Michael A. Yukish

Visual Steering Commands and Test Problems to Support Research in Trade Space Exploration

Designers can simulate thousands, if not millions, of design alternatives more cheaply and quickly than ever before with today’s computing power; however, the resulting data can overwhelm designers without proper tools to support multi-dimensional data visualization. In this paper, we discuss the use of a multi-dimensional data visualization tool and visual steering commands which allow designers to navigate multi-attribute trade spaces. The novelty in our work is providing designers with a set of visual steering commands to simultaneously explore the trade space and exploit new information and insights as they are gained. Specifically, designers can explore the entire design space (either sampled randomly or manually) or along the entire Pareto front using the Basic Sampler, Point Sampler, and/or Pareto Sampler. Alternatively, they can exploit information they have gained during the exploration process by searching near a specific point of interest or within a region of high preference using the Attractor, Preference Sampler, and/or Guided Pareto Sampler. Examples of each are included in this paper. Meanwhile, a suite of test problems is being formalized to support our trade space exploration – algorithmic development as well as empirical studies involving human decision-makers. This work supports our long-term goal of quantifying the benefits of putting humans back “in-the-loop” during design optimization.

ATTRIBUTE-BASED MULTIDISCIPLINARY OPTIMIZATION OF UNDERSEA VEHICLES

In an existing environment for designing undersea exploratory vehicles, the customer sets target values for certain attributes such as Range, Speed, Power, etc. and then desires a virtual prototype of the vehicle which includes variables such as dimensions and parameters associated with propulsion, guidance and control, hydraulics and other sub-systems. After a design is determined from the attribute specifications, simulation programs are used to estimate the performance and associated cost. However, while subsystem design servers exist, there is no coordination procedure among these various servers leading to a significant lack of automation. There is a need for a coordination/optimization strategy while maintaining the attribute driven environment currently used, the design process is currently done manually owing to the absence of a strategy to coordinate the various design servers. A methodology for automated design synthesis is presented here, using Collaborative Optimization. An example problem is presented. Non-gradient methods are used to circumvent non-smoothness of the problem and that of the collaborative optimization strategy. Future directions are identified.

Trade Space Exploration of a Wing Design Problem Using Visual Steering and Multi-Dimensional Data Visualization

Trade space exploration is a promising decision-making paradigm that provides a visual and intuitive means for formulating, adjusting, and ultimately solving multi-objective design optimization problems. This is achieved by combining multi-dimensional data visualization techniques with visual steering commands to allow designers to “steer” the optimization process while searching for the best, or Pareto optimal, designs. In this paper, we investigate the impact of constraint handling on the trade space exploration process. Specifically we consider three different constraint handling methods: (1) no constraint handling, (2) manual constraint handling, and (3) automatic constraint handling, and assess their impact on the efficiency and effectiveness of the visual steering commands used to explore the trade space. We find that the performance of the constraint handling method is highly correlated with the visual steering command that is being used and is consistent with the user’s a priori knowledge about the constraints, which is reflected in how constraints are handled in each method. The implications of these findings on the trade space exploration process are also discussed in conjunction with future work.

Preference Construction, Sequential Decision Making, and Trade Space Exploration

This paper develops and explores the interface between two related concepts in design decision making. First, design decision making is a process of simultaneously constructing one’s preferences while satisfying them. Second, design using computational models (e.g., simulation-based design and model-based design) is a sequential process that starts with low fidelity models for initial trades and progresses through models of increasing detail. Thus, decision making during design should be treated as a sequential decision process rather than as a single decision problem. This premise is supported by research from the domains of behavioral economics, psychology, judgment and decision making, neuroeconomics, marketing, and engineering design as reviewed herein. The premise is also substantiated by our own experience in conducting trade studies for numerous customers across engineering domains. The paper surveys the pertinent literature, presents supporting case studies and identifies use cases from our experiences, synthesizes a preliminary model of the sequential process, presents ongoing research in this area, and provides suggestions for future efforts.Copyright

Design as a Sequential Decision Process: A Method for Reducing Design Set Space Using Models to Bound Objectives

Design is a sequential decision process that increases the detail of modeling and analysis while simultaneously decreasing the space of alternatives considered. In a decision theoretic framework, low-fidelity models help decision-makers identify regions of interest in the tradespace and cull others prior to constructing more computationally expensive models of higher fidelity. The method presented herein demonstrates design as a sequence of finite decision epochs through a search space defined by the extent of the set of designs under consideration, and the level of analytic fidelity subjected to each design. Previous work has shown that multi-fidelity modeling can aid in rapid optimization of the design space when high-fidelity models are coupled with low-fidelity models. This paper offers two contributions to the design community: (1) a model of design as a sequential decision process of refinement using progressively more accurate and expensive models, and (2) a connected approach for how conceptual models couple with detailed models. Formal definitions of the process are provided, and a simple one-dimensional example is presented to demonstrate the use of sequential multi-fidelity modeling in determining an optimal modeling selection policy.Copyright

Toward a Value-Driven Design Approach for Complex Engineered Systems Using Trade Space Exploration Tools

Design decision-making involves trade-offs between many design variables and attributes, which can be difficult to model and capture in complex engineered systems. To choose the best design, the decision-maker is often required to analyze many different combinations of these variables and attributes and process the information internally. Trade Space Exploration (TSE) tools, including interactive and multi-dimensional data visualization, can be used to aid in this process and provide designers with a means to make better decisions, particularly during the design of complex engineered systems. In this paper, we investigate the use of TSE tools to support decision-makers using a Value-Driven Design (VDD) approach for complex engineered systems. A VDD approach necessitates a rethinking of trade space exploration. In this paper, we investigate the different uses of trade space exploration in a VDD context. We map a traditional TSE process into a value-based trade environment to provide greater decision support to a design team during complex systems design. The research leverages existing TSE paradigms and multi-dimensional data visualization tools to identify optimal designs using a value function for a system. The feasibility of using these TSE tools to help formulate value functions is also explored. A satellite design example is used to demonstrate the differences between a VDD approach to design complex engineered systems and a multi-objective approach to capture the Pareto frontier. Ongoing and future work is also discussed.Copyright

Analysis of an Algorithm for Identifying Pareto Points in Multi-Dimensional Data Sets

In this paper we present results from analytical and experimental investigations into the performance of divide & conquer algorithms for determining Pareto points in multidimensional data sets of size n and dimension d. The focus in this work is on the worst-case, where all points are Pareto, but extends to problem sets where only a partial subset of the points is Pareto. Analysis supported by experiment shows that the number of comparisons is bounded by two different curves, one that is O(n (log n)^(d-2)), and the other that is O(n^log 3). Which one is active depends on the relative values of n and d. Also, the number of comparisons is very sensitive to the structure of the data, varying by orders of magnitude for data sets with the same number of Pareto points.

Research topics in trade space exploration

This paper presents a number of new, open research topics in design methods emerging from our use of an internally developed method for supporting trade space exploration for complex systems, to include spacecraft. The research topics focus on how the trade space exploration process affects how models are assembled and exercised, how features in the trade space can be traced back to their source in the models, and on how the process of increasing refinement in modeling is itself a tropic of research

Simulation Model of an Autonomous Underwater Vehicle for Design Optimization

In this research, a stochastic simulation based design tool that facilitates design in a dynamic environment is developed. The simulation includes a full dynamic model of an Autonomous Underwater Vehicle (AUV) that was developed to evaluate the efiectiveness of such a craft given a set of physical attributes. These attributes include but are not limited to: speed, mass, moments of inertia, control gains in the auto-pilot, and target detection capabilities. The efiectiveness of the AUV is based upon the probability that it can successfully complete a given mission and how quickly it can complete this mission. The model is coupled with the Applied Research Lab’s unclassifled AUV problem that can compute weights, speeds, and e‐ciencies based on propulsor types, sonar conflgurations, and various other subsystems. In order to use this model in an optimization framework a mission was selected. This mission was to hit an oncoming torpedo before the torpedo was able to hit its target. The objective of this mission was to maximize the probability of successfully hitting the torpedo before the torpedo reaches its own target. In order to calculate this probability, the simulation was run with many difierent starting conflgurations including: difierent speeds of the oncoming torpedo, evasive maneuvers of the oncoming torpedo, and also various spacial orientations between the AUV and the targeted torpedo. This paper includes a detailed description of the simulation model, the development of the multidisciplinary design problem, and results obtained from the optimization of this problem.

Adding Value to Trade Space Exploration When Designing Complex Engineered Systems

Design decision-making involves tradeoffs between many design variables and attributes, which can be difficult to model and capture in complex engineered systems. To choose the best design, the decision maker is often required to analyze many different combinations of these variables and attributes and process the information internally. Trade Space Exploration (TSE) tools, including interactive and multidimensional data visualization, can be used to aid in this process and provide designers with a means to make better decisions, particularly during the design of complex engineered systems that have multiple, competing objectives. In this paper, we investigate the use of TSE tools to support decision makers using a Value-Driven Design (VDD) approach for complex engineered systems. A VDD approach necessitates a rethinking of TSE, and we outline and illustrate four different uses of a VDD approach to TSE. The research leverages existing TSE paradigms and multidimensional data visualization tools to identify optimal designs when using a value function for a system. A satellite design example is used to demonstrate the differences between a VDD approach to design complex engineered systems and a multiobjective approach to capture the Pareto frontier. Ongoing and future work is also discussed.