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Worst-Case Design in Head Impact Crashworthiness Optimization

Worst-Case Design in Head Impact Crashworthiness Optimization

K. J. Craig, Nielen Stander, Suri Balasubramanyam

This paper outlines a worst-case design process for automotive safety, using a unique optimization algorithm to find saddlepoints by simultaneously maximizing and minimizing different sets of design variables. This method is shown to be effective in solving the complex, noise-affected head impact problem.

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A Comparison of Metamodeling Techniques for Crashworthiness Optimization

A Comparison of Metamodeling Techniques for Crashworthiness Optimization

This paper compares three metamodeling techniques used for crashworthiness optimization, demonstrating that Successive Linear Response Surface, Neural Network, and Kriging methods are all comparably efficient. It highlights the advantage of the Neural Network and Kriging methods in creating more accurate global approximations.

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Optimization of Shell Buckling Incorporating Karhunen-Loeve-Based Geometrical Imperfections

Optimization of Shell Buckling Incorporating Karhunen-Loeve-Based Geometrical Imperfections

Ken Craig, Nielen Stander

This paper presents a design optimization study for shell buckling, incorporating geometric imperfections defined through Karhunen-Loève expansions. The methodology utilizes Monte Carlo simulation with LS-OPT to minimize the average peak normal force and internal energy, demonstrating the power of a parametric approach in creating robust designs.

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Uncertainty in finite Element Modeling and Failure Analysis: A Metrology-Based Approach

Uncertainty in finite Element Modeling and Failure Analysis: A Metrology-Based Approach

This paper proposes a metrology-based approach to address uncertainty in finite element modeling and failure analysis, treating parametric variations like mesh size as "numerical experiments" to quantify uncertainty. This methodology is critical for creating a more robust framework for design and failure prediction, moving beyond traditional deterministic models.

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Cancellous Bone Strains Indicate Efficacy of Stem Augmentation in Constrained Condylar Knees

Cancellous Bone Strains Indicate Efficacy of Stem Augmentation in Constrained Condylar Knees

This paper investigates the use of modular augmented stems in knee implants to improve tibial fixation using both cadaveric experiments and specimen-specific computational models. The models demonstrated that stems consistently reduce strain in the bone, especially benefiting patients with lower bone quality by transferring load to the distal cortical bone.

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