Non-Probabilistic Based Topology Optimization Under External Load Uncertainty With Eigenvalue-Superposition of Convex Models

In this paper the Eigenvalue-Superposition of Convex Models (ESCM) based topology optimization method for solving topology optimization problems under external load uncertainties is presented. The load uncertainties are formulated using the non-probabilistic based unknown-but-bounded convex model. The sensitivities are derived and the problem is solved using gradient based algorithm. The proposed ESCM based method yields the material distribution which would optimize the worst structure response under the uncertain loads. Comparing to the deterministic based topology optimization formulation the ESCM based method provided more reasonable solutions when load uncertainties were involved. The simplicity, efficiency and versatility of the proposed ESCM based topology optimization method can be considered as a supplement to the sophisticated reliability based topology optimization methods.

A Copula-Based Sampling Method for Data-Driven Prognostics and Health Management

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

Design Optimization of a Solar-Powered Reverse Osmosis Desalination System for Small Communities

Fresh water availability is essential for the economic development in small communities in remote areas. In desert climate, where naturally occurring fresh water is scarce, seawater or brackish water from wells is often more abundant. Since water desalination approaches are energy intensive, a strong motivation exists for the design of cost-effective desalination systems that utilize the abundant renewable energy resource; solar energy. This paper presents an optimization model of a solar-powered reverse osmosis (RO) desalination system. RO systems rely on pumping salty water at high pressure through semi-permeable membrane modules. Under sufficient pressure, water molecules will flow through the membranes, leaving salt ions behind, and are collected in a fresh water stream. Since RO system are primarily powered via electricity, the system model incorporates photovoltaic (PV) panels, and battery storage for smoothing out fluctuations in the PV power output, as well as allowing system operation for a number of hours after sunset. Design variables include sizing of the PV solar collectors, battery storage capacity, as well as the sizing of the RO system membrane module and power elements. The objective is to minimize the cost of unit volume produced fresh water, subject to constraints on production capacity. A genetic algorithm is used to generate and compare optimal designs for two different locations near the Red Sea and Sinai.

A Heat Transfer Model for the Conceptual Design of a Biomass Cookstove for Developing Countries

The use of biomass cookstoves to meet household energy needs has a profound impact on the life and health of individuals, families, and communities in the developing world. This paper introduces an experimentally validated heat transfer analysis model for use during the conceptual design process of a biomass cookstove to be used in the developing world. This steady-state model of a shielded, natural-draft biomass cookstove fitted with a flat-bottomed pot with pot-shield was developed using published experimental data that included 63 variations of 15 operating, geometrical, and material variables. The model provides the essential information needed to support decision making during the cookstove conceptual design process by predicting heat transfer efficiency as a function of stove geometry, construction material, firepower, and fuel moisture content.

A Knowledge-Based Approach for the Automated Design of Robotic/Human Manufacturing Workcells in Hazardous Environments

The design of manufacturing systems in hazardous environments is complex, requiring interdisciplinary knowledge to determine which components and operators (human or robotic) are feasible. When conceptualizing designs, some options may be overlooked or unknowingly infeasible due to the design engineers’ lack of knowledge in a particular field or ineffective communication of requirements between disciplines. Computational design tools can help alleviate many of the problems encountered in this design task. We create a knowledge-based system (KBS) utilizing CLIPS to automate the synthesis of conceptual manufacturing system designs in radioactive environments. The KBS takes a high-level functional description of a process and uses FBS modeling to generate multiple designs with generic components retrieved from a database and low-level manufacturing task sequences. Using this approach, many options are explored and operator task compatibility is directly addressed. The KBS is applied to the design of glovebox processing systems at Los Alamos National Laboratory (LANL).

Design Methods for Integrated Design of Blast Resistance Panels and Materials

Integrated Materials and Products Design (IMPD) differs in the way that materials as well as product layout are designed or optimized in a concurrent manner to meet design requirements. IMPD allows the specific performance required in a product to be achieved by tailoring materials and product, since system performance will not be limited by a pre-chosen material employed in conventional, material-selection-based design. In this study, Blast Resistance Panels (BRPs) with square honeycomb core are designed based on this new design approach to further enhance the performance of BRPs. We employ multi-level design methods for the integrated design of blast resistance panels and materials. Along with the traditional multi-level optimization of BRP, another design approach, Analytical Target Cascading (ATC) is introduced for a comparative design study in the BRP design. In this article, we compare the design results and design exploration efficiency of the two multi-level design methods in designing the blast resistance panels as well as those materials. We also discuss the advantage and disadvantage of the methods observed in this study.

Resilient Design of Complex Engineered Systems

This paper presents a complex network and graph spectral approach to calculate the resiliency of complex engineered systems. Resiliency is a key driver in how systems are developed to operate in an unexpected operating environment, and how systems change and respond to the environments in which they operate. This paper deduces resiliency properties of complex engineered systems based on graph spectra calculated from their adjacency matrix representations, which describes the physical connections between components in a complex engineered systems. In conjunction with the adjacency matrix, the degree and Laplacian matrices also have eigenvalue and eigenspectrum properties that can be used to calculate the resiliency of the complex engineered system. One such property of the Laplacian matrix is the algebraic connectivity. The algebraic connectivity is defined as the second smallest eigenvalue of the Laplacian matrix and is proven to be directly related to the resiliency of a complex network. Our motivation in the present work is to calculate the algebraic connectivity and other graph spectra properties to predict the resiliency of the system under design.

Application of Regional Strain Energy in Topology Optimization With Inertia Relief Analysis

In finite element analysis, inertia relief solves the response of an unconstrained structure subject to constant or slowly varying external loads with static analysis computational cost. It is very attractive to utilize it in topology optimization to design structures under unbalanced loads, such as in impact and drop phenomena. In this paper, regional strain energy formulation and inertia relief is integrated into topology optimization to design protective structure under unbalanced loads. For background, the equations of inertia relief are introduced and a commonly used solving method is revisited. Then the regional strain energy formulation for topology optimization with inertia relief is proposed and its sensitivity is derived from the adjoint method. Based on the solving method, the sensitivity is evaluated term by term to simplify the results. The simplified sensitivity can be calculated easily using the output of commercial finite element packages. Finally, the effectiveness of this formulation is shown in the first example and the proposed regional strain energy formulation for topology optimization with inertia relief are presented and discussed in the protective structure design examples.

Early-Stage Design of Rheologically Complex Materials via Material Function Design Targets

Rheological material properties are high-dimensional function-valued quantities, such as frequency-dependent viscoelastic moduli or non-Newtonian shear viscosity. Here we describe a process to model and optimize design targets for such rheological material functions. For linear viscoelastic systems, we demonstrate that one can avoid specific a priori assumptions of spring-dashpot topology by writing governing equations in terms of a time-dependent relaxation modulus function. Our approach embraces rheological design freedom, connecting system-level performance to optimal material functions that transcend specific material classes or structure. This technique is therefore material agnostic, applying to any material class including polymers, colloids, metals, composites, or any rheologically complex material. These early-stage design targets allow for broadly creative ideation of possible material solutions, which can then be used for either material-specific selection or later-stage design of novel materials.

Extracting Consumer Preference From User-Generated Content Sources Using Classification

The use of online, user-generated content for consumer preference modeling has been a recent topic of interest among the engineering and marketing communities. With the rapid growth of many different types of user-generate content sources, the tasks of reliable opinion extraction and data interpretation are critical challenges. This research investigates one of the largest and most-active content sources, Twitter, and its viability as a content source for preference modeling. Support Vector Machine (SVM) is used for sentiment classification of the messages, and a Twitter query strategy is developed to categorize messages according to product attributes and attribute levels. Over 7,000 messages are collected for a smartphone design case study. The preference modeling results are compared with those from a typical product review study, including over 2,500 product reviews. Overall, the results demonstrate that consumers do express their product opinions through Twitter; thus, this content source could potentially facilitate product design and decision-making via preference modeling.