bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of Differences Between FEA Predictions With Geometric Variations and Tensile Tests of Strut Specimens of Lattice Structures Fabricated by Material Extrusion
Authors: Gorguluarslan Recep M. , Gungor O. Utku , Karabiyik Huseyin 
Published: January 2022

Abstract
The aim of this study is to evaluate the differences between the tensile test results of individual strut members fabricated by the material extrusion process and finite element analysis (FEA) predictions of the struts modeled with the geometric variations introduced by the material extrusion method using advanced statistical methods based on the spatial dependency. For this purpose, strut member specimens with different diameter values and angles are fabricated using the material extrusion technique from PLA material. The fabricated strut specimens are examined by a digital light microscope and the measurements are done for the fabricated diameter variations. These variations are characterized using the random field method and integrated into the FEA models by using voxel elements. Tensile tests of the fabricated strut specimens at different diameters and build angles are also conducted. The reasons for the differences of FEA results from the experimental results and solutions to improve the prediction accuracy are discussed.
A Modeling Tool to Analyze the Performance of Industrial Cooling Towers
Authors: Hooper Josh , Languri Ethan , Cunningham Glenn , Guo Wei 
Published: January 2022

Abstract
Energy efficiency and energy savings have become an important factor as industries look for ways to save energy and minimize their energy consumption while reducing their carbon footprint. Cooling towers are utilized significantly in industries for either serving chillers or process cooling. Depending on the size of the cooling tower, it can use a surprising amount of energy and water, which is why it is crucial to make sure that the facility has optimized their cooling tower.
A modeling tool has been developed to perform a thorough analysis of a cooling tower and its various operations to ensure that the facility has optimized its cooling tower. This model analyzes an annual base case in comparison with the revised case of a cooling tower operation. This modeling tool simulates 8,760 hourly calculations for fan power for various fan controls, water consumption, and pumping energy consumption and demand based on the user’s location and its corresponding Typical Meteorological Year 3 (TMY3) weather data. This model is capable to simulate up to five cooling tower cells as one large tower utilizing one pump or parallel pumping. The successful validated cooling tower model should assist industry to save energy at their facility through their cooling tower, whether they use it for process cooling or heating, ventilation and air conditioning (HVAC) applications. The entering/leaving water temperatures based on a monthly operating schedule or a wet-bulb temperature schedule will be used as inputs, and the model can calculate savings in energy for various conditions including variable-frequency drive (VFD) on the fan/pump, drift eliminators, number of cycles of concentration, reduced water flow rate, etc.
Robot Learning From Human Demonstration of Activities of Daily Living (ADL) Tasks
Authors: Trivedi Urvish , Alqasemi Redwan , Dubey Rajiv 
Published: January 2022

Abstract
The redundant kinematic structure of the human upper body provides sufficient dexterity in performing daily living activities, such as drinking, eating, and other manipulation tasks. Most of these Activities of Daily Living (ADL) tasks can be performed using various trajectories; however, healthy humans employ some possible trajectories to accomplish these tasks. Assistive robots can explore these Cartesian trajectories and joint angles’ trajectories from human demonstrations and utilize artificial intelligence techniques to recognize, learn, and perform ADLs using robotic arms. This paper aims to develop a platform for Assistive Robots to identify and perform ADLs through human demonstration. In this paper, two Mask Regional Convolutional Neural Networks (Mask R-CNN) were developed to predict human intention for a given task, and the Reinforcement Learning approach was implemented for optimum trajectory planning.
The First Mask R-CNN network, which was used for the detection of an object, was trained on a novel dataset. This dataset comprises images captured by the research team as well as gathered images from the internet. The first Mask R-CNN is expected to achieve two main objectives. The first objective is related to structure learning, which detects an object in a cluttered environment. The second objective is to estimate parameters that maximize the weight and detection rate of diverse types of similar objects. The first Mask R-CNN tries to detect the object with its location in the image and provides a soft mask on it. The second Mask R-CNN, which tries to detect human posture, was trained on a pre-existing dataset comprising human posture images. The output of these two Mask R-CNN’s then feed-forward to recognize human intensions that can be achieved through human-pose key points.
Prediction of human intention was performed if the detected object and human pose match a range of pre-stored values specific to ADLs. Once the human intention has been identified via Mask R-CNN networks, the system starts planning for an optimal trajectory. For the trajectory planning, the Q-learning approach was implemented, which uses the concept of reward and penalty while exploring the unstructured environment in order to find an optimal trajectory. Experiments were conducted to determine the level of accuracy and reliability provided by our assistive platform in predicting human actions and performing trajectory planning. Results show that our assistive platform successfully recognized human actions such as water pouring, cereal making, and opening the door, as well as finding optimized trajectories to perform those actions.
Effects of HVAC Settings and Windows Open or Close on the SARS-CoV-2 Virus Transmission Inside a Mass Transit System Bus
Authors: Zafar Muhammad Usman , Lee Vincent , Timms Will , Bounds Patrick , Uddin Mesbah 
Published: January 2022

Abstract
With the current outbreak of SARS-CoV-2, public transportation is a key area which must be investigated to ensure both passenger safety and efficiency of passenger transport to best serve the community and reduce environmental footprint. In this paper, the transport of the SARS-CoV-2 virus through human respiratory particles is examined using transient Computational Fluid Dynamics (CFD) simulations to determine the impacts different ventilation configurations on the probability of viral exposure. The motion of the viral particles was simulated first by solving for the flow field inside the bus using a proprietary Navier-Stokes finite-volume solver, RavenCFD by Corvid Technologies, and then using Lagrangian particle tracking (LPT) post processing techniques. The LPT methods used allowed for the injection of respiratory particles, according to distributions found in literature, which included sneezing, coughing, and speaking. To fully investigate the problem space the moving bus was modeled with the windows in various states of closure and with various HVAC configurations. In all scenarios, a volumetric Viral Mean Exposure Time (VMET), which considers the viral load calculations, was used to quantify the various exposure risk of all passengers on the bus. It was found that the most efficient ventilation system on a public transport bus was to keep the windows closed and HVAC of main cabin at maximum to minimize the viral exposure within the bus.
Evaluating the Implementation of Distributed Ledger Technology for the Licensing and Regulation of Nuclear Power Plants
Authors: Pandit Priyanka , Tezbasaran Alp , Earthperson Arjun , Diaconeasa Mihai A. 
Published: January 2022

Abstract
The approval process from the U.S. Nuclear Regulatory Commission (NRC) for nuclear power plants is sequential. It involves several government bodies such as the Advisory Committee on Reactor Safeguards (ACRS), public meetings, and hearings. If the submissions made to the NRC do not contain enough information to meet the regulation requirements, the NRC issues a Request for Additional Information (RAI). Thus, the licensee has to go through a paperwork-intensive process that involves multiple regulatory agencies for the various licensing requirements. Moreover, sending applications to the NRC is limited to using an electronic submission generation tool called the Packing Slip Wizard (PSW).
This paper presents a methodology to implement Distributed Ledger Technology (DLT) to address the need for a real-time, digitized documentation platform in the nuclear power industry’s licensing and regulation process.
The evaluation of DLT’s implementation resulted in the formulation of a methodology to accept submissions from an applicant on a web application and storing the received data on a distributed ledger. The presented method offers a real-time submission of the available information of an application. It facilitates the NRC with a real-time feedback capability expediting the review process. RAI’s can be reduced in number by ensuring that the NRC’s information requirements are defined as smart contracts.
Parametric Analysis and Validation of Machine Learning in Chaotic Transitions of the Lorenz System
Authors: Tribelhorn Ben , Dillon H. E. 
Published: January 2022

Abstract
We are continuing an examination of unsupervised machine learning methods in natural convection systems. We are leveraging the Lorenz system to consider turbulent transitions and how machine learning models assist understanding these transitions. The Lorenz equations were selected to test the machine learning algorithms due to the relative simplicity of the equations, and the well-documented chaotic behavior.
We developed a robust numerical solution to the Lorenz equations using a fourth order Runge-Kutta method with a time step of 0.001 seconds. We solved the Lorenz equations for a large range of Raleigh ratios from 1–1000. We also used a finer time resolution and larger Rayleigh range than prior work. We calculated the power spectral density, various descriptive statistics, and a cluster analysis using unsupervised machine learning. To identify behaviors and regions in the data, we utilize unsupervised learning as it is designed to assist in recognizing patterns without being told or trained by prior knowledge. We confirmed the performance of the machine learning system’s ability to identify chaotic transitions independent of expert selection of Raleigh ratio ranges.
In prior work we found that the automated cluster analysis aligns well with well known key transition regions of the convection system. In this paper we further explore the inputs to the machine learning system and new methods of analysis. We further validated our methods with a windowing approach to the Rayleigh ratio range. By running this methodology over multiple Raleigh ratio ranges, the system is able to locate transitional regions. The system correctly identifies the transitional behaviors described in prior mathematical work. The results indicate that the power spectral density is very important for the correct clustering. We also found that examining machine learning clusters by dimension (x, y, and z) was important to understand many of the facets of the chaotic transitions.
This simplified model serves as a test case for more complex natural convection flows. This unsupervised learning approach can be utilized on other systems where numerical analysis is computationally difficult. The results provide a visual mapping of the regions where chaotic transitions may occur based on variations in the Prandtl number and geometry constant. We plan to apply these methods to more complex natural convection systems and compare to experimental data. Unsupervised machine learning may be used as a tool to characterize the transition regions for these geometries, providing new lenses for the heat transfer community.
Designing and Analyzing an Underbody Plate for an Armored Vehicle Subjected to Blast
Authors: Carrillo Jesus , Justice Jarrett , Kim Tyler , Loomis Courtney , McMullen Kevin 
Published: January 2022

Abstract
The purpose of this project was to design the next generation of additive underbody blast protection plates for military combat vehicles. Buried improvised explosive devices (IEDs) pose a serious threat to armored vehicles causing significant deflection to the underbody. Such deflections can result in potential lifelong and life-threatening injuries. The proposed plate is designed to be modular and easily replaceable. The structural design aimed to minimize areal density and depth of the panel, thereby reducing the weight and increasing the ground clearance of the vehicle. Prototypes of the panel designs were additively manufactured from sintered Ti6Al4V, a high strength titanium alloy. The designs were experimentally tested at the Army Research Laboratory in Aberdeen, Maryland under a small-scale buried blast event. Numerical finite element models were created in LS-DYNA to analyze each potential design. The designs used a sacrificial sandwich structure between two monolithic plates. The sandwiched structure was designed to provide optimal stiffness and energy absorption. The final panel design decreased the areal density and thickness of the panel by 14% and 9%, respectively. The experimental results were validated by an LS-Dyna numerical model, which projected a maximum deflection of 7.4 mm within 20.7% of the experimental deflection.
A Review of Graphite Properties Relevant to Micro-Reactor Design
Authors: Ozaltun Hakan , Liepinya Diana , Avincola Valentina Angelici 
Published: January 2022

Abstract
Micro-reactors are advanced reactors that are designed to be small, scalable, and portable with up to 20 MW power. These reactors could operate independently, or as part of a microgrid, without need of frequent refueling. Micro-reactors are designed to be deployable to provide electric power in remote locations, therefore providing greater flexibility in energy distribution and access.
Several micro-reactors are under development in the US. These reactors are based on advanced gas-cooled or heat-pipe concepts. While graphite can be found in various applications, some high-temperature reactors plan to employ graphite in their core components due to its role as a moderator and its ability to maintain its structural integrity when subjected to elevated temperatures and high neutron fluence. Although there is significant experience with use of graphite in nuclear applications, there are still unknowns that should be addressed. Graphite properties depend on the graphite type and are affected by the fabrication procedures. Material properties are functions of the temperature and radiation dose and are often affected by the oxidation environment. Therefore, in-depth understanding of graphite properties, including the effect of irradiation, oxidation, and other influencing parameters are required to evaluate the design accurately.
This paper aims to provide a review of material properties for graphite relevant to micro-reactor design. For this work, two distinct grades of graphite were considered, namely IG-110 and Mersen-2114. Physical, thermal, and mechanical properties for these grades were comparatively evaluated. The properties that are discussed in this draft aim to serve as a basis for design and safety analysis.
Ballistic Performance of Sandwich Composite Armor System
Authors: Alam Shah , Nguyen Diem 
Published: January 2022

Abstract
This research focuses on simulating and experimenting the ballistic impact of composite materials. The projectile used is 7.62 mm AP composed of alloy steel 4340 and the target armor is made of Alumina 99.5% with size of 100mm × 100mm × 5mm and Kevlar 29/epoxy with dimension of 100mm × 100mm × 2mm. The Johnson-Holmquist, Johnson-Cook and Hashin damage models have been employed to analyse damage of the sandwich structured composite armor and deformation of steel projectile. The fracture of target armor under normal impact was investigated numerically with the aid of VUMAT, a user defined material adds on in Abaqus/Explicit. Finite element model validation was conducted by comparing the numerical findings with one experimental result obtained from other researches that provide similar experimental scenarios. The obtained data compare well with experimental results.
Wind Farm Layout Optimization: A Multi-Stage Approach
Authors: Yang Puyi , Najafi Hamidreza 
Published: January 2022

Abstract
The Wind Farm Layout Optimization (WFLO) problem is a complex and non-convex optimization problem. Even though a lot of heuristic algorithms and mathematical programming methods have been tested and discussed, there is not a consensus about which algorithm is the most suitable one to solve the WFLO problems. Every algorithm has its own advantages and disadvantages on solving different problems, thus the multi-stage approaches have been picked up. One multi-stage approach applied in solving WFLO problems is to apply an algorithm in stage 1 to capture a coarse, initial optimized layout and import it to stage 2 as an initial condition for another algorithm for further refinement. This paper compared two types of multi-stage methods: The Heuristic-Gradient-based (H-G) model which consists of a heuristic algorithm in stage 1 and a gradient-based algorithm in stage 2; The Discrete-Continuous (D-C) model which consists of a heuristic algorithm in discrete scheme in stage 1 and an algorithm in continuous scheme in stage 2. Annual Energy Production (AEP) is used as the objective function while the computational time associated with each approach is documented. The results illustrate most of the multistage models can improve the optimization procedure both in terms of AEP and computational time. Overall, it is found that the D-C approach is better than the H-G approach. Particularly, the combination of Greedy+Random Search provides the highest AEP and the combination of Greedy and SLSQP provides the lowest computational time.
Processing and Characterization of Continuous Carbon Fiber Reinforced Silicon Oxycarbide Ceramic Matrix Composites
Authors: Song Haonan , Saltzman Derek , Kapat Jayanta , Gou Jihua 
Published: January 2022

Abstract
There are significant challenges for materials in extreme environments for a variety of applications such as aircraft engines, gas turbines, nuclear reactors, reentry vehicles, and hypersonic structures. Ceramic matrix composites (CMCs) could be ideal candidates to meet these stringent requirements for materials due to their high melting temperatures, high oxidation, corrosion and ablation resistance, low creep, and thermal cycling behavior in such extreme environments. Particularly, continuous fibers can bridge cracks in CMCs and therefore improve the strength and fracture toughness of composites. CMCs are traditionally manufactured by the melting infiltration method. With this method, the high porosity and brittle structure of fabricated CMCs are not capable of withstanding high mechanical and thermal loads. Alternatively, polymer derived ceramic composites are fabricated by incorporating carbon fibers into polymer derived ceramic matrix to achieve high fracture toughness. With the aid of protective coatings with metallic or ceramic materials, such as Nickel and boride nitride, carbon fiber could potentially withstand high temperatures without oxidation. In this study, continuous fiber reinforced silicon oxycarbide composite was manufactured with polysiloxane (PSX) resin and woven carbon fabrics through the polymer infiltration and pyrolysis process (PIP). Re-infiltration of the PSX resin into the composites, curing in an autoclave, and pyrolysis for additional 2 to 10 cycles can increase the yield of ceramics of the composites. A dense structure of the composites was observed by SEM. The EDS results showed that the elemental composition of the composites mainly consisted of carbon, silicon and oxygen. The crystalline structure of the composites was examined through XRD to indicate the degree of polymer pyrolysis to ceramics. The results of four-point bending testing of the composites showed a flexural strength of 62.17MPa, a flexural modulus of 51.30GPa, and a fracture toughness of 1.3 × 108J/m3.
RADAR Modeling for Autonomous Vehicle Simulation Environment With Open-Source Utilities
Authors: Kesury Tayabali Akhtar , Cardoza Chris Orlin , Anwar Sohel 
Published: January 2022

Abstract
This research presents a RADAR modeling technique suitable for autonomous vehicle simulation environment using open source utilities. This study proposes to customize an onboard LiDAR model to the specification of a desired RADAR field of view, resolution, and range and then utilizes a density based clustering algorithm to generate the RADAR output on an open source graphical engine such as Unreal Engine (UE). High fidelity RADAR models have recently been developed for proprietary simulation platforms such as MATLAB under its automated driving toolbox. However, open source RADAR models for open source simulation platform such as UE are not available. This research focuses on developing a RADAR model on UE using blueprint visual scripting for off-road vehicles. This model offers the developers a base to build upon, and help them develop and test autonomous control algorithms requiring RADAR sensor data. Preliminary simulation results show promise for the proposed RADAR model.
Stress Analysis of Bolted Flange Joints With Different Shell Connections
Authors: Choulaei Mohammad , Bouzid Abdel-Hakim 
Published: January 2022

Abstract
Bolted flange joints are widely implemented in various pressure vessel applications of different industries due to their simplicity in installation and operation. However, the most challenging concern during their operation is the level of leakage tightness that they can maintain over time. In addition, the performance of the different flange connection configurations is not well established. Unfortunately, the current ASME BPV Code flange design is not based on a leakage criterion nor a flexibility analysis to give a precise evaluation of the different parameters.
This study deals with an evaluation of the integrity and leakage tightness of different types of shells connected to the flange ring. The proposed study is to use the different shell theories to analyze the different parameters such as flange rotation, gasket contact stress and stress distribution at the flange to shell junction. Several types of shell connections, namely cylindrical, spherical, dish and conical are compared. All these types of shells are connected to the raised-face flange ring without the hub. Moreover, to support the analytical approach and validate the study, these shell connections are modelled using a general-purpose finite element software. It is worth noting that, the gasket nonlinear behavior is considered in the FE analysis but not in the analytical modeling.