Optimal Design of Mechanical Engineering Systems

1995 ◽  
Vol 117 (B) ◽  
pp. 55-62 ◽  
Author(s):  
P. Y. Papalambros
Keyword(s):  
Design Optimization ◽  
Large Scale ◽  
Mechanical Design ◽  
Mathematical Optimization ◽  
Structural Systems ◽  
Engineering Systems ◽  
Design Problems ◽  
Future Design ◽  
And Topology ◽  
Key Issues

The capability of mathematical optimization to support the mechanical design process is finally reaching wide recognition, as evidenced by significant applications in industry. The article reviews key issues and challenges for design optimization theorists and practitioners. Large scale system design and topology or configuration design are identified as the most important areas of future design optimization research. Some new approaches for partitioning large design problems and for topology optimization of multi-component structural systems are introduced.

Optimal Design of Mechanical Engineering Systems

1995 ◽  
Vol 117 (B) ◽  
pp. 55-62
Author(s):  
P. Y. Papalambros
Keyword(s):  
Design Optimization ◽  
Large Scale ◽  
Mechanical Design ◽  
Mathematical Optimization ◽  
Structural Systems ◽  
Engineering Systems ◽  
Design Problems ◽  
Future Design ◽  
And Topology ◽  
Key Issues

The capability of mathematical optimization to support the mechanical design process is finally reaching wide recognition, as evidenced by significant applications in industry. The article reviews key issues and challenges for design optimization theorists and practitioners. Large scale system design and topology or configuration design are identified as the most important areas of future design optimization research. Some new approaches for partitioning large design problems and for topology optimization of multi-component structural systems are introduced.

A Distributed Pool Architecture for Highly Constrained Optimization Problems in Complex Systems Design

2013 ◽  
Vol 13 (3) ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Solution Process ◽  
Systems Design ◽  
Mixed Integer ◽  
Engineering Systems ◽  
Design Problems ◽  
Design Variables ◽  
Complex Engineering

Optimal design of complex engineering systems is challenging because numerous design variables and constraints are present. Dynamic changes in design requirements and lack of complete knowledge of subsystem requirements add to the complexity. We propose an enhanced distributed pool architecture to aid distributed solving of design optimization problems. The approach not only saves solution time but is also resilient against failures of some processors. It is best suited to handle highly constrained design problems, with dynamically changing constraints, where finding even a feasible solution (FS) is challenging. In our work, this task is distributed among many processors. Constraints can be easily added or removed without having to restart the solution process. We demonstrate the efficacy of our method in terms of computational savings and resistance to partial failures of some processors, using two mixed integer nonlinear programming (MINLP)-class mechanical design optimization problems.

Reliability-Based Design Optimization for Design Problems with Random Fuzzy and Interval Uncertainties

2019 ◽  
Vol 17 (06) ◽  
pp. 1950018 ◽  
Author(s):  
Li-Xiang Zhang ◽  
Xin-Jia Meng ◽  
He Zhang
Keyword(s):  
Design Optimization ◽  
Mechanical Design ◽  
Computational Cost ◽  
Reliability Based Design Optimization ◽  
Design Problems ◽  
Main Obstacle ◽  
Reliability Based Design ◽  
Interval Uncertainties ◽  
Mixed Uncertainties ◽  
Measurement Approach

Reliability-based design optimization (RBDO) has been widely used in mechanical design. However, the treatment of various uncertainties and associated computational burden are still the main obstacle of its application. A methodology of RBDO under random fuzzy and interval uncertainties (RFI-RBDO) is proposed in this paper. In the proposed methodology, two reliability analysis approaches, respectively named as FORM-[Formula: see text]-URA and interpolation-based sequential performance measurement approach (ISPMA), are developed for the mixed uncertainties assessment, and a parallel-computing-based SOMUA (PCSOMUA) method is proposed to reduce the computational cost of RFI-RBDO. Finally, two examples are provided to verify the validity of the methods.

Adaptive Refined-Model-Based Approach for Robust Design Optimization

2018 ◽  
pp. 19-43
Author(s):  
Tanmoy Chatterjee ◽  
Rajib Chowdhury
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Large Scale ◽  
Model Building ◽  
Kriging Model ◽  
Robust Design Optimization ◽  
Engineering Systems ◽  
Selection Scheme ◽  
Meta Model ◽  
Systems Implementation

Robust design optimization (RDO) has been noteworthy in realizing optimal design of engineering systems in presence of uncertainties. However, computations involved in RDO prove to be intensive for real-time applications. For addressing such issues, a meta-model-assisted RDO framework has been proposed. It has been further observed in such approximation-based RDO frameworks that accuracy of the meta-model is an important factor and even slight deviation in intermediate iterations may eventually lead to false optima. Therefore, two-tier improvement has been incorporated within existing Kriging model so as to ensure accurate approximation of response quantities. Firstly, the trend portion has been refined so that the model is capable of approximating higher order non-linearity. Secondly, a sequential basis selection scheme has been merged during model building, which reduces computational complexity significantly in case of large-scale systems. Implementation of the proposed approach in a few examples clearly illustrates its potential for further complex problems.

The research of the decomposition‐coordination method of multidisciplinary collaboration design optimization

2005 ◽  
Vol 22 (3) ◽  
pp. 274-285 ◽  
Author(s):  
Jianjiang Chen ◽  
Yifang Zhong ◽  
Renbin Xiao ◽  
Jianxun Sun
Keyword(s):  
Design Optimization ◽  
Large Scale ◽  
Global Optimum ◽  
Engineering Problem ◽  
Engineering Systems ◽  
Decomposition Approach ◽  
Content Type ◽  
Complex Design ◽  
Complex Engineering ◽  
Artificial Neural Network Ann

PurposeTo obtain the global optimum of large‐scale complex engineering systems, the paper proposes a decomposition‐coordination method of multidisciplinary design optimization (MDO).Design/methodology/approachA rational decomposition approach based on artificial neural network (ANN) and genetic algorithms is proposed for partitioning the complex design problem into smaller, more tractable subsystems. Once the problem is decomposed into subsystems, each subsystem may be solved in parallel provided that there is some mechanism to coordinate the solutions in the different subsystems. So the response surface approximation model based on the ANN as a coordination method is described and a MDO framework is presented.FindingsThe proposed method was implemented in the design of a tactical missile. Numerical results show the effectiveness of the decomposition‐coordination method, as indicated by both better performance and lower computational requirements.Originality/valueThis paper adopts a novel MDO method to solve complex engineering problem and offers a potential and efficient MDO framework to researchers.

Optimal design of mechanical components using the Bees Algorithm

2008 ◽  
Vol 223 (5) ◽  
pp. 1051-1056 ◽  
Author(s):  
D T Pham ◽  
A Ghanbarzadeh ◽  
S Otri ◽  
E Koç
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Honey Bees ◽  
Mechanical Design ◽  
Optimization Techniques ◽  
Bees Algorithm ◽  
Search Procedure ◽  
Robust Performance ◽  
Beam Structure ◽  
Design Problems

This paper describes the first application of the Bees Algorithm to mechanical design optimization. The Bees Algorithm is a search procedure inspired by the way honey bees forage for food. Two standard mechanical design problems, the design of a welded beam structure and the design of coil springs, were used to benchmark the Bees Algorithm against other optimization techniques. The paper presents the results obtained showing the robust performance of the Bees Algorithm.

scholarly journals TRACING THE EMERGENCE OF DESIGN PROBLEMS AND THEIR IMPACTS ON THE COMPLEXITY OF ENGINEERING SOLUTIONS

2021 ◽  
Vol 1 ◽  
pp. 3229-3238
Author(s):  
Torben Beernaert ◽  
Pascal Etman ◽  
Maarten De Bock ◽  
Ivo Classen ◽  
Marco De Baar
Keyword(s):  
Large Scale ◽  
Fusion Reactor ◽  
Design Stage ◽  
Engineering Model ◽  
Design Problems ◽  
Nuclear Fusion Reactor ◽  
Early Design Stage ◽  
Iterative Design ◽  
Problems And Solutions ◽  
Emergent Design

AbstractThe design of ITER, a large-scale nuclear fusion reactor, is intertwined with profound research and development efforts. Tough problems call for novel solutions, but the low maturity of those solutions can lead to unexpected problems. If designers keep solving such emergent problems in iterative design cycles, the complexity of the resulting design is bound to increase. Instead, we want to show designers the sources of emergent design problems, so they may be dealt with more effectively. We propose to model the interplay between multiple problems and solutions in a problem network. Each problem and solution is then connected to a dynamically changing engineering model, a graph of physical components. By analysing the problem network and the engineering model, we can (1) derive which problem has emerged from which solution and (2) compute the contribution of each design effort to the complexity of the evolving engineering model. The method is demonstrated for a sequence of problems and solutions that characterized the early design stage of an optical subsystem of ITER.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

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