Decision-Based Collaborative Optimization

2000 ◽  
Vol 124 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyu Gu ◽  
John E. Renaud ◽  
Leah M. Ashe ◽  
Stephen M. Batill ◽  
Amrjit S. Budhiraja ◽  
...  
Keyword(s):  
Systems Design ◽  
Collaborative Optimization ◽  
System Level ◽  
Optimization Strategy ◽  
Business Decisions ◽  
Single Level ◽  
Performance Targets ◽  
Optimization Framework ◽  
Multidisciplinary Systems ◽  
Decision Based Design

In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg [1,2] is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

Decision-Based Collaborative Optimization Under Uncertainty

2000 ◽  
Author(s):  
Xiaoyu Gu ◽  
John E. Renaud ◽  
Leah M. Ashe ◽  
Stephen M. Batill ◽  
Amarjit S. Budhiraja ◽  
...  
Keyword(s):  
Systems Design ◽  
Collaborative Optimization ◽  
Optimization Under Uncertainty ◽  
System Level ◽  
Optimization Strategy ◽  
Business Decisions ◽  
Single Level ◽  
Optimization Framework ◽  
Multidisciplinary Systems ◽  
Decision Based Design

Abstract In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg (1996a, 1998) is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming the Hazelrigg framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

Implicit Uncertainty Propagation for Robust Collaborative Optimization

2006 ◽  
Vol 128 (4) ◽  
pp. 1001-1013 ◽  
Author(s):  
Xiaoyu (Stacey) Gu ◽  
John E. Renaud ◽  
Charles L. Penninger
Keyword(s):  
System Performance ◽  
Uncertainty Propagation ◽  
Bilevel Optimization ◽  
Parallel Execution ◽  
Collaborative Optimization ◽  
System Level ◽  
Optimization Strategy ◽  
Optimization Framework ◽  
Uncertainty Estimates ◽  
Consistency Constraints

In this research we develop a mathematical construct for estimating uncertainties within the bilevel optimization framework of collaborative optimization. The collaborative optimization strategy employs decomposition techniques that decouple analysis tools in order to facilitate disciplinary autonomy and parallel execution. To ensure consistency of the physical artifact being designed, interdisciplinary consistency constraints are introduced at the system level. These constraints implicitly enforce multidisciplinary consistency when satisfied. The decomposition employed in collaborative optimization prevents the use of explicit propagation techniques for estimating uncertainties of system performance. In this investigation, we develop and evaluate an implicit method for estimating system performance uncertainties within the collaborative optimization framework. The methodology accounts for both the uncertainty associated with design inputs and the uncertainty of performance predictions from other disciplinary simulation tools. These implicit uncertainty estimates are used as the basis for a new robust collaborative optimization (RCO) framework. The bilevel robust optimization strategy developed in this research provides for disciplinary autonomy in system design, while simultaneously accounting for performance uncertainties to ensure feasible robustness of the resulting system. The method is effective in locating a feasible robust optimum in application studies involving a multidisciplinary aircraft concept sizing problem. The system-level consistency constraint formulation used in this investigation avoids the computational difficulties normally associated with convergence in collaborative optimization. The consistency constraints are formulated to have the inherent properties necessary for convergence of general nonconvex problems when performing collaborative optimization.

Implicit Uncertainty Propagation for Robust Collaborative Optimization

2001 ◽  
Author(s):  
Xiaoyu Gu ◽  
John E. Renaud
Keyword(s):  
System Performance ◽  
Uncertainty Propagation ◽  
Bilevel Optimization ◽  
Parallel Execution ◽  
Collaborative Optimization ◽  
System Level ◽  
Optimization Strategy ◽  
Optimization Framework ◽  
Uncertainty Estimates ◽  
Consistency Constraints

Abstract In this research we develop a mathematical construct for estimating uncertainties within the bilevel optimization framework of collaborative optimization. The collaborative optimization strategy employs decomposition techniques that decouple analysis tools in order to facilitate disciplinary autonomy and parallel execution. To ensure consistency of the physical artifact being designed, interdisciplinary consistency constraints are introduced at the system level. These constraints implicitly enforce multidisciplinary consistency when satisfied. The decomposition employed in collaborative optimization prevents the use of explicit propagation techniques for estimating uncertainties of system performance. In this investigation we develop and evaluate an implicit method for estimating system performance uncertainties within the collaborative optimization framework. The methodology accounts for both the uncertainty associated with design inputs and the uncertainty of performance predictions from other disciplinary simulation tools. These implicit uncertainty estimates are used as the basis for a new robust collaborative optimization (RCO) framework. The bilevel robust optimization strategy developed in this research provides for disciplinary autonomy in system design, while simultaneously accounting for performance uncertainties to ensure feasible robustness of the resulting system. The method is effective in locating a feasible robust optima in application studies involving a multidisciplinary aircraft concept sizing problem. The system-level consistency constraint formulation used in this investigation avoids the computational difficulties normally associated with convergence in collaborative optimization. The consistency constraints are formulated to have the inherent properties necessary for convergence of general non-convex problems when performing collaborative optimization.

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.

Adaptive Collaborative Optimization Strategy Based on Genetic Algorithm

2011 ◽  
Vol 311-313 ◽  
pp. 32-36
Author(s):  
Ji Hong Liu ◽  
Hao Jiang ◽  
Qi Xie
Keyword(s):  
Genetic Algorithm ◽  
System Optimization ◽  
Collaborative Optimization ◽  
System Level ◽  
Optimization Strategy ◽  
Speed Reducer ◽  
Adaptive Penalty ◽  
Adaptive Penalty Function ◽  
Optimization Function ◽  
Definition Of

The genetic algorithm and the adaptive mechanism are adopted to tackle the inefficiency of optimization and the convergence difficulty of collaborative optimization (CO). Based on the further analysis of collaborative optimization process, the constraint conditions are converged into part of the optimization function. The system optimization model of CO has been reconstructed according to the adaptive penalty function which is based on the information of different disciplines and the transformation of system-level constraints. Therefore, the global and local search capabilities of optimization algorithm and searching efficiency of CO have been improved. Meanwhile, the difficulty of convergence caused by the internal definition of CO has been resolved. Finally, an example of speed reducer is demonstrated to verify the proposed method, showing that the convergence rate and search efficiency have been improved.

Component-Oriented Decomposition for Collaborative Optimization in Reinforcement Concrete Bridge Design

2011 ◽  
Vol 374-377 ◽  
pp. 2405-2410
Author(s):  
Lian Fa Wang ◽  
Ai Ping Tang
Keyword(s):  
Optimization Design ◽  
Target Function ◽  
Inequality Constraints ◽  
Optimization Methods ◽  
Collaborative Optimization ◽  
System Level ◽  
Optimization Strategy ◽  
Bridge Design ◽  
Direct Construction ◽  
Consistency Constraints

In order to implement the bi-level optimization strategy-collaborative optimization (CO) to bridge design, bridge optimization design process is subdivided into three subsystems in terms of component-oriented decomposition: superstructure subsystem, bearing subsystem and substructure subsystem. For system level, target function is formulated with the total direct construction cost, and inequality constraints induced relaxation factors are adopted to relax the intersubsystem consistency constraints. For subsystems, target functions are formulated with discrepancy expressions and constraints are formulated according to corresponding codes demands respectively. The feasibility and validity of the proposed approach are examined with an optimization process of reinforcement concrete box girder bridge. Optimization results from proposed approach are compared with that from mono-discipline optimization. The proposed approach shows high computing efficiency than mono-discipline optimization methods when achieving same optimization results.

Teaching Classical Control System Course With Portable Student-Owned Mechatronic Kits

2012 ◽  
Author(s):  
Eniko T. Enikov ◽  
Estelle Eke
Keyword(s):  
Carbon Fiber ◽  
Mechanical Engineering ◽  
Low Cost ◽  
Systems Design ◽  
Circuit Board ◽  
System Level ◽  
Linear Feedback ◽  
Phase System ◽  
Phase Lead ◽  
Controls Systems

Teaching classical controls systems design to mechanical engineering students presents unique challenges. While most mechanical engineering programs prepare students to be well-versed in the application of physical principles and modeling aspects of physical systems, implementation of closed loop control and system-level analysis is lagging. It is not uncommon that students report difficulty in conceptualizing even common controls systems terms such as steady-state error and disturbance rejection. Typically, most courses focus on the theoretical analysis and modeling, but students are left asking the questions…How do I implement a phase-lead compensator? …What is a non-minimum phase system? This paper presents an innovative approach in teaching control systems design course based on the use of a low-cost apparatus that has the ability to directly communicate with MATLAB and its Simulink toolbox, allowing students to drag-and-drop controllers and immediately test their effect on the response of the physical plant. The setup consists of a DC micro-motor driving a propeller attached to a carbon-fiber rod. The angular displacement of the rod is measured with an analog potentiometer, which acts as the pivot point for the carbon fiber rod. The miniature circuit board is powered by the USB port of a laptop and communicates to the host computer using the a virtual COM port. MATLAB/Simulink communicates to the board using its serial port read/write blocks to command the motor and detect the deflection angle. This presentation describes a typical semester-long experimental protocol facilitated by the low-cost kit. The kit allows demonstration of classical PID, phase lead and lag controllers, as well as non-linear feedback linearization techniques. Comparison between student gains before and after the introduction of the mechatronic kits are also provided.

scholarly journals An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls

2021 ◽  
Vol 11 (18) ◽  
pp. 8379
Author(s):  
Seongmin Kim
Keyword(s):  
System Level ◽  
Instruction Set ◽  
Optimization Strategy ◽  
Design Decisions ◽  
Systematic Analysis ◽  
Kernel Module ◽  
Recent Innovation ◽  
Optimization Methodology ◽  
Application Specific ◽  
Trusted Execution Environment

A recent innovation in the trusted execution environment (TEE) technologies enables the delegation of privacy-preserving computation to the cloud system. In particular, Intel SGX, an extension of x86 instruction set architecture (ISA), accelerates this trend by offering hardware-protected isolation with near-native performance. However, SGX inherently suffers from performance degradation depending on the workload characteristics due to the hardware restriction and design decisions that primarily concern the security guarantee. The system-level optimizations on SGX runtime and kernel module have been proposed to resolve this, but they cannot effectively reflect application-specific characteristics that largely impact the performance of legacy SGX applications. This work presents an optimization strategy to achieve application-level optimization by utilizing asynchronous switchless calls to reduce enclave transition, one of the dominant overheads of using SGX. Based on the systematic analysis, our methodology examines the performance benefit for each enclave transition wrapper and selectively applies switchless calls without modifying the legacy codebases. The evaluation shows that our optimization strategy successfully improves the end-to-end performance of our showcasing application, an SGX-enabled network middlebox.

scholarly journals Biased Information Passing Between Subsystems Over Time in Complex System Design

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Bo Yang Yu ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
Large Scale ◽  
Early Stage ◽  
Systems Design ◽  
System Level ◽  
Worst Case ◽  
Early Stage Design ◽  
Complex System Design ◽  
Biased Information

During the early stage design of large-scale engineering systems, design teams are challenged to balance a complex set of considerations. The established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice suboptimal system-level results are often reached due to factors such as satisficing, ill-defined problems, or other project constraints. Twelve subsystem and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate subsystems in their own work. Responses showed subsystem team members often presented conservative, worst-case scenarios to other subsystems when negotiating a tradeoff as a way of hedging against their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled in this paper with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias, and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

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