Glen A. Hansen

Multiphysics simulations: Challenges and opportunities

We consider multiphysics applications from algorithmic and architectural perspectives, where “algorithmic” includes both mathematical analysis and computational complexity, and “architectural” includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.

Model-based control of a thermal regenerator. Part 1: dynamic model

Abstract This paper describes the dynamic process model and solution technique developed for the hot blast stoves used with the No. 7 blast furnace at the Ispat Inland Steel facility in East Chicago, IN, USA. A detailed, distributed parameter, heat transfer model of this thermal regenerator system is developed and verified using plant data. The model is capable of predicting accurately the temperature and energy content of the stoves during the thermal regenerative cycles. It was developed for a predictive controller that determines the minimum amount of fuel necessary to achieve the energy requirements from the system.

Model-based control of a thermal regenerator. Part 2: control and estimation

Abstract This paper describes the model-based control and estimation techniques implemented on the hot blast stoves for the number 7 blast furnace at the Ispat Inland Steel facility in East Chicago, IN. The process model is a detailed heat transfer model of this thermal regenerator system used as part of a predictive control scheme to determine the minimum amount of fuel necessary to achieve the energy requirements. Batch nonlinear least squares estimation is used to update the predicted temperature profile and heat transfer coefficients. These estimated parameters are then used by the model-based controller to determine the minimum fuel required for the subsequent regenerative cycle.

Automatic Differentiation for Numerically Exact Computation of Tangent Operators in Small‐ and Large‐Deformation Computational Inelasticity

Advances in computer software tools and technologies have transformed the way in which finite element codes and associated material models are developed. In this work, we propose a numerically exact approach for computing the sensitivites required to construct local consistent tangent operators in computational inelasticity applications. The tangent operators that come from the derivatives of constitutive equations are necessary for achieving quadratic convergence in integrating material models at the integration point level. Unlike finite difference-based numerical methods, the approach proposed in this work is based on an exact differentiation technique called automatic differentiation (AD). The method is efficient, robust and easy to incorporate. Numerical examples in both small- and large-deformation inelasticity problems with complicated material models are presented to illustrate the efficiency and applicability of the proposed method.

Lagrangian solution methods for nonlinear model predictive control

This work presents a simultaneous approach to the solution of the receding horizon, open-loop optimal model predictive control law for nonlinear systems using first-order Lagrangian methods. The nonlinear model considered is a general form of the initial value advective-diffusion parabolic partial differential equation. Others forms may be considered in a similar manner. The Lagrangian is formed from the discretized objective function, model and constraint equations. A finite volume approach is used to discretize the partial differential model equations. Inequality constraints on the model states and control inputs are handled with an active set method. The nonlinear equations resulting from the first order necessary conditions are then solved directly using a Newton-Krylov technique.

Component-based workflows for parallel thermomechanical analysis of arrayed geometries

Component-based simulation workflows can increase the agility of the design process by streamlining adaptation of new simulation methods. We present one such workflow for parallel unstructured mesh-based simulations and demonstrate its usefulness in the thermomechanical analysis of an array of solder joints used in microelectronics fabrication. We automate the simulation process from problem specification to the solution of the underlying PDEs, including problem setup, domain definition, and mesh generation. We establish the utility of the proposed approach by demonstrating that qualitatively different stress concentrations are seen in solder joints near the center of such an array and a solder joint seen at the edge of the same array.

NEAMS VLTS project

The objective of the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) Very Long Term Storage (VLTS) Project is to develop a simple, benchmark model that describes the performance of Zry4 d-hydrides in cladding, under conditions of long-term storage of used fuel. This model will be used to further explore the requirements of hydride modeling for used fuel storage and transport. It is expected that this model will be further developed as its weaknesses are understood, and as a basis of comparison as the Used Fuel Disposition (UFD) Campaign explores more comprehensive, multiscale approaches. Cladding hydride processes, a thermal model, a hydride model API, and the initial implementation of the J2Fiber hydride model is documented in this report.