bluuebunny/crossref_metadata_2025_split huggingface dataset:
CFD Modelling of an Internal Gear Pump for High Viscosity Fluids
Authors: Fresia Paola , Rundo Massimo , Brignolo Gianpaolo , Ferrarese Giulio 
Published: November 2023

Abstract
The paper presents the CFD modelling of an internal gear positive displacement pump used for pumping high viscosity fluids. The aim of the study is to show the proper methodology for the evaluation of the delivered flow rate in conditions of incomplete filling. The model was developed in SimericsMP+ with the general gear template. It features axial and radial feeding of the variable chambers and all leakage passageways. The model was calibrated in terms of leakage flow rate in conditions of complete filling, then validated on the steady-state flow rate as a function of the suction pressure. Good agreement was found in the evaluation of the critical operating condition for which the incomplete filling begins. Based on the obtained results, it has been demonstrated that the simulation methodology can be used with a good degree of confidence for predicting the pump behavior. Hence, the model seems suitable for designing and optimizing such kinds of machines.
An Integrated Holistic Approach Toward Sustainable Product Design Using Life Cycle Assessment
Authors: Basereh Taramsari Hossein , Mudhar Simerjeet , Hoffenson Steven 
Published: November 2023

Abstract
The number of sustainable design methods emerging in recent years has increased due to global environmental concerns. Several proposed methods focus exclusively on the environmental impacts of products rather than taking a holistic perspective that includes social and economic sustainability. Despite the advancements in tools for incorporating sustainability in the product design phase, designers are not universally adopting these tools mainly due to their fragmented or underdeveloped characteristics. This research aims to integrate change propagation and sustainable design tools within a holistic framework that can enable simultaneous consideration of design parameters’ impacts on the three dimensions of sustainability. A reusable water bottle is used to demonstrate the application of the proposed holistic, sustainable design framework. The interdependencies among these design parameters are captured using a multi-domain matrix (MDM), and then equations are defined to quantify these dependencies. Life cycle assessment (LCA) is then performed in an automated way to evaluate the product’s environmental impacts, and the investigation of its results provides details to identify critical unit processes contributing to the environmental categories. Using this framework, sensitivity analyses are able to reveal how changes to individual design parameters propagate through the model to influence the three dimensions of sustainability.
Facilitating Cost-Effective Circular Economy With an Example on Plastics
Authors: Thomas Douglas 
Published: November 2023

Abstract
In the global economy, plastics have become a ubiquitous material, as have the environmental impact and waste that accompany them. This article examines causes of unsustainable production activities and potential cost-effective solutions in the U.S. plastics industry. This paper estimates that approximately 3.5 % of the total U.S. environmental impact is due to plastics, measured as a weighted composite of twelve types of environmental impact. It further discusses five potential solutions to reduce these impacts: increasing product life expectancy, improving the repairability of products, increasing the use of recycling and bioplastics, and improving manufacturing efficiency. Several potential needs were identified for increasing the sustainability of plastics: the ability to differentiate product brands and models by life-expectancy, ability to differentiate product brands and models by repairability, the aggregation of plastic streams to increase recycling volume, standards for separating plastics, and the ability to differentiate products by recyclability. Bioplastics are associated with different types of environmental impact than traditional plastic and are generally considered to be less severe (environmental) impacts; however, their cost is higher, leaving a need for cost reduction efforts. For traditional plastic resin production, efficiency improvements in energy management were identified as having an average per establishment net present value of $0.92 million for a 10-year study period and for the use of plastics the net present value was $0.67 million, suggesting that there are opportunities for cost-effectively reducing their environmental impact by improving energy management.
Process-Aware Prediction of Geometric Accuracy for Additive Manufacturing via Transfer Learning
Authors: Lin Daphne , Seepersad Carolyn 
Published: November 2023

Abstract
While additive manufacturing offers unprecedented design freedom to engineers, challenges associated with part accuracy and quality prevent additive manufacturing from becoming as widespread as traditional manufacturing techniques. Statistical quality control is typically applied to mass-produced parts in which large numbers of identical (or similar) parts are fabricated in a production line context, but for customized, additively manufactured parts, part qualification must be performed with a much more limited number of samples — ideally, just one customized part — or the benefit of customized production is lost to the expense of repeated production and refinement. Building upon previous work that established a statistical database of geometric design allowables for a laser sintering additive manufacturing process via extensive geometric metrology, this research establishes transfer learning techniques for transferring those databases to new materials, machines, or parts. The goal of this research is to utilize transfer learning to maximize the accuracy of design allowables for new materials, machines, or parts, while limiting the required number of expensive new metrology studies. Transfer learning is implemented via a two-stage Gaussian process regression method. In the first stage, a trend model is built using pre-existing metrology data to capture general geometric characteristics of the process. In the second stage, a deviation model is trained on the difference between the pre-existing data and a small amount of data gathered from the new material/machine/process. The trend and deviation models are then aggregated to create a surrogate model referred to as the transfer learning model. The proposed method is evaluated using a multi-material polymer selective laser sintering metrology dataset. The performance of the transfer learning model is compared to that of simpler alternative models. Results show that transfer learning models are generally more accurate in predicting geometric dimensions and less sensitive to changes in training data points.
Overview of Design Considerations for Data-Driven Time Stepping Schemes Applied to Non-Linear Mechanical Systems
Authors: Slimak Tomas , Zwölfer Andreas , Todorov Bojidar , Rixen Daniel J. 
Published: November 2023

Abstract
Artificial neural networks (NNs) are a type of machine learning (ML) algorithm that mimics the functioning of the human brain to learn and generalize patterns from large amounts of data without the need for explicit knowledge of the system’s physics. Employing NNs to predict time responses in the field of mechanical system dynamics is still in its infancy. The aim of this contribution is to give an overview of design considerations for NN-based time stepping schemes for non-linear mechanical systems. To this end numerous design parameters and choices available when creating a NN are presented and their effects on the accuracy of predicting the dynamics of non-linear mechanical systems are discussed. The findings are presented with the support of three test cases: a double pendulum, a duffing oscillator, and a gyroscope. Factors such as initial conditions, external forcing as well as system parameters were varied to demonstrate the robustness of the proposed approaches. Furthermore, practical design considerations such as noise-sensitivity as well as the ability to extrapolate are examined. Ultimately, we are able to show that NNs are capable of functioning as a time-stepping schemes for non-linear mechanical system dynamics applications.
A Framework of Real-Time Knowledge Capture and Formalization for Model-Based Design With Spoken Annotation and Design Operations
Authors: Nomaguchi Yutaka , Morikawa Hiroyuki , Nishida Rempei , Fujita Kikuo 
Published: November 2023

Abstract
Knowledge capture without an extra burden on engineers is one of the most challenging issues in the engineering domain. This study proposes a framework of real-time knowledge capture and systematic formalization for the model-based design (MBD), the systems design approach where a simulation is a key component. The framework records the screen of the modeling tool as video data and spoken annotation in which an engineer verbally explains the rationale of the modeling conducted with Modelica-based software commonly used for MBD. The captured modeling tool log and spoken annotation are divided into chunks, each corresponding to a design operation, e.g., defining functions, defining system components, setting model elements, setting model parameters, and running simulations. gIBIS, the argumentation model for representing design rationale, represents the logical structure of the design process. We define design operation templates at the system and subsystem levels, which act as rules to convert the modeling tool log into gIBIS format systematically. We conduct a design case of a spinach harvesting machine with MBD software to verify the proposed framework. The results show that through non-intrusive recording of the design process, the proposed framework is effective in capturing knowledge that can be used to help understand how a model was built.
Design of Tooth Flank Modifications in Transmission Systems Considering Dynamic Misalignments
Authors: Westphal Christian , Brimmers Jens , Brecher Christian 
Published: November 2023

Abstract
Current challenges in gear design, such as higher speeds and increasing power density, are leading to the increasing use of power-split and high-ratio transmission systems. These requirements can be achieved with planetary and stepped planetary gear systems. However, the power split of these gearbox types results in kinematic overdetermination and a pronounced misalignment behavior. In this report, a method is developed for the design of tooth flank modifications in transmission systems, taking into account dynamic misalignments caused by manufacturing or assembly deviations. For this purpose, the simulation of the dynamic operational behavior of transmission systems with assembly or manufacturing deviations in the dynamic multi-body simulation (MBS) is described. The focus is on the modeling of the tooth contact with the force element GearForce6D developed for the MBS software Dassault Systèmes Simpack. In the following, a novel combination of the MBS with the quasi-static tooth contact analysis (TCA) is presented. Here, the misalignment and load time histories simulated in the MBS are used for the detailed calculation of the gear meshes. With the combination of the MBS and the TCA, a significant reduction of the calculation time for the variation of tooth flank modifications is possible.
The developed method is used for the design of tooth flank modifications of a stepped planetary gearbox considering a misalignment of the planet carrier. The optimized tooth flank modifications show that a significant improvement in operational behavior can be achieved by considering the dynamic misalignment in the design. The load-related misalignment of the gear components has a high influence on the dynamic operational behavior.
A Tale of Two Turrets: Transitioning Hands-On, Project-Based Learning to a Remote Environment
Authors: Kutzer Michael D. M. , DeVries Levi D. , Severson Tracie A. 
Published: November 2023

Abstract
This paper presents an open-source MATLAB tool developed as a partial “digital twin” for a hands-on, project-based learning (PBL) course. The aforementioned course is a semester-long, mechatronic design exercise that guides students through the interface, modeling, and control of a one-dimensional turret system integrated with an electronic NERF RIVAL-series blaster. The digital twin presented is a MATLAB-based simulation that replicates key elements of the existing in-person course for use in a remote setting. Results show a highly visual, open-source tool that accomplishes 55% of the key tasks associated with the in-person PBL course; allowing students to explore experimental modeling, control tuning, and camera-based feedback techniques in a remote setting. Source code, documentation, and installation functions for the Turret Simulation Toolbox are available at https://github.com/USNA-WRCE/TurretSimToolbox.
A GPU-Based Parallel Bound-and-Classify Method for Continuous Constraint Satisfaction Problems
Authors: Feng Wangchuan , Zhang Guanglu , Cagan Jonathan 
Published: November 2023

Abstract
Continuous constraint satisfaction is prevalent in many science and engineering fields. When solving continuous constraint satisfaction problems, it is more advantageous for practitioners to derive all feasible regions (i.e., the solution space) rather than a limited number of solution points, since these feasible regions facilitate innovative design concept generation and design tradeoff evaluation. Several CPU-based branch-and-prune methods and geometric approximation methods have been proposed in prior research to derive feasible regions for continuous constraint satisfaction problems. However, these methods have not been extensively adopted in practice, mainly because of their high computational expense. To overcome the computational bottleneck of extant CPU-based methods, this paper introduces a GPU-based parallel bound-and-classify method to derive feasible regions for continuous constraint satisfaction problems in a reasonable computational time. Using interval arithmetic, coupled with the computational power of GPU, this method iteratively partitions the design space into many subregions and classifies these subregions as feasible, infeasible, and indeterminate regions. To visualize these classified regions in the design space, a planar visualization approach that projects all classified regions into one figure is also proposed in this paper. The GPU-based parallel bound-and-classify method and the planar visualization approach are validated through a case study of welded beam design. The case study shows that the method and the approach can solve the welded beam design problem and visualize the result effectively and efficiently. A four-step procedure for implementing the GPU-based parallel bound-and-classify method and the planar visualization approach in practice is also outlined.
Topology Optimization in Consideration of Overhang Constraint for Additive Manufacturing Based on Coupled Fictitious Physical Model in Thermal Design Problem
Authors: Tajima Mikihiro , Yamada Takayuki 
Published: November 2023

Abstract
Additive manufacturing can be used to create complex optimal structures through topology optimization. However, the overhang constraint, which requires costly support materials, is a problem when part of the structure is unsupported. Previously, the fictitious physical model was proposed to consider geometric manufacturing requirements, e.g., the overhang constraint. However, converging to a single optimal structure is challenging in the fictitious physical model because the model acts noninteractively as the mechanical model describing physical phenomena. To solve the convergence problem, we propose a coupled fictitious physical model, where two models interact by changing the material constants of the mechanical model in regions where geometric constraints defined in the fictitious physical model are violated such that the objective function worsens. In this paper, the proposed model is formulated based on the steady-state heat conduction problem with overhang constraint. Then, the optimization problem and sensitivity analysis are discussed. Finally, the proposed model is verified using an example two-dimensional heat conduction problem.
Mechanical Logic Gates and Processors Using Hinge-Connected Pinned-Pinned Arches
Authors: Maharana Priyabrata , Shekhar Sudhanshu , Ananthasuresh G. K. 
Published: November 2023

Abstract
Physical limitations such as extreme temperature and intense radiation restrict the use of electronic circuits in military, space, and geothermal applications. Hence, there is a need for alternative ways to process information for specific applications using mechanical devices that provide functions similar to that of electronic systems. Furthermore, mechanical devices are preferred over others as they consume less energy and dissipate less. In this paper, we present compact mechanical logic gates using connected pinned-pinned bistable arches that are stress-free in their as-fabricated state. The connected arches possess multiple stable states for different inputs and do not require external axial compression as a preload. Our mechanical logic gates consist of three pinned-pinned arches, which makes them compact compared to other designs. The three serially connected arches have alternate signs of curvatures, and they can flip and switch to their second stable state when input is given to their neighbors. We present criteria for flipping and switching as design charts using non-dimensional geometric parameters. As a first step, with the appropriate selection of geometric parameters of input and output arches, we designed, prototyped, and validated AND, NAND, OR, and NOR gates. Next, we used the mechanical gates to design logic processors. The cases of encoder and de-multiplexer are used as demonstrative examples with experimental validation.
Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction
Authors: Park Jangseop , Kang Namwoo 
Published: November 2023

Abstract
In the field of engineering design, surrogate models for 3D computer-aided design (CAD) have been widely used to replace computationally expensive simulations. However, the conventional surrogate modeling process, which relies on the geometric parameters (or design variables) of CAD, has limitations when dealing with complex structural shapes commonly found in industry datasets. These limitations include information loss in low dimensions and difficulty in parametrization. This study proposes a Bayesian graph neural network (GNN) framework for a 3D deep-learning-based surrogate model that predicts engineering performance by directly learning the geometric features of CAD with mesh representation. Our proposed framework derives the optimal size of the mesh elements, creating a high-accuracy surrogate model with Bayesian optimization. It also solves the heterogeneity problem of 3D CAD data in that 2D images have regular pixel structures, whereas 3D CADs have irregular structures. From the experimental results, the mesh quality is highly correlated with the prediction accuracy of the surrogate model, and there exists an optimal mesh size that satisfies the high-performance requirements of the surrogate model. We expect that our proposed framework has the potential to be applied to mesh-based simulations in various engineering fields, reflecting the physics-based information widely used in computer-aided engineering.
Feasibility of Small-Scale, Off-Grid Desalination in Navajo Nation
Authors: Brei Melissa 
Published: November 2023

Abstract
Water scarcity is affecting billions of people around the world and the utilization of brackish water, defined as having total dissolved salt content between 500–10,000 mg/L, could provide a potential solution. This paper focuses on the Navajo Nation in the southwest of the United States, an extremely water stressed region, due to many homes lacking piped water and electricity. Previous work points to membrane desalination being a suitable off-grid technology for providing potable water. By analyzing overlaps of various maps, feasible regions for desalination were identified due to high potential for positive impact and opportunity for desalination to succeed. This geographic analysis revealed the southern region as the focal point for this study, which was corroborated by various partners. Water haulers and stakeholders in diverse organizations affiliated with water management were interviewed to elucidate design requirements for a highly-adoptable desalination system. Engaging with stakeholders at the beginning of the design process validated the proposal of desalination as a technical solution. Additionally, the interviews revealed sensitive design requirements and unique challenges that will aid the designers in the following stages of the design process. Future work will include a robust evaluation of membrane desalination technologies to select the best suited for this application.
Reducing Data Requirements for Wiener Series Identification With Gaussian Process Priors
Authors: Massingham Joseph , Nielsen Ole , Butlin Tore 
Published: November 2023

Abstract
System identification of nonlinear dynamical systems aims to predict the output of a system for a given input. In many engineering applications, the underlying physics are not fully understood and so there is no analytical solution. The Wiener series is a classical data-driven technique that decomposes the system response into a set of orthogonal functionals of increasing order. Unlike standard black-box algorithms, such as neural networks, the series is highly interpretable and can offer insight into the nonlinearities present. To date, in order to calculate higher order terms in the Wiener series, vast quantities of data are needed. In this paper, a novel formulation of the Wiener series is developed in the frequency domain which applies to general stochastic inputs with an arbitrary spectrum. It is enhanced by placing Gaussian process priors over the Wiener kernels to enforce prior knowledge of their structure. This significantly reduces the quantity of data required for inference and has the benefit of enabling the calculation of the third order kernel for systems with long memory. The benefits were demonstrated in initial investigations using an idealised nonlinear oscillatory system. Decomposition of the system response into Wiener functionals also sheds light on the learnability of nonlinear dynamical systems, which could be used to assess the value of collecting additional data.