bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Study of Deformation of Viscoelastic Drop Migrating Through Microchannel With Sudden Constriction
Authors: Prasad Niraj Kr , Sankar Ghosh Siddhartha , Dalal Amaresh
Published: January 2022

In this paper, deformation of viscoelastic drop in a microscale flow with sudden constriction is studied numerically. . The effect of polymeric viscosity on deformation of viscoelastic fluid is studied for two different solvent viscosity ratio and two different channel confinement in two-phase viscoelastic drop-Newtonian matrix system. The Finitely Extensible Non-linear Elastic–Peterlin (FENE-P) model was used to model the viscoelastic drop. The simulation was performed using open source solver Basilisk. The viscoelastic drop with lower polymeric viscosity and higher solvent viscosity seemed to undergo appreciable deformation inside constricted microchannel when subjected to sudden contraction. The low polymeric viscosity of a viscoelastic fluid corresponds to a dilute polymeric solution, its viscoelastic behavior is best explained by seeing its deformation under high shear flows inside microchannel. The isolated arrangement of polymeric coils in dilute polymeric solutions assists independent molecular movement, which supports their deformation under high shear flows. The FENE-P drop model used in the present study, at higher solvent viscosity, exhibit faster stress relaxation and remarkable elastic recovery in the expansion zone of the microchannel. The dilute FENE-P viscoelastic fluid can also be considered as a significant model to predict the viscoelastic behavior of useful polymeric fluids like hydrogels, paints, and vesicles, etc.
Evaluation of “Natural Gas/Hydrogen” Mixtures for Power to Gas Application
Authors: Incer-Valverde Jimena , Olaniyi Oyeniyi , Morosuk Tatiana , Tsatsaronis George
Published: January 2022

Utilization of hydrogen (H2)/natural gas (NG) mixtures or pure hydrogen in gas-fired power plants poses a lower carbon footprint instead of the regular 100% NG fuel. Reducing carbon emissions (CO2) in electricity production is fast gaining huge traction in gas power plants, as the attention is shifting from soon eradicated coal power plants to low carbon power plants. Increased interest in the hydrogen economy has further aroused discussions for hydrogen to replace natural gas. This paper evaluates the impact of hydrogen mixtures on existing power plants in three countries: Denmark, Germany, and the United Kingdom. The investigation is carried out using energy, exergy, and economic analysis to depict implications of the various mixtures on each of the power plants. The simulation of the power plants was performed using Ebsilon software, while the calculations of CO2, and NOx emissions were carried out with the aid of the Cantera software and the exergy-based analysis was computed in Excel VBA. The analyzed mixtures of H2/NG presented advantages in all the power plants studied such as lower CO2 emissions, higher energetic and exergetic efficiencies, and, therefore, lower mass flowrates of the fuel mixture. However, NOx discharge, levelized fuel cost (except in the Viborg power plant), and volumetric flowrate increased drastically. Conclusions of this paper will enlighten readers on the technological and economic constraints of using H2-NG mixtures in gas-fired power plants.
Mixed-Integer Nonlinear Programming (MINLP) Based Optimization of Refrigeration Systems
Authors: Mussati Sergio F. , Morosuk Tatiana , Mussati Miguel C.
Published: January 2022

This paper addresses the optimization of refrigeration systems by proposing a deterministic model involving both discrete and continuous decisions. The superstructure-based representation of vapor compression-absorption cascade refrigeration system, recently presented by the authors, is properly extended to include more candidate configurations as well as a cost model considering capital and operation expenditures. The proposed superstructure representation is then formulated as a mixed-integer nonlinear programming (MINLP) model and solved using an optimization algorithm based on the generalized reduced gradient method. The mass flowrate, pressure, temperature, composition of all streams, and the energy transferred with the associated heat transfer area and driving force of all process units are simultaneously optimized. The optimal configuration is selected from the proposed superstructure. The resulting optimization model is inherently combinatorial in nature and involves more than 20 candidate combinations. A study case is presented in order to show the utility of the proposed model. Given the desired design specifications — refrigeration temperature and capacity — the model allows obtaining the optimal configuration, process-unit sizes, and operating conditions that minimize the total annual cost. As a result of the model, the optimal configuration consisting of a stand-alone compression cycle with R717 is selected from the proposed superstructure to achieve 100 kW at −20 °C. Then, this optimal solution is compared with a solution obtained with a fixed configuration consisting of a vapor compression-absorption cascade refrigeration system which is a sub-optimal solution compared to the stand-alone compression cycle.
Mechanical-Electrical Behavior of Multifunctional Energy Storage Composites
Authors: Bombik Anthony , Ha Sung Yeon Sara , Nasrollahi Amir , Haider Mohammad Faisal , Chang Fu-Kuo
Published: January 2022

Multi-functional Energy Storage Composites (MESC) are composite sandwich structures where battery stack layers are placed between two layers of CFRP and sealed by low-density polyethylene (LDPE), forming a unified material. Because the layered Li-ion stacks have negligible out-of-plain shear stiffness, the two CFRP sheets on both sides of the battery are connected using LDPE rivets that pass through holes cut through the battery layers. The shear transfer mechanism of the rivets substantially enhances the shear stiffness and strength of the MESC. As the first step of preparing a guide for MESC design, the highly coupled mechanical and electrical behavior of MESC was studied through experiments. Several MESC cells were tested under three-point-bending loads. The load, deformation, and electric potential of the MESC were measured, and the electrical and mechanical failures were observed. A finite element model was developed to simulate the electro-chemo-mechanical coupling effect in MESC. In this model, a new constitutive relation of the battery material is proposed and verified by the experimental results. The resulting model can be used to simulate MESCs with various configurations and material properties to provide a design guideline of MESCs in multiple applications.
On the Modeling of Wildfires-Induced Release and Atmospheric Dispersion in Radioactively Contaminated Regions
Authors: Polat Damla , Diaconeasa Mihai A.
Published: January 2022

Nuclear energy is one of the most efficient types of electricity production. However, it is one of the biggest fears of people due to the potential radiation effects on human health. Despite the major developments in the nuclear sector, some gaps need to be studied for the higher safety scrutiny of nuclear power plants (NPPs). Besides technical advances for the safer management of an NPP, another important part is having a well-constructed and planned probabilistic risk assessment and management. Realistic probabilistic risk assessment and management provide proper emergency response in case of an accident or hazardous situation to human health. On the other hand, aside from the radiation emitted directly from radioactive sources inside the NPP, there may be indirect radiation emission from dispersions outside the plant’s protected area. For example, we can look at forest fires occurring in radioactively contaminated areas surrounding NPPs that suffered accidents with releases, such as Chernobyl or Fukushima Daiichi. Radioactive particles produced by burning contaminated forests could spread in the air and threaten public health. It has already been observed that fires in forests around Chernobyl can increase the level of radiation in the air. Such events have the possibility to occur in all areas where nuclear facilities are located. The forests contaminated after the Fukushima Daiichi NPP accident, resemble the ones at Chernobyl. This study aims to develop the knowledge for an early sensing and emergency response by doing an atmospheric dispersion modeling and supporting a probabilistic risk assessment for a wildfire scenario in radioactively contaminated areas, such as Chernobyl and Fukushima Daiichi. Also, this study provides a pathway to assessing the risk of nuclear contamination caused by wildfires around nuclear facilities.
Constitutive Modeling and Validation of Sintered Metal Powders Subjected to Large Strains and High Strain Rates
Authors: West Ashby , Venable Garrett , Flanagan Michael , Harris Evan , Davis Brad G. , Davidson F. Todd , Hanus Joseph
Published: January 2022

The development of advanced small caliber weapon systems has resulted in rounds with more material penetration capabilities. The increased capabilities may mean that existing live-fire facilities will no longer be adequate for the training and certification of military and law enforcement personnel, which could result in training constraints and possibly expensive upgrades to improve the safety of existing facilities. New training ammunition manufactured from novel structural materials are needed to allow for the safe, continued use of live-fire shoot house facilities. The goal of this project is to characterize a sintered metal powder and fit a suitable constitutive model for simulation in support of numerical design. A pressed and sintered blend of copper-tin was selected as a suitable representative material for this application. Samples were tested in uniaxial compression under quasi-static conditions and elevated temperatures. Dynamic compression testing at strain rates up to approximately 105 s−1 was conducted using a split-Hopkinson bar. The results of these tests were then used to fit Johnson-Cook and Zerilli-Armstrong strength models to the test data. The models were fit by selecting points from test data at different strain rates and elevated temperatures. This system of equations was then solved for each model while using the same test data to ensure a fair comparison of the results. A Mie-Gruneisen equation of state for the material was estimated using a rule of mixtures and existing shock and particle velocity data. Taylor cylinder tests were conducted and the rate of change in length was measured using high-speed video. Simulation of the Taylor tests was conducted using the developed strength and equation of state model and compared to the experimental results for model validation and comparison. Both the Johnson-Cook and Zerilli-Armstrong models resulted in less than 1% error of the Taylor cylinder results before material fracture. Further development of a fracture model for this material is recommended for use in high strain rate modeling applications.
Design of a 3D Aerospace Bracket Using Lattice Structures and Topology Optimization for Additive Manufacturing
Authors: Ates Gorkem Can , Demirtunç Mehmet , Göçer Ali Cem , Doğru Abdulhamid Han , Gorguluarslan Recep M. , Gökdağ İstemihan , Yavaş Hakan
Published: January 2022

In this study, a design optimization framework is presented that utilizes the topology and lattice structure optimization approaches to design an aerospace component for additive manufacturing (AM). In this framework, the topology optimization is first utilized to find the relative density distribution in the design space of the component that maximizes its stiffness under the volume and strength constraints. The optimized density distribution is used to generate an initial lattice structure topology. A two-step size optimization is also carried out using the beam element formulation in the FEA. The diameters of the strut members in the lattice structure are aimed to be kept within the manufacturability limits of the selective laser melting (SLM) process with AlSi10Mg alloy to satisfy the volume and stress constraints while maximizing the overall stiffness. Optimized designs are determined with four different lattice types. The best design among them is analyzed to ensure an additional natural frequency constraint using modal analysis. Thus, a novel lattice structure design is achieved that satisfies the strength and vibration-specific requirements of the aerospace component for a real-world application. The developed lattice structure design of the aerospace component is achieved with a 30% reduction in weight while still satisfying the desired requirements compared to the existing design in use. The presented lattice design optimization framework is presented in a way that is not application-specific so that it can also be used for the design of different components for AM. The future work includes experimental validation of the strength and vibration performances of the SLM-fabricated design.
Operational Modal Analysis of a Rotating Structure Subject to Random Excitation Using a Tracking Continuously Scanning Laser Doppler Vibrometer via a Two-Dimensional Scan Scheme
Authors: Lyu Linfeng , Zhu Weidong
Published: January 2022

A two-dimensional (2D) scan scheme is developed for a tracking continuously scanning laser Doppler vibrometer (CSLDV) system to scan the whole surface of a rotating structure excited by a random force. A tracking CSLDV system is developed to track a rotating structure and sweep its laser spot on its surface. The measured response of the structure using the 2D scan scheme of the tracking CSLDV system is considered as the response of the whole surface of the structure subject to random excitation. The measured response can be processed by an operational modal analysis (OMA) method called the improved demodulation method based on a rigorous model of a rotating plate to obtain modal parameters of the rotating structure, such as damped natural frequencies and undamped full-field mode shapes. Damped natural frequencies of the rotating structure are estimated from the fast Fourier transform of the measured response. Undamped full-field mode shapes are estimated by multiplying the measured response using sinusoids whose frequencies are estimated damped natural frequencies. Experimental investigation of the 2D scan scheme of the tracking CSLDV system and OMA method is conducted, and damped natural frequencies and undamped full-field mode shapes of a rotating fan blade with different constant speeds are estimated. It is theoretically and experimentally shown that damped natural frequencies of the rotating fan blade increases with its rotation speed.
Reliability Analysis of Flexible PCBs
Authors: Shejwal Amogh , Srivastava Ashutosh , Medikonda Sandeep , Aminjikarai S. Babu
Published: January 2022

Flexible Circuit Boards (FCB) are ubiquitous in most electronic devices used today. These are utilized in mobile phones, display cables in laptops, cameras, smart watches, robotic arms and more. They are mainly used in applications where space, flexibility and construction constraints limit the usage of conventional Printed Circuit Board (PCB). While FCBs offer numerous advantages over traditional PCBs, like enhanced reliability, capabilities, reduced weight, and lesser space utilization, on the other hand, they present different set of challenges like assembly, installation, and difficulty in repairing and reworking after installation. The flexes are generally bent at several points before conforming to the installed state which induces stresses before the actual operation or the working phase. These stresses are further magnified during the cyclic loading which can lead to breakage of these flexes. Due to intricacies involved in FCBs, numerical modeling of these components is challenging.

In this work, a methodology is developed in Ansys Mechanical™ to model the installation and operating phase of the FCB. Stresses generated in both the phases are calculated and fatigue life is computed after the operational phase. Two different models are analyzed. The first model is a Rigid Flex PCB, where a FCB connects with the rigid PCBs. The second model is a standalone FCB cable. For both the models, shell elements are used to mesh the FCBs, which are typically thin structures and experience a large amount of rotation and bending loads. Trace mapping feature is used to accurately model the large number of intricate features such as copper traces, vias and other Electronic-CAD data. The trace mapping feature simplifies the model by modeling the geometry as dielectric layers and includes the effect of traces by mapping the metal fraction onto the dielectric layers.

The loop forming capability of both the models is analyzed where they are subjected to a 180° bend. The fatigue induced due to this bending load is calculated for both the models. For the FCB cable case, the work is extended to study the stresses developed in the installation phase as it impacts the overall fatigue life of the FCB. Here the rigid surface bodies are used to push/deform the FCB cable to its final installation stage. Lastly, a detailed High-Performance Computing (HPC) scalability study is performed in-order to find the best balance between the number of cores and solution time.
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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Investigation of 3D Printed Antibacterial Nanocomposites for Improved Public Health
Authors: Billings Christopher , Cai Changjie , Liu Yingtao
Published: January 2022

Bacterial infections have been recognized as a critical challenge to public health, resulting in substantial morbidity, mortality, and enormous costs. In this paper, a digital light processing (DLP) based 3D printing system is employed to rapidly manufacture photocurable thermoset polymers and nanocomposites for potential antibacterial applications. This work shows how nanoparticles that present antibacterial properties can be added to traditional DLP manufacturing and their effects on the physical properties. In this paper, titanium dioxide (TiO2) and zinc oxide (ZnO) nanoparticles in the 10 to 30-nm range are mixed with photocurable resins for rapid 3D printing and prototyping. The two resins used are a standard photopolymer rapid resin and an ABS-like photopolymer rapid resin. A 1% composite percentage is utilized to avoid the requirement of modification to the printing system due to greatly increased viscosity. Tensile testing data, contact angle data, and abrasion data are performed on a total of four different composites and two controls. These composites have shown a tensile strength of 29.53 MPa. At the 1% nanoparticle weight concentration, the 3D printing nanocomposites are transparent and demonstrate a complete penetration of particles throughout the entire print. The detailed experimental characterization will be conducted to understand the 3D printed material’s mechanical properties and microstructures fully. This research can enhance public health by providing a novel approach to control the spread of bacteria and other microbial.
Design of Human Head and Neck Replica to Facilitate Concussion and TBI Research
Authors: Honarmandi Peyman , Awikeh Elias
Published: January 2022

Mitigating concussion and researching more about the motion of the brain is vital to develop safer protection gears for athletes and others around the world. The key to furthering research surrounding concussions is to dive deeper into the effect a hit to the head has on the brain. Most areas of this research look to analyze and track the movement of the head. As this may offer an idea as to what happens to the brain, there is still a completely different motion that the brain experiences during a hit. The goal of this project is to offer a realistic method to analyze the motion of the brain via a head and neck replica model. By creating a model with a human like brain within it, the motion of the brain can be tracked via accelerometers. Our goals with this project are to shed light on the lasting damage concussions can do to the brain and potentially study on further concussion prevention technology. What this project aims to accomplish is to create a head and neck model that can more accurately track the actual motion of the head and brain. Selection of the corresponding materials and validation of the model have been conducted. The purpose of creating this model would be to further the understanding of how the brain reacts to certain impacts within different scenarios.
Human Machine Interaction: A Design Proposal of a Ticket Vending Machine for Public Transport
Authors: Ramos Alvarez Adrielly Nahomee , Rodriguez-Tenorio J. Carlos , Borja Vicente , Escalera Matamoros Yesica , Ramirez-Reivich Alejandro C.
Published: January 2022

Mobility as a key element in the dynamics of any city, a development potentiator and resilience factor, is vital for transforming megalopolis into Smart Cities. However, due to numerous elements that interact within urban mobility, solutions are hard to accomplish. Since public transport is crowded in large cities, the process for buying a ticket must be shortened as much as possible. However, even when machines are used for selling tickets in the Mexican public transportation system, not the entire population has access to the variety of payment methods other than the use of cash. Moreover, there is a wide mixture of others who also interact with the machine such as those involved in maintenance, surveillance, and cash collectors. Based on the information gathered and the steps from the user centered design methodology, a design proposal for improving the interaction was conceived.

This paper presents the process that was followed for designing a Ticket Vending Machine (TVM) for Mexico City’s public transport, through which human-machine interaction was made simpler and easier to follow compared to previous TVMs and thus refining a key interaction in mobility for a more efficient transport system. The results and the design proposal could be useful for tackling human-machine interaction concerning other products such as automated teller machines, parking ticket machines and other TVMs for public transport in other cities or countries with a similar panorama.

The implementation of the user-centered design methodology involves the users at each step of the process to understand and tackle their needs when prototyping and testing are conducted. The methodology boosts the innovation and allows faster implementation of ideas, due to its flexibility throughout the iterative process, which is an advantage compared to linear methodologies where feedback is gathered until the end of the process and not during the development of new products and services.
A New Variant of the Dynamic Hybrid RANS-LES Model for Complex Turbulent Flows
Authors: Jamal Tausif , Shobayo Olalekan O. , Walters D. Keith
Published: January 2022

This study proposes a new variant of the Dynamic Hybrid RANS-LES (DHRL) model. Since the baseline DHRL model uses the ratio of resolved to modeled turbulent stress in RANS-to-LES blending, it was observed that for some types of flows the DHRL model remained in RANS mode despite the presence of appreciable levels of LES fluctuations. A new statistical variable is introduced in this study and implemented into a modified blending function to facilitate model transition from RANS to LES based on the presence of appreciable levels of resolved turbulence. Numerical simulations are carried out using the density-based finite-volume solver Loci-CHEM for canonical test cases such as fully developed channel flow, flow over a backward facing step, and flow over a three-dimensional axisymmetric hill. A comprehensive analysis of results indicate that the new model successfully addresses some of the shortcomings of the baseline DHRL model and it is concluded that new DHRL model variant is a useful alternative to traditional HRL models.