A Virtual Engineering Tool for Product Design Using High Fidelity CFD Analysis Models

2005 ◽  
Author(s):  
Gengxun Huang ◽  
Angran Xiao ◽  
Kenneth M. Bryden
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Engineering Design ◽  
Direct Interaction ◽  
Design Tool ◽  
Optimization Process ◽  
Initial Population ◽  
High Fidelity ◽  
Design And Optimization ◽  
Virtual Engineering

Product design optimization is a complex decision-making process requiring intensive interactions between designers and the designed product. However, most current optimization tools do not support this type of direct interaction. Typically, resolving a converged result with an optimization tool takes a long solution time and high computing cost. However, designers are not involved in the optimization process and cannot control the quality of the so-called optimal result. In this paper, we introduce a virtual engineering design tool that expands the application scope of virtual reality from visualization to interaction and decision support. This design tool allows designers to easily experiment with different product designs using high fidelity CFD solver and observe the effects in an almost real-time manner. This can help designers understand the nature of the product and make superior decisions. Most importantly, the design tool enables designers to control the optimization computing process by selecting superior starting points or changing an obviously unpromising search direction. Hence, by adding human creativity and experience into the optimization process, designers can resolve the design optimization problem more efficiently. A coal pipe design and optimization scenario is presented to demonstrate the efficacy of this virtual engineering design tool. The goal of this tool is to enable a designer to modify the size and shape of a coal pipe to obtain evenly distributed coal at the outlet. In this tool after the initial population was chosen, a standard evolutionary algorithm was used to find the most superior pipe design within a much shorter time.

An Integrated Intelligent System Model and Its Applications in Virtual Product Design and Development

2008 ◽  
pp. 449-473
Author(s):  
Xuan F. Zha
Keyword(s):  
Product Design ◽  
Engineering Design ◽  
Intelligent System ◽  
Design And Development ◽  
Virtual Engineering ◽  
Modeling Analysis ◽  
Neuro Fuzzy ◽  
Intelligent Approach ◽  
Design Customization ◽  
Virtual Product

In this Chapter, a novel integrated intelligent framework is first proposed for virtual engineering design and development based on the soft computing and hybrid intelligent techniques. Then, an evolutionary neuro-fuzzy (EFNN) model is developed and used for supporting modeling, analysis and evaluation, and optimization tasks in the design process, which combines fuzzy logic with neural networks and genetic algorithms. The developed system HIDS-EFNN provides a unified integrated intelligent environment for virtual engineering design and simulation. The focus of this Chapter is to present a hybrid intelligent approach with evolutionary neuro-fuzzy modeling and its applications in virtual product design, customization and simulation (product performance prediction). Case studies are provided to illustrate and verify the proposed model and approach.

Evolutionary Multi-Agent Systems: An Adaptive Approach to Optimization in Dynamic Environments

2008 ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Engineering Design ◽  
Dynamic Environments ◽  
Optimization Process ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Engineering Design Problems ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a team of autonomous software agents to be effective in unknown and changing optimization environments by evolving to use the most successful algorithms at the points in the optimization process where they will be the most effective. We present the core framework and methodology which has potential applications in layout, scheduling, manufacturing, and other engineering design areas. The communal agent team organizational structure employed allows cooperation of agents through the products of their work and creates an ever changing set of individual solutions for the agents to work on. In addition, the organizational structure allows the framework to be adaptive to changes in the design space that occur during the optimization process — making our approach extremely flexible to the kinds of dynamic environments encountered in engineering design problems. An evolutionary approach is used, but evolution occurs at the strategic, rather than solution level — where the strategies of agents in the team (the decisions for picking, altering, and inserting a solution) evolve over time. As an application of this approach, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, each agent running a different solution strategy, must evolve to apply the solution strategies which are most useful given the set at any point in the process. As a team, the evolutionary agents produce better solutions than any individual algorithm. We discuss the extensions to our preliminary work that will make our framework highly useful to the design and optimization community.

Combustor Design Optimization Using Co-Kriging of Steady and Unsteady Turbulent Combustion

2011 ◽  
Vol 133 (12) ◽  
Author(s):  
Moresh J. Wankhede ◽  
Neil W. Bressloff ◽  
Andy J. Keane
Keyword(s):  
Design Optimization ◽  
Optimization Process ◽  
Design Stage ◽  
Reacting Flow ◽  
High Fidelity ◽  
Optimization Strategy ◽  
Function Calls ◽  
Unsteady Rans ◽  
Computational Budget ◽  
High Level

In the gas turbine industry, computational fluid dynamics (CFD) simulations are often used to predict and visualize the complex reacting flow dynamics, combustion environment and emissions performance of a combustor at the design stage. Given the complexity involved in obtaining accurate flow predictions and due to the expensive nature of simulations, conventional techniques for CFD based combustor design optimization are often ruled out, primarily due to the limits on available computing resources and time. The design optimization process normally requires a large number of analyses of the objective and constraint functions which necessitates a careful selection of fast, reliable and efficient computational methods for the CFD analysis and the optimization process. In this study, given a fixed computational budget, an assessment of a co-Kriging based optimization strategy against a standard Kriging based optimization strategy is presented for the design of a 2D combustor using steady and unsteady Reynolds-averaged Navier Stokes (RANS) formulation. Within the fixed computational budget, using a steady RANS formulation, the Kriging strategy successfully captures the underlying response; however with unsteady RANS the Kriging strategy fails to capture the underlying response due to the existence of a high level of noise. The co-Kriging strategy is then applied to two design problems, one using two levels of grid resolutions in a steady RANS formulation and the other using steady and unsteady RANS formulations on the same grid resolution. With the co-Kriging strategy, the multifidelity analysis is expected to find an optimum design in comparatively less time than that required using the high-fidelity model alone since less high-fidelity function calls should be required. However, using the applied computational setup for co-Kriging, the Kriging strategy beats the co-Kriging strategy under the steady RANS formulation whereas under the unsteady RANS formulation, the high level of noise stalls the co-Kriging optimization process.

Applications of Cultural Ant Colony Optimization in Optimal Excavator Mechanisms Design

2012 ◽  
Vol 479-481 ◽  
pp. 1857-1862
Author(s):  
Pei Qing Xie ◽  
Shu Wen Lin
Keyword(s):  
Design Optimization ◽  
Ant Colony Optimization ◽  
Engineering Design ◽  
Optimization Algorithms ◽  
Ant Colony ◽  
Optimization Process ◽  
Unsatisfactory Result ◽  
Engineering Design Optimization ◽  
Low Efficiency

In allusion to the low efficiency and unsatisfactory result of the tradional optimization algorithms in existence for engineering design optimization,this paper proposes a cultural ant colony optimization(CACO) algorithm for application in design optimization of excavator’s mechanisms to improve the excavator’s performance efficiently. Through testing and verifying experiments,it is concluded that CACO can discovery knowledge during optimization process and use the knowledge to guide the heuristic searching process,furthermore,it is an appropriate algorithm for the optimization of excavator mechanisms. CACO costs less time and can get better quality solution to improve excavator’s main porformances.

A Fuzzy Sequential Linear Programming Algorithm for Engineering Design Optimization

1995 ◽  
Author(s):  
Yeh-Liang Hsu ◽  
Yu-Fa Lin ◽  
Yu-Shuei Guo
Keyword(s):  
Linear Programming ◽  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Optimization Process ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Original Algorithm ◽  
Engineering Design Optimization

Abstract An optimization process can be viewed as a closed-loop control system. Traditional “controllers”, the numerical optimization algorithms, are usually “crisply” designed for well defined mathematical models. However, when applied to engineering design optimization problems in which function evaluations can be expensive and imprecise, very often the crisp algorithms will become impractical or will not converge. A common strategy for designers is to monitor the optimization process and keep “tuning” the process in an interactive manner, using their judgment on the information obtained from previous iterations, and their knowledge of the problem. This paper presents how the heuristics of this human supervision can be modeled into the optimization algorithms using fuzzy set theory. A fuzzy version of sequential linear programming is used to demonstrate this idea. Fuzzy rules, which describe the human supervision during the optimization process, are combined with the numerical rules of the original algorithm to refine the output of each iteration. Several design optimization problems are used to show the feasibility and practicality of this approach.

An Integrated Intelligent System Model and Its Applications in Virtual Product Design and Development

2011 ◽  
pp. 342-370 ◽  
Author(s):  
Xuan F. Zha
Keyword(s):  
Product Design ◽  
Engineering Design ◽  
Intelligent System ◽  
Design And Development ◽  
Virtual Engineering ◽  
Modeling Analysis ◽  
Neuro Fuzzy ◽  
Intelligent Approach ◽  
Design Customization ◽  
Virtual Product

In this Chapter, a novel integrated intelligent framework is first proposed for virtual engineering design and development based on the soft computing and hybrid intelligent techniques. Then, an evolutionary neuro-fuzzy (EFNN) model is developed and used for supporting modeling, analysis and evaluation, and optimization tasks in the design process, which combines fuzzy logic with neural networks and genetic algorithms. The developed system HIDS-EFNN provides a unified integrated intelligent environment for virtual engineering design and simulation. The focus of this Chapter is to present a hybrid intelligent approach with evolutionary neuro-fuzzy modeling and its applications in virtual product design, customization and simulation (product performance prediction). Case studies are provided to illustrate and verify the proposed model and approach.

Towards a Methodology for Multidisciplinary Design Optimization of Haptic Devices

2015 ◽  
Author(s):  
Xuan Sun ◽  
Kjell Andersson ◽  
Ulf Sellgren
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Haptic Device ◽  
Optimization Process ◽  
Multidisciplinary Design ◽  
Medical Applications ◽  
Initial Population ◽  
Haptic Devices ◽  
Multi Objective ◽  
Computer Aided

Design of haptic devices requires trade-off between many conflicting requirements, such as high stiffness, large workspace, small inertia, low actuator force/torque, and a small size of the device. With the traditional design and optimization process, it is difficult to effectively fulfill the system requirements by separately treating the different discipline domains. To solve this problem and to avoid sub-optimization, this work proposes a design methodology, based on Multidisciplinary Design Optimization (MDO) methods and tools, for design optimization of six degree-of-freedom (DOF) haptic devices for medical applications, e.g. simulators for surgeon and dentist training or for remote surgery. The proposed model-based and simulation-driven methodology aims to enable different disciplines and subsystems to be included in the haptic device optimization process by using a robust model architecture that integrates discipline-specific models in an optimization framework and thus enables automation of design activities in the concept and detail design phase. Because of the multi-criteria character of the performance requirements, multi-objective optimization is included as part of the proposed methodology. Because of the high-level requirements on haptic devices for medical applications in combination with a complex structure, models such as CAD (Computer Aided Design), CAE (Computer Aided Engineering), and kinematic models are considered to be integrated in the optimization process and presenting a systems view to the design engineers. An integration tool for MDO is used as framework to manage, integrate, and execute the optimization process. A case study of a 6-DOF haptic device based on a TAU structure is used to illustrate the proposed methodology. With this specific case, a Multi-objective Genetic Algorithm (MOGA) with an initial population based on a pseudo random SOBOL sequence and Monte Carlo samplings is used for the optimization.

scholarly journals Machine–Learning in Optimization of Expensive Black–Box Functions

2017 ◽  
Vol 27 (1) ◽  
pp. 105-118 ◽  
Author(s):  
Yoel Tenne
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Engineering Design ◽  
Computer Simulations ◽  
Numerical Experiments ◽  
Black Box ◽  
Optimization Process ◽  
Modern Engineering ◽  
Engineering Design Optimization ◽  
Unknown Reason

Abstract Modern engineering design optimization often uses computer simulations to evaluate candidate designs. For some of these designs the simulation can fail for an unknown reason, which in turn may hamper the optimization process. To handle such scenarios more effectively, this study proposes the integration of classifiers, borrowed from the domain of machine learning, into the optimization process. Several implementations of the proposed approach are described. An extensive set of numerical experiments shows that the proposed approach improves search effectiveness.

Automated Fault Tree Analysis for Engineering Design Optimization

2007 ◽  
Author(s):  
Tiefu Shao ◽  
Zongfang Lin ◽  
Sundar Krishnamurty ◽  
Ian R. Grosse ◽  
Leon J. Osterweil
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Process Model ◽  
Autonomous Agents ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Optimization Process ◽  
Tree Analysis ◽  
Use Of Resources ◽  
Engineering Design Optimization

This paper presents an automated fault tree analysis for engineering design optimization process. Specifically, a novel approach is presented in which Little-JIL, a process programming language, is applied to create a process model of engineering optimization. The process model uses a graphical language in the form of easy-to-understand block diagrams for defining processes that coordinate the activities of autonomous agents and their use of resources during the performance of a task. The use of Little-JIL facilitates agent coordination in the design optimization process and helps to model the order of and the communications between units of sub-processes. The resulting process model is easy to debug and is rigorous for simulation and formal reasoning in engineering design optimization. Furthermore, it enables the development of a clear and precise design optimization process model at different levels of granularity as perhaps preferred by the user. Moreover, since the process model allows for generation of fault trees automatically, it can be expected to be less errorprone than manually generated ones. A case study is shown to demonstrate the effectiveness and efficiency of the automated fault tree approach to design optimization and its usefulness in engineering decision making and in improving reliability of engineering design process.

A Marine Turbocharger Compressor Multi-Point 3D Design Optimization Tool

2021 ◽  
Author(s):  
Konstantinos Ntonas ◽  
Nikolaos Aretakis ◽  
Konstantinos Mathioudakis
Keyword(s):  
Diesel Engine ◽  
Design Optimization ◽  
Structural Integrity ◽  
Engine Performance ◽  
Design Tool ◽  
Optimization Process ◽  
3D Design ◽  
Element Analysis ◽  
Turbocharged Diesel Engine ◽  
Compressor Design

Abstract A marine turbocharger 3D compressor design tool, implemented on an existing marine turbocharger retrofit platform is presented. It produces 3D centrifugal compressor geometry for optimal compressor retrofit. It encompasses two modules, allowing the design process to become fully automatic. First, a 1D compressor multi-point design optimization process is carried out, aiming to provide a fast and reliable solution based on Turbocharged diesel Engine range of operation. Structural integrity is ensured by using simplified structural analysis. Dimensionless parameters are used as optimization variables, for a given nominal compressor mass flow and power. Then a CFD compressor multi-point design optimization process is carried out, producing optimized 3D compressor geometry. It complies with the Turbocharged diesel Engine range of operation, while structural integrity is ensured by using Finite Element analysis. A turbocharger compressor design case study is presented. First, a turbocharger 1D compressor design is carried out, aiming to at least reconstituting the original diesel engine performance. This first module provides a reliable compressor initial geometry for the 3D design module. A fully 3D compressor design is then performed, using a CFD-FEA optimization process, in order to provide an improved retrofitting solution. Comparison between the multi-point and the traditional one-point design method, shows that the multi-point method provides a wider SFC reduction in the range that the Diesel engine normally operates.

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