bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Method to Specify Part of a System to Change in Improvement Design
Authors: Oizumi Kazuya , Aoyama Kazuhiro 
Published: November 2020

Abstract
Improvement design, which is characterized by minimum necessary changes to a past product, is employed to reuse verified and experienced parts to create a new product faster, reliable, and economically sound. However, as products become so complex that changes to some parts propagates concomitant changes to other parts, which makes it difficult to enjoy the advantages of improvement design. Therefore, design changes have to be contemplated at the early phase of design. This paper proposes an improvement design planning method based on House of quality. Parts of a product to be changed and directions on which they are changed are explored in terms of opportunity to improve product and risk of worsening other parts. By exploring the possible design in the trade-off between the opportunity and the risk, design change plan that comprises proper parts of a product to be changed can be decided. A case study on a solar boat design demonstrates the applicability and validity of the proposed method.
Applications of Polynomial Chaos-Based Cokriging to Simulation-Based Analysis and Design Under Uncertainty
Authors: Nagawkar Jethro , Leifsson Leifur 
Published: November 2020

Abstract
This paper demonstrates the use of the polynomial chaos-based Cokriging (PC-Cokriging) on various simulation-based problems, namely an analytical borehole function, an ultrasonic testing (UT) case and a robust design optimization of an airfoil case. This metamodel is compared to Kriging, polynomial chaos expansion (PCE), polynomial chaos-based Kriging (PC-Kriging) and Cokriging. The PC-Cokriging model is a multi-variate variant of PC-Kriging and its construction is similar to Cokriging. For the borehole function, the PC-Cokriging requires only three high-fidelity samples to accurately capture the global accuracy of the function. For the UT case, it requires 20 points. Sensitivity analysis is performed for the UT case showing that the F-number has negligible effect on the output response. For the robust design case, a 75 and 31 drag count reduction is reported on the mean and standard deviation of the drag coefficient, respectively, when compared to the baseline shape.
Development of a Modal Selection Method for Full Strain Field Estimation
Authors: Fu Gen , Untaroiu Alexandrina 
Published: November 2020

Abstract
Full field response of a structure is critical for evaluating the performance of large slender structures. Since only several discrete measurements can be acquired during operation, the data expansion method is important for the estimation of the full field responses of the large complex structure. In previous studies, modal transformation methods were mainly applied in model reduction/expansion and global shape sensing. Compared to other expansion methods, the modal method is straightforward to implement and computational efficient, which makes it the most suitable approach for real-time expansion. However, only the first several modes were included in the modal transformation method in previous studies. Since the errors due to truncated mode components can occur under high frequency band excitations, it is necessary to include all of the modes that contribute significantly to the responses of the structure. Therefore, in this study, a modal selection method based on operational modal analysis (OMA) is proposed for selecting proper modes. The modal characteristics of the system were derived with the strain data at several discrete locations. The contribution of each mode was quantified. By sorting the modes based on their contribution, the most significant modes can be used in the expansion process. Two operational modal analysis methods, stochastic system identification (SSI) and frequency domain decomposition (FDD), were considered and compared. The proposed approach was implemented with a computational model. Considerable improvement has been observed when high bandwidth excitations were added. The proposed modal selection method can successfully rank the participated modes. It can improve the accuracy of the modal transformation approach as shown in the impact loading case. It can be used for data expansion even when high frequency band is excited. Finally, we believe the novel methods presented in this study could be used in the development of more reliable health monitoring systems for turbomachinery.
A Method for Solving Multi-Objective Optimization Problems Containing an Infinite Number of Parameterized Objectives
Authors: Rudnick-Cohen Eliot 
Published: November 2020

Abstract
Multi-objective decision making problems can sometimes involve an infinite number of objectives. In this paper, an approach is presented for solving multi-objective optimization problems containing an infinite number of parameterized objectives, termed “infinite objective optimization”. A formulation is given for infinite objective optimization problems and an approach for checking whether a Pareto frontier is a solution to this formulation is detailed. Using this approach, a new sampling based approach is developed for solving infinite objective optimization problems. The new approach is tested on several different example problems and is shown to be faster and perform better than a brute force approach.
Extending the Use of Bio-Inspiration for Water Distribution Networks to Urban Settings
Authors: Dave Tirth , Layton Astrid 
Published: November 2020

Abstract
Prior work investigated the use of biological food webs as a source of quantitative guidance to balance the efficiency and redundancy in industrial water distribution networks. Urban water distribution networks, however, present unique challenges that prevent a direct replication of the bio-inspired methods used for industrial network settings. Many of the benefits bio-inspiration can impart on human networks, including resilience-related improvements, makes use of reuse- and recycling-related pathways — maximizing the use of all available value in the network. This work looks at the application of bio-inspiration to urban water network settings, modeling these networks to include local wastewater treatment options and testing actor aggregation options to best fit the ecosystem analogy. The results suggest that biological ecosystems do in fact have characteristics that can be beneficial to urban water distribution, but a change of system boundaries is required to recognize their potential.
Towards a Zero Trust Hybrid Security and Safety Risk Analysis Method
Authors: Papakonstantinou Nikolaos , Van Bossuyt Douglas L. , Linnosmaa Joonas , Hale Britta , O’Halloran Bryan 
Published: November 2020

Abstract
Designing and improving the resilience of complex sociotechnical/cyber-physical systems is not a simple task. Interdependencies between engineering domains can lead to emerging behavior which is difficult to predict and handle. Early identification of safety and security weaknesses of a safety-critical system leads to reduced redesign costs in later design phases. The scientific contribution of this paper is a method for early combined safety and security assessment based on interdisciplinary dependency models of the system. The focus is on the factors contributing to the estimation of the probabilities of successful attacks to system components. The Zero Trust paradigm is applied, in which all humans part of, or external to, the system pose a security risk. The estimation of security-related probabilities enables a combined safety and security overall risk calculation for the probability of losing specific key components or safety functions. The calculation of the security-related probabilities is a dynamic and difficult process that heavily depends on the domain and current global security environment. The methodology of this paper is demonstrated with a fictional case study of a spent fuel pool cooling system. The results of the case study showed that the overall risk of losing one key system component doubled when combining security and safety compared to only assessing safety events.
Extrapolation With Gaussian Random Processes and Evolutionary Programming
Authors: Planas Robert , Oune Nicholas , Bostanabad Ramin 
Published: November 2020

Abstract
Emulation plays an indispensable role in engineering design. However, the majority of emulation methods are formulated for interpolation purposes and their performance significantly deteriorates in extrapolation. In this paper, we develop a method for extrapolation by integrating Gaussian processes (GPs) and evolutionary programming (EP). Our underlying assumption is that there is a set of free-form parametric bases that can model the data source reasonably well. Consequently, if we can find these bases via some training data over a region, we can do predictions outside of that region. To systematically and efficiently find these bases, we start by learning a GP without any parametric mean function. Then, a rich dataset is generated by this GP and subsequently used in EP to find some parametric bases. Afterwards, we retrain the GP while using the bases found by EP. This retraining essentially allows to validate and/or correct the discovered bases via maximum likelihood estimation. By iterating between GP and EP we robustly and efficiently find the underlying bases that can be used for extrapolation. We validate our approach with a host of analytical problems in the absence or presence of noise. We also study an engineering example on finding the constitutive law of a composite microstructure.
Introducing Likelihood of Occurrence and Expected Cost to Human Error and Functional Failure Reasoning Framework
Authors: Irshad Lukman , Hulse Daniel , Demirel H. Onan , Tumer Irem Y. , Jensen David C. 
Published: November 2020

Abstract
Risk-based design uses severity and occurrence quantification to determine overall system risk and prioritize the most important hazards. To fully understand and effectively mitigate potential risks, the effects of component failures and human errors (acting alone and in tandem) need to be considered early. Then one can determine whether to allocate resources to proactively mitigate human errors in the design process. In previous work, the Human Error and Functional Failure Reasoning (HEFFR) framework was developed to model effects of human errors and component failures in a system, taking critical event scenarios as inputs and producing functional failures, human errors, and their propagation paths as outputs. With automated scenario generation, this framework can model millions of scenarios that cause system critical functions to fail. However, the outputs of this framework do not include any quantifiable measures to assess the risk of the hazards or prioritize fault scenarios. This work addresses these shortcomings by using a scenario probability and cost model to quantify the expected cost of failures in the HEFFR framework. A coolant tank case study is used to demonstrate this approach. The results show that the quantifiable measures enable HEFFR to identify worst-case scenarios, prioritize scenarios with the highest impact, and improve human-product interactions. However, the underlying likelihood and cost models are subject to uncertainties which may affect the assessments.
Substructure Interface Reduction Techniques to Capture Nonlinearities in Bolted Structures
Authors: Singh Aabhas , Allen Matthew S. , Kuether Robert J. 
Published: November 2020

Abstract
Structural dynamic finite element models typically use multipoint constraints (MPC) to condense the degrees of freedom (DOF) near bolted joints down to a single node, which can then be joined to neighboring structures with linear springs or nonlinear elements. Scalability becomes an issue when multiple joints are present in a system, because each requires its own model to capture the nonlinear behavior. While this increases the computational cost, the larger problem is that the parameters of the joint models are not known, and so one must solve a nonlinear model updating problem with potentially hundreds of unknown variables to fit the model to measurements. Furthermore, traditional MPC approaches are limited in how the flexibility of the interface is treated (i.e. with rigid bar elements the interface has no flexibility). To resolve this shortcoming, this work presents an alternative approach where the contact interface is reduced to a set of modal DOF which retain the flexibility of the interface and are capable of modeling multiple joints simultaneously. Specifically, system-level characteristic constraint (S-CC) reduction is used to reduce the motion at the contact interface to a small number of shapes. To capture the hysteresis and energy dissipation that is present during microslip of joints, a hysteretic element is applied to a small number of the S-CC Shapes. This method is compared against a traditional MPC method (using rigid bar elements) on a two-dimensional finite element model of a cantilever beam with a single joint near the free end. For all methods, a four-parameter Iwan element is applied to the interface DOF to capture how the amplitude dependent modal frequency and damping change with vibration amplitude.
A Comparison of Vector and Network-Based Measures for Assessing Design Similarity
Authors: Nandy Ananya , Dong Andy , Goucher-Lambert Kosa 
Published: November 2020

Abstract
In order to retrieve analogous designs for design-by-analogy, computational systems require the calculation of similarity between the target design and a repository of source designs. Representing designs as functional abstractions can support designers in practicing design-by-analogy by minimizing fixation on surface-level similarities. In addition, when a design is represented by a functional model using a function-flow format, many measures are available to determine functional similarity. In most current function-based design-by-analogy systems, the functions are represented as vectors and measures like cosine similarity are used to retrieve analogous designs. However, it is hypothesized that changing the similarity measure can significantly change the examples that are retrieved. In this paper, several similarity measures are empirically tested across a set of functional models of energy harvesting products. In addition, the paper explores representing the functional models as networks to find functionally similar designs using graph similarity measures. Surprisingly, the types of designs that are considered similar by vector-based and one of the graph similarity measures are found to vary significantly. Even among a set of functional models that share known similar technology, the different measures find inconsistent degrees of similarity — some measures find the set of models to be very similar and some find them to be very dissimilar. The findings have implications on the choice of similarity metric and its effect on finding analogous designs that, in this case, have similar pairs of functions and flows in their functional models. Since literature has shown that the types of designs presented can impact their effectiveness in aiding the design process, this work intends to spur further consideration of the impact of using different similarity measures when assessing design similarity computationally.
Development of Spatial Abilities in Engineering Education: An Empirical Study of the Influence of Visualisation Media
Authors: Zorn Stefan , Gericke Kilian 
Published: November 2020

Abstract
Spatial ability is one of the critical components of human intelligence. It has been proven that it is particularly crucial for success, especially in engineering, where interpreting views of an object presented by drawings, visualizing parts, or manipulating geometry in CAD are fundamental skills. Research has confirmed that spatial skills can be improved through instruction and teaching, for example, sketching and technical drawing, which are also included in the basic engineering classes. This study tested the hypothesis that the development of spatial skills during the fundamental design engineering class can be positively influenced due to the use of different visualization media for sketching and technical drawing tasks, whereas the used visualization media offer varying possibilities of interaction. Seventy students were pre- and post-tested with the Mental Rotation Test. All participants received the same training during the class but were given individual tasks with varying media. The analysis revealed a significant increase in mental rotation performance for all participants throughout the semester with a big effect size. Moreover, the mean performance improvement differed considerably depending on the visualization media and its offered interaction possibilities.
Surrogate Model Assisted Lithium-Ion Battery Co-Design for Fast Charging and Cycle Life Performances
Authors: Cui Tonghui , Zheng Zhuoyuan , Wang Pingfeng 
Published: November 2020

Abstract
As one of the significant enablers of portable devices and electric vehicles, lithium-ion batteries are drawing much attention for their high energy density and low self-discharging rate. A major hindrance to their further development has been the “range anxiety”, that fast-charging of Li-ion battery is not attainable without sacrificing battery life. In the past, much effort has been carried out to resolve such a problem by either improve the battery design or optimize the charging/discharging protocols, while limited work has been done to address the problem simultaneously, or through a control co-design framework, for a system-level optimum. The control co-design framework is ideal for lithium-ion batteries due to the strong coupling effects between battery design and control optimization. The integration of such coupling effects can lead to improved performances as compared with traditional sequential optimization approaches. However, the challenge of implementing such a co-design framework has been updating the dynamics efficiently for design variations. In this study, we optimize the charging time and cycle life of a lithium-ion battery as a control co-design problem. Specifically, the anode volume fraction and particle size, and the corresponding charging current profile are optimized for a minimum charging time with health-management considerations. The battery is modeled as a coupled electro-thermal-aging dynamical system. The design-dependent dynamics is parameterized thru a Gaussian Processes model, that has been trained with high-fidelity multiphysics simulation samples. A nested co-design approach was implemented using direct transcription, which achieves a better performance than the sequential design approach.
Mission-Level Optimization: Complex Systems Design for Highly Stochastic Life Cycle Use Case Scenarios
Authors: Chell Brian , Hoffenson Steven , Kruse Benjamin , Blackburn Mark R. 
Published: November 2020

Abstract
Mission engineering is a growing field with many practical opportunities and challenges. The goal of mission engineering is to increase system effectiveness, reduce life cycle costs, and aid in communicating system capabilities to key stakeholders. Optimizing system designs for their mission context is important to achieving these goals. However, system optimization is generally done using multiple key performance indicators (KPIs), which are not always directly representative of, nor easily translatable to, mission success. This paper introduces, motivates, and proposes a new approach for performing mission-level optimization (MLO), where the objective is to design systems that maximize the probability of mission success over the system life cycle. This builds on previous literature related to mission engineering, modeling, and analysis, as well as optimization under uncertainty. MLO problems are unique in their high levels of design, operational, and environmental uncertainty, as well as the single binary objective representing mission success or failure. By optimizing for mission success, designers can account for large numbers of KPIs and external factors when determining the best possible system design.
An Initial Analysis of Undergraduate Student Mental Models of Product Design
Authors: Driscoll Jessica R. , Hoffenson Steven , Pitterson Nicole 
Published: November 2020

Abstract
Design is a concept that means different things to different people. Even in the engineering design research community, there is little agreement on a consistent definition of design. This study looks into how engineering students understand product design, using a concept mapping exercise to elicit the key concepts and relationships present in their mental models. An analysis of concept maps from 130 third-year undergraduate engineering students shows how these students think about design, the common themes and relationships that are seen across the population, and variations across different groups of students. By understanding how students in the midst of ABET-accredited programs conceptualize design, conclusions can be drawn regarding the effectiveness of existing curricula in instilling a complete understanding of holistic product design. This can lead to recommendations regarding future engineering design learning objectives, teaching materials, and activities.
Design and Manufacturing of 3D Printed Foods With User Validation
Authors: Chirico Scheele Stefania , Binks Martin , Egan Paul F. 
Published: November 2020

Abstract
Additive manufacturing is becoming widely practical for diverse engineering applications, with emerging approaches showing great promise in the food industry. From the realization of complex food designs to the automated preparation of personalized meals, 3D printing promises many innovations in the food manufacturing sector. However, its use is limited due to the need to better understand manufacturing capabilities for different food materials and user preferences for 3D food prints. Our study aims to explore the 3D food printability of design features, such as overhangs and holes, and assess how well they print through quantitative and qualitative measurements. Designs with varied angles and diameters based on the standard design limitations for additive manufacturing were printed and measured using marzipan and chocolate. It was found that marzipan material has a minimum feature size for overhang design at 55° and for hole design at 4mm, while chocolate material has a minimum overhang angle size of 35° and does not reliably print holes. Users were presented a series of designs to determine user preference (N = 30) towards the importance of fidelity and accuracy between the expected design and the 3D printed sample, and how much they liked each sample. Results suggest that users prefer designs with high fidelity to their original shape and perceive the current accuracy/precision of 3D printers sufficient for accurately printing three-dimensional geometries. These results demonstrate the current manufacturing capabilities for 3D food printing and success in achieving high fidelity designs for user satisfaction. Both of these considerations are essential steps in providing automated and personalized manufacturing for specific user needs and preferences.