Jean-Daniel Beley

Application of higher order derivatives to parameterization

Research on automatic differentiation is mainly motivated by gradient computation and optimization. However, in the optimal design area, it is quite difficult to use optimization tools. Some constraints (e.g., aesthetics constraints, manufacturing constraints) are quite difficult to describe by mathematical expressions. In practice, the optimal design process is a dialog between the designer and the analysis software (structural analysis, electromagnetism, computational fluid dynamics, etc.). One analysis may take a while. Hence, parameterization tools such as design of experiments (D.O.E.) and neural networks are used. The aim of those tools is to build surrogate models. We present a parameterization method based on higher order derivatives computation obtained by automatic differentiation.

Analytical Derivatives Technology for Parametric Shape Design and Analysis in Structural Applications

The ability to perform and evaluate the effect of shape changes on the stress, modal and thermal response of components is an important ingredient in the ‘design’ of aircraft engine components. The classical design of experiments (DOE) based approach that is motivated from statistics (for physical experiments) is one of the possible approaches for the evaluation of the component response with respect to design parameters [1]. Since the underlying physical model used for the component response is deterministic and understood through a computer simulation model, one needs to re-think the use of the classical DOE techniques for this class of problems. In this paper, we explore an alternate sensitivity analysis based technique where a deterministic parametric response is constructed using exact derivatives of the complex finite-element (FE) based computer models to design parameters. The method is based on a discrete sensitivity analysis formulation using semi-automatic differentiation [2,3] to compute the Taylor series or its Pade equivalent for finite element based responses. Shape design or optimization in the context of finite element modeling is challenging because the evaluation of the response for different shape requires the need for a meshing consistent with the new geometry. This paper examines the differences in the nature and performance (accuracy and efficiency) of the analytical derivatives approach against other existing approaches with validation on several benchmark structural applications. The use of analytical derivatives for parametric analysis is demonstrated to have accuracy benefits on certain classes of shape applications.Copyright

Fast and efficient multi-domain system simulation based on coupled heterogeneous model structures

With the ever increasing complexity of designs, the ability to validate and optimize the overall design while simultaneously considering all of the sub-systems, has become increasingly important. System simulation tools seek to address this need by combining control system models and physical device models with links to detailed physics based tools, thereby enabling more complex designs and encouraging collaboration. In this paper, we describe a new state-ofthe- art approach to link a high level system simulation tool (Simplorer) with a fast and accurate rigid dynamics tool (RBD) and its application to multi-physics system design. This approach allows the designer to combine detailed rigid mechanics models with system models such as complex electronic semiconductor device models used in controls.