Engineering Commons^™

Special Collections As Muse: The Use Of Rare Books And Archives To Inspire Creative Works, Tracy Grimm, Adriana Harmeyer

Faculty & Staff Scholarship

The unique and varied collections held by archives and special collections within many academic libraries offer fertile ground for the creative endeavors of students, faculty, and professional artists. This chapter explores direct and indirect methods librarians and archivists may engage creators with primary source materials. Academic libraries do not necessarily need to build art-focused collections in order to support the research of creators. More than subject content, successful engagement with creators is developed by means of collaborative relationships with arts faculty, artists, and galleries to reach student creators and introduce concepts of primary source research as a source of inspiration. …

Grey-Taguchi Approach To Optimize Fused Deposition Modeling Process In Terms Of Mechanical Properties And Dimensional Accuracy, Md Asif Bin Syed, Qausar Rhaman, Hasan Md Shahriar, Mohammad Muhshin Aziz Khan

Graduate Student Scholarship

Fused Deposition Modeling (FDM) is a process that allows for the rapid production of functional parts through the deposition of fused material layers in a sequential manner. FDM has flexibility and the potential to create complicated parts. This study aims to optimize the FDM process parameters in terms of tensile strength, flexural strength, and longitudinal shrinkage using the Grey-Taguchi approach. The input parameters chosen to study the effects on dimension and mechanical properties are layer thickness, the raster angle, fill density, the number of contours, printing temperature, and printing speed. The Taguchi L27 orthogonal array is used as the statistical …

Autonomous Design Of New Chemical Reactions Using A Variational Autoencoder, Terence Musho, Robert Tempke

Faculty & Staff Scholarship

Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based on experimental synthesis are typically reported only for the optimal synthesis reactions. This leads to an inherited bias in the model predictions. Therefore, robust datasets that span the entirety of the solution space are necessary to remove inherited bias and permit complete training of the space. In this study, an artificial intelligence model based on a Variational AutoEncoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions. …

Direct Foam Writing In Microgravity, Guy Jacob Cordonier, Cicely Sharafati, Spencer Mays, Lukas Thackery, Ellena Gemmen, Samuel Cyphert, Megan Brown, John Quinn Napolillo, Savannah Toney, Hunter Moore, John M. Kuhlman `, Konstantinos A. Sierros

Faculty & Staff Scholarship

Herein we report 2D printing in microgravity of aqueous-based foams containing metal oxide nanoparticles. Such hierarchical foams have potential space applications, for example for in situ habitat repair work, or for UV shielding. Foam line patterns of a TiO2-containing foam have been printed onto glass substrates via Direct Foam Writing (DFW) under microgravity conditions through a parabolic aircraft flight. Initial characterization of the foam properties (printed foam line width, bubble size, etc.) are presented. It has been found that gravity plays a significant role in the process of direct foam writing. The foam spread less over the substrate when deposited …

Trip Based Modeling Of Fuel Consumption In Modern Heavy-Duty Vehicles Using Artificial Intelligence, Sasanka Katreddi, Arvind Thiruvengadam

Faculty & Staff Scholarship

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a …

Exploring Dft+U Parameter Space With A Bayesian Calibration Assisted By Markov Chain Monte Carlo Sampling, Pedram Tavadze, Reese Boucher, Guillermo Avendaño-Franco, Keenan X. Kocan, Sobhit Singh, Viviana Dovale-Farelo, Wilfredo Ibarra-Hernández, Matthew B. Johnson, David S. Mebane, Aldo H. Romero

Faculty & Staff Scholarship

The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo …

Graphene Flake Self-Assembly Enhancement Via Stretchable Platforms And External Mechanical Stimuli, Harrison A. Loh, Claudio Marchi, Luca Magagnin, Konstantinos A. Sierros

Faculty & Staff Scholarship

While the green production and application of 2D functional nanomaterials, such as graphene flakes, in films for stretchable and wearable technologies is a promising platform for advanced technologies, there are still challenges involved in the processing of the deposited material to improve properties such as electrical conductivity. In applications such as wearable biomedical and flexible energy devices, the widely used flexible and stretchable substrate materials are incompatible with high-temperature processing traditionally employed to improve the electrical properties, which necessitates alternative manufacturing approaches and new steps for enhancing the film functionality. We hypothesize that a mechanical stimulus, in the form of …

An Application Of An Unequal-Area Facilities Layout Problem With Fixed-Shape Facilities, Alan Mckendall, Artak Hakobyan

Faculty & Staff Scholarship

The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, …

An Application Of An Unequal-Area Facilities Layout Problem With Fixed-Shape Facilities, Alan Mckendall, Artak Hakobyan

Faculty & Staff Scholarship

The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, …

A Scalable Framework For Map Matching Based Cooperative Localization, Chizhao Yang, Jared Strader, Yu Gu

Faculty & Staff Scholarship

Localization based on scalar field map matching (e.g., using gravity anomaly, magnetic anomaly, topographics, or olfaction maps) is a potential solution for navigating in Global Navigation Satellite System (GNSS)-denied environments. In this paper, a scalable framework is presented for cooperatively localizing a group of agents based on map matching given a prior map modeling the scalar field. In order to satisfy the communication constraints, each agent in the group is assigned to different subgroups. A locally centralized cooperative localization method is performed in each subgroup to estimate the poses and covariances of all agents inside the subgroup. Each agent in …

Subsurface Analytics: Contribution Of Artificial Intelligence And Machine Learning To Reservoir Engineering, Reservoir Modeling, And Reservoir Management, Shahab D. Mohaghegh

Faculty & Staff Scholarship

Subsurface Analytics is a new technology that changes the way reservoir simulation and modeling is performed. Instead of starting with the construction of mathematical equations to model the physics of the fluid flow through porous media and then modification of the geological models in order to achieve history match, Subsurface Analytics that is a completely AI-based reservoir simulation and modeling technology takes a completely different approach. In AI-based reservoir modeling, field measurements form the foundation of the reservoir model. Using data-driven, pattern recognition technologies; the physics of the fluid flow through porous media is modeled through discovering the best, most …

Integrate A Conflict Resolution Session Into The Freshman Engineering Problem Solving Course To Improve Students’ Ability To Solve Interpersonal Team Conflicts, Xinyu Zhang, Jeremy G. Roberts

Faculty & Staff Scholarship

The project-based freshman engineering course entitled “Engineering Problem Solving I” in the Fundamentals of Engineering Program (FEP) at West Virginia University (WVU) requires students to work in teams to complete engineering design projects. Many students are lacking in their team dynamics, particularly in handling inter-team conflicts, which can seriously hinder their learning and future coping with conflict. When a conflict happened within a team, students hesitated to enforce their team charters, which defines the project parameters and the team’s standard of conduct. Some students avoided confronting the difficult team members, some waited for the instructor to handle the issues for …

Engaging First Year Students With Intellectual Property, Marian G. Armour-Gemmen

Faculty & Staff Scholarship

Since intellectual property is so important to engineers, creating enthusiasm from the beginning of their engineering studies is imperative. Since first year students have not learned how to apply technological concepts to real life, demonstrating intellectual property could be a challenge. To engage first year engineering students in the concept and the value of intellectual property, students were introduced to basic concepts and applications. Different concepts were applied to real life examples allowing them to interface with technology from an intellectual property perspective. This paper highlights not only patents, but also trademarks and trade secrets.

Separation Of Radionuclides From A Rare Earth-Containing Solution By Zeolite Adsorption, Deniz Talan, Qingqing Huang

Faculty & Staff Scholarship

The increasing industrial demand for rare earths requires new or alternative sources to be found. Within this context, there have been studies validating the technical feasibility of coal and coal byproducts as alternative sources for rare earth elements. Nonetheless, radioactive materials, such as thorium and uranium, are frequently seen in the rare earths’ mineralization, and causes environmental and health concerns. Consequently, there exists an urgent need to remove these radionuclides in order to produce high purity rare earths to diversify the supply chain, as well as maintain an environmentally-favorable extraction process for the surroundings. In this study, an experimental design …

Tangle-Free Exploration With A Tethered Mobile Robot, Danylo Shapovalov, Guilherme Pereira

Faculty & Staff Scholarship

Exploration and remote sensing with mobile robots is a well known field of research, but current solutions cannot be directly applied for tethered robots. In some applications, tethers may be very important to provide power or allow communication with the robot. This paper presents an exploration algorithm that guarantees complete exploration of arbitrary environments within the length constraint of the tether, while keeping the tether tangle-free at all times. While we also propose a generalized algorithm that can be used with several exploration strategies, our implementation uses a modified frontier-based exploration approach, where the robot chooses its next goal in …

Tuning The Size And Composition Of Manganese Oxide Nanoparticles Through Varying Temperature Ramp And Aging Time, Celia Martinez De La Torre, Jasmine H. Grossman, Andrey A. Bobko, Margaret F. Bennewitz

Faculty & Staff Scholarship

Manganese oxide (MnO) nanoparticles (NPs) can serve as robust pH-sensitive contrast agents for magnetic resonance imaging (MRI) due to Mn2+ release at low pH, which generates a ~30 fold change in T1 relaxivity. Strategies to control NP size, composition, and Mn2+ dissolution rates are essential to improve diagnostic performance of pH-responsive MnO NPs. We are the first to demonstrate that MnO NP size and composition can be tuned by the temperature ramping rate and aging time used during thermal decomposition of manganese(II) acetylacetonate. Two different temperature ramping rates (10°C/min and 20°C/min) were applied to reach 300°C and NPs were aged …

Continuous Otm 33a Analysis Of Controlled Releases Of Methane With Various Time Periods, Data Rates And Wind Filters, Robert S. Heltzel, Mohammed T. Zaki, Aron K. Gebreslase, Omar I. Abdul-Aziz, Derek R. Johnson

Faculty & Staff Scholarship

Other test method (OTM) 33A has been used to quantify emissions from natural gas sites since it was introduced by the Environmental Protection Agency (EPA). The method relies on point source Gaussian (PSG) assumptions to estimate emissions rates from a targeted site or source. However, the method often results in low accuracy (typically ±70%, even under conducive conditions). These accuracies were verified with controlled-release experiments. Typically, controlled releases were performed for short periods (15–20 min) under atmospheric conditions that were ideal for effective plume transport. We examined three methane release rates from three distances over various periods of time ranging …

Vehicle-To-Grid Integration For Enhancement Of Grid: A Distributed Resource Allocation Approach, Deepak Tiwari, Mohd Adil Anwar Sheikh, Joseph Moyalan, Mayur Sawant, Sarika Khushalani Solanki, Jignesh Solanki

Faculty & Staff Scholarship

In the future grids, to reduce greenhouse gas emissions Electric Vehicles (EVs) seems to be an important means of transportation. One of the major disadvantages of the future grid is the demand-supply mismatch which can be mitigated by incorporating the EVs into the grid. The paper introduces the concept of the Distributed Resource Allocation (DRA) approach for incorporating a large number of Plug-in EV (PEVs) with the power grid utilizing the concept of achieving output consensus. The charging/discharging time of all the participating PEVs are separated with respect to time slots and are considered as strategies. The major aim of …

Effects Of Oxygen Modification On The Structural And Magnetic Properties Of Highly Epitaxial La0.7sr0.3mno3 (Lsmo) Thin Films, Shalini Kumari, Navid Mottaghi, Chih-Yeh Huang, Robbyn Trappen, Ghadendra Bhandari, Saeed Yousefi, Guerau Cabrera, Mohindar S. Seehra, Mikel B. Holcomb

Faculty & Staff Scholarship

La0.7Sr0.3MnO3, a strong semi-metallic ferromagnet having robust spin polarization and magnetic transition temperature (TC) well above 300 K, has attracted significant attention as a possible candidate for a wide range of memory, spintronic, and multifunctional devices. Since varying the oxygen partial pressure during growth is likely to change the structural and other physical functionalities of La0.7Sr0.3MnO3 (LSMO) films, here we report detailed investigations on structure, along with magnetic behavior of LSMO films with same thickness (~30 nm) but synthesized at various oxygen partial pressures: 10, 30, 50, 100, 150, 200 and 250 mTorr. The observation of only ( …

Large-Scale Inflatable Structures For Tunnel Protection: A Review Of The Resilient Tunnel Plug Project, Eduardo M. Sosa, Gregory J. Thompson, Gregory M. Holter, John M. Fortune

Faculty & Staff Scholarship

The protection of underground civil infrastructure continues to be a high priority for transportation and transit security agencies. In particular, rail transit tunnels running under bodies of water are susceptible to disruptions due to flooding caused by extraordinary climatic events such as hurricanes or other events resulting from human activities. Several events have taken place in the past decades that have demonstrated the need to mitigate vulnerabilities or, at least, minimize the consequences of catastrophic events. Although it is impossible to prevent all situations that can lead to flooding, damage can be substantially decreased by reducing the area affected by …

An Elastic-Net Logistic Regression Approach To Generate Classifiers And Gene Signatures For Types Of Immune Cells And T Helper Cell Subsets, Arezo Torang, Paraag Gupta, David J. Klinke Ii

Faculty & Staff Scholarship

Background: Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously. Results: Elastic-net logistic regression, a type of …

From Statistical Correlations To Stochasticity And Size Effects In Sub-Micron Crystal Plasticity, Hengxu Song, Stefanos Papanikolaou

Faculty & Staff Scholarship

Metals in small volumes display a strong dependence on initial conditions, which translates into size effects and stochastic mechanical responses. In the context of crystal plasticity, this amounts to the role of pre-existing dislocation configurations that may emerge due to prior processing. Here, we study a minimal but realistic model of uniaxial compression of sub-micron finite volumes. We show how the statistical correlations of pre-existing dislocation configurations may influence the mechanical response in multi-slip crystal plasticity, in connection to the finite volume size and the initial dislocation density. In addition, spatial dislocation correlations display evidence that plasticity is strongly influenced …

Temporal Variations In Methane Emissions From An Unconventional Well Site, Derek Johnson, Robert Heltzel, Dakota Oliver

Faculty & Staff Scholarship

Studies have aimed to quantify methane emissions associated with the growing natural gas infrastructure. Quantification is completed using direct or indirect methods—both of which typically represent only a snapshot in time. Most studies focused on collecting emissions data from multiple sites to increase sample size, thus combining the effects of geospatial and temporal variability (spatio-temporal variability). However, we examined the temporal variability in methane emissions from a single unconventional well site over the course of nearly 2 years (21 months) by conducting six direct quantification audits. We used a full flow sampling system that quantified methane mass emissions with an …

Neutrophil Extracellular Traps In Breast Cancer And Beyond: Current Perspectives On Net Stimuli, Thrombosis And Metastasis, And Clinical Utility For Diagnosis And Treatment, Hunter T. Snoderly, Brian A. Boone, Margaret F. Bennewitz

Faculty & Staff Scholarship

Abstract

The formation of neutrophil extracellular traps (NETs), known as NETosis, was first observed as a novel immune response to bacterial infection, but has since been found to occur abnormally in a variety of other inflammatory disease states including cancer. Breast cancer is the most commonly diagnosed malignancy in women. In breast cancer, NETosis has been linked to increased disease progression, metastasis, and complications such as venous thromboembolism. NET-targeted therapies have shown success in preclinical cancer models and may prove valuable clinical targets in slowing or halting tumor progression in breast cancer patients. We will briefly outline the mechanisms by …

Bending Nanoindentation And Plasticity Noise In Fcc Single And Polycrystals, Ryder Bolin, Hakan Yavas, Hengxu Song, Kevin J. Hemker, Stefanos Papanikolaou

Faculty & Staff Scholarship

Abstract: We present a high-throughput nanoindentation study of in situ bending effects on incipient plastic deformation behavior of polycrystalline and single-crystalline pure aluminum and pure copper at ultranano depths (< 200 nm). We find that hardness displays a statistically inverse dependence on in-plane stress for indentation depths smaller than 10 nm, and the dependence disappears for larger indentation depths. In contrast, plastic noise in the nanoindentation force and displacement displays statistically robust noise features, independently of applied stresses. Our experimental results suggest the existence of a regime in Face Centered Cubic (FCC) crystals where ultranano hardness is sensitive to residual applied stresses, but plasticity pop-in noise is insensitive to it.

Drilling Performance Monitoring And Optimization: A Data-Driven Approach, Shan E Zehra Lashari, Ali Takbiri-Borujeni, Ebrahim Fathi, Ting Sun, Reza Rahmani, Mehdi Khazaeli

Faculty & Staff Scholarship

Abstract Drilling performance monitoring and optimization are crucial in increasing the overall NPV of an oil and gas project. Even after rigorous planning, drilling phase of any project can be hindered by unanticipated problems, such as bit balling. The objective of this paper is to implement artifcial intelligence technique to develop a smart model for more accurate and robust real-time drilling performance monitoring and optimization. For this purpose, the back propagation, feed forward neural network model was developed to predict rate of penetration (ROP) using diferent input parameters such as weight on bit, rotations per minute, mud fow (GPM) and …

A Review Of 2d And 3d Plasmonic Nanostructure Array Patterns: Fabrication, Light Management And Sensing Applications, Sujan Kassani, Katherine Curtin, Nianqiang Wu

Faculty & Staff Scholarship

Abstract: This review article discusses progress in surface plasmon resonance (SPR) of two-dimensional (2D) and three-dimensional (3D) chip-based nanostructure array patterns. Recent advancements in fabrication techniques for nano-arrays have endowed researchers with tools to explore a material’s plasmonic optical properties. In this review, fabrication techniques including electron-beam lithography, focused-ion lithography, dip-pen lithography, laser interference lithography, nanosphere lithography, nanoimprint lithography, and anodic aluminum oxide (AAO) template-based lithography are introduced and discussed. Nano-arrays have gained increased attention because of their optical property dependency (lightmatter interactions) on size, shape, and periodicity. In particular, nano-array architectures can be tailored to produce and tune plasmonic …

Exploration Of High Entropy Ceramics (Hecs) With Computational Thermodynamics - A Case Study With Lamno3±Δ, Yu Zhong, Hooman Sabarou, Xiaotian Yan, Mei Yang, Michael C. Gao, Xingbo Liu, Richard D. Sisson Jr.

Faculty & Staff Scholarship

The concept of the new category materials high entropy ceramics (HECs) has been proposed several years ago, which is directly borrowed from high entropy alloys (HEAs). It quickly attracts a lot of interests and displays promising properties. However, there is no clear definition of HECs differentiating it from HEAs, as it is still in its early research stage. In the current work, we are trying to use the classic perovskite LaMnO3±δ (LMO) to demonstrate the fundamental differences between HECs and HEAs. We have adopted the integrated defect chemistry and CALPHAD approach to investigate the mixing behavior and how it is …

A Regulated Charge Pump With Extremely Low Output Ripple, Mir Mohammad Navidi, David W. Graham

Faculty & Staff Scholarship

Abstract: In this paper, we present a regulated charge pump with extremely low output ripple (mV) that can be used for accurate programming of nonvolatile memory. We present a technique to include a low-drop-out regulator inside the charge-pump regulation loop to reduce the ripple. This charge pump was fabricated in a 0.35 µm standard CMOS process. The die area of this charge pump is 0.163 mm2 . While operating from a 2.5 V supply, this charge pump generates regulated voltages up to 10 V.

Cancer Nanotechnology: Enhancing Tumor Cell Response To Chemotherapy For Hepatocellular Carcinoma Therapy, Sun Yongbing, Ma Wen, Yang Yuanyuan, He Mengxue, Li Aimin, Bai Lei, Yu Bin, Yu Zhiqiang

Faculty & Staff Scholarship

Abstract

Hepatocellular carcinoma (HCC) is one of the deadliest cancers due to its complexities, reoccurrence after surgical resection, metastasis and heterogeneity. In addition to sorafenib and lenvatinib for the treatment of HCC approved by FDA, various strategies including transarterial chemoembolization, radiotherapy, locoregional therapy and chemotherapy have been investigated in clinics. Recently, cancer nanotechnology has got great attention for the treatment of various cancers including HCC. Both passive and active targetings are progressing at a steady rate. Herein, we describe the lessons learned from pathogenesis of HCC and the understanding of targeted and non-targeted nanoparticles used for the delivery of small …