This paper introduces a new technique for designing nonlinear feedback controllers that can effectively and efficiently control nonlinear and unstable dynamical systems. The technique, called State-Parameterized Nonlinear Programming Control (sp-NLPC), constructs an optimal control strategy that is a function of dynamical system states. This is achieved through an offline parametric optimization process using the predictive parameterized Pareto genetic algorithm (P3GA) and representing the optimized state-varying policy using radial basis function (RBF) metamodeling. The sp-NLPC technique avoids many limitations of alternative methods, such as the need to make strong assumptions about model form (e.g., linearity) and the demands of online optimization processes. The proposed method is benchmarked on the problems of controlling the highly nonlinear and inherently unstable systems: single and double inverted pendulums on a cart. Performance and computational efficiency are compared to several competing control design techniques. Results show that sp-NLPC outperforms and is more efficient than competing methods. The parametric solution strategy for sp-NLPC lends itself to use in Control Co-Design (CCD). Such extensions are discussed as part of future work.

This paper presents a new methodology for calculating the hypervolume indicator (HVI)for multi-objective and parametric data. Existing multi-objective HVI calculation techniques cannot be directly used for parametric data because designers do not have preferences for parameters like they do for objectives. The novel method presented herein allows for the consideration of both objectives and parameters through the newly introduced hypercone heuristic (HCH). This heuristic relaxes the strict rules of parametric Pareto dominance for a more practical dominance assessment when comparing designs of differing parameter values without violating Pareto dominance rules. A parametric HVI (pHVI) enhances a design engineer’s toolkit by enabling both online and offline evaluation of parametric optimization results. The pHVI measure allows designers to compare solution sets, detect optimization convergence, and to better inform optimization procedures in a parametric context. Results show that the HCH-based pHVI yields a similar quality measure to the existing technique based on a support vector domain description (SVDD) in a fraction of the computational time. Furthermore, the novel HCH-based pHVI technique satisfies multi-objective HVI properties allowing previous applications of the HVI to be applied to multi-objective parametric optimization. This contribution enables the field of parametric optimization, and thus parametric design, to benefit from prior and future advances in the multi-objective optimization domain involving the HVI.

Parametric optimization solves optimization problems as a function of uncontrollable or unknown parameters. Such an approach allows an engineer to gather more information than traditional optimization procedures during design. Existing methods for parametric optimization of computationally or monetarily expensive functions can be too time-consuming or impractical to solve. Therefore, new methods for the parametric optimization of expensive functions need to be explored. This work proposes a novel algorithm that leverages the advantages of two existing optimization algorithms. This new algorithm is called the efficient parametric optimization (EPO) algorithm. EPO enables adaptive sampling of a high-fidelity design space using an inexpensive low-fidelity response surface model. Such an approach largely reduces the required number of expensive high-fidelity computations. The proposed method is benchmarked using analytic test problems and used to evaluate a case study requiring finite element analysis. Results show that EPO performs as well as or better than the existing alternative, P3GA, for these problems given an allowable number of function evaluations.

Searching for and selecting among design solutions is not an effortless task. The principle of least effort suggests people seek to minimize the amount of effort they apply towards completing their tasks. In the context of engineering design, it is conceivable that expending more effort on the design problem yields a design solution with greater performance. In this paper, we investigate the impact of incentives on motivating engineering designers to increase the amount of effort they apply to solving design problems. Specifically, we formulate an analytical model of effort provision towards design tasks to compare two incentive structures: a probabilistic incentive and a deterministic incentive. With the probabilistic incentive, a designer’s final reward or penalty is uncertain, e.g. it is uncertain if a proposed design solution will meet requirements. With the deterministic incentive, a designer’s final reward or penalty is tied directly to the quality of the design solution as it is presented, e.g. a proposed design solution meets requirements with a certain probability as the figure of merit and the designer is directly rewarded or penalized on producing a design solution with that figure of merit. We parameterize the proposed analytical model and perform a parameter study to determine which incentive produces a more optimal design solution in the parameter space. Results show that there is no one dominant incentive structure, and the preferred incentive structure depends on how intense the reward or penalty is and how a designer subjectively valuates his effort.

Origami engineering is the study of the construction principles found in the art of origami and application of those principles to various engineering applications. Although origami engineering researchers have addressed the tasks of lifting a load and supporting a static load, to our knowledge no one has demonstrated a system combining these two important capabilities. In this paper, we describe an origami-inspired actuator that can fold flat, lift a load, and then support that load structurally with no electrical or mechanical input. The design is based on a bistable origami pattern similar to the classical origami waterbomb base, and is actuated by shape memory alloy (SMA) wires in torsion. We detail a framework for the generalized structure and actuation method of SMA wires as well as the construction and testing of a proof-of-concept lifter. This proof of concept structure, weighing 22 grams, is able to lift a 50g load in 8 seconds using a 2.5 Volt input, and statically support 200g even after the cessation of voltage input. We conduct a preliminary analysis into the parameters of the structure that most affect its lifting capabilities.

Delegation of decision authority is a fundamental characteristic of systems engineering problems. Engineers and managers at higher levels within the organization allocate responsibility and resources to other individuals through requirements flowdown and other processes. The prevalence of schedule slippages and budget overruns on systems engineering projects raises questions about the adequacy of and potential to improve existing methods. However, at present the community lacks a foundational understanding of these processes that would be valuable in identifying and validating candidate improvements to the systems engineering process. In this paper, we develop a conceptual modeling framework for delegation in systems engineering based on the principal-agent problem, a game-theoretic model of agent interaction across hierarchical levels. Several variations on the basic model are possible. We study the model and its variations on an illustrative example of a passenger vehicle engineering process. Numerical results highlight the impact of various assumptions, including whether engineers act normatively or according to proposed behavioral decision models. Implications and extensions are discussed, including the need for behavioral validation of engineering decision models and the potential to use the modeling framework to evaluate newly proposed delegation schemes.

Origami-inspired active structures have important characteristics such as reconfigurability and the ability to adopt compact flat forms for storage. A self-folding shape memory alloy (SMA)-based laminated sheet is considered in this work wherein SMA wire meshes comprise the top and bottom layers and a thermally insulating compliant elastomer comprises the middle layer. Uncertainty in various parameters (e.g. material properties) may affect the performance of the sheet, which is explored here. Different modeling approaches are studied in order to compare their accuracy and computational cost. A numerical approach based on the Euler-Bernoulli beam theory is selected due to its accuracy when compared to higher fidelity finite element simulations and its low computational cost, necessary to perform a large number of design evaluations as required for uncertainty analysis. Optimization is performed considering uncertainty in the material properties. Failure probabilities under mechanical constraints and expected values of fold curvature and blocking moment are considered during optimization of the self-folding sheet. The multiobjective genetic algorithm for technology characterization P3GA is used to obtain the Pareto dominant designs. Most designs forming the Pareto frontier have the same values for certain design parameters such as the distance between the wires in the SMA meshes non-dimensionalized by SMA wire thickness, elastomer layer thickness non-dimensionalized by SMA wire thickness, and applied temperature. The design parameter deciding the trade-off between fold curvature and blocking moment is found to be the SMA wire thickness.

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.

In this paper, we investigate the use of serious gaming as a research tool for design and systems engineering research. Design and systems engineering research produces theories and methods aimed at bettering the engineering process and improving the quality of end products. However, the engineering process is largely human-driven and thus the way in which theories and methods are implemented and used depends greatly on the human element. Serious gaming provides a fully controllable environment in which to test and validate theories and methods against human behavior. To evaluate the viability of serious gaming as a research tool, we construct a game to study the sunk cost effect in the context of a manned Mars mission, and compare game results with results from a survey centered on the scenario. We observe a difference in results between the game and survey; those in the game fall victim to the sunk cost effect despite treatment whereas those in the survey do not fall for the sunk cost effect in a control treatment. We hypothesize that this may be caused by a preference for continued gameplay as opposed to the objectives in the narrative. Therefore, we conclude that gaming, at least in our case, induces behavior that may not be representative of real-world engineer behavior and must be mitigated.

Origami inspired structures possess attractive characteristics such as the potential to be reconfigurable and the capability to be folded into compact forms for storage. Self-folding structures, which are systems able to perform folding operations without external mechanical input, are desirable in certain circumstances such as remote applications (e.g., space applications, underwater robotics). A self-folding structural sheet consisting of two outer layers of shape memory alloy (SMA) orthogonal wire meshes separated by an inner insulating layer is considered in this work. The inner layer consists of ABS plastic columns that connect the SMA wire mesh intersections of the top and bottom layers, which are co-located and co-oriented (denoted sparse middle layer/aligned meshes design). Significant reduction on the heat transfer between the SMA layers is expected in this design compared to previously considered designs with continuous or perforated elastomeric middle layers. The geometric and power input parameters of the sparse middle layer/aligned meshes design are optimized under mechanical and thermal constraints considering finite element and reduced order analytical models. The optimal folding performance of the sparse middle layer/aligned meshes design is compared to that of the previous designs. The results show that the sparse middle layer/aligned meshes design has promising characteristics as a self-folding structural sheet and provides for tighter folds compared to the designs with elastomeric middle layers.

The goal of this research is to experimentally characterize the capabilities of a concept for a self-folding reconfigurable sheet for use in origami-inspired engineering design and to use this characterization to validate simulations of physics-based models of the sheet. The sheet consists of an active, self-morphing laminate that contains two shape memory alloy (SMA) mesh layers and a passive compliant medium between these layers. The SMA layers are thermally actuated, allowing bending to occur in both positive and negative directions to create soft hill and valley folds. These folds are completely reversible, allowing the structure to fold and unfold without permanent deformation. Unlike past work on self-folding structures, these sheets can have folds along any line, be subsequently unfolded, and then be folded again in a new way. To explore the effect of changing design parameters on the performance metrics of the sheet, it is desirable to use Finite Element Analysis (FEA) simulations instead of relying on time consuming experiments. Such models have been created incorporating user material subroutines (UMATs) in an FEA solver such as Abaqus to capture material behavior, but these must now be validated against experimental data to establish how well they match experimental performance. The primary performance metric of the sheet was chosen to be the radius of curvature measured perpendicularly to the line of heating. Both experiment and simulation focus on the radius of curvature achieved by the sheet for a given set of design parameters and actuation path. The goal of validation is to achieve a desirable level of agreement and repeatability in these results. To measure the deformation and curvature in the sheet as it actuates, a 3D Digital Image Correlation (3D DIC) system is employed to track the movement of points along the surface of the sample as it is heated to a temperature above the transformation temperature of the SMA and allowed to fully actuate. These tools are utilized for a number of samples so that validation of the sheet encompasses multiple values for each of the primary design parameters.

Engineering design projects can be characterized by three dimensions: duration, budget, and scope. As projects grow in any of these three dimensions, there will be increased opportunity for uncertainty, and subsequently, increased risk. This study explored the relationship between the number of project activities and the presence of risk indicators. A risk indicator is a particular state of affairs that has potential predictive power about the likelihood of a risk occurring during the course of a project. This study was completed by examining eleven projects that were conducted within one division of a large engineering design firm. Projects were divided into groups according to the number of activities associated with each project, and interviews were conducted with employees, who worked in a variety of positions. A comparative analysis of risk indicators was then completed to identify potential “watch” areas and provide a basis for risk mitigation planning. The study provided conclusive evidence that the most frequently occurring risk indicators vary with the number of activities involved in a project. Project managers can use this information to create more specific risk management plans tailored to the types of risk indicators most frequently associated with the number of activities within a project.

Design is an uncertain human activity involving decisions with uncertain outcomes. Sources of uncertainty in product design include uncertainty in modeling methods, market preferences, and performance levels of subsystem technologies, among many others. The performance of a technology evolves over time, typically exhibiting improving performance. As the performance of a technology in the future is uncertain, quantifying the evolution of these technologies poses a challenge in making long-term design decisions. Here, we focus on how to make decisions using formal models of technology evolution. The scenario of a wind turbine energy company deciding which technology to invest in demonstrates a new technology evolution modeling technique and decision making method. The design of wind turbine arrays is a complex problem involving decisions such as location and turbine model selection. Wind turbines, like many other technologies, are currently evolving as the research and development efforts push the performance limits. In this research, the development of technology performance is modeled as an S-curve; slowly at first, quickly during heavy research and development effort, and slowly again as the performance approaches its limits. The S-curve model typically represents the evolution of just one performance attribute, but designers generally deal with problems involving multiple important attributes. Pareto frontiers representing the set of optimal solutions that the decision maker can select from at any point in time allow for modeling the evolution of technologies with multiple attributes. As the performance of a technology develops, the Pareto frontier shifts to a new location. The assumed S-curve form of technology development allows the designer to apply the uncertainty of technology development directly to the S-curve evolution model rather than applying the uncertainty to the future performance, giving a more focused application of uncertainty in the problem. The multi-attribute technology evolution modeling technique applied in decision-making gives designers greater insight when making long-term decisions involving technologies that evolve.