bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of a Control Co-Design Modeling Tool for Marine Hydrokinetic Turbines
Authors: Ross Hannah , Hall Matthew , Herber Daniel R. , Jonkman Jason , Sundarrajan Athul Krishna , Tran Thanh Toan , Wright Alan , Zalkind Daniel , Johnson Nick 
Published: February 2023

Abstract
This report describes the ongoing and planned development of the software package CT-Opt (Current/Tidal Optimization), a control co-design modeling tool for marine hydrokinetic turbines. The commercialization of these turbines has faced significant challenges due to the complex, multidisciplinary nature of their design and the extreme environmental conditions of their operation. This project aims to create a modeling tool that will enable the efficient design of robust, cost-competitive hydrokinetic turbine systems. Rather than using traditional optimization methods, CT-Opt combines multiple models across a range of fidelities to enable coupled optimization of the system design and system controller via a control co-design approach. With this method, the parameters that affect system performance are considered more comprehensively at every stage of the design process. The lowest-fidelity, frequency-domain model called by CT-Opt is RAFT (Response Amplitudes of Floating Turbines), which was originally developed by the National Renewable Energy Laboratory (NREL) to model response amplitudes of floating offshore wind turbines. The highest-fidelity, time-domain model is OpenFAST, which was developed by NREL for land-based and offshore wind turbines. As part of the CT-Opt project, new functionalities will be added to RAFT and OpenFAST to enable the accurate simulation of fixed and floating marine hydrokinetic turbines. In addition to expanding the capabilities of RAFT and OpenFAST, new mid-fidelity models will be developed. These models will be based on RAFT and OpenFAST and will consist of linearized, state-space models derived from the fully coupled, nonlinear OpenFAST equations and derivative function surrogate models that approximate the nonlinear system behavior. Each model will be coupled with controllers to allow control co-design methods to be applied both within models and across fidelity levels, enabling efficient system optimization.
An Artificial Neural Network Model for Multiple Piezoelectric Actuation of Plates
Authors: Jiang Linjin , Yu Pengcheng , Fan Mu 
Published: February 2023

Abstract
With the increasing use of piezoelectric smart structures in various fields, the search for further optimization of the devices is inevitable. In this work, both artificial neural network (ANN) models and finite element models were used to explore the effect of piezoelectric patch size and numbers on the first order natural frequency and transverse displacement of a plate. The research objective is to investigate the efficiency and factors influencing the actuation of thin plate structures under single/multi-channel piezoelectric control conditions. A Finite element model built with COMSOL was used to analyze the effect of structural parameters, including the number of channels and other key parameters on the control of the main natural frequencies of the thin plate structure. With the obtained data from finite element simulations, an ANN model was used to predict the dynamic response of the plate, including the first-order natural frequency and displacement amplitude for finite element verification, so that structural optimization design can be achieved. A well-trained neural network model can quickly and efficiently predict the displacement amplitude and natural frequency of a piezoelectric driven rectangular plate. This study provides a convenient and effective method for predicting the dynamic response of a piezoelectric drive plate considering the number of piezoelectric patch channels and size effects.
Numerical Simulation of Aged Reinforced Concrete Column Nonlinear Creep due to High Sustained Load
Authors: Ma Wenchen 
Published: February 2023

Abstract
A numerical simulation method for predicting reinforced concrete (RC) column creep under high axial sustained stresses exceeding 75% of concrete short-time strength f’c with loading ages greater than 200 days is explored. The objective is to investigate a comprehensive approach to predicting reinforced columns’ long-term nonlinear behavior. Nonlinear creep of RC columns is modeled based on combining a theoretical framework for the time-dependent uniaxial behavior of plain concrete under high axial sustained loads and an age-adjusted effective modulus method for calculating linear creep of reinforced concrete columns. The mechanism of the theoretical framework is improved by extending the prediction of linear concrete creep estimated by the GL2000 model to that of nonlinear concrete creep. The measured experimental results of concentrically loaded column tests are compared with the predictions from the numerical simulation method. For the RC columns loaded to 76% of their short-time strength calculated by the ACI equations without considering any strength reduction factor, the difference between the measured and predicted creeps at the end of sustained loading was less than 6%. However, for the RC columns loaded with 98% of column shoer-time strength, the predicted creep was 26% and 18% greater than the measured creep at the end of sustained loading. The research is concluded that the reason caused the predicted creep is greater than the measured creep because transverse reinforcement can restrain concrete creep under higher sustained stresses, especially in the early stage of sustained loading.
Investigation of Undeformed Chip Thickness Model and Surface Roughness Prediction in Belt Grinding
Authors: Li Heng , Zou Lai , Wang Wenxi , Li Mingcong 
Published: February 2023

Abstract
Undeformed chip thickness plays a very important role in studying the materials remove mechanism in flexible belt grinding and predicting ground surface roughness. A calculation model that can be used to effectively predict the undeformed chip thickness in flexible belt grinding is established based on the height distribution of abrasive grains. It can lay the foundation for in-depth study of belt grinding mechanism. In this model, the real contact arc length is derived based on the elastic deformation of the contact area between the abrasive belt and workpiece. The real depth of cut prediction model is introduced considering the belt grinding processing characteristics. Based on the established undeformed chip thickness model, a ground surface roughness calculation model is further reached, and a series of verification experiments are carried out. The results show that the predicted value and the measured value have the same trend basically under different grinding process parameters. The error of the predicted value is mostly distributed between 5% and 16%, which proved the reliability of the developed models preliminary.
Contact Search Using a Kd-Tree for Non-Rigid Variation Simulation
Authors: Sadeghi Tabar Roham , Lindau Björn , Lindkvist Lars , Wärmefjord Kristina , Söderberg Rikard 
Published: February 2023

Abstract
Geometric variation is one of the causes of aesthetic and functional issues in mechanical assemblies. To predict the geometric variation in assemblies of rigid and non-rigid parts, statistical variation simulation is introduced. For non-rigid parts, bending and deformation occur during the assembly process. In non-rigid variation simulation, contact modeling is utilized to avoid the virtual penetration of the components in the adjacent areas. Contact modeling imposes non-linear behavior to the MIC approach for variation simulation, and thereby the problem complexity and simulation time increase. Traditionally, iterative node search is used to identify and define the computational contact nodes. However, iterative search is time-demanding, specifically in large-scale models, as the search space increases by the number of nodes included in the assembly. To allow for faster contact search, a data structuring method using Kd-trees and nearest neighbor search (NN) is implemented and integrated into a computer aided tolerancing tool, enhancing the search functionality and reducing the search time compared to iterative one-by-one node search. The method is applied to three reference assemblies of different size, and the identified contact nodes and the time needed to perform the search is compared to an iterative node search. The results show that the K-tree structure and nearest neighbor search perform considerably, 96%, faster than the iterative node search. The method increases the search performance, while the identified contact points are similar to the ones identified by an iterative search. The approach efficiently enables the contact search of large models and reduces the modeling time required for non-rigid variation simulation.
Monitoring and Diagnosis of Dimensional Deviation in Assembly Process Using Tensor Regression
Authors: Sun Rui , Jin Sun , Liu Yinhua 
Published: February 2023

Abstract
The monitoring and diagnosis of dimensional deviation is an important means for the continuous improvement of assembly capacity in manufacturing systems. As optical scanning measurements become more common, statistical models based on 3D point cloud data have gained widespread attention. This paper introduces a general tensor regression method that builds a linear regression model between input and output. First, voxel technology is used to transform the 3D point cloud with deviation information into tensor form. Secondly, a Tensor-on-Scalar regression model is established for quality monitoring, and a Tensor-on-Tensor regression model is established for fault diagnosis. Then, tensor decomposition is used to reduce the number of parameters to be estimated in the regression model. Finally, the learning of parameter values is achieved by combining alternating least squares and gradient descent or coordinate descent. A case study of the door inner panel assembly process evaluates the predictive performance of a tensor regression model. The results show that the proposed method outperforms most existing methods in terms of prediction accuracy. Hence, the tensor regression model can achieve high-performance quality monitoring and fault diagnosis.
Numerical Simulation and Occupant Injury Prediction Under Side Impact Loading Using Human Surrogate Model
Authors: Arathanaikotti Prasanna , Prakash Raghu V. 
Published: February 2023

Abstract
Side Impact, after frontal impact is one of the major causes of concern for seated occupants during road accidents. Study of injury to occupants under side impact scenarios is important to design and develop reliable and efficient safety systems that can reduce the effect during crashes. In the present work, a typical side impact scenario is modelled using finite element method and human surrogates. Aluminum plates were modelled that represent a scenario in a side impact road accident where a door may infringe into the occupant seating area. The plates were impacted at a constant velocity of 4.3 m/s along the right side of the 50th Percentile Male LSTC World Side Impact Dummy (WSID) Finite Element Model and the biofidelic response was evaluated. The pelvis and lower extremity plates were offset by 10 cm closer to the subject to simulate a typical geometry of a side door panel during a side impact. The model is validated against experimental data available in the literature by considering the forces on the plates and nodal acceleration response on full body side impact studies using Anthropomorphic Test Devices (ATD) and Post Mortem Human Subjects (PMHS). The severity of injury in this scenario is found to be more than frontal impact since the occupant is struck on the side and is seated in close proximity to the side structures. Also, safety mechanisms in the vehicle like the seat belt may not be effective in negating the injury from a side impact. The simulation results were found to be well within the PMHS response available in the experimental data. The biofidelic response of the FE surrogates was also studied to estimate the severity of the injury based on a set of injury criteria/injury assessment risk curves (IARC) that has been developed for the WSID dummy. The simulation predicted severe pelvic injuries due to the offset of the pelvis plate. Also the pelvic forces obtained on the plates in the simulation were higher than the actual PMHS response. The simulation predicted that there was a 30% risk of thoracic injuries. Injury patterns in side impact scenarios were studied in detail and probabilities based on IARC were estimated to predict injury severity and compared with standard IARV (Injury Assessment Reference Values). The numerical model developed in the present approach can be a valuable tool in assessing the performance of passenger side automotive airbags during its design and initial development phase.
Urban Air Mobility: Design of a Virtual Reality Testbed and Experiments for Human Factors Evaluation
Authors: Shankar Praveen , Marayong Panadda , Strybel Thomas , Battiste Vernol , Nguyen Hanson , Cheung Justin , Viramontes Jesus 
Published: February 2023

Abstract
Urban Air Mobility (UAM) has the potential to transform the transportation landscape for movement of small number of passengers and good across citywide distances. However, the successful integration of such a concept into the National Air Space will require a significant effort focused on development and testing of both the vehicles and the supporting infrastructure. In this paper, the development of a virtual reality (VR) environment for testing urban air mobility operation concepts has been presented. The VR environment that is simulated in a CAVE system includes a quadcopter type vehicle that can transport passengers across citywide distances. The design features are primarily focused on cockpit input and display interfaces while the vehicle’s motion is sufficiently realistic. An experimental design to execute a mission in San Francisco city has been tested by participants with and without prior piloting experience. Preliminary analysis of the flight data collected indicates minimal impact of prior flight experience in completing the mission.
Gravity Impact on Post-Resonance Backward Whirl Excitation by the Nonsynchronous Whirl
Authors: Abdul Nasar Rafath , A. AL-Shudeifat Mohammad 
Published: February 2023

Abstract
Rotordynamical systems are usually exposed to recurrent transition through their resonance rotational speeds during runup and coast-down operations. As a result, a nonsynchronous whirl between the shaft rotation and its lateral whirling occurs at the neighborhood of the critical resonance speeds. This nonsynchronous whirl has a significant impact on the system when a breathing crack starts to propagate. The horizontal Jeffcott rotor model with a breathing crack is considered here to study the combined effect of the gravity force vector and the nonsynchronous whirl on post-resonance backward whirl (Po-BW) excitation. The numerical simulation results show that the gravity further intensifies the excitation of the Po-BW in the horizontal cracked rotor compared with the vertical one at relatively small crack depths. In addition, the unbalance force vector orientation has been found to significantly affect the Po-BW excitation and its recurrence.
Environmental Sustainability of Additive Manufacturing: A Case Study of Indian Manufacturing Industry
Authors: Yadav Alok , Sachdeva Anish Kumar , Agrawal Rajeev , Garg Rajiv Kumar 
Published: February 2023

Abstract
Manufacturing industries across the globe are now adopting additive manufacturing (AM) practices over the traditional manufacturing process due to less waste generation and other economic and emissions benefits. Additive manufacturing can also be considered a key enabling technology for the high technology plan. However, the practices related to additive manufacturing in developing and developed economies are limited due to knowledge and technological advancements. Environmental impact assessment is an emerging research area highlighted in the last few years. At this point, research and industry attention is focused on determining where AM can replace or create new manufacturing systems. This study compares the environmental impact assessment of traditional and additive manufacturing processes by considering a case of pattern making. This paper also investigates how additive manufacturing technologies contribute to creating a sustainable environment in the manufacturing industry. A real-time case study is performed in SMEs of India, showing that additive manufacturing processes help lower the environmental impacts.
The present study also highlighted the opportunities for additive manufacturing in sustainability. The gate-to-gate analysis is performed, and impact assessment is done by GABI software version 9 on Windows 10 operating system. In this report, a case study has been done to compare the traditional IC and AM processes using Aluminum alloy (Al-Si-Cu) in terms of environmental Sustainability. Results show that 28.77% reduction in GWP, 74.04% reduction in emission to Air, 36.30% reduction in AP and 45.62% reduction in HTP by using AM process. Overall, AM process is an environmentally sustainable process. This study would help researchers and the manufacturing industry determine the emissions associated with pattern making using the conventional IC process under Indian climatic conditions. It assists manufacturing industries in choosing the best available IC alternative regarding reduced environmental emissions.
Distributed Acoustic Sensing (DAS) for Intelligent In-Motion Transportation Condition Monitoring
Authors: Taheri Hossein , Jones Michael , Bueso Quan Suyen , Gonzalez Bocanegra Maria , Taheri Mohammad 
Published: February 2023

Abstract
Safety is the top priority for every transportation system. Although various aspects of transportation infrastructure’s safety have been studied, in-motion monitoring and detection of defect is still a big concern. Understanding the trend of anomalies, and how to monitor undesired conditions are of high interest in transportation. In this study, the technology of Distributed Acoustic Sensing (DAS) for in-motion rail condition monitoring is studied through experimental testing and simulation modeling. DAS uses fiber optic cables along the track to detect any anomaly indicator. DAS permit the measurement of a desired parameter as a function of length along the fiber. Despite any conventional Nondestructive Testing (NDT) technique where the coverage or scanning area of the sensors are very limited, DAS provides a full, fast and accurate coverage of all section under the test. The objective of this research is to provide an assessment of anomaly detection and monitoring techniques based on DAS for transportation investigation. It presents the experimental evaluations and numerical simulations on the current methodologies in DAS systems. DAS was used to evaluate the transportation traffic condition in a rural area by connecting an available underground dark fiber to the DAS interrogator and system as well as simulated traffic condition in smaller scale in a parking lot. COMSOL Multiphysics software was used to model the interaction of ambient vibration with the fiber optic. Results show that the condition of the transportation can be monitored and detected by DAS with an appropriate accuracy. DAS information can be used for traffic condition monitoring, object tracking and flaw detections in the transportation lines.
Circulating Tumor Cell Separation in a Zigzag Channel Using Dielectrophoresis Based Inertial Microfluidics
Authors: Islam Md Sadiqul , Uddin Mohammed Raihan , Chen Xiaolin 
Published: February 2023

Abstract
Circulating tumor cells (CTCs) are known to be a primary indicator of vital diagnostic and clinical information for early-stage cancer detection. Effective separation of CTCs from blood is crucial for genetic characterization of CTCs, drug development, and improvement of cell cycle-targeted therapies. Many conventional microfluidic platforms isolate CTCs based on their size difference from other blood cells which renders them impractical for sorting overlapping-sized cells. To address this issue, we propose a method using a zigzag channel for continuous, label-free, and high throughput separation of CTCs coupling Dielectrophoresis (DEP) with inertial microfluidics. This hybrid channel exhibits enhanced similar-sized cell separation resolution and single-step retrieval of viable CTCs by combining inertial lift force, DEP force, and alternating curvature-induced Dean force. Through numerical investigation, separation of MDA-231 CTCs from identical-sized WBCs has been achieved at a relatively high Reynolds number of 125. Furthermore, the working parameters such as Reynolds number, voltage, and electrode configuration have been optimized for enhancing the separation efficiency. The proposed design can provide valuable insight into the development of a versatile, efficient, inexpensive, and novel platform with reduced analysis time for cancer diagnosis and prognosis.
Supervised and Unsupervised Deep Learning Applications for Visual SLAM: A Review
Authors: Ukaegbu Uchechi Faithful , Tartibu Lagouge Kwanda , Lim Chee Wah 
Published: February 2023

Abstract
Visual Simultaneous Localization and Mapping (V-SLAM) is a trending robotics research concept as well as the basis for autonomous and smart navigation. It is an integral part of vision-based applications which include virtual reality, unmanned aerial vehicles, augmented reality, and unmanned ground vehicles. V-SLAM carries out localization and mapping by learning relevant feature points from images and estimating their pose based on the correlation between the camera and the feature points. It also represents the ability of a robot to effectively navigate itself, employing visual sensors and prior information of the given location, in an uncharted environment while updating and constructing a coordinated map of the scene. However, due to the challenges of data association triggered by illumination, different viewpoints and environment dynamics, there has been rapid adoption of deep learning in the area of feature extraction/description, pose/depth estimation, mapping, loop closure detection and global optimization as it concerns visual SLAM. This paper sets out to elucidate diverse applications of supervised and unsupervised deep learning methods in all aspects of visual SLAM. It also briefly explains a case study regarding the application of both deep learning and SLAM for underground mining applications. It highlights recent research developments in addition to limitations hindering their effective application and investigates how a combination of deep learning with other methods offers a promising direction for visual SLAM research.