Topology Optimization of Piezoelectric Energy Harvesting Devices Subjected to Stochastic Excitation

Converting ambient vibration energy into electrical energy using piezoelectric energy harvester has attracted much interest in the past decades. In this paper, topology optimization is applied to design the optimal layout of the piezoelectric energy harvesting devices. The objective function is defined as to maximize the energy harvesting performance over a range of ambient vibration frequencies. Pseudo excitation method (PEM) is applied to analyze structural stationary random responses. Sensitivity analysis is derived by the adjoint method. Numerical examples are presented to demonstrate the validity of the proposed approach.

An Engineering Design Strategy for Reconfigurable Products That Support Poverty Alleviation

Reconfigurable products can adapt to new and changing customer needs. One potential, high-impact, area for product reconfiguration is in the design of income-generating products for poverty alleviation. Non-reconfigurable income-generating products such as manual irrigation pumps have helped millions of people sustainably escape poverty. However, millions of other impoverished people are unwilling to invest in these relatively costly products because of the high perceived and actual financial risk involved. As a result, these individuals do not benefit from such technologies. Alternatively, when income-generating products are designed to be reconfigurable, the window of affordability can be expanded to attract more individuals, while simultaneously making the product adaptable to the changing customer needs that accompany an increased income. The method provided in this paper significantly reduces the risks associated with purchasing income-generating products while simultaneously allowing the initial purchase to serve as a foundation for future increases in income. The method presented builds on principles of multiobjective optimization and Pareto optimality, by allowing the product to move from one location on the Pareto frontier to another through the addition of modules and reconfiguration. Elements of product family design are applied as each instantiation of the reconfigurable product is considered in the overall design optimization of the product. The design of a modular irrigation pump for developing nations demonstrates the methodology.

Designing a Product Package Platform

An essential part of designing a successful product family is establishing a recognizable, familiar, product family identity. It is very often the case that consumers first identify products based on their physical embodiment. The Apple iPod, DeWalt power tools, and KitchenAid appliances are all examples of product families that have successfully branded themselves based on physical principles. While physical branding is often the first trait apparent to designers, there are some products that cannot be differentiated based on physical appearance. This is especially common for consumable products. For example, it is impossible to differentiate between diet Coke, Classic Coke, and Pepsi when each is poured into separate glasses. When differentiation is difficult to achieve from a product’s physical characteristics, the product’s package becomes a vital part of establishing branding and communicating membership to a product family while maintaining individual product identity. In this paper, product packaging is investigated with a focus on the graphic packaging components that identify product families. These components include: color, shape, typography, and imagery. Through the application of tools used in facilities layout planning, graph theory, social network theory, and display design theory an approach to determine an optimal arrangement of graphic components is achieved. This approach is validated using a web based survey that tracks user-package interactions across a range of commonly used cereal boxes.

System RBDO With Correlated Variables Using Probabilistic Re-Analysis and Local Metamodels

A simulation-based, system reliability-based design optimization (RBDO) method is presented that can handle problems with multiple failure regions and correlated random variables. Copulas are used to represent dependence between random variables. The method uses a Probabilistic Re-Analysis (PRRA) approach in conjunction with a sequential trust-region optimization approach and local metamodels covering each trust region. PRRA calculates very efficiently the system reliability of a design by performing a single Monte Carlo (MC) simulation per trust region. Although PRRA is based on MC simulation, it calculates “smooth” sensitivity derivatives, allowing the use of a gradient-based optimizer. The PRRA method is based on importance sampling. One requirement for providing accurate results is that the support of the sampling PDF must contain the support of the joint PDF of the input random variables. The trust-region optimization approach satisfies this requirement. Local metamodels are constructed sequentially for each trust region taking advantage of the potential overlap of the trust regions. The metamodels are used to determine the value of the indicator function in MC simulation. An example with correlated input random variables demonstrates the accuracy and efficiency of the proposed RBDO method.

Development of Game Theoretic Protocols for Multilevel Design

The effectiveness of the use of game theory in addressing multi-objective design problems has been illustrated. For the most part, researchers have focused on design problems at single level. In this paper, we illustrate the efficacy of using game theoretic protocols to model the relationship between multidisciplinary engineering teams and facilitate decision making at multiple levels. We will illustrate the protocols in the context of an underwater vehicle with three levels that span material and geometric modeling associated with microstructure mediated design of the material and vehicle.

Design and Analysis of Hybrid Guideway Heating System for Morgantown Personal Rapid Transit

The Morgantown Personal Rapid Transit (M-PRT) system is a comfortable conveyance for travel in Morgantown, WV. One of its operating concerns is the increasing cost of heat to the guideway during winter. As the vehicles cannot run safely during snow, the system includes a guideway heating system to melt the ice from the guideway. To reduce the use of expensive natural gas, an interest has been expressed to define a hybrid heating system using an alternate fuel supply. Solid Oxide Fuel Cell (SOFC) was incorporated in the hybrid heating system. This hybrid heating system was designed, and then a detailed analysis was performed to ascertain the performance parameters like heat produced, thermal efficiency, cost of the system and the emissions involved. This high temperature fuel cell releases large amounts of usable heat in the form of exhaust gases. The exhaust gases are deprived of any undesired emissions that pollute the atmosphere. A USDOE EPSCoR WV State Implementation Award conducted by Advance Power Electricity Research Center (APERC) at West Virginia University provided support for conducting this research.

An Improved Kriging Assisted Multi-Objective Genetic Algorithm

Although Genetic Algorithms (GAs) and Multi-Objective Genetic Algorithms (MOGAs) have been widely used in engineering design optimization, the important challenge still faced by researchers in using these methods is their high computational cost due to the population-based nature of these methods. For these problems it is important to devise MOGAs that can significantly reduce the number of simulation calls compared to a conventional MOGA. We present an improved kriging assisted MOGA, called Circled Kriging MOGA (CK-MOGA), in which kriging metamodels are embedded within the computation procedure of a traditional MOGA. In the proposed approach, the decision as to whether the original simulation or its kriging metamodel should be used for evaluating an individual is based on a new objective switch criterion and an adaptive metamodeling technique. The effect of the possible estimated error from the metamodel is mitigated by applying the new switch criterion. Three numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed approach. The results show that, on the average, CK-MOGA outperforms both a conventional MOGA and our developed Kriging MOGA in terms of the number of simulation calls.

Risk Management in Product Design: Current State, Conceptual Model and Future Research

Risk management is an important element of product design. It helps to minimize the project- and product-related risks such as project budget and schedule overrun, or missing product cost and quality targets. Risk management is especially important for complex, international product design projects that involve a high degree of novel technology. This paper reviews the literature on risk management in product design. It examines the newly released international standard ISO 31000 “Risk management — Principles and guidelines” and explores its applicability to product design. The new standard consists of the seven process steps communication and consultation; establishing the context; risk identification; risk analysis; risk evaluation; risk treatment; and monitoring and review. A literature review reveals, among other findings, that the general ISO 31000 process model seems applicable to risk management in product design; the literature addresses different process elements to varying degrees, but none fully according to ISO recommendations; and that the integration of product design risk management with risk management of other disciplines, or between project and portfolio level in product design, is not well developed.

Computation of the Usage Contexts Coverage of a Jigsaw With CSP Techniques

In the context of the Usage Context Based Design (UCBD) of a product-service, a taxonomy of variables is suggested to setup the link between the design parameters of a product-service and the part of a set of expected usages that may be covered. This paper implements a physics-based model to provide a performance prediction for each usage context that also depends on the user skill. The physics describing the behavior and consequently the performances of a jigsaw are established. Simulating numerically the usage coverage is non trivial for two reasons: the presence of circular references in physical relations and the need to efficiently propagate value sets or domains instead of accurate values. For these two reasons, we modeled the usage coverage issue as a Constraint Satisfaction Problem and we result in the expected service performances and a value of a covered usage indicator.

Multiscale Variability and Uncertainty Quantification Based on a Generalized Multiscale Markov Model

Variability is inherent randomness in systems, whereas uncertainty is due to lack of knowledge. In this paper, a generalized multiscale Markov (GMM) model is proposed to quantify variability and uncertainty simultaneously in multiscale system analysis. The GMM model is based on a new imprecise probability theory that has the form of generalized interval, which is a Kaucher or modal extension of classical set-based intervals to represent uncertainties. The properties of the new definitions of independence and Bayesian inference are studied. Based on a new Bayes’ rule with generalized intervals, three cross-scale validation approaches that incorporate variability and uncertainty propagation are also developed.