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| HEEDS Software > Application Examples |
| White Papers |
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The Real Benefits of Choosing Superior Optimization Technology |
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How to Select the Right Optimization Method for Your Problem |
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Optimal Design of Medical Devices |
| Benchmark Papers |
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SHERPA - An Efficient and Robust Optimization/Search Algorithm |
| Application Briefs |
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Optimization of a Tube Hydroforming Process |
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Shape Optimization for Improved Vehicle Safety and Reliability |
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Improved Vehicle Crashworthiness Via Shape Optimization |
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Design Optimization of Hydroformed Crashworthy Automotive Body Structures |
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Advanced Technologies for Design and Fabrication of Composite Automotive Components |
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Shape Optimization Of Crashworthy Structures |
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Optimization of a Vascular Stent |
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Design Optimization for Fatigue Life |
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Design Optimization of Progressively Crushing Rails |
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Optimization of Steam Usage for a Chemical Process |
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SHERPA - An Efficient and Robust Optimization/Search Algorithm
One of the keys to a successful optimization study is the effectiveness of the search algorithm used. This paper provides brief answers to the following questions about optimization algorithms:
• What does it mean for an algorithm to be efficient and robust, and why is it important?
• How do various algorithms compare on these important characteristics?
• What makes the algorithm SHERPA so effective?
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How to Select the Right Optimization Method for Your Problem
Automated design optimization technology is rapidly being adopted by engineers in nearly all major industries. The potential for delivering better designs in less time compared to manual optimization approaches makes automated design optimization very attractive from both a technical and a business point of view. However, one of the main barriers to widespread usage of design optimization in industry is the difficulty of choosing an appropriate optimization search algorithm for a given problem. This white paper describes the root of this issue and proposes a solution useful for many optimization problems.
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Optimization of a Tube Hydroforming Process
An approach is presented to optimize a tube hydroforming process using a Genetic Algorithm (GA) search method. The goal of the study is to maximize formability by identifying the optimal internal hydraulic pressure and feed rate while satisfying the forming limit diagram (FLD). The optimization software HEEDS is used in combination with the nonlinear structural finite element code LS-DYNA to carry out the investigation. In particular, a sub-region of a circular tube blank is formed into a square die. Compared to the best results of a manual optimization procedure, a 55% increase in expansion was achieved when using the pressure and feed profiles identified by the automated optimization procedure.
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Shape Optimization for Improved Vehicle Safety and Reliability
Advancements in high-performance computing and in nonlinear dynamic structural simulation software such as ABAQUS have made it possible to virtually test potential designs prior to building and testing expensive prototypes. These tools alone, however, still require an engineer to develop a design based on intuition and numerous time-consuming and error-prone iterations. The next level of advancement is software to automate the process of iterating over a large number of design scenarios and intelligently seek optimal values for those parameters that strongly affect product performance and cost. While many design optimization approaches are limited to a small number of continuous design variables, the approach described here leads to a productive search over hundreds of variables at a time. This capability has been implemented in a software product called HEEDS (Hierarchical Evolutionary Engineering Design System). HEEDS uses multiple autonomous agents to hierarchically decompose a problem into subsets of highly decomposed overlapped relationships. Decomposition is effected by using different numbers of design variables, different levels of design variable dicretization, and/or other problem-specific divide-and-conquer rules. The system combines evolutionary search algorithms with local optimization techniques. Using Abaqus/Explicit as the finite element solver within the HEEDS optimization environment, this process has been applied to several automotive lower compartment rail designs, resulting in significant gains in performance along with up to 20% reductions in mass compared to baseline rails designed by experienced engineers. An example application of this method is described herein. A second example demonstrates the shape optimization capabilities of HEEDS when used in conjunction with ABAQUS/CAE.
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Improved Vehicle Crashworthiness Via Shape Optimization
Advancements in high-performance computing and in nonlinear dynamic structural simulation have made it possible to virtually test potential designs prior to building and testing expensive prototypes. These tools alone, however, still require an engineer to develop a design based on intuition and numerous time-consuming and error-prone iterations. The next level of advancement is software to automate the process of iterating over a large number of design scenarios and intelligently seek optimal values for those parameters that strongly affect product performance and cost. While many design optimization approaches are limited to a small number of continuous design variables, the approach described here leads to a productive search over hundreds of variables at a time. This capability has been implemented in a software product called HEEDS (Hierarchical Evolutionary Engineering Design System). HEEDS uses multiple autonomous agents to hierarchically decompose a problem into subsets with highly decomposed overlapped relationships. Decomposition is effected by using different numbers of design variables, different levels of design variable discretization, and/or other problem-specific divide-and-conquer rules. The system combines evolutionary search algorithms with local optimization techniques. Using ABAQUS/Explicit and LS-DYNA as the finite element solver within the HEEDS optimization environment, this process has been applied to several automotive rail designs, resulting in significant gains in performance along with up to 20% reductions in mass compared to baseline rails designed by experienced engineers. Two example applications of this method are described herein.
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Design Optimization of Hydroformed Crashworthy Automotive Body Structures
While many design optimization approaches are limited to a small number of design variables, the approach described here leads to a productive search over hundreds of variables at a time. This capability has been implemented in a software product called HEEDS (Hierarchical Evolutionary Engineering Design System). HEEDS uses multiple autonomous agents to hierarchically decompose a problem into subsets with highly decomposed overlapped relationships. Decomposition is effected by using different numbers of design variables, different levels of design variable discretization, and/or other problem-specific divide-and-conquer rules. The system combines evolutionary search algorithms with local optimization techniques. Using explicit finite element codes such as LS-DYNA as the finite element solver within the HEEDS optimization environment, this process has been applied to several automotive rail designs, resulting in significant gains in performance in addition to substantial reductions in mass compared to baseline rails designed by experienced engineers. Two example applications of this method are described herein.
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Advanced Technologies for Design and Fabrication of Composite Automotive Components
Structural composites are available in various forms and functionality, providing the designer a tremendous amount of flexibility to develop innovative composite design solutions. But these advantages often cannot be realized without novel manufacturing methods that can accommodate heterogeneous parts of complex shape. Today, new manufacturing methods allow the designer to satisfy specific local strength criteria by judicious selection and placement of materials. At the same time, the freedom of complex component geometry provides the added benefits of combining multiple components/operations into a one-piece compression molded component.
These new material combinations and manufacturing techniques provide a vast and comprehensive set of new opportunities for novel design solutions that exceed previous performance, overcome previous limitations and stretch the limits of previous engineering design intuition. In order to take full advantage of these new materials and manufacturing techniques, advanced automated design optimization technologies can be used to discover creative solutions. These methods dramatically improve the relevance and speed of complex manual design processes, truncating them from months to days or even hours. They concurrently explore hundreds of design parameters and their relationships in product and process design scenarios, and intelligently seek optimal values for parameters that affect performance and cost. These design tools have been used in the development of several FRP structural programs solely focused on replacing traditional materials like steel, aluminum, and cast iron.
In this paper, a new composite manufacturing method and a new design optimization technique are discussed briefly. Several example applications to real automotive composite components are described to illustrate the benefits of combining advanced manufacturing and design methods to realize novel composite solutions at approximately one-half the weight of equivalent metallic parts.
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Shape Optimization Of Crashworthy Structures
Crashworthiness problems, which are highly dynamic and nonlinear, do not lend themselves well to classical gradient optimization techniques. Evolutionary-based design approaches that employ a form of guided stochastic search algorithm have been successfully applied to these problems. While many design optimization approaches are limited to a small number of continuous design variables, the approach described here can productively search over hundreds at a time. The power of classical evolutionary algorithms can be increased by allowing flexible design variable decomposition and incorporating classical local optimization methods and/or by embedding them within adaptive agents, which communicate but work semi-independently on a common problem. The authors have developed a system that allows for flexible design variable decomposition while combining evolutionary algorithms with local optimization. Within this approach, autonomous agents break down a problem hierarchically, using problem-specific divide-and-conquer rules to organize design variables and design criteria into a set of highly decomposed, overlapped relationships. These agents simultaneously search a discretized design space at various levels of resolution and use different design variable representations, performance measures (combinations of objectives and constraints), and local search methods. The agents exchange information about the decomposed solution space with each other, helping them jointly to satisfy multiple constraints and objectives. This technology has been implemented into a software code called HEEDS (Hierarchical Evolutionary Engineering Design System), which can be run on a single processor or in a networked computing environment, including clusters of personal computers or simple networks of workstations. Using LS-DYNA explicit as the finite element solver within the HEEDS optimization environment, this process has been applied to several automotive lower compartment rail designs, resulting in significant gains in performance along with up to 20% reductions in mass compared to baseline rails designed by experienced engineers. An example application of this method is described herein.
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Optimization of a Vascular Stent
Placing a stent into a human blood vessel presents many engineering design challenges and requires extremely high reliability and biocompatibility. From a mechanical design standpoint, the main challenges are choosing the material and the design geometry.
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Optimal Design of Medical Devices
The design of medical devices presents many challenges, as evidenced by special requirements for innovative materials, rigorous dimensional tolerances and uncompromised product safety and reliability. Advanced engineering skills supported by the use of sophisticated simulation technology are the norm, rather than an exception. The engineering process used today in the design of medical devices emphasizes numerical simulation, or Computer Aided Engineering, over the physical build-and-break prototyping activities for reasons of efficiency and savings in time and costs. But manually iterating on these virtual prototypes still requires a great deal of time and effort, and often does not lead to an optimal solution. Now, automated design optimization tools provide the means to accelerate the design process while increasing the quality of the end result.
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Design Optimization for Fatigue Life
To ensure reliability of designs, fatigue life performance is often an integral part of today’s CAE evaluations. In addition to meeting design targets for allowable deformations, stresses, and strains under the prescribed loading conditions, a reliable structure should also perform as intended for a minimum number of loading cycles. Fatigue life predictions are generally calculated from the results of finite element based simulations.
When a design does not meet all of the performance targets, the design must be modified iteratively until a suitable design is found. This process is very inefficient and expensive if done manually, since many CAE models must be built, executed and evaluated.
Red Cedar Technology’s HEEDS software is a multi-disciplinary design optimization software package that eliminates frustrating manual iterations while providing better and more efficient solutions to challenging design problems. It is able to effectively search broad and complicated design spaces using intelligent and adaptive techniques, locating those designs that best satisfy all performance specifications. HEEDS works with existing CAD and CAE tools to modify a design’s attributes and to predict the performance of a design. It performs these steps automatically and iteratively while searching for the best possible design(s).
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The Real Benefits of Choosing Superior Optimization Technology
Does it matter which optimization technology an engineering team chooses? The answer is, unequivocally, yes. Choosing a superior optimization technology can
• reduce design cycle time
• improve design performance
• increase innovation
• support efficient use of resources
Prior to automated, mathematics-based optimization, engineers performed manual search based primarily on intuition. In this process, a key indicator of success was the education and experience of the lead project engineer. A smarter and more efficient engineer could often develop far better designs in much less time than someone with less training and experience.
In the same way, a smarter and more efficient mathematical search algorithm can add significant value to an automated design optimization process, particularly when it comes to finding better designs in less time. In real terms, this equates to higher quality designs at lower cost, greater innovation, increased competitive advantage, and more.
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Design Optimization of Progressively Crushing Rails
To increase robustness of the crush mode and to decrease repair costs after a crash, it is desirable for front and rear rails in an automotive vehicle to crush progressively. Here, progressive crush refers to a mode of axial crush that initiates near the tip of the rail and then progresses rearward in a controlled fashion. In this study, a new strategy is investigated to achieve progressively crushing designs during an automated design optimization study. This strategy employs the definition of crush zones along the length of a rail, and a design optimization problem statement that encourages maximum energy absorption in any particular crush zone to occur prior to any energy absorption in rearward zones. It is demonstrated that high performing designs with progressive crush can be obtained using the proposed approach.
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Optimization of Steam Usage for a Chemical Process
HEEDS Professional was used with the Aspen Plus 2006 simulation tool to minimize the cost of producing Dichloromethane (DCM), a common chemical byproduct that can be used as a solvent and in the production processes of certain food products (such as decaffeinating coffee). HEEDS successfully designed the process to use 14% less steam as compared to the baseline design, while at the same time meeting the constraint on the concentration of Dichloromethane in the effluent stream.
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Red Cedar Technology • 4572 S. Hagadorn • Suite 3-A • East Lansing, Michigan USA 48823
Phone: 517.664.1137 • FAX: 517.664.1175 • info@redcedartech.com
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