Towards the Design of Resilient Waste-to-Energy Systems Using Bayesian Networks

The concept of resilience has emerged from various domains to address how systems, people and organizations can handle uncertainty. This paper presents a method to improve the resilience of an engineering system by maximizing the system economic lifecycle value, as measured by Net Present Value, under uncertainty. The method is applied to a Waste-to-Energy system based in Singapore and the impact of combining robust and flexible design strategies to improve resilience are discussed. Robust strategies involve optimizing the initial capacity of the system while Bayesian Networks are implemented to choose the flexible expansion strategy that should be deployed given the current observations of demand uncertainties. The Bayesian Network shows promise and should be considered further where decisions are more complex. Resilience is further assessed by varying the volatility of the stochastic demand in the simulation. Increasing volatility generally made the system perform worse since not all demand could be converted to revenue due to capacity constraints. Flexibility shows increased value compared to a fixed design. However, when the system is allowed to upgrade too often, the costs of implementation negates the revenue increase. The better design is to have a high initial capacity, such that there is less restriction on the demand with two or three expansions.

Design for Additive Manufacturing of Cellular Compliant Mechanism Using Thermal History Feedback

With growing interest in metal additive manufacturing, one area of interest for design for additive manufacturing is the ability to understand how part geometry combined with the manufacturing process will affect part performance. In addition, many researchers are pursuing design for additive manufacturing with the goal of generating designs for stiff and lightweight applications as opposed to tailored compliance. A compliant mechanism has unique advantages over traditional mechanisms but previously, complex 3D compliant mechanisms have been limited by manufacturability. Recent advances in additive manufacturing enable fabrication of more complex and 3D metal compliant mechanisms, an area of research that is relatively unexplored. In this paper, a design for additive manufacturing workflow is proposed that incorporates feedback to a designer on both the structural performance and manufacturability. Specifically, a cellular contact-aided compliant mechanism for energy absorption is used as a test problem. Insights gained from finite element simulations of the energy absorbed as well as the thermal history from an AM build simulation are used to further refine the design. Using the proposed workflow, several trends on the performance and manufacturability of the test problem are determined and used to redesign the compliant unit cell. When compared to a preliminary unit cell design, a redesigned unit cell showed decreased energy absorption capacity of only 7.8% while decreasing thermal distortion by 20%. The workflow presented provides a systematic approach to inform a designer about methods to redesign an AM part.

Modeling Spatiotemporal Heterogeneity of Customer Preferences in Engineering Design

Customer preferences are found to evolve over time and correlate with geographical locations. Studying spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for a thorough understanding of preference trend. However, existing analytical models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. To fill this research gap, a spatial panel modeling approach is developed in this study to investigate the spatiotemporal heterogeneity of customer preferences by introducing engineering attributes explicitly as model inputs in support of demand forecasting in engineering design. In addition, a step-by-step procedure is proposed to aid the implementation of the approach. To demonstrate this approach, a case study is conducted on small SUV in China’s automotive market. Our results show that small SUVs with lower prices, higher power, and lower fuel consumption tend to have a positive impact on their sales in each region. In understanding the spatial patterns of China’s small SUV market, we found that each province has a unique spatial specific effect influencing the small SUV demand, which suggests that even if changing the design attributes of a product to the same extent, the resulting effects on product demand might be different across different regions. In understanding the underlying social-economic factors that drive the regional differences, it is found that Gross Domestic Product (GDP) per capita, length of paved roads per capita and household consumption expenditure have significantly positive influence on small SUV sales. These results demonstrate the potential capability of our approach in handling spatial variations of customers for product design and marketing strategy development. The main contribution of this research is the development of an analytical approach integrating spatiotemporal heterogeneity into demand modeling to support engineering design.

Optimal Flow Control and Single Split Architecture Exploration for Fluid-Based Thermal Management

High-performance cooling is often necessary for thermal management of high power density systems. Both human intuition and vast experience may not be adequate to identify optimal thermal management designs as systems increase in size and complexity. This paper presents a design framework supporting comprehensive exploration of a class of single phase fluid-based cooling architectures. The candidate cooling system architectures are represented using labeled rooted tree graphs. Dynamic models are automatically generated from these trees using a graph-based thermal modeling framework. Optimal performance is determined by solving an appropriate fluid flow control problem, handling temperature constraints in the presence of exogenous heat loads. Rigorous case studies are performed in simulation, with components having variable sets of heat loads and temperature constraints. Results include optimization of thermal endurance for an enumerated set of 4,051 architectures. In addition, cooling system architectures capable of steady-state operation under a given loading are identified.

Topology, Shape, and Size Optimization of Additively Manufactured Lattice Structures Based on the Superformula

This paper investigates the application of Superformula for structural synthesis. The focus is set on the lightweight design of parts that can be realized using discrete lattice structures. While the design domain will be obtained using the Superformula, a tetrahedral meshing technique will be applied to this domain to generate the topology of the lattice structure. The motivation for this investigation stems from the property of the Superformula to easily represent complex biological shapes, which opens a possibility to directly link a structural synthesis to a biomimetic design. Currently, numerous results are being reported showing the development of a wide range of design methods and tools that first study and then utilize the solutions and principles from the nature to solve technical problems. However, none of these methods and tools quantitatively utilizes these principles in the form of nature inspired shapes that can be controlled parametrically. The motivation for this work is also in part due to the mathematical formulation of the Superformula as a generalization of a superellipse, which, in contrast to the normal surface modeling offers a very compact and easy way to handle set of rich shape variants with promising applications in structural synthesis. The structural synthesis approach is organized as a volume minimization using Simulated Annealing (SA) to search over the topology and shape of the lattice structure. The fitness of each of candidate solutions generated by SA is determined based on the outcome of lattice member sizing for which an Interior Point based method is applied. The approach is validated with a case study involving inline skate wheel spokes.

Data-Driven Additive Manufacturing Constraints for Topology Optimization

The topology optimization (TO) of structures to be produced using additive manufacturing (AM) is explored using a data-driven constraint function that predicts the minimum producible size of small features in different shapes and orientations. This shape- and orientation-dependent manufacturing constraint, derived from experimental data, is implemented within a TO framework using a modified version of the Moving Morphable Components (MMC) approach. Because the analytic constraint function is fully differentiable, gradient-based optimization can be used. The MMC approach is extended in this work to include a “bootstrapping” step, which provides initial component layouts to the MMC algorithm based on intermediate Solid Isotropic Material with Penalization (SIMP) topology optimization results. This “bootstrapping” approach improves convergence compared to reference MMC implementations. Results from two compliance design optimization example problems demonstrate the successful integration of the manufacturability constraint in the MMC approach, and the optimal designs produced show minor changes in topology and shape compared to designs produced using fixed-radius filters in the traditional SIMP approach. The use of this data-driven manufacturability constraint makes it possible to take better advantage of the achievable complexity in additive manufacturing processes, while resulting in typical penalties to the design objective function of around only 2% when compared to the unconstrained case.

Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks

Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.

Redesign of the Femoral Stem for a Total Hip Arthroplasty for Additive Manufacturing

Total hip arthroplasty (THA) is an increasingly common procedure that replaces all or part of the hip joint. The average age of patients is decreasing, which in turn increases the need for more durable implants. Revisions in hip implants are frequently caused by three primary issues: femoral loading, poor fixation, and stress shielding. First, as the age of hip implant patients decreases, the hip implants are seeing increased loading, beyond what they were traditionally designed for. Second, traditional implants may have roughened surfaces but are not fully porous which would allow bone to grow in and through the implant. Third, traditional implants are too stiff, causing more load to be carried by the implant and shielding the bone from stress. Ultimately this stress shielding leads to bone resorption and implant loosening. Additive manufacturing (AM) presents a unique opportunity for enhanced performance by allowing for personalized medicine and increased functionality through geometrically complex parts. Much research has been devoted to how AM can be used to improve surgical implants through lattice structures. To date, the authors have found no studies that have performed a complete 3D lattice structure optimization in patient specific anatomy. This paper discusses the general design of an AM hip implant that is personalized for patient specific anatomy and proposes a workflow for optimizing a lattice structure within the implant. Using this design workflow, several lattice structured AM hip implants of various unit cell types are optimized. A solid hip implant is compared against the optimized hip implants. It appears the AM hip implant with a tetra lattice outperforms the other implant by reducing stiffness and allowing for greater bone ingrowth. Ultimately it was found that AM software still has many limitations associated with attempting complex optimizations with multiple materials in patient specific anatomy. Though software limitations prevented a full 3D optimization in patient specific anatomy, the challenges associated such an approach and limitations of the current software are discussed.

Understanding the Effect of Task Complexity and Problem-Solving Skills on the Design Performance of Agents in Systems Engineering

Systems engineering processes coordinate the efforts of many individuals to design a complex system. However, the goals of the involved individuals do not necessarily align with the system-level goals. Everyone, including managers, systems engineers, subsystem engineers, component designers, and contractors, is self-interested. It is not currently understood how this discrepancy between organizational and personal goals affects the outcome of complex systems engineering processes. To answer this question, we need a systems engineering theory that accounts for human behavior. Such a theory can be ideally expressed as a dynamic hierarchical network game of incomplete information. The nodes of this network represent individual agents and the edges the transfer of information and incentives. All agents decide independently on how much effort they should devote to a delegated task by maximizing their expected utility; the expectation is over their beliefs about the actions of all other individuals and the moves of nature. An essential component of such a model is the quality function, defined as the map between an agent’s effort and the quality of their job outcome. In the economics literature, the quality function is assumed to be a linear function of effort with additive Gaussian noise. This simplistic assumption ignores two critical factors relevant to systems engineering: (1) the complexity of the design task, and (2) the problem-solving skills of the agent. Systems engineers establish their beliefs about these two factors through years of job experience. In this paper, we encode these beliefs in clear mathematical statements about the form of the quality function. Our approach proceeds in two steps: (1) we construct a generative stochastic model of the delegated task, and (2) we develop a reduced order representation suitable for use in a more extensive game-theoretic model of a systems engineering process. Focusing on the early design stages of a systems engineering process, we model the design task as a function maximization problem and, thus, we associate the systems engineer’s beliefs about the complexity of the task with their beliefs about the complexity of the function being maximized. Furthermore, we associate an agent’s problem solving-skills with the strategy they use to solve the underlying function maximization problem. We identify two agent types: “naïve” (follows a random search strategy) and “skillful” (follows a Bayesian global optimization strategy). Through an extensive simulation study, we show that the assumption of the linear quality function is only valid for small effort levels. In general, the quality function is an increasing, concave function with derivative and curvature that depend on the problem complexity and agent’s skills.

Over-Design Versus Redesign As a Response to Future Requirements

Though little research has been done in the field of over-design as a product development strategy, an over-design approach can help products avoid the issue of premature obsolescence. This paper compares over-design to redesign as approaches to address the emergence of future requirements. Net present value (NPV) analyses of several real world applications are examined from the perspective of manufacturers and customers. This analysis is used to determine the conditions under which an over-design approach provides a greater benefit than a redesign approach. Over-design is found to have a higher net present value than redesign when future requirements occur soon after the initial release, discount rates are low, initial research and development cost or price is high, and when the incremental costs of the future requirements are low.