Mixed integer evolution strategies for parameter optimization.
Li, Rui; Emmerich, Michael T M; Eggermont, Jeroen; Bäck, Thomas; Schütz, M; Dijkstra, J; Reiber, J H C
2013-01-01
Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems. PMID:22122384
Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R.
2015-01-01
Motivation: Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. Results: In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: julio@iim.csic.es or saezrodriguez@ebi.ac.uk PMID:26002881
Mixed integer optimization in automobile sheet metal forming processes
Schenk, Olaf
Mixed integer optimization in automobile sheet metal forming processes Madan Sathe, Olaf Schenk in the automobile industry. In automobile sheet metal forming processes the blank sheet has to be processed on some
Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach
NASA Astrophysics Data System (ADS)
Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin
2014-09-01
Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.
A Mixed Integer Linear Programming Model for Dynamic Route Guidance
Smith, Robert L.
. Implicit within this MILP formulation of the dynamic traffic assignment problem is therefore in the context of Intelligent Transportation Sys- tems is the problem of assigning vehicular traffic to paths and achieve a strictly lower trip time). The static version of this traffic assignment problem assumes
Gorissen, Bram L; Hoffmann, Aswin L
2014-01-01
Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the solver at an early stage, without deterioration of the plan quality. For a fixed catheter configuration, the dwell time optimization model LP solves to optimality in less than 15 seconds, which confirms earlier results. We propose an iterative procedure for QP that allows to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iter...
Optimization of a wood dryer kiln using the mixed integer programming technique: A case study
Gustafsson, S.I.
1999-07-01
When wood is to be utilized as a raw material for furniture, buildings, etc., it must be dried from approximately 100% to 6% moisture content. This is achieved at least partly in a drying kiln. Heat for this purpose is provided by electrical means, or by steam from boilers fired with wood chips or oil. By making a close examination of monitored values from an actual drying kiln it has been possible to optimize the use of steam and electricity using the so called mixed integer programming technique. Owing to the operating schedule for the drying kiln it has been necessary to divide the drying process in very short time intervals, i.e., a number of minutes. Since a drying cycle takes about two or three weeks, a considerable mathematical problem is presented and this has to be solved.
NASA Astrophysics Data System (ADS)
Tang, Jiafu; Liu, Yang; Fung, Richard; Luo, Xinggang
2008-12-01
Manufacturers have a legal accountability to deal with industrial waste generated from their production processes in order to avoid pollution. Along with advances in waste recovery techniques, manufacturers may adopt various recycling strategies in dealing with industrial waste. With reuse strategies and technologies, byproducts or wastes will be returned to production processes in the iron and steel industry, and some waste can be recycled back to base material for reuse in other industries. This article focuses on a recovery strategies optimization problem for a typical class of industrial waste recycling process in order to maximize profit. There are multiple strategies for waste recycling available to generate multiple byproducts; these byproducts are then further transformed into several types of chemical products via different production patterns. A mixed integer programming model is developed to determine which recycling strategy and which production pattern should be selected with what quantity of chemical products corresponding to this strategy and pattern in order to yield maximum marginal profits. The sales profits of chemical products and the set-up costs of these strategies, patterns and operation costs of production are considered. A simulated annealing (SA) based heuristic algorithm is developed to solve the problem. Finally, an experiment is designed to verify the effectiveness and feasibility of the proposed method. By comparing a single strategy to multiple strategies in an example, it is shown that the total sales profit of chemical products can be increased by around 25% through the simultaneous use of multiple strategies. This illustrates the superiority of combinatorial multiple strategies. Furthermore, the effects of the model parameters on profit are discussed to help manufacturers organize their waste recycling network.
Mixed integer programming model for optimizing the layout of an ICU vehicle
2009-01-01
Background This paper presents a Mixed Integer Programming (MIP) model for designing the layout of the Intensive Care Units' (ICUs) patient care space. In particular, this MIP model was developed for optimizing the layout for materials to be used in interventions. This work was developed within the framework of a joint project between the Madrid Technical Unverstity and the Medical Emergency Services of the Madrid Regional Government (SUMMA 112). Methods The first task was to identify the relevant information to define the characteristics of the new vehicles and, in particular, to obtain a satisfactory interior layout to locate all the necessary materials. This information was gathered from health workers related to ICUs. With that information an optimization model was developed in order to obtain a solution. From the MIP model, a first solution was obtained, consisting of a grid to locate the different materials needed for the ICUs. The outcome from the MIP model was discussed with health workers to tune the solution, and after slightly altering that solution to meet some requirements that had not been included in the mathematical model, the eventual solution was approved by the persons responsible for specifying the characteristics of the new vehicles. According to the opinion stated by the SUMMA 112's medical group responsible for improving the ambulances (the so-called "coaching group"), the outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public tender. Results As a result from solving the Optimization model, a grid was obtained to locate the different necessary materials for the ICUs. This grid had to be slightly altered to meet some requirements that had not been included in the mathematical model. The results were discussed with the persons responsible for specifying the characteristics of the new vehicles. Conclusion The outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public tender. The authors advocate this approach to address similar problems within the field of Health Services to improve the efficiency and the effectiveness of the processes involved. Problems such as those in operation rooms or emergency rooms, where the availability of a large amount of material is critical are eligible to be dealt with in a simmilar manner. PMID:19995438
Optimizing well-stimulation treatment size using mixed integer linear programming
Picon Aranguren, Oscar
2002-01-01
This thesis presents a model for optimum sizing of well stimulation treatments. The optimization scheme is based on a combination of a well stimulation type selection and treatment size, therefore determining the optimum type of stimulation...
Ferrari-Trecate, Giancarlo
of the method is tested on simulated data generated by a simple model of Escherichia coli nutrient stressCanalizing structure of genetic network dynamics: modelling and identification via mixed discuss the identification of genetic networks based on a class of boolean gene activation rules known
Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks
NASA Technical Reports Server (NTRS)
Cheung, Kar-Ming; Lee, Charles H.
2012-01-01
We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.
DOI 10.1007/s11081-013-9226-6 A mixed-integer nonlinear program for the optimal
the system include lead-acid batteries, photovoltaic cells, solid ox- ide fuel cells, heat exchangers present an optimization model that determines the mix, capacity, and operational schedule of DG, and a hot water storage tank. Modeling the acquisition and operation of discrete technologies requires
Mixed Integer Programming and Heuristic Scheduling for Space Communication
NASA Technical Reports Server (NTRS)
Lee, Charles H.; Cheung, Kar-Ming
2013-01-01
Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.
convex segmentation and mixed-integer footstep planning for a walking robot
Deits, Robin L. H. (Robin Lloyd Henderson)
2014-01-01
This work presents a novel formulation of the footstep planning problem as a mixed-integer convex optimization. The footstep planning problem involves choosing a set of footstep locations which a walking robot can follow ...
Mixed-Integer Formulations for Constellation Scheduling
NASA Astrophysics Data System (ADS)
Valicka, C.; Hart, W.; Rintoul, M.
Remote sensing systems have expanded the set of capabilities available for and critical to national security. Cooperating, high-fidelity sensing systems and growing mission applications have exponentially increased the set of potential schedules. A definitive lack of advanced tools places an increased burden on operators, as planning and scheduling remain largely manual tasks. This is particularly true in time-critical planning activities where operators aim to accomplish a large number of missions through optimal utilization of single or multiple sensor systems. Automated scheduling through identification and comparison of alternative schedules remains a challenging problem applicable across all remote sensing systems. Previous approaches focused on a subset of sensor missions and do not consider ad-hoc tasking. We have begun development of a robust framework that leverages the Pyomo optimization modeling language for the design of a tool to assist sensor operators planning under the constraints of multiple concurrent missions and uncertainty. Our scheduling models have been formulated to address the stochastic nature of ad-hoc tasks inserted under a variety of scenarios. Operator experience is being leveraged to select appropriate model objectives. Successful development of the framework will include iterative development of high-fidelity mission models that consider and expose various schedule performance metrics. Creating this tool will aid time-critical scheduling by increasing planning efficiency, clarifying the value of alternative modalities uniquely provided by multi-sensor systems, and by presenting both sets of organized information to operators. Such a tool will help operators more quickly and fully utilize sensing systems, a high interest objective within the current remote sensing operations community. Preliminary results for mixed-integer programming formulations of a sensor scheduling problem will be presented. Assumptions regarding sensor geometry and sensing activity time constraints, durations, priorities, etc. will be outlined. Finally, solver speed and stochastic programming details for uncertain activities and scheduling impediments will be discussed.
Grossmann, Ignacio E.
A Lagrangean based Branch-and-Cut algorithm for global optimization of nonconvex Mixed present a global optimization algorithm for solving a class of large-scale nonconvex optimization models-and-cut algorithm to solve these models to global optimality, wherein bounds on the global optimum are obtained
Smalley, Hannah K; Keskinocak, Pinar; Swann, Julie; Hinman, Alan
2015-11-17
In addition to improved sanitation, hygiene, and better access to safe water, oral cholera vaccines can help to control the spread of cholera in the short term. However, there is currently no systematic method for determining the best allocation of oral cholera vaccines to minimize disease incidence in a population where the disease is endemic and resources are limited. We present a mathematical model for optimally allocating vaccines in a region under varying levels of demographic and incidence data availability. The model addresses the questions of where, when, and how many doses of vaccines to send. Considering vaccine efficacies (which may vary based on age and the number of years since vaccination), we analyze distribution strategies which allocate vaccines over multiple years. Results indicate that, given appropriate surveillance data, targeting age groups and regions with the highest disease incidence should be the first priority, followed by other groups primarily in order of disease incidence, as this approach is the most life-saving and cost-effective. A lack of detailed incidence data results in distribution strategies which are not cost-effective and can lead to thousands more deaths from the disease. The mathematical model allows for what-if analysis for various vaccine distribution strategies by providing the ability to easily vary parameters such as numbers and sizes of regions and age groups, risk levels, vaccine price, vaccine efficacy, production capacity and budget. PMID:26458806
First-Order Mixed Integer Linear Programming
Gordon, Geoffrey; Dudik, Miroslav
2012-01-01
Mixed integer linear programming (MILP) is a powerful representation often used to formulate decision-making problems under uncertainty. However, it lacks a natural mechanism to reason about objects, classes of objects, and relations. First-order logic (FOL), on the other hand, excels at reasoning about classes of objects, but lacks a rich representation of uncertainty. While representing propositional logic in MILP has been extensively explored, no theory exists yet for fully combining FOL with MILP. We propose a new representation, called first-order programming or FOP, which subsumes both FOL and MILP. We establish formal methods for reasoning about first order programs, including a sound and complete lifted inference procedure for integer first order programs. Since FOP can offer exponential savings in representation and proof size compared to FOL, and since representations and proofs are never significantly longer in FOP than in FOL, we anticipate that inference in FOP will be more tractable than inferen...
Computational Mixed Integer Nonlinear Programming Jeff "Obi-Wan" Linderoth
Willett, Rebecca
Linderoth (UW-Madison) MINLP Wars WIDDOW 5 / 70 #12;Introduction Applications Application: Death Star Core Linderoth (UW-Madison) MINLP Wars WIDDOW 1 / 70 #12;Introduction MINLP Our Quest Mixed Integer Nonlinear Linderoth (UW-Madison) MINLP Wars WIDDOW 2 / 70 #12;Introduction MINLP Our Quest Mixed Integer Nonlinear
Orbital rendezvous mission planning using mixed integer nonlinear programming
NASA Astrophysics Data System (ADS)
Zhang, Jin; Tang, Guo-jin; Luo, Ya-Zhong; Li, Hai-yang
2011-04-01
The rendezvous and docking mission is usually divided into several phases, and the mission planning is performed phase by phase. A new planning method using mixed integer nonlinear programming, which investigates single phase parameters and phase connecting parameters simultaneously, is proposed to improve the rendezvous mission's overall performance. The design variables are composed of integers and continuous-valued numbers. The integer part consists of the parameters for station-keeping and sensor-switching, the number of maneuvers in each rendezvous phase and the number of repeating periods to start the rendezvous mission. The continuous part consists of the orbital transfer time and the station-keeping duration. The objective function is a combination of the propellant consumed, the sun angle which represents the power available, and the terminal precision of each rendezvous phase. The operational requirements for the spacecraft-ground communication, sun illumination and the sensor transition are considered. The simple genetic algorithm, which is a combination of the integer-coded and real-coded genetic algorithm, is chosen to obtain the optimal solution. A practical rendezvous mission planning problem is solved by the proposed method. The results show that the method proposed can solve the integral rendezvous mission planning problem effectively, and the solution obtained can satisfy the operational constraints and has a good overall performance.
PySP : modeling and solving stochastic mixed-integer programs in Python.
Woodruff, David L.; Watson, Jean-Paul
2010-08-01
Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its widespread use. One key factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of deterministic models, which are often formulated first. A second key factor relates to the difficulty of solving stochastic programming models, particularly the general mixed-integer, multi-stage case. Intricate, configurable, and parallel decomposition strategies are frequently required to achieve tractable run-times. We simultaneously address both of these factors in our PySP software package, which is part of the COIN-OR Coopr open-source Python project for optimization. To formulate a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree with associated uncertain parameters in the Pyomo open-source algebraic modeling language. Given these two models, PySP provides two paths for solution of the corresponding stochastic program. The first alternative involves writing the extensive form and invoking a standard deterministic (mixed-integer) solver. For more complex stochastic programs, we provide an implementation of Rockafellar and Wets Progressive Hedging algorithm. Our particular focus is on the use of Progressive Hedging as an effective heuristic for approximating general multi-stage, mixed-integer stochastic programs. By leveraging the combination of a high-level programming language (Python) and the embedding of the base deterministic model in that language (Pyomo), we are able to provide completely generic and highly configurable solver implementations. PySP has been used by a number of research groups, including our own, to rapidly prototype and solve difficult stochastic programming problems.
NASA Technical Reports Server (NTRS)
Laird, Philip
1992-01-01
We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.
Linderoth, Jeff T.; Luedtke, James R.
2013-05-30
The mathematical modeling of systems often requires the use of both nonlinear and discrete components. Problems involving both discrete and nonlinear components are known as mixed-integer nonlinear programs (MINLPs) and are among the most challenging computational optimization problems. This research project added to the understanding of this area by making a number of fundamental advances. First, the work demonstrated many novel, strong, tractable relaxations designed to deal with non-convexities arising in mathematical formulation. Second, the research implemented the ideas in software that is available to the public. Finally, the work demonstrated the importance of these ideas on practical applications and disseminated the work through scholarly journals, survey publications, and conference presentations.
Noncommercial Software for Mixed-Integer Linear Programming J. T. Linderoth
Ralphs, Ted
Noncommercial Software for Mixed-Integer Linear Programming J. T. Linderoth T. K. Ralphs December for the solution of mixed-integer linear programs (MILPs). We first review solution methodologies for MILPs, 2004. Revised: January, 2005. Abstract We present an overview of noncommercial software tools
Noncommercial Software for MixedInteger Linear Programming J. T. Linderoth # T. K. Ralphs +
Ralphs, Ted
Noncommercial Software for MixedInteger Linear Programming J. T. Linderoth # T. K. Ralphs for the solution of mixedinteger linear programs (MILPs). We first review solution methodologies for MILPs + December, 2004. Revised: January, 2005. Abstract We present an overview of noncommercial software tools
ERIC Educational Resources Information Center
Han, Kyung T.; Rudner, Lawrence M.
2014-01-01
This study uses mixed integer quadratic programming (MIQP) to construct multiple highly equivalent item pools simultaneously, and compares the results from mixed integer programming (MIP). Three different MIP/MIQP models were implemented and evaluated using real CAT item pool data with 23 different content areas and a goal of equal information…
On the Complexity of Inverse Mixed Integer Linear Optimization
Ralphs, Ted
) min xP d x (1) E-mail: aykut@lehigh.edu E-mail: ted@lehigh.edu 2 #12;where d Rn and P = {x Rn |Ax function d is unknown. We are given A, b, c, and x0 Rn . To formalize our statement of the problem, we will be made clear. First, consider the following candidate for formulating the inverse problem, min c - d s
Learning oncogenetic networks by reducing to mixed integer linear programming.
Shahrabi Farahani, Hossein; Lagergren, Jens
2013-01-01
Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog. PMID:23799047
The use of mixed-integer programming for inverse treatment planning with pre-defined field segments
NASA Astrophysics Data System (ADS)
Bednarz, Greg; Michalski, Darek; Houser, Chris; Saiful Huq, M.; Xiao, Ying; Rani Anne, Pramila; Galvin, James M.
2002-07-01
Complex intensity patterns generated by traditional beamlet-based inverse treatment plans are often very difficult to deliver. In the approach presented in this work the intensity maps are controlled by pre-defining field segments to be used for dose optimization. A set of simple rules was used to define a pool of allowable delivery segments and the mixed-integer programming (MIP) method was used to optimize segment weights. The optimization problem was formulated by combining real variables describing segment weights with a set of binary variables, used to enumerate voxels in targets and critical structures. The MIP method was compared to the previously used Cimmino projection algorithm. The field segmentation approach was compared to an inverse planning system with a traditional beamlet-based beam intensity optimization. In four complex cases of oropharyngeal cancer the segmental inverse planning produced treatment plans, which competed with traditional beamlet-based IMRT plans. The mixed-integer programming provided mechanism for imposition of dose-volume constraints and allowed for identification of the optimal solution for feasible problems. Additional advantages of the segmental technique presented here are: simplified dosimetry, quality assurance and treatment delivery.
Grossmann, Ignacio E.
Systems Engineering Ignacio E. Grossmann and Francisco Trespalacios Department of Chemical Engineering of the applications of mixed-integer nonlinear programming (MINLP) in process systems engineering (PSE). A review
CONCRETE STRUCTURE DESIGN USING MIXED-INTEGER NONLINEAR PROGRAMMING WITH COMPLEMENTARITY
CONCRETE STRUCTURE DESIGN USING MIXED-INTEGER NONLINEAR PROGRAMMING WITH COMPLEMENTARITY programming (MINLP) formulation to achieve mini- mum-cost designs for reinforced concrete (RC) structures for concrete, steel reinforcing bars, and formwork according to typical contractor methods. Restrictions
A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem
NASA Technical Reports Server (NTRS)
Montoya, Justin Vincent; Wood, Zachary Paul; Rathinam, Sivakumar; Malik, Waqar Ahmad
2010-01-01
Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.
Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming.
Bravo, Héctor Corrada; Wright, Stephen; Eng, Kevin H; Keles, Sündüz; Wahba, Grace
2009-01-01
We present a novel method for estimating tree-structured covariance matrices directly from observed continuous data. Specifically, we estimate a covariance matrix from observations of p continuous random variables encoding a stochastic process over a tree with p leaves. A representation of these classes of matrices as linear combinations of rank-one matrices indicating object partitions is used to formulate estimation as instances of well-studied numerical optimization problems.In particular, our estimates are based on projection, where the covariance estimate is the nearest tree-structured covariance matrix to an observed sample covariance matrix. The problem is posed as a linear or quadratic mixed-integer program (MIP) where a setting of the integer variables in the MIP specifies a set of tree topologies of the structured covariance matrix. We solve these problems to optimality using efficient and robust existing MIP solvers.We present a case study in phylogenetic analysis of gene expression and a simulation study comparing our method to distance-based tree estimating procedures. PMID:22081761
Synchronic interval Gaussian mixed-integer programming for air quality management.
Cheng, Guanhui; Huang, Guohe Gordon; Dong, Cong
2015-12-15
To reveal the synchronism of interval uncertainties, the tradeoff between system optimality and security, the discreteness of facility-expansion options, the uncertainty of pollutant dispersion processes, and the seasonality of wind features in air quality management (AQM) systems, a synchronic interval Gaussian mixed-integer programming (SIGMIP) approach is proposed in this study. A robust interval Gaussian dispersion model is developed for approaching the pollutant dispersion process under interval uncertainties and seasonal variations. The reflection of synchronic effects of interval uncertainties in the programming objective is enabled through introducing interval functions. The proposition of constraint violation degrees helps quantify the tradeoff between system optimality and constraint violation under interval uncertainties. The overall optimality of system profits of an SIGMIP model is achieved based on the definition of an integrally optimal solution. Integer variables in the SIGMIP model are resolved by the existing cutting-plane method. Combining these efforts leads to an effective algorithm for the SIGMIP model. An application to an AQM problem in a region in Shandong Province, China, reveals that the proposed SIGMIP model can facilitate identifying the desired scheme for AQM. The enhancement of the robustness of optimization exercises may be helpful for increasing the reliability of suggested schemes for AQM under these complexities. The interrelated tradeoffs among control measures, emission sources, flow processes, receptors, influencing factors, and economic and environmental goals are effectively balanced. Interests of many stakeholders are reasonably coordinated. The harmony between economic development and air quality control is enabled. Results also indicate that the constraint violation degree is effective at reflecting the compromise relationship between constraint-violation risks and system optimality under interval uncertainties. This can help decision makers mitigate potential risks, e.g. insufficiency of pollutant treatment capabilities, exceedance of air quality standards, deficiency of pollution control fund, or imbalance of economic or environmental stress, in the process of guiding AQM. PMID:26367068
NASA Astrophysics Data System (ADS)
Guo, P.; Huang, G. H.; Li, Y. P.
2010-01-01
In this study, an inexact fuzzy-chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is developed for flood diversion planning under multiple uncertainties. A concept of the distribution with fuzzy boundary interval probability is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets and probability distributions. IFCTIP integrates the inexact programming, two-stage stochastic programming, integer programming and fuzzy-stochastic programming within a general optimization framework. IFCTIP incorporates the pre-regulated water-diversion policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised targets are violated. More importantly, it can facilitate dynamic programming for decisions of capacity-expansion planning under fuzzy-stochastic conditions. IFCTIP is applied to a flood management system. Solutions from IFCTIP provide desired flood diversion plans with a minimized system cost and a maximized safety level. The results indicate that reasonable solutions are generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of flood flows.
A stochastic mixed integer programming approach to wildfire management systems
Lee, Won Ju
2009-06-02
is maximized. To achieve this objective, wildfire attack-bases should be optimally located such that any wildfire is suppressed within the effective attack range from some bases. In addition, the optimal fire-fighting resources should be deployed...
Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks
NASA Technical Reports Server (NTRS)
Lee, Charles H.; Cheung, Kar-Ming
2012-01-01
In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.
Solution of Mixed-Integer Programming Problems on the XT5
Hartman-Baker, Rebecca J; Busch, Ingrid Karin; Hilliard, Michael R; Middleton, Richard S; Schultze, Michael
2009-01-01
In this paper, we describe our experience with solving difficult mixed-integer linear programming problems (MILPs) on the petaflop Cray XT5 system at the National Center for Computational Sciences at Oak Ridge National Laboratory. We describe the algorithmic, software, and hardware needs for solving MILPs and present the results of using PICO, an open-source, parallel, mixed-integer linear programming solver developed at Sandia National Laboratories, to solve canonical MILPs as well as problems of interest arising from the logistics and supply chain management field.
Mixed-integer Linear Programming in the Analysis of Trivium and Ktantan
International Association for Cryptologic Research (IACR)
Introduction There is an increasing number of low-end computing devices such as RFID tags. These devices [4], a small scale variant of the cipher Trivium [9]. The algebraic description of the cipher apply mixed-integer programming to analyze the stream cipher Triv- ium [9], its small scale variant
A local search heuristic for a mixed integer nonlinear integrated airline schedule planning problem
Bierlaire, Michel
A local search heuristic for a mixed integer nonlinear integrated airline schedule planning problem present a local search heuristic method for an integrated airline scheduling, fleeting and pricing model management decisions. The local search heuristic tackles the complexity of the problem decomposing
Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure after
Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure after-control study of acute kidney injury after surgery among Medicare patients illustrates these features in detail: Motivating Example; Review of Matching; Outline 1.1 Motivating example: obesity and acute kidney injuries
October 27, 2008 1 Abstract--The use of Mixed Integer Programming (MIP) within
Oren, Shmuel S.
programming, generation unit commitment, power generation dispatch, power system economics, valid inequalities, it is important to have a sound understanding of mixed integer programming. LR is commonly taught within power or they are already using MIP. There are various MIP formulations published for generation unit commitment with little
Solving the Double Digestion Problem as a MixedInteger Linear Program \\Lambda
Zhang, Yin
Solving the Double Digestion Problem as a MixedInteger Linear Program \\Lambda Zhijun Wu y and Yin Zhang z August, 2001 Abstract. The double digestion problem for DNA restriction mapping is knownscale double digestion problems. Key Words. DNA sequencing, restriction mapping, double digestion, NP
Grossmann, Ignacio E.
Algorithmic approach for improved mixed-integer reformulations of convex Generalized Disjunctive-M and Hull reformulation. We illustrate the application of this algorithm with several examples. The results University, Pittsburgh, PA 15213 July, 2013 Abstract In this work, we propose an algorithmic approach
Combinatorial therapy discovery using mixed integer linear programming
Pang, Kaifang; Wan, Ying-Wooi; Choi, William T.; Donehower, Lawrence A.; Sun, Jingchun; Pant, Dhruv; Liu, Zhandong
2014-01-01
Motivation: Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Results: Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. Availability: Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. Contact: zhandong.liu@bcm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24463180
Optimization Dynamical Systems
Moore, John Barratt
Optimization and Dynamical Systems Uwe Helmke1 John B. Moore2 2nd Edition March 1996 1. Department at mathematics and engineering graduate students and researchers in the areas of optimization, dynamical systems or directly concerned with dynamical systems themselves, so there is feedback in that dynamical systems
DOTcvpSB, a software toolbox for dynamic optimization in systems biology
Hirmajer, Tomáš; Balsa-Canto, Eva; Banga, Julio R
2009-01-01
Background Mathematical optimization aims to make a system or design as effective or functional as possible, computing the quality of the different alternatives using a mathematical model. Most models in systems biology have a dynamic nature, usually described by sets of differential equations. Dynamic optimization addresses this class of systems, seeking the computation of the optimal time-varying conditions (control variables) to minimize or maximize a certain performance index. Dynamic optimization can solve many important problems in systems biology, including optimal control for obtaining a desired biological performance, the analysis of network designs and computer aided design of biological units. Results Here, we present a software toolbox, DOTcvpSB, which uses a rich ensemble of state-of-the-art numerical methods for solving continuous and mixed-integer dynamic optimization (MIDO) problems. The toolbox has been written in MATLAB and provides an easy and user friendly environment, including a graphical user interface, while ensuring a good numerical performance. Problems are easily stated thanks to the compact input definition. The toolbox also offers the possibility of importing SBML models, thus enabling it as a powerful optimization companion to modelling packages in systems biology. It serves as a means of handling generic black-box models as well. Conclusion Here we illustrate the capabilities and performance of DOTcvpSB by solving several challenging optimization problems related with bioreactor optimization, optimal drug infusion to a patient and the minimization of intracellular oscillations. The results illustrate how the suite of solvers available allows the efficient solution of a wide class of dynamic optimization problems, including challenging multimodal ones. The toolbox is freely available for academic use. PMID:19558728
Grossmann, Ignacio E.
Dehydration Succinic acidSuccinic acid Fumaric acidFumaric acid Malic acidMalic acid FurfuralFurfural DehydrationHydrolysis Fractionation Catalyst Steam Acid Enzymes Fractionation Catalyst Steam Acid Enzymes Biooils (Sugars, Acids) & Lignin Biooils (Sugars, Acids) & Lignin LigninLignin Demethoxylation BTXBTX Hydrodeoxygenation
Final Report---Optimization Under Nonconvexity and Uncertainty: Algorithms and Software
Jeff Linderoth
2011-11-06
the goal of this work was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems. The focus of the work done in the continuation was on Mixed Integer Nonlinear Programs (MINLP)s and Mixed Integer Linear Programs (MILP)s, especially those containing a great deal of symmetry.
Least quantile regression via modern optimization
Mazumder, Rahul
We address the Least Quantile of Squares (LQS) (and in particular the Least Median of Squares) regression problem using modern optimization methods. We propose a Mixed Integer Optimization (MIO) formulation of the LQS ...
Regression under a modern optimization lens
King, Angela, Ph. D. Massachusetts Institute of Technology
2015-01-01
In the last twenty-five years (1990-2014), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 200 billion factor speedup in solving mixed integer optimization ...
Zou, Meng; Zhang, Peng-Jun; Wen, Xin-Yu; Chen, Luonan; Tian, Ya-Ping; Wang, Yong
2015-07-15
Multi-biomarker panels can capture the nonlinear synergy among biomarkers and they are important to aid in the early diagnosis and ultimately battle complex diseases. However, identification of these multi-biomarker panels from case and control data is challenging. For example, the exhaustive search method is computationally infeasible when the data dimension is high. Here, we propose a novel method, MILP_k, to identify serum-based multi-biomarker panel to distinguish colorectal cancers (CRC) from benign colorectal tumors. Specifically, the multi-biomarker panel detection problem is modeled by a mixed integer programming to maximize the classification accuracy. Then we measured the serum profiling data for 101 CRC patients and 95 benign patients. The 61 biomarkers were analyzed individually and further their combinations by our method. We discovered 4 biomarkers as the optimal small multi-biomarker panel, including known CRC biomarkers CEA and IL-10 as well as novel biomarkers IMA and NSE. This multi-biomarker panel obtains leave-one-out cross-validation (LOOCV) accuracy to 0.7857 by nearest centroid classifier. An independent test of this panel by support vector machine (SVM) with threefold cross validation gets an AUC 0.8438. This greatly improves the predictive accuracy by 20% over the single best biomarker. Further extension of this 4-biomarker panel to a larger 13-biomarker panel improves the LOOCV to 0.8673 with independent AUC 0.8437. Comparison with the exhaustive search method shows that our method dramatically reduces the searching time by 1000-fold. Experiments on the early cancer stage samples reveal two panel of biomarkers and show promising accuracy. The proposed method allows us to select the subset of biomarkers with best accuracy to distinguish case and control samples given the number of selected biomarkers. Both receiver operating characteristic curve and precision-recall curve show our method's consistent performance gain in accuracy. Our method also shows its advantage in capturing synergy among selected biomarkers. The multi-biomarker panel far outperforms the simple combination of best single features. Close investigation of the multi-biomarker panel illustrates that our method possesses the ability to remove redundancy and reveals complementary biomarker combinations. In addition, our method is efficient and can select multi-biomarker panel with more than 5 biomarkers, for which the exhaustive methods fail. In conclusion, we propose a promising model to improve the clinical data interpretability and to serve as a useful tool for other complex disease studies. Our small multi-biomarker panel, CEA, IL-10, IMA, and NSE, may provide insights on the disease status of colorectal diseases. The implementation of our method in MATLAB is available via the website: http://doc.aporc.org/wiki/MILP_k. PMID:25980368
Integer optimization methods for machine learning
Chang, Allison An
2012-01-01
In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks in ...
Solving mixed integer nonlinear programming problems for mine production planning with stockpiling
Froyland, Gary
, Sydney, Australia 4 Resource and Business Optimization Group Function, BHP Billiton Ltd., Melbourne an integer feasible solution of OPMPSP which violates these constraints. Combining these two techniques computational challenges. In combination, however, they produce an NP-hard problem (see eg. [11]), often known
Optimal operation of a cogeneration plant in combination with electric heat pumps
Ito, K.; Shiba, T.; Yokoyama, R. . Dept. of Energy Systems Engineering)
1994-03-01
An operational problem is discussed for a gas turbine cogeneration plant in combination with a heat pump/thermal storage system that utilizes time-of-use pricing of the electrical utility. An optimal planning method is presented by which the operational policy of constituent equipment is determined so as to minimize the daily operational cost. An algorithm is proposed to solve this optimization problem efficiently by combining the dynamic programming method with the mixed-integer programming one. A case study is carried out to investigate the effect of introducing a heat pump system into a cogeneration plant.
Planning using dynamic optimization
from each state, lead to "curse of dimensionality": Rx dx Â· Ru du Â· We will talk about special purpose. continuous time? Â· Globally optimal? Â· Stochastic vs. deterministic? Â· Clocked vs. autonomous? Â· What should in 3D Trajectory Segments Mountain Car Value Function Value Function Discretizations #12;6 Policy
New numerical methods for open-loop and feedback solutions to dynamic optimization problems
NASA Astrophysics Data System (ADS)
Ghosh, Pradipto
The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development is that the resulting control law has an algebraic closed-form structure. The proposed method uses an optimal spatial statistical predictor called universal kriging to construct the surrogate model of a feedback controller, which is capable of quickly predicting an optimal control estimate based on current state (and time) information. With universal kriging, an approximation to the optimal feedback map is computed by conceptualizing a set of state-control samples from pre-computed extremals to be a particular realization of a jointly Gaussian spatial process. Feedback policies are computed for a variety of example dynamic optimization problems in order to evaluate the effectiveness of this methodology. This feedback synthesis approach is found to combine good numerical accuracy with low computational overhead, making it a suitable candidate for real-time applications. Particle swarm and universal kriging are combined for a capstone example, a near optimal, near-admissible, full-state feedback control law is computed and tested for the heat-load-limited atmospheric-turn guidance of an aeroassisted transfer vehicle. The performance of this explicit guidance scheme is found to be very promising; initial errors in atmospheric entry due to simulated thruster misfirings are found to be accurately corrected while closely respecting the algebraic state-inequality constraint.
Optimal dynamic detection of explosives
Moore, David Steven; Mcgrane, Shawn D; Greenfield, Margo T; Scharff, R J; Rabitz, Herschel A; Roslund, J
2009-01-01
The detection of explosives is a notoriously difficult problem, especially at stand-off distances, due to their (generally) low vapor pressure, environmental and matrix interferences, and packaging. We are exploring optimal dynamic detection to exploit the best capabilities of recent advances in laser technology and recent discoveries in optimal shaping of laser pulses for control of molecular processes to significantly enhance the standoff detection of explosives. The core of the ODD-Ex technique is the introduction of optimally shaped laser pulses to simultaneously enhance sensitivity of explosives signatures while reducing the influence of noise and the signals from background interferents in the field (increase selectivity). These goals are being addressed by operating in an optimal nonlinear fashion, typically with a single shaped laser pulse inherently containing within it coherently locked control and probe sub-pulses. With sufficient bandwidth, the technique is capable of intrinsically providing orthogonal broad spectral information for data fusion, all from a single optimal pulse.
2013-01-01
Background Phylogeny estimation from aligned haplotype sequences has attracted more and more attention in the recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from medical research, to drug discovery, to epidemiology, to population dynamics. The literature on molecular phylogenetics proposes a number of criteria for selecting a phylogeny from among plausible alternatives. Usually, such criteria can be expressed by means of objective functions, and the phylogenies that optimize them are referred to as optimal. One of the most important estimation criteria is the parsimony which states that the optimal phylogeny T?for a set H?of n haplotype sequences over a common set of variable loci is the one that satisfies the following requirements: (i) it has the shortest length and (ii) it is such that, for each pair of distinct haplotypes hi,hj?H, the sum of the edge weights belonging to the path from hi to hj in T? is not smaller than the observed number of changes between hi and hj. Finding the most parsimonious phylogeny for H?involves solving an optimization problem, called the Most Parsimonious Phylogeny Estimation Problem (MPPEP), which is NP-hard in many of its versions. Results In this article we investigate a recent version of the MPPEP that arises when input data consist of single nucleotide polymorphism haplotypes extracted from a population of individuals on a common genomic region. Specifically, we explore the prospects for improving on the implicit enumeration strategy of implicit enumeration strategy used in previous work using a novel problem formulation and a series of strengthening valid inequalities and preliminary symmetry breaking constraints to more precisely bound the solution space and accelerate implicit enumeration of possible optimal phylogenies. We present the basic formulation and then introduce a series of provable valid constraints to reduce the solution space. We then prove that these constraints can often lead to significant reductions in the gap between the optimal solution and its non-integral linear programming bound relative to the prior art as well as often substantially faster processing of moderately hard problem instances. Conclusion We provide an indication of the conditions under which such an optimal enumeration approach is likely to be feasible, suggesting that these strategies are usable for relatively large numbers of taxa, although with stricter limits on numbers of variable sites. The work thus provides methodology suitable for provably optimal solution of some harder instances that resist all prior approaches. PMID:23343437
OPTIMAL CONTROL WITH ADAPTIVE INTERNAL DYNAMICS MODELS
Vijayakumar, Sethu
OPTIMAL CONTROL WITH ADAPTIVE INTERNAL DYNAMICS MODELS Djordje Mitrovic, Stefan Klanke, and Sethu, optimal control, adaptive control, robot simulation Abstract: Optimal feedback control has been proposed. The optimal feedback control law for systems with non-linear dynamics and non-quadratic costs can be found
Final Report-Optimization Under Uncertainty and Nonconvexity: Algorithms and Software
Jeff Linderoth
2008-10-10
The goal of this research was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems.
Universal Locally Optimized Dynamical Decoupling
Uys, Hermann; Bollinger, John J
2009-01-01
One approach to maintaining phase coherence of qubits through dynamical decoupling consists of applying a sequence of Hahn spin-echo pulses. Recent studies have shown that, in certain noise environments, judicious choice of the delay times between these pulses can greatly improve the suppression of phase errors compared to traditional approaches. By enforcing a simple analytical condition, we obtain sets of optimized dynamical decoupling sequences that are spectrum-independent up to a single scaling factor set by the coherence time of the system. We demonstrate the efficacy of these sequences in suppressing phase errors through experimental measurements on $^{9}$Be$^{+}$ ion qubits in a Penning trap. Our combined theoretical and experimental studies show that in high-frequency-dominated noise environments this approach has the potential to suppress phase errors orders of magnitude more efficiently than comparable techniques can.
Benavides Serrano, Alberto J.
2014-11-13
part of this work, this assumption is relaxed via formulation SPqt, which considers non-uniform dynamic detector unavailabilities. Relaxing this assumption results in a Mixed-Integer NonLinear Programming (MINLP) formulation. SPqt, being an extension...
Optimal control with adaptive internal dynamics models
Mitrovic, Djordje; Klanke, Stefan; Vijayakumar, Sethu
2008-01-01
Optimal feedback control has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. The optimal feedback control law for systems with non-linear dynamics ...
McCalley, James D.
process and some selected comparisons with our previously existing Lagrangian Relaxation (LR) algorithm Relaxation (LR) include: 1) Global optimality. 2) a more accurate measure of optimality, 3) improved modeling. These are followed by a description of some newly developed MIP-Based models (e.g. combined cycle modeling
Drees, Lawrence David
1999-01-01
. A11 optimizing strategies and the heuristic were tested using CPLEX 6.0. The results of these tests were compared with the CPLEX default settings for solving MIPs. One particular optimizing strategy worked especially well in terms of the number...
Optimal signal patterns for dynamical cellular communication
Hasegawa, Yoshihiko
2015-01-01
Cells transmit information via signaling pathways, using temporal dynamical patterns. As optimality with respect to environments is the universal principle in biological systems, organisms have acquired an optimal way of transmitting information. Here we obtain optimal dynamical signal patterns which can transmit information efficiently (low power) and reliably (high accuracy) using the optimal control theory. Adopting an activation-inactivation decoding network, we reproduced several dynamical patterns found in actual signals, such as steep, gradual and overshooting dynamics. Notably, when minimizing the power of the input signal, optimal signals exhibit the overshooting pattern, which is a biphasic pattern with transient and steady phases; this pattern is prevalent in actual dynamical patterns as it can be generated by an incoherent feed-forward loop (FFL), a common motif in biochemical networks. We also identified conditions when the three patterns, steep, gradual and overshooting, confer advantages.
Real-time path-planning using mixed-integer linear programming and global cost-to-go maps
Toupet, Olivier
2006-01-01
With the advance in the fields of computer science, control and optimization, it is now possible to build aerial vehicles which do not need pilots. An important capability for such autonomous vehicles is to be able to ...
Dynamic Optimization for Optimal Control of Water Distribution Systems
Ertin, Emre
the water demand while maintaining the tank levels at acceptable margins. Figure 1 shows a schematic is at low levels. A pump scheduler should anticipate water demand and choose control actions to maximizeDynamic Optimization for Optimal Control of Water Distribution Systems Emre Ertin, Anthony N. Dean
TRACKING CODE DEVELOPMENT FOR BEAM DYNAMICS OPTIMIZATION
Yang, L.
2011-03-28
Dynamic aperture (DA) optimization with direct particle tracking is a straight forward approach when the computing power is permitted. It can have various realistic errors included and is more close than theoretical estimations. In this approach, a fast and parallel tracking code could be very helpful. In this presentation, we describe an implementation of storage ring particle tracking code TESLA for beam dynamics optimization. It supports MPI based parallel computing and is robust as DA calculation engine. This code has been used in the NSLS-II dynamics optimizations and obtained promising performance.
NASA Astrophysics Data System (ADS)
Kim, Seong-In; Han, Junghee; Lee, Youngho; Shim, Bo-Kyung; Hameed, Ibrahim A.
2008-12-01
The blending procedure, selecting from various kinds of zinc-bearing materials containing different contents of components and determining their mixing ratios, is the first step in the hydrometallurgical zinc process. Whenever a setup takes place to change the selection of materials or their ratios in the blending process, the setup itself and the adjustments required in the subsequent processes incur cost, time, and effort. Reducing the number of setups by keeping the blending ratios of the selected materials for the longest possible period is critical for consistent operation and processing and, thus, for assuring the quality of the final zinc product. In this article, we formulate a mixed integer programming model for the blending schedule that minimizes the number of setups over a planning horizon, given the constraints on the contents of components and the daily schedules of material supply and zinc production. Also, we propose an efficient heuristic, which provides a solution of good quality within reasonable time bounds. The applicability and efficiency of the model along with its heuristic are verified through simulation experiments as well as the model’s application to a real zinc refinery.
Dynamic Currency Determination in Optimized Programs
Dhamdhere, Dhananjay Madhav
Dynamic Currency Determination in Optimized Programs D. M. DHAMDHERE and K. V. SANKARANARAYANAN is the same as its expected value. We use the notion of dynamic currency of a variable in source currency determination. We prove that the minimal unrolled graph is an adequate basis for performing bit
Control of quantum dynamics by optimized measurements
Feng Shuang; Mianlai Zhou; Alexander Pechen; Rebing Wu; Ofer M. Shir; Herschel Rabitz
2009-02-16
Quantum measurements are considered for optimal control of quantum dynamics with instantaneous and continuous observations utilized to manipulate population transfer. With an optimal set of measurements, the highest yield in a two-level system can be obtained. The analytical solution is given for the problem of population transfer by measurement-assisted coherent control in a three-level system with a dynamical symmetry. The anti-Zeno effect is recovered in the controlled processes. The demonstrations in the paper show that suitable observations can be powerful tools in the manipulation of quantum dynamics.
Efficient dynamic optimization of logic programs
NASA Technical Reports Server (NTRS)
Laird, Phil
1992-01-01
A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.
Streak camera dynamic range optimization
Wiedwald, J.D.; Lerche, R.A.
1987-09-01
The LLNL optical streak camera is used by the Laser Fusion Program in a wide range of applications. Many of these applications require a large recorded dynamic range. Recent work has focused on maximizing the dynamic range of the streak camera recording system. For our streak cameras, image intensifier saturation limits the upper end of the dynamic range. We have developed procedures to set the image intensifier gain such that the system dynamic range is maximized. Specifically, the gain is set such that a single streak tube photoelectron is recorded with an exposure of about five times the recording system noise. This ensures detection of single photoelectrons, while not consuming intensifier or recording system dynamic range through excessive intensifier gain. The optimum intensifier gain has been determined for two types of film and for a lens-coupled CCD camera. We have determined that by recording the streak camera image with a CCD camera, the system is shot-noise limited up to the onset of image intensifier nonlinearity. When recording on film, the film determines the noise at high exposure levels. There is discussion of the effects of slit width and image intensifier saturation on dynamic range. 8 refs.
Supercomputer Optimization for Stochastic Dynamic Programming.
NASA Astrophysics Data System (ADS)
Siu-Leung, Chung
Optimizations for computational stochastic dynamic programming are presented. The computational methods are valid for a general class of optimal control problems that are nonlinear and perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian noise and jump Poisson noise. Codes are developed and tested on vector multiprocessors or vectorizing supercomputers such as the Alliant FX/8 and the Crays. Optimization techniques include advanced data structure, loop restructuring and compiler directives. The finite element method is adapted to stochastic dynamic programming. Its computational efficiency is compared to that of the finite difference method. These advanced numerical and computing techniques and fast supercomputing hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations by permitting the solution of larger problems in a fast and efficient way.
Use of profilers for studying Java dynamic optimizations
Machkasova, Elena
Use of profilers for studying Java dynamic optimizations Kevin Arhelger, Fernando Trinciante, Elena dynamically (i.e. at run-time) by the JVM. A just-in-time compiler (JIT) is a part of the JVM that performs dynamic optimizations. Our research goal is to be able to detect and study dynamic optimizations performed
Optimal BLS: Optimizing transit-signal detection for Keplerian dynamics
NASA Astrophysics Data System (ADS)
Ofir, Aviv
2015-08-01
Transit surveys, both ground- and space-based, have already accumulated a large number of light curves that span several years. We optimize the search for transit signals for both detection and computational efficiencies by assuming that the searched systems can be described by Keplerian, and propagating the effects of different system parameters to the detection parameters. Importnantly, we mainly consider the information content of the transit signal and not any specific algorithm - and use BLS (Kovács, Zucker, & Mazeh 2002) just as a specific example.We show that the frequency information content of the light curve is primarily determined by the duty cycle of the transit signal, and thus the optimal frequency sampling is found to be cubic and not linear. Further optimization is achieved by considering duty-cycle dependent binning of the phased light curve. By using the (standard) BLS, one is either fairly insensitive to long-period planets or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3 yr long dataset). We also show how the physical system parameters, such as the host star's size and mass, directly affect transit detection. This understanding can then be used to optimize the search for every star individually.By considering Keplerian dynamics explicitly rather than implicitly one can optimally search the transit signal parameter space. The presented Optimal BLS enhances the detectability of both very short and very long period planets, while allowing such searches to be done with much reduced resources and time. The Matlab/Octave source code for Optimal BLS is made available.
Optimized Dynamical Decoupling via Genetic Algorithms
Gregory Quiroz; Daniel A. Lidar
2013-08-07
We utilize genetic algorithms to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse-intervals and perform the optimization with respect to pulse type and order. In this manner we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
MOBILE ROBOTS PATH PLANNING OPTIMIZATION IN STATIC AND DYNAMIC ENVIRONMENTS
Areibi, Shawki M
MOBILE ROBOTS PATH PLANNING OPTIMIZATION IN STATIC AND DYNAMIC ENVIRONMENTS A Thesis Presented PATH PLANNING OPTIMIZATION IN STATIC AND DYNAMIC ENVIRONMENTS Ahmed Elshamli University of Guelph, 2004 Advisor: Professor: Hussein A. Abdullah Professor: Shawki Areibi Path planning for mobile robots
Optimization and stabilization of trajectories for constrained dynamical systems
Tedrake, Russ
Optimization and stabilization of trajectories for constrained dynamical systems Michael Posa1 constraints. We introduce an extension to the direct collocation trajectory optimization algorithm advances in motion planning techniques based on trajectory optimization that now make it possible
IMPACT OF DYNAMIC VOLTAGE SCALING (DVS) ON CIRCUIT OPTIMIZATION
Esquit Hernandez, Carlos A.
2010-01-16
Circuit designers perform optimization procedures targeting speed and power during the design of a circuit. Gate sizing can be applied to optimize for speed, while Dual-VT and Dynamic Voltage Scaling (DVS) can be applied to optimize for leakage...
Application of optimal prediction to molecular dynamics
Barber IV, John Letherman
2004-12-01
Optimal prediction is a general system reduction technique for large sets of differential equations. In this method, which was devised by Chorin, Hald, Kast, Kupferman, and Levy, a projection operator formalism is used to construct a smaller system of equations governing the dynamics of a subset of the original degrees of freedom. This reduced system consists of an effective Hamiltonian dynamics, augmented by an integral memory term and a random noise term. Molecular dynamics is a method for simulating large systems of interacting fluid particles. In this thesis, I construct a formalism for applying optimal prediction to molecular dynamics, producing reduced systems from which the properties of the original system can be recovered. These reduced systems require significantly less computational time than the original system. I initially consider first-order optimal prediction, in which the memory and noise terms are neglected. I construct a pair approximation to the renormalized potential, and ignore three-particle and higher interactions. This produces a reduced system that correctly reproduces static properties of the original system, such as energy and pressure, at low-to-moderate densities. However, it fails to capture dynamical quantities, such as autocorrelation functions. I next derive a short-memory approximation, in which the memory term is represented as a linear frictional force with configuration-dependent coefficients. This allows the use of a Fokker-Planck equation to show that, in this regime, the noise is {delta}-correlated in time. This linear friction model reproduces not only the static properties of the original system, but also the autocorrelation functions of dynamical variables.
Optimal reduction of flexible dynamic system
Jankovic, J.
1994-12-31
Dynamic system reduction is basic procedure in various problems of active control synthesis of flexible structures. In this paper is presented direct method for system reduction by explicit extraction of modes included in reduced model form. Criterion for optimal system discrete approximation in synthesis reduced dynamic model is also presented. Subjected method of system decomposition is discussed in relation to the Schur method of solving matrix algebraic Riccati equation as condition for system reduction. By using exposed method procedure of flexible system reduction in addition with corresponding example is presented. Shown procedure is powerful in problems of active control synthesis of flexible system vibrations.
Dynamics of Dengue epidemics using optimal control
Rodrigues, Helena Sofia; Torres, Delfim F M
2010-01-01
We present an application of optimal control theory to Dengue epidemics. This epidemiologic disease is an important theme in tropical countries due to the growing number of infected individuals. The dynamic model is described by a set of nonlinear ordinary differential equations, that depend on the dynamic of the Dengue mosquito, the number of infected individuals, and the people's motivation to combat the mosquito. The cost functional depends not only on the costs of medical treatment of the infected people but also on the costs related to educational and sanitary campaigns. Two approaches to solve the problem are considered: one using optimal control theory, another one by discretizing first the problem and then solving it with nonlinear programming. The results obtained with OC-ODE and IPOPT solvers are given and discussed. We observe that with current computational tools it is easy to obtain, in an efficient way, better solutions to Dengue problems, leading to a decrease of infected mosquitoes and individ...
Direct Optimal Control of Duffing Dynamics
NASA Technical Reports Server (NTRS)
Oz, Hayrani; Ramsey, John K.
2002-01-01
The "direct control method" is a novel concept that is an attractive alternative and competitor to the differential-equation-based methods. The direct method is equally well applicable to nonlinear, linear, time-varying, and time-invariant systems. For all such systems, the method yields explicit closed-form control laws based on minimization of a quadratic control performance measure. We present an application of the direct method to the dynamics and optimal control of the Duffing system where the control performance measure is not restricted to a quadratic form and hence may include a quartic energy term. The results we present in this report also constitute further generalizations of our earlier work in "direct optimal control methodology." The approach is demonstrated for the optimal control of the Duffing equation with a softening nonlinear stiffness.
Rigorous bounds for optimal dynamical decoupling
Uhrig, Goetz S.; Lidar, Daniel A.
2010-07-15
We present rigorous performance bounds for the optimal dynamical decoupling pulse sequence protecting a quantum bit (qubit) against pure dephasing. Our bounds apply under the assumption of instantaneous pulses and of bounded perturbing environment and qubit-environment Hamiltonians such as those realized by baths of nuclear spins in quantum dots. We show that if the total sequence time is fixed the optimal sequence can be used to make the distance between the protected and unperturbed qubit states arbitrarily small in the number of applied pulses. If, on the other hand, the minimum pulse interval is fixed and the total sequence time is allowed to scale with the number of pulses, then longer sequences need not always be advantageous. The rigorous bound may serve as a testbed for approximate treatments of optimal decoupling in bounded models of decoherence.
Trajectory optimization for domains with contacts using inverse dynamics
Todorov, Emanuel
Trajectory optimization for domains with contacts using inverse dynamics Tom Erez and Emanuel the control that yields a trajectory of minimal total cost. Global methods of optimal control find an optimal of trajectory optimization -- by identifying only locally-optimal trajectories, these algo- rithms achieve
Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming
Neumaier, Arnold
Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming locally optimal plans are actually globally optimal, up to the resolution of our search procedures answers can play a useful role in speeding up the process of computing or learning a globally optimal
Direct Trajectory Optimization of Rigid Body Dynamical Systems Through Contact
Tedrake, Russ
Direct Trajectory Optimization of Rigid Body Dynamical Systems Through Contact Michael Posa and Russ Tedrake Abstract Direct methods for trajectory optimization are widely used for planning locally optimal trajectories of robotic systems. Most state-of-the-art techniques treat the discontinuous dynamics
Optimized Noise Filtration through Dynamical Decoupling
Hermann Uys; Michael J. Biercuk; John J. Bollinger
2009-06-24
One approach to maintaining phase coherence of qubits through dynamical decoupling consists of applying a sequence of Hahn spin-echo pulses. Recent studies have shown that, in certain noise environments, judicious choice of the delay times between these pulses can greatly improve the suppression of phase errors compared to traditional approaches. By enforcing a simple analytical condition, we obtain sets of dynamical decoupling sequences that are designed for optimized noise filtration and are spectrum-independent up to a single scaling factor set by the coherence time of the system. We demonstrate the efficacy of these sequences in suppressing phase errors through measurements on a model qubit system, $^{9}$Be$^{+}$ ions in a Penning trap. Our combined theoretical and experimental studies show that in high-frequency-dominated noise environments this approach may suppress phase errors orders of magnitude more efficiently than comparable techniques can.
Optimal Empirical Prognostic Models of Climate Dynamics
NASA Astrophysics Data System (ADS)
Loskutov, E. M.; Mukhin, D.; Gavrilov, A.; Feigin, A. M.
2014-12-01
In this report the empirical methodology for prediction of climate dynamics is suggested. We construct the dynamical models of data patterns connected with climate indices, from observed spatially distributed time series. The models are based on artificial neural network (ANN) parameterization and have a form of discrete stochastic evolution operator mapping some sequence of systems state on the next one [1]. Different approaches to reconstruction of empirical basis (phase variables) for system's phase space representation, which is appropriate for forecasting the climate index of interest, are discussed in the report; for this purpose both linear and non-linear data expansions are considered. The most important point of the methodology is finding the optimal structural parameters of the model such as dimension of variable vector, i.e. number of principal components used for modeling, the time lag used for prediction, and number of neurons in ANN determining the quality of approximation. Actually, we need to solve the model selection problem, i.e. we want to obtain a model of optimal complexity in relation to analyzed time series. We use MDL approach [2] for this purpose: the model providing best data compression is chosen. The method is applied to space-distributed time-series of sea surface temperature and sea level pressure taken from IRI datasets [3]: the ability of proposed models to predict different climate indices (incl. Multivariate ENSO index, Pacific Decadal Oscillation index, North-Atlantic Oscillation index) is investigated. References:1. Molkov Ya. I., E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, Random dynamical models from time series. Phys. Rev. E, 85, 036216, 2012.2. Molkov, Ya.I., D.N. Mukhin, E.M. Loskutov, A.M. Feigin, and G.A. Fidelin, Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series. Phys. Rev. E, 80, 046207, 2009.3. IRI/LDEO Climate Data Library (http://iridl.ldeo.columbia.edu/)
Structural optimization for nonlinear dynamic response.
Dou, Suguang; Strachan, B Scott; Shaw, Steven W; Jensen, Jakob S
2015-09-28
Much is known about the nonlinear resonant response of mechanical systems, but methods for the systematic design of structures that optimize aspects of these responses have received little attention. Progress in this area is particularly important in the area of micro-systems, where nonlinear resonant behaviour is being used for a variety of applications in sensing and signal conditioning. In this work, we describe a computational method that provides a systematic means for manipulating and optimizing features of nonlinear resonant responses of mechanical structures that are described by a single vibrating mode, or by a pair of internally resonant modes. The approach combines techniques from nonlinear dynamics, computational mechanics and optimization, and it allows one to relate the geometric and material properties of structural elements to terms in the normal form for a given resonance condition, thereby providing a means for tailoring its nonlinear response. The method is applied to the fundamental nonlinear resonance of a clamped-clamped beam and to the coupled mode response of a frame structure, and the results show that one can modify essential normal form coefficients by an order of magnitude by relatively simple changes in the shape of these elements. We expect the proposed approach, and its extensions, to be useful for the design of systems used for fundamental studies of nonlinear behaviour as well as for the development of commercial devices that exploit nonlinear behaviour. PMID:26303922
Nonsmooth dynamic optimization of systems with varying structure
Yunt, Mehmet, 1975-
2011-01-01
In this thesis, an open-loop numerical dynamic optimization method for a class of dynamic systems is developed. The structure of the governing equations of the systems under consideration change depending on the values of ...
Dynamic optimization of a copolymerization reactor using tabu search.
Anand, P; Rao, M Bhagvanth; Venkateswarlu, Ch
2015-03-01
A novel multistage dynamic optimization strategy based on meta-heuristic tabu search (TS) is proposed and evaluated through sequential and simultaneous implementation procedures by applying it to a semi-batch styrene-acrylonitrile (SAN) copolymerization reactor. The adaptive memory and responsive exploration features of TS are exploited to design the dynamic optimization strategy and compute the optimal control policies for temperature and monomer addition rate so as to achieve the desired product quality parameters expressed in terms of single and multiple objectives. The dynamic optimization results of TS sequential and TS simultaneous implementation strategies are analyzed and compared with those of a conventional optimization technique based on iterative dynamic programming (IDP). The simulation results demonstrate the usefulness of TS for optimal control of transient dynamic systems. PMID:25466914
Optimization with extremal dynamics for the traveling salesman problem
NASA Astrophysics Data System (ADS)
Chen, Yu-Wang; Lu, Yong-Zai; Chen, Peng
2007-11-01
By mapping the optimization problems to physical systems, the paper presents a general-purpose stochastic optimization method with extremal dynamics. It is built up with the traveling salesman problem (TSP) being a typical NP-complete problem. As self-organized critical processes of extremal dynamics, the optimization dynamics successively updates the states of those cities with high energy. Consequently, a near-optimal solution can be quickly obtained through the optimization processes combining the two phases of greedy searching and fluctuated explorations (ergodic walk near the phase transition). The computational results demonstrate that the proposed optimization method may provide much better performance than other optimization techniques developed from statistical physics, such as simulated annealing (SA). Since the proposed fundamental solution is based on the principles and micromechanisms of computational systems, it can provide systematic viewpoints and effective computational methods on a wide spectrum of combinatorial and physical optimization problems.
Distributed Adaptive Particle Swarm Optimizer in Dynamic Environment
Cui, Xiaohui; Potok, Thomas E
2007-01-01
In the real world, we have to frequently deal with searching and tracking an optimal solution in a dynamical and noisy environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the changing solution. Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. In PSO algorithm, the problem solution emerges from the interactions between many simple individual agents called particles, which make PSO an inherently distributed algorithm. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic and noisy environment. In this paper, we present a distributed adaptive PSO (DAPSO) algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing and noisy environment.
March 19, 2008 1 Abstract--In this paper, we continue to analyze optimal
Oren, Shmuel S.
: generator d: load Variables n: voltage angle at node n Pnmk: real power flow from node m to n optimal power flow (DCOPF) used to dispatch generators to meet load in an efficient manner. We then use--Mixed integer programming, Power generation dispatch, Power system economics, Power transmission control, Power
Grossmann, Ignacio E.
1 Optimal supply chain design and management over a multi-period horizon under demand uncertainty motors. Keywords: supply chain, demand uncertainty, inventory management, mixed integer non An optimization model is proposed to redesign the supply chain of spare part delivery under demand uncertainty
Ant Colony Optimization with Immigrants Schemes in Dynamic Environments
Yang, Shengxiang
Ant Colony Optimization with Immigrants Schemes in Dynamic Environments Michalis Mavrovouniotis1 of the pop- ulation and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants immigrants schemes are applied to ant colony optimization (ACO) for the dynamic travelling salesman problem
Dynamic Multidrug Therapies for HIV: Optimal and STI Control Approaches
the corresponding optimality systems. In addition, using ideas from dynamic programming, we formulate and deriveDynamic Multidrug Therapies for HIV: Optimal and STI Control Approaches B. M. Adams 1, H. T. Banks mathematical model that describes the interaction of the immune system with the human immunodeficiency virus
April 12, 2007 Dynamic Real-Time Optimization
Grossmann, Ignacio E.
April 12, 2007 Dynamic Real-Time Optimization: Linking Off-line Planning with On-line Optimization, PA 15213 1 #12;Overview Introduction · Pyramid of operations · Off-line vs On-line Tasks · Steady state vs Dynamics · Enablers and open questions On-line Issues · Model predictive control · Treatment
Optimal control of HIV/AIDS dynamic: Education and treatment
NASA Astrophysics Data System (ADS)
Sule, Amiru; Abdullah, Farah Aini
2014-07-01
A mathematical model which describes the transmission dynamics of HIV/AIDS is developed. The optimal control representing education and treatment for this model is explored. The existence of optimal Control is established analytically by the use of optimal control theory. Numerical simulations suggest that education and treatment for the infected has a positive impact on HIV/AIDS control.
Optimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments
Guo, Yi
Optimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments Yi Guo and Tang Tang Abstract-- We study optimal trajectory generation for non- holonomic mobile robots in the presence criterion, optimal collision-free trajectory can be generated real time as the solution is expressed in its
Optimal Control of Switched Dynamical Systems under Dwell Time Constraints
Egerstedt, Magnus
Optimal Control of Switched Dynamical Systems under Dwell Time Constraints Usman Ali1 and Magnus constraint has not received attention in research on optimal control of switched systems. In this paper, we consider the common optimal control problem of minimizing a cost functional defined on the state trajectory
Chaotic dynamics in optimal monetary policy
NASA Astrophysics Data System (ADS)
Gomes, O.; Mendes, V. M.; Mendes, D. A.; Sousa Ramos, J.
2007-05-01
There is by now a large consensus in modern monetary policy. This consensus has been built upon a dynamic general equilibrium model of optimal monetary policy as developed by, e.g., Goodfriend and King [ NBER Macroeconomics Annual 1997 edited by B. Bernanke and J. Rotemberg (Cambridge, Mass.: MIT Press, 1997), pp. 231 282], Clarida et al. [J. Econ. Lit. 37, 1661 (1999)], Svensson [J. Mon. Econ. 43, 607 (1999)] and Woodford [ Interest and Prices: Foundations of a Theory of Monetary Policy (Princeton, New Jersey, Princeton University Press, 2003)]. In this paper we extend the standard optimal monetary policy model by introducing nonlinearity into the Phillips curve. Under the specific form of nonlinearity proposed in our paper (which allows for convexity and concavity and secures closed form solutions), we show that the introduction of a nonlinear Phillips curve into the structure of the standard model in a discrete time and deterministic framework produces radical changes to the major conclusions regarding stability and the efficiency of monetary policy. We emphasize the following main results: (i) instead of a unique fixed point we end up with multiple equilibria; (ii) instead of saddle-path stability, for different sets of parameter values we may have saddle stability, totally unstable equilibria and chaotic attractors; (iii) for certain degrees of convexity and/or concavity of the Phillips curve, where endogenous fluctuations arise, one is able to encounter various results that seem intuitively correct. Firstly, when the Central Bank pays attention essentially to inflation targeting, the inflation rate has a lower mean and is less volatile; secondly, when the degree of price stickiness is high, the inflation rate displays a larger mean and higher volatility (but this is sensitive to the values given to the parameters of the model); and thirdly, the higher the target value of the output gap chosen by the Central Bank, the higher is the inflation rate and its volatility.
Multi-objective optimization for deepwater dynamic umbilical installation analysis
NASA Astrophysics Data System (ADS)
Yang, HeZhen; Wang, AiJun; Li, HuaJun
2012-08-01
We suggest a method of multi-objective optimization based on approximation model for dynamic umbilical installation. The optimization aims to find out the most cost effective size, quantity and location of buoyancy modules for umbilical installation while maintaining structural safety. The approximation model is constructed by the design of experiment (DOE) sampling and is utilized to solve the problem of time-consuming analyses. The non-linear dynamic analyses considering environmental loadings are executed on these sample points from DOE. Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to obtain the Pareto solution set through an evolutionary optimization process. Intuitionist fuzzy set theory is applied for selecting the best compromise solution from Pareto set. The optimization results indicate this optimization strategy with approximation model and multiple attribute decision-making method is valid, and provide the optimal deployment method for deepwater dynamic umbilical buoyancy modules.
Dynamic systems of regional economy management optimization
NASA Astrophysics Data System (ADS)
Trofimov, S.; Kudzh, S.
One of the most actual problems of the Russian economic life is a regional economic systems formation. The hierarchy of economic and branch priorities should follow from the general idea of an industrial policy. The matter is that the concept of an industrial policy is defined by the system of priorities mainly incorporated in it. The problem of priorities is not solved yet neither on federal, nor at a regional level. It is necessary to recognize, that a substantiation of this or that variant of priorities - objectively a challenge. Such substantiation can be received with the help of dynamic structural modeling and management technology. At formation of the regional industrial policy program the special attention is given to creation of modern type commercial structures. In regions there are headquarters and branches of many largest corporations, holdings and banks. Besides it, many regional enterprises already became inter-regional or even the transnational companies. In this connection an assistance of transformation of the industrial enterprises and their groups in vertically integrated companies and modern type holdings can become a prominent aspect of an industrial policy. Regional economic structures should be reconstructed gradually on the general model of the world class competitive companies. Assistance to creation of new corporational control systems, the organization of headquarters and the central services work - all this can be included into the sphere of regional administration industrial policy. The special attention should be turned on necessity of development of own system of the corporate structures, capable to provide to the region an independent participation in use of the natural resources and industrial-technological potential, at the stage of a regional industrial policy program formation. Transformation of the industrial enterprises and their groups into modern type vertically-integrated companies and holdings can become one of the major directions of an industrial policy of region. The situational-analytical centers (SAC) of regional administration The major component of SAC is dynamic modeling, analysis, forecasting and optimization systems, based on modern intellectual information technologies. Spheres of SAC are not only financial streams management and investments optimization, but also strategic forecasting functions, which provide an optimum choice, "aiming", search of optimum ways of regional development and corresponding investments. It is expedient to consider an opportunity of formation of the uniform organizational-methodical center of an industrial policy of region. This organization can be directly connected to the scheduled-analytical services of the largest economic structures, local authorities, the ministries and departments. Such "direct communication" is capable to provide an effective regional development strategic management. Anyway, the output on foreign markets demands concentration of resources and support of authorities. Offered measures are capable to provide a necessary coordination of efforts of a various level economic structures. For maintenance of a regional industrial policy an attraction of all newest methods of strategic planning and management is necessary. Their activity should be constructed on the basis of modern approaches of economic systems management, cause the essence of an industrial policy is finally reduced to an effective regional and corporate economic activities control centers formation. Opportunities of optimum regional economy planning and management as uniform system Approaches to planning regional economic systems can be different. We will consider some most effective methods of planning and control over a regional facilities condition. All of them are compact and evident, that allows to put them into the group of average complexity technologies. At the decision of problems of a regional resource management is rather perspective the so-called "topographical" approach, which is used by intellectual information tec
Noise-optimal capture for high dynamic range photography
Hasinoff, Samuel W.
Taking multiple exposures is a well-established approach both for capturing high dynamic range (HDR) scenes and for noise reduction. But what is the optimal set of photos to capture? The typical approach to HDR capture ...
Validated global multiobjective optimization of trajectories in nonlinear dynamical systems
Coffee, Thomas Merritt
2015-01-01
We introduce a new approach for global multiobjective optimization of trajectories in continuous nonlinear dynamical systems that can provide rigorous, arbitrarily tight bounds on the objective values and state paths ...
An Optimization Framework for Dynamic Hybrid Energy Systems
Wenbo Du; Humberto E Garcia; Christiaan J.J. Paredis
2014-03-01
A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problem takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.
Review of dynamic optimization methods in renewable natural resource management
Williams, B.K.
1989-01-01
In recent years, the applications of dynamic optimization procedures in natural resource management have proliferated. A systematic review of these applications is given in terms of a number of optimization methodologies and natural resource systems. The applicability of the methods to renewable natural resource systems are compared in terms of system complexity, system size, and precision of the optimal solutions. Recommendations are made concerning the appropriate methods for certain kinds of biological resource problems.
Optimal Routing-Conscious Dynamic Placement for Reconfigurable Devices
Fekete, Sándor P.
Optimal Routing-Conscious Dynamic Placement for Reconfigurable Devices Ali Ahmadinia1 , Christophe and O(n) for heuristic results. We also show that finding an optimal feasible communication-conscious quadratic. The more difficult task of routing-conscious placement has received less attention: Clearly
Nonlinear Hybrid Dynamical Systems: Modeling, Optimal Control, and Applications
Stryk, Oskar von
Nonlinear Hybrid Dynamical Systems: Modeling, Optimal Control, and Applications Martin Buss1 Systems Group, Technische Universit¨at Berlin, Berlin, Germany 2 Simulation and Systems Optimization Group, Technische Universit¨at Darmstadt, Darmstadt, Germany 3 Zentrum Mathematik, Technische Universit¨at M
de Weerdt, Mathijs
methods over varying levels of uncertainty in comparison to traditional optimization methods are generally utilizes a mixed integer program to obtain a feasible route at 30-second intervals. The second solution container traffic grew to approximately 417 million twenty foot equivalent units (TEUs) in 2006 (BTS, 2007
First principles molecular dynamics without self-consistent field optimization
Souvatzis, Petros; Niklasson, Anders M. N.
2014-01-28
We present a first principles molecular dynamics approach that is based on time-reversible extended Lagrangian Born-Oppenheimer molecular dynamics [A. M. N. Niklasson, Phys. Rev. Lett. 100, 123004 (2008)] in the limit of vanishing self-consistent field optimization. The optimization-free dynamics keeps the computational cost to a minimum and typically provides molecular trajectories that closely follow the exact Born-Oppenheimer potential energy surface. Only one single diagonalization and Hamiltonian (or Fockian) construction are required in each integration time step. The proposed dynamics is derived for a general free-energy potential surface valid at finite electronic temperatures within hybrid density functional theory. Even in the event of irregular functional behavior that may cause a dynamical instability, the optimization-free limit represents a natural starting guess for force calculations that may require a more elaborate iterative electronic ground state optimization. Our optimization-free dynamics thus represents a flexible theoretical framework for a broad and general class of ab initio molecular dynamics simulations.
Optimized dynamical control of state transfer through noisy spin chains
Analia Zwick; Gonzalo A. Alvarez; Guy Bensky; Gershon Kurizki
2015-01-09
We propose a method of optimally controlling the tradeoff of speed and fidelity of state transfer through a noisy quantum channel (spin-chain). This process is treated as qubit state-transfer through a fermionic bath. We show that dynamical modulation of the boundary-qubits levels can ensure state transfer with the best tradeoff of speed and fidelity. This is achievable by dynamically optimizing the transmission spectrum of the channel. The resulting optimal control is robust against both static and fluctuating noise in the channel's spin-spin couplings. It may also facilitate transfer in the presence of diagonal disorder (on site energy noise) in the channel.
Optimized dynamical control of state transfer through noisy spin chains
NASA Astrophysics Data System (ADS)
Zwick, Analia; Álvarez, Gonzalo A.; Bensky, Guy; Kurizki, Gershon
2014-06-01
We propose a method of optimally controlling the tradeoff of speed and fidelity of state transfer through a noisy quantum channel (spin-chain). This process is treated as qubit state-transfer through a fermionic bath. We show that dynamical modulation of the boundary-qubits levels can ensure state transfer with the best tradeoff of speed and fidelity. This is achievable by dynamically optimizing the transmission spectrum of the channel. The resulting optimal control is robust against both static and fluctuating noise in the channel's spin-spin couplings. It may also facilitate transfer in the presence of diagonal disorder (on site energy noise) in the channel.
Neural dynamic optimization for control systems.II. Theory.
Seong, C Y; Widrow, B
2001-01-01
The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the theory of NDO, while the two other companion papers of this topic explain the background for the development of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively. PMID:18244816
Neural dynamic optimization for control systems. I. Background.
Seong, C Y; Widrow, B
2001-01-01
The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the background and motivations for the development of NDO, while the two other subsequent papers of this topic present the theory of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively. PMID:18244815
Dynamic positioning configuration and its first-order optimization
NASA Astrophysics Data System (ADS)
Xue, Shuqiang; Yang, Yuanxi; Dang, Yamin; Chen, Wu
2014-02-01
Traditional geodetic network optimization deals with static and discrete control points. The modern space geodetic network is, on the other hand, composed of moving control points in space (satellites) and on the Earth (ground stations). The network configuration composed of these facilities is essentially dynamic and continuous. Moreover, besides the position parameter which needs to be estimated, other geophysical information or signals can also be extracted from the continuous observations. The dynamic (continuous) configuration of the space network determines whether a particular frequency of signals can be identified by this system. In this paper, we employ the functional analysis and graph theory to study the dynamic configuration of space geodetic networks, and mainly focus on the optimal estimation of the position and clock-offset parameters. The principle of the D-optimization is introduced in the Hilbert space after the concept of the traditional discrete configuration is generalized from the finite space to the infinite space. It shows that the D-optimization developed in the discrete optimization is still valid in the dynamic configuration optimization, and this is attributed to the natural generalization of least squares from the Euclidean space to the Hilbert space. Then, we introduce the principle of D-optimality invariance under the combination operation and rotation operation, and propose some D-optimal simplex dynamic configurations: (1) (Semi) circular configuration in 2-dimensional space; (2) the D-optimal cone configuration and D-optimal helical configuration which is close to the GPS constellation in 3-dimensional space. The initial design of GPS constellation can be approximately treated as a combination of 24 D-optimal helixes by properly adjusting the ascending node of different satellites to realize a so-called Walker constellation. In the case of estimating the receiver clock-offset parameter, we show that the circular configuration, the symmetrical cone configuration and helical curve configuration are still D-optimal. It shows that the given total observation time determines the optimal frequency (repeatability) of moving known points and vice versa, and one way to improve the repeatability is to increase the rotational speed. Under the Newton's law of motion, the frequency of satellite motion determines the orbital altitude. Furthermore, we study three kinds of complex dynamic configurations, one of which is the combination of D-optimal cone configurations and a so-called Walker constellation composed of D-optimal helical configuration, the other is the nested cone configuration composed of n cones, and the last is the nested helical configuration composed of n orbital planes. It shows that an effective way to achieve high coverage is to employ the configuration composed of a certain number of moving known points instead of the simplex configuration (such as D-optimal helical configuration), and one can use the D-optimal simplex solutions or D-optimal complex configurations in any combination to achieve powerful configurations with flexile coverage and flexile repeatability. Alternately, how to optimally generate and assess the discrete configurations sampled from the continuous one is discussed. The proposed configuration optimization framework has taken the well-known regular polygons (such as equilateral triangle and quadrangular) in two-dimensional space and regular polyhedrons (regular tetrahedron, cube, regular octahedron, regular icosahedron, or regular dodecahedron) into account. It shows that the conclusions made by the proposed technique are more general and no longer limited by different sampling schemes. By the conditional equation of D-optimal nested helical configuration, the relevance issues of GNSS constellation optimization are solved and some examples are performed by GPS constellation to verify the validation of the newly proposed optimization technique. The proposed technique is potentially helpful in maintenance and quadratic optimization of single GNSS of which the orbi
Bridging Developmental Systems Theory and Evolutionary Psychology Using Dynamic Optimization
ERIC Educational Resources Information Center
Frankenhuis, Willem E.; Panchanathan, Karthik; Clark Barrett, H.
2013-01-01
Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic…
OPTIMAL EXPOSURE CONTROL FOR HIGH DYNAMIC RANGE IMAGING Keigo Hirakawa
Hirakawa, Keigo
OPTIMAL EXPOSURE CONTROL FOR HIGH DYNAMIC RANGE IMAGING Keigo Hirakawa University of Dayton (ECE used to acquire high dynamic range im- age data is that of exposure bracketing--short exposure times are required to capture bright regions of the image without saturation, whereas long exposure times are needed
February 15, 2006 Dynamic Optimization for
Grossmann, Ignacio E.
(Foxboro/SimSci/Esscor) , LIC Energy/ESI , Pavilion Technologies, Shell Global Solutions, SST, STN ATLAS with A+ models · Dow/MDC/Emerson - RTO of system of 4 cogeneration plants, optimization run every 30 min
Fault tolerant and dynamic evolutionary optimization engines
Morales Reyes, Alicia
2011-01-01
Mimicking natural evolution to solve hard optimization problems has played an important role in the artificial intelligence arena. Such techniques are broadly classified as Evolutionary Algorithms (EAs) and have been ...
DYNAMIC RISK MANAGEMENT IN ELECTRICITY PORTFOLIO OPTIMIZATION
Römisch, Werner
post, i.e., after power production planning, derivative products such as futures or options are traded in electricity portfolio optimization. The approach of polyhedral risk functionals, motivated through tra
Stochastic optimal control with learned dynamics models
Mitrovic, Djordje
2011-01-01
The motor control of anthropomorphic robotic systems is a challenging computational task mainly because of the high levels of redundancies such systems exhibit. Optimality principles provide a general strategy to resolve ...
Practical synchronization on complex dynamical networks via optimal pinning control.
Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu
2015-07-01
We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications. PMID:26274112
Practical synchronization on complex dynamical networks via optimal pinning control
NASA Astrophysics Data System (ADS)
Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu
2015-07-01
We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.
Dynamic optimization of metabolic networks coupled with gene expression.
Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander
2015-01-21
The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. PMID:25451533
Observation-assisted optimal control of quantum dynamics
Feng Shuang; Alexander Pechen; Tak-San Ho; Herschel Rabitz
2007-05-31
This paper explores the utility of instantaneous and continuous observations in the optimal control of quantum dynamics. Simulations of the processes are performed on several multilevel quantum systems with the goal of population transfer. Optimal control fields are shown to be capable of cooperating or fighting with observations to achieve a good yield, and the nature of the observations may be optimized to more effectively control the quantum dynamics. Quantum observations also can break dynamical symmetries to increase the controllability of a quantum system. The quantum Zeno and anti-Zeno effects induced by observations are the key operating principles in these processes. The results indicate that quantum observations can be effective tools in the control of quantum dynamics.
A generalized gradient algorithm for dynamic optimization
NASA Technical Reports Server (NTRS)
Zhao, Yiyuan; Bryson, A. E.; Slattery, R.
1989-01-01
A gradient algorithm is developed that determines optimal trajectories with path equality constraints and terminal constraints. A generalized gradient is formed which improves both the performance index and the path equality constraints simultaneously. The algorithm is extended to treat terminal constraints by using Bryson's impulse response technique. The main features of this algorithm are its numerical stability and smooth convergence near the optimum.
Global dynamic optimization approach to predict activation in metabolic pathways
2014-01-01
Background During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been succesfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. Results In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. Conclusions The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints. PMID:24393148
Aerospace applications of integer and combinatorial optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in solving combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on a large space structure and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.
Aerospace applications on integer and combinatorial optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem. for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.
Aerospace Applications of Integer and Combinatorial Optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.
Recursive multibody dynamics and discrete-time optimal control
NASA Technical Reports Server (NTRS)
Deleuterio, G. M. T.; Damaren, C. J.
1989-01-01
A recursive algorithm is developed for the solution of the simulation dynamics problem for a chain of rigid bodies. Arbitrary joint constraints are permitted, that is, joints may allow translational and/or rotational degrees of freedom. The recursive procedure is shown to be identical to that encountered in a discrete-time optimal control problem. For each relevant quantity in the multibody dynamics problem, there exists an analog in the context of optimal control. The performance index that is minimized in the control problem is identified as Gibbs' function for the chain of bodies.
MULTIOBJECTIVE DYNAMIC APERTURE OPTIMIZATION AT NSLS-II
Yang, L.; Li, Y.; Guo, W.; Krinsky, S.
2011-03-28
In this paper we present a multiobjective approach to the dynamic aperture (DA) optimization. Taking the NSLS-II lattice as an example, we have used both sextupoles and quadrupoles as tuning variables to optimize both on-momentum and off-momentum DA. The geometric and chromatic sextupoles are used for nonlinear properties while the tunes are independently varied by quadrupoles. The dispersion and emittance are fixed during tunes variation. The algorithms, procedures, performances and results of our optimization of DA will be discussed and they are found to be robust, general and easy to apply to similar problems.
Speeding up critical system dynamics through optimized evolution
Caneva, Tommaso; Calarco, Tommaso; Montangero, Simone; Fazio, Rosario; Santoro, Giuseppe E.
2011-07-15
The number of defects which are generated upon crossing a quantum phase transition can be minimized by choosing properly designed time-dependent pulses. In this work we determine what are the ultimate limits of this optimization. We discuss under which conditions the production of defects across the phase transition is vanishing small. Furthermore we show that the minimum time required to enter this regime is T{approx}{pi}/{Delta}, where {Delta} is the minimum spectral gap, unveiling an intimate connection between an optimized unitary dynamics and the intrinsic measure of the Hilbert space for pure states. Surprisingly, the dynamics is nonadiabatic; this result can be understood by assuming a simple two-level dynamics for the many-body system. Finally we classify the possible dynamical regimes in terms of the action s=T{Delta}.
Dynamic optimization of district energy grid
NASA Astrophysics Data System (ADS)
Salsbery, Scott
The University of Iowa Power Plant operates utility generation and distribution for campus facilities, including electricity, steam, and chilled water. It is desirable to evaluate the optimal load combination of boilers, engines and chillers to meet the demand at minimal cost, particularly for future demand scenarios. An algorithm has been developed which takes into account the performance of individual units as part of the mix which ultimately supplies the campus and determine the degree that each should be operating to most efficiently meet demand. The algorithm is part of an integrated simulation tool which is specifically designed to apply traditional optimization techniques for a given (both current and possible) circumstance. The second component is to couple the algorithm with accurate estimates and historical data through which expected demand could be predicted. The simulation tool can account for any theoretical circumstance, which will be highly beneficial for strategic planning. As part of the process it is also necessary to determine the unique operating characteristics of the system components. The algorithms rely upon performance curves of individual system components (boiler, chiller, etc.) and those must be developed and refined when possible from experimental testing and commissioning or manufacturer supplied data.
Structural dynamics test simulation and optimization for aerospace components
Klenke, S.E.; Baca, T.J.
1996-06-01
This paper initially describes an innovative approach to product realization called Knowledge Based Testing (KBT). This research program integrates test simulation and optimization software, rapid fabrication techniques and computational model validation to support a new experimentally-based design concept. This design concept implements well defined tests earlier in the design cycle enabling the realization of highly reliable aerospace components. A test simulation and optimization software environment provides engineers with an essential tool needed to support this KBT approach. This software environment, called the Virtual Environment for Test Optimization (VETO), integrates analysis and test based models to support optimal structural dynamic test design. A goal in developing this software tool is to provide test and analysis engineers with a capability of mathematically simulating the complete structural dynamics test environment within a computer. A developed computational model of an aerospace component can be combined with analytical and/or experimentally derived models of typical structural dynamic test instrumentation within the VETO to determine an optimal test design. The VETO provides the user with a unique analysis and visualization environment to evaluate new and existing test methods in addition to simulating specific experiments designed to maximize test based information needed to validate computational models. The results of both a modal and a vibration test design are presented for a reentry vehicle and a space truss structure.
Multiobjective Optimization of Low-Energy Trajectories Using Optimal Control on Dynamical Channels
NASA Technical Reports Server (NTRS)
Coffee, Thomas M.; Anderson, Rodney L.; Lo, Martin W.
2011-01-01
We introduce a computational method to design efficient low-energy trajectories by extracting initial solutions from dynamical channels formed by invariant manifolds, and improving these solutions through variational optimal control. We consider trajectories connecting two unstable periodic orbits in the circular restricted 3-body problem (CR3BP). Our method leverages dynamical channels to generate a range of solutions, and approximates the areto front for impulse and time of flight through a multiobjective optimization of these solutions based on primer vector theory. We demonstrate the application of our method to a libration orbit transfer in the Earth-Moon system.
Aerodynamic design optimization with sensitivity analysis and computational fluid dynamics
NASA Technical Reports Server (NTRS)
Baysal, Oktay
1995-01-01
An investigation was conducted from October 1, 1990 to May 31, 1994 on the development of methodologies to improve the designs (more specifically, the shape) of aerodynamic surfaces of coupling optimization algorithms (OA) with Computational Fluid Dynamics (CFD) algorithms via sensitivity analyses (SA). The study produced several promising methodologies and their proof-of-concept cases, which have been reported in the open literature.
HIV Dynamics: Modeling, Data Analysis, and Optimal Treatment Protocols
HIV Dynamics: Modeling, Data Analysis, and Optimal Treatment Protocols B. M. Adams 1 , H. T. Banks in model- ing HIV pathogenesis. After a brief discussion of motivation for and previous efforts in the development of mathematical models for progression of HIV infection and treatment, we discuss mathematical
Optimal dynamical range of excitable networks at criticality
Loss, Daniel
ARTICLES Optimal dynamical range of excitable networks at criticality OSAME KINOUCHI1 * AND MAURO is to study how physical stimuli transduce into psychological sensation, probably the most basic mindbrain for the twenty-first-century brain sciences. As the intensity of physical stimuli (light, sound, pressure
Optimal motor control may mask sensory dynamics
Kiemel, Tim; Cowan, Noah J.; Jeka, John J.
2009-01-01
Properties of neural controllers for closed-loop sensorimotor behavior can be inferred with system identification. Under the standard paradigm, the closed-loop system is perturbed (input), measurements are taken (output), and the relationship between input and output reveals features of the system under study. Here we show that under common assumptions made about such systems (e.g. the system implements optimal control with a penalty on mechanical, but not sensory, states) important aspects of the neural controller (its zeros mask the modes of the sensors) remain hidden from standard system identification techniques. Only by perturbing or measuring the closed-loop system “between” the sensor and the control can these features be exposed with closed-loop system identification methods; while uncommon, there exist noninvasive techniques such as galvanic vestibular stimulation that perturb between sensor and controller in this way. PMID:19408009
Neural dynamic optimization for control systems.III. Applications.
Seong, C Y; Widrow, B
2001-01-01
For pt.II. see ibid., p. 490-501. The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper demonstrates NDO with several applications including control of autonomous vehicles and of a robot-arm, while the two other companion papers of this topic describes the background for the development of NDO and present the theory of the method, respectively. PMID:18244817
An optimized replica exchange molecular dynamics method.
Kamberaj, Hiqmet; van der Vaart, Arjan
2009-02-21
We introduce a new way to perform swaps between replicas in replica exchange molecular dynamics simulations. The method is based on a generalized canonical probability distribution function and flattens the potential of mean force along the temperature coordinate, such that a random walk in temperature space is achieved. Application to a Go model of protein A showed that the method is more efficient than conventional replica exchange. The method results in a constant probability distribution of the replicas over the thermostats, yields a minimum round-trip time between extremum temperatures, and leads to faster ergodic convergence. PMID:19239315
Optimal dynamic bandwidth allocation for complex networks
NASA Astrophysics Data System (ADS)
Jiang, Zhong-Yuan; Liang, Man-Gui; Li, Qian; Guo, Dong-Chao
2013-03-01
Traffic capacity of one network strongly depends on the link’s bandwidth allocation strategy. In previous bandwidth allocation mechanisms, once one link’s bandwidth is allocated, it will be fixed throughout the overall traffic transmission process. However, the traffic load of every link changes from time to time. In this paper, with finite total bandwidth resource of the network, we propose to dynamically allocate the total bandwidth resource in which each link’s bandwidth is proportional to the queue length of the output buffer of the link per time step. With plenty of data packets in the network, the traffic handling ability of all links of the network achieves full utilization. The theoretical analysis and the extensive simulation results on complex networks are consistent. This work is valuable for network service providers to improve network performance or to do reasonable network design efficiently.
Experimental Testing of Dynamically Optimized Photoelectron Beams
Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J.; Musumeci, P.; Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; Di Pirro, G.; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Vicario, C.; Serafini, L.; Jones, S.
2006-11-27
We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC photoinjector. The scheme under study employs the tendency of intense electron beams to rearrange to produce uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation. We show that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.
Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem
NASA Astrophysics Data System (ADS)
Chen, Wei
2015-07-01
In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.
Optimization Research of Generation Investment Based on Linear Programming Model
NASA Astrophysics Data System (ADS)
Wu, Juan; Ge, Xueqian
Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.
Vasquez, Juan Carlos
Consensus Algorithm Based Distributed Global Efficiency Optimization of a Droop Controlled DC Microgrid Consensus Algorithm Based Distributed Global Efficiency Optimization of a Droop Controlled DC MicrogridAalborg Universitet Dynamic Consensus Algorithm Based Distributed Global Efficiency Optimization
Global Optimization using a Dynamical Systems Stefan Sertl and Michael Dellnitz
Global Optimization using a Dynamical Systems Approach Stefan Sertl and Michael Dellnitz Faculty algorithms for global optimization by combining well known branch and bound methods with multilevel of the new algorithms and present a particular implementation. Keywords: global optimization, branch
Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics
NASA Technical Reports Server (NTRS)
Baysal, Oktay; Eleshaky, Mohamed E.
1991-01-01
A new and efficient method is presented for aerodynamic design optimization, which is based on a computational fluid dynamics (CFD)-sensitivity analysis algorithm. The method is applied to design a scramjet-afterbody configuration for an optimized axial thrust. The Euler equations are solved for the inviscid analysis of the flow, which in turn provides the objective function and the constraints. The CFD analysis is then coupled with the optimization procedure that uses a constrained minimization method. The sensitivity coefficients, i.e. gradients of the objective function and the constraints, needed for the optimization are obtained using a quasi-analytical method rather than the traditional brute force method of finite difference approximations. During the one-dimensional search of the optimization procedure, an approximate flow analysis (predicted flow) based on a first-order Taylor series expansion is used to reduce the computational cost. Finally, the sensitivity of the optimum objective function to various design parameters, which are kept constant during the optimization, is computed to predict new optimum solutions. The flow analysis of the demonstrative example are compared with the experimental data. It is shown that the method is more efficient than the traditional methods.
Sequential activation of metabolic pathways: a dynamic optimization approach.
Oyarzún, Diego A; Ingalls, Brian P; Middleton, Richard H; Kalamatianos, Dimitrios
2009-11-01
The regulation of cellular metabolism facilitates robust cellular operation in the face of changing external conditions. The cellular response to this varying environment may include the activation or inactivation of appropriate metabolic pathways. Experimental and numerical observations of sequential timing in pathway activation have been reported in the literature. It has been argued that such patterns can be rationalized by means of an underlying optimal metabolic design. In this paper we pose a dynamic optimization problem that accounts for time-resource minimization in pathway activation under constrained total enzyme abundance. The optimized variables are time-dependent enzyme concentrations that drive the pathway to a steady state characterized by a prescribed metabolic flux. The problem formulation addresses unbranched pathways with irreversible kinetics. Neither specific reaction kinetics nor fixed pathway length are assumed.In the optimal solution, each enzyme follows a switching profile between zero and maximum concentration, following a temporal sequence that matches the pathway topology. This result provides an analytic justification of the sequential activation previously described in the literature. In contrast with the existent numerical approaches, the activation sequence is proven to be optimal for a generic class of monomolecular kinetics. This class includes, but is not limited to, Mass Action, Michaelis-Menten, Hill, and some Power-law models. This suggests that sequential enzyme expression may be a common feature of metabolic regulation, as it is a robust property of optimal pathway activation. PMID:19412635
Optimizing airport runway improvement program - A dynamic programming approach
NASA Technical Reports Server (NTRS)
Yu, J. C.; Gibson, D. R.
1975-01-01
In order to reduce the air traffic delay in the terminal area, an immediate remedy is to increase airport capacity by an expansion of the existing runway system. The runway expansion program is often limited by budgetary constraints; the expensive facilities for a long-term improvement cannot be built at once. When a runway improvement strategy is being considered for a longer planning horizon, the investiment decision depends upon the interrelations of its composite periods. The problem, therefore, is to determine how time factor and investment decisions interact to yield an optimal improvement scheme that meets demand at a minimum cost. With this objective in mind, a dynamic programming methodology is employed to determine the optimal planning scheme. Also, an example runway improvement problem is tested to illustrate how a dynamic programming model is practical in actual application.
Tensor-optimized antisymmetrized molecular dynamics in nuclear physics
Myo, Takayuki; Ikeda, Kiyomi; Horiuchi, Hisashi; Suhara, Tadahiro
2015-01-01
We develop a new formalism to treat nuclear many-body systems using bare nucleon-nucleon interaction. It has become evident that the tensor interaction plays important role in nuclear many-body systems due to the role of the pion in strongly interacting system. We take the antisymmetrized molecular dynamics (AMD) as a basic framework and add a tensor correlation operator acting on the AMD wave function using the concept of the tensor-optimized shell model (TOSM). We demonstrate a systematical and straightforward formulation utilizing the Gaussian integration and differentiation method and the antisymmetrization technique to calculate all the matrix elements of the many-body Hamiltonian. We can include the three-body interaction naturally and calculate the matrix elements systematically in the progressive order of the tensor correlation operator. We call the new formalism "tensor-optimized antisymmetrized molecular dynamics".
Tensor-optimized antisymmetrized molecular dynamics in nuclear physics
Takayuki Myo; Hiroshi Toki; Kiyomi Ikeda; Hisashi Horiuchi; Tadahiro Suhara
2015-11-23
We develop a new formalism to treat nuclear many-body systems using bare nucleon-nucleon interaction. It has become evident that the tensor interaction plays important role in nuclear many-body systems due to the role of the pion in strongly interacting system. We take the antisymmetrized molecular dynamics (AMD) as a basic framework and add a tensor correlation operator acting on the AMD wave function using the concept of the tensor-optimized shell model (TOSM). We demonstrate a systematical and straightforward formulation utilizing the Gaussian integration and differentiation method and the antisymmetrization technique to calculate all the matrix elements of the many-body Hamiltonian. We can include the three-body interaction naturally and calculate the matrix elements systematically in the progressive order of the tensor correlation operator. We call the new formalism "tensor-optimized antisymmetrized molecular dynamics".
Optimal Control of a Parabolic Equation with Dynamic Boundary Condition
Hoemberg, D. Krumbiegel, K.; Rehberg, J.
2013-02-15
We investigate a control problem for the heat equation. The goal is to find an optimal heat transfer coefficient in the dynamic boundary condition such that a desired temperature distribution at the boundary is adhered. To this end we consider a function space setting in which the heat flux across the boundary is forced to be an L{sup p} function with respect to the surface measure, which in turn implies higher regularity for the time derivative of temperature. We show that the corresponding elliptic operator generates a strongly continuous semigroup of contractions and apply the concept of maximal parabolic regularity. This allows to show the existence of an optimal control and the derivation of necessary and sufficient optimality conditions.
Dynamic route optimization with time-expanded graphs
NASA Astrophysics Data System (ADS)
Zuniga, Miguel R.
1996-06-01
An important computer calculation in military planning is determining the optimal paths that a set of flying objects (airplanes, cruise missiles, UAVs) should take toward their targets. The current practice is to represent the problem with static graphs, then apply search algorithms (e.g. network flow) that can yield least cost paths. A number of significant problems must be solved in order to determine optimal and realistic paths. These include the difficulty associated with representing dynamic (time dependent) and realistic phenomena, and the associated difficulties of solving the graphs with polynomial-time algorithms. In this paper we discuss the limitations of modeling with graph theoretic techniques, and we present some results that permit significantly more accurate problem representation and solution than the previous state of the art. These include: a new method for graph representation of dynamic of phenomena associated with strike routing; some general relations that are important in modeling with graphs whose edge traversals represent independent probabilistic events; and a number of new models for use in optimizations. Some of the issues and solutions that we present are very general and they are also given detailed discussion in the context of two important military problems: general radar detection representation for optimization, and representation of the overflight problem (the increased threat to strike assets as they repeat flights over threats). Finally, the models and algorithms are considered in relation to single asset routing and joint routing of multiple assets.
Coarse-graining two-dimensional turbulence via dynamical optimization
Bruce Turkington; Qian-Yong Chen; Simon Thalabard
2015-09-24
A model reduction technique based on an optimization principle is employed to coarse-grain inviscid, incompressible fluid dynamics in two dimensions. In this reduction the spectrally-truncated vorticity equation defines the microdynamics, while the macroscopic state space consists of quasi-equilibrium trial probability densities on the microscopic phase space, which are parameterized by the means and variances of the low modes of the vorticity. A macroscopic path therefore represents a coarse-grained approximation to the evolution of a nonequilibrium ensemble of microscopic solutions. Closure in terms of the vector of resolved variables, namely, the means and variances of the low modes, is achieved by minimizing over all feasible paths the time integral of their mean-squared residual with respect to the Liouville equation. The equations governing the optimal path are deduced from Hamilton-Jacobi theory. The coarse-grained dynamics derived by this optimization technique contains a scale-dependent eddy viscosity, modified nonlinear interactions between the low mode means, and a nonlinear coupling between the mean and variance of each low mode. The predictive skill of this optimal closure is validated quantitatively by comparing it against direct numerical simulations. These tests show that good agreement is achieved without adjusting any closure parameters.
Rethinking design parameters in the search for optimal dynamic seating.
Pynt, Jennifer
2015-04-01
Dynamic seating design purports to lessen damage incurred during sedentary occupations by increasing sitter movement while modifying muscle activity. Dynamic sitting is currently defined by O'Sullivan et al. ( 2013a) as relating to 'the increased motion in sitting which is facilitated by the use of specific chairs or equipment' (p. 628). Yet the evidence is conflicting that dynamic seating creates variation in the sitter's lumbar posture or muscle activity with the overall consensus being that current dynamic seating design fails to fulfill its goals. Research is needed to determine if a new generation of chairs requiring active sitter involvement fulfills the goals of dynamic seating and aids cardio/metabolic health. This paper summarises the pursuit of knowledge regarding optimal seated spinal posture and seating design. Four new forms of dynamic seating encouraging active sitting are discussed. These are 1) The Core-flex with a split seatpan to facilitate a walking action while seated 2) the Duo balans requiring body action to create rocking 3) the Back App and 4) Locus pedestal stools both using the sitter's legs to drive movement. Unsubstantiated claims made by the designers of these new forms of dynamic seating are outlined. Avenues of research are suggested to validate designer claims and investigate whether these designs fulfill the goals of dynamic seating and assist cardio/metabolic health. Should these claims be efficacious then a new definition of dynamic sitting is suggested; 'Sitting in which the action is provided by the sitter, while the dynamic mechanism of the chair accommodates that action'. PMID:25892386
Confronting dynamics and uncertainty in optimal decision making for conservation
Williams, Byron K.; Johnson, Fred A.
2013-01-01
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making--a careful consideration of values, actions, and outcomes.
Confronting dynamics and uncertainty in optimal decision making for conservation
NASA Astrophysics Data System (ADS)
Williams, Byron K.; Johnson, Fred A.
2013-06-01
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making—a careful consideration of values, actions, and outcomes.
Optimization of Dynamic Aperture of PEP-X Baseline Design
Wang, Min-Huey; Cai, Yunhai; Nosochkov, Yuri; ,
2010-08-23
SLAC is developing a long-range plan to transfer the evolving scientific programs at SSRL from the SPEAR3 light source to a much higher performing photon source. Storage ring design is one of the possibilities that would be housed in the 2.2-km PEP-II tunnel. The design goal of PEPX storage ring is to approach an optimal light source design with horizontal emittance less than 100 pm and vertical emittance of 8 pm to reach the diffraction limit of 1-{angstrom} x-ray. The low emittance design requires a lattice with strong focusing leading to high natural chromaticity and therefore to strong sextupoles. The latter caused reduction of dynamic aperture. The dynamic aperture requirement for horizontal injection at injection point is about 10 mm. In order to achieve the desired dynamic aperture the transverse non-linearity of PEP-X is studied. The program LEGO is used to simulate the particle motion. The technique of frequency map is used to analyze the nonlinear behavior. The effect of the non-linearity is tried to minimize at the given constrains of limited space. The details and results of dynamic aperture optimization are discussed in this paper.
Human opinion dynamics: An inspiration to solve complex optimization problems
NASA Astrophysics Data System (ADS)
Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P.; Kapur, Pawan
2013-10-01
Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making.
Khawaja, Sajid Gul; Mushtaq, Mian Hamza; Khan, Shoab A; Akram, M Usman; Jamal, Habib Ullah
2015-01-01
With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows. The novelty of the proposed system lies in derivation of a Mixed Integer Linear Programming model which is then used to generate a resource optimal Network on Chip (NoC) topology and architecture while considering traffic and QoS requirements. We also present the micro-architectural design features used for enabling traffic and latency guarantees and discuss how the solution adapts for dynamic variations in the application traffic. The paper highlights the effectiveness of proposed method by generating resource efficient NoC solutions for both industrial and benchmark applications. The area-optimized results are generated in few seconds by proposed technique, without resorting to heuristics, even for an application with 48 traffic flows. PMID:25898016
Khawaja, Sajid Gul; Mushtaq, Mian Hamza; Khan, Shoab A.; Akram, M. Usman; Jamal, Habib ullah
2015-01-01
With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows. The novelty of the proposed system lies in derivation of a Mixed Integer Linear Programming model which is then used to generate a resource optimal Network on Chip (NoC) topology and architecture while considering traffic and QoS requirements. We also present the micro-architectural design features used for enabling traffic and latency guarantees and discuss how the solution adapts for dynamic variations in the application traffic. The paper highlights the effectiveness of proposed method by generating resource efficient NoC solutions for both industrial and benchmark applications. The area-optimized results are generated in few seconds by proposed technique, without resorting to heuristics, even for an application with 48 traffic flows. PMID:25898016
Optimized Dynamical Decoupling in a Model Quantum Memory
Michael J. Biercuk; Hermann Uys; Aaron P. VanDevender; Nobuyasu Shiga; Wayne M. Itano; John J. Bollinger
2009-01-05
We present experimental measurements on a model quantum system that demonstrate our ability to dramatically suppress qubit error rates by the application of optimized dynamical decoupling pulse sequences in a variety of experimentally relevant noise environments. We provide the first demonstration of an analytically derived pulse sequence developed by Uhrig, and find novel sequences through active, real-time experimental feedback. These new sequences are specially tailored to maximize error suppression without the need for a priori knowledge of the ambient noise environment. We compare these sequences against the Uhrig sequence, and the well established CPMG-style spin echo, demonstrating that our locally optimized pulse sequences outperform all others under test. Numerical simulations show that our locally optimized pulse sequences are capable of suppressing errors by orders of magnitude over other existing sequences. Our work includes the extension of a treatment to predict qubit decoherence under realistic conditions, including the use of finite-duration, square $\\pi$ pulses, yielding strong agreement between experimental data and theory for arbitrary pulse sequences. These results demonstrate the robustness of qubit memory error suppression through dynamical decoupling techniques across a variety of qubit technologies.
Optimizing spread dynamics on graphs by message passing
NASA Astrophysics Data System (ADS)
Altarelli, F.; Braunstein, A.; Dall'Asta, L.; Zecchina, R.
2013-09-01
Cascade processes are responsible for many important phenomena in natural and social sciences. Simple models of irreversible dynamics on graphs, in which nodes activate depending on the state of their neighbors, have been successfully applied to describe cascades in a large variety of contexts. Over the past decades, much effort has been devoted to understanding the typical behavior of the cascades arising from initial conditions extracted at random from some given ensemble. However, the problem of optimizing the trajectory of the system, i.e. of identifying appropriate initial conditions to maximize (or minimize) the final number of active nodes, is still considered to be practically intractable, with the only exception being models that satisfy a sort of diminishing returns property called submodularity. Submodular models can be approximately solved by means of greedy strategies, but by definition they lack cooperative characteristics which are fundamental in many real systems. Here we introduce an efficient algorithm based on statistical physics for the optimization of trajectories in cascade processes on graphs. We show that for a wide class of irreversible dynamics, even in the absence of submodularity, the spread optimization problem can be solved efficiently on large networks. Analytic and algorithmic results on random graphs are complemented by the solution of the spread maximization problem on a real-world network (the Epinions consumer reviews network).
Optimized Uncertainty Quantification Algorithm Within a Dynamic Event Tree Framework
J. W. Nielsen; Akira Tokuhiro; Robert Hiromoto
2014-06-01
Methods for developing Phenomenological Identification and Ranking Tables (PIRT) for nuclear power plants have been a useful tool in providing insight into modelling aspects that are important to safety. These methods have involved expert knowledge with regards to reactor plant transients and thermal-hydraulic codes to identify are of highest importance. Quantified PIRT provides for rigorous method for quantifying the phenomena that can have the greatest impact. The transients that are evaluated and the timing of those events are typically developed in collaboration with the Probabilistic Risk Analysis. Though quite effective in evaluating risk, traditional PRA methods lack the capability to evaluate complex dynamic systems where end states may vary as a function of transition time from physical state to physical state . Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. A limitation of DPRA is its potential for state or combinatorial explosion that grows as a function of the number of components; as well as, the sampling of transition times from state-to-state of the entire system. This paper presents a method for performing QPIRT within a dynamic event tree framework such that timing events which result in the highest probabilities of failure are captured and a QPIRT is performed simultaneously while performing a discrete dynamic event tree evaluation. The resulting simulation results in a formal QPIRT for each end state. The use of dynamic event trees results in state explosion as the number of possible component states increases. This paper utilizes a branch and bound algorithm to optimize the solution of the dynamic event trees. The paper summarizes the methods used to implement the branch-and-bound algorithm in solving the discrete dynamic event trees.
Aero-gravity assist maneuvers: controlled dynamics modeling and optimization
NASA Astrophysics Data System (ADS)
Armellin, R.; Lavagna, M.; Ercoli-Finzi, A.
2006-05-01
The aero-gravity assist maneuver is here proposed as a tool to improve the efficiency of the gravity assist as, thanks to the interaction with the planetary atmospheres, the angular deviation of the velocity vector can be definitely increased. Even though the drag reduces the spacecraft velocity, the overall ?? gain could be remarkable whenever a high lift-to-drag vehicle is supposed to fly. Earlier studies offer simplified approaches according to both the dynamics modeling and the atmospheric trajectory constraints. In this paper a 3D dynamical model is adopted and a more realistic L/ D performance for the hypersonic vehicle is assumed. Some relevant aspects related to the multidisciplinary design have been considered such as heating rates and structural loads bounding. Comparisons between in and out of plane maneuvering have been performed by assuming, as control variables, either the angle of attack or the bank angle, respectively. The optimal control problem has been solved by selecting a direct method approach. The dynamics has been transcribed into a set of non-linear constraints and the arising non-linear programming problem has been solved with a sequential quadratic programming solver. To gain the global optimum convergence the initial guess has been supplied by solving the same problem by a direct shooting technique and a genetic optimizer.
Modelling the Transmission Dynamics of Hepatitis B & Optimal control
Mehmood, Nayyar
2011-01-01
Hepatitis B is a potentially life-threatening viral infection that can cause illness and even death. The Hepatitis B virus is fifty to hundred times more infectious than HIV, and world wide an estimated two billion people have been affected (WHO2008). HBV is the most common serious viral infection and leading cause of deaths in main land in china South Asia and Africa and almost all parts of the world. Every year 300,000 people from HBV related diseases only in China. Hepatitis B is a common disease in Pakistan as every tenth person is carrier and every fifth person has been exposed to hepatitis B. We proposed a model to understand the transmission dynamics and prevalence of HBV. To control the prevalence of diseases, we use optimal control strategies, which reduce the latent, infected and carrier individuals and increase the total number of recovered and immune individuals. In order to do this, we show that an optimal control exists for this optimal control problem and then we use optimal control techniques ...
Exposure time optimization for highly dynamic star trackers.
Wei, Xinguo; Tan, Wei; Li, Jian; Zhang, Guangjun
2014-01-01
Under highly dynamic conditions, the star-spots on the image sensor of a star tracker move across many pixels during the exposure time, which will reduce star detection sensitivity and increase star location errors. However, this kind of effect can be compensated well by setting an appropriate exposure time. This paper focuses on how exposure time affects the star tracker under highly dynamic conditions and how to determine the most appropriate exposure time for this case. Firstly, the effect of exposure time on star detection sensitivity is analyzed by establishing the dynamic star-spot imaging model. Then the star location error is deduced based on the error analysis of the sub-pixel centroiding algorithm. Combining these analyses, the effect of exposure time on attitude accuracy is finally determined. Some simulations are carried out to validate these effects, and the results show that there are different optimal exposure times for different angular velocities of a star tracker with a given configuration. In addition, the results of night sky experiments using a real star tracker agree with the simulation results. The summarized regularities in this paper should prove helpful in the system design and dynamic performance evaluation of the highly dynamic star trackers. PMID:24618776
Exposure Time Optimization for Highly Dynamic Star Trackers
Wei, Xinguo; Tan, Wei; Li, Jian; Zhang, Guangjun
2014-01-01
Under highly dynamic conditions, the star-spots on the image sensor of a star tracker move across many pixels during the exposure time, which will reduce star detection sensitivity and increase star location errors. However, this kind of effect can be compensated well by setting an appropriate exposure time. This paper focuses on how exposure time affects the star tracker under highly dynamic conditions and how to determine the most appropriate exposure time for this case. Firstly, the effect of exposure time on star detection sensitivity is analyzed by establishing the dynamic star-spot imaging model. Then the star location error is deduced based on the error analysis of the sub-pixel centroiding algorithm. Combining these analyses, the effect of exposure time on attitude accuracy is finally determined. Some simulations are carried out to validate these effects, and the results show that there are different optimal exposure times for different angular velocities of a star tracker with a given configuration. In addition, the results of night sky experiments using a real star tracker agree with the simulation results. The summarized regularities in this paper should prove helpful in the system design and dynamic performance evaluation of the highly dynamic star trackers. PMID:24618776
Active flutter control using discrete optimal constrained dynamic compensators
NASA Technical Reports Server (NTRS)
Broussard, J. R.; Halyo, N.
1983-01-01
A method for synthesizing digital active flutter suppression controllers using the concept of optimal output feedback is presented. A recently developd convergent algorithm is employed to determine constrained control law parameters that minimize an infinite-time discrete quadratic performance index. Low-order compensator dynamics are included in the control law and the compensator parameters are computed along with the output feedback gain as part of the optimization process. An input noise adjustment procedure is used to improve the stability margins of the digital active flutter controller. Results from investigations into sample rate variation, prefilter pole variation, and effects of varying flight condtions are discussed. The study indicates that a digital control law which accommodates computation delay can stabilize the wing with reasonable rms performance and adequate stability margins.
Code Cache Management Schemes for Dynamic Optimizers Kim Hazelwood Michael D. Smith
Hazelwood, Kim
Code Cache Management Schemes for Dynamic Optimizers Kim Hazelwood Michael D. Smith Division optimizer is a software-based system that performs code modifications at runtime, and several such systems of code. Since the dynamic optimizers produce variable-length code traces that are modified copies
Optimal Allocation of Shunt Dynamic Var Source SVC and STATCOM: A Survey
Tolbert, Leon M.
1 Optimal Allocation of Shunt Dynamic Var Source SVC and STATCOM: A Survey Abstract-- Optimal categorizes the literature relevant to optimal allocation of shunt dynamic Var source SVC and STATCOM, based allocation, voltage stability analysis, CPF, modal analysis, OPF, SVC, STATCOM I. INTRODUCTION Recently
Optimal dynamic control of invasions: applying a systematic conservation approach.
Adams, Vanessa M; Setterfield, Samantha A
2015-06-01
The social, economic, and environmental impacts of invasive plants are well recognized. However, these variable impacts are rarely accounted for in the spatial prioritization of funding for weed management. We examine how current spatially explicit prioritization methods can be extended to identify optimal budget allocations to both eradication and control measures of invasive species to minimize the costs and likelihood of invasion. Our framework extends recent approaches to systematic prioritization of weed management to account for multiple values that are threatened by weed invasions with a multi-year dynamic prioritization approach. We apply our method to the northern portion of the Daly catchment in the Northern Territory, which has significant conservation values that are threatened by gamba grass (Andropogon gayanus), a highly invasive species recognized by the Australian government as a Weed of National Significance (WONS). We interface Marxan, a widely applied conservation planning tool, with a dynamic biophysical model of gamba grass to optimally allocate funds to eradication and control programs under two budget scenarios comparing maximizing gain (MaxGain) and minimizing loss (MinLoss) optimization approaches. The prioritizations support previous findings that a MinLoss approach is a better strategy when threats are more spatially variable than conservation values. Over a 10-year simulation period, we find that a MinLoss approach reduces future infestations by ~8% compared to MaxGain in the constrained budget scenarios and ~12% in the unlimited budget scenarios. We find that due to the extensive current invasion and rapid rate of spread, allocating the annual budget to control efforts is more efficient than funding eradication efforts when there is a constrained budget. Under a constrained budget, applying the most efficient optimization scenario (control, minloss) reduces spread by ~27% compared to no control. Conversely, if the budget is unlimited it is more efficient to fund eradication efforts and reduces spread by ~65% compared to no control. PMID:26465047
Dynamic Simulation and Optimization of Nuclear Hydrogen Production Systems
Paul I. Barton; Mujid S. Kaximi; Georgios Bollas; Patricio Ramirez Munoz
2009-07-31
This project is part of a research effort to design a hydrogen plant and its interface with a nuclear reactor. This project developed a dynamic modeling, simulation and optimization environment for nuclear hydrogen production systems. A hybrid discrete/continuous model captures both the continuous dynamics of the nuclear plant, the hydrogen plant, and their interface, along with discrete events such as major upsets. This hybrid model makes us of accurate thermodynamic sub-models for the description of phase and reaction equilibria in the thermochemical reactor. Use of the detailed thermodynamic models will allow researchers to examine the process in detail and have confidence in the accurary of the property package they use.
Optimized dynamical decoupling for power-law noise spectra
Pasini, S.; Uhrig, G. S.
2010-01-15
We analyze the suppression of decoherence by means of dynamical decoupling in the pure-dephasing spin-boson model for baths with power law spectra. The sequence of ideal pi pulses is optimized according to the power of the bath. We expand the decoherence function and separate the canceling divergences from the relevant terms. The proposed sequence is chosen to be the one minimizing the decoherence function. By construction, it provides the best performance. We analytically derive the conditions that must be satisfied. The resulting equations are solved numerically. The solutions are very close to the Carr-Purcell-Meiboom-Gill sequence for a soft cutoff of the bath while they approach the Uhrig dynamical-decoupling sequence as the cutoff becomes harder.
Hybridizing Particle Filters and Population-based Metaheuristics for Dynamic Optimization Problems
Pantrigo Fernández, Juan José
-reconstruction procedure [15]. On the other hand, many dynamic problems require the estimation of the system stateHybridizing Particle Filters and Population-based Metaheuristics for Dynamic Optimization Problems Many real-world optimization problems are dynamic. These problems require from powerful methods
Global Optimization using a Dynamical Systems Stefan Sertl and Michael Dellnitz
Neumaier, Arnold
Global Optimization using a Dynamical Systems Approach Stefan Sertl and Michael Dellnitz Faculty://math-www.uni-paderborn.de/~agdellnitz July 21, 2003 Abstract We develop new algorithms for global optimization by combining well known branch: global optimization, branch and bound optimization, dy- namical systems, computation of fixed points MSC
Clustering molecular dynamics trajectories for optimizing docking experiments.
De Paris, Renata; Quevedo, Christian V; Ruiz, Duncan D; Norberto de Souza, Osmar; Barros, Rodrigo C
2015-01-01
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20?ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. PMID:25873944
Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments
De Paris, Renata; Quevedo, Christian V.; Ruiz, Duncan D.; Norberto de Souza, Osmar; Barros, Rodrigo C.
2015-01-01
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20?ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. PMID:25873944
Dynamics and real-time optimal control of satellite attitude and satellite formation systems
Yan, Hui
2006-10-30
In this dissertation the solutions of the dynamics and real-time optimal control of magnetic attitude control and formation flying systems are presented. In magnetic attitude control, magnetic actuators for the time-optimal ...
Aircraft path planning for optimal imaging using dynamic cost functions
NASA Astrophysics Data System (ADS)
Christie, Gordon; Chaudhry, Haseeb; Kochersberger, Kevin
2015-05-01
Unmanned aircraft development has accelerated with recent technological improvements in sensing and communications, which has resulted in an "applications lag" for how these aircraft can best be utilized. The aircraft are becoming smaller, more maneuverable and have longer endurance to perform sensing and sampling missions, but operating them aggressively to exploit these capabilities has not been a primary focus in unmanned systems development. This paper addresses a means of aerial vehicle path planning to provide a realistic optimal path in acquiring imagery for structure from motion (SfM) reconstructions and performing radiation surveys. This method will allow SfM reconstructions to occur accurately and with minimal flight time so that the reconstructions can be executed efficiently. An assumption is made that we have 3D point cloud data available prior to the flight. A discrete set of scan lines are proposed for the given area that are scored based on visibility of the scene. Our approach finds a time-efficient path and calculates trajectories between scan lines and over obstacles encountered along those scan lines. Aircraft dynamics are incorporated into the path planning algorithm as dynamic cost functions to create optimal imaging paths in minimum time. Simulations of the path planning algorithm are shown for an urban environment. We also present our approach for image-based terrain mapping, which is able to efficiently perform a 3D reconstruction of a large area without the use of GPS data.
Data-driven optimization of dynamic reconfigurable systems of systems.
Tucker, Conrad S.; Eddy, John P.
2010-11-01
This report documents the results of a Strategic Partnership (aka University Collaboration) LDRD program between Sandia National Laboratories and the University of Illinois at Urbana-Champagne. The project is titled 'Data-Driven Optimization of Dynamic Reconfigurable Systems of Systems' and was conducted during FY 2009 and FY 2010. The purpose of this study was to determine and implement ways to incorporate real-time data mining and information discovery into existing Systems of Systems (SoS) modeling capabilities. Current SoS modeling is typically conducted in an iterative manner in which replications are carried out in order to quantify variation in the simulation results. The expense of many replications for large simulations, especially when considering the need for optimization, sensitivity analysis, and uncertainty quantification, can be prohibitive. In addition, extracting useful information from the resulting large datasets is a challenging task. This work demonstrates methods of identifying trends and other forms of information in datasets that can be used on a wide range of applications such as quantifying the strength of various inputs on outputs, identifying the sources of variation in the simulation, and potentially steering an optimization process for improved efficiency.
An inverse dynamics approach to trajectory optimization and guidance for an aerospace plane
NASA Technical Reports Server (NTRS)
Lu, Ping
1992-01-01
The optimal ascent problem for an aerospace planes is formulated as an optimal inverse dynamic problem. Both minimum-fuel and minimax type of performance indices are considered. Some important features of the optimal trajectory and controls are used to construct a nonlinear feedback midcourse controller, which not only greatly simplifies the difficult constrained optimization problem and yields improved solutions, but is also suited for onboard implementation. Robust ascent guidance is obtained by using combination of feedback compensation and onboard generation of control through the inverse dynamics approach. Accurate orbital insertion can be achieved with near-optimal control of the rocket through inverse dynamics even in the presence of disturbances.
An inverse dynamics approach to trajectory optimization and guidance for an aerospace plane
NASA Astrophysics Data System (ADS)
Lu, Ping
1992-08-01
The optimal ascent problem for an aerospace planes is formulated as an optimal inverse dynamic problem. Both minimum-fuel and minimax type of performance indices are considered. Some important features of the optimal trajectory and controls are used to construct a nonlinear feedback midcourse controller, which not only greatly simplifies the difficult constrained optimization problem and yields improved solutions, but is also suited for onboard implementation. Robust ascent guidance is obtained by using combination of feedback compensation and onboard generation of control through the inverse dynamics approach. Accurate orbital insertion can be achieved with near-optimal control of the rocket through inverse dynamics even in the presence of disturbances.
A Formal Approach to Empirical Dynamic Model Optimization and Validation
NASA Technical Reports Server (NTRS)
Crespo, Luis G; Morelli, Eugene A.; Kenny, Sean P.; Giesy, Daniel P.
2014-01-01
A framework was developed for the optimization and validation of empirical dynamic models subject to an arbitrary set of validation criteria. The validation requirements imposed upon the model, which may involve several sets of input-output data and arbitrary specifications in time and frequency domains, are used to determine if model predictions are within admissible error limits. The parameters of the empirical model are estimated by finding the parameter realization for which the smallest of the margins of requirement compliance is as large as possible. The uncertainty in the value of this estimate is characterized by studying the set of model parameters yielding predictions that comply with all the requirements. Strategies are presented for bounding this set, studying its dependence on admissible prediction error set by the analyst, and evaluating the sensitivity of the model predictions to parameter variations. This information is instrumental in characterizing uncertainty models used for evaluating the dynamic model at operating conditions differing from those used for its identification and validation. A practical example based on the short period dynamics of the F-16 is used for illustration.
Performance Study and Dynamic Optimization Design for Thread Pool Systems
Dongping Xu
2004-12-19
Thread pools have been widely used by many multithreaded applications. However, the determination of the pool size according to the application behavior still remains problematic. To automate this process, in this thesis we have developed a set of performance metrics for quantitatively analyzing thread pool performance. For our experiments, we built a thread pool system which provides a general framework for thread pool research. Based on this simulation environment, we studied the performance impact brought by the thread pool on different multithreaded applications. Additionally, the correlations between internal characterizations of thread pools and their throughput were also examined. We then proposed and evaluated a heuristic algorithm to dynamically determine the optimal thread pool size. The simulation results show that this approach is effective in improving overall application performance.
Ultra-sensitive Diamond Magnetometry Using Optimal Dynamic Decoupling
Hall, Liam T; Cole, Jared H; Hollenberg, Lloyd C L
2010-01-01
New magnetometry techniques based on Nitrogen Vacancy (NV) defects in diamond have received much attention of late as a means to probe nanoscale magnetic environments. The sensitivity of a single NV magnetometer is primarily determined by the transverse spin relaxation time, $T_2$. Current approaches to improving the sensitivity employ crystals with a high NV density at the cost of spatial resolution, or extend $T_2$ via the manufacture of novel isotopically pure diamond crystals. We adopt a complementary approach, in which optimal dynamic decoupling techniques extend coherence times out to the self-correlation time of the spin bath. This suggests single spin, room temperature magnetometer sensitivities as low as 5\\,pT\\,Hz$^{-1/2}$ with current technology.
Ultra-sensitive Diamond Magnetometry Using Optimal Dynamic Decoupling
Liam T. Hall; Charles D. Hill; Jared H. Cole; Lloyd C. L. Hollenberg
2010-03-19
New magnetometry techniques based on Nitrogen Vacancy (NV) defects in diamond have received much attention of late as a means to probe nanoscale magnetic environments. The sensitivity of a single NV magnetometer is primarily determined by the transverse spin relaxation time, $T_2$. Current approaches to improving the sensitivity employ crystals with a high NV density at the cost of spatial resolution, or extend $T_2$ via the manufacture of novel isotopically pure diamond crystals. We adopt a complementary approach, in which optimal dynamic decoupling techniques extend coherence times out to the self-correlation time of the spin bath. This suggests single spin, room temperature magnetometer sensitivities as low as 5\\,pT\\,Hz$^{-1/2}$ with current technology.
Differential dynamic programming technique for constrained optimal control
NASA Astrophysics Data System (ADS)
Lin, T. C.; Arora, J. S.
1991-01-01
In Part 1, two approaches for constrained optimal control problems (OCP) using the differential dynamic programming (DDP) are presented. In the present paper, three illustrative examples are used to evaluate the approaches: (1) A linear quadratic problem (LQP) with an inequality constraint on the state variable, (2) a single degree of freedom nonlinear impact absorber with an inequality constraint on the state variable, and (3) a linear structural control problem with inequality constraints on the state and control variables. The solutions are compared with the results available in the literature. It is found that the DDP is far more efficient compared to the NLP approach. Also, the continuous-time DDP formulation is superior to the discrete-time formulation. The continuous-time DDP with the multiplier approach is recommended for applications since it is more general compared to the one based on quadratic programming.
Differential dynamic programming technique for constrained optimal control
NASA Astrophysics Data System (ADS)
Lin, T. C.; Arora, J. S.
1991-01-01
In this paper, two computational procedures based on the differential dynamic programming (DDP) are presented for linear or nonlinear constrained optimal control problems (OCP). The first procedure solves a quadratic programming problem at every time step and thus constructs the differential control law, and the other one uses the multiplier method to sum up all the point-wise constraints and then uses the unconstrained DDP approach to construct the differential control law. In the first approach, the constraints need to be explicitly dependent on the control variables when continuous-time model is used, whereas in the second approach, there is no such limitation. Therefore the second approach is more general than the first one. Detailed algorithms are given for numerical implementation. Numerical evaluation of the techniques is presented in Part 2 of the paper.
Geometry optimization for micro-pressure sensor considering dynamic interference
Yu, Zhongliang; Zhao, Yulong Li, Lili; Tian, Bian; Li, Cun
2014-09-15
Presented is the geometry optimization for piezoresistive absolute micro-pressure sensor. A figure of merit called the performance factor (PF) is defined as a quantitative index to describe the comprehensive performances of a sensor including sensitivity, resonant frequency, and acceleration interference. Three geometries are proposed through introducing islands and sensitive beams into typical flat diaphragm. The stress distributions of sensitive elements are analyzed by finite element method. Multivariate fittings based on ANSYS simulation results are performed to establish the equations about surface stress, deflection, and resonant frequency. Optimization by MATLAB is carried out to determine the dimensions of the geometries. Convex corner undercutting is evaluated. Each PF of the three geometries with the determined dimensions is calculated and compared. Silicon bulk micromachining is utilized to fabricate the prototypes of the sensors. The outputs of the sensors under both static and dynamic conditions are tested. Experimental results demonstrate the rationality of the defined performance factor and reveal that the geometry with quad islands presents the highest PF of 210.947 Hz{sup 1/4}. The favorable overall performances enable the sensor more suitable for altimetry.
Optimal Pulse-Tube Design Using Computational Fluid Dynamics
NASA Astrophysics Data System (ADS)
Taylor, R. P.; Nellis, G. F.; Klein, S. A.
2008-03-01
Over the past few decades, the pulse-tube cryocooler has advanced from a curiosity to one of the most attractive systems for providing reliable cryogenic cooling; it is now used in aerospace, medical and superconductor applications. This technology development has been enabled by the simulation tools that are available for regenerator, compressor, and inertance tube design. However, a dedicated design tool for the pulse-tube component in a pulse-tube cryocooler and the associated flow transitions between the pulse tube and the regenerator and the pulse tube and the inertance network is not currently available. This paper describes the development of a two-dimensional, axisymmetric computational fluid dynamic (CFD) model of the pulse-tube and its associated flow transitions. The model is implemented in the commercial CFD package FLUENT. The CFD simulations are sufficient to calculate and delineate the various loss mechanisms; these are reported as a percentage of the acoustic power that is present at the cold end. A gross figure of merit (the pulse tube efficiency) is defined as the ratio of the useful cooling provided to the available acoustic power. The practical uses (e.g., identification of the optimal geometric design) and limitations of the model are discussed and some initial optimization results are presented.
Remy, Christian D.; Thelen, Darryl G.
2010-01-01
Background Forward dynamic simulation provides a powerful framework for characterizing in vivo loads, and for predicting changes in movement due to injury, impairment or surgical intervention. However, the computational challenge of generating simulations has greatly limited the use and application of forward dynamic models for simulating human gait. Methods In this study, we introduce an optimal estimation approach to effciently solve for generalized accelerations that satisfy the overall equations of motion and best agree with measured kinematics and ground reaction forces. The estimated accelerations are numerically integrated to enforce dynamic consistency over time, resulting in a forward dynamic simulation. Numerical optimization is then used to determine a set of initial generalized coordinates and speeds that produce a simulation that is most consistent with the measured motion over a full cycle of gait. The proposed method was evaluated with synthetically created kinematics and forceplate data in which both random noise and bias errors were introduced. We also applied the method to experimental gait data collected from five young healthy adults walking at a preferred speed. Results and Conclusions We show that the proposed residual elimination algorithm (REA) converges to an accurate solution, reduces the detrimental effects of kinematic measurement errors on joint moments, and eliminates the need for residual forces that arise in standard inverse dynamics. The greatest improvements in joint kinetics were observed proximally, with the algorithm reducing joint moment errors due to marker noise by over 20% at the hip and over 50% at the low back. Simulated joint angles were generally within 1 deg of recorded values when REA was used to generate a simulation from experimental gait data. REA can thus be used as a basis for generating accurate simulations of subject-specific gait dynamics. PMID:19154064
4500 V SPT+ IGBT optimization on static and dynamic losses
NASA Astrophysics Data System (ADS)
Qingyun, Dai; Xiaoli, Tian; Wenliang, Zhang; Shuojin, Lu; Yangjun, Zhu
2015-09-01
This paper concerns the need for improving the static and dynamic performance of the high voltage insulated gate bipolar transistor (HV IGBTs). A novel structure with a carrier stored layer on the cathode side, known as an enhanced planar IGBT of the 4500 V voltage class is investigated. With the adoption of a soft punch through (SPT) concept as the vertical structure and an enhanced planar concept as the top structure, signed as SPT+ IGBT, the simulation results indicate the turn-off switching waveform of the 4500 V SPT+ IGBT is soft and also realizes an improved trade-off relationship between on-state voltage drop (Von) and turn-off loss (Eoff) in comparison with the SPT IGBT. Attention is also paid to the influences caused by different carrier stored layer doping dose on static and dynamic performances, to optimize on-state and switching losses of SPT+ IGBT. Project supported by the National Major Science and Technology Special Project of China (No. 2011ZX02504-002).
Estimating Optimal Dynamic Regimes: Correcting Bias under the Null
Moodie, Erica E. M.; Richardson, Thomas S.
2009-01-01
A dynamic regime provides a sequence of treatments that are tailored to patient-specific characteristics and outcomes. In 2004 James Robins proposed g-estimation using structural nested mean models for making inference about the optimal dynamic regime in a multi-interval trial. The method provides clear advantages over traditional parametric approaches. Robins’ g-estimation method always yields consistent estimators, but these can be asymptotically biased under a given structural nested mean model for certain longitudinal distributions of the treatments and covariates, termed exceptional laws. In fact, under the null hypothesis of no treatment effect, every distribution constitutes an exceptional law under structural nested mean models which allow for interaction of current treatment with past treatments or covariates. This paper provides an explanation of exceptional laws and describes a new approach to g-estimation which we call Zeroing Instead of Plugging In (ZIPI). ZIPI provides nearly identical estimators to recursive g-estimators at non-exceptional laws while providing substantial reduction in the bias at an exceptional law when decision rule parameters are not shared across intervals. PMID:20526433
) and muscle co-contraction. For direct comparison between models, the shoulder and elbow muscle moment armsA comparison of static and dynamic optimization muscle force predictions during wheelchair t The primary purpose of this study was to compare static and dynamic optimization muscle force and work
Optimal Execution Comparison Across Risks and Dynamics, with Solutions for Displaced Diffusions
Brigo, Damiano
Optimal Execution Comparison Across Risks and Dynamics, with Solutions for Displaced Diffusions FSP and not of the author employers, present and past. (c) 2014 D. Brigo (www.damianobrigo.it) Optimal Trade Execution under DD FPL Conference 1 / 36 #12;Agenda I 1 The trade execution problem under different dynamics Context
One-Dimensional Infinite Horizon Nonconcave Optimal Control Problems Arising in Economic Dynamics
Zaslavski, Alexander J.
2011-12-15
We study the existence of optimal solutions for a class of infinite horizon nonconvex autonomous discrete-time optimal control problems. This class contains optimal control problems without discounting arising in economic dynamics which describe a model with a nonconcave utility function.
Analia Zwick; Gonzalo A. Alvarez; Guy Bensky; Gershon Kurizki
2013-10-06
We propose a method of optimally controlling state transfer through a noisy quantum channel (spin-chain). This process is treated as qubit state-transfer through a fermionic bath. We show that dynamical modulation of the boundary-qubits levels suffices to ensure fast and high-fidelity state transfer. This is achievable by dynamically optimizing the transmission spectrum of the channel. The resulting optimal control is robust against both static and fluctuating noise.
Dai, C; Li, Y P; Huang, G H
2011-12-01
In this study, a two-stage support-vector-regression optimization model (TSOM) is developed for the planning of municipal solid waste (MSW) management in the urban districts of Beijing, China. It represents a new effort to enhance the analysis accuracy in optimizing the MSW management system through coupling the support-vector-regression (SVR) model with an interval-parameter mixed integer linear programming (IMILP). The developed TSOM can not only predict the city's future waste generation amount, but also reflect dynamic, interactive, and uncertain characteristics of the MSW management system. Four kernel functions such as linear kernel, polynomial kernel, radial basis function, and multi-layer perception kernel are chosen based on three quantitative simulation performance criteria [i.e. prediction accuracy (PA), fitting accuracy (FA) and over all accuracy (OA)]. The SVR with polynomial kernel has accurate prediction performance for MSW generation rate, with all of the three quantitative simulation performance criteria being over 96%. Two cases are considered based on different waste management policies. The results are valuable for supporting the adjustment of the existing waste-allocation patterns to raise the city's waste diversion rate, as well as the capacity planning of waste management system to satisfy the city's increasing waste treatment/disposal demands. PMID:21872384
NASA Astrophysics Data System (ADS)
Bulgakov, V. K.; Strigunov, V. V.
2009-05-01
The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.
Orellana, Liliana; Rotnitzky, Andrea; Robins, James M
2010-01-01
In this companion article to "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content" [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption. PMID:20405047
Orellana, Liliana; Rotnitzky, Andrea; Robins, James M.
2010-01-01
In this companion article to “Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content” [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption. PMID:20405047
Dynamics and Trajectory Optimization for a Soft Spatial Fluidic Elastomer Manipulator
Tedrake, Russ
Dynamics and Trajectory Optimization for a Soft Spatial Fluidic Elastomer Manipulator Andrew D manipulation system that is capable of autonomous, dynamic, and safe interactions with humans and its environment. First, we develop a dynamic model for a multi-body fluidic elastomer manipulator that is composed
On Designing Truthful and Optimal Spectrum Assignment Mechanisms for Dynamic Spectrum
Krovi, Venkat
1 On Designing Truthful and Optimal Spectrum Assignment Mechanisms for Dynamic Spectrum Access of cognitive radio technologies, Dynamic Spectrum Access (DSA) has received increasing attention. For dynamic spectrum access, a good spectrum assignment algorithm often considers the spectrum demands of different
Optimization of conventional water treatment plant using dynamic programming.
Mostafa, Khezri Seyed; Bahareh, Ghafari; Elahe, Dadvar; Pegah, Dadras
2015-12-01
In this research, the mathematical models, indicating the capability of various units, such as rapid mixing, coagulation and flocculation, sedimentation, and the rapid sand filtration are used. Moreover, cost functions were used for the formulation of conventional water and wastewater treatment plant by applying Clark's formula (Clark, 1982). Also, by applying dynamic programming algorithm, it is easy to design a conventional treatment system with minimal cost. The application of the model for a case reduced the annual cost. This reduction was approximately in the range of 4.5-9.5% considering variable limitations. Sensitivity analysis and prediction of system's feedbacks were performed for different alterations in proportion from parameters optimized amounts. The results indicated (1) that the objective function is more sensitive to design flow rate (Q), (2) the variations in the alum dosage (A), and (3) the sand filter head loss (H). Increasing the inflow by 20%, the total annual cost would increase to about 12.6%, while 20% reduction in inflow leads to 15.2% decrease in the total annual cost. Similarly, 20% increase in alum dosage causes 7.1% increase in the total annual cost, while 20% decrease results in 7.9% decrease in the total annual cost. Furthermore, the pressure decrease causes 2.95 and 3.39% increase and decrease in total annual cost of treatment plants. PMID:23625909
Function-valued adaptive dynamics and optimal control theory.
Parvinen, Kalle; Heino, Mikko; Dieckmann, Ulf
2013-09-01
In this article we further develop the theory of adaptive dynamics of function-valued traits. Previous work has concentrated on models for which invasion fitness can be written as an integral in which the integrand for each argument value is a function of the strategy value at that argument value only. For this type of models of direct effect, singular strategies can be found using the calculus of variations, with singular strategies needing to satisfy Euler's equation with environmental feedback. In a broader, more mechanistically oriented class of models, the function-valued strategy affects a process described by differential equations, and fitness can be expressed as an integral in which the integrand for each argument value depends both on the strategy and on process variables at that argument value. In general, the calculus of variations cannot help analyzing this much broader class of models. Here we explain how to find singular strategies in this class of process-mediated models using optimal control theory. In particular, we show that singular strategies need to satisfy Pontryagin's maximum principle with environmental feedback. We demonstrate the utility of this approach by studying the evolution of strategies determining seasonal flowering schedules. PMID:22763388
Photocathode Optimization for a Dynamic Transmission Electron Microscope: Final Report
Ellis, P; Flom, Z; Heinselman, K; Nguyen, T; Tung, S; Haskell, R; Reed, B W; LaGrange, T
2011-08-04
The Dynamic Transmission Electron Microscope (DTEM) team at Harvey Mudd College has been sponsored by LLNL to design and build a test setup for optimizing the performance of the DTEM's electron source. Unlike a traditional TEM, the DTEM achieves much faster exposure times by using photoemission from a photocathode to produce electrons for imaging. The DTEM team's work is motivated by the need to improve the coherence and current density of the electron cloud produced by the electron gun in order to increase the image resolution and contrast achievable by DTEM. The photoemission test setup is nearly complete and the team will soon complete baseline tests of electron gun performance. The photoemission laser and high voltage power supply have been repaired; the optics path for relaying the laser to the photocathode has been finalized, assembled, and aligned; the internal setup of the vacuum chamber has been finalized and mostly implemented; and system control, synchronization, and data acquisition has been implemented in LabVIEW. Immediate future work includes determining a consistent alignment procedure to place the laser waist on the photocathode, and taking baseline performance measurements of the tantalum photocathode. Future research will examine the performance of the electron gun as a function of the photoemission laser profile, the photocathode material, and the geometry and voltages of the accelerating and focusing components in the electron gun. This report presents the team's progress and outlines the work that remains.
Morphological Optimization of Perovskite Thin Films via Dynamic Zone Annealing
NASA Astrophysics Data System (ADS)
Sun, Yan; Wang, Kai; Gong, Xiong; Karim, Alamgir
2015-03-01
Organolead Halide Perovskites have been proved to be excellent candidates for application in low-cost high-efficient solar cells owing to their superior desired optical and electrical properties, as well as compatibility with low-temperature solution-processed manufacturing. However, most perovskites applications in photovoltaics require high quality perovskite films. Although tremendous works on tuning perovskite film morphology have been reported previously, it is still a challenge to realize high quality perovskite film with controllable film uniformity and surface coverage, neither the mechanisms in the formation of perovskite. To address the issues above, here we demonstrate the effect of Dynamic Zone Annealing (DZA) on perovskite morphologies, which is proved as an efficient method to control the structure and morphology in crystalline polymer and block copolymers. Via applying the DZA method, the mechanism in perovskite film formation is studied. Furthermore, by optimizing DZA parameter such as maximum temperature, temperature gradient and zone velocity to control dendritic morphology and the grain growth, enhanced device performance was realized eventually. Equal contribution.
An optimal operational advisory system for a brewery's energy supply plant
Ito, K.; Shiba, T.; Yokoyama, R. . Dept. of Energy Systems Engineering); Sakashita, S. . Mayekawa Energy Management Research Center)
1994-03-01
An optimal operational advisory system is proposed to operate rationally a brewery's energy supply plant from the economical viewpoint. A mixed-integer linear programming problem is formulated so as to minimize the daily operational cost subject to constraints such as equipment performance characteristics, energy supply-demand relations, and some practical operational restrictions. This problem includes lots of unknown variables and a hierarchical approach is adopted to derive numerical solutions. The optimal solution obtained by this methods is indicated to the plant operators so as to support their decision making. Through the numerical study for a real brewery plant, the possibility of saving operational cost is ascertained.
Yokoyama, R.; Ito, K.
1995-12-01
An optimal operational planning method is proposed for cogeneration systems with thermal storage. The daily operational strategy of constituent equipment is determined so as to minimize the daily operational cost subject to the energy demand requirement. This optimization problem is formulated as a large-scale mixed-integer linear programming one, and it is solved by means of the decomposition method. Effects of thermal storage on the operation of cogeneration systems are examined through a numerical study on a gas engine-driven cogeneration system installed in a hotel. This method is a useful tool for evaluating the economic and energy-saving properties of cogeneration systems with thermal storage.
Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping
2014-01-01
A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. PMID:24701180
Power flow response based dynamic topology optimization of bi-material plate Structures
NASA Astrophysics Data System (ADS)
Xue, Xiaoguang; Li, Guoxi; Xiong, Yeping; Gong, Jingzhong
2013-05-01
Work on dynamic topology optimization of engineering structures for vibration suppression has mainly addressed the maximization of eigenfrequencies and gaps between consecutive eigenfrequencies of free vibration, minimization of the dynamic compliance subject to forced vibration, and minimization of the structural frequency response. A dynamic topology optimization method of bi-material plate structures is presented based on power flow analysis. Topology optimization problems formulated directly with the design objective of minimizing the power flow response are dealt with. In comparison to the displacement or velocity response, the power flow response takes not only the amplitude of force and velocity into account, but also the phase relationship of the two vector quantities. The complex expression of power flow response is derived based on time-harmonic external mechanical loading and Rayleigh damping. The mathematical formulation of topology optimization is established based on power flow response and bi-material solid isotropic material with penalization(SIMP) model. Computational optimization procedure is developed by using adjoint design sensitivity analysis and the method of moving asymptotes(MMA). Several numerical examples are presented for bi-material plate structures with different loading frequencies, which verify the feasibility and effectiveness of this method. Additionally, optimum results between topological design of minimum power flow response and minimum dynamic compliance are compared, showing that the present method has strong adaptability for structural dynamic topology optimization problems. The proposed research provides a more accurate and effective approach for dynamic topology optimization of vibrating structures.
Optimal GENCO bidding strategy
NASA Astrophysics Data System (ADS)
Gao, Feng
Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies' (GENCOs) bidding strategies. After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system.
Phase balancing optimization for radial feeder systems
NASA Astrophysics Data System (ADS)
Zhu, Jinxiang
In the power distribution systems, unbalanced feeder causes deteriorating power quality and increases investment and operating costs. There are two approaches to balance a feeder system. One approach is feeder reconfiguration, the other is phase swapping. Feeder reconfiguration is an on-line operation of sectionalizing switches. However, feeder reconfiguration is difficult to find the phase balancing solution for a unbalanced feeder system. On the other hand, phase swapping is a direct and effective approach for phase balancing. Phase swapping is an off-line operation of re-tapping loads/laterals to the phase lines during maintenance and restoration periods. Unfortunately, phase balancing problem has not received its deserved importance. Deregulation in power industry has arisen phase balancing issue because it can improve power quality, improve service reliability, and reduce total costs. Thus, phase balancing can enhance utility competitive capability. This research provides utilities the optimization tools to maximize the benefits of phase balancing and minimize the costs. This dissertation proposed several mathematical formulations, including Mixed-integer programming (MIP), Dynamic programming (DP), Simulated annealing (SA), and Fuzzy logic (FL), to perform phase swapping to balance phase in a radial feeder. Due to the discrete nature of phase swapping, a MIP model is proposed to find the optimal phase swapping scheme for small-sized feeder. Candidate sets and monitored branches are then introduced to solve the large-scale feeder systems. Possibilistic integer programming (PIP) is proposed to incorporate load uncertainties in phase swapping problems. The tests show that the load uncertainties may change the optimal phase swapping. DP computation is significant reduced by introducing the tighter upper/lower bounds. The upper bound can be quickly obtained by assignment algorithm. Even though SA is a time-consuming heuristic method, it can provide better solution than other heuristic methods. FL can efficiently identify the most effective phase swapping for large-scale feeder systems. The results can be easily fine-tuned to match the expectation of decision-maker. Finally, the computation efforts and optimality are compared between the proposed methods. Several examples are used to illustrate the effectiveness of the proposed methods.
Evacuation dynamic and exit optimization of a supermarket based on particle swarm optimization
NASA Astrophysics Data System (ADS)
Li, Lin; Yu, Zhonghai; Chen, Yang
2014-12-01
A modified particle swarm optimization algorithm is proposed in this paper to investigate the dynamic of pedestrian evacuation from a fire in a public building-a supermarket with multiple exits and configurations of counters. Two distinctive evacuation behaviours featured by the shortest-path strategy and the following-up strategy are simulated in the model, accounting for different categories of age and sex of the pedestrians along with the impact of the fire, including gases, heat and smoke. To examine the relationship among the progress of the overall evacuation and the layout and configuration of the site, a series of simulations are conducted in various settings: without a fire and with a fire at different locations. Those experiments reveal a general pattern of two-phase evacuation, i.e., a steep section and a flat section, in addition to the impact of the presence of multiple exits on the evacuation along with the geographic locations of the exits. For the study site, our simulations indicated the deficiency of the configuration and the current layout of this site in the process of evacuation and verified the availability of proposed solutions to resolve the deficiency. More specifically, for improvement of the effectiveness of the evacuation from the site, adding an exit between Exit 6 and Exit 7 and expanding the corridor at the right side of Exit 7 would significantly reduce the evacuation time.
Optimal motion planning with the half-car dynamical model for autonomous high-speed driving
Jeon, Jeong hwan
We discuss an implementation of the RRT* optimal motion planning algorithm for the half-car dynamical model to enable autonomous high-speed driving. To develop fast solutions of the associated local steering problem, we ...
Optimal Regulation of Heating Systems with Metering Based on Dynamic Simulation
Zhao, H.; Wang, P.; Zeng, G.; Tian, Y.
2006-01-01
is applied, and the optimal supply water temperatures are worked out by the estimate method of linear summation with weights based on dynamic simulation. The simulative results reflect the relationship between energy loss of heating systems and users' comfort....
Optimal foreign borrowing in a multisector dynamic equilibrium model for Brazil
Tourinho, Octv?io A. F.
1985-01-01
This paper shows how a dynamic multisector equilibrium model can be formulated to be able to analyze the optimal borrowing policy of a developing country. It also describes how a non-linear programming model with the ...
An archived multi-objective simulated annealing for a dynamic cellular manufacturing system
NASA Astrophysics Data System (ADS)
Shirazi, Hossein; Kia, Reza; Javadian, Nikbakhsh; Tavakkoli-Moghaddam, Reza
2014-05-01
To design a group layout of a cellular manufacturing system (CMS) in a dynamic environment, a multi-objective mixed-integer non-linear programming model is developed. The model integrates cell formation, group layout and production planning (PP) as three interrelated decisions involved in the design of a CMS. This paper provides an extensive coverage of important manufacturing features used in the design of CMSs and enhances the flexibility of an existing model in handling the fluctuations of part demands more economically by adding machine depot and PP decisions. Two conflicting objectives to be minimized are the total costs and the imbalance of workload among cells. As the considered objectives in this model are in conflict with each other, an archived multi-objective simulated annealing (AMOSA) algorithm is designed to find Pareto-optimal solutions. Matrix-based solution representation, a heuristic procedure generating an initial and feasible solution and efficient mutation operators are the advantages of the designed AMOSA. To demonstrate the efficiency of the proposed algorithm, the performance of AMOSA is compared with an exact algorithm (i.e., ?-constraint method) solved by the GAMS software and a well-known evolutionary algorithm, namely NSGA-II for some randomly generated problems based on some comparison metrics. The obtained results show that the designed AMOSA can obtain satisfactory solutions for the multi-objective model.
Simultaneous model discrimination and parameter estimation in dynamic models of cellular systems
2013-01-01
Background Model development is a key task in systems biology, which typically starts from an initial model candidate and, involving an iterative cycle of hypotheses-driven model modifications, leads to new experimentation and subsequent model identification steps. The final product of this cycle is a satisfactory refined model of the biological phenomena under study. During such iterative model development, researchers frequently propose a set of model candidates from which the best alternative must be selected. Here we consider this problem of model selection and formulate it as a simultaneous model selection and parameter identification problem. More precisely, we consider a general mixed-integer nonlinear programming (MINLP) formulation for model selection and identification, with emphasis on dynamic models consisting of sets of either ODEs (ordinary differential equations) or DAEs (differential algebraic equations). Results We solved the MINLP formulation for model selection and identification using an algorithm based on Scatter Search (SS). We illustrate the capabilities and efficiency of the proposed strategy with a case study considering the KdpD/KdpE system regulating potassium homeostasis in Escherichia coli. The proposed approach resulted in a final model that presents a better fit to the in silico generated experimental data. Conclusions The presented MINLP-based optimization approach for nested-model selection and identification is a powerful methodology for model development in systems biology. This strategy can be used to perform model selection and parameter estimation in one single step, thus greatly reducing the number of experiments and computations of traditional modeling approaches. PMID:23938131
An inverse dynamics approach to trajectory optimization for an aerospace plane
NASA Technical Reports Server (NTRS)
Lu, Ping
1992-01-01
An inverse dynamics approach for trajectory optimization is proposed. This technique can be useful in many difficult trajectory optimization and control problems. The application of the approach is exemplified by ascent trajectory optimization for an aerospace plane. Both minimum-fuel and minimax types of performance indices are considered. When rocket augmentation is available for ascent, it is shown that accurate orbital insertion can be achieved through the inverse control of the rocket in the presence of disturbances.
Optimal unsteady convection over a duty cycle for arbitrary unsteady flow under dynamic thermal load
Bahrami, Majid
systems Pulsating flow Transient Optimal frequency APEEM a b s t r a c t Developing next generation- cles (EV), and emerging fuel cell vehicles (FCV) [1,2] and green power systems (wind, solar, tidal) [3Optimal unsteady convection over a duty cycle for arbitrary unsteady flow under dynamic thermal
Liu, Ping; Li, Guodong; Liu, Xinggao
2015-09-01
Control vector parameterization (CVP) is an important approach of the engineering optimization for the industrial dynamic processes. However, its major defect, the low optimization efficiency caused by calculating the relevant differential equations in the generated nonlinear programming (NLP) problem repeatedly, limits its wide application in the engineering optimization for the industrial dynamic processes. A novel highly effective control parameterization approach, fast-CVP, is first proposed to improve the optimization efficiency for industrial dynamic processes, where the costate gradient formulae is employed and a fast approximate scheme is presented to solve the differential equations in dynamic process simulation. Three well-known engineering optimization benchmark problems of the industrial dynamic processes are demonstrated as illustration. The research results show that the proposed fast approach achieves a fine performance that at least 90% of the computation time can be saved in contrast to the traditional CVP method, which reveals the effectiveness of the proposed fast engineering optimization approach for the industrial dynamic processes. PMID:26117286
Optimization of the Dynamic Aperture for SPEAR3 Low-Emittance Upgrade
Wang, Lanfa; Huang, Xiaobiao; Nosochkov, Yuri; Safranek, James A.; Borland, Michael; /Argonne
2012-05-30
A low emittance upgrade is planned for SPEAR3. As the first phase, the emittance is reduced from 10nm to 7nm without additional magnets. A further upgrade with even lower emittance will require a damping wiggler. There is a smaller dynamic aperture for the lower emittance optics due to a stronger nonlinearity. Elegant based Multi-Objective Genetic Algorithm (MOGA) is used to maximize the dynamic aperture. Both the dynamic aperture and beam lifetime are optimized simultaneously. Various configurations of the sextupole magnets have been studied in order to find the best configuration. The betatron tune also can be optimized to minimize resonance effects. The optimized dynamic aperture increases more than 15% from the nominal case and the lifetime increases from 14 hours to 17 hours. It is important that the increase of the dynamic aperture is mainly in the beam injection direction. Therefore the injection efficiency will benefit from this improvement.
Optimal Dynamic Advertising Strategy Under Age-Specific Market Segmentation
NASA Astrophysics Data System (ADS)
Krastev, Vladimir
2011-12-01
We consider the model proposed by Faggian and Grosset for determining the advertising efforts and goodwill in the long run of a company under age segmentation of consumers. Reducing this model to optimal control sub problems we find the optimal advertising strategy and goodwill.
Optimal Campaign Strategies in Fractional-Order Smoking Dynamics
NASA Astrophysics Data System (ADS)
Zeb, Anwar; Zaman, Gul; Jung, Il Hyo; Khan, Madad
2014-06-01
This paper deals with the optimal control problem in the giving up smoking model of fractional order. For the eradication of smoking in a community, we introduce three control variables in the form of education campaign, anti-smoking gum, and anti-nicotive drugs/medicine in the proposed fractional order model. We discuss the necessary conditions for the optimality of a general fractional optimal control problem whose fractional derivative is described in the Caputo sense. In order to do this, we minimize the number of potential and occasional smokers and maximize the number of ex-smokers. We use Pontryagin's maximum principle to characterize the optimal levels of the three controls. The resulting optimality system is solved numerically by MATLAB.
Fictitious play for nding system optimal routings in dynamic trac networks q
Smith, Robert L.
Fictitious play for ®nding system optimal routings in dynamic trac networks q Alfredo Garcia routings in a dynamic trac network. Fictitious play is utilized within a game of identical interests in the network. This decentralized approach via repeated play of the ®ctitious game is proven to converge
On the optimality of parsing in dynamic dictionary based data compression
Matias, Yossi
On the optimality of parsing in dynamic dictionary based data compression (preliminary version) Yossi Matias Suleyman Cenk S.ahinalp y May 15, 1998 Since the introduction of dynamic dictionary based data compression by Ziv and Lempel two decades ago, many dictionary construction schemes have been
Optimal Dynamic Sleep Time Control in Wireless Sensor Networks Xu Ning and Christos G. Cassandras
Cassandras, Christos G.
Optimal Dynamic Sleep Time Control in Wireless Sensor Networks Xu Ning and Christos G. Cassandras the sleep interval between consecutive wake- ups of the receiver so that the expected total energy spent fixed sleep times to our dynamic control policy. I. INTRODUCTION A Wireless Sensor Network (WSN
OPTIMAL TASK ALLOCATION AND DYNAMIC TRAJECTORY PLANNING FOR MULTI-VEHICLE
Stryk, Oskar von
appeared in the 1st IFAC Workshop on Multivehicle Systems, October 2-3, 2006, Salvador, Brazil, pages 38-64289 Darmstadt, Germany www.sim.tu-darmstadt.de Abstract: Based on a nonlinear hybrid dynamical systemsOPTIMAL TASK ALLOCATION AND DYNAMIC TRAJECTORY PLANNING FOR MULTI-VEHICLE SYSTEMS USING NONLINEAR
Real-Time Optimized Trajectory Planning for a Fixed-Wing Vehicle Flying in a Dynamic Environment
Qu, Zhihua
Real-Time Optimized Trajectory Planning for a Fixed-Wing Vehicle Flying in a Dynamic Environment an optimal feasible trajectory, for a fixed wing flying vehicle moving in a dynamical three- dimensional by optimizing a performance index with respect to L2 norm. This trajectory and its associated steering controls
Adaptive Optimal Feedback Control with Learned Internal Dynamics Models
Mitrovic, Djordje; Klanke, Stefan; Vijayakumar, Sethu
2010-01-01
Optimal Feedback Control (OFC) has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. Recent developments, such as the Iterative Linear Quadratic ...
NASA Astrophysics Data System (ADS)
Xu, Da; Zhao, Jian-xun; Hu, Jun-biao; Li, Hua; Wang, Chuan-you; Li, Bing-wei
2009-07-01
The dynamical simulation model of the exposure device of laser detector is built up in ADAMS software. Aiming at optimizing the movement law of the pole and minimizing the maximal value of load of key parts, the influences of the spring stiffness coefficient,the damping coefficient, preload of spring and the mass of pole on the optimal goal are discussed. The virtual prototype of the exposure device of laser detector has been optimized and the optimized parameters are obtained. In order to choose the electromotor and material, intensity of key parts is checked based on ANSYS. And the problem of TQC is solved effectively by this way.
Morrow, Melissa M; Rankin, Jeffery W; Neptune, Richard R; Kaufman, Kenton R
2014-11-01
The primary purpose of this study was to compare static and dynamic optimization muscle force and work predictions during the push phase of wheelchair propulsion. A secondary purpose was to compare the differences in predicted shoulder and elbow kinetics and kinematics and handrim forces. The forward dynamics simulation minimized differences between simulated and experimental data (obtained from 10 manual wheelchair users) and muscle co-contraction. For direct comparison between models, the shoulder and elbow muscle moment arms and net joint moments from the dynamic optimization were used as inputs into the static optimization routine. RMS errors between model predictions were calculated to quantify model agreement. There was a wide range of individual muscle force agreement that spanned from poor (26.4% Fmax error in the middle deltoid) to good (6.4% Fmax error in the anterior deltoid) in the prime movers of the shoulder. The predicted muscle forces from the static optimization were sufficient to create the appropriate motion and joint moments at the shoulder for the push phase of wheelchair propulsion, but showed deviations in the elbow moment, pronation-supination motion and hand rim forces. These results suggest the static approach does not produce results similar enough to be a replacement for forward dynamics simulations, and care should be taken in choosing the appropriate method for a specific task and set of constraints. Dynamic optimization modeling approaches may be required for motions that are greatly influenced by muscle activation dynamics or that require significant co-contraction. PMID:25282075
NASA Technical Reports Server (NTRS)
Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.
1995-01-01
This paper describes an integrated aerodynamic/dynamic/structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general-purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of global quantities (stiffness, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic designs are performed at a global level and the structural design is carried out at a detailed level with considerable dialog and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several examples.
NASA Technical Reports Server (NTRS)
Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.
1994-01-01
This paper describes an integrated aerodynamic, dynamic, and structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of local quantities (stiffnesses, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic design is performed at a global level and the structural design is carried out at a detailed level with considerable dialogue and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several cases.
Basu, Sohini; Sen, Srikanta
2013-02-25
Structure and dynamics both are known to be important for the activity of a protein. A fundamental question is whether a thermophilic protein and its mesophilic homologue exhibit similar dynamics at their respective optimal growth temperatures. We have addressed this question by performing molecular dynamics (MD) simulations of a natural mesophilic-thermophilic homologue pair at their respective optimal growth temperatures to compare their structural, dynamical, and solvent properties. The MD simulations were done in explicit aqueous solvent under periodic boundary and constant pressure and temperature (CPT) conditions and continued for 10.0 ns using the same protocol for the two proteins, excepting the temperatures. The trajectories were analyzed to compare the properties of the two proteins. Results indicated that the dynamical behaviors of the two proteins at the respective optimal growth temperatures were remarkably similar. For the common residues in the thermophilic protein, the rms fluctuations have a general trend to be slightly higher compared to that in the mesophilic counterpart. Lindemann parameter values indicated that only a few residues exhibited solid-like dynamics while the protein as a whole appeared as a molten globule in each case. Interestingly, the water-water interaction was found to be strikingly similar in spite of the difference in temperatures while, the protein-water interaction was significantly different in the two simulations. PMID:23267663
Optimal sizing of a gas turbine cogeneration plant in consideration of its operational strategy
Yokoyama, R.; Ito, K. . Dept. of Energy Systems Engineering); Matsumoto, Y. . Technical Research Center)
1994-01-01
An optimal planning method is proposed for the fundamental design of cogeneration plants. Equipment capacities and utility maximum demands are determined so as to minimize the annual total cost in consideration of the plants' annual operational strategies for the variations of both electricity and thermal energy demands. These sizing and operational planning problems are formulated as a nonlinear programming problem and a mixed-integer linear programming problem, respectively. They are solved efficiently in consideration of their hierarchical relationship by a penalty method. A numerical example about a gas turbine plant is given to ascertain the validity and effectiveness of the proposed method.
Conte, Thomas M.
improve performance on any computer system, but plays an especially important role in statically scheduled be used in any modern dynamic optimizer to balance control flow and predication based on actual runtime misprediction rates into the if- conversion decision process. This method acts as an effective means
Data-driven learning in dynamic pricing using adaptive optimization
of observations. These modeling features enable us to unify optimization and estimation as the uncertainty set for also affecting revenues provided the population to which the product is offered is price of the internet as a sales channel and the increasing use of digital price tags have drastically decreased
Human Kicking Motion Using Efficient Forward Dynamics Simulation And Optimization
Stryk, Oskar von
. von Stryk Technische Universität Darmstadt, Simulation and Systems Optimization Group, Darmstadt collocation, goal oriented motions, analysis of measured motion I. INTRODUCTION Biomechanical systems are very SYSTEMS The human body is modeled as multibody system in two or three dimensions including mass, center
Was Your Glass Left Half Full? Family Dynamics and Optimism
ERIC Educational Resources Information Center
Buri, John R.; Gunty, Amy
2008-01-01
Students' levels of a frequently studied adaptive schema (optimism) as a function of parenting variables (parental authority, family intrusiveness, parental overprotection, parentification, parental psychological control, and parental nurturance) were investigated. Results revealed that positive parenting styles were positively related to the…
Robust time-optimal control of uncertain structural dynamic systems
NASA Technical Reports Server (NTRS)
Wie, Bong; Sinha, Ravi; Liu, Qiang
1993-01-01
A time-optimal open-loop control problem of flexible spacecraft in the presence of modeling uncertainty has been investigated. The results indicate that the proposed approach significantly reduces the residual structural vibrations caused by modeling uncertainty. The results also indicate the importance of proper jet placement for practical tradeoffs among the maneuvering time, fuel consumption, and performance robustness.
INCREASING STABILITY IN DYNAMIC GAITS USING NUMERICAL OPTIMIZATION
Stryk, Oskar von
Simulation and Systems Optimization Group, Technische Universit¨at Darmstadt, Alexanderstr. 10, 64283 of the popular method of quantifying This is a preprint of the paper that appeared in: Proc. 15th IFAC World Darmstadt, Germany Email: ¢¡¤£¦¥¨§©©¥ !"#%$'&(©0) 10§£¦¥$23©¤£§0©4&(§¨5 ¡¨©©687@99BAAA4&C¨#%$D&E#3F
Orellana, Liliana; Rotnitzky, Andrea; Robins, James M
2010-01-01
Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results. PMID:21969994
Performance evaluation of the inverse dynamics method for optimal spacecraft reorientation
NASA Astrophysics Data System (ADS)
Ventura, Jacopo; Romano, Marcello; Walter, Ulrich
2015-05-01
This paper investigates the application of the inverse dynamics in the virtual domain method to Euler angles, quaternions, and modified Rodrigues parameters for rapid optimal attitude trajectory generation for spacecraft reorientation maneuvers. The impact of the virtual domain and attitude representation is numerically investigated for both minimum time and minimum energy problems. Owing to the nature of the inverse dynamics method, it yields sub-optimal solutions for minimum time problems. Furthermore, the virtual domain improves the optimality of the solution, but at the cost of more computational time. The attitude representation also affects solution quality and computational speed. For minimum energy problems, the optimal solution can be obtained without the virtual domain with any considered attitude representation.
NASA Astrophysics Data System (ADS)
Butler, Thomas; Goldenfeld, Nigel; Mathew, Damien; Luthey-Schulten, Zaida
2009-06-01
A molecular dynamics calculation of the amino acid polar requirement is used to score the canonical genetic code. Monte Carlo simulation shows that this computational polar requirement has been optimized by the canonical genetic code, an order of magnitude more than any previously known measure, effectively ruling out a vertical evolution dynamics. The sensitivity of the optimization to the precise metric used in code scoring is consistent with code evolution having proceeded through the communal dynamics of statistical proteins using horizontal gene transfer, as recently proposed. The extreme optimization of the genetic code therefore strongly supports the idea that the genetic code evolved from a communal state of life prior to the last universal common ancestor.
NASA Astrophysics Data System (ADS)
Zhou, Xu; Liu, Yanheng; Li, Bin; Sun, Geng
2015-10-01
Identifying community structures in static network misses the opportunity to capture the evolutionary patterns. So community detection in dynamic network has attracted many researchers. In this paper, a multiobjective biogeography based optimization algorithm with decomposition (MBBOD) is proposed to solve community detection problem in dynamic networks. In the proposed algorithm, the decomposition mechanism is adopted to optimize two evaluation objectives named modularity and normalized mutual information simultaneously, which measure the quality of the community partitions and temporal cost respectively. A novel sorting strategy for multiobjective biogeography based optimization is presented for comparing quality of habitats to get species counts. In addition, problem-specific migration and mutation model are introduced to improve the effectiveness of the new algorithm. Experimental results both on synthetic and real networks demonstrate that our algorithm is effective and promising, and it can detect communities more accurately in dynamic networks compared with DYNMOGA and FaceNet.
Discrete Adjoint-Based Design Optimization of Unsteady Turbulent Flows on Dynamic Unstructured Grids
NASA Technical Reports Server (NTRS)
Nielsen, Eric J.; Diskin, Boris; Yamaleev, Nail K.
2009-01-01
An adjoint-based methodology for design optimization of unsteady turbulent flows on dynamic unstructured grids is described. The implementation relies on an existing unsteady three-dimensional unstructured grid solver capable of dynamic mesh simulations and discrete adjoint capabilities previously developed for steady flows. The discrete equations for the primal and adjoint systems are presented for the backward-difference family of time-integration schemes on both static and dynamic grids. The consistency of sensitivity derivatives is established via comparisons with complex-variable computations. The current work is believed to be the first verified implementation of an adjoint-based optimization methodology for the true time-dependent formulation of the Navier-Stokes equations in a practical computational code. Large-scale shape optimizations are demonstrated for turbulent flows over a tiltrotor geometry and a simulated aeroelastic motion of a fighter jet.
NASA Technical Reports Server (NTRS)
Athans, M.; Ku, R.; Gershwin, S. B.
1977-01-01
This note shows that the optimal control of dynamic systems with uncertain parameters has certain limitations. In particular, by means of a simple scalar linear-quadratic optimal control example, it is shown that the infinite horizon solution does not exist if the parameter uncertainty exceeds a certain quantifiable threshold; we call this the uncertainty threshold principle. The philosophical and design implications of this result are discussed.
NASA Technical Reports Server (NTRS)
Athans, M.; Ku, R.; Gershwin, S. B.
1977-01-01
This note shows that the optimal control of dynamic systems with uncertain parameters has certain limitations. In particular, by means of a simple scalar linear-quadratic optimal control example, it is shown that the infinite horizon solution does not exist if the parameter uncertainty exceeds a certain quantifiable threshold; we call this the uncertainty threshold principle. The philosophical and design implications of this result are discussed.
Optimal post-experiment estimation of poorly modeled dynamic systems
NASA Technical Reports Server (NTRS)
Mook, D. Joseph
1988-01-01
Recently, a novel strategy for post-experiment state estimation of discretely-measured dynamic systems has been developed. The method accounts for errors in the system dynamic model equations in a more general and rigorous manner than do filter-smoother algorithms. The dynamic model error terms do not require the usual process noise assumptions of zero-mean, symmetrically distributed random disturbances. Instead, the model error terms require no prior assumptions other than piecewise continuity. The resulting state estimates are more accurate than filters for applications in which the dynamic model error clearly violates the typical process noise assumptions, and the available measurements are sparse and/or noisy. Estimates of the dynamic model error, in addition to the states, are obtained as part of the solution of a two-point boundary value problem, and may be exploited for numerous reasons. In this paper, the basic technique is explained, and several example applications are given. Included among the examples are both state estimation and exploitation of the model error estimates.
INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization
Groer, Christopher S; Sullivan, Blair D; Weerapurage, Dinesh P
2012-10-01
It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.
Notes on optimality of direct characterisation of quantum dynamics
Mario Ziman
2006-03-17
We argue that the claimed optimality of a new process tomography method suggested in [quant-ph/0601033] and [quant-ph/0601034] is based on not completely fair comparison that does not take into account the available information in an equal way. We also argue that the method is not a new process tomography scheme, but rather represents an interesting modification of ancilla assisted process tomography method. In our opinion these modifications require deeper understanding and further investigation.
DYNAMIC APERTURE OPTIMIZATION FOR LOW EMITTANCE LIGHT SOURCES.
KRAMER, S.; BENGTSSON, J.
2005-05-15
State of the art low emittance light source lattices, require small bend angle dipole magnets and strong quadrupoles. This in turn creates large chromaticity and small value of dispersion in the lattice. To counter the high linear chromaticity, strong sextupoles are required which limit the dynamic aperture. Traditional methods for expanding the dynamic aperture use harmonic sextupoles to counter the tune shift with amplitude. This has been successful up to now, but is non-deterministic and limited as the sextupole strength increases, driving higher order nonlinearities. We have taken a different approach that makes use of the tune flexibility of a TBA lattice to minimize the lowest order nonlinearities, freeing the harmonic sextupoles to counter the higher order nonlinearities. This procedure is being used to improve the nonlinear dynamics of the NSLS-II lattice.
Biological network design strategies: discovery through dynamic optimization.
Adiwijaya, Bambang S; Barton, Paul I; Tidor, Bruce
2006-12-01
An important challenge in systems biology is the inherent complexity of biological network models, which complicates the task of relating network structure to function and of understanding the conceptual design principles by which a given network operates. Here we investigate an approach to analyze the relationship between a network structure and its function using the framework of optimization. A common feature found in a variety of biochemical networks involves the opposition of a pair of enzymatic chemical modification reactions such as phosphorylation-dephosphorylation or methylation-demethylation. The modification pair frequently adjusts biochemical properties of its target, such as activating and deactivating function. We applied optimization methodology to study a reversible modification network unit commonly found in signal transduction systems, and we explored the use of this methodology to discover design principles. The results demonstrate that different sets of rate constants used to parameterize the same network topology represent different compromises made in the resulting network operating characteristics. Moreover, the same topology can be used to encode different strategies for achieving performance goals. The ability to adopt multiple strategies may lead to significantly improved performance across a range of conditions through rate modulation or evolutionary processes. The optimization framework explored here is a practical approach to support the discovery of design principles in biological networks. PMID:17216046
NASA Technical Reports Server (NTRS)
Athans, M.; Ku, R.; Gershwin, S. B.
1976-01-01
The fundamental limitations of the optimal control of dynamic systems with random parameters are analyzed by studying a scalar linear-quadratic optimal control example. It is demonstrated that optimum long-range decision making is possible only if the dynamic uncertainty (quantified by the means and covariances of the random parameters) is below a certain threshold. If this threshold is exceeded, there do not exist optimum decision rules. This phenomenon is called the 'uncertainty threshold principle'. The implications of this phenomenon to the field of modelling, identification, and adaptive control are discussed.
Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; Klein, Vladislav
1990-01-01
A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.
Discrete-time dynamic user-optimal departure time/route choice model
Chen, H.K.; Hsueh, C.F.
1998-05-01
This paper concerns a discrete-time, link-based, dynamic user-optimal departure time/route choice model using the variational inequality approach. The model complies with a dynamic user-optimal equilibrium condition in which for each origin-destination pair, the actual route travel times experienced by travelers, regardless the departure time, is equal and minimal. A nested diagonalization procedure is proposed to solve the model. Numerical examples are then provided for demonstration and detailed elaboration for multiple solutions and Braess`s paradox.
A new method of optimal design for a two-dimensional diffuser by using dynamic programming
NASA Technical Reports Server (NTRS)
Gu, Chuangang; Zhang, Moujin; Chen, XI; Miao, Yongmiao
1991-01-01
A new method for predicting the optimal velocity distribution on the wall of a two dimensional diffuser is presented. The method uses dynamic programming to solve the optimal control problem with inequality constraints of state variables. The physical model of optimization is designed to prevent the separation of the boundary layer while approaching the maximum pressure ratio in a diffuser of a specified length. The computational results are in fair agreement with the experimental ones. Optimal velocity distribution on a diffuser wall is said to occur when the flow decelerates quickly at first and then smoothly, while the flow is near separation, but always protected from it. The optimal velocity distribution can be used to design the contour of the diffuser.
THE COUPLING OF OPTIMAL ECONOMIC GROWTH AND CLIMATE DYNAMICS
Edwards, Neil
economic growth programs coupled with climate change dynamics. The study is based on models derived from climate model, allow us to identity a domain intended to preserve the Atlantic thermohaline circulation of MERGE and an intermediate complexity "3-D" climate model (C-GOLDSTEIN) which computes the changes
Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes
the vast amount of research on anticipatory gaze behaviour in natural situations, such as action execution by introducing camera motion, jump cuts, and film-editing. Keywords Eye movements Ã Anticipatory gaze behaviour Ã collected and examined on a wide variety of dynamic realistic stimulus types (i.e. video cat- egories). Gaze
InformationBased Optimization Approaches to Dynamical System Safety Verification
Neller, Todd W.
parameters of a stepper motor (e.g. viscous fric tion, inertial load), bounds on initial conditions (e.g. angular displacement and velocity), and an openloop motor acceleration control, verify that no scenario exists in which the motor stalls. We model the motor's continuous dynamics using ODEs given in [2
Optimization and Frustration: The Dynamical Lattice Model of Proteins
Kobe, Sigismund
offlattice model high complexity computability #12;Dynamical Lattice Model Assumptions: » Atoms: hard spheres. » Atomic bonds are fixed in length and orientation » Sidechains: spheres (diameter according.53 CCN' / º 114 N-C / Å 1.47 NCC / º 110 180 C ' lies in the plane C CN': » two degrees of freedom
Shu, Chi-Wang
Words: Anisotropic, Continuum model, Dynamic traffic assignment, Predictive user equilibrium the practicality of the continuum model and govern its dynamic changes, dynamic traffic assignment (DTA) (Chow user-optimal dynamic assignment in con- tinuous time and space. Although their model was proposed
2012-01-01
Background Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations. This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Results Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. Conclusions In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems. PMID:22748139
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962
Credibility theory based dynamic control bound optimization for reservoir flood limited water level
NASA Astrophysics Data System (ADS)
Jiang, Zhiqiang; Sun, Ping; Ji, Changming; Zhou, Jianzhong
2015-10-01
The dynamic control operation of reservoir flood limited water level (FLWL) can solve the contradictions between reservoir flood control and beneficial operation well, and it is an important measure to make sure the security of flood control and realize the flood utilization. The dynamic control bound of FLWL is a fundamental key element for implementing reservoir dynamic control operation. In order to optimize the dynamic control bound of FLWL by considering flood forecasting error, this paper took the forecasting error as a fuzzy variable, and described it with the emerging credibility theory in recent years. By combining the flood forecasting error quantitative model, a credibility-based fuzzy chance constrained model used to optimize the dynamic control bound was proposed in this paper, and fuzzy simulation technology was used to solve the model. The FENGTAN reservoir in China was selected as a case study, and the results show that, compared with the original operation water level, the initial operation water level (IOWL) of FENGTAN reservoir can be raised 4 m, 2 m and 5.5 m respectively in the three division stages of flood season, and without increasing flood control risk. In addition, the rationality and feasibility of the proposed forecasting error quantitative model and credibility-based dynamic control bound optimization model are verified by the calculation results of extreme risk theory.
Optimal purchasing of raw materials: A data-driven approach
Muteki, K.; MacGregor, J.F.
2008-06-15
An approach to the optimal purchasing of raw materials that will achieve a desired product quality at a minimum cost is presented. A PLS (Partial Least Squares) approach to formulation modeling is used to combine databases on raw material properties and on past process operations and to relate these to final product quality. These PLS latent variable models are then used in a sequential quadratic programming (SQP) or mixed integer nonlinear programming (MINLP) optimization to select those raw-materials, among all those available on the market, the ratios in which to combine them and the process conditions under which they should be processed. The approach is illustrated for the optimal purchasing of metallurgical coals for coke making in the steel industry.
Bacterial Temporal Dynamics Enable Optimal Design of Antibiotic Treatment
Meredith, Hannah R.; Lopatkin, Allison J.; Anderson, Deverick J.; You, Lingchong
2015-01-01
There is a critical need to better use existing antibiotics due to the urgent threat of antibiotic resistant bacteria coupled with the reduced effort in developing new antibiotics. ?-lactam antibiotics represent one of the most commonly used classes of antibiotics to treat a broad spectrum of Gram-positive and -negative bacterial pathogens. However, the rise of extended spectrum ?-lactamase (ESBL) producing bacteria has limited the use of ?-lactams. Due to the concern of complex drug responses, many ?-lactams are typically ruled out if ESBL-producing pathogens are detected, even if these pathogens test as susceptible to some ?-lactams. Using quantitative modeling, we show that ?-lactams could still effectively treat pathogens producing low or moderate levels of ESBLs when administered properly. We further develop a metric to guide the design of a dosing protocol to optimize treatment efficiency for any antibiotic-pathogen combination. Ultimately, optimized dosing protocols could allow reintroduction of a repertoire of first-line antibiotics with improved treatment outcomes and preserve last-resort antibiotics. PMID:25905796
Global optimal design of ground water monitoring network using embedded kriging.
Dhar, Anirban; Datta, Bithin
2009-01-01
We present a methodology for global optimal design of ground water quality monitoring networks using a linear mixed-integer formulation. The proposed methodology incorporates ordinary kriging (OK) within the decision model formulation for spatial estimation of contaminant concentration values. Different monitoring network design models incorporating concentration estimation error, variance estimation error, mass estimation error, error in locating plume centroid, and spatial coverage of the designed network are developed. A big-M technique is used for reformulating the monitoring network design model to a linear decision model while incorporating different objectives and OK equations. Global optimality of the solutions obtained for the monitoring network design can be ensured due to the linear mixed-integer programming formulations proposed. Performances of the proposed models are evaluated for both field and hypothetical illustrative systems. Evaluation results indicate that the proposed methodology performs satisfactorily. These performance evaluation results demonstrate the potential applicability of the proposed methodology for optimal ground water contaminant monitoring network design. PMID:19563421
Improving the Dynamic Characteristics of Body-in-White Structure Using Structural Optimization
Yahaya Rashid, Aizzat S.; Mohamed Haris, Sallehuddin; Alias, Anuar
2014-01-01
The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312
Improving the dynamic characteristics of body-in-white structure using structural optimization.
Yahaya Rashid, Aizzat S; Ramli, Rahizar; Mohamed Haris, Sallehuddin; Alias, Anuar
2014-01-01
The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312
Optimal control landscape for the generation of unitary transformations with constrained dynamics
Hsieh, Michael; Wu, Rebing; Rabitz, Herschel; Lidar, Daniel
2010-06-15
The reliable and precise generation of quantum unitary transformations is essential for the realization of a number of fundamental objectives, such as quantum control and quantum information processing. Prior work has explored the optimal control problem of generating such unitary transformations as a surface-optimization problem over the quantum control landscape, defined as a metric for realizing a desired unitary transformation as a function of the control variables. It was found that under the assumption of nondissipative and controllable dynamics, the landscape topology is trap free, which implies that any reasonable optimization heuristic should be able to identify globally optimal solutions. The present work is a control landscape analysis, which incorporates specific constraints in the Hamiltonian that correspond to certain dynamical symmetries in the underlying physical system. It is found that the presence of such symmetries does not destroy the trap-free topology. These findings expand the class of quantum dynamical systems on which control problems are intrinsically amenable to a solution by optimal control.
Michael Hsieh; Rebing Wu; Herschel Rabitz; Daniel Lidar
2009-06-24
The reliable and precise generation of quantum unitary transformations is essential to the realization of a number of fundamental objectives, such as quantum control and quantum information processing. Prior work has explored the optimal control problem of generating such unitary transformations as a surface optimization problem over the quantum control landscape, defined as a metric for realizing a desired unitary transformation as a function of the control variables. It was found that under the assumption of non-dissipative and controllable dynamics, the landscape topology is trap-free, implying that any reasonable optimization heuristic should be able to identify globally optimal solutions. The present work is a control landscape analysis incorporating specific constraints in the Hamiltonian corresponding to certain dynamical symmetries in the underlying physical system. It is found that the presence of such symmetries does not destroy the trap-free topology. These findings expand the class of quantum dynamical systems on which control problems are intrinsically amenable to solution by optimal control.
Dynamic Resource Allocation for Optimizing Population Diffusion Shan Xue Alan Fern Daniel Sheldon
Sheldon, Daniel R.
growth of a species by purchasing and reserving land parcels to best support population spread-term population growth. While this work focuses on a par- ticular population diffusion process, it is importantDynamic Resource Allocation for Optimizing Population Diffusion Shan Xue Alan Fern Daniel Sheldon
A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION
Kuhl, Michael E.
A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION Michael E INTRODUCTION Project management is a tool that is used by many compa- nies to help improve performance and competitiveness. Projects and their execution, in general, require resources. Project management, which
Optimal control of transient dynamics in balanced networks supports generation of complex movements
Gerstner, Wulfram
of limb movements. Traditional network models with stochastically assigned synapses cannot reproduce are manifold. It must be highly malleable during the preparatory period, excitable and fast when movementOptimal control of transient dynamics in balanced networks supports generation of complex movements
Bipedal Robotic Running with Partial Hybrid Zero Dynamics and Human-Inspired Optimization
Ames, Aaron
Bipedal Robotic Running with Partial Hybrid Zero Dynamics and Human-Inspired Optimization Huihua for achieving stable "human-like" running in simulation by using human-inspired control. Data from human running experiments are processed, analyzed and split into the two domains: stance phase and flight phase
Stentz, Tony
recommended path to the goal. Mission planning is the process of determining what each robot should doAbstract Planning for multiple mobile robots in dynamic environ ments involves determining the optimal path each robot should follow to accomplish the goals of the mission, given the current knowledge
Post-Doctoral position Optimal Image Transport; Application to Dynamic Medical Image Analysis
Maume-Deschamps, VÃ©ronique
Post-Doctoral position Optimal Image Transport; Application to Dynamic Medical Image Analysis.clarysse@creatis.insa-lyon.fr Context Current medical imaging techniques allow the analysis of the anatomy and of several functions. Imaging , Vol. 24, pages 209Â228, 2006. [3] S. Keeling, W. Ring, "Medical image registration
Pedram, Massoud
}@elpl.snu.ac.kr, pedram@usc.edu ABSTRACT Partial shading is a serious obstacle to effective utilization of photovoltaicNear-Optimal, Dynamic Module Reconfiguration in a Photovoltaic System to Combat Partial Shading Effects Xue Lin1 , Yanzhi Wang1 , Siyu Yue1 , Donghwa Shin2 , Naehyuck Chang2 , and Massoud Pedram1 1
Power System Dynamics as Primal-Dual Algorithm for Optimal Load Control
Wierman, Adam
1 Power System Dynamics as Primal-Dual Algorithm for Optimal Load Control Changhong Zhao , Ufuk power flows, coupled with frequency-based load control, serve as a distributed primal-dual algorithm that provides fast and inexpensive power system regulation. Indeed the feasibility and efficiency of load
External optimal control of self-organisation dynamics in a chemotaxis reaction diffusion system
Maurer, Helmut
External optimal control of self-organisation dynamics in a chemotaxis reaction diffusion system D a highly organised spatiotemporal system structure. In particular chemotaxis is crucial for various chemotaxis based on a 1-D two-component partial differential equation (PDE) model of reaction diffusion type
Chalkiadakis, Georgios
into account the energy con- sumption caused by the tracking itself, nor do they consider the systemTowards Optimal Solar Tracking: A Dynamic Programming Approach Athanasios Aris Panagopoulos solar tracking tech- niques. However, current techniques suffer from several drawbacks in their tracking
Optimizing MLC-based STT-RAM Caches by Dynamic Block Size Reconfiguration
Wong, Weng Fai
Optimizing MLC-based STT-RAM Caches by Dynamic Block Size Reconfiguration Jianxing Wang, Pooja Roy that MLC STT-RAM can achieve 2× the storage density of SLC and thus improves system performance to the multi-step access scheme. Compared to SLC, a typical 2-bit MLC STT-RAM introduces an additional data
Optimal Dynamic Pricing with Patient Customers Yan Liu William L. Cooper
Cooper, William L.
a purchase. If the price does not fall below a patient customer's valuation at any time during those periodsOptimal Dynamic Pricing with Patient Customers Yan Liu William L. Cooper Department of Industrial an infinite-horizon single-product pricing problem in which a fraction of cus- tomers is patient
Multi-host transmission dynamics of schistosomiasis and its optimal control.
Ding, Chunxiao; Qiu, Zhipeng; Zhu, Huaiping
2015-10-01
In this paper we formulate a dynamical model to study the transmission dynamics of schistosomiasis in humans and snails. We also incorporate bovines in the model to study their impact on transmission and controlling the spread of Schistosoma japonicum in humans in China. The dynamics of the model is rigorously analyzed by using the theory of dynamical systems. The theoretical results show that the disease free equilibrium is globally asymptotically stable if R0 < 1, and if R0 > 1 the system has only one positive equilibrium. The local stability of the unique positive equilibrium is investigated and sufficient conditions are also provided for the global stability of the positive equilibrium. The optimal control theory are further applied to the model to study the corresponding optimal control problem. Both analytical and numerical results suggest that: (a) the infected bovines play an important role in the spread of schistosomiasis among humans, and killing the infected bovines will be useful to prevent transmission of schistosomiasis among humans; (b) optimal control strategy performs better than the constant controls in reducing the prevalence of the infected human and the cost for implementing optimal control is much less than that for constant controls; and PMID:26280190
Locusts use dynamic thermoregulatory behaviour to optimize nutritional outcomes
Coggan, Nicole; Clissold, Fiona J.; Simpson, Stephen J.
2011-01-01
Because key nutritional processes differ in their thermal optima, ectotherms may use temperature selection to optimize performance in changing nutritional environments. Such behaviour would be especially advantageous to small terrestrial animals, which have low thermal inertia and often have access to a wide range of environmental temperatures over small distances. Using the locust, Locusta migratoria, we have demonstrated a direct link between nutritional state and thermoregulatory behaviour. When faced with chronic restrictions to the supply of nutrients, locusts selected increasingly lower temperatures within a gradient, thereby maximizing nutrient use efficiency at the cost of slower growth. Over the shorter term, when locusts were unable to find a meal in the normal course of ad libitum feeding, they immediately adjusted their thermoregulatory behaviour, selecting a lower temperature at which assimilation efficiency was maximal. Thus, locusts use fine scale patterns of movement and temperature selection to adjust for reduced nutrient supply and thereby ameliorate associated life-history consequences. PMID:21288941
Optimization of multi-stage dynamic treatment regimes utilizing accumulated data.
Huang, Xuelin; Choi, Sangbum; Wang, Lu; Thall, Peter F
2015-11-20
In medical therapies involving multiple stages, a physician's choice of a subject's treatment at each stage depends on the subject's history of previous treatments and outcomes. The sequence of decisions is known as a dynamic treatment regime or treatment policy. We consider dynamic treatment regimes in settings where each subject's final outcome can be defined as the sum of longitudinally observed values, each corresponding to a stage of the regime. Q-learning, which is a backward induction method, is used to first optimize the last stage treatment then sequentially optimize each previous stage treatment until the first stage treatment is optimized. During this process, model-based expectations of outcomes of late stages are used in the optimization of earlier stages. When the outcome models are misspecified, bias can accumulate from stage to stage and become severe, especially when the number of treatment stages is large. We demonstrate that a modification of standard Q-learning can help reduce the accumulated bias. We provide a computational algorithm, estimators, and closed-form variance formulas. Simulation studies show that the modified Q-learning method has a higher probability of identifying the optimal treatment regime even in settings with misspecified models for outcomes. It is applied to identify optimal treatment regimes in a study for advanced prostate cancer and to estimate and compare the final mean rewards of all the possible discrete two-stage treatment sequences. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26095711
Ghosh, Arka P.
are employed to prove the asymptotic optimality of this policy. In addition, we identify the parameter regimes with impatient customers (see [25, 31, 30, 26, 27, 32]). Here we consider a system optimization problem relatedOptimal buffer size and dynamic rate control for a queueing network with impatient customers
Shadmehr, Reza
Effects of changing noise in dynamics of reaching on changes in control of reaching: An optimal trajectory'. Recently, optimal feedback control framework has been proposed as a model of motor control. This hypothesis in conjunction ii #12;with stochastic optimal feedback control framework has quite interesting
Optimized explicit-solvent replica exchange molecular dynamics from scratch.
Nadler, Walter; Hansmann, Ulrich H E
2008-08-28
Replica exchange molecular dynamics (REMD) simulations have become an important tool to study proteins and other biological molecules in silico. However, such investigations require considerable, and often prohibitive, numerical effort when the molecules are simulated in explicit solvents. In this communication we show that in this case the cost can be minimized by choosing the number of replicas as N(opt) approximately 1+0.594 radical C ln(Tmax/Tmin), where C is the specific heat, and the temperatures distributed according to Ti(opt) approximately T min(Tmax/Tmin)(i-1)/(N-1). PMID:18671362
Mixed-Integer Nonlinear Programming Michael R. Bussieck Armin Pruessner
Neumaier, Arnold
and the financial, engineering, management science and operations research sectors. It includes problems in process flow sheets, portfolio selection, batch processing in chemical engineering (consisting of mixing of interest include the automobile, aircraft, and VLSI manufacturing ar- eas. An impressive collection
Applications and algorithms for mixed integer nonlinear programming
Linderoth, Jeffrey T.
insulation layer for the large hadron collider: A thermal insulation system uses a series of heat intercepts of Thermal Insulation In [6], by using MINLP, we have identified solutions that were as much as 4% better MINLP tools to address their challenging scientific problems. 2. Applications Design of thermal
Safe bounds in linear and mixed-integer linear programming
Neumaier, Arnold
of the 300 year old Kepler conjecture; see, e.g., the discussion in Lagarias [12]), where the linear programs by Ord#19; o~ nez & Freund [19] revealed that 72% of the (real life!) problems of the Netlib LP
Application of mixed-integer programming in chemical engineering
Pogiatzis, Thomas
2013-06-11
in Part II posi position of city i in decimal base Q set of vertices (cities) in Part I; heat duty in Part II Q0 heat duty for a clean heat exchanger qi Lagrange interpolation polynomial rc coke formation rate Rf thermal fouling resistance rf function... heuristic procedures are mentioned. Chapter 3 introduces the novel mathematical description of the Travelling Salesman Problem. A new class of mathematical programming formulations is developed, based on work in Binary Arithmetic. The detailed presentation...
NASA Technical Reports Server (NTRS)
Zang, Thomas A.; Green, Lawrence L.
1999-01-01
A challenge for the fluid dynamics community is to adapt to and exploit the trend towards greater multidisciplinary focus in research and technology. The past decade has witnessed substantial growth in the research field of Multidisciplinary Design Optimization (MDO). MDO is a methodology for the design of complex engineering systems and subsystems that coherently exploits the synergism of mutually interacting phenomena. As evidenced by the papers, which appear in the biannual AIAA/USAF/NASA/ISSMO Symposia on Multidisciplinary Analysis and Optimization, the MDO technical community focuses on vehicle and system design issues. This paper provides an overview of the MDO technology field from a fluid dynamics perspective, giving emphasis to suggestions of specific applications of recent MDO technologies that can enhance fluid dynamics research itself across the spectrum, from basic flow physics to full configuration aerodynamics.
Multiple criteria dynamic spatial optimization to manage water quality on a watershed scale
Randhir, T.O.; Lee, J.G.; Engel, B.
2000-04-01
This article develops a dynamic spatial optimization algorithm for watershed modeling that reduces dimensionality and incorporates multiple objectives. Spatial optimization methods, which include spatially linear and nonlinear formulations, are applied to an experimental watershed and tested against a full enumeration frontier. The integrated algorithm includes biophysical simulation and economic decision-making within a geographic information system. It was observed that it is possible to achieve economic and water quality objectives in a watershed by spatially optimizing site-specific practices. It was observed that a spatially diversified watershed plan could achieve multiple goals in a watershed. The algorithm can be used to develop efficient policies towards environmental management of watersheds to address water quality issues by identifying optimal tradeoffs across objectives.
Dynamical Arrest, Structural Disorder, and Optimization of Organic Photovoltaic Devices
Gould, Ian; Dmitry, Matyushov
2014-09-11
This project describes fundamental experimental and theoretical work that relates to charge separation and migration in the solid, heterogeneous or aggregated state. Marcus theory assumes a system in equilibrium with all possible solvent (dipolar) configurations, with rapid interconversion among these on the ET timescale. This project has addressed the more general situation where the medium is at least partially frozen on the ET timescale, i.e. under conditions of dynamical arrest. The approach combined theory and experiment and includes: (1) Computer simulations of model systems, (2) Development of analytical procedures consistent with computer experiment and (3) Experimental studies and testing of the formal theories on this data. Electron transfer processes are unique as a consequence of the close connection between kinetics, spectroscopy and theory, which is an essential component of this work.
OPTIMIZING THE DYNAMIC APERTURE FOR TRIPLE BEND ACHROMATIC LATTICES.
KRAMER, S.L.; BENGTSSON, J.
2006-06-26
The Triple Bend Achromatic (TBA) lattice has the potential for lower natural emittance per period than the Double Bend Achromatic (DBA) lattice for high brightness light sources. However, the DBA has been chosen for 3rd generation light sources more often due to the higher number of undulator straight section available for a comparable emittance. The TBA has considerable flexibility in linear optics tuning while maintaining this emittance advantage. We have used the tune and chromaticity flexibility of a TBA lattice to minimize the lowest order nonlinearities to implement a 3rd order achromatic tune, while maintaining a constant emittance. This frees the geometric sextupoles to counter the higher order nonlinearities. This procedure is being used to improve the nonlinear dynamics of the TBA as a proposed lattice for NSLS-II facility. The flexibility of the TBA lattice will also provide for future upgrade capabilities of the beam parameters.
Mdluli, Thembi; Buzzard, Gregery T; Rundell, Ann E
2015-09-01
This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm's scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements. PMID:26379275
Mdluli, Thembi; Buzzard, Gregery T.; Rundell, Ann E.
2015-01-01
This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm’s scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements. PMID:26379275
Optimizing electromagnetic induction sensors for dynamic munitions classification surveys
NASA Astrophysics Data System (ADS)
Miller, Jonathan S.; Keranen, Joe; Schultz, Gregory
2014-06-01
Standard protocol for detection and classification of Unexploded Ordnance (UXO) comprises a two-step process that includes an initial digital geophysical mapping (DGM) survey to detect magnetic field anomalies followed by a cued survey at each anomaly location that enables classification of these anomalies. The initial DGM survey is typically performed using a low resolution single axis electromagnetic induction (EMI) sensor while the follow-up cued survey requires revisiting each anomaly location with a multi-axis high resolution EMI sensor. The DGM survey comprises data collection in tightly spaced transects over the entire survey area. Once data collection in this area is complete, a threshold analysis is applied to the resulting magnetic field anomaly map to identify anomalies corresponding to potential targets of interest (TOI). The cued sensor is deployed in static mode where this higher resolution sensor is placed over the location of each anomaly to record a number of soundings that may be stacked and averaged to produce low noise data. These data are of sufficient quality to subsequently classify the object as either TOI or clutter. While this approach has demonstrated success in producing effective classification of UXO, conducting successive surveys is time consuming. Additionally, the low resolution of the initial DGM survey often produces errors in the target picking process that results in poor placement of the cued sensor and often requires several revisits to the anomaly location to ensure adequate characterization of the target space. We present data and test results from an advanced multi-axis EMI sensor optimized to provide both detection and classification from a single survey. We demonstrate how the large volume of data from this sensor may be used to produce effective detection and classification decisions while only requiring one survey of the munitions response area.
NASA Astrophysics Data System (ADS)
Koch, Caleb; Winfrey, Leigh
2014-10-01
Natural Gas is a major energy source in Europe, yet political instabilities have the potential to disrupt access and supply. Energy resilience is an increasingly essential construct and begins with transmission network design. This study proposes a new way of thinking about modelling natural gas flow. Rather than relying on classical economic models, this problem is cast into a time-dependent Hamiltonian dynamics discussion. Traditional Natural Gas constraints, including inelastic demand and maximum/minimum pipe flows, are portrayed as energy functions and built into the dynamics of each pipe flow. Doing so allows the constraints to be built into the dynamics of each pipeline. As time progresses in the model, natural gas flow rates find the minimum energy, thus the optimal gas flow rates. The most important result of this study is using dynamical principles to ensure the output of natural gas at demand nodes remains constant, which is important for country to country natural gas transmission. Another important step in this study is building the dynamics of each flow in a decentralized algorithm format. Decentralized regulation has solved congestion problems for internet data flow, traffic flow, epidemiology, and as demonstrated in this study can solve the problem of Natural Gas congestion. A mathematical description is provided for how decentralized regulation leads to globally optimized network flow. Furthermore, the dynamical principles and decentralized algorithm are applied to a case study of the Fluxys Belgium Natural Gas Network.
Simulating the Dynamics of Scale-Free Networks via Optimization
Schieber, Tiago Alves; Ravetti, Martín Gómez
2013-01-01
We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of how information theory quantifiers can be applied to characterize networks evolution. With the objective of dynamically capture the topological changes of a network's evolution, we propose a model able to quantify and reproduce several characteristics of a given network, by using the square root of the Jensen-Shannon divergence in combination with the mean degree and the clustering coefficient. To support our hypothesis, we test the model by copying the evolution of well-known models and real systems. The results show that the methodology was able to mimic the test-networks. By using this copycat model, the user is able to analyze the networks behavior over time, and also to conjecture about the main drivers of its evolution, also providing a framework to predict its evolution. PMID:24353752
Long term dynamics and optimal control of nano-satellite deorbit using a short electrodynamic tether
NASA Astrophysics Data System (ADS)
Zhong, R.; Zhu, Z. H.
2013-10-01
This paper studies the long term dynamics and optimal control of a nano-satellite deorbit by a short electrodynamic tether. The long term deorbit process is discretized into intervals and within each interval a two-phase optimal control law is proposed to achieve libration stability and fast deorbit simultaneously. The first-phase formulates an open-loop fast-deorbit control trajectory by a simplified model that assumes the slow-varying orbital elements of electrodynamic tethered system as constant and ignores perturbation forces other than the electrodynamic force. The second phase tracks the optimal trajectory derived in the first phase by a finite receding horizon control method while considering a full dynamic model of electrodynamic tether system. Both optimal control problems are solved by direct collocation method base on the Hermite-Simpson discretization schemes with coincident nodes. The resulting piecewise nonlinear programing problems in the sequential intervals reduces the problem size and improve the computational efficiency, which enable an on-orbit control application. Numerical results for deorbit control of a short electrodynamic tethered nano-satellite system in both equatorial and highly inclined orbits demonstrate the efficiency of the proposed control method. An optimal balance between the libration stability and a fast deorbit of satellite with minimum control efforts is achieved.
Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing
NASA Technical Reports Server (NTRS)
Ono, Masahiro; Kuwata, Yoshiaki
2013-01-01
A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.
Arkun, Yaman; Erman, Burak
2010-01-01
An optimization model is introduced in which proteins try to evade high energy regions of the folding landscape, and prefer low entropy loss routes during folding. We make use of the framework of optimal control whose convenient solution provides practical and useful insight into the sequence of events during folding. We assume that the native state is available. As the protein folds, it makes different set of contacts at different folding steps. The dynamic contact map is constructed from these contacts. The topology of the dynamic contact map changes during the course of folding and this information is utilized in the dynamic optimization model. The solution is obtained using the optimal control theory. We show that the optimal solution can be cast into the form of a Gaussian Network that governs the optimal folding dynamics. Simulation results on three examples (CI2, Sso7d and Villin) show that folding starts by the formation of local clusters. Non-local clusters generally require the formation of several local clusters. Non-local clusters form cooperatively and not sequentially. We also observe that the optimal controller prefers "zipping" or small loop closure steps during folding. The folding routes predicted by the proposed method bear strong resemblance to the results in the literature. PMID:20967269
Shu, Chuan-Cun; Edwalds, Melanie; Shabani, Alireza; Ho, Tak-San; Rabitz, Herschel
2015-07-28
The efficacy of optimal control of quantum dynamics depends on the topology and associated local structure of the underlying control landscape defined as the objective as a function of the control field. A commonly studied control objective involves maximization of the transition probability for steering the quantum system from one state to another state. This paper invokes landscape Hessian analysis performed at an optimal solution to gain insight into the controlled dynamics, where the Hessian is the second-order functional derivative of the control objective with respect to the control field. Specifically, we consider a quantum system composed of coupled primary and secondary subspaces of energy levels with the initial and target states lying in the primary subspace. The primary and secondary subspaces may arise in various scenarios, for example, respectively, as sub-manifolds of ground and excited electronic states of a poly-atomic molecule, with each possessing a set of rotational-vibrational levels. The control field may engage the system through electric dipole transitions that occur either (I) only in the primary subspace, (II) between the two subspaces, or (III) only in the secondary subspace. Important insights about the resultant dynamics in each case are revealed in the structural patterns of the corresponding Hessian. The Fourier spectrum of the Hessian is shown to often be complementary to mechanistic insights provided by the optimal control field and population dynamics. PMID:26119871
Design method of dynamical decoupling sequences integrated with optimal control theory
Yutaka Tabuchi; Masahiro Kitagawa
2012-08-26
A method for synthesizing dynamical decoupling (DD) sequences is presented, which can tailor these sequences to a given set of qubits, environments, instruments, and available resources using partial information of the system. The key concept behind the generation of the DD sequences involves not only extricating the strong dependence on the coupling strengths according to the "optimal control," but also exploiting the "refocus" technique used conventionally to obtain DD sequences. The concept is a generalized one that integrates optimal control and designing of DD sequences.
Approximate dynamic programming for optimal stationary control with control-dependent noise.
Jiang, Yu; Jiang, Zhong-Ping
2011-12-01
This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Itô calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology. PMID:21954203
Optimal placement of excitations and sensors for verification of large dynamical systems
NASA Technical Reports Server (NTRS)
Salama, M.; Rose, T.; Garba, J.
1987-01-01
The computationally difficult problem of the optimal placement of excitations and sensors to maximize the observed measurements is studied within the framework of combinatorial optimization, and is solved numerically using a variation of the simulated annealing heuristic algorithm. Results of numerical experiments including a square plate and a 960 degrees-of-freedom Control of Flexible Structure (COFS) truss structure, are presented. Though the algorithm produces suboptimal solutions, its generality and simplicity allow the treatment of complex dynamical systems which would otherwise be difficult to handle.
Discrete-time system optimal dynamic traffic assignment (SO-DTA) with partial control Traffic Assignment problem with Partial Control (SO- DTA-PC) for general networks with horizontal queuing the discrete adjoint method. 1 Introduction Dynamic traffic assignment overview. Dynamic traffic assignment
Optimal control based dynamics exploration of a rigid car with load transfer
Rucco, Alessandro; Hauser, John
2011-01-01
In this paper we provide optimal control based strategies to explore the dynamic capabilities of a single-track rigid car which includes tire models and load transfer. Using an explicit formulation of the holonomic constraints imposed on the unconstrained rigid car, we design a car model which includes load transfer without adding suspension models. With this model in hand, we perform an analysis of the equilibrium manifold of the vehicle. That is, we design a continuation and predictor-corrector numerical strategy to compute cornering equilibria on the entire range of operation of the tires. Finally, as main contribution of the paper, we explore the system dynamics by use of novel nonlinear optimal control techniques. The proposed strategies allow to compute aggressive car trajectories and study how the vehicle behaves depending on its parameters. To show the effectiveness of the proposed strategies we compute aggressive maneuvers of the vehicle inspired to testing maneuvers from virtual and real prototyping...
Cooperating or Fighting with Decoherence in the Optimal Control of Quantum Dynamics
Shuang, F; Shuang, Feng; Rabitz, Herschel
2006-01-01
This paper explores the use of laboratory closed-loop learning control to either fight or cooperate with decoherence in the optimal manipulation of quantum dynamics. Simulations of the processes are performed in a Lindblad formulation on multilevel quantum systems strongly interacting with the environment without spontaneous emission. When seeking a high control yield it is possible to find fields that successfully fight with decoherence while attaining a good quality yield. When seeking modest control yields, fields can be found which are optimally shaped to cooperate with decoherence and thereby drive the dynamics more efficiently. In the latter regime when the control field and the decoherence strength are both weak, a theoretical foundation is established to describe how they cooperate with each other. In general, the results indicate that the population transfer objectives can be effectively met by appropriately either fighting or cooperating with decoherence.
Cooperating or Fighting with Decoherence in the Optimal Control of Quantum Dynamics
Feng Shuang; Herschel Rabitz
2006-06-20
This paper explores the use of laboratory closed-loop learning control to either fight or cooperate with decoherence in the optimal manipulation of quantum dynamics. Simulations of the processes are performed in a Lindblad formulation on multilevel quantum systems strongly interacting with the environment without spontaneous emission. When seeking a high control yield it is possible to find fields that successfully fight with decoherence while attaining a good quality yield. When seeking modest control yields, fields can be found which are optimally shaped to cooperate with decoherence and thereby drive the dynamics more efficiently. In the latter regime when the control field and the decoherence strength are both weak, a theoretical foundation is established to describe how they cooperate with each other. In general, the results indicate that the population transfer objectives can be effectively met by appropriately either fighting or cooperating with decoherence.
Reply to M. Ziman's "Notes on optimality of direct characterization of quantum dynamics"
M. Mohseni; D. A. Lidar
2006-04-16
Recently M. Ziman [quant-ph/0603151] criticized our approach for quantifying the required physical resources in the theory of Direct Characterization of Quantum Dynamics (DCQD) [quant-ph/0601033, quant-ph/0601034] in comparison to other quantum process tomography (QPT) schemes. Here we argue that Ziman's comments regarding optimality, quantumness, and the novelty of DCQD are inaccurate. Specifically, we demonstrate that DCQD is optimal with respect to both the required number of experimental configurations and the number of possible outcomes over all known QPT schemes in the 2^{2n} dimensional Hilbert space of n system and n ancilla qubits. Moreover, we show DCQD is more efficient than all known QPT schemes in the sense of overall required number of quantum operations. Furthermore, we argue that DCQD is a new method for characterizing quantum dynamics and cannot be considered merely as a subclass of previously known QPT schemes.
Reply to M. Ziman's "Notes on optimality of direct characterization of quantum dynamics"
Mohseni, M
2006-01-01
Recently M. Ziman [quant-ph/0603151] criticized our approach for quantifying the required physical resources in the theory of Direct Characterization of Quantum Dynamics (DCQD) [quant-ph/0601033, quant-ph/0601034] in comparison to other quantum process tomography (QPT) schemes. Here we argue that Ziman's comments regarding optimality, quantumness, and the novelty of DCQD are inaccurate. Specifically, we demonstrate that DCQD is optimal with respect to both the required number of experimental configurations and the number of possible outcomes over all known QPT schemes in the 2^{2n} dimensional Hilbert space of n system and n ancilla qubits. Moreover, we show DCQD is more efficient than all known QPT schemes in the sense of overall required number of quantum operations. Furthermore, we argue that DCQD is a new method for characterizing quantum dynamics and cannot be considered merely as a subclass of previously known QPT schemes.
Human motion planning based on recursive dynamics and optimal control techniques
NASA Technical Reports Server (NTRS)
Lo, Janzen; Huang, Gang; Metaxas, Dimitris
2002-01-01
This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A quasi-Newton nonlinear programming technique (super-linear convergence) is implemented to solve minimum torque-based human motion-planning problems. The explicit analytical gradients needed in the dynamics are derived using a matrix exponential formulation and Lie algebra. Cubic spline functions are used to make the search space for an optimal solution finite. Based on our formulations, our method is well conditioned and robust, in addition to being computationally efficient. To better illustrate the efficiency of our method, we present results of natural looking and physically correct human motions for a variety of human motion tasks involving open and closed loop kinematic chains.
NASA Astrophysics Data System (ADS)
Culver, Teresa B.; Shoemaker, Christine A.
1993-04-01
Differential dynamic programming with quasi-Newton approximations (QNDDP) is combined with a finite element groundwater quality simulation model to determine optimal time-varying pumping policies for reclamation of a contaminated aquifer. The purpose of the QNDDP model is to significantly reduce the large computational effort associated with calculation of optimal time-varying policies. A Broyden rank-one quasi-Newton technique is developed to approximate the second derivatives of the groundwater quality model; these second derivatives are difficult to calculate directly. The performance of the QNDDP algorithm is compared to the successive approximation linear quadratic regulator (SALQR) technique, which sets the complicated second derivatives to 0. QNDDP converged to the optimal pumping policy in approximately half the time that the SALQR technique required. The QNDDP algorithm thus shows great promise for the management of complex, time-varying systems.
Balasubramonian, Rajeev (Sandy, UT); Dwarkadas, Sandhya (Rochester, NY); Albonesi, David (Ithaca, NY)
2012-01-24
In a processor having multiple clusters which operate in parallel, the number of clusters in use can be varied dynamically. At the start of each program phase, the configuration option for an interval is run to determine the optimal configuration, which is used until the next phase change is detected. The optimum instruction interval is determined by starting with a minimum interval and doubling it until a low stability factor is reached.
NASA Astrophysics Data System (ADS)
Grafton, R. Quentin; Chu, Hoang Long; Stewardson, Michael; Kompas, Tom
2011-12-01
A key challenge in managing semiarid basins, such as in the Murray-Darling in Australia, is to balance the trade-offs between the net benefits of allocating water for irrigated agriculture, and other uses, versus the costs of reduced surface flows for the environment. Typically, water planners do not have the tools to optimally and dynamically allocate water among competing uses. We address this problem by developing a general stochastic, dynamic programming model with four state variables (the drought status, the current weather, weather correlation, and current storage) and two controls (environmental release and irrigation allocation) to optimally allocate water between extractions and in situ uses. The model is calibrated to Australia's Murray River that generates: (1) a robust qualitative result that "pulse" or artificial flood events are an optimal way to deliver environmental flows over and above conveyance of base flows; (2) from 2001 to 2009 a water reallocation that would have given less to irrigated agriculture and more to environmental flows would have generated between half a billion and over 3 billion U.S. dollars in overall economic benefits; and (3) water markets increase optimal environmental releases by reducing the losses associated with reduced water diversions.
Stochastic Optimal Foraging: Tuning Intensive and Extensive Dynamics in Random Searches
Bartumeus, Frederic; Raposo, Ernesto P.; Viswanathan, Gandhimohan M.; da Luz, Marcos G. E.
2014-01-01
Recent theoretical developments had laid down the proper mathematical means to understand how the structural complexity of search patterns may improve foraging efficiency. Under information-deprived scenarios and specific landscape configurations, Lévy walks and flights are known to lead to high search efficiencies. Based on a one-dimensional comparative analysis we show a mechanism by which, at random, a searcher can optimize the encounter with close and distant targets. The mechanism consists of combining an optimal diffusivity (optimally enhanced diffusion) with a minimal diffusion constant. In such a way the search dynamics adequately balances the tension between finding close and distant targets, while, at the same time, shifts the optimal balance towards relatively larger close-to-distant target encounter ratios. We find that introducing a multiscale set of reorientations ensures both a thorough local space exploration without oversampling and a fast spreading dynamics at the large scale. Lévy reorientation patterns account for these properties but other reorientation strategies providing similar statistical signatures can mimic or achieve comparable efficiencies. Hence, the present work unveils general mechanisms underlying efficient random search, beyond the Lévy model. Our results suggest that animals could tune key statistical movement properties (e.g. enhanced diffusivity, minimal diffusion constant) to cope with the very general problem of balancing out intensive and extensive random searching. We believe that theoretical developments to mechanistically understand stochastic search strategies, such as the one here proposed, are crucial to develop an empirically verifiable and comprehensive animal foraging theory. PMID:25216191
Optimal discrete-time dynamic output-feedback design - A w-domain approach
NASA Technical Reports Server (NTRS)
Ha, Cheolkeun; Ly, Uy-Loi
1991-01-01
An alternative method for optimal digital control design is described in this paper. The method is based on the usage of the w-transform and has many attractive design features. One of these is its immediate connection with frequency loop-shaping techniques that are now popular and effective for multivariable control synthesis in continuous-time domain. Furthermore, any design algorithms originally developed for continuous-time systems can now be immediately extended to the discrete-time domain. The main results presented in this paper are the exact problem formulation and solution of an optimal discrete-time dynamic output-feedback design in the w-domain involving a quadratic performance index to random disturbances. In addition, necessary conditions for optimality are obtained for the numerical solution of the optimal output-feedback compensator design. A numerical example is presented illustrating its application to the design of a low-order dynamic compensator in a stability augmentation system of a commercial transport.
NASA Astrophysics Data System (ADS)
Lali, Mehdi
2009-03-01
A comprehensive computer program is designed in MATLAB to analyze, design and optimize the propulsion, dynamics, thermodynamics, and kinematics of any serial multi-staging rocket for a set of given data. The program is quite user-friendly. It comprises two main sections: "analysis and design" and "optimization." Each section has a GUI (Graphical User Interface) in which the rocket's data are entered by the user and by which the program is run. The first section analyzes the performance of the rocket that is previously devised by the user. Numerous plots and subplots are provided to display the performance of the rocket. The second section of the program finds the "optimum trajectory" via billions of iterations and computations which are done through sophisticated algorithms using numerical methods and incremental integrations. Innovative techniques are applied to calculate the optimal parameters for the engine and designing the "optimal pitch program." This computer program is stand-alone in such a way that it calculates almost every design parameter in regards to rocket propulsion and dynamics. It is meant to be used for actual launch operations as well as educational and research purposes.
NASA Astrophysics Data System (ADS)
Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.-L.
2015-05-01
The most widely used community weather forecast and research model in the world is the Weather Research and Forecast (WRF) model. Two distinct varieties of WRF exist. The one we are interested is the Advanced Research WRF (ARW) is an experimental, advanced research version featuring very high resolution. The WRF Nonhydrostatic Mesoscale Model (WRF-NMM) has been designed for forecasting operations. WRF consists of dynamics code and several physics modules. The WRF-ARW core is based on an Eulerian solver for the fully compressible nonhydrostatic equations. In the paper, we optimize a meridional (north-south direction) advection subroutine for Intel Xeon Phi coprocessor. Advection is of the most time consuming routines in the ARW dynamics core. It advances the explicit perturbation horizontal momentum equations by adding in the large-timestep tendency along with the small timestep pressure gradient tendency. We will describe the challenges we met during the development of a high-speed dynamics code subroutine for MIC architecture. Furthermore, lessons learned from the code optimization process will be discussed. The results show that the optimizations improved performance of the original code on Xeon Phi 7120P by a factor of 1.2x.
Optimizing zonal advection of the Advanced Research WRF (ARW) dynamics for Intel MIC
NASA Astrophysics Data System (ADS)
Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.
2014-10-01
The Weather Research and Forecast (WRF) model is the most widely used community weather forecast and research model in the world. There are two distinct varieties of WRF. The Advanced Research WRF (ARW) is an experimental, advanced research version featuring very high resolution. The WRF Nonhydrostatic Mesoscale Model (WRF-NMM) has been designed for forecasting operations. WRF consists of dynamics code and several physics modules. The WRF-ARW core is based on an Eulerian solver for the fully compressible nonhydrostatic equations. In the paper, we will use Intel Intel Many Integrated Core (MIC) architecture to substantially increase the performance of a zonal advection subroutine for optimization. It is of the most time consuming routines in the ARW dynamics core. Advection advances the explicit perturbation horizontal momentum equations by adding in the large-timestep tendency along with the small timestep pressure gradient tendency. We will describe the challenges we met during the development of a high-speed dynamics code subroutine for MIC architecture. Furthermore, lessons learned from the code optimization process will be discussed. The results show that the optimizations improved performance of the original code on Xeon Phi 5110P by a factor of 2.4x.
arXiv:cs/0406035v3[cs.DS]28Sep2005 Optimal Free-Space Management and Routing-Conscious Dynamic
Fekete, Sándor P.
arXiv:cs/0406035v3[cs.DS]28Sep2005 Optimal Free-Space Management and Routing-Conscious Dynamic is optimal. We also show that finding an optimal feasible communication-conscious placement (which minimizes space manager, routing-conscious placement, geometric optimization, line sweep technique, optimal
Optimal control for nonlinear dynamical system of microbial fed-batch culture
NASA Astrophysics Data System (ADS)
Liu, Chongyang
2009-10-01
In fed-batch culture of glycerol bio-dissimilation to 1, 3-propanediol (1, 3-PD), the aim of adding glycerol is to obtain as much 1, 3-PD as possible. So a proper feeding rate is required during the process. Taking the concentration of 1, 3-PD at the terminal time as the performance index and the feeding rate of glycerol as the control function, we propose an optimal control model subject to a nonlinear dynamical system and constraints of continuous state and non-stationary control. A computational approach is constructed to seek the solution of the above model in two aspects. On the one hand we transcribe the optimal control model into an unconstrained one based on the penalty functions and an extension of the state space; on the other hand, by approximating the control function with simple functions, we transform the unconstrained optimal control problem into a sequence of nonlinear programming problems, which can be solved using gradient-based optimization techniques. The convergence analysis of this approximation is also investigated. Numerical results show that, by employing the optimal control policy, the concentration of 1, 3-PD at the terminal time can be increased considerably.
Use of Ultrafast Molecular Dynamics and Optimal Control for Identifying Biomolecules
NASA Astrophysics Data System (ADS)
Wolf, Jean-Pierre
2008-03-01
With F.COURVOISIER,L.GUYON,V.BOUTOU, and M.ROTH,J. ROSLUND, H. RABITZ, Princeton University. The identification and discrimination of molecules that exhibit almost identical structures and spectra using fluorescence spectroscopy is considered quite difficult. In order to evaluate the capability of optimal control for discriminating between the optical emissions of nearly identical molecules, we developed a new approach called ``optimal dynamic discrimination (ODD). A proof of principle ODD experiment has been performed using Riboflavin (RBF) and Flavin Mononucleotide (FMN) as model system. We used a complex multipulse control field made of a pair of pulses (UV and IR). The UV part (400 nm) is optimally shaped using a control learning loop while the IR component (800 nm) is FT-limited (100 fs) and set at a definite time delay with respect to the UV pulse. Clear discrimination was observed for optimally shaped pulses, although the linear spectra from both molecules are virtually identical. A further experiment showed that, by using the optimal pulse shapes that maximize the fluorescence depletion in FMN and RBF in a differential manner, the concentration of both molecules could be retrieved while they were mixed in the same solution. The ODD demonstration sets out a promising path for future applications, as for example fluorescence microscopy where endogenous fluorescence spectra of many biomolecules overlap.
H? optimization of dynamic vibration absorber variant for vibration control of damped linear systems
NASA Astrophysics Data System (ADS)
Chun, Semin; Lee, Youngil; Kim, Tae-Hyoung
2015-01-01
This study focuses on the H? optimal design of a dynamic vibration absorber (DVA) variant for suppressing high-amplitude vibrations of damped primary systems. Unlike traditional DVA configurations, the damping element in this type of DVA is connected directly to the ground instead of the primary mass. First, a thorough graphical analysis of the variations in the maximum amplitude magnification factor depending on two design parameters, natural frequency and absorber damping ratios, is performed. The results of this analysis clearly show that any fixed-points-theory-based conventional method could provide, at best, only locally but not globally optimal parameters. Second, for directly handling the H? optimization for its optimal design, a novel meta-heuristic search engine, called the diversity-guided cyclic-network-topology-based constrained particle swarm optimization (Div-CNT-CPSO), is developed. The variant DVA system developed using the proposed Div-CNT-CPSO scheme is compared with those reported in the literature. The results of this comparison verified that the proposed system is better than the existing methods for suppressing the steady-state vibration amplitude of a controlled primary system.
North Carolina at Chapel Hill, University of
Real-time Optimization-based Planning in Dynamic Environments using GPUs Chonhyon Park and Jia Pan trajectories in parallel to explore Chonhyon Park, Jia Pan and Dinesh Manocha are with the Department
Blamey, Peter J.
2005-01-01
Adaptive dynamic range optimization (ADRO) is an amplification strategy that uses digital signal processing techniques to improve the audibility, comfort, and intelligibility of sounds for people who use cochlear implants and/or hearing aids. The strategy uses statistical analysis to select the most information-rich section of the input dynamic range in multiple-frequency channels. Fuzzy logic rules control the gain in each frequency channel so that the selected section of the dynamic range is presented at an audible and comfortable level. The ADRO processing thus adaptively optimizes the dynamic range of the signal in multiple-frequency channels. Clinical studies show that ADRO can be fitted easily to all degrees of hearing loss for hearing aids and cochlear implants in a direct and intuitive manner, taking the preferences of the listener into account. The result is high acceptance by new and experienced hearing aid users and strong preferences for ADRO compared with alternative amplification strategies. The ADRO processing is particularly well suited to bimodal and hybrid stimulation which combine electric and acoustic stimulation in opposite ears or in the same ear, respectively. PMID:16012705
Blamey, Peter J
2005-01-01
Adaptive dynamic range optimization (ADRO) is an amplification strategy that uses digital signal processing techniques to improve the audibility, comfort, and intelligibility of sounds for people who use cochlear implants and/or hearing aids. The strategy uses statistical analysis to select the most information-rich section of the input dynamic range in multiple-frequency channels. Fuzzy logic rules control the gain in each frequency channel so that the selected section of the dynamic range is presented at an audible and comfortable level. The ADRO processing thus adaptively optimizes the dynamic range of the signal in multiple-frequency channels. Clinical studies show that ADRO can be fitted easily to all degrees of hearing loss for hearing aids and cochlear implants in a direct and intuitive manner, taking the preferences of the listener into account. The result is high acceptance by new and experienced hearing aid users and strong preferences for ADRO compared with alternative amplification strategies. The ADRO processing is particularly well suited to bimodal and hybrid stimulation which combine electric and acoustic stimulation in opposite ears or in the same ear, respectively. PMID:16012705
Optimal life-history strategies in seasonal consumer-resource dynamics.
Akhmetzhanov, Andrei R; Grognard, Frederic; Mailleret, Ludovic
2011-11-01
The interplay between individual adaptive life histories and populations dynamics is an important issue in ecology. In this context, we considered a seasonal consumer-resource model with nonoverlapping generations. We focused on the consumers decision-making process through which they maximize their reproductive output via a differential investment into foraging for resources or reproducing. Our model takes a semi-discrete form, and is composed of a continuous time within-season part, similar to a dynamic model of energy allocation, and of a discrete time part, depicting the between seasons reproduction and mortality processes. We showed that the optimal foraging-reproduction strategies of the consumers may be "determinate" or "indeterminate" depending on the season length. More surprisingly, it depended on the consumers population density as well, with large densities promoting indeterminacy. A bifurcation analysis showed that the long-term dynamics produced by this model were quite rich, ranging from both populations' extinction, coexistence at some season-to-season equilibrium or on (quasi)-periodic motions, to initial condition-dependent dynamics. Interestingly, we observed that any long-term sustainable situation corresponds to indeterminate consumers' strategies. Finally, a comparison with a model involving typical nonoptimal consumers highlighted the stabilizing effects of the optimal life histories of the consumers. PMID:22023579
Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Castro, Joseph Pete Jr.; Giunta, Anthony Andrew
2006-01-01
Many engineering application problems use optimization algorithms in conjunction with numerical simulators to search for solutions. The formulation of relevant objective functions and constraints dictate possible optimization algorithms. Often, a gradient based approach is not possible since objective functions and constraints can be nonlinear, nonconvex, non-differentiable, or even discontinuous and the simulations involved can be computationally expensive. Moreover, computational efficiency and accuracy are desirable and also influence the choice of solution method. With the advent and increasing availability of massively parallel computers, computational speed has increased tremendously. Unfortunately, the numerical and model complexities of many problems still demand significant computational resources. Moreover, in optimization, these expenses can be a limiting factor since obtaining solutions often requires the completion of numerous computationally intensive simulations. Therefore, we propose a multifidelity optimization algorithm (MFO) designed to improve the computational efficiency of an optimization method for a wide range of applications. In developing the MFO algorithm, we take advantage of the interactions between multi fidelity models to develop a dynamic and computational time saving optimization algorithm. First, a direct search method is applied to the high fidelity model over a reduced design space. In conjunction with this search, a specialized oracle is employed to map the design space of this high fidelity model to that of a computationally cheaper low fidelity model using space mapping techniques. Then, in the low fidelity space, an optimum is obtained using gradient or non-gradient based optimization, and it is mapped back to the high fidelity space. In this paper, we describe the theory and implementation details of our MFO algorithm. We also demonstrate our MFO method on some example problems and on two applications: earth penetrators and groundwater remediation.
NASA Technical Reports Server (NTRS)
Whiffen, Gregory J.
2006-01-01
Mystic software is designed to compute, analyze, and visualize optimal high-fidelity, low-thrust trajectories, The software can be used to analyze inter-planetary, planetocentric, and combination trajectories, Mystic also provides utilities to assist in the operation and navigation of low-thrust spacecraft. Mystic will be used to design and navigate the NASA's Dawn Discovery mission to orbit the two largest asteroids, The underlying optimization algorithm used in the Mystic software is called Static/Dynamic Optimal Control (SDC). SDC is a nonlinear optimal control method designed to optimize both 'static variables' (parameters) and dynamic variables (functions of time) simultaneously. SDC is a general nonlinear optimal control algorithm based on Bellman's principal.
A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization
Bhattacharya, Sayantani; Konar, Amit; Das, Swagatam; Han, Sang Yong
2009-01-01
The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov's stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy. PMID:22303158
NASA Astrophysics Data System (ADS)
Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.
2015-08-01
We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS)-based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAllenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction of random errors (standard deviations) of optimized bending angles down to about half of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; and (4) realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well-characterized and high-quality atmospheric profiles over the entire stratosphere.
NASA Astrophysics Data System (ADS)
Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.
2015-01-01
We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS) based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically-varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAMP and COSMIC measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction in random errors (standard deviations) of optimized bending angles down to about two-thirds of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; (4) produces realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well characterized and high-quality atmospheric profiles over the entire stratosphere.
Matias, Yossi
in the dictionary, all its pre xes are also phrases in the dictionary. Department of Computer Science, TelOn the optimality of parsing in dynamic dictionary based data compression Yossi Matias Suleyman with respect to the dictionary in use (2) a one- lookahead greedy parsing scheme obtains optimality with re
Bank, Peter
Optimal Dynamic Choice of Durable and Perishable Goods By Peter Bank, Humboldt University choice model for multiple goods, focus- ing on the distinction between durables and perishables with one perishable good, the optimal consump- tion plan is not myopic. Instead, it depends on past as well
Dynamics of hepatitis C under optimal therapy and sampling based analysis
NASA Astrophysics Data System (ADS)
Pachpute, Gaurav; Chakrabarty, Siddhartha P.
2013-08-01
We examine two models for hepatitis C viral (HCV) dynamics, one for monotherapy with interferon (IFN) and the other for combination therapy with IFN and ribavirin. Optimal therapy for both the models is determined using the steepest gradient method, by defining an objective functional which minimizes infected hepatocyte levels, virion population and side-effects of the drug(s). The optimal therapies for both the models show an initial period of high efficacy, followed by a gradual decline. The period of high efficacy coincides with a significant decrease in the viral load, whereas the efficacy drops after hepatocyte levels are restored. We use the Latin hypercube sampling technique to randomly generate a large number of patient scenarios and study the dynamics of each set under the optimal therapy already determined. Results show an increase in the percentage of responders (indicated by drop in viral load below detection levels) in case of combination therapy (72%) as compared to monotherapy (57%). Statistical tests performed to study correlations between sample parameters and time required for the viral load to fall below detection level, show a strong monotonic correlation with the death rate of infected hepatocytes, identifying it to be an important factor in deciding individual drug regimens.
Fast optimization of binary clusters using a novel dynamic lattice searching method
Wu, Xia Cheng, Wen
2014-09-28
Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd){sub 79} clusters with DFT-fit parameters of Gupta potential.
Pharmacokinetic modeling of dynamic MR images using a simulated annealing-based optimization
NASA Astrophysics Data System (ADS)
Sawant, Amit R.; Reece, John H.; Reddick, Wilburn E.
2000-04-01
The aim of this work was to use dynamic contrast enhanced MR image (DEMRI) data to generate 'parameter images' which provide functional information about contrast agent access, in bone sarcoma. A simulated annealing based technique was applied to optimize the parameters of a pharmacokinetic model used to describe the kinetics of the tissue response during and after intravenous infusion of a paramagnetic contrast medium, Gd-DTPA. Optimization was performed on a pixel by pixel basis so as to minimize the sum of square deviations of the calculated values from the values obtained experimentally during dynamic contrast enhanced MR imaging. A cost function based on a priori information was introduced during the annealing procedure to ensure that the values obtained were within the expected ranges. The optimized parameters were used in the model to generate parameter images, which reveal functional information that is normally not visible in conventional Gd-DTPA enhanced MR images. This functional information, during and upon completion of pre-operative chemotherapy, is useful in predicting the probability of disease free survival.
Empirical prediction of climate dynamics: optimal models, derived from time series
NASA Astrophysics Data System (ADS)
Mukhin, D.; Loskutov, E. M.; Gavrilov, A.; Feigin, A. M.
2013-12-01
The new empirical method for prediction of climate indices by the analysis of climatic fields' time series is suggested. The method is based on construction of prognostic models of evolution operator (EO) in low-dimensional subspaces of system's phase space. One of the main points of suggested analysis is reconstruction of appropriate basis of dynamical variables (predictors) from spatially distributed data: different ways of data decomposition are discussed in the report including EOFs, MSSA and other relevant data rotations. We consider the models of different complexity for EO reconstruction, from linear statistical models of particular indices to more complex artificial neural network (ANN) models of climatic patterns dynamics, which take the form of discrete random dynamical systems [1]. Very important problem, that always arises in empirical modeling approaches, is optimal model selection criterium: appropriate regularization procedure is needed to avoid overfitted model. Particulary, it includes finding the optimal structural parameters of the model such as dimension of variables vector, i.e. number of principal components used for modeling, number of states used for prediction, and number of parameters determining quality of approximation. In this report the minimal descriptive length (MDL) approach [2] is proposed for this purpose: the model providing most data compression is chosen. Results of application of suggested method to analysis of SST and SLP fields' time series [3] covering last 30-50 years are presented: predictions of different climate indices time series including NINO 3.4, MEI, PDO, NOA are shown. References: 1. Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, Random dynamical models from time series, Phys. Rev. E 85, 036216, 2012 2. Molkov, Ya.I., D.N. Mukhin, E.M. Loskutov, A.M. Feigin, and G.A. Fidelin, Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series. Phys. Rev. E, 80, 046207, 2009 3. IRI/LDEO (http://iridl.ldeo.columbia.edu/)
A model of bi-mode transmission dynamics of hepatitis C with optimal control.
Imran, Mudassar; Rafique, Hassan; Khan, Adnan; Malik, Tufail
2014-06-01
In this paper, we present a rigorous mathematical analysis of a deterministic model for the transmission dynamics of hepatitis C. The model is suitable for populations where two frequent modes of transmission of hepatitis C virus, namely unsafe blood transfusions and intravenous drug use, are dominant. The susceptible population is divided into two distinct compartments, the intravenous drug users and individuals undergoing unsafe blood transfusions. Individuals belonging to each compartment may develop acute and then possibly chronic infections. Chronically infected individuals may be quarantined. The analysis indicates that the eradication and persistence of the disease is completely determined by the magnitude of basic reproduction number R(c). It is shown that for the basic reproduction number R(c) < 1, the disease-free equilibrium is locally and globally asymptotically stable. For R(c) > 1, an endemic equilibrium exists and the disease is uniformly persistent. In addition, we present the uncertainty and sensitivity analyses to investigate the influence of different important model parameters on the disease prevalence. When the infected population persists, we have designed a time-dependent optimal quarantine strategy to minimize it. The Pontryagin's Maximum Principle is used to characterize the optimal control in terms of an optimality system which is solved numerically. Numerical results for the optimal control are compared against the constant controls and their efficiency is discussed. PMID:24374404
Modeling forest stand dynamics from optimal balances of carbon and nitrogen.
Valentine, Harry T; Mäkelä, Annikki
2012-06-01
We formulate a dynamic evolutionary optimization problem to predict the optimal pattern by which carbon (C) and nitrogen (N) are co-allocated to fine-root, leaf, and wood production, with the objective of maximizing height growth rate, year by year, in an even-aged stand. Height growth is maximized with respect to two adaptive traits, leaf N concentration and the ratio of fine-root mass to sapwood cross-sectional area. Constraints on the optimization include pipe-model structure, the C cost of N acquisition, and agreement between the C and N balances. The latter is determined by two models of height growth rate, one derived from the C balance and the other from the N balance; agreement is defined by identical growth rates. Predicted time-courses of maximized height growth rate accord with general observations. Across an N gradient, higher N availability leads to greater N utilization and net primary productivity, larger trees, and greater stocks of leaf and live wood biomass, with declining gains as a result of saturation effects at high N availability. Fine-root biomass is greatest at intermediate N availability. Predicted leaf and fine-root stocks agree with data from coniferous stands across Finland. Optimal C-allocation patterns agree with published observations and model analyses. PMID:22463713
NASA Astrophysics Data System (ADS)
Eisa, Fabian; Brauweiler, Robert; Peetz, Alexander; Hupfer, Martin; Nowak, Tristan; Kalender, Willi A.
2012-05-01
One of the biggest challenges in dynamic contrast-enhanced CT is the optimal synchronization of scan start and duration with contrast medium administration in order to optimize image contrast and to reduce the amount of contrast medium. We present a new optically based approach, which was developed to investigate and optimize bolus timing and shape. The time-concentration curve of an intravenously injected test bolus of a dye is measured in peripheral vessels with an optical sensor prior to the diagnostic CT scan. The curves can be used to assess bolus shapes as a function of injection protocols and to determine contrast medium arrival times. Preliminary results for phantom and animal experiments showed the expected linear behavior between dye concentration and absorption. The kinetics of the dye was compared to iodinated contrast medium and was found to be in good agreement. The contrast enhancement curves were reliably detected in three mice with individual bolus shapes and delay times of 2.1, 3.5 and 6.1 s, respectively. The optical sensor appears to be a promising approach to optimize injection protocols and contrast enhancement timing and is applicable to all modalities without implying any additional radiation dose. Clinical tests are still necessary.
NASA Technical Reports Server (NTRS)
Welstead, Jason; Crouse, Gilbert L., Jr.
2014-01-01
Empirical sizing guidelines such as tail volume coefficients have long been used in the early aircraft design phases for sizing stabilizers, resulting in conservatively stable aircraft. While successful, this results in increased empty weight, reduced performance, and greater procurement and operational cost relative to an aircraft with optimally sized surfaces. Including flight dynamics in the conceptual design process allows the design to move away from empirical methods while implementing modern control techniques. A challenge of flight dynamics and control is the numerous design variables, which are changing fluidly throughout the conceptual design process, required to evaluate the system response to some disturbance. This research focuses on addressing that challenge not by implementing higher order tools, such as computational fluid dynamics, but instead by linking the lower order tools typically used within the conceptual design process so each discipline feeds into the other. In thisresearch, flight dynamics and control was incorporated into the conceptual design process along with the traditional disciplines of vehicle sizing, weight estimation, aerodynamics, and performance. For the controller, a linear quadratic regulator structure with constant gains has been specified to reduce the user input. Coupling all the disciplines in the conceptual design phase allows the aircraft designer to explore larger design spaces where stabilizers are sized according to dynamic response constraints rather than historical static margin and volume coefficient guidelines.
Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.
2010-01-01
Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998
Coherent control of plasma dynamics by feedback-optimized wavefront manipulation
He, Z.-H.; Hou, B.; Gao, G.; Nees, J. A.; Krushelnick, K.; Thomas, A. G. R.; Lebailly, V.; Clarke, R.
2015-05-15
Plasmas generated by an intense laser pulse can support coherent structures such as large amplitude wakefield that can affect the outcome of an experiment. We investigate the coherent control of plasma dynamics by feedback-optimized wavefront manipulation using a deformable mirror. The experimental outcome is directly used as feedback in an evolutionary algorithm for optimization of the phase front of the driving laser pulse. In this paper, we applied this method to two different experiments: (i) acceleration of electrons in laser driven plasma waves and (ii) self-compression of optical pulses induced by ionization nonlinearity. The manipulation of the laser wavefront leads to orders of magnitude improvement to electron beam properties such as the peak charge, beam divergence, and transverse emittance. The demonstration of coherent control for plasmas opens new possibilities for future laser-based accelerators and their applications.
Customer Equilibrium and Optimal Strategies in an M/M/1 Queue with Dynamic Service Control
Dimitrakopoulos, Y
2011-01-01
We consider the problem of customer equilibrium strategies in an M/M/1 queue under dynamic service control. The service rate switches between a low and a high value depending on system congestion. Arriving customers do not observe the system state at the moment of arrival. We show that due to service rate variation, the customer equilibrium strategy is not generally unique, and derive an upper bound on the number of possible equilibria. For the problem of social welfare optimization, we numerically analyze the relationship between the optimal arrival rate, which maximizes the overall welfare of the customers, and the equilibrium ones as a function of various parameter values. We finally derive analytic solutions for the special case where the service rate switch occurs when the queue ceases to be empty.
Lipschitzian Regularity of Minimizers for Optimal Control Problems with Control-Affine Dynamics
Sarychev, A. V.; Torres, D. F. M. delfim@mat.ua.pt
2000-03-15
We study the Lagrange Problem of Optimal Control with a functional {integral}{sub a}{sup b}L(t,x(t),u(t)) dt and control-affine dynamics x-dot= f(t,x) + g(t,x)u and (a priori) unconstrained control u element of bf R{sup m}. We obtain conditions under which the minimizing controls of the problem are bounded-a fact which is crucial for the applicability of many necessary optimality conditions, like, for example, the Pontryagin Maximum Principle. As a corollary we obtain conditions for the Lipschitzian regularity of minimizers of the Basic Problem of the Calculus of Variations and of the Problem of the Calculus of Variations with higher-order derivatives.
Optimization of a hydrodynamic separator using a multiscale computational fluid dynamics approach.
Schmitt, Vivien; Dufresne, Matthieu; Vazquez, Jose; Fischer, Martin; Morin, Antoine
2013-01-01
This article deals with the optimization of a hydrodynamic separator working on the tangential separation mechanism along a screen. The aim of this study is to optimize the shape of the device to avoid clogging. A multiscale approach is used. This methodology combines measurements and computational fluid dynamics (CFD). A local model enables us to observe the different phenomena occurring at the orifice scale, which shows the potential of expanded metal screens. A global model is used to simulate the flow within the device using a conceptual model of the screen (porous wall). After validation against the experimental measurements, the global model was used to investigate the influence of deflectors and disk plates in the structure. PMID:24135107
NASA Technical Reports Server (NTRS)
Nguyen, Howard; Willacy, Karen; Allen, Mark
2012-01-01
KINETICS is a coupled dynamics and chemistry atmosphere model that is data intensive and computationally demanding. The potential performance gain from using a supercomputer motivates the adaptation from a serial version to a parallelized one. Although the initial parallelization had been done, bottlenecks caused by an abundance of communication calls between processors led to an unfavorable drop in performance. Before starting on the parallel optimization process, a partial overhaul was required because a large emphasis was placed on streamlining the code for user convenience and revising the program to accommodate the new supercomputers at Caltech and JPL. After the first round of optimizations, the partial runtime was reduced by a factor of 23; however, performance gains are dependent on the size of the data, the number of processors requested, and the computer used.
Optimizing the dynamic range extension of a radiochromic film dosimetry system
Devic, Slobodan; Tomic, Nada; Soares, Christopher G.; Podgorsak, Ervin B.
2009-02-15
The authors present a radiochromic film dosimetry protocol for a multicolor channel radiochromic film dosimetry system consisting of the external beam therapy (EBT) model GAFCHROMIC film and the Epson Expression 1680 flat-bed document scanner. Instead of extracting only the red color channel, the authors are using all three color channels in the absorption spectrum of the EBT film to extend the dynamic dose range of the radiochromic film dosimetry system. By optimizing the dose range for each color channel, they obtained a system that has both precision and accuracy below 1.5%, and the optimized ranges are 0-4 Gy for the red channel, 4-50 Gy for the green channel, and above 50 Gy for the blue channel.
Optimal dynamics for quantum-state and entanglement transfer through homogeneous quantum systems
Banchi, L.; Apollaro, T. J. G.; Cuccoli, A.; Vaia, R.; Verrucchi, P.
2010-11-15
The capability of faithfully transmit quantum states and entanglement through quantum channels is one of the key requirements for the development of quantum devices. Different solutions have been proposed to accomplish such a challenging task, which, however, require either an ad hoc engineering of the internal interactions of the physical system acting as the channel or specific initialization procedures. Here we show that optimal dynamics for efficient quantum-state and entanglement transfer can be attained in generic quantum systems with homogeneous interactions by tuning the coupling between the system and the two attached qubits. We devise a general procedure to determine the optimal coupling, and we explicitly implement it in the case of a channel consisting of a spin-(1/2)XY chain. The quality of quantum-state and entanglement transfer is found to be very good and, remarkably, almost independent of the channel length.
The importance of functional form in optimal control solutions of problems in population dynamics
Runge, M.C.; Johnson, F.A.
2002-01-01
Optimal control theory is finding increased application in both theoretical and applied ecology, and it is a central element of adaptive resource management. One of the steps in an adaptive management process is to develop alternative models of system dynamics, models that are all reasonable in light of available data, but that differ substantially in their implications for optimal control of the resource. We explored how the form of the recruitment and survival functions in a general population model for ducks affected the patterns in the optimal harvest strategy, using a combination of analytical, numerical, and simulation techniques. We compared three relationships between recruitment and population density (linear, exponential, and hyperbolic) and three relationships between survival during the nonharvest season and population density (constant, logistic, and one related to the compensatory harvest mortality hypothesis). We found that the form of the component functions had a dramatic influence on the optimal harvest strategy and the ultimate equilibrium state of the system. For instance, while it is commonly assumed that a compensatory hypothesis leads to higher optimal harvest rates than an additive hypothesis, we found this to depend on the form of the recruitment function, in part because of differences in the optimal steady-state population density. This work has strong direct consequences for those developing alternative models to describe harvested systems, but it is relevant to a larger class of problems applying optimal control at the population level. Often, different functional forms will not be statistically distinguishable in the range of the data. Nevertheless, differences between the functions outside the range of the data can have an important impact on the optimal harvest strategy. Thus, development of alternative models by identifying a single functional form, then choosing different parameter combinations from extremes on the likelihood profile may end up producing alternatives that do not differ as importantly as if different functional forms had been used. We recommend that biological knowledge be used to bracket a range of possible functional forms, and robustness of conclusions be checked over this range.
NASA Astrophysics Data System (ADS)
Hanaoka, T.
The paper presents a dynamic-programing algorithm for solving general nonlinear continuous-state optimal-control problems with state and control constraints. The dynamic-programing solution is obtained by the iterative functional equation of backward dynamic programing. Numerical examples from optimal-control problems are examined in order to illustrate the technique, and its effectiveness in solving practical problems is demonstrated by its application to the sun encounter mission with a low-thrust trajectory, in which Venus is taken as a swinged-by planet if a planet swingby is required. The results are compared with those of the cases utilizing simple nonoptimal control laws and Jupiter swingby.
Ali, Hussnain; Hazrati, Oldooz; Tobey, Emily A.; Hansen, John H. L
2014-01-01
The aim of this study is to investigate the effect of Adaptive Dynamic Range Optimization (ADRO) on speech identification for cochlear implant (CI) users in adverse listening conditions. In this study, anechoic quiet, noisy, reverberant, noisy reverberant, and reverberant noisy conditions are evaluated. Two scenarios are considered when modeling the combined effects of reverberation and noise: (a) noise is added to the reverberant speech, and (b) noisy speech is reverberated. CI users were tested in different listening environments using IEEE sentences presented at 65?dB sound pressure level. No significant effect of ADRO processing on speech intelligibility was observed. PMID:25190428
Demonstration of open-quantum-system optimal control in dynamic nuclear polarization
NASA Astrophysics Data System (ADS)
Sheldon, S.; Cory, D. G.
2015-10-01
Dynamic nuclear polarization (DNP) is used in nuclear magnetic resonance to transfer polarization from electron spins to nuclear spins. The resulting nuclear polarization enhancement can, in theory, be two or three orders of magnitude depending on the sample. In solid-state systems, however, there are competing mechanisms of DNP, which, when occurring simultaneously, reduce the net polarization enhancement of the nuclear spin. We present a simple quantum description of DNP and apply optimal control theory (OCT) with an open-quantum-system framework to design pulses that select one DNP process and suppress the others. We demonstrate experimentally an order of magnitude improvement in the DNP enhancement using OCT pulses.
Progress on Optimization of the Nonlinear Beam Dynamics in the MEIC Collider Rings
Morozov, Vasiliy S.; Derbenev, Yaroslav S.; Lin, Fanglei; Pilat, Fulvia; Zhang, Yuhong; Cai, Y.; Nosochkov, Y. M.; Sullivan, Michael; Wang, M.-H.; Wienands, Uli
2015-09-01
One of the key design features of the Medium-energy Electron-Ion Collider (MEIC) proposed by Jefferson Lab is a small beta function at the interaction point (IP) allowing one to achieve a high luminosity of up to 1034 cm-2s-1. The required strong beam focusing unavoidably causes large chromatic effects such as chromatic tune spread and beam smear at the IP, which need to be compensated. This paper reports recent progress in our development of a chromaticity correction scheme for the ion ring including optimization of dynamic aperture and momentum acceptance.
Demonstration of Open Quantum System Optimal Control in Dynamic Nuclear Polarization
Sarah Sheldon; David G. Cory
2015-07-20
Dynamic nuclear polarization (DNP) is used in nuclear magnetic resonance (NMR) to transfer polarization from electron spins to nuclear spins. The resulting nuclear polarization enhancement can, in theory, be two or three orders of magnitude depending on the sample. In solid state systems, however, there are competing mechanisms of DNP, which, when occurring simultaneously, reduce the net polarization enhancement of the nuclear spin. We present a simple quantum description of DNP and apply optimal control theory (OCT) with an open quantum system framework to design pulses that select one DNP process and suppress the others. We demonstrate experimentally an order of magnitude improvement in the DNP enhancement using OCT pulses.
NASA Astrophysics Data System (ADS)
Praveena, P.; Vaisakh, K.; Rama Mohana Rao, S.
The Dynamic economic dispatch (DED) problem is an optimization problem with an objective to determine the optimal combination of power outputs for all generating units over a certain period of time in order to minimize the total fuel cost while satisfying dynamic operational constraints and load demand in each interval. Recently social foraging behavior of Escherichia coli bacteria has been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA) is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. This article comes up with a hybrid approach involving Particle Swarm Optimization (PSO) and BFO algorithms with varying chemo tactic step size for solving the DED problem of generating units considering valve-point effects. The proposed hybrid algorithm has been extensively compared with those methods reported in the literature. The new method is shown to be statistically significantly better on two test systems consisting of five and ten generating units.
Efficiency optimization of a fast Poisson solver in beam dynamics simulation
NASA Astrophysics Data System (ADS)
Zheng, Dawei; Pöplau, Gisela; van Rienen, Ursula
2016-01-01
Calculating the solution of Poisson's equation relating to space charge force is still the major time consumption in beam dynamics simulations and calls for further improvement. In this paper, we summarize a classical fast Poisson solver in beam dynamics simulations: the integrated Green's function method. We introduce three optimization steps of the classical Poisson solver routine: using the reduced integrated Green's function instead of the integrated Green's function; using the discrete cosine transform instead of discrete Fourier transform for the Green's function; using a novel fast convolution routine instead of an explicitly zero-padded convolution. The new Poisson solver routine preserves the advantages of fast computation and high accuracy. This provides a fast routine for high performance calculation of the space charge effect in accelerators.
Optimization for Hub-and-Spoke Port Logistics Network of Dynamic Hinterland
NASA Astrophysics Data System (ADS)
Ming-Jun, Ji; Yan-Ling, Chu
The port logistics and its regional economic react on each other and develop in unison. This paper studies the Hub-and-Spoke port logistics network which is a transportation system between the sea routes and ports hinterland transport routes. An optimization model is proposed with the objective of the total transportation cost in the regional port group based on the conditions of dynamic hinterland. This paper not only ensures every port in the hub-and spoke port logistics network to achieve its maximum economic benefits, but also makes the entire system get the minimum total transportation cost in the view of quantitative point. In order to illustrate the validity of the model, the example was solved. The results show that the model is feasible. Furthermore, the competitiveness power of the port, the demarcation of hinterland and the traffic capacity are changed dynamically in the model, which is closer to the real system.
Optimization of stomatal conductance for maximum carbon gain under dynamic soil moisture
NASA Astrophysics Data System (ADS)
Manzoni, Stefano; Vico, Giulia; Palmroth, Sari; Porporato, Amilcare; Katul, Gabriel
2013-12-01
Optimization theories explain a variety of forms and functions in plants. At the leaf scale, it is often hypothesized that carbon gain is maximized, thus providing a quantifiable objective for a mathematical definition of optimality conditions. Eco-physiological trade-offs and limited resource availability introduce natural bounds to this optimization process. In particular, carbon uptake from the atmosphere is inherently linked to water losses from the soil as water is taken up by roots and evaporated. Hence, water availability in soils constrains the amount of carbon that can be taken up and assimilated into new biomass. The problem of maximizing photosynthesis at a given water availability by modifying stomatal conductance, the plant-controlled variable to be optimized, has been traditionally formulated for short time intervals over which soil moisture changes can be neglected. This simplification led to a mathematically open solution, where the undefined Lagrange multiplier of the optimization (equivalent to the marginal water use efficiency, ?) is then heuristically determined via data fitting. Here, a set of models based on different assumptions that account for soil moisture dynamics over an individual dry-down are proposed so as to provide closed analytical expressions for the carbon gain maximization problem. These novel solutions link the observed variability in ? over time, across soil moisture changes, and at different atmospheric CO2 concentrations to water use strategies ranging from intensive, in which all soil water is consumed by the end of the dry-down period, to more conservative, in which water stress is avoided by reducing transpiration.
NASA Astrophysics Data System (ADS)
Hai-yang, Zhao; Min-qiang, Xu; Jin-dong, Wang; Yong-bo, Li
2015-05-01
In order to improve the accuracy of dynamics response simulation for mechanism with joint clearance, a parameter optimization method for planar joint clearance contact force model was presented in this paper, and the optimized parameters were applied to the dynamics response simulation for mechanism with oversized joint clearance fault. By studying the effect of increased clearance on the parameters of joint clearance contact force model, the relation of model parameters between different clearances was concluded. Then the dynamic equation of a two-stage reciprocating compressor with four joint clearances was developed using Lagrange method, and a multi-body dynamic model built in ADAMS software was used to solve this equation. To obtain a simulated dynamic response much closer to that of experimental tests, the parameters of joint clearance model, instead of using the designed values, were optimized by genetic algorithms approach. Finally, the optimized parameters were applied to simulate the dynamics response of model with oversized joint clearance fault according to the concluded parameter relation. The dynamics response of experimental test verified the effectiveness of this application.
Setrak J. Balian; Ren-Bao Liu; T. S. Monteiro
2015-04-22
There are two distinct techniques of proven effectiveness for extending the coherence lifetime of spin qubits in environments of other spins. One is dynamical decoupling, whereby the qubit is subjected to a carefully timed sequence of control pulses; the other is tuning the qubit towards 'optimal working points' (OWPs), which are sweet-spots for reduced decoherence in magnetic fields. By means of quantum many-body calculations, we investigate the effects of dynamical decoupling pulse sequences far from and near OWPs for a central donor qubit subject to decoherence from a nuclear spin bath. Key to understanding the behavior is to analyse the degree of suppression of the usually dominant contribution from independent pairs of flip-flopping spins within the many-body quantum bath. We find that to simulate recently measured Hahn echo decays at OWPs (lowest-order dynamical decoupling), one must consider clusters of three interacting spins, since independent pairs do not even give finite $T_2$ decay times. We show that while operating near OWPs, dynamical decoupling sequences require hundreds of pulses for a single order of magnitude enhancement of $T_2$, in contrast to regimes far from OWPs, where only about ten pulses are required.
NASA Astrophysics Data System (ADS)
Balian, S. J.; Liu, Ren-Bao; Monteiro, T. S.
2015-06-01
There are two distinct techniques of proven effectiveness for extending the coherence lifetime of spin qubits in environments of other spins. One is dynamical decoupling, whereby the qubit is subjected to a carefully timed sequence of control pulses; the other is tuning the qubit towards "optimal working points" (OWPs), which are sweet spots for reduced decoherence in magnetic fields. By means of quantum many-body calculations, we investigate the effects of dynamical decoupling pulse sequences far from and near OWPs for a central donor qubit subject to decoherence from a nuclear spin bath. Key to understanding the behavior is to analyze the degree of suppression of the usually dominant contribution from independent pairs of flip-flopping spins within the many-body quantum bath. We find that to simulate recently measured Hahn echo decays at OWPs (lowest-order dynamical decoupling), one must consider clusters of three interacting spins since independent pairs do not even give finite-T2 decay times. We show that while operating near OWPs, dynamical decoupling sequences require hundreds of pulses for a single order of magnitude enhancement of T2, in contrast to regimes far from OWPs, where only about 10 pulses are required.
Dynamic nuclear polarization and optimal control spatial-selective 13C MRI and MRS
NASA Astrophysics Data System (ADS)
Vinding, Mads S.; Laustsen, Christoffer; Maximov, Ivan I.; Søgaard, Lise Vejby; Ardenkjær-Larsen, Jan H.; Nielsen, Niels Chr.
2013-02-01
Aimed at 13C metabolic magnetic resonance imaging (MRI) and spectroscopy (MRS) applications, we demonstrate that dynamic nuclear polarization (DNP) may be combined with optimal control 2D spatial selection to simultaneously obtain high sensitivity and well-defined spatial restriction. This is achieved through the development of spatial-selective single-shot spiral-readout MRI and MRS experiments combined with dynamic nuclear polarization hyperpolarized [1-13C]pyruvate on a 4.7 T pre-clinical MR scanner. The method stands out from related techniques by facilitating anatomic shaped region-of-interest (ROI) single metabolite signals available for higher image resolution or single-peak spectra. The 2D spatial-selective rf pulses were designed using a novel Krotov-based optimal control approach capable of iteratively fast providing successful pulse sequences in the absence of qualified initial guesses. The technique may be important for early detection of abnormal metabolism, monitoring disease progression, and drug research.
Dynamic Allocation of SPM Based on Time-Slotted Cache Conflict Graph for System Optimization
NASA Astrophysics Data System (ADS)
Wu, Jianping; Ling, Ming; Zhang, Yang; Mei, Chen; Wang, Huan
This paper proposes a novel dynamic Scratch-pad Memory allocation strategy to optimize the energy consumption of the memory sub-system. Firstly, the whole program execution process is sliced into several time slots according to the temporal dimension; thereafter, a Time-Slotted Cache Conflict Graph (TSCCG) is introduced to model the behavior of Data Cache (D-Cache) conflicts within each time slot. Then, Integer Nonlinear Programming (INP) is implemented, which can avoid time-consuming linearization process, to select the most profitable data pages. Virtual Memory System (VMS) is adopted to remap those data pages, which will cause severe Cache conflicts within a time slot, to SPM. In order to minimize the swapping overhead of dynamic SPM allocation, a novel SPM controller with a tightly coupled DMA is introduced to issue the swapping operations without CPU's intervention. Last but not the least, this paper discusses the fluctuation of system energy profit based on different MMU page size as well as the Time Slot duration quantitatively. According to our design space exploration, the proposed method can optimize all of the data segments, including global data, heap and stack data in general, and reduce the total energy consumption by 27.28% on average, up to 55.22% with a marginal performance promotion. And comparing to the conventional static CCG (Cache Conflicts Graph), our approach can obtain 24.7% energy profit on average, up to 30.5% with a sight boost in performance.
Fluid-Dynamic Optimal Design of Helical Vascular Graft for Stenotic Disturbed Flow
Ha, Hojin; Hwang, Dongha; Choi, Woo-Rak; Baek, Jehyun; Lee, Sang Joon
2014-01-01
Although a helical configuration of a prosthetic vascular graft appears to be clinically beneficial in suppressing thrombosis and intimal hyperplasia, an optimization of a helical design has yet to be achieved because of the lack of a detailed understanding on hemodynamic features in helical grafts and their fluid dynamic influences. In the present study, the swirling flow in a helical graft was hypothesized to have beneficial influences on a disturbed flow structure such as stenotic flow. The characteristics of swirling flows generated by helical tubes with various helical pitches and curvatures were investigated to prove the hypothesis. The fluid dynamic influences of these helical tubes on stenotic flow were quantitatively analysed by using a particle image velocimetry technique. Results showed that the swirling intensity and helicity of the swirling flow have a linear relation with a modified Germano number (Gn*) of the helical pipe. In addition, the swirling flow generated a beneficial flow structure at the stenosis by reducing the size of the recirculation flow under steady and pulsatile flow conditions. Therefore, the beneficial effects of a helical graft on the flow field can be estimated by using the magnitude of Gn*. Finally, an optimized helical design with a maximum Gn* was suggested for the future design of a vascular graft. PMID:25360705
Roslund, Jonathan; Roth, Matthias; Guyon, Laurent; Boutou, Véronique; Courvoisier, Francois; Wolf, Jean-Pierre; Rabitz, Herschel
2011-01-21
Fundamental molecular selectivity limits are probed by exploiting laser-controlled quantum interferences for the creation of distinct spectral signatures in two flavin molecules, erstwhile nearly indistinguishable via steady-state methods. Optimal dynamic discrimination (ODD) uses optimally shaped laser fields to transiently amplify minute molecular variations that would otherwise go unnoticed with linear absorption and fluorescence techniques. ODD is experimentally demonstrated by combining an optimally shaped UV pump pulse with a time-delayed, fluorescence-depleting IR pulse for discrimination amongst riboflavin and flavin mononucleotide in aqueous solution, which are structurally and spectroscopically very similar. Closed-loop, adaptive pulse shaping discovers a set of UV pulses that induce disparate responses from the two flavins and allows for concomitant flavin discrimination of ?16?. Additionally, attainment of ODD permits quantitative, analytical detection of the individual constituents in a flavin mixture. The successful implementation of ODD on quantum systems of such high complexity bodes well for the future development of the field and the use of ODD techniques in a variety of demanding practical applications. PMID:21261372
NASA Astrophysics Data System (ADS)
Roslund, Jonathan; Roth, Matthias; Guyon, Laurent; Boutou, Véronique; Courvoisier, Francois; Wolf, Jean-Pierre; Rabitz, Herschel
2011-01-01
Fundamental molecular selectivity limits are probed by exploiting laser-controlled quantum interferences for the creation of distinct spectral signatures in two flavin molecules, erstwhile nearly indistinguishable via steady-state methods. Optimal dynamic discrimination (ODD) uses optimally shaped laser fields to transiently amplify minute molecular variations that would otherwise go unnoticed with linear absorption and fluorescence techniques. ODD is experimentally demonstrated by combining an optimally shaped UV pump pulse with a time-delayed, fluorescence-depleting IR pulse for discrimination amongst riboflavin and flavin mononucleotide in aqueous solution, which are structurally and spectroscopically very similar. Closed-loop, adaptive pulse shaping discovers a set of UV pulses that induce disparate responses from the two flavins and allows for concomitant flavin discrimination of ˜16?. Additionally, attainment of ODD permits quantitative, analytical detection of the individual constituents in a flavin mixture. The successful implementation of ODD on quantum systems of such high complexity bodes well for the future development of the field and the use of ODD techniques in a variety of demanding practical applications.
Dynamic response and optimal design of curved metallic sandwich panels under blast loading.
Qi, Chang; Yang, Shu; Yang, Li-Jun; Han, Shou-Hong; Lu, Zhen-Hua
2014-01-01
It is important to understand the effect of curvature on the blast response of curved structures so as to seek the optimal configurations of such structures with improved blast resistance. In this study, the dynamic response and protective performance of a type of curved metallic sandwich panel subjected to air blast loading were examined using LS-DYNA. The numerical methods were validated using experimental data in the literature. The curved panel consisted of an aluminum alloy outer face and a rolled homogeneous armour (RHA) steel inner face in addition to a closed-cell aluminum foam core. The results showed that the configuration of a "soft" outer face and a "hard" inner face worked well for the curved sandwich panel against air blast loading in terms of maximum deflection (MaxD) and energy absorption. The panel curvature was found to have a monotonic effect on the specific energy absorption (SEA) and a nonmonotonic effect on the MaxD of the panel. Based on artificial neural network (ANN) metamodels, multiobjective optimization designs of the panel were carried out. The optimization results revealed the trade-off relationships between the blast-resistant and the lightweight objectives and showed the great use of Pareto front in such design circumstances. PMID:25126606
Time-optimal path planning in dynamic flows using level set equations: theory and schemes
NASA Astrophysics Data System (ADS)
Lolla, Tapovan; Lermusiaux, Pierre F. J.; Ueckermann, Mattheus P.; Haley, Patrick J.
2014-10-01
We develop an accurate partial differential equation-based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents, obviating the need for heuristics. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid currents to minimize their travel time. Inspired by the level set method, we derive and demonstrate that a modified level set equation governs the time-optimal path in any continuous flow. We show that our algorithm is computationally efficient and apply it to a number of experiments. First, we validate our approach through a simple benchmark application in a Rankine vortex flow for which an analytical solution is available. Next, we apply our methodology to more complex, simulated flow fields such as unsteady double-gyre flows driven by wind stress and flows behind a circular island. These examples show that time-optimal paths for multiple vehicles can be planned even in the presence of complex flows in domains with obstacles. Finally, we present and support through illustrations several remarks that describe specific features of our methodology.
Time-optimal path planning in dynamic flows using level set equations: theory and schemes
NASA Astrophysics Data System (ADS)
Lolla, Tapovan; Lermusiaux, Pierre F. J.; Ueckermann, Mattheus P.; Haley, Patrick J.
2014-09-01
We develop an accurate partial differential equation-based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents, obviating the need for heuristics. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid currents to minimize their travel time. Inspired by the level set method, we derive and demonstrate that a modified level set equation governs the time-optimal path in any continuous flow. We show that our algorithm is computationally efficient and apply it to a number of experiments. First, we validate our approach through a simple benchmark application in a Rankine vortex flow for which an analytical solution is available. Next, we apply our methodology to more complex, simulated flow fields such as unsteady double-gyre flows driven by wind stress and flows behind a circular island. These examples show that time-optimal paths for multiple vehicles can be planned even in the presence of complex flows in domains with obstacles. Finally, we present and support through illustrations several remarks that describe specific features of our methodology.
Fluid dynamic optimization of a ventricular assist device using particle image velocimetry.
Mussivand, T; Day, K D; Naber, B C
1999-01-01
Thrombus formation and resulting thromboembolism are major risks that can impede the widespread use of ventricular assist devices (VADs). Adverse flow patterns (turbulence and stasis) have been implicated in thrombogenesis. This study focuses on optimization of VAD geometry, port orientation, and fluid dynamics to reduce thrombus formation. Particle image velocimetry with cross-correlation was performed using Amberlite particles suspended in distilled water. The transparent VADs were illuminated by halogen lamps. Four different VADs were tested in an iterative approach toward optimization. A peak shear stress of 9,100 dynes/cm2 was noted in the first configuration immediately after the end of systole at the outlet port. Modifications in chamber geometry, port diameters and orientation, and valve enclosure design yielded shear stresses in the two subsequent geometries of 5,100 dynes/cm2 and 1,900 dynes/cm2, respectively. For the third iteration, a region of stasis occurred during the transition between the inlet port and the blood chamber. Further modifications were implemented, including a reduction in port diameters and further smoothing of the port entry region. This eliminated stasis and yielded a maximum shear level of 4,100 dynes/cm2. In conclusion, optimization was achieved through geometric modification of the VAD, thus minimizing adverse flow conditions. PMID:9952002
Dynamic Response and Optimal Design of Curved Metallic Sandwich Panels under Blast Loading
Yang, Shu; Han, Shou-Hong; Lu, Zhen-Hua
2014-01-01
It is important to understand the effect of curvature on the blast response of curved structures so as to seek the optimal configurations of such structures with improved blast resistance. In this study, the dynamic response and protective performance of a type of curved metallic sandwich panel subjected to air blast loading were examined using LS-DYNA. The numerical methods were validated using experimental data in the literature. The curved panel consisted of an aluminum alloy outer face and a rolled homogeneous armour (RHA) steel inner face in addition to a closed-cell aluminum foam core. The results showed that the configuration of a “soft” outer face and a “hard” inner face worked well for the curved sandwich panel against air blast loading in terms of maximum deflection (MaxD) and energy absorption. The panel curvature was found to have a monotonic effect on the specific energy absorption (SEA) and a nonmonotonic effect on the MaxD of the panel. Based on artificial neural network (ANN) metamodels, multiobjective optimization designs of the panel were carried out. The optimization results revealed the trade-off relationships between the blast-resistant and the lightweight objectives and showed the great use of Pareto front in such design circumstances. PMID:25126606
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2013-01-01
This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.
NASA Astrophysics Data System (ADS)
Liu, Xinning; Mei, Chen; Cao, Peng; Zhu, Min; Shi, Longxing
This paper proposes a novel sub-architecture to optimize the data flow of REMUS-II (REconfigurable MUltimedia System 2), a dynamically coarse grain reconfigurable architecture. REMUS-II consists of a µPU (Micro-Processor Unit) and two RPUs (Reconfigurable Processor Unit), which are used to speeds up control-intensive tasks and data-intensive tasks respectively. The parallel computing capability and flexibility of REMUS-II makes itself an excellent candidate to process multimedia applications, which require a large amount of memory accesses. In this paper, we specifically optimize the data flow to deal with those performance-hazard and energy-hungry memory accessing in order to meet the bandwidth requirement of parallel computing. The RPU internal memory could work in multiple modes, like 2D-access mode and transformation mode, according to different multimedia access patterns. This novel design can improve the performance up to 26% compared to traditional on-chip memory. Meanwhile, the block buffer is implemented to optimize the off-chip data flow through reducing off-chip memory accesses, which reducing up to 43% compared to direct DDR access. Based on RTL simulation, REMUS-II can achieve 1080p@30fps of H.264 High Profile@ Level 4 and High Level MPEG2 at 200MHz clock frequency. The REMUS-II is implemented into 23.7mm2 silicon on TSMC 65nm logic process with a 400MHz maximum working frequency.
Reduced-order model for dynamic optimization of pressure swing adsorption processes
Agarwal, A.; Biegler, L.; Zitney, S.
2007-01-01
Over the past decades, pressure swing adsorption (PSA) processes have been widely used as energy-efficient gas and liquid separation techniques, especially for high purity hydrogen purification from refinery gases. The separation processes are based on solid-gas equilibrium and operate under periodic transient conditions. Models for PSA processes are therefore multiple instances of partial differential equations (PDEs) in time and space with periodic boundary conditions that link the processing steps together. The solution of this coupled stiff PDE system is governed by steep concentrations and temperature fronts moving with time. As a result, the optimization of such systems for either design or operation represents a significant computational challenge to current differential algebraic equation (DAE) optimization techniques and nonlinear programming algorithms. Model reduction is one approach to generate cost-efficient low-order models which can be used as surrogate models in the optimization problems. The study develops a reduced-order model (ROM) based on proper orthogonal decomposition (POD), which is a low-dimensional approximation to a dynamic PDE-based model. Initially, a representative ensemble of solutions of the dynamic PDE system is constructed by solving a higher-order discretization of the model using the method of lines, a two-stage approach that discretizes the PDEs in space and then integrates the resulting DAEs over time. Next, the ROM method applies the Karhunen-Loeve expansion to derive a small set of empirical eigenfunctions (POD modes) which are used as basis functions within a Galerkin's projection framework to derive a low-order DAE system that accurately describes the dominant dynamics of the PDE system. The proposed method leads to a DAE system of significantly lower order, thus replacing the one obtained from spatial discretization before and making optimization problem computationally-efficient. The method has been applied to the dynamic coupled PDE-based model of a two-bed four-step PSA process for separation of hydrogen from methane. Separate ROMs have been developed for each operating step with different POD modes for each of them. A significant reduction in the order of the number of states has been achieved. The gas-phase mole fraction, solid-state loading and temperature profiles from the low-order ROM and from the high-order simulations have been compared. Moreover, the profiles for a different set of inputs and parameter values fed to the same ROM were compared with the accurate profiles from the high-order simulations. Current results indicate the proposed ROM methodology as a promising surrogate modeling technique for cost-effective optimization purposes. Moreover, deviations from the ROM for different set of inputs and parameters suggest that a recalibration of the model is required for the optimization studies. Results for these will also be presented with the aforementioned results.
Combating obesity through healthy eating behavior: a call for system dynamics optimization.
Abidin, Norhaslinda Zainal; Mamat, Mustafa; Dangerfield, Brian; Zulkepli, Jafri Haji; Baten, Md Azizul; Wibowo, Antoni
2014-01-01
Poor eating behavior has been identified as one of the core contributory factors of the childhood obesity epidemic. The consequences of obesity on numerous aspects of life are thoroughly explored in the existing literature. For instance, evidence shows that obesity is linked to incidences of diseases such as heart disease, type-2 diabetes, and some cancers, as well as psychosocial problems. To respond to the increasing trends in the UK, in 2008 the government set a target to reverse the prevalence of obesity (POB) back to 2000 levels by 2020. This paper will outline the application of system dynamics (SD) optimization to simulate the effect of changes in the eating behavior of British children (aged 2 to 15 years) on weight and obesity. This study also will identify how long it will take to achieve the government's target. This paper proposed a simulation model called Intervention Childhood Obesity Dynamics (ICOD) by focusing the interrelations between various strands of knowledge in one complex human weight regulation system. The model offers distinct insights into the dynamics by capturing the complex interdependencies from the causal loop and feedback structure, with the intention to better understand how eating behaviors influence children's weight, body mass index (BMI), and POB measurement. This study proposed a set of equations that are revised from the original (baseline) equations. The new functions are constructed using a RAMP function of linear decrement in portion size and number of meal variables from 2013 until 2020 in order to achieve the 2020 desired target. Findings from the optimization analysis revealed that the 2020 target won't be achieved until 2026 at the earliest, six years late. Thus, the model suggested that a longer period may be needed to significantly reduce obesity in this population. PMID:25502170
Combating Obesity through Healthy Eating Behavior: A Call for System Dynamics Optimization
Zainal Abidin, Norhaslinda; Mamat, Mustafa; Dangerfield, Brian; Zulkepli, Jafri Haji; Baten, Md. Azizul; Wibowo, Antoni
2014-01-01
Poor eating behavior has been identified as one of the core contributory factors of the childhood obesity epidemic. The consequences of obesity on numerous aspects of life are thoroughly explored in the existing literature. For instance, evidence shows that obesity is linked to incidences of diseases such as heart disease, type-2 diabetes, and some cancers, as well as psychosocial problems. To respond to the increasing trends in the UK, in 2008 the government set a target to reverse the prevalence of obesity (POB) back to 2000 levels by 2020. This paper will outline the application of system dynamics (SD) optimization to simulate the effect of changes in the eating behavior of British children (aged 2 to 15 years) on weight and obesity. This study also will identify how long it will take to achieve the government’s target. This paper proposed a simulation model called Intervention Childhood Obesity Dynamics (ICOD) by focusing the interrelations between various strands of knowledge in one complex human weight regulation system. The model offers distinct insights into the dynamics by capturing the complex interdependencies from the causal loop and feedback structure, with the intention to better understand how eating behaviors influence children’s weight, body mass index (BMI), and POB measurement. This study proposed a set of equations that are revised from the original (baseline) equations. The new functions are constructed using a RAMP function of linear decrement in portion size and number of meal variables from 2013 until 2020 in order to achieve the 2020 desired target. Findings from the optimization analysis revealed that the 2020 target won’t be achieved until 2026 at the earliest, six years late. Thus, the model suggested that a longer period may be needed to significantly reduce obesity in this population. PMID:25502170
NASA Astrophysics Data System (ADS)
Kollet, S. J.
2014-12-01
Theories of optimality and self-organization are appealing when dealing with non-linear systems, because based on first principles of thermodynamic these theories may lead to an intuitive interpretation and prediction of absolute values, directions, and interactions of gradients and fluxes, and universal inference laws for effective conductances. In this context, for example, the maximum entropy production principle received attention, because of its foundation in non-equilibrium thermodynamics, which appears to be useful in e.g., eco-hydrologic and atmospheric applications. A number of studies successfully applied this principle in the optimization of conductances in simplified and well-mixed open systems with external (periodic) forcing. In support-scale simulations of a variably saturated hillslope, the study presented here relaxes major simplifying assumptions by applying a realistic, arid atmospheric time series in spinup simulations to create a dynamic equilibrium utilizing the integrated hydrologic model ParFlow-CLM. The simulated hillslope exhibits time-varying internal circulation patterns due to the periodic atmospheric forcing, topography, and also heterogeneity by utilizing and optimizing all degrees of freedom provided by the soil-water retention relationship and free-moving water table. Because of the extreme non-linearity of variably saturated flow under arid climate conditions, the system is never well mixed and optimality principles relying on time-integrated gradients and fluxes do not appear to be applicable in the currently available theoretical framework. Here, integrated support-scale simulations may be useful in deriving novel theories for the application to real systems in future.
Trajectory optimization for dynamic couch rotation during volumetric modulated arc radiotherapy.
Smyth, Gregory; Bamber, Jeffrey C; Evans, Philip M; Bedford, James L
2013-11-21
Non-coplanar radiation beams are often used in three-dimensional conformal and intensity modulated radiotherapy to reduce dose to organs at risk (OAR) by geometric avoidance. In volumetric modulated arc radiotherapy (VMAT) non-coplanar geometries are generally achieved by applying patient couch rotations to single or multiple full or partial arcs. This paper presents a trajectory optimization method for a non-coplanar technique, dynamic couch rotation during VMAT (DCR–VMAT), which combines ray tracing with a graph search algorithm. Four clinical test cases (partial breast, brain, prostate only, and prostate and pelvic nodes) were used to evaluate the potential OAR sparing for trajectory-optimized DCR–VMAT plans, compared with standard coplanar VMAT. In each case, ray tracing was performed and a cost map reflecting the number of OAR voxels intersected for each potential source position was generated. The least-cost path through the cost map, corresponding to an optimal DCR–VMAT trajectory, was determined using Dijkstra's algorithm. Results show that trajectory optimization can reduce dose to specified OARs for plans otherwise comparable to conventional coplanar VMAT techniques. For the partial breast case, the mean heart dose was reduced by 53%. In the brain case, the maximum lens doses were reduced by 61% (left) and 77% (right) and the globes by 37% (left) and 40% (right). Bowel mean dose was reduced by 15% in the prostate only case. For the prostate and pelvic nodes case, the bowel V50?Gy and V60?Gy were reduced by 9% and 45% respectively. Future work will involve further development of the algorithm and assessment of its performance over a larger number of cases in site-specific cohorts. PMID:24200876
NASA Astrophysics Data System (ADS)
Ribeiro, Eduardo Afonso; Pereira, Jucélio Tomás; Alberto Bavastri, Carlos
2015-09-01
One of the major reasons for inserting damping into bearings is that rotating machines are often requested in critical functioning conditions having sometimes to function under dynamic instability or close to critical speeds. Hydrodynamic and magnetic bearings have usually been used for this purpose, but they present limitations regarding costs and operation, rendering the use of viscoelastic supports a feasible solution for vibration control in rotating machines. Most papers in the area use simple analytic or single degree of freedom models for the rotor as well as classic mechanical models of linear viscoelasticity for the support - like Maxwell, Kelvin-Voigt, Zenner, four-element, GHM models and even frequency independent models - but they lack the accuracy of fractional models in a large range of frequency and temperature regarding the same number of coefficients. Even in those works, the need to consider the addition of degrees of freedom to the support is evident. However, so far no paper has been published focusing on a methodology to determine the optimal constructive form for any viscoelastic support in which the rotor is discretized by finite elements associated to an accurate model for characterizing the viscoelastic material. In general, the support is meant to be a simple isolation system, and the fact the stiffness matrix is complex and frequency-temperature dependent - due to its viscoelastic properties - forces the traditional methods to require an extremely long computing time, thus rendering them too time consuming in an optimization environment. The present work presents a robust methodology based mainly on generalized equivalent parameters (GEP) - for an optimal design of viscoelastic supports for rotating machinery - aiming at minimizing the unbalance frequency response of the system using a hybrid optimization technique (genetic algorithms and Nelder-Mead method). The rotor is modeled based on the finite element method using Timoshenko's thick beam formulation, and the viscoelastic material is modeled based on four-parameter fractional derivatives. The insertion of supports - a two-degree-of-freedom isolation system - into rotor's motion equations is performed in two different ways: (1) by adding degrees of freedom and (2) by using the GEP technique. The results show that both techniques are consistent, but the GEP technique proves to be less time consuming, regarding computing time. In the presented simulations it is possible to observe the reduction in vibration amplitudes and transmissibility in a system using optimized viscoelastic supports when compared to ball and hydrodynamic bearings. One concludes that the methodology presented is robust and allows obtaining an optimal design of any viscoelastic support - using GEP - in an efficient and viable way for vibration passive control in rotors of rotating machines.
Optimal variable flip angle schemes for dynamic acquisition of exchanging hyperpolarized substrates
NASA Astrophysics Data System (ADS)
Xing, Yan; Reed, Galen D.; Pauly, John M.; Kerr, Adam B.; Larson, Peder E. Z.
2013-09-01
In metabolic MRI with hyperpolarized contrast agents, the signal levels vary over time due to T1 decay, T2 decay following RF excitations, and metabolic conversion. Efficient usage of the nonrenewable hyperpolarized magnetization requires specialized RF pulse schemes. In this work, we introduce two novel variable flip angle schemes for dynamic hyperpolarized MRI in which the flip angle is varied between excitations and between metabolites. These were optimized to distribute the magnetization relatively evenly throughout the acquisition by accounting for T1 decay, prior RF excitations, and metabolic conversion. Simulation results are presented to confirm the flip angle designs and evaluate the variability of signal dynamics across typical ranges of T1 and metabolic conversion. They were implemented using multiband spectral-spatial RF pulses to independently modulate the flip angle at various chemical shift frequencies. With these schemes we observed increased SNR of [1-13C]lactate generated from [1-13C]pyruvate, particularly at later time points. This will allow for improved characterization of tissue perfusion and metabolic profiles in dynamic hyperpolarized MRI.
Optimization of the dynamic and thermal performance of a resonant micro heat engine
NASA Astrophysics Data System (ADS)
Bardaweel, H. K.; Anderson, M. J.; Richards, R. F.; Richards, C. D.
2008-10-01
The dynamic behavior of a flexing membrane micro heat engine is presented. The micro heat engine consists of a cavity filled with a saturated, two-phase working fluid bounded on the top by a flexible expander membrane and on the bottom by a stiff evaporator membrane. A lumped parameter model is developed to simulate the dynamic behavior of the micro heat engine. First, the model is validated against experimental data. Then, the model is used to investigate the effect of the duration of the heat addition process, the mass of the expander membrane and the thermal storage or thermal inertia associated with the engine cavity on the dynamic behavior of the micro engine. The results show the optimal duration for the heat addition process to be less than 10% of the engine cycle period. Increasing the mass of the flexible expander membrane is shown to reduce the resonant frequency of the engine to 130 Hz. Operating the engine at resonance leads to increased power output. The thermal storage or thermal inertia associated with the engine cavity is shown to have a strong effect on engine performance.
Dynamic Modeling, Model-Based Control, and Optimization of Solid Oxide Fuel Cells
NASA Astrophysics Data System (ADS)
Spivey, Benjamin James
2011-07-01
Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs.
Bouncing between model and data: stability, passivity, and optimality in hybrid dynamics.
Ronsse, Renaud; Sternad, Dagmar
2010-11-01
Rhythmically bouncing a ball with a racket is a seemingly simple task, but it poses all the challenges critical for coordinative behavior: perceiving the ball's trajectory to adapt position and velocity of the racket for the next ball contact. To gain insight into the underlying control strategies, the authors conducted a series of studies that tested models with experimental data, with an emphasis on deriving model-based hypotheses and trying to falsify them. Starting with a simple dynamical model of the racket and ball interactions, stability analyses showed that open-loop dynamics affords dynamical stability, such that small perturbations do not require corrections. To obtain this passive stability, the ball has to be impacted with negative acceleration--a strategy that subjects adopted in a variety of conditions at steady state. However, experimental tests that applied perturbations revealed that after perturbations, subjects applied active perceptually guided corrections to reestablish steady state faster than by relying on the passive model's relaxation alone. Hence, the authors derived a model with active control based on optimality principles that considered each impact as a separate reaching-like movement. This model captured some additional features of the racket trajectory but failed to predict more fine-grained aspects of performance. The authors proceed to present a new model that accounts not only for fine-grained behavior but also reconciles passive and active control approaches with new predictions that will be put to test in the next set of experiments. PMID:21184357
Bouncing between Model and Data: Stability, Passivity, and Optimality in Hybrid Dynamics
Ronsse, Renaud; Sternad, Dagmar
2012-01-01
Rhythmically bouncing a ball with a racket is a seemingly simple task, but it poses all the challenges critical for coordinative behavior: perceiving the ball’s trajectory to adapt position and velocity of the racket for the next ball contact. To gain insight into the underlying control strategies, the authors conducted a series of studies that tested models with experimental data, with an emphasis on deriving model-based hypotheses and trying to falsify them. Starting with a simple dynamical model of the racket and ball interactions, stability analyses showed that open-loop dynamics affords dynamical stability, such that small perturbations do not require corrections. To obtain this passive stability, the ball has to be impacted with negative acceleration—a strategy that subjects adopted in a variety of conditions at steady state. However, experimental tests that applied perturbations revealed that after perturbations, subjects applied active perceptually guided corrections to reestablish steady state faster than by relying on the passive model’s relaxation alone. Hence, the authors derived a model with active control based on optimality principles that considered each impact as a separate reaching-like movement. This model captured some additional features of the racket trajectory but failed to predict more fine-grained aspects of performance. The authors proceed to present a new model that accounts not only for fine-grained behavior but also reconciles passive and active control approaches with new predictions that will be put to test in the next set of experiments. PMID:21184357
Griffin, Joshua D. (Sandai National Labs, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L.; Watson, Jean-Paul; Kolda, Tamara Gibson; Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J.; Hough, Patricia Diane; Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Guinta, Anthony A.; Brown, Shannon L.
2006-10-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.
Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane; Gay, David M.; Eddy, John P.; Haskell, Karen H.
2010-05-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.
Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes
Schulte, Phillip J; Laber, Eric B; Davidian, Marie
2012-01-01
In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a key decision point and dictates the next treatment action among the options available as a function of accrued information on the patient. Using data from a clinical trial or observational study, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q-learning and advantage (A-)learning are two main approaches for this purpose. We provide a detailed account of Q- and A-learning and study systematically the performance of these methods. The methods are illustrated using data from a study of depression.
An 'optimal' spawning algorithm for adaptive basis set expansion in nonadiabatic dynamics
Yang, Sandy; Coe, Joshua D.; Kaduk, Benjamin; Martinez, Todd J.
2009-04-07
The full multiple spawning (FMS) method has been developed to simulate quantum dynamics in the multistate electronic problem. In FMS, the nuclear wave function is represented in a basis of coupled, frozen Gaussians, and a 'spawning' procedure prescribes a means of adaptively increasing the size of this basis in order to capture population transfer between electronic states. Herein we detail a new algorithm for specifying the initial conditions of newly spawned basis functions that minimizes the number of spawned basis functions needed for convergence. 'Optimally' spawned basis functions are placed to maximize the coupling between parent and child trajectories at the point of spawning. The method is tested with a two-state, one-mode avoided crossing model and a two-state, two-mode conical intersection model.
Optimizing an HIV testing program using a system dynamics model of the continuum of care.
Kok, Sarah; Rutherford, Alexander R; Gustafson, Reka; Barrios, Rolando; Montaner, Julio S G; Vasarhelyi, Krisztina
2015-09-01
Realizing the full individual and population-wide benefits of antiretroviral therapy for human immunodeficiency virus (HIV) infection requires an efficient mechanism of HIV-related health service delivery. We developed a system dynamics model of the continuum of HIV care in Vancouver, Canada, which reflects key activities and decisions in the delivery of antiretroviral therapy, including HIV testing, linkage to care, and long-term retention in care and treatment. To measure the influence of operational interventions on population health outcomes, we incorporated an HIV transmission component into the model. We determined optimal resource allocations among targeted and routine testing programs to minimize new HIV infections over five years in Vancouver. Simulation scenarios assumed various constraints informed by the local health policy. The project was conducted in close collaboration with the local health care providers, Vancouver Coastal Health Authority and Providence Health Care. PMID:25595433
NASA Astrophysics Data System (ADS)
Zhao, Hong-Liang; Lv, Chao; Liu, Yan; Zhang, Ting-An
2015-07-01
The complex fluid flow in a large-scale tank stirred with multiple Ekato Intermig impellers used in the seed precipitation process was numerically analyzed by the computational fluid dynamics method. The flow field, liquid-solid mixing, and power consumption were simulated by adopting the Eulerian granular multiphase model and standard k- ? turbulence model. A steady multiple reference frame approach was used to represent impeller rotation. The simulated results showed that the five-stage multiple Intermig impeller coupled with sloped baffles could generate circulation loops in axial, which is good for solid uniform mixing. The fluid is overmixed under the current industrial condition. Compared with the current process conditions, a three-stage impeller with L/ D of 1.25 not only could meet the industrial requirements, but also more than 20% power could be saved. The results have important implications for reliable design and optimal performance for industry.
Yan, Xiaoxu; Wu, Qing; Sun, Jianyu; Liang, Peng; Zhang, Xiaoyuan; Xiao, Kang; Huang, Xia
2016-01-01
Geometry property would affect the hydrodynamics of membrane bioreactor (MBR), which was directly related to membrane fouling rate. The simulation of a bench-scale MBR by computational fluid dynamics (CFD) showed that the shear stress on membrane surface could be elevated by 74% if the membrane was sandwiched between two baffles (baffled MBR), compared with that without baffles (unbaffled MBR). The effects of horizontal geometry characteristics of a bench-scale membrane tank were discussed (riser length index Lr, downcomer length index Ld, tank width index Wt). Simulation results indicated that the average cross flow of the riser was negatively correlated to the ratio of riser and downcomer cross-sectional area. A relatively small tank width would also be preferable in promoting shear stress on membrane surface. The optimized MBR had a shear elevation of 21.3-91.4% compared with unbaffled MBR under same aeration intensity. PMID:26512855
Rapid maneuvering of multi-body dynamic systems with optimal motion compensation
NASA Astrophysics Data System (ADS)
Bishop, B.; Gargano, R.; Sears, A.; Karpenko, M.
2015-12-01
Rapid maneuvering of multi-body dynamical systems is an important, yet challenging, problem in many applications. Even in the case of rigid bodies, it can be difficult to maintain precise control over nominally stationary links if it is required to move some of the other links quickly because of the various nonlinearities and coupled interactions that occur between the bodies. Typical control concepts treat the multi-body motion control problem in two-stages. First, the nonlinear and coupling terms are treated as disturbances and a trajectory tracking control law is designed in order to attenuate their effects. Next, motion profiles are designed, based on kinematics parameterizations, and these are used as inputs to the closed loop system to move the links. This paper describes an approach for rapid maneuvering of multi-body systems that uses optimal control theory to account for dynamic nonlinearities and coupling as part of the motion trajectory design. Incorporating appropriate operational constraints automatically compensates for these multi-body effects so that motion time can be reduced while simultaneously achieving other objectives such as reducing the excitation of selected links. Since the compensatory effect is embedded within the optimal motion trajectories, the performance improvement can be obtained even when using simple closed-loop architectures for maneuver implementation. Simulation results for minimum time control of a two-axis gimbal system and for rapid maneuvering of a TDRS single-access antenna, wherein it is desired to limit the excitation of the satellite body to which the antenna is attached, are presented to illustrate the concepts.
NASA Technical Reports Server (NTRS)
Brown, Jonathan M.; Petersen, Jeremy D.
2014-01-01
NASA's WIND mission has been operating in a large amplitude Lissajous orbit in the vicinity of the interior libration point of the Sun-Earth/Moon system since 2004. Regular stationkeeping maneuvers are required to maintain the orbit due to the instability around the collinear libration points. Historically these stationkeeping maneuvers have been performed by applying an incremental change in velocity, or (delta)v along the spacecraft-Sun vector as projected into the ecliptic plane. Previous studies have shown that the magnitude of libration point stationkeeping maneuvers can be minimized by applying the (delta)v in the direction of the local stable manifold found using dynamical systems theory. This paper presents the analysis of this new maneuver strategy which shows that the magnitude of stationkeeping maneuvers can be decreased by 5 to 25 percent, depending on the location in the orbit where the maneuver is performed. The implementation of the optimized maneuver method into operations is discussed and results are presented for the first two optimized stationkeeping maneuvers executed by WIND.
Optimizing a Dynamical Decoupling Protocol for Solid-State Electronic Spin Ensembles in Diamond
Demitry Farfurnik; Andrey Jarmola; Linh M. Pham; Zhi-Hui Wang; Viatcheslav V. Dobrovitski; Ronald L. Walsworth; Dmitry Budker; Nir Bar-Gill
2015-07-14
We demonstrate significant improvements of the spin coherence time of a dense ensemble of nitrogen-vacancy (NV) centers in diamond through optimized dynamical decoupling (DD). Cooling the sample down to $77$ K suppresses longitudinal spin relaxation $T_1$ effects and DD microwave pulses are used to increase the transverse coherence time $T_2$ from $\\sim 0.7$ ms up to $\\sim 30$ ms. We extend previous work of single-axis (CPMG) DD towards the preservation of arbitrary spin states. Following a theoretical and experimental characterization of pulse and detuning errors, we compare the performance of various DD protocols. We identify that the optimal control scheme for preserving an arbitrary spin state is a recursive protocol, the concatenated version of the XY8 pulse sequence. The improved spin coherence might have an immediate impact on improvements of the sensitivities of AC magnetometry. Moreover, the protocol can be used on denser diamond samples to increase coherence times up to NV-NV interaction time scales, a major step towards the creation of quantum collective NV spin states.
Optimizing a dynamical decoupling protocol for solid-state electronic spin ensembles in diamond
Farfurnik, D.; Jarmola, A.; Pham, L. M.; Wang, Z. H.; Dobrovitski, V. V.; Walsworth, R. L.; Budker, D.; Bar-Gill, N.
2015-08-24
In this study, we demonstrate significant improvements of the spin coherence time of a dense ensemble of nitrogen-vacancy (NV) centers in diamond through optimized dynamical decoupling (DD). Cooling the sample down to 77 K suppresses longitudinal spin relaxation T1 effects and DD microwave pulses are used to increase the transverse coherence time T2 from ~0.7ms up to ~30ms. Furthermore, we extend previous work of single-axis (Carr-Purcell-Meiboom-Gill) DD towards the preservation of arbitrary spin states. Following a theoretical and experimental characterization of pulse and detuning errors, we compare the performance of various DD protocols. We also identify that the optimal controlmore »scheme for preserving an arbitrary spin state is a recursive protocol, the concatenated version of the XY8 pulse sequence. The improved spin coherence might have an immediate impact on improvements of the sensitivities of ac magnetometry. Moreover, the protocol can be used on denser diamond samples to increase coherence times up to NV-NV interaction time scales, a major step towards the creation of quantum collective NV spin states.« less
Optimizing a dynamical decoupling protocol for solid-state electronic spin ensembles in diamond
Farfurnik, D.; Jarmola, A.; Pham, L. M.; Wang, Z. H.; Dobrovitski, V. V.; Walsworth, R. L.; Budker, D.; Bar-Gill, N.
2015-08-24
In this study, we demonstrate significant improvements of the spin coherence time of a dense ensemble of nitrogen-vacancy (NV) centers in diamond through optimized dynamical decoupling (DD). Cooling the sample down to 77 K suppresses longitudinal spin relaxation T_{1} effects and DD microwave pulses are used to increase the transverse coherence time T_{2} from ~0.7ms up to ~30ms. Furthermore, we extend previous work of single-axis (Carr-Purcell-Meiboom-Gill) DD towards the preservation of arbitrary spin states. Following a theoretical and experimental characterization of pulse and detuning errors, we compare the performance of various DD protocols. We also identify that the optimal control scheme for preserving an arbitrary spin state is a recursive protocol, the concatenated version of the XY8 pulse sequence. The improved spin coherence might have an immediate impact on improvements of the sensitivities of ac magnetometry. Moreover, the protocol can be used on denser diamond samples to increase coherence times up to NV-NV interaction time scales, a major step towards the creation of quantum collective NV spin states.
Lang, Katharina M H; Kittelmann, Jörg; Pilgram, Florian; Osberghaus, Anna; Hubbuch, Jürgen
2015-09-25
The performance of functionalized materials, e.g., ion exchange resins, depends on multiple resin characteristics, such as type of ligand, ligand density, the pore accessibility for a molecule, and backbone characteristics. Therefore, the screening and identification process for optimal resin characteristics for separation is very time and material consuming. Previous studies on the influence of resin characteristics have focused on an experimental approach and to a lesser extent on the mechanistic understanding of the adsorption mechanism. In this in silico study, a previously developed molecular dynamics (MD) tool is used, which simulates any given biomolecule on resins with varying ligand densities. We describe a set of simulations and experiments with four proteins and six resins varying in ligand density, and show that simulations and experiments correlate well in a wide range of ligand density. With this new approach simulations can be used as pre-experimental screening for optimal adsorber characteristics, reducing the actual number of screening experiments, which results in a faster and more knowledge-based development of custom-tailored adsorbers. PMID:26319376
Optimization strategies for molecular dynamics programs on Cray computers and scalar work stations
NASA Astrophysics Data System (ADS)
Unekis, Michael J.; Rice, Betsy M.
1994-12-01
We present results of timing runs and different optimization strategies for a prototype molecular dynamics program that simulates shock waves in a two-dimensional (2-D) model of a reactive energetic solid. The performance of the program may be improved substantially by simple changes to the Fortran or by employing various vendor-supplied compiler optimizations. The optimum strategy varies among the machines used and will vary depending upon the details of the program. The effect of various compiler options and vendor-supplied subroutine calls is demonstrated. Comparison is made between two scalar workstations (IBM RS/6000 Model 370 and Model 530) and several Cray supercomputers (X-MP/48, Y-MP8/128, and C-90/16256). We find that for a scientific application program dominated by sequential, scalar statements, a relatively inexpensive high-end work station such as the IBM RS/60006 RISC series will outperform single processor performance of the Cray X-MP/48 and perform competitively with single processor performance of the Y-MP8/128 and C-9O/16256.
NASA Astrophysics Data System (ADS)
Kodali, Anuradha
In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a facility, respectively. The set-covering matrix encapsulates the relationship among the rows (tests or demand points) and columns (faults or locations) of the system at each time. By relaxing the coupling constraints using Lagrange multipliers, the DSC problem can be decoupled into independent subproblems, one for each column. Each subproblem is solved using the Viterbi decoding algorithm, and a primal feasible solution is constructed by modifying the Viterbi solutions via a heuristic. The proposed Viterbi-Lagrangian relaxation algorithm (VLRA) provides a measure of suboptimality via an approximate duality gap. As a major practical extension of the above problem, we also consider the problem of diagnosing faults with delayed test outcomes, termed delay-dynamic set-covering (DDSC), and experiment with real-world problems that exhibit masking faults. Also, we present simulation results on OR-library datasets (set-covering formulations are predominantly validated on these matrices in the literature), posed as facility location problems. Finally, we implement these algorithms to solve problems in aerospace and automotive applications. Firstly, we address the diagnostic ambiguity problem in aerospace and automotive applications by developing a dynamic fusion framework that includes dynamic multiple fault diagnosis algorithms. This improves the correct fault isolation rate, while minimizing the false alarm rates, by considering multiple faults instead of the traditional data-driven techniques based on single fault (class)-single epoch (static) assumption. The dynamic fusion problem is formulated as a maximum a posteriori decision problem of inferring the fault sequence based on uncertain outcomes of multiple binary classifiers over time. The fusion process involves three steps: the first step transforms the multi-class problem into dichotomies using error correcting output codes (ECOC), thereby solving the concomitant binary classification problems; the second step fuses the outcomes of multiple binary classifiers over time using a sliding window or block dynamic fusi
A dynamic programming model for optimal planning of aquifer storage and recovery facility operations
NASA Astrophysics Data System (ADS)
Uddameri, V.
2007-01-01
Aquifer storage recovery (ASR) is an innovative technology with the potential to augment dwindling water resources in regions experiencing rapid growth and development. Planning and design of ASR systems requires quantifying how much water should be stored and appropriate times for storage and withdrawals within a planning period. A monthly scale planning model has been developed in this study to derive optimal (least cost) long-term policies for operating ASR systems and is solved using a recursive deterministic dynamic programming approach. The outputs of the model include annual costs of operation, the amount of water to be imported each month as well as the schedule for storage and extraction. A case study modeled after a proposed ASR system for Mustang Island and Padre Island service areas of the city of Corpus Christi is used to illustrate the utility of the developed model. The results indicate that for the assumed baseline demands, the ASR system is to be kept operational for a period of 4 months starting from May through August. Model sensitivity analysis indicated that increased seasonal shortages can be met using ASR with little additional costs. For the assumed cost structure, a 16% shortage increased the costs by 1.6%. However, the operation time of ASR increased from 4 to 8 months. The developed dynamic programming model is a useful tool to assess the feasibility of evaluating the use of ASR systems during regional-scale water resources planning endeavors.
Study of the Bus Dynamic Coscheduling Optimization Method under Urban Rail Transit Line Emergency
Yan, Xuedong; Wang, Jiaxi; Chen, Shasha
2014-01-01
As one of the most important urban commuter transportation modes, urban rail transit (URT) has been acting as a key solution for supporting mobility needs in high-density urban areas. However, in recent years, high frequency of unexpected events has caused serious service disruptions in URT system, greatly harming passenger safety and resulting in severe traffic delays. Therefore, there is an urgent need to study emergency evacuation problem in URT. In this paper, a method of bus dynamic coscheduling is proposed and two models are built based on different evacuation destinations including URT stations and surrounding bus parking spots. A dynamic coscheduling scheme for buses can be obtained by the models. In the model solution process, a new concept—the equivalent parking spot—is proposed to transform the nonlinear model into an integer linear programming (ILP) problem. A case study is conducted to verify the feasibility of models. Also, sensitivity analysis of two vital factors is carried out to analyze their effects on the total evacuation time. The results reveal that the designed capacity of buses has a negative influence on the total evacuation time, while an increase in the number of passengers has a positive effect. Finally, some significant optimizing strategies are proposed. PMID:25530750
Optimal GPS/accelerometer integration algorithm for monitoring the vertical structural dynamics
NASA Astrophysics Data System (ADS)
Meng, Xiaolin; Wang, Jian; Han, Houzeng
2014-11-01
The vertical structural dynamics is a crucial factor for structural health monitoring (SHM) of civil structures such as high-rise buildings, suspension bridges and towers. This paper presents an optimal GPS/accelerometer integration algorithm for an automated multi-sensor monitoring system. The closed loop feedback algorithm for integrating the vertical GPS and accelerometer measurements is proposed based on a 5 state extended KALMAN filter (EKF) and then the narrow moving window Fast Fourier Transform (FFT) analysis is applied to extract structural dynamics. A civil structural vibration is simulated and the analysed result shows the proposed algorithm can effectively integrate the online vertical measurements produced by GPS and accelerometer. Furthermore, the accelerometer bias and scale factor can also be estimated which is impossible with traditional integration algorithms. Further analysis shows the vibration frequencies detected in GPS or accelerometer are all included in the integrated vertical defection time series and the accelerometer can effectively compensate the short-term GPS outages with high quality. Finally, the data set collected with a time synchronised and integrated GPS/accelerometer monitoring system installed on the Nottingham Wilford Bridge when excited by 15 people jumping together at its mid-span are utilised to verify the effectiveness of this proposed algorithm. Its implementations are satisfactory and the detected vibration frequencies are 1.720 Hz, 1.870 Hz, 2.104 Hz, 2.905 Hz and also 10.050 Hz, which is not found in GPS or accelerometer only measurements.
High dynamic range image compression by optimizing tone mapped image quality index.
Ma, Kede; Yeganeh, Hojatollah; Zeng, Kai; Wang, Zhou
2015-10-01
Tone mapping operators (TMOs) aim to compress high dynamic range (HDR) images to low dynamic range (LDR) ones so as to visualize HDR images on standard displays. Most existing TMOs were demonstrated on specific examples without being thoroughly evaluated using well-designed and subject-validated image quality assessment models. A recently proposed tone mapped image quality index (TMQI) made one of the first attempts on objective quality assessment of tone mapped images. Here, we propose a substantially different approach to design TMO. Instead of using any predefined systematic computational structure for tone mapping (such as analytic image transformations and/or explicit contrast/edge enhancement), we directly navigate in the space of all images, searching for the image that optimizes an improved TMQI. In particular, we first improve the two building blocks in TMQI—structural fidelity and statistical naturalness components—leading to a TMQI-II metric. We then propose an iterative algorithm that alternatively improves the structural fidelity and statistical naturalness of the resulting image. Numerical and subjective experiments demonstrate that the proposed algorithm consistently produces better quality tone mapped images even when the initial images of the iteration are created by the most competitive TMOs. Meanwhile, these results also validate the superiority of TMQI-II over TMQI. PMID:26011881
Stentz, Tony
recommended path to the goal. Mission planning is the process of determining what each robot should doAbstract Planning for multiple mobile robots in dynamic environ- ments involves determining the optimal path each robot should follow to accomplish the goals of the mission, given the current knowledge
Stefanopoulou, Anna
Dynamics, Optimization and Control of a Fuel Cell Based Combined Heat Power (CHP) System burner (CB) are integrated in a combined heat power (CHP) generation plant. The FC provides the power to the baseline when no feedback control is employed. I. INTRODUCTION AND MOTIVATION Combined heat power (CHP
Zhang, Shuo; Zhang, Chengning; Han, Guangwei; Wang, Qinghui
2014-01-01
A dual-motor coupling-propulsion electric bus (DMCPEB) is modeled, and its optimal control strategy is studied in this paper. The necessary dynamic features of energy loss for subsystems is modeled. Dynamic programming (DP) technique is applied to find the optimal control strategy including upshift threshold, downshift threshold, and power split ratio between the main motor and auxiliary motor. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement in reducing energy loss due to the dual-motor coupling-propulsion system (DMCPS) running is realized without increasing the frequency of the mode switch. PMID:25540814
TAS: 89 0227: TAS Recovery Act - Optimization and Control of Electric Power Systems: ARRA
Chiang, Hsiao-Dong
2014-02-01
The name SuperOPF is used to refer several projects, problem formulations and soft-ware tools intended to extend, improve and re-define some of the standard methods of optimizing electric power systems. Our work included applying primal-dual interior point methods to standard AC optimal power flow problems of large size, as well as extensions of this problem to include co-optimization of multiple scenarios. The original SuperOPF problem formulation was based on co-optimizing a base scenario along with multiple post-contingency scenarios, where all AC power flow models and constraints are enforced for each, to find optimal energy contracts, endogenously determined locational reserves and appropriate nodal energy prices for a single period optimal power flow problem with uncertainty. This led to example non-linear programming problems on the order of 1 million constraints and half a million variables. The second generation SuperOPF formulation extends this by adding multiple periods and multiple base scenarios per period. It also incorporates additional variables and constraints to model load following reserves, ramping costs, and storage resources. A third generation of the multi-period SuperOPF, adds both integer variables and a receding horizon framework in which the problem type is more challenging (mixed integer), the size is even larger, and it must be solved more frequently, pushing the limits of currently available algorithms and solvers. The consideration of transient stability constraints in optimal power flow (OPF) problems has become increasingly important in modern power systems. Transient stability constrained OPF (TSCOPF) is a nonlinear optimization problem subject to a set of algebraic and differential equations. Solving a TSCOPF problem can be challenging due to (i) the differential-equation constraints in an optimization problem, (ii) the lack of a true analytical expression for transient stability in OPF. To handle the dynamics in TSCOPF, the set of differential equations can be approximated or converted into equivalent algebraic equations before they are included in an OPF formulation. In Chapter 4, a rigorous evaluation of using a predefined and fixed threshold for rotor angles as a mean to determine transient stability of the system is developed. TSCOPF can be modeled as a large-scale nonlinear programming problem including the constraints of differential-algebraic equations (DAE). Solving a TSCOPF problem can be challenging due to (i) the differential-equation constraints in an optimization problem, (ii) the lack of a true analytical expression for transient stability constraint in OPF. Unfortunately, even the current best TSCOPF solvers still suffer from the curse of dimensionality and unacceptable computational time, especially for large-scale power systems with multiple contingencies. In chapter 5, thse issues will be addressed and a new method to incorporate the transient stability constraints will be presented.
Sabar, Nasser R; Ayob, Masri; Kendall, Graham; Qu, Rong
2015-02-01
Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite. PMID:24951713
Analysis of green algal growth via dynamic model simulation and process optimization.
Zhang, Dongda; Chanona, Ehecatl Antonio Del-Rio; Vassiliadis, Vassilios S; Tamburic, Bojan
2015-10-01
Chlamydomonas reinhardtii is a green microalga with the potential to generate sustainable biofuels for the future. Process simulation models are required to predict the impact of laboratory-scale growth experiments on future scaled-up system operation. Two dynamic models were constructed to simulate C. reinhardtii photo-autotrophic and photo-mixotrophic growth. A novel parameter estimation methodology was applied to determine the values of key parameters in both models, which were then verified using experimental results. The photo-mixotrophic model was used to accurately predict C. reinhardtii growth under different light intensities and in different photobioreactor configurations. The optimal dissolved CO2 concentration for C. reinhardtii photo-autotrophic growth was determined to be 0.0643 g·L(-1) , and the optimal light intensity for algal growth was 47 W·m(-2) . Sensitivity analysis revealed that the primary factor limiting C. reinhardtii growth was its intrinsic cell decay rate rather than light attenuation, regardless of the growth mode. The photo-mixotrophic growth model was also applied to predict the maximum biomass concentration at different flat-plate photobioreactors scales. A double-exposure-surface photobioreactor with a lower light intensity (less than 50 W·m(-2) ) was the best configuration for scaled-up C. reinhardtii cultivation. Three different short-term (30-day) C. reinhardtii photo-mixotrophic cultivation processes were simulated and optimised. The maximum biomass productivity was 0.053 g·L(-1) ·hr(-1) , achieved under continuous photobioreactor operation. The continuous stirred-tank reactor was the best operating mode, as it provides both the highest biomass productivity and lowest electricity cost of pump operation. PMID:25855209
Optimized particle-mesh Ewald/multiple-time step integration for molecular dynamics simulations
NASA Astrophysics Data System (ADS)
Batcho, Paul F.; Case, David A.; Schlick, Tamar
2001-09-01
We develop an efficient multiple time step (MTS) force splitting scheme for biological applications in the AMBER program in the context of the particle-mesh Ewald (PME) algorithm. Our method applies a symmetric Trotter factorization of the Liouville operator based on the position-Verlet scheme to Newtonian and Langevin dynamics. Following a brief review of the MTS and PME algorithms, we discuss performance speedup and the force balancing involved to maximize accuracy, maintain long-time stability, and accelerate computational times. Compared to prior MTS efforts in the context of the AMBER program, advances are possible by optimizing PME parameters for MTS applications and by using the position-Verlet, rather than velocity-Verlet, scheme for the inner loop. Moreover, ideas from the Langevin/MTS algorithm LN are applied to Newtonian formulations here. The algorithm's performance is optimized and tested on water, solvated DNA, and solvated protein systems. We find CPU speedup ratios of over 3 for Newtonian formulations when compared to a 1 fs single-step Verlet algorithm using outer time steps of 6 fs in a three-class splitting scheme; accurate conservation of energies is demonstrated over simulations of length several hundred ps. With modest Langevin forces, we obtain stable trajectories for outer time steps up to 12 fs and corresponding speedup ratios approaching 5. We end by suggesting that modified Ewald formulations, using tailored alternatives to the Gaussian screening functions for the Coulombic terms, may allow larger time steps and thus further speedups for both Newtonian and Langevin protocols; such developments are reported separately.
A dynamic optimization model of the diel vertical distribution of a pelagic planktivorous fish
NASA Astrophysics Data System (ADS)
Rosland, Rune; Giske, Jarl
A stochastic dynamic optimization model for the diel depth distribution of juveniles and adults of the mesopelagic planktivore Maurolicus muelleri (Gmelin) is developed and used for a winter situation. Observations from Masfjorden, western Norway, reveal differences in vertical distribution, growth and mortality between juveniles and adults in January. Juveniles stay within the upper 100m with high feeding rates, while adults stay within the 100-150m zone with very low feeding rates during the diel cycle. The difference in depth profitability is assumed to be caused by age-dependent processes, and are calculated from a mechanistic model for visual feeding. The environment is described as a set of habitats represented by discrete depth intervals along the vertical axis, differing with respect to light intensity, food abundance, predation risk and temperature. The short time interval (24h) allows fitness to be linearly related to growth (feeding), assuming that growth increases the future reproductive output of the fish. Optimal depth position is calculated from balancing feeding opportunity against mortality risk, where the fitness reward gained by feeding is weighted against the danger of being killed by a predator. A basic run is established, and the model is validated by comparing predictions and observations. The sensitivity for different parameter values is also tested. The modelled vertical distributions and feeding patterns of juvenile and adult fish correspond well with the observations, and the assumption of age differences in mortality-feeding trade-offs seems adequate to explain the different depth profitability of the two age groups. The results indicate a preference for crepuscular feeding activity of the juveniles, and the vertical distribution of zooplankton seems to be the most important environmental factor regulating the adult depth position during the winter months in Masfjorden.
A Bell-Curved Based Algorithm for Mixed Continuous and Discrete Structural Optimization
NASA Technical Reports Server (NTRS)
Kincaid, Rex K.; Weber, Michael; Sobieszczanski-Sobieski, Jaroslaw
2001-01-01
An evolutionary based strategy utilizing two normal distributions to generate children is developed to solve mixed integer nonlinear programming problems. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu + mu) evolutionary strategies and evolutionary programs but with fewer parameters to adjust and no mechanism for self adaptation. First, a new version of BCB to solve purely discrete optimization problems is described and its performance tested against a tabu search code for an actuator placement problem. Next, the performance of a combined version of discrete and continuous BCB is tested on 2-dimensional shape problems and on a minimum weight hub design problem. In the latter case the discrete portion is the choice of the underlying beam shape (I, triangular, circular, rectangular, or U).
Combined optimization model for sustainable energization strategy
NASA Astrophysics Data System (ADS)
Abtew, Mohammed Seid
Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.
Using Maximal Isometric Force to Determine the Optimal Load for Measuring Dynamic Muscle Power
NASA Technical Reports Server (NTRS)
Spiering, Barry A.; Lee, Stuart M. C.; Mulavara, Ajitkumar P.; Bentley, Jason R.; Nash, Roxanne E.; Sinka, Joseph; Bloomberg, Jacob J.
2009-01-01
Maximal power output occurs when subjects perform ballistic exercises using loads of 30-50% of one-repetition maximum (1-RM). However, performing 1-RM testing prior to power measurement requires considerable time, especially when testing involves multiple exercises. Maximal isometric force (MIF), which requires substantially less time to measure than 1-RM, might be an acceptable alternative for determining the optimal load for power testing. PURPOSE: To determine the optimal load based on MIF for maximizing dynamic power output during leg press and bench press exercises. METHODS: Twenty healthy volunteers (12 men and 8 women; mean +/- SD age: 31+/-6 y; body mass: 72 +/- 15 kg) performed isometric leg press and bench press movements, during which MIF was measured using force plates. Subsequently, subjects performed ballistic leg press and bench press exercises using loads corresponding to 20%, 30%, 40%, 50%, and 60% of MIF presented in randomized order. Maximal instantaneous power was calculated during the ballistic exercise tests using force plates and position transducers. Repeated-measures ANOVA and Fisher LSD post hoc tests were used to determine the load(s) that elicited maximal power output. RESULTS: For the leg press power test, six subjects were unable to be tested at 20% and 30% MIF because these loads were less than the lightest possible load (i.e., the weight of the unloaded leg press sled assembly [31.4 kg]). For the bench press power test, five subjects were unable to be tested at 20% MIF because these loads were less than the weight of the unloaded aluminum bar (i.e., 11.4 kg). Therefore, these loads were excluded from analysis. A trend (p = 0.07) for a main effect of load existed for the leg press exercise, indicating that the 40% MIF load tended to elicit greater power output than the 60% MIF load (effect size = 0.38). A significant (p . 0.05) main effect of load existed for the bench press exercise; post hoc analysis indicated that the effect of load on power output was: 30% > 40% > 50% = 60%. CONCLUSION: Loads of 40% and 30% of MIF elicit maximal power output during dynamic leg presses and bench presses, respectively. These findings are similar to those obtained when loading is based on 1-RM.
DAKOTA Design Analysis Kit for Optimization and Terascale
Energy Science and Technology Software Center (ESTSC)
2010-02-24
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes (computational models) and iterative analysis methods. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and analysis of computational models on high performance computers.A user provides a set of DAKOTA commands in an input file andmore »launches DAKOTA. DAKOTA invokes instances of the computational models, collects their results, and performs systems analyses. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, polynomial chaos, stochastic collocation, and epistemic methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. Services for parallel computing, simulation interfacing, approximation modeling, fault tolerance, restart, and graphics are also included.« less
REopt: A Platform for Energy System Integration and Optimization: Preprint
Simpkins, T.; Cutler, D.; Anderson, K.; Olis, D.; Elgqvist, E.; Callahan, M.; Walker, A.
2014-08-01
REopt is NREL's energy planning platform offering concurrent, multi-technology integration and optimization capabilities to help clients meet their cost savings and energy performance goals. The REopt platform provides techno-economic decision-support analysis throughout the energy planning process, from agency-level screening and macro planning to project development to energy asset operation. REopt employs an integrated approach to optimizing a site?s energy costs by considering electricity and thermal consumption, resource availability, complex tariff structures including time-of-use, demand and sell-back rates, incentives, net-metering, and interconnection limits. Formulated as a mixed integer linear program, REopt recommends an optimally-sized mix of conventional and renewable energy, and energy storage technologies; estimates the net present value associated with implementing those technologies; and provides the cost-optimal dispatch strategy for operating them at maximum economic efficiency. The REopt platform can be customized to address a variety of energy optimization scenarios including policy, microgrid, and operational energy applications. This paper presents the REopt techno-economic model along with two examples of recently completed analysis projects.
Robar, James L.; Thomas, Christopher
2012-01-01
This investigation focuses on possible dosimetric and efficiency advantages of HybridArc-a novel treatment planning approach combining optimized dynamic arcs with intensity-modulated radiation therapy (IMRT) beams. Application of this technique to two disparate sites, complex cranial tumors, and prostate was examined. HybridArc plans were compared with either dynamic conformal arc (DCA) or IMRT plans to determine whether HybridArc offers a synergy through combination of these 2 techniques. Plans were compared with regard to target volume dose conformity, target volume dose homogeneity, sparing of proximal organs at risk, normal tissue sparing, and monitor unit (MU) efficiency. For cranial cases, HybridArc produced significantly improved dose conformity compared with both DCA and IMRT but did not improve sparing of the brainstem or optic chiasm. For prostate cases, conformity was improved compared with DCA but not IMRT. Compared with IMRT, the dose homogeneity in the planning target volume was improved, and the maximum doses received by the bladder and rectum were reduced. Both arc-based techniques distribute peripheral dose over larger volumes of normal tissue compared with IMRT, whereas HybridArc involved slightly greater volumes of normal tissues compared with DCA. Compared with IMRT, cranial cases required 38% more MUs, whereas for prostate cases, MUs were reduced by 7%. For cranial cases, HybridArc improves dose conformity to the target. For prostate cases, dose conformity and homogeneity are improved compared with DCA and IMRT, respectively. Compared with IMRT, whether required MUs increase or decrease with HybridArc was site-dependent.
Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques
NASA Astrophysics Data System (ADS)
Benarab, D.; Napoléon, T.; Alfalou, A.; Verney, A.; Hellard, P.
2015-12-01
To design a robust swimmer tracking system, we took into account two well-known tracking techniques: the nonlinear joint transform correlation (NL-JTC) and the color histogram. The two techniques perform comparably well, yet they both have substantial limitations. Interestingly, they also seem to show some complementarity. The correlation technique yields accurate detection but is sensitive to rotation, scale and contour deformation, whereas the color histogram technique is robust for rotation and contour deformation but shows low accuracy and is highly sensitive to luminosity and confusing background colors. These observations suggested the possibility of a dynamic fusion of the correlation plane and the color scores map. Before this fusion, two steps are required. First is the extraction of a sub-plane of correlation that describes the similarity between the reference and target images. This sub-plane has the same size as the color scores map but they have different interval values. Thus, the second step is required which is the normalization of the planes in the same interval so they can be fused. In order to determine the benefits of this fusion technique, first, we tested it on a synthetic image containing different forms with different colors. We thus were able to optimize the correlation plane and color histogram techniques before applying our fusion technique to real videos of swimmers in international competitions. Last, a comparative study of the dynamic fusion technique and the two classical techniques was carried out to demonstrate the efficacy of the proposed technique. The criteria of comparison were the tracking percentage, the peak to correlation energy (PCE), which evaluated the sharpness of the peak (accuracy), and the local standard deviation (Local-STD), which assessed the noise in the planes (robustness).
A Two-Stage Method to Determine Optimal Product Sampling considering Dynamic Potential Market
Hu, Zhineng; Lu, Wei; Han, Bing
2015-01-01
This paper develops an optimization model for the diffusion effects of free samples under dynamic changes in potential market based on the characteristics of independent product and presents a two-stage method to figure out the sampling level. The impact analysis of the key factors on the sampling level shows that the increase of the external coefficient or internal coefficient has a negative influence on the sampling level. And the changing rate of the potential market has no significant influence on the sampling level whereas the repeat purchase has a positive one. Using logistic analysis and regression analysis, the global sensitivity analysis gives a whole analysis of the interaction of all parameters, which provides a two-stage method to estimate the impact of the relevant parameters in the case of inaccuracy of the parameters and to be able to construct a 95% confidence interval for the predicted sampling level. Finally, the paper provides the operational steps to improve the accuracy of the parameter estimation and an innovational way to estimate the sampling level. PMID:25821847
Design optimization of an axial blood pump with computational fluid dynamics.
Zhang, Yan; Zhan, Zhao; Gui, Xing-Min; Sun, Han-Song; Zhang, Hao; Zheng, Zhe; Zhou, Jian-Ye; Zhu, Xiao-Dong; Li, Guo-Rong; Hu, Sheng-Shou; Jin, Dong-Hai
2008-01-01
A fully implantable, axial flow blood pump has been developed in our hospital. Both in vitro and in vivo tests showed that the hemolysis and thrombus characteristics of the pump were in an acceptable but not in an ideal range. Computational fluid dynamics (CFD) and in vitro test results showed that the pump worked at off-design point with a low hydraulic efficiency; CFD analysis also showed regions of reverse flow in the diffuser, which not only decreases the pump's hydrodynamic efficiency, but also increases its overall potential for blood trauma and thrombosis. To make a blood pump atraumatic and nonthrombogenic, several methods were taken to reach a final model of the optimized blood pump using CFD, which decreased the rotational speed from 9,000 to 8,000 rpm, and the design flow rate from 11 to 6 L/min. More significantly, the flow separation and recirculation in the diffuser region were eliminated, which mitigated the traumatic and thrombus effect on blood. The acceptable results of the numerical simulations encourage additional in vitro and in vivo studies. PMID:18356647
NASA Astrophysics Data System (ADS)
Bousige, Colin; BoÅ£an, Alexandru; Ulm, Franz-Josef; Pellenq, Roland J.-M.; Coasne, Benoît
2015-03-01
We report an efficient atom-scale reconstruction method that consists of combining the Hybrid Reverse Monte Carlo algorithm (HRMC) with Molecular Dynamics (MD) in the framework of a simulated annealing technique. In the spirit of the experimentally constrained molecular relaxation technique [Biswas et al., Phys. Rev. B 69, 195207 (2004)], this modified procedure offers a refined strategy in the field of reconstruction techniques, with special interest for heterogeneous and disordered solids such as amorphous porous materials. While the HRMC method generates physical structures, thanks to the use of energy penalties, the combination with MD makes the method at least one order of magnitude faster than HRMC simulations to obtain structures of similar quality. Furthermore, in order to ensure the transferability of this technique, we provide rational arguments to select the various input parameters such as the relative weight ? of the energy penalty with respect to the structure optimization. By applying the method to disordered porous carbons, we show that adsorption properties provide data to test the global texture of the reconstructed sample but are only weakly sensitive to the presence of defects. In contrast, the vibrational properties such as the phonon density of states are found to be very sensitive to the local structure of the sample.
Bousige, Colin; Bo?an, Alexandru; Coasne, Benoît; Ulm, Franz-Josef; Pellenq, Roland J.-M.
2015-03-21
We report an efficient atom-scale reconstruction method that consists of combining the Hybrid Reverse Monte Carlo algorithm (HRMC) with Molecular Dynamics (MD) in the framework of a simulated annealing technique. In the spirit of the experimentally constrained molecular relaxation technique [Biswas et al., Phys. Rev. B 69, 195207 (2004)], this modified procedure offers a refined strategy in the field of reconstruction techniques, with special interest for heterogeneous and disordered solids such as amorphous porous materials. While the HRMC method generates physical structures, thanks to the use of energy penalties, the combination with MD makes the method at least one order of magnitude faster than HRMC simulations to obtain structures of similar quality. Furthermore, in order to ensure the transferability of this technique, we provide rational arguments to select the various input parameters such as the relative weight ? of the energy penalty with respect to the structure optimization. By applying the method to disordered porous carbons, we show that adsorption properties provide data to test the global texture of the reconstructed sample but are only weakly sensitive to the presence of defects. In contrast, the vibrational properties such as the phonon density of states are found to be very sensitive to the local structure of the sample.
Merging spatially variant physical process models under an optimized systems dynamics framework.
Cain, William O.; Lowry, Thomas Stephen; Pierce, Suzanne A.; Tidwell, Vincent Carroll
2007-10-01
The complexity of water resource issues, its interconnectedness to other systems, and the involvement of competing stakeholders often overwhelm decision-makers and inhibit the creation of clear management strategies. While a range of modeling tools and procedures exist to address these problems, they tend to be case specific and generally emphasize either a quantitative and overly analytic approach or present a qualitative dialogue-based approach lacking the ability to fully explore consequences of different policy decisions. The integration of these two approaches is needed to drive toward final decisions and engender effective outcomes. Given these limitations, the Computer Assisted Dispute Resolution system (CADRe) was developed to aid in stakeholder inclusive resource planning. This modeling and negotiation system uniquely addresses resource concerns by developing a spatially varying system dynamics model as well as innovative global optimization search techniques to maximize outcomes from participatory dialogues. Ultimately, the core system architecture of CADRe also serves as the cornerstone upon which key scientific innovation and challenges can be addressed.
Optimal Intrinsic Dynamics for Bursting in a Three-Cell Network
NASA Astrophysics Data System (ADS)
Dunmyre, Justin R.; Rubin, Jonathan E.
2010-01-01
Previous numerical and analytical work has shown that synaptic coupling can allow a network of model neurons to synchronize despite heterogeneity in intrinsic parameter values. In particular, synchronous bursting oscillations can arise in a network with excitatory synaptic coupling, even in the absence of intrinsically bursting neurons. In this work, we explore how the intrinsic dynamics of neurons within a reduced three-cell network influence its ability to exhibit synchronous bursting and the frequency range over which such activity can occur. We establish necessary and sufficient conditions for the existence of synchronous bursting solutions and perform related numerical experiments in three-cell networks that include a quiescent cell, a tonically active cell, and a third added cell. Our results show that, in most cases, the addition of a quiescent cell is optimal for synchronous network bursting, in a variety of ways, and that intrinsically bursting cells can be detrimental to synchronous bursting, and we explain the mechanisms underlying these effects. These findings may help explain how robust synchronous oscillations arise in neuronal central pattern generators, such as the mammalian inspiratory network, despite the presence of significant cellular heterogeneity. They also support the idea that intrinsic burst capabilities of individual cells need not be central to these networks' rhythms.
Paramfit: automated optimization of force field parameters for molecular dynamics simulations.
Betz, Robin M; Walker, Ross C
2015-01-15
The generation of bond, angle, and torsion parameters for classical molecular dynamics force fields typically requires fitting parameters such that classical properties such as energies and gradients match precalculated quantum data for structures that scan the value of interest. We present a program, Paramfit, distributed as part of the AmberTools software package that automates and extends this fitting process, allowing for simplified parameter generation for applications ranging from single molecules to entire force fields. Paramfit implements a novel combination of a genetic and simplex algorithm to find the optimal set of parameters that replicate either quantum energy or force data. The program allows for the derivation of multiple parameters simultaneously using significantly fewer quantum calculations than previous methods, and can also fit parameters across multiple molecules with applications to force field development. Paramfit has been applied successfully to systems with a sparse number of structures, and has already proven crucial in the development of the Assisted Model Building with Energy Refinement Lipid14 force field. PMID:25413259
Optimization of a Continuous Hybrid Impeller Mixer via Computational Fluid Dynamics
Othman, N.; Kamarudin, S. K.; Takriff, M. S.; Rosli, M. I.; Engku Chik, E. M. F.; Meor Adnan, M. A. K.
2014-01-01
This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-? turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination. PMID:25170524
Using Plate Finite Elements for Modeling Fillets in Design, Optimization, and Dynamic Analysis
NASA Technical Reports Server (NTRS)
Brown, A. M.; Seugling, R. M.
2003-01-01
A methodology has been developed that allows the use of plate elements instead of numerically inefficient solid elements for modeling structures with 90 degree fillets. The technique uses plate bridges with pseudo Young's modulus (Eb) and thickness (tb) values placed between the tangent points of the fillets. These parameters are obtained by solving two nonlinear simultaneous equations in terms of the independent variables rlt and twallt. These equations are generated by equating the rotation at the tangent point of a bridge system with that of a fillet, where both rotations are derived using beam theory. Accurate surface fits of the solutions are also presented to provide the user with closed-form equations for the parameters. The methodology was verified on the subcomponent level and with a representative filleted structure, where the technique yielded a plate model exhibiting a level of accuracy better than or equal to a high-fidelity solid model and with a 90-percent reduction in the number of DOFs. The application of this method for parametric design studies, optimization, and dynamic analysis should prove extremely beneficial for the finite element practitioner. Although the method does not attempt to produce accurate stresses in the filleted region, it can also be used to obtain stresses elsewhere in the structure for preliminary analysis. A future avenue of study is to extend the theory developed here to other fillet geometries, including fillet angles other than 90 and multifaceted intersections.
Materials optimization and ghz spin dynamics of metallic ferromagnetic thin film heterostructures
NASA Astrophysics Data System (ADS)
Cheng, Cheng
Metallic ferromagnetic (FM) thin film heterostructures play an important role in emerging magnetoelectronic devices, which introduce the spin degree of freedom of electrons into conventional charge-based electronic devices. As the majority of magnetoelectronic devices operate in the GHz frequency range, it is critical to understand the high-frequency magnetization dynamics in these structures. In this thesis, we start with the static magnetic properties of FM thin films and their optimization via the field-sputtering process incorporating a specially designed in-situ electromagnet. We focus on the origins of anisotropy and hysteresis/coercivity in soft magnetic thin films, which are most relevant to magentic susceptibility and power dissipation in applications in the sub-GHz frequency regime, such as magnetic-core integrated inductors. Next we explore GHz magnetization dynamics in thin-film heterostructures, both in semi-infinite samples and confined geometries. All investigations are rooted in the Landau-Lifshitz-Gilbert (LLG) equation, the equation of motion for magnetization. The phenomenological Gilbert damping parameter in the LLG equation has been interpreted, since the 1970's, in terms of the electrical resistivity. We present the first interpretation of the size effect in Gilbert damping in single metallic FM films based on this electron theory of damping. The LLG equation is intrinsically nonlinear, which provides possibilities for rf signal processing. We analyze the frequency doubling effect at small-angle magnetization precession from the first-order expansion of the LLG equation, and demonstrate second harmonic generation from Ni81 Fe19 (Permalloy) thin film under ferromagnetic resonance (FMR), three orders of magnitude more efficient than in ferrites traditionally used in rf devices. Though the efficiency is less than in semiconductor devices, we provide field- and frequency-selectivity in the second harmonic generation. To address further the relationship between the rf excitation and the magnetization dynamics in systems with higher complexity, such as multilayered thin films consisting of nonmagnetic (NM) and FM layers, we employ the powerful time-resolved x-ray magnetic circular dichroism (TR-XMCD) spectroscopy. Soft x-rays have element-specific absorption, leading to layer-specific magnetization detection provided the FM layers have distinctive compositions. We discovered that in contrast to what has been routinely assumed, for layer thicknesses well below the skin depth of the EM wave, a significant phase difference exists between the rf magnetic fields H rf in different FM layers separated by a Cu spacer layer. We propose an analysis based on the distribution of the EM waves in the film stack and substrate to interpret this striking observation. For confined geometries with lateral dimensions in the sub-micron regime, there has been a critical absence of experimental techniques which can image small-amplitude dynamics of these structures. We extend the TR-XMCD technique to scanning transmission x-ray microscopy (STXM), to observe directly the local magnetization dynamics in nanoscale FM thin-film elements, demonstrated at picosecond temporal, 40 nm spatial and < 6° angular resolution. The experimental data are compared with our micromagnetic simulations based on the finite element analysis of the time-dependent LLG equation. We resolve standing spin wave modes in nanoscale Ni81 Fe19 thin film ellipses (1000 nm x 500 nm x 20 nm) with clear phase information to distinguish between degenerate eigenmodes with different symmetries for the first time. With the element-specific imaging capability of soft x-rays, spatial resolution up to 15 nm with improved optics, we see great potential for this technique to investigate functional devices with multiple FM layers, and provide insight into the studies of spin injection, manipulation and detection.
NASA Astrophysics Data System (ADS)
Dakovski, Georgi; Durakiewicz, Tomasz; Zhu, Jian-Xin; Riseborough, Peter; Gu, Genda; Gilbertson, Steve; Rodriguez, George
2012-02-01
We have employed the novel technique of time- and angle-resolved photoelectron spectroscopy (t-ARPES) to investigate the quasiparticle dynamics in photoexcited, optimally-doped Bi2Sr2CaCu2O8 superconductor across the superconductor-metal transition. In this talk, we will present and analyze the substantially different ultrafast dynamics, tracked in momentum-dependent fashion, by probing the nodal and antinodal regions of the Brillouin zone, on a 35 femtosecond timescale. The consequences of these findings in terms of reentrant superconductivity will be discussed.
Optimal response to attacks on the open science grids.
Altunay, M.; Leyffer, S.; Linderoth, J. T.; Xie, Z.
2011-01-01
Cybersecurity is a growing concern, especially in open grids, where attack propagation is easy because of prevalent collaborations among thousands of users and hundreds of institutions. The collaboration rules that typically govern large science experiments as well as social networks of scientists span across the institutional security boundaries. A common concern is that the increased openness may allow malicious attackers to spread more readily around the grid. We consider how to optimally respond to attacks in open grid environments. To show how and why attacks spread more readily around the grid, we first discuss how collaborations manifest themselves in the grids and form the collaboration network graph, and how this collaboration network graph affects the security threat levels of grid participants. We present two mixed-integer program (MIP) models to find the optimal response to attacks in open grid environments, and also calculate the threat level associated with each grid participant. Given an attack scenario, our optimal response model aims to minimize the threat levels at unaffected participants while maximizing the uninterrupted scientific production (continuing collaborations). By adopting some of the collaboration rules (e.g., suspending a collaboration or shutting down a site), the model finds optimal response to subvert an attack scenario.
NASA Astrophysics Data System (ADS)
Martel, François; Kelouwani, Sousso; Dubé, Yves; Agbossou, Kodjo
2015-01-01
This work analyses the economical dynamics of an optimized battery degradation management strategy intended for plug-in hybrid electric vehicles (PHEVs) with consideration given to low-cost technologies, such as lead-acid batteries. The optimal management algorithm described herein is based on discrete dynamic programming theory (DDP) and was designed for the purpose of PHEV battery degradation management; its operation relies on simulation models using data obtained experimentally on a physical PHEV platform. These tools are first used to define an optimal management strategy according to the economical weights of PHEV battery degradation and the secondary energy carriers spent to manage its deleterious effects. We then conduct a sensitivity study of the proposed optimization process to the fluctuating economic parameters associated with the fuel and energy costs involved in the degradation management process. Results demonstrate the influence of each parameter on the process's response, including daily total operating costs and expected battery lifetime, as well as establish boundaries for useful application of the method; in addition, they provide a case for the relevance of inexpensive battery technologies, such as lead-acid batteries, for economy-centric PHEV applications where battery degradation is a major concern.
NASA Astrophysics Data System (ADS)
Zhu, Z. W.; Zhang, W. D.; Xu, J.
2014-03-01
The non-linear dynamic characteristics and optimal control of a giant magnetostrictive film (GMF) subjected to in-plane stochastic excitation were studied. Non-linear differential items were introduced to interpret the hysteretic phenomena of the GMF, and the non-linear dynamic model of the GMF subjected to in-plane stochastic excitation was developed. The stochastic stability was analysed, and the probability density function was obtained. The condition of stochastic Hopf bifurcation and noise-induced chaotic response were determined, and the fractal boundary of the system's safe basin was provided. The reliability function was solved from the backward Kolmogorov equation, and an optimal control strategy was proposed in the stochastic dynamic programming method. Numerical simulation shows that the system stability varies with the parameters, and stochastic Hopf bifurcation and chaos appear in the process; the area of the safe basin decreases when the noise intensifies, and the boundary of the safe basin becomes fractal; the system reliability improved through stochastic optimal control. Finally, the theoretical and numerical results were proved by experiments. The results are helpful in the engineering applications of GMF.
Zhu, Z. W.; Zhang, W. D. Xu, J.
2014-03-15
The non-linear dynamic characteristics and optimal control of a giant magnetostrictive film (GMF) subjected to in-plane stochastic excitation were studied. Non-linear differential items were introduced to interpret the hysteretic phenomena of the GMF, and the non-linear dynamic model of the GMF subjected to in-plane stochastic excitation was developed. The stochastic stability was analysed, and the probability density function was obtained. The condition of stochastic Hopf bifurcation and noise-induced chaotic response were determined, and the fractal boundary of the system's safe basin was provided. The reliability function was solved from the backward Kolmogorov equation, and an optimal control strategy was proposed in the stochastic dynamic programming method. Numerical simulation shows that the system stability varies with the parameters, and stochastic Hopf bifurcation and chaos appear in the process; the area of the safe basin decreases when the noise intensifies, and the boundary of the safe basin becomes fractal; the system reliability improved through stochastic optimal control. Finally, the theoretical and numerical results were proved by experiments. The results are helpful in the engineering applications of GMF.
Lee, Chang Jun
2015-12-01
In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines and pumping between connecting equipment under various constraints. However, what is the lacking of considerations in previous researches is to transform various heuristics or safety regulations into mathematical equations. For example, proper safety distances between equipments have to be complied for preventing dangerous accidents on a complex plant. Moreover, most researches have handled single-floor plant. However, many multi-floor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multi-floor plant should be developed. In this study, the Mixed Integer Non-Linear Programming (MINLP) problem including safety distances, maintenance spaces, etc. is suggested based on mathematical equations. The objective function is a summation of pipeline and pumping costs. Also, various safety and maintenance issues are transformed into inequality or equality constraints. However, it is really hard to solve this problem due to complex nonlinear constraints. Thus, it is impossible to use conventional MINLP solvers using derivatives of equations. In this study, the Particle Swarm Optimization (PSO) technique is employed. The ethylene oxide plant is illustrated to verify the efficacy of this study. PMID:26027708
LEE, Chang Jun
2015-01-01
In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines and pumping between connecting equipment under various constraints. However, what is the lacking of considerations in previous researches is to transform various heuristics or safety regulations into mathematical equations. For example, proper safety distances between equipments have to be complied for preventing dangerous accidents on a complex plant. Moreover, most researches have handled single-floor plant. However, many multi-floor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multi-floor plant should be developed. In this study, the Mixed Integer Non-Linear Programming (MINLP) problem including safety distances, maintenance spaces, etc. is suggested based on mathematical equations. The objective function is a summation of pipeline and pumping costs. Also, various safety and maintenance issues are transformed into inequality or equality constraints. However, it is really hard to solve this problem due to complex nonlinear constraints. Thus, it is impossible to use conventional MINLP solvers using derivatives of equations. In this study, the Particle Swarm Optimization (PSO) technique is employed. The ethylene oxide plant is illustrated to verify the efficacy of this study. PMID:26027708
Application of particle swarm optimization to parameter search in dynamical systems
NASA Astrophysics Data System (ADS)
Matsushita, Haruna; Saito, Toshimichi
This paper proposes an application of the particle swarm optimization (PSO) to analysis of switched dynamical systems (SDS). This is the first application of PSO to bifurcation analysis. We consider the application to an example of the SDS which relates to a simplified model of photovoltaic systems such that the input is a single solar cell and is converted to the output via a boost converter. Our SDS includes a piecewise linear current-controlled voltage source that is a simplified model of the solar cell and the switching rule is a variant of peak-current-controlled switching. We derive two equations that give period-doubling bifurcation set and the maximum power point (MPP) for the parameter: they are objective of the analysis. The two equations are transformed into an multi objective problem (MOP) described by the hybrid fitness function consisting of two functions evaluating the validity of parameters and criteria. The proposed method permits increase (deteriorate) of some component below the criterion and the increase can help to exclude the bad component. This criteria effect helps an improvement of trade-off problems in existing MOP solvers. Furthermore, by using the piecewise exact solution and return map for the simulation, the MOP is described exactly and the PSO can find the precise (approximate) solution. From simulation results, we confirm that the PSO for the MOP can easily find the solution parameters although a standard numerical calculation needs huge calculation amount. The efficiency of the proposed algorithm is confirmed by measuring in terms of accuracy, computation amount and robustness.
NASA Astrophysics Data System (ADS)
Peralta, Richard C.; Forghani, Ali; Fayad, Hala
2014-04-01
Many real water resources optimization problems involve conflicting objectives for which the main goal is to find a set of optimal solutions on, or near to the Pareto front. E-constraint and weighting multiobjective optimization techniques have shortcomings, especially as the number of objectives increases. Multiobjective Genetic Algorithms (MGA) have been previously proposed to overcome these difficulties. Here, an MGA derives a set of optimal solutions for multiobjective multiuser conjunctive use of reservoir, stream, and (un)confined groundwater resources. The proposed methodology is applied to a hydraulically and economically nonlinear system in which all significant flows, including stream-aquifer-reservoir-diversion-return flow interactions, are simulated and optimized simultaneously for multiple periods. Neural networks represent constrained state variables. The addressed objectives that can be optimized simultaneously in the coupled simulation-optimization model are: (1) maximizing water provided from sources, (2) maximizing hydropower production, and (3) minimizing operation costs of transporting water from sources to destinations. Results show the efficiency of multiobjective genetic algorithms for generating Pareto optimal sets for complex nonlinear multiobjective optimization problems.
NASA Astrophysics Data System (ADS)
Walker, M. D.; Matthews, J. C.; Asselin, M.-C.; Watson, C. C.; Saleem, A.; Dickinson, C.; Charnley, N.; Julyan, P. J.; Price, P. M.; Jones, T.
2010-11-01
The precision of biological parameter estimates derived from dynamic PET data can be limited by the number of acquired coincidence events (prompts and randoms). These numbers are affected by the injected activity (A0). The benefits of optimizing A0 were assessed using a new model of data variance which is formulated as a function of A0. Seven cancer patients underwent dynamic [15O]H2O PET scans (32 scans) using a Biograph PET-CT scanner (Siemens), with A0 varied (142-839 MBq). These data were combined with simulations to (1) determine the accuracy of the new variance model, (2) estimate the improvements in parameter estimate precision gained by optimizing A0, and (3) examine changes in precision for different size regions of interest (ROIs). The new variance model provided a good estimate of the relative variance in dynamic PET data across a wide range of A0s and time frames for FBP reconstruction. Patient data showed that relative changes in estimate precision with A0 were in reasonable agreement with the changes predicted by the model: Pearson's correlation coefficients were 0.73 and 0.62 for perfusion (F) and the volume of distribution (VT), respectively. The between-scan variability in the parameter estimates agreed with the estimated precision for small ROIs (<5 mL). An A0 of 500-700 MBq was near optimal for estimating F and VT from abdominal [15O]H2O scans on this scanner. This optimization improved the precision of parameter estimates for small ROIs (<5 mL), with an injection of 600 MBq reducing the standard error on F by a factor of 1.13 as compared to the injection of 250 MBq, but by the more modest factor of 1.03 as compared to A0 = 400 MBq.
NASA Astrophysics Data System (ADS)
Borhan, Hoseinali
Modern hybrid electric vehicles and many stationary renewable power generation systems combine multiple power generating and energy storage devices to achieve an overall system-level efficiency and flexibility which is higher than their individual components. The power or energy management control, "brain" of these "hybrid" systems, determines adaptively and based on the power demand the power split between multiple subsystems and plays a critical role in overall system-level efficiency. This dissertation proposes that a receding horizon optimal control (aka Model Predictive Control) approach can be a natural and systematic framework for formulating this type of power management controls. More importantly the dissertation develops new results based on the classical theory of optimal control that allow solving the resulting optimal control problem in real-time, in spite of the complexities that arise due to several system nonlinearities and constraints. The dissertation focus is on two classes of hybrid systems: hybrid electric vehicles in the first part and wind farms with battery storage in the second part. The first part of the dissertation proposes and fully develops a real-time optimization-based power management strategy for hybrid electric vehicles. Current industry practice uses rule-based control techniques with "else-then-if" logic and look-up maps and tables in the power management of production hybrid vehicles. These algorithms are not guaranteed to result in the best possible fuel economy and there exists a gap between their performance and a minimum possible fuel economy benchmark. Furthermore, considerable time and effort are spent calibrating the control system in the vehicle development phase, and there is little flexibility in real-time handling of constraints and re-optimization of the system operation in the event of changing operating conditions and varying parameters. In addition, a proliferation of different powertrain configurations may result in the need for repeated control system redesign. To address these shortcomings, we formulate the power management problem as a nonlinear and constrained optimal control problem. Solution of this optimal control problem in real-time on chronometric- and memory-constrained automotive microcontrollers is quite challenging; this computational complexity is due to the highly nonlinear dynamics of the powertrain subsystems, mixed-integer switching modes of their operation, and time-varying and nonlinear hard constraints that system variables should satisfy. The main contribution of the first part of the dissertation is that it establishes methods for systematic and step-by step improvements in fuel economy while maintaining the algorithmic computational requirements in a real-time implementable framework. More specifically a linear time-varying model predictive control approach is employed first which uses sequential quadratic programming to find sub-optimal solutions to the power management problem. Next the objective function is further refined and broken into a short and a long horizon segments; the latter approximated as a function of the state using the connection between the Pontryagin minimum principle and Hamilton-Jacobi-Bellman equations. The power management problem is then solved using a nonlinear MPC framework with a dynamic programming solver and the fuel economy is further improved. Typical simplifying academic assumptions are minimal throughout this work, thanks to close collaboration with research scientists at Ford research labs and their stringent requirement that the proposed solutions be tested on high-fidelity production models. Simulation results on a high-fidelity model of a hybrid electric vehicle over multiple standard driving cycles reveal the potential for substantial fuel economy gains. To address the control calibration challenges, we also present a novel and fast calibration technique utilizing parallel computing techniques. ^ The second part of this dissertation presents an optimization-based control str
ERIC Educational Resources Information Center
Wu, Jason H.
2013-01-01
This study was designed to examine the construct of academic optimism and its relationship with collective responsibility in a sample of Taiwan elementary schools. The construct of academic optimism was tested using confirmatory factor analysis, and the whole structural model was tested with a structural equation modeling analysis. The data were…
Optimization of municipal solid waste collection and transportation routes.
Das, Swapan; Bhattacharyya, Bidyut Kr
2015-09-01
Optimization of municipal solid waste (MSW) collection and transportation through source separation becomes one of the major concerns in the MSW management system design, due to the fact that the existing MSW management systems suffer by the high collection and transportation cost. Generally, in a city different waste sources scatter throughout the city in heterogeneous way that increase waste collection and transportation cost in the waste management system. Therefore, a shortest waste collection and transportation strategy can effectively reduce waste collection and transportation cost. In this paper, we propose an optimal MSW collection and transportation scheme that focus on the problem of minimizing the length of each waste collection and transportation route. We first formulize the MSW collection and transportation problem into a mixed integer program. Moreover, we propose a heuristic solution for the waste collection and transportation problem that can provide an optimal way for waste collection and transportation. Extensive simulations and real testbed results show that the proposed solution can significantly improve the MSW performance. Results show that the proposed scheme is able to reduce more than 30% of the total waste collection path length. PMID:26152365
Optimal design of zero-water discharge rinsing systems.
Thöming, Jorg
2002-03-01
This paper is about zero liquid discharge in processes that use water for rinsing. Emphasis was given to those systems that contaminate process water with valuable process liquor and compounds. The approach involved the synthesis of optimal rinsing and recycling networks (RRN) that had a priori excluded water discharge. The total annualized costs of the RRN were minimized by the use of a mixed-integer nonlinear program (MINLP). This MINLP was based on a hyperstructure of the RRN and contained eight counterflow rinsing stages and three regenerator units: electrodialysis, reverse osmosis, and ion exchange columns. A "large-scale nickel plating process" case study showed that by means of zero-water discharge and optimized rinsing the total waste could be reduced by 90.4% at a revenue of $448,000/yr. Furthermore, with the optimized RRN, the rinsing performance can be improved significantly at a low-cost increase. In all the cases, the amount of valuable compounds reclaimed was above 99%. PMID:11917998
Robust optimization of contaminant sensor placement for community water systems.
Konjevod, Goran; Carr, Robert D.; Greenberg, Harvey J.; Hart, William Eugene; Morrison, Tod; Phillips, Cynthia Ann; Lin, Henry; Lauer, Erik
2004-09-01
We present a series of related robust optimization models for placing sensors in municipal water networks to detect contaminants that are maliciously or accidentally injected.We formulate sensor placement problems as mixed-integer programs, for which the objective coefficients are not known with certainty. We consider a restricted absolute robustness criteria that is motivated by natural restrictions on the uncertain data, and we define three robust optimization models that differ in how the coefficients in the objective vary. Under one set of assumptions there exists a sensor placement that is optimal for all admissible realizations of the coefficients. Under other assumptions, we can apply sorting to solve each worst-case realization efficiently, or we can apply duality to integrate the worst-case outcome and have one integer program. The most difficult case is where the objective parameters are bilinear, and we prove its complexity is NP-hard even under simplifying assumptions. We consider a relaxation that provides an approximation, giving an overall guarantee of nearoptimality when used with branch-and-bound search. We present preliminary computational experiments that illustrate the computational complexity of solving these robust formulations on sensor placement applications.
On the optimal design of the disassembly and recovery processes
Xanthopoulos, A.; Iakovou, E.
2009-05-15
This paper tackles the problem of the optimal design of the recovery processes of the end-of-life (EOL) electric and electronic products, with a special focus on the disassembly issues. The objective is to recover as much ecological and economic value as possible, and to reduce the overall produced quantities of waste. In this context, a medium-range tactical problem is defined and a novel two-phased algorithm is presented for a remanufacturing-driven reverse supply chain. In the first phase, we propose a multicriteria/goal-programming analysis for the identification and the optimal selection of the most 'desirable' subassemblies and components to be disassembled for recovery, from a set of different types of EOL products. In the second phase, a multi-product, multi-period mixed-integer linear programming (MILP) model is presented, which addresses the optimization of the recovery processes, while taking into account explicitly the lead times of the disassembly and recovery processes. Moreover, a simulation-based solution approach is proposed for capturing the uncertainties in reverse logistics. The overall approach leads to an easy-to-use methodology that could support effectively middle level management decisions. Finally, the applicability of the developed methodology is illustrated by its application on a specific case study.
A Theoretical Analysis of How Segmentation of Dynamic Visualizations Optimizes Students' Learning
ERIC Educational Resources Information Center
Spanjers, Ingrid A. E.; van Gog, Tamara; van Merrienboer, Jeroen J. G.
2010-01-01
This article reviews studies investigating segmentation of dynamic visualizations (i.e., showing dynamic visualizations in pieces with pauses in between) and discusses two not mutually exclusive processes that might underlie the effectiveness of segmentation. First, cognitive activities needed for dealing with the transience of dynamic…
NASA Astrophysics Data System (ADS)
Piccardi, Carlo; Soncini-Sessa, Rodolfo
1991-05-01
The solution via dynamic programming (DP) of a reservoir optimal control problem is often computationally prohibitive when the proper description of the inflow process leads to a system model having several state variables and/or when a sufficiently dense state discretization is required to achieve numerical accuracy. Thus, to simplify, the inflow correlation is usually neglected and/or a coarse state discretization is adopted. However, these simplifications may significantly affect the reliability of the solution of the optimization problem. Nowadays, the availability of very powerful computers based on innovative architectures (vector and parallel machines), even in the domain of personal computers (transputer architectures), stimulates the reformulation of the standard dynamic programming algorithm in a form able to exploit these new machine architectures. The reformulated DP algorithm and new machines enable faster and less costly solution of optimization problems involving a system model having two state variables (storage and previous period inflow, then taking into account the inflow correlation) and a number of states (of the order of 104) such as to guarantee a high numerical accuracy.
Krivov, Sergei V
2011-01-01
Dimensionality reduction is ubiquitous in analysis of complex dynamics. The conventional dimensionality reduction techniques, however, focus on reproducing the underlying configuration space, rather than the dynamics itself. The constructed low-dimensional space does not provide complete and accurate description of the dynamics. Here I describe how to perform dimensionality reduction while preserving the essential properties of the dynamics. The approach is illustrated by analyzing the chess game - the archetype of complex dynamics. A variable that provides complete and accurate description of chess dynamics is constructed. Winning probability is predicted by describing the game as a random walk on the free energy landscape associated with the variable. The approach suggests a possible way of obtaining a simple yet accurate description of many important complex phenomena. The analysis of the chess game shows that the approach can quantitatively describe the dynamics of processes where human decision-making pl...
Peng, Huei
(Ressler et al. 1988; Kizu et al. 1988) for improved vehicle dynamic response. Recently, integrated chassisVehicle System Dynamics, Vol. 26, No.4, October 1996, pp.301-320. Traction/Braking Force The optimal tire force distribution to maximize acceleration/deceleration of a four-wheel vehicle during
Xu, Ling; Liu, Rui; Wang, Feng; Liu, Chun-Zhao
2012-09-01
The key parameters of the inner structure of a cylindrical airlift bioreactor, including the ratio of the cross-section area of the downcomer to the cross-section area of the riser, clearance from the upper edge of the draft tube to the water level, and clearance from the low edge of the draft tube to the bottom of the reactor, significantly affected the biomass production of Botryococcus braunii. In order to achieve high algal cultivation performance, the optimal structural parameters of the bioreactor were determined using computational fluid dynamics (CFD) simulation. The simulated results were validated by experimental data collected from the microalgal cultures in both 2 and 40-L airlift bioreactors. The CFD model developed in this study provides a powerful means for optimizing bioreactor design and scale-up without the need to perform numerous time-consuming bioreactor experiments. PMID:22750496
NASA Technical Reports Server (NTRS)
Lisano, Michael E.
2004-01-01
Controlled flight of a solar sail-propelled spacecraft ('sailcraft') is a six-degree-of-freedom dynamics problem. Current state-of-the-art tools that simulate and optimize the trajectories flown by sailcraft do not treat the full kinetic (i.e. force and torque-constrained) motion, instead treating a discrete history of commanded sail attitudes, and either neglecting the sail attitude motion over an integration timestep, or treating the attitude evolution kinematically with a spline or similar treatment. The present paper discusses an aspect of developing a next generation sailcraf trajectory designing optimization tool JPL, for NASA's Solar Sail Spaceflight Simulation Software (SS). The aspect discussed in an experimental approach to modeling full six-degree-of-freedom kinetic motion of a solar sail in a trajectory propagator. Early results from implementing this approach in a new trajectory propagation tool are given.
NASA Astrophysics Data System (ADS)
Seko, Atsuto; Togo, Atsushi; Hayashi, Hiroyuki; Tsuda, Koji; Chaput, Laurent; Tanaka, Isao
2015-11-01
Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease LTC. However, such trials have yielded successes only within a limited exploration space. Here, we report the virtual screening of a library containing 54 779 compounds. Our strategy is to search the library through Bayesian optimization using for the initial data the LTC obtained from first-principles anharmonic lattice-dynamics calculations for a set of 101 compounds. We discovered 221 materials with very low LTC. Two of them even have an electronic band gap <1 eV , which makes them exceptional candidates for thermoelectric applications. In addition to those newly discovered thermoelectric materials, the present strategy is believed to be powerful for many other applications in which the chemistry of materials is required to be optimized.
Yun, Jong Pil; Jeon, Yong-Ju; Choi, Doo-chul; Kim, Sang Woo
2012-05-01
We propose a new defect detection algorithm for scale-covered steel wire rods. The algorithm incorporates an adaptive wavelet filter that is designed on the basis of lattice parameterization of orthogonal wavelet bases. This approach offers the opportunity to design orthogonal wavelet filters via optimization methods. To improve the performance and the flexibility of wavelet design, we propose the use of the undecimated discrete wavelet transform, and separate design of column and row wavelet filters but with a common cost function. The coefficients of the wavelet filters are optimized by the so-called univariate dynamic encoding algorithm for searches (uDEAS), which searches the minimum value of a cost function designed to maximize the energy difference between defects and background noise. Moreover, for improved detection accuracy, we propose an enhanced double-threshold method. Experimental results for steel wire rod surface images obtained from actual steel production lines show that the proposed algorithm is effective. PMID:22561939
Seko, Atsuto; Togo, Atsushi; Hayashi, Hiroyuki; Tsuda, Koji; Chaput, Laurent; Tanaka, Isao
2015-11-13
Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease LTC. However, such trials have yielded successes only within a limited exploration space. Here, we report the virtual screening of a library containing 54?779 compounds. Our strategy is to search the library through Bayesian optimization using for the initial data the LTC obtained from first-principles anharmonic lattice-dynamics calculations for a set of 101 compounds. We discovered 221 materials with very low LTC. Two of them even have an electronic band gap <1??eV, which makes them exceptional candidates for thermoelectric applications. In addition to those newly discovered thermoelectric materials, the present strategy is believed to be powerful for many other applications in which the chemistry of materials is required to be optimized. PMID:26613454
Biswas, Abhishek; Ranjan, Desh; Zubair, Mohammad; He, Jing
2015-09-01
The determination of secondary structure topology is a critical step in deriving the atomic structures from the protein density maps obtained from electron cryomicroscopy technique. This step often relies on matching the secondary structure traces detected from the protein density map to the secondary structure sequence segments predicted from the amino acid sequence. Due to inaccuracies in both sources of information, a pool of possible secondary structure positions needs to be sampled. One way to approach the problem is to first derive a small number of possible topologies using existing matching algorithms, and then find the optimal placement for each possible topology. We present a dynamic programming method of ?(Nq(2)h) to find the optimal placement for a secondary structure topology. We show that our algorithm requires significantly less computational time than the brute force method that is in the order of ?(q(N) h). PMID:26244416
NASA Astrophysics Data System (ADS)
Hager, P.; Czupalla, M.; Walter, U.
2010-11-01
In this paper we report on the development of a dynamic MATLAB SIMULINK® model for the water and electrolyte balance inside the human body. This model is part of an environmentally sensitive dynamic human model for the optimization and verification of environmental control and life support systems (ECLSS) in space flight applications. An ECLSS provides all vital supplies for supporting human life on board a spacecraft. As human space flight today focuses on medium- to long-term missions, the strategy in ECLSS is shifting to closed loop systems. For these systems the dynamic stability and function over long duration are essential. However, the only evaluation and rating methods for ECLSS up to now are either expensive trial and error breadboarding strategies or static and semi-dynamic simulations. In order to overcome this mismatch the Exploration Group at Technische Universität München (TUM) is developing a dynamic environmental simulation, the "Virtual Habitat" (V-HAB). The central element of this simulation is the dynamic and environmentally sensitive human model. The water subsystem simulation of the human model discussed in this paper is of vital importance for the efficiency of possible ECLSS optimizations, as an over- or under-scaled water subsystem would have an adverse effect on the overall mass budget. On the other hand water has a pivotal role in the human organism. Water accounts for about 60% of the total body mass and is educt and product of numerous metabolic reactions. It is a transport medium for solutes and, due to its high evaporation enthalpy, provides the most potent medium for heat load dissipation. In a system engineering approach the human water balance was worked out by simulating the human body's subsystems and their interactions. The body fluids were assumed to reside in three compartments: blood plasma, interstitial fluid and intracellular fluid. In addition, the active and passive transport of water and solutes between those compartments was modeled dynamically. A kidney model regulates the electrolyte concentration in body fluids (osmolality) in narrow confines and a thirst mechanism models the urge to ingest water. A controlled exchange of water and electrolytes with other human subsystems, as well as with the environment, is implemented. Finally, the changes in body composition due to muscle growth are accounted for. The outcome of this is a dynamic water and electrolyte balance, which is capable of representing body reactions like thirst and headaches, as well as heat stroke and collapse, as a response to its work load and environment.
NASA Astrophysics Data System (ADS)
Raso, Luciano; Dorchies, David; Malaterre, Pierre-Olivier
2015-04-01
We developed an Objective Oriented Programming (OOP) tool for optimal management of complex water systems by use of Stochastic Dual Dynamic Programming (SDDP). OOP is a powerful programming paradigm. OOP minimizes code redundancies, making code modification and maintenance very effective. This is especially welcome in research, in which, often, code must be modified to meet new requirements that were not initially considered. SDDP is an advanced method for optimal operation of complex dynamic systems under uncertainty. SDDP can deal with large and complex systems, such as a multi-reservoir system. The objective of this tool is making SDDP usable for Water Management Analysts. Thanks to this tool, the Analyst can bypass the SDDP programming complexity, and his/her task is simplified to the definition of system elements, topology and objectives, and experiments characteristics. In this tool, the main classes are: Experiment, System, Element, and Objective. Experiments are run on a system. A system is made of many elements interconnected among them. Class Element is made of the following sub-classes: (stochastic) hydrological scenario, (deterministic) water demand scenario, reservoir, river reach, off-take, and irrigation basin. Objectives are used in the optimization procedure to find the optimal operational rules, for a given system and experiment. OOP flexibility allows the Water Management Analyst to extend easily existing classes in order to answer his/her specific research questions. The tool is implemented in Python, and will be initially tested on two applications: the Senegal River water system, in West Africa, and the Seine River, in France.
Energy optimal path planning using stochastic dynamically orthogonal level set equations
Narayanan Subramani, Deepak
2014-01-01
The growing use of autonomous underwater vehicles and underwater gliders for a variety of applications gives rise to new requirements in the operation of these vehicles. One such important requirement is optimization of ...
Time-optimal path planning in dynamic flows using level set equations: realistic applications
Lermusiaux, Pierre F. J.
The level set methodology for time-optimal path planning is employed to predict collision-free and fastest-time trajectories for swarms of underwater vehicles deployed in the Philippine Archipelago region. To simulate the ...
Optimal Acceleration-Bounded Trajectory Planning in Dynamic Environments Along a Specified Path
Hauser, Kris
are the second- highest cause of accidents for elderly drivers in the United States [1] and are a challenge for automated urban driving. We present a polynomial-time, optimal, exact algorithm for the problem of planning
Design optimization of a centrifugal pump impeller and volute using computational fluid dynamics
NASA Astrophysics Data System (ADS)
Kim, J. H.; Oh, K. T.; Pyun, K. B.; Kim, C. K.; Choi, Y. S.; Yoon, J. Y.
2012-11-01
In this study, optimization of the impeller and design of volute were carried out in order to improve the performance of a centrifugal pump. Design parameters from vane plane development for impeller design were selected and effect of the design parameters on the performance of the pump was analyzed using CFD and Response Surface Method to optimized impeller. This study also proposed the optimization geometry of pump impeller for performance improvement through the results from numerical analysis that was obtained optimum design pump; efficiency 98.2% and head 64.5m. In addition, the pump design method was suggested by designing volute which was suitable for the optimized impeller through volute design where Stepanoff theory was applied and numerical analysis.
Triep, Michael; Brücker, Christoph; Schröder, Wolfgang; Siess, Thorsten
2006-05-01
A detailed knowledge of the flow field in a blood pump is indispensable in order to increase the efficiency of the pump and to reduce the shear-induced hemolysis. Thus, three different impeller designs were developed and tested by means of computational fluid dynamics (CFD) and digital particle image velocimetry (DPIV). The results show a good agreement of CFD and DPIV data. An optimization of the impeller could be achieved by following the concept of turbulent drag reduction for the axisymmetric center body. PMID:16683957
NASA Astrophysics Data System (ADS)
Yu, Feng; Li, Yanjun; Wu, Tie-Jun
2010-02-01
A large number of networks in the real world have a scale-free structure, and the parameters of the networks change stochastically with time. Searching for the shortest paths in a scale-free dynamic and stochastic network is not only necessary for the estimation of the statistical characteristics such as the average shortest path length of the network, but also challenges the traditional concepts related to the “shortest path” of a network and the design of path searching strategies. In this paper, the concept of shortest path is defined on the basis of a scale-free dynamic and stochastic network model, and a temporal ant colony optimization (TACO) algorithm is proposed for searching for the shortest paths in the network. The convergence and the setup for some important parameters of the TACO algorithm are discussed through theoretical analysis and computer simulations, validating the effectiveness of the proposed algorithm.
NASA Astrophysics Data System (ADS)
Wang, Rongrong; Hu, Chuan; Wang, Zejiang; Yan, Fengjun; Chen, Nan
2015-08-01
This paper presents an integrated optimal dynamics control of four-wheel driving and four-wheel steering (4WD4WS) electric ground vehicles via hierarchical control methodology. In the higher-level design, an LQR controller is proposed to obtain the integrated lateral force and yaw moment, according to their respective reference values. The lower-level controller is designed to ensure all the tires work in the stable region while realizing the tracking control of the vehicle dynamics. The tire-road friction coefficient is estimated through the integrated longitudinal force and lateral force, respectively, using a brush tire model. To reduce the estimation error, a novel data fusion function is employed to generate the final estimation value. Finally, the effectiveness of the proposed control and estimation strategies is validated via CarSim-Simulink joint simulation.
Borland, M.; Sajaev, V.; Emery, L.; Xiao, A.; Accelerator Systems Division
2010-08-24
The Advanced Photon Source (APS) is a 7 GeV storage ring light source that has been in operation for well over a decade. In the near future, the ring may be upgraded, including changes to the lattice such as provision of several long straight sections (LSS). Because APS beamlines are nearly fully built out, we have limited freedom to place LSSs in a symmetric fashion. Arbitrarily-placed LSSs will drastically reduce the symmetry of the optics and would typically be considered unworkable. We apply a recently-developed multi-objective direct optimization technique that relies on particle tracking to compute the dynamic aperture and Touschek lifetime. We show that this technique is able to tune sextupole strengths and select the working point in such a way as to recover the dynamic and momentum acceptances. We also show the results of experimental tests of lattices developed using these techniques.
Jenny, Richard M; Jasper, Micah N; Simmons, Otto D; Shatalov, Max; Ducoste, Joel J
2015-10-15
Alternative disinfection sources such as ultraviolet light (UV) are being pursued to inactivate pathogenic microorganisms such as Cryptosporidium and Giardia, while simultaneously reducing the risk of exposure to carcinogenic disinfection by-products (DBPs) in drinking water. UV-LEDs offer a UV disinfecting source that do not contain mercury, have the potential for long lifetimes, are robust, and have a high degree of design flexibility. However, the increased flexibility in design options will add a substantial level of complexity when developing a UV-LED reactor, particularly with regards to reactor shape, size, spatial orientation of light, and germicidal emission wavelength. Anticipating that LEDs are the future of UV disinfection, new methods are needed for designing such reactors. In this research study, the evaluation of a new design paradigm using a point-of-use UV-LED disinfection reactor has been performed. ModeFrontier, a numerical optimization platform, was coupled with COMSOL Multi-physics, a computational fluid dynamics (CFD) software package, to generate an optimized UV-LED continuous flow reactor. Three optimality conditions were considered: 1) single objective analysis minimizing input supply power while achieving at least (2.0) log10 inactivation of Escherichia coli ATCC 11229; and 2) two multi-objective analyses (one of which maximized the log10 inactivation of E. coli ATCC 11229 and minimized the supply power). All tests were completed at a flow rate of 109 mL/min and 92% UVT (measured at 254 nm). The numerical solution for the first objective was validated experimentally using biodosimetry. The optimal design predictions displayed good agreement with the experimental data and contained several non-intuitive features, particularly with the UV-LED spatial arrangement, where the lights were unevenly populated throughout the reactor. The optimal designs may not have been developed from experienced designers due to the increased degrees of freedom offered by using UV-LEDs. The results of this study revealed that the coupled optimization routine with CFD was effective at significantly decreasing the engineer's design decision space and finding a potentially near-optimal UV-LED reactor solution. PMID:26179637
Microgrid optimal scheduling considering impact of high penetration wind generation
NASA Astrophysics Data System (ADS)
Alanazi, Abdulaziz
The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.
Optimal Operation System of the Integrated District Heating System with Multiple Regional Branches
NASA Astrophysics Data System (ADS)
Kim, Ui Sik; Park, Tae Chang; Kim, Lae-Hyun; Yeo, Yeong Koo
This paper presents an optimal production and distribution management for structural and operational optimization of the integrated district heating system (DHS) with multiple regional branches. A DHS consists of energy suppliers and consumers, district heating pipelines network and heat storage facilities in the covered region. In the optimal management system, production of heat and electric power, regional heat demand, electric power bidding and sales, transport and storage of heat at each regional DHS are taken into account. The optimal management system is formulated as a mixed integer linear programming (MILP) where the objectives is to minimize the overall cost of the integrated DHS while satisfying the operation constraints of heat units and networks as well as fulfilling heating demands from consumers. Piecewise linear formulation of the production cost function and stairwise formulation of the start-up cost function are used to compute nonlinear cost function approximately. Evaluation of the total overall cost is based on weekly operations at each district heat branches. Numerical simulations show the increase of energy efficiency due to the introduction of the present optimal management system.
Eldred, Michael Scott; Vigil, Dena M.; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Lefantzi, Sophia; Hough, Patricia Diane; Eddy, John P.
2011-12-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.
NASA Technical Reports Server (NTRS)
Kasahara, Hironori; Honda, Hiroki; Narita, Seinosuke
1989-01-01
Parallel processing of real-time dynamic systems simulation on a multiprocessor system named OSCAR is presented. In the simulation of dynamic systems, generally, the same calculation are repeated every time step. However, we cannot apply to Do-all or the Do-across techniques for parallel processing of the simulation since there exist data dependencies from the end of an iteration to the beginning of the next iteration and furthermore data-input and data-output are required every sampling time period. Therefore, parallelism inside the calculation required for a single time step, or a large basic block which consists of arithmetic assignment statements, must be used. In the proposed method, near fine grain tasks, each of which consists of one or more floating point operations, are generated to extract the parallelism from the calculation and assigned to processors by using optimal static scheduling at compile time in order to reduce large run time overhead caused by the use of near fine grain tasks. The practicality of the scheme is demonstrated on OSCAR (Optimally SCheduled Advanced multiprocessoR) which has been developed to extract advantageous features of static scheduling algorithms to the maximum extent.
NASA Astrophysics Data System (ADS)
Teoh, Lay Eng; Khoo, Hooi Ling
2013-09-01
This study deals with two major aspects of airlines, i.e. supply and demand management. The aspect of supply focuses on the mathematical formulation of an optimal fleet management model to maximize operational profit of the airlines while the aspect of demand focuses on the incorporation of mode choice modeling as parts of the developed model. The proposed methodology is outlined in two-stage, i.e. Fuzzy Analytic Hierarchy Process is first adopted to capture mode choice modeling in order to quantify the probability of probable phenomena (for aircraft acquisition/leasing decision). Then, an optimization model is developed as a probabilistic dynamic programming model to determine the optimal number and types of aircraft to be acquired and/or leased in order to meet stochastic demand during the planning horizon. The findings of an illustrative case study show that the proposed methodology is viable. The results demonstrate that the incorporation of mode choice modeling could affect the operational profit and fleet management decision of the airlines at varying degrees.
Wu, Jingchun; Antaki, James F; Wagner, William R; Snyder, Trevor A; Paden, Bradley E; Borovetz, Harvey S
2005-01-01
We investigated a miniature magnetically levitated centrifugal blood pump intended to deliver 0.3-1.5 l/min of support to neonates and infants. The back clearance gap between the housing and large volume of the rotor, where the suspension and motor bearings are located, forms a continuous leakage flow path. Within the gap, flow demonstrates a very complex three-dimensional structure: the fluid adjacent to the rotating disk tends to accelerate by centrifugal force to flow radially outwards toward the outlet of the impeller against an unfavorable pressure gradient, which in turn forces blood to return along the stationary housing surfaces. Consequently, one or multiple vortices may be generated in the gap to block blood flow and cause the formation of a retrograde and antegrade leakage flow phenomenon at the gap outlet using an optimization process including extensive computational fluid dynamics (CFD) analysis of impeller refinements, we found that secondary blades located along the back or extended to the side surfaces of the rotor have the capacity to reduce and eliminate the retrograde flow in the back clearance gap. Flow visualization confirmed the CFD-predicted flow patterns. This work demonstrates the utility of CFD-based design optimization to optimize the fluid path of a miniature centrifugal pump. PMID:16322730
Patnaik, Lalit; Umanand, Loganathan
2015-01-01
The inverted pendulum is a popular model for describing bipedal dynamic walking. The operating point of the walker can be specified by the combination of initial mid-stance velocity (v 0) and step angle ([Formula: see text]) chosen for a given walk. In this paper, using basic mechanics, a framework of physical constraints that limit the choice of operating points is proposed. The constraint lines thus obtained delimit the allowable region of operation of the walker in the v 0-[Formula: see text] plane. A given average forward velocity [Formula: see text] can be achieved by several combinations of v 0 and [Formula: see text]. Only one of these combinations results in the minimum mechanical power consumption and can be considered the optimum operating point for the given [Formula: see text]. This paper proposes a method for obtaining this optimal operating point based on tangency of the power and velocity contours. Putting together all such operating points for various [Formula: see text], a family of optimum operating points, called the optimal locus, is obtained. For the energy loss and internal energy models chosen, the optimal locus obtained has a largely constant step angle with increasing speed but tapers off at non-dimensional speeds close to unity. PMID:26502096
Li, Ji-Qing; Zhang, Yu-Shan; Ji, Chang-Ming; Wang, Ai-Jing; Lund, Jay R
2013-01-01
This paper examines long-term optimal operation using dynamic programming for a large hydropower system of 10 reservoirs in Northeast China. Besides considering flow and hydraulic head, the optimization explicitly includes time-varying electricity market prices to maximize benefit. Two techniques are used to reduce the 'curse of dimensionality' of dynamic programming with many reservoirs. Discrete differential dynamic programming (DDDP) reduces the search space and computer memory needed. Object-oriented programming (OOP) and the ability to dynamically allocate and release memory with the C++ language greatly reduces the cumulative effect of computer memory for solving multi-dimensional dynamic programming models. The case study shows that the model can reduce the 'curse of dimensionality' and achieve satisfactory results. PMID:24334896
Jung, HoHyun; Chun, Keyoung Jin; Hong, Jaesoo; Lim, Dohyung
2015-01-01
Balance is important in daily activities and essential for maintaining an independent lifestyle in the elderly. Recent studies have shown that balance rehabilitation training can improve the balance ability of the elderly, and diverse balance rehabilitation training equipment has been developed. However, there has been little research into optimized strategies for balance rehabilitation training. To provide an optimized strategy, we analyzed the balance characteristics of participants in response to the rotation of a base plate on multiple axes. Seven male adults with no musculoskeletal or nervous system-related diseases (age: 25.5±1.7 years; height: 173.9±6.4 cm; body mass: 71.3±6.5 kg; body mass index: 23.6±2.4 kg/m2) were selected to investigate the balance rehabilitation training using customized rehabilitation equipment. Rotation of the base plate of the equipment was controlled to induce dynamic rotation of participants in the anterior–posterior, right-diagonal, medial–lateral, and left-diagonal directions. We used a three-dimensional motion capture system employing infrared cameras and the Pedar Flexible Insoles System to characterize the major lower-extremity joint angles, center of body mass, and center of pressure. We found statistically significant differences between the changes in joint angles in the lower extremities in response to dynamic rotation of the participants (P<0.05). The maximum was greater with anterior–posterior and medial–lateral dynamic rotation than with that in other directions (P<0.05). However, there were no statistically significant differences in the frequency of center of body mass deviations from the base of support (P>0.05). These results indicate that optimizing rotation control of the base plate of balance rehabilitation training equipment to induce anterior–posterior and medial–lateral dynamic rotation preferentially can lead to effective balance training. Additional tests with varied speeds and ranges of angles of base plate rotation are expected to be useful as well as an analysis of the balance characteristics considering a balance index that reflects the muscle activity and cooperative characteristics. PMID:26508847
Adjoint-based Unsteady Airfoil Design Optimization with Application to Dynamic Stall
Mavripli, Dimitri J.
-Stokes simula- tion capability with a suitable turbulence model. Com- putational fluid dynamics (CFD) analyses contribute to poor comparisons. Difficult cases, such as smooth surface separation and trailing edge stall
Computational fluid dynamic (CFD) optimization of microfluidic mixing in a MEMS steam generator
Collins, Kimberlee C. (Kimberlee Chiyoko)
2008-01-01
The challenge of achieving rapid mixing in microchannels is addressed through a computational fluid dynamics (CFD) study using the ADINA-F finite element program. The study is motivated by the need to design an adequate ...
Integrated method to create optimal dynamic strategic plans for corporate technology start-ups
Mikati, Samir Omar
2009-01-01
This thesis presents an innovative method for evaluating and dynamically planning the development of uncertain technology investments. Its crux centers on a paradigm shift in the way managers assess investments, toward an ...
Temperature-Aware Dynamic Resource Provisioning in a Power-Optimized Datacenter
Pedram, Massoud
of their carbon dioxide (CO2) footprint. Motivated by the need for datacenters to be put on a more scalable the effectiveness of the proposed dynamic resource provisioning method. 1 Keywords-datacenter, cloud computing
Optimization of a high-efficiency jet ejector by computational fluid dynamic software
Watanawanavet, Somsak
2005-08-29
Research was performed to optimize high-efficiency jet ejector geometry (Holtzapple, 2001) by varying nozzle diameter ratios from 0.03 to 0.21, and motive velocities from Mach 0.39 to 1.97. The high-efficiency jet ejector was simulated by Fluent...