Edgar Galvan

Design and Optimization of a Shape Memory Alloy-Based Self-Folding Sheet

Origami engineering—the practice of creating useful three-dimensional structures through folding and fold-like operations on two-dimensional building-blocks—has the potential to impact several areas of design and manufacturing. In this article, we study a new concept for a self-folding system. It consists of an active, self-morphing laminate that includes two meshes of thermally-actuated shape memory alloy (SMA) wire separated by a compliant passive layer. The goal of this article is to analyze the folding behavior and examine key engineering tradeoffs associated with the proposed system. We consider the impact of several design variables including mesh wire thickness, mesh wire spacing, thickness of the insulating elastomer layer, and heating power. Response parameters of interest include effective folding angle, maximum von Mises stress in the SMA, maximum temperature in the SMA, maximum temperature in the elastomer, and radius of curvature at the fold line. We identify an optimized physical realization for maximizing folding capability under mechanical and thermal failure constraints. Furthermore, we conclude that the proposed self-folding system is capable of achieving folds of significant magnitude (as measured by the effective folding angle) as required to create useful 3D structures.

Parameterized Design Optimization of a Magnetohydrodynamic Liquid Metal Active Cooling Concept

The success of model-based multifunctional material design efforts relies on the proper development of multiphysical models and advanced optimization algorithms. This paper addresses both in the context of a structure that includes a liquid metal (LM) circuit for integrated cooling. We demonstrate for the first time on a complex engineering problem the use of a parameterized approach to design optimization that solves a family of optimization problems as a function of parameters exogenous to the subsystem of interest. This results in general knowledge about the capabilities of the subsystem rather than a restrictive point solution. We solve this specialized problem using the predictive parameterized Pareto genetic algorithm (P3GA) and show that it efficiently produces results that are accurate and useful for design exploration and reasoning. A “population seeding” approach allows an efficient multifidelity approach that combines a computationally efficient reduced-fidelity algebraic model with a computationally intensive finite-element model. Using data output from P3GA, we explore different design scenarios for the LM thermal management concept and demonstrate how engineers can make a final design selection once the exogenous parameters are resolved.

P3GA: An Algorithm for Technology Characterization

It is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. However, communicating this information can be a challenge. Mathematical characterizations of technical capabilities are of interest as a means to reduce ambiguity in communication and to increase opportunities to utilize design automation methods. The parameterized Pareto frontier (PPF) was introduced in prior work as a mathematical basis for modeling technical capabilities. One advantage of PPFs is that, in many cases, engineers can model a system by composing frontiers of its components. This allows for rapid technology evaluation and design space exploration. However, finding the PPF can be difficult. The contribution of this article is a new algorithm for approximating the PPF, called predictive parameterized Pareto genetic algorithm (P3GA). The proposed algorithm uses concepts and methods from multi-objective genetic optimization and machine learning to generate a discrete approximation of the PPF. If needed, designers can generate a continuous approximation of the frontier by generalizing beyond these data. The algorithm is explained, its performance is analyzed on numerical test problems, and its use is demonstrated on an engineering example. The results of the investigation indicate that P3GA may be effective in practice. [DOI: 10.1115/1.4028101]

A Genetic Algorithm Approach for Technology Characterization

It is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. Several researchers have investigated approaches for modeling the capabilities of a technology with the aim of supporting the design process. In these works, the information about the physical form is typically abstracted away. However, the efficient generation of an accurate model of technical capabilities remains a challenge. Pareto frontier based methods are often used but yield results that are of limited use for subsequent decision making and analysis. Models based on parameterized Pareto frontiers—termed Technology Characterization Models (TCMs)—are much more reusable and composable. However, there exists no efficient technique for modeling the parameterized Pareto frontier. The contribution of this paper is a new algorithm for modeling the parameterized Pareto frontier to be used as a model of the characteristics of a technology. The proposed algorithm uses fundamental concepts from multiobjective genetic optimization and machine learning to generate a model of the technology frontier.Copyright

Constraint Satisfaction Approach to the Design of Multi-Component, Multi-Phase Alloys

The development of new materials must start with an understanding of their phase stability. Researchers have used the CALPHAD method to develop self-consistent databases encoding the thermodynamics of phases. In this forward approach, thermo dynamic conditions (processing conditions such as composition, temperature, pressure, etc.) are mapped to equilibrium states. In this research, we are instead interested in the inverse problem of mapping a set of desired phase constitutions to the set of thermodynamic conditions that give rise to them. Recently, search and optimization techniques have been used to determine thermodynamic conditions that yield a particular phase stability state (point-to-point mapping). In this research, we focus on a more general problem: mapping of specific regions in multi-dimensional phase constitution spaces to ranges in values of thermodynamic conditions(set-to-set mapping). In the context of search theory, we are interested in finding all solutions to a Continuous Constraint Satisfaction Problem (CCSP). The problem is typically multi-dimensional, highly nonlinear, and, importantly, contains non-isolated solutions (the solution is ranges of values rather than finite points). Numerical methods for finding all solutions to CCSPs typically rely on branch-and-prune methods, which interleave branching with pruning steps. These methods are mainly designed to address CCSPs with isolated solutions and would be inefficient if applied to the problem at hand. In this work, we describe a novel algorithm to search the thermodynamic phase field for the full set of thermodynamic conditions that result in user-specified phase constitutions. The approach combines techniques from computational materials science, evolutionary computation, and machine learning to approximate the non-isolated solution set to the CCSP. We investigate the performance of the algorithm on an Fe-Ti binary alloy system using ThermoCalc with TCFE7 database. For this system, the algorithm is able to generate solutions with low error rates.Copyright

Technology Characterization Models and Their Use in Systems Design

Prior research suggests that set-based design representations can be useful for facilitating collaboration among engineers in a design project. However, existing set-based methods are limited in terms of how the sets are constructed and in their representational capability. The focus of this article is on the problem of modeling the capabilities of a component technology in a way that can be communicated and used in support of system-level decision making. The context is the system definition phases of a systems engineering project, when engineers still are considering various technical concepts. The approach under investigation requires engineers familiar with the component- or subsystem-level technologies to generate a set-based model of their achievable technical attributes, called a technology characterization model (TCM). Systems engineers then use these models to explore system-level alternatives and choose the combination of technologies that are best suited to the design problem. Previously, this approach was shown to be theoretically sound from a decision making perspective under idealized circumstances. This article is an investigation into the practical effectiveness of different TCM representational methods under realistic conditions such as having limited data. A power plant systems engineering problem is used as an example, with TCMs generated for different technical concepts for the condenser component. Samples of valid condenser realizations are used as inputs to the TCM representation methods. Two TCM representation methods are compared based on their solution accuracy and computational effort required: a Kriging-based interpolation and a machine learning technique called support vector domain description (SVDD). The results from this example hold that the SVDD-based method provides the better combination of accuracy and efficiency.