Margie and Bill Klesse College of Engineering and Integrated Design Faculty Research

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12588/829

Browse

Now showing 1 - 20 of 279

Developing an eco-driving strategy in a hybrid traffic network using reinforcement learning
(SAGE Publications, 2024-07-23) Jamil, Umar; Malmir, Mostafa; Chen, Alan; Filipovska, Monika; Xie, Mimi; Ding, Caiwen; Jin, Yu-Fang
Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles.
Examining the Impact of Natural Ventilation versus Heat Recovery Ventilation Systems on Indoor Air Quality: A Tiny House Case Study
(MDPI, 2024-06-14) Karaiskos, Panos; Martinez-Molina, Antonio; Alamaniotis, Miltiadis
Adverse health effects can arise from indoor air pollutants, resulting in allergies, asthma, and other respiratory problems among occupants. Concurrently, the energy consumption of residential buildings, particularly concerning heating, ventilation, and air conditioning (HVAC) systems, significantly contributes to global energy usage. To address these intertwined challenges, heat recovery ventilation (HRV) has emerged as a viable solution to reduce heating and cooling demands while providing fresh ventilation rates. This study aims to investigate the indoor air quality (IAQ) of an experimental tiny house building equipped with an HRV unit by simulating real-life scenarios contributing to IAQ. The research evaluates the effectiveness of HRV compared to natural ventilation in managing particle matter (PM), total volatile organic compounds (TVOC), formaldehyde (CH₂O), carbon monoxide (CO), and carbon dioxide (CO₂) levels. This research significantly contributes to the understanding of the different ventilation strategies’ impact on IAQ in tiny houses and offers valuable insights for improving living conditions in a unique building typology that is underrepresented in the research literature.
Thermal Evaluation of the Initial Concept 3.X Vehicle at Mach 7
(MDPI, 2024-06-13) Dhanagopal, Abinayaa; Strasser, Nathan S.; Andrade, Angelina; Posladek, Kevin R.; Hoffman, Eugene N. A.; Combs, Christopher S.
High-speed global surface temperature distributions and heat flux measurements on the Initial Concept 3.X vehicle (IC3X) model were investigated at the UTSA Mach 7 wind tunnel, examining angles of attack of 0° and 5° at a freestream unit Reynolds number (Re) ~7 × 10⁶ m⁻¹. A ruthenium-based, fast-responding, temperature-sensitive paint (fast-TSP) prepared in-house was applied to a 7.1% scale model of the vehicle. Static calibration was performed to convert the intensity measurements into surface temperature values. The surface temperatures and derived heat flux fields conformed to the predicted trends, which was corroborated by Schlieren flow visualization. Notably, the average surface temperature variation was identified to range from 6 to 34 K at a 0° angle of attack and from 11 to 44 K at a 5° angle of attack, with the most pronounced gradient detected at the stagnation point. Additional measurements provided a detailed thermal assessment of the model, including estimations of the stagnation point heat flux, the convective heat transfer coefficient, and the modified Stanton number. Statistical and time series analyses of the data collected revealed the absence of prevailing unsteady phenomena, suggesting that the tested design geometry is well suited for hypersonic flight applications. These experimental outcomes not only shed light on the aerothermodynamics experienced during high-speed flight but also underscore the effectiveness of fast-TSP in capturing both quantitative and qualitative thermal data.
Designing Multifunctional Multiferroic Composites for Advanced Electronic Applications
(MDPI, 2024-06-09) Pereira, Lilian Nunes; Pastoril, Julio Cesar Agreira; Dias, Gustavo Sanguino; Santos, Ivair Aparecido dos; Guo, Ruyan; Bhalla, Amar S.; Cotica, Luiz Fernando
This paper presents a novel approach for the fabrication of magnetoelectric composites aimed at enhancing cross-coupling between electrical and magnetic phases for potential applications in intelligent sensors and electronic components. Unlike previous methodologies known for their complexity and expense, our method offers a simple and cost-effective assembly process conducted at room temperature, preserving the original properties of the components and avoiding undesired phases. The composites, composed of PZT fibers, cobalt (CoFe₂O₄), and a polymeric resin, demonstrate the uniform distribution of PZT-5A fibers within the cobalt matrix, as demonstrated by scanning electron microscopy. Detailed morphological analyses reveal the interface characteristics crucial for determining overall performance. Dielectric measurements indicate stable behaviors, particularly when PZT-5A fibers are properly poled, showcasing potential applications in sensors or medical devices. Furthermore, H-dependence studies illustrate strong magnetoelectric interactions, suggesting promising avenues for enhancing coupling efficiency. Overall, this study lays the basic work for future optimization of composite composition and exploration of its long-term stability, offering valuable insights into the potential applications of magnetoelectric composites in various technological domains.
Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm
(MDPI, 2024-05-20) Shahin, Mohammad; Chen, F. Frank; Maghanaki, Mazdak; Hosseinzadeh, Ali; Zand, Neda; Khodadadi Koodiani, Hamid
Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.
The Dolan Fire of Central Coastal California: Burn Severity Estimates from Remote Sensing and Associations with Environmental Factors
(MDPI, 2024-05-10) Oseghae, Iyare; Bhaganagar, Kiran; Mestas-Nuñez, Alberto M.
In 2020, wildfires scarred over 4,000,000 hectares in the western United States, devastating urban populations and ecosystems alike. The significant impact that wildfires have on plants, animals, and human environments makes wildfire adaptation, management, and mitigation strategies a critical task. This study uses satellite imagery from Landsat to calculate burn severity and map the fire progression for the Dolan Fire of central Coastal California which occurred in August 2020. Several environmental factors, such as temperature, humidity, fuel type, topography, surface conditions, and wind velocity, are known to affect wildfire spread and burn severity. The aim of this study is the investigation of the relationship between these environmental factors, estimates of burn severity, and fire spread patterns. Burn severity is calculated and classified using the Difference in Normalized Burn Ratio (dNBR) before being displayed as a time series of maps. The Dolan Fire had a moderate severity burn with an average dNBR of 0.292. The ignition site location, when paired with the patterns of fire spread, is consistent with wind speed and direction data, suggesting fire movement to the southeast of the fire ignition site. Patterns of increased burn severity are compared with both topography (slope and aspect) and fuel type. Locations that were found to be more susceptible to high burn severity featured Long Needle Timber Litter and Mature Timber fuels, intermediate slope angles between 15 and 35°, and north- and east-facing slopes. This study has implications for the future predictive modeling of wildfires that may serve to develop wildfire mitigation strategies, manage climate change impacts, and protect human lives.
Computational Investigation of the Mechanical Response of a Bioinspired Nacre-like Nanocomposite under Three-Point Bending
(MDPI, 2024-05-07) Yang, Xingzi; Rumi, Md Jalal Uddin; Zeng, Xiaowei
Natural biological nanocomposites, like nacre, demonstrate extraordinary fracture toughness, surpassing their base materials, attributed to their intricate staggered hierarchical architectures integrating hard and soft phases. The enhancement of toughness in these composites is often linked to the crack-deflection mechanism. Leveraging the core design principles that enhance durability, resilience, and robustness in organic materials, this paper describes the use of computational modeling and simulation to perform a three-point bending test on a 3D staggered nanocomposite intentionally crafted to mimic the detailed microstructure of nacre. We adopted a previously proposed interfacial zone model that conceptualizes the "relatively soft" layer as an interface between the "hard" mineral tablets and the microstructure's interlayer spaces to examine how the microstructure and interface characteristics affect the mechanical responses and failure mechanisms. By comparing the model's predictions with experimental data on natural nacre, the simulations unveil the mechanisms of tablet separation through adjacent layer sliding and crack deflection across interfacial zones. This study offers a robust numerical method for investigating the fracture toughening mechanisms and damage evolution and contributes to a deeper understanding of the complex interplays within biomimetic materials.
Novel Entropy for Enhanced Thermal Imaging and Uncertainty Quantification
(MDPI, 2024-04-28) Ayunts, Hrach; Grigoryan, Artyom; Agaian, Sos
This paper addresses the critical need for precise thermal modeling in electronics, where temperature significantly impacts system reliability. We emphasize the necessity of accurate temperature measurement and uncertainty quantification in thermal imaging, a vital tool across multiple industries. Current mathematical models and uncertainty measures, such as Rényi and Shannon entropies, are inadequate for the detailed informational content required in thermal images. Our work introduces a novel entropy that effectively captures the informational content of thermal images by combining local and global data, surpassing existing metrics. Validated by rigorous experimentation, this method enhances thermal images’ reliability and information preservation. We also present two enhancement frameworks that integrate an optimized genetic algorithm and image fusion techniques, improving image quality by reducing artifacts and enhancing contrast. These advancements offer significant contributions to thermal imaging and uncertainty quantification, with broad applications in various sectors.
Integrating an Ensemble Reward System into an Off-Policy Reinforcement Learning Algorithm for the Economic Dispatch of Small Modular Reactor-Based Energy Systems
(MDPI, 2024-04-26) Arvanitidis, Athanasios Ioannis; Alamaniotis, Miltiadis
Nuclear Integrated Energy Systems (NIES) have emerged as a comprehensive solution for navigating the changing energy landscape. They combine nuclear power plants with renewable energy sources, storage systems, and smart grid technologies to optimize energy production, distribution, and consumption across sectors, improving efficiency, reliability, and sustainability while addressing challenges associated with variability. The integration of Small Modular Reactors (SMRs) in NIES offers significant benefits over traditional nuclear facilities, although transferring involves overcoming legal and operational barriers, particularly in economic dispatch. This study proposes a novel off-policy Reinforcement Learning (RL) approach with an ensemble reward system to optimize economic dispatch for nuclear-powered generation companies equipped with an SMR, demonstrating superior accuracy and efficiency when compared to conventional methods and emphasizing RL's potential to improve NIES profitability and sustainability. Finally, the research attempts to demonstrate the viability of implementing the proposed integrated RL approach in spot energy markets to maximize profits for nuclear-driven generation companies, establishing NIES' profitability over competitors that rely on fossil fuel-based generation units to meet baseload requirements.
Unsteady Subsonic/Supersonic Flow Simulations in 3D Unstructured Grids over an Acoustic Cavity
(MDPI, 2024-04-17) Araya, Guillermo
In this study, the unsteady Reynolds-averaged Navier–Stokes (URANS) equations are employed in conjunction with the Menter Shear Stress Transport (SST)-Scale-Adaptive Simulation (SAS) turbulence model in compressible flow, with an unstructured mesh and complex geometry. While other scale-resolving approaches in space and time, such as direct numerical simulation (DNS) and large-eddy simulation (LES), supply more comprehensive information about the turbulent energy spectrum of the fluctuating component of the flow, they imply computationally intensive situations, usually performed over structured meshes and relatively simple geometries. In contrast, the SAS approach is designed according to "physically" prescribed length scales of the flow. More precisely, it operates by locally comparing the length scale of the modeled turbulence to the von Karman length scale (which depends on the local first- and second fluid velocity derivatives). This length-scale ratio allows the flow to dynamically adjust the local eddy viscosity in order to better capture the large-scale motions (LSMs) in unsteady regions of URANS simulations. While SAS may be constrained to model only low flow frequencies or wavenumbers (i.e., LSM), its versatility and low computational cost make it attractive for obtaining a quick first insight of the flow physics, particularly in those situations dominated by strong flow unsteadiness. The selected numerical application is the well-known M219 three-dimensional rectangular acoustic cavity from the literature at two different free-stream Mach numbers, M_∞ (0.85 and 1.35) and a length-to-depth ratio of 5:1. Thus, we consider the "deep configuration" in experiments by Henshaw. The SST-SAS model demonstrates a satisfactory compromise between simplicity, accuracy, and flow physics description.
Correction: Gautam et al. Experimental Thermal Conductivity Studies of Agar-Based Aqueous Suspensions with Lignin Magnetic Nanocomposites. Magnetochemistry 2024, 10, 12
(MDPI, 2024-04-15) Gautam, Bishal; Nabat Al-Ajrash, Saja M.; Hasan, Mohammad Jahid; Saini, Abhishek; Watzman, Sarah J.; Ureña-Benavides, Esteban; Vasquez-Guardado, Erick S.
In the original publication [1] there was a unit error in Figure 5. [...]
Statistical and Spatial Analysis of Large Truck Crashes in Texas (2017–2021)
(MDPI, 2024-03-27) Billah, Khondoker; Sharif, Hatim O.; Dessouky, Samer
Freight transportation, dominated by trucks, is an integral part of trade and production in the USA. Given the prevalence of large truck crashes, a comprehensive investigation is imperative to ascertain the underlying causes. This study analyzed 2017–2021 Texas crash data to identify factors impacting large truck crash rates and injury severity and to locate high-risk zones for severe incidents. Logistic regression models and bivariate analysis were utilized to assess the impacts of various crash-related variables individually and collectively. Heat maps and hotspot analysis were employed to pinpoint areas with a high frequency of both minor and severe large truck crashes. The findings of the investigation highlighted night-time no-passing zones and marked lanes as primary road traffic control, highway or FM roads, a higher posted road speed limit, dark lighting conditions, male and older drivers, and curved road alignment as prominent contributing factors to large truck crashes. Furthermore, in cases where the large truck driver was determined not to be at fault, the likelihood of severe collisions significantly increased. The study's findings urge policymakers to prioritize infrastructure improvements like dual left-turn lanes and extended exit ramps while advocating for wider adoption of safety technologies like lane departure warnings and autonomous emergency braking. Additionally, public awareness campaigns aimed at reducing distracted driving and drunk driving, particularly among truck drivers, could significantly reduce crashes. By implementing these targeted solutions, we can create safer roads for everyone in Texas.
Balancing Sustainability: An Analysis of Habitat for Humanity Affiliates in Mississippi
(MDPI, 2024-02-15) Doleac, Alex; Langar, Sandeep; Sulbaran, Tulio
Non-profit organizations (NPOs) support economically disadvantaged communities by improving housing conditions and building homes, despite limited resources. With rising housing costs and poverty causing homelessness and poor housing quality, NPOs’ efforts are crucial. However, operating constraints (such as financial, policy, and others), often lead NPOs to prioritize initial costs over sustainability and environmental impacts. Therefore, this research investigated the adoption, implementation, and routinization patterns for sustainability and green efforts in Mississippi (US) by a leading NPO. The research used a two-phased combined design methodology, with the first phase involving explorative design that involved the identification of criteria that led to selecting the affiliates of Habitat for Humanity (HFH) as the unit of analysis. The selected NPO (HFH) had 38 affiliates across Mississippi, US, at the time of the study. The second phase involved a cross-sectional design, with data collected by utilizing a structured telephone survey. All collected data were subjected to descriptive and inferential statistics, and thematic analysis. Twenty-five affiliates (66% response rate) participated in the study, and the results indicate that a small proportion of affiliates were actively adopting sustainability practices for projects constructed, and most were located in the southern part of the state. The research identified factors that lead to the routinization of sustainability practices, the most commonly used third-party benchmarking tools, and perceptions of NPOs towards such tools to evaluate the greenness of residential projects. Some crucial implications were identifying uneven project completion within HFH, limited adoption of green strategies, and perception of green certification as non-vital.
Synergism of Fuzzy Leaky Bucket with Virtual Buffer for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones
(MDPI, 2024-02-14) Alamaniotis, Miltiadis; Alexiou, Michail
Smart cities can be viewed as expansive systems that optimize operational quality and deliver a range of services, particularly in the realm of energy management. Identifying energy zones within smart cities marks an initial step towards ensuring equitable energy distribution driven by factors beyond energy considerations. This study introduces a socially oriented methodology for energy allocation during emergencies, implemented at the zone level to address justice concerns. The proposed method integrates a fuzzy leaky bucket model with an energy virtual buffer, leveraging extensive data from diverse city zones to allocate energy resources during emergent situations. By employing fuzzy sets and rules, the leaky bucket mechanism distributes buffered energy to zones, aiming to maximize energy utilization while promoting social justice principles. Evaluation of the approach utilizes consumption data from simulated smart city zones during energy-constrained emergencies, comparing it against a uniform allocation method. Results demonstrate the socially equitable allocation facilitated by the proposed methodology.
Experimental Thermal Conductivity Studies of Agar-Based Aqueous Suspensions with Lignin Magnetic Nanocomposites
(MDPI, 2024-02-10) Gautam, Bishal; Nabat Al-Ajrash, Saja M.; Hasan, Mohammad Jahid; Saini, Abhishek; Watzman, Sarah J.; Ureña-Benavides, Esteban; Vasquez-Guardado, Erick S.
Nanoparticle additives increase the thermal conductivity of conventional heat transfer fluids at low concentrations, which leads to improved heat transfer fluids and processes. This study investigates lignin-coated magnetic nanocomposites (lignin@Fe₃O₄) as a novel bio-based magnetic nanoparticle additive to enhance the thermal conductivity of aqueous-based fluids. Kraft lignin was used to encapsulate the Fe₃O₄ nanoparticles to prevent agglomeration and oxidation of the magnetic nanoparticles. Lignin@Fe₃O₄ nanoparticles were prepared using a pH-driven co-precipitation method with a 3:1 lignin to magnetite ratio and characterized by X-ray diffraction, FT-IR, thermogravimetric analysis, and transmission electron microscopy. The magnetic properties were characterized using a vibrating sample magnetometer. Once fully characterized, lignin@Fe₃O₄ nanoparticles were dispersed in aqueous 0.1% w/v agar–water solutions at five different concentrations, from 0.001% w/v to 0.005% w/v. Thermal conductivity measurements were performed using the transient line heat source method at various temperatures. A maximum enhancement of 10% in thermal conductivity was achieved after adding 0.005% w/v lignin@Fe₃O₄ to the agar-based aqueous suspension at 45 °C. At room temperature (25 °C), the thermal conductivity of lignin@Fe₃O₄ and uncoated Fe₃O₄ agar-based suspensions was characterized at varying magnetic fields from 0 to 0.04 T, which were generated using a permanent magnet. For this analysis, the thermal conductivity of lignin magnetic nanosuspensions initially increased, showing a 5% maximum peak increase after applying a 0.02 T magnetic field, followed by a decreasing thermal conductivity at higher magnetic fields up to 0.04 T. This result is attributed to induced magnetic nanoparticle aggregation under external applied magnetic fields. Overall, this work demonstrates that lignin-coated Fe₃O₄ nanosuspension at low concentrations slightly increases the thermal conductivity of agar aqueous-based solutions, using a simple permanent magnet at room temperature or by adjusting temperature without any externally applied magnetic field.
A Spectral/hp-Based Stabilized Solver with Emphasis on the Euler Equations
(MDPI, 2024-01-08) Ranjan, Rakesh; Catabriga, Lucia; Araya, Guillermo
The solution of compressible flow equations is of interest with many aerospace engineering applications. Past literature has focused primarily on the solution of Computational Fluid Dynamics (CFD) problems with low-order finite element and finite volume methods. High-order methods are more the norm nowadays, in both a finite element and a finite volume setting. In this paper, inviscid compressible flow of an ideal gas is solved with high-order spectral/hp stabilized formulations using uniform high-order spectral element methods. The Euler equations are solved with high-order spectral element methods. Traditional definitions of stabilization parameters used in conjunction with traditional low-order bilinear Lagrange-based polynomials provide diffused results when applied to the high-order context. Thus, a revision of the definitions of the stabilization parameters was needed in a high-order spectral/hp framework. We introduce revised stabilization parameters, τsupg, with low-order finite element solutions. We also reexamine two standard definitions of the shock-capturing parameter, δ: the first is described with entropy variables, and the other is the YZβ parameter. We focus on applications with the above introduced stabilization parameters and analyze an array of problems in the high-speed flow regime. We demonstrate spectral convergence for the Kovasznay flow problem in both L¹ and L² norms. We numerically validate the revised definitions of the stabilization parameter with Sod’s shock and the oblique shock problems and compare the solutions with the exact solutions available in the literature. The high-order formulation is further extended to solve shock reflection and two-dimensional explosion problems. Following, we solve flow past a two-dimensional step at a Mach number of 3.0 and numerically validate the shock standoff distance with results obtained from NASA Overflow 2.2 code. Compressible flow computations with high-order spectral methods are found to perform satisfactorily for this supersonic inflow problem configuration. We extend the formulation to solve the implosion problem. Furthermore, we test the stabilization parameters on a complex flow configuration of AS-202 capsule analyzing the flight envelope. The proposed stabilization parameters have shown robustness, providing excellent results for both simple and complex geometries.
Development of Intensity–Duration–Frequency (IDF) Curves over the United Arab Emirates (UAE) Using CHIRPS Satellite-Based Precipitation Products
(MDPI, 2023-12-20) Alsumaiti, Tareefa S.; Hussein, Khalid A.; Ghebreyesus, Dawit T.; Petchprayoon, Pakorn; Sharif, Hatim O.; Abdalati, Waleed
The recent flooding events in the UAE have emphasized the need for a reassessment of flood frequencies to mitigate risks. The exponential urbanization and climatic changes in the UAE require a reform for developing and updating intensity–duration–frequency (IDF) curves. This study introduces a methodology to develop and update IDF curves for the UAE at a high spatial resolution using CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data. A bias correction was applied to the CHIRPS data, resulting in an improved capture of extreme events across the country. The Gumbel distribution was the most suitable theoretical distribution for the UAE, exhibiting a strong fit to the observed data. The study also revealed that the CHIRPS-derived IDF curves matched the shape of IDF curves generated using rain gauges. Due to orographic rainfall in the northeastern region, the IDF intensities were at their highest there, while the aridity of inland regions resulted in the lowest intensities. These findings enhance our understanding of rainfall patterns in the UAE and support effective water resource management and infrastructure planning. This study demonstrates the potential of the CHIRPS dataset for IDF curve development, emphasizes the importance of performing bias corrections, and recommends tailoring adjustments to the intended application.
A p-Refinement Method Based on a Library of Transition Elements for 3D Finite Element Applications
(MDPI, 2023-12-14) Shahriar, Adnan; Mostafa, Ahmed Jenan
Wave propagation or acoustic emission waves caused by impact load can be simulated using the finite element (FE) method with a refined high-fidelity mesh near the impact location. This paper presents a method to refine a 3D finite element mesh by increasing the polynomial order near the impact location. Transition elements are required for such a refinement operation. Three protocols are defined to implement the transition elements within the low-order FE mesh. Due to the difficulty of formulating shape functions and verification, there are no transition elements beyond order two in the current literature for 3D elements. This paper develops a complete set of transition elements that facilitate the transition from first- to fourth-order Lagrangian elements, which facilitates mesh refinement following the protocols. The shape functions are computed and verified, and the interelement compatibility conditions are checked for each element case. The integration quadratures and shape function derivative matrices are also computed and made readily available for FE users. Finally, two examples are presented to illustrate the applicability of this method.
A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids
(MDPI, 2023-11-18) Beg, Omar A.; Khan, Asad Ali; Rehman, Waqas Ur; Hassan, Ali
In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.
Green Infrastructure and Urban Vacancies: Land Cover and Natural Environment as Predictors of Vacant Land in Austin, Texas
(MDPI, 2023-11-08) Kim, Young-Jae; Lee, Ryun Jung; Lee, Taehwa; Shin, Yongchul
Urban vacancies have been a concern for neighborhood distress and economic decline and have gained more recent attention as potential green infrastructure is known to benefit communities in diverse ways. To investigate this, this study looked into the relationship between land cover, natural environment, and urban vacancies in Austin, Texas. Additionally, we investigated the spatial patterns of green infrastructure and urban vacancies by different income groups to see if low income communities would potentially lack the benefits of green infrastructure. To measure green infrastructure, we used different land covers such as forests and shrublands, as well as natural environments such as tree canopies and vegetation richness, using remote sensing data. Urban vacancy information was retrieved from the USPS vacant addresses and parcel land uses. Through a series of multivariate analyses examining green infrastructure variables one by one, the study results indicate that green infrastructure interacts with residential and business vacancies differently. Additionally, low-income communities lack green infrastructure compared with the rest of the city and are exposed to more urban vacancies in their neighborhoods. Further study is required to understand the dynamics of vacancies in underserved communities and examine how existing vacant land can benefit the communities as ecological resources.

Browse

Recent Submissions