Extension of models in dynamic system design problems

1993 ◽  
Vol 4 (3) ◽  
pp. 280-287
Author(s):  
V. A. Vyazgin
Keyword(s):  
Dynamic System ◽  
System Design ◽  
Design Problems

Robust MDSDO for Co-Design of Stochastic Dynamic Systems

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
System Design ◽  
Design Problem ◽  
Integrated Design ◽  
Problem Formulation ◽  
Robust Design Optimization ◽  
Design Problems ◽  
Embodiment Design ◽  
And Control

Abstract Optimization of dynamic engineering systems generally requires problem formulations that account for the coupling between embodiment design and control system design simultaneously. Such formulations are commonly known as combined optimal design and control (co-design) problems, and their application to deterministic systems is well established in the literature through a variety of methods. However, an issue that has not been addressed in the co-design literature is the impact of the inherent uncertainties within a dynamic system on its integrated design solution. Accounting for these uncertainties transforms the standard, deterministic co-design problem into a stochastic one, thus requiring appropriate stochastic optimization approaches for its solution. This paper serves as the starting point for research on stochastic co-design problems by proposing and solving a novel problem formulation based on robust design optimization (RDO) principles. Specifically, a co-design method known as multidisciplinary dynamic system design optimization (MDSDO) is used as the basis for an RDO problem formulation and implementation. The robust objective and inequality constraints are computed per usual as functions of their first-order-approximated means and variances, whereas analysis-based equality constraints are evaluated deterministically at the means of the random decision variables. The proposed stochastic co-design problem formulation is then implemented for two case studies, with the results indicating the importance of the robust approach on the integrated design solutions and performance measures.

Robust MDSDO for Co-Design of Stochastic Dynamic Systems

2018 ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
System Design ◽  
Design Problem ◽  
Integrated Design ◽  
Problem Formulation ◽  
Robust Design Optimization ◽  
Design Problems ◽  
Embodiment Design ◽  
And Control

Optimization of dynamic engineering systems generally requires problem formulations that account for the coupling between embodiment design and control system design simultaneously. Such formulations are commonly known as combined optimal design and control (co-design) problems, and their application to deterministic systems is well-established in the literature through a variety of methods. However, an issue that has not been addressed in the co-design literature is the impact of the inherent uncertainties within a dynamic system on its integrated design solution. Accounting for these uncertainties transforms the standard, deterministic co-design problem into a stochastic one, thus requiring appropriate stochastic optimization approaches for its solution. This paper serves as the starting point for research on stochastic co-design problems by proposing and solving a novel problem formulation based on robust design optimization (RDO) principles. Specifically, a co-design method known as multidisciplinary dynamic system design optimization (MDSDO) is used as the basis for a RDO problem formulation and implementation. The robust objective and inequality constraints are computed per usual as functions of their first-order-approximated means and variances, whereas analysis-based equality constraints are evaluated deterministically at the means of the random decision variables. The proposed stochastic co-design problem formulation is then implemented for two case studies, with the results indicating a significant impact of the robust approach on the integrated design solutions and performance measures.

Automated System Design for Classification of Chronic Lung Viruses using Non-Linear Dynamic System Features and K-Nearest Neighbour

2021 ◽  
Author(s):  
Misha Urooj Khan ◽  
Ayesha Farman ◽  
Asad Ur Rehman ◽  
Nida Israr ◽  
Muhammad Zulqarnain Haider Ali ◽  
...  
Keyword(s):  
Dynamic System ◽  
System Design ◽  
Linear Dynamic System ◽  
Automated System ◽  
Nearest Neighbour ◽  
Linear Dynamic ◽  
Non Linear

Multiobjective Collaborative Optimization

1997 ◽  
Author(s):  
Ravindra V. Tappeta ◽  
John E. Renaud
Keyword(s):  
System Design ◽  
Systems Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Accurate Estimation ◽  
Successful Implementation ◽  
Design Environment ◽  
Comprehensive Overview ◽  
Design Problems ◽  
Multidisciplinary Systems

Abstract This investigation focuses on the development of modifications to the Collaborative Optimization (CO) approach to multidisciplinary systems design, that will provide solution capabilities for multiobjective problems. The primary goal of this research is to provide a comprehensive overview and development of mathematically rigorous optimization strategies for MultiObjective Collaborative Optimization (MOCO). Collaborative Optimization strategies provide design optimization capabilities to discipline designers within a multidisciplinary design environment. To date these CO strategies have primarily been applied to system design problems which have a single objective function. Recent investigations involving multidisciplinary design simulators have reported success in applying CO to multiobjective system design problems. In this research three MultiObjective Collaborative Optimization (MOCO) strategies are developed, reviewed and implemented in a comparative study. The goal of this effort is to provide an in depth comparison of different MOCO strategies available to system designers. Each of the three strategies makes use of parameter sensitivities within multilevel solution strategies. In implementation studies, each of the three MOCO strategies is effective in solving two multiobjective multidisciplinary systems design problems. Results indicate that these MOCO strategies require an accurate estimation of parameter sensitivities for successful implementation. In each of the three MOCO strategies these parameter sensitivities are obtained using post-optimality analysis techniques.

Multiobjective Collaborative Optimization

1997 ◽  
Vol 119 (3) ◽  
pp. 403-411 ◽  
Author(s):  
R. V. Tappeta ◽  
J. E. Renaud
Keyword(s):  
System Design ◽  
Systems Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Accurate Estimation ◽  
Successful Implementation ◽  
Design Environment ◽  
Comprehensive Overview ◽  
Design Problems ◽  
Multidisciplinary Systems

This investigation focuses on the development of modifications to the Collaborative Optimization (CO) approach to multidisciplinary systems design, that will provide solution capabilities for multiobjective problems. The primary goal of this paper is to provide a comprehensive overview and development of mathematically rigorous optimization strategies for Multiobjective Collaborative Optimization (MOCO). Collaborative Optimization strategies provide design optimization capabilities to discipline designers within a multidisciplinary design environment. To date these CO strategies have primarily been applied to system design problems which have a single objective function. Recent investigations involving multidisciplinary design simulators have reported success in applying CO to multiobjective system design problems. In this research three Multiobjective Collaborative Optimization (MOCO) strategies are developed, reviewed and implemented in a comparative study. The goal of this effort is to provide an in depth comparison of different MOCO strategies available to system designers. Each of the three strategies makes use of parameter sensitivities within multilevel solution strategies. In implementation studies, each of the three MOCO strategies is effective in solving a multiobjective multidisciplinary systems design problem. Results indicate that these MOCO strategies require an accurate estimation of parameter sensitivities for successful implementation. In each of the three MOCO strategies these parameter sensitivities are obtained using post-optimality analysis techniques.

A CONVEX SUBMODEL WITH APPLICATION TO SYSTEM DESIGN

2004 ◽  
Vol 21 (01) ◽  
pp. 9-33
Author(s):  
JAVIER SALMERÓN ◽  
ÁNGEL MARÍN
Keyword(s):  
Convex Optimization ◽  
System Design ◽  
Optimality Conditions ◽  
Benders Decomposition ◽  
Optimal Solution ◽  
Lagrangean Relaxation ◽  
Optimization Techniques ◽  
Convex Model ◽  
Design Problems ◽  
Primal And Dual

In this paper, we present an algorithm to solve a particular convex model explicitly. The model may massively arise when, for example, Benders decomposition or Lagrangean relaxation-decomposition is applied to solve large design problems in facility location and capacity expansion. To attain the optimal solution of the model, we analyze its Karush–Kuhn–Tucker optimality conditions and develop a constructive algorithm that provides the optimal primal and dual solutions. This approach yields better performance than other convex optimization techniques.

Modeling Design Patterns for Semi-Automatic Reuse in System Design

2012 ◽  
pp. 29-56
Author(s):  
Galia Shlezinger ◽  
Iris Reinhartz-Berger ◽  
Dov Dori
Keyword(s):  
System Design ◽  
Software Quality ◽  
Design Pattern ◽  
Design Patterns ◽  
Design Problems ◽  
Suggested Approach ◽  
Main Design ◽  
Analysis And Design ◽  
Correct Usage

Design patterns provide reusable solutions for recurring design problems. They constitute an important tool for improving software quality. However, correct usage of design patterns depends to a large extent on the designer. Design patterns often include models that describe the suggested solutions, while other aspects of the patterns are neglected or described informally only in text. Furthermore, design pattern solutions are usually described in an object-oriented fashion that is too close to the implementation, masking the essence of and motivation behind a particular design pattern. We suggest an approach to modeling the different aspects of design patterns and semi-automatically utilizing these models to improve software design. Evaluating our approach on commonly used design patterns and a case study of an automatic application for composing, taking, checking, and grading analysis and design exams, we found that the suggested approach successfully locates the main design problems modeled by the selected design patterns.

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