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Meminductor- Physical Realization and Edge of Chaos Operation
No full textInherently nonlinear, two-terminal circuit elements with state-dependent electrical properties are collectively termed ���memelements��� and include the memristor, the memcapacitor, and the meminductor. The first intentional memristor and memcapacitor were physically realized in 2008 and 2019 respectively, and in this work, I report a two-terminal passive element bearing the three fingerprints of a meminductor thus providing the first physical evidence of meminductance. I discuss the role of series resistance as a parasitic component which obscures potential meminductive behavior and present a technique to extract meminductance in resistance dominated systems by removing parasitic resistance as ���resistive flux���.
Memelements are capable of being biased at ���locally active��� steady states and may therefore generate persistent dynamical response to a constant excitation when passively coupled to an appropriate passive network. Reports on mathematical proof of the existence of such locally active steady states in memristors and experimental evidence of resultant persistent oscillations can be found in the literature. However, similar results for a meminductor are currently missing due to the absence of its physical realization until recently and an associated unavailability of realistic compact models. In this work, I tackle this challenge by fabricating a volatile meminductor whose small signal model mathematically identifies locally active bias points where the meminductor operates at the ���edge-of-chaos (EOC)���. I present experimental evidence of persistent oscillatory behavior when this EOC meminductor is coupled to a passive network and explain the rationale behind the choice of the coupling elements. I show that local activity/passivity of each bias point depends on the meminductor's material properties and geometry and quantify this dependence through the parameters of a ���circle of inversion (COI)���. Further, I show the emergence of the COI to be a consequence of state-dependent inductance, thus highlighting that local activity- and thereby, complexity- cannot emerge without ���mem���-behavior. Finally, I present results demonstrating that the coupled meminductor system emulates second order neuronal behavior in its dynamical response thus experimentally affirming the potential of meminductors in neuromorphic computing applications
Predicting and Optimizing the Power Performance of Dye Sensitized Solar Cells Using Machine Learning Techniques
No full textSolar cells hold great potential for the future of energy production. Among the different generations of solar cells, the third generation marks the latest and most significant improvement in solar cell technology, showcasing a notable leap forward in modernizing energy capture and utilization. Among third-generation solar cells, Dye Sensitized Solar Cells (DSSCs) emerge as particularly promising, thanks to their unique features. These include the utilization of a dye absorbed semiconducting oxide material, which enhances both cost-effectiveness and flexibility. DSSCs, especially when paired with various transition metals, exhibit encouraging results. Ongoing experiments aim to further enhance their performance by exploring alternative materials, with a specific emphasis on refining the photoanode for increased efficiency. Within the photoanode, the semi-conducting oxides demonstrate excellent conductivity and porosity, rendering them valuable components of DSSCs for further exploration in electrochemical energy production and integration. However, despite their potential, testing various material variations in solar cell and energy storage systems is challenging due to time and resource constraints. This research builds on past studies by introducing machine learning to predict efficiencies for low cost Dye-Sensitized Solar Cell (DSSC) configurations, specifically emphasizing the semiconducting oxide. The main goal is to use this machine learning algorithm as a cost effective way to identify high-performance configurations for DSSCs. This study aims to be applicable to all materials in the periodic table, with a specific focus on transition metals and metal oxides. The proposed machine learning model aims to predict the system's performance across various parameters, including Power Conversion Efficiency and Fill Factor. By using machine learning, this study aims to simplify the evaluation process and speed up the identification of optimal materials and weight-percentages for the semi-conducting oxide, contributing to the advancement of sustainable and efficient solar energy technologies
Effects of Aluminum Foam on Boiling Heat Transfer for Thermosyphon Evaporator
No full textOpen cell metal foams are found to be one of the better solutions for heat transfer enhancement; especially for two-phase heat transfer like pool boiling. This is due to their ability to provide a higher number of nucleation sites with a greater amount of surface area. Due to additional advantages like high thermal conductivity, compact size, and lightweight, it has potential in a host of several thermal applications. However, the utilization of open cell metal foams in thermosyphon systems has not been fully explored yet. In this study, uniform aluminum metal foam substrates with different pore densities (10 and 40 PPI), as well as composite metal foam substrates were investigated in terms of two-phase heat transfer enhancement in a vertical orientation. Each experiment considered different liquid fill ratios of (10%, 30%, 60% and 100%) and different heat flux levels (2.56 kW/m^2, 8.67 kW/m^2, 16.82 kW/m2 and 26.68 kW/m^2). The working fluid used for this study was 3M Novec-7000.
This investigation employed comprehensive experimental methods, utilizing IR thermometry to gain an in-depth understanding of the distribution of surface temperatures of each substrate. This study also involved a comparative analysis between all substrates and baseline condition (plain substrate) using the heat transfer coefficient (HTC) and enhancement HTC ratio as performance indicators. The findings unveiled that the 40 PPI foam performed consistently better at low fill ratios and at different heat flux values. However, under specific conditions such as a fill ratio of 100% and heat flux of 26.68 kW/m^2, the composite foam led to superior performance when compared to the other cases. Notably, all metal foam substrates exhibited greater enhancement over baseline at low heat flux of 8.67 kW/m^2 (near the onset of nucleate boiling) compared to high heat flux of 26.68 kW/m^2. In summary, use of aluminum metal foams in an evaporator should help improve the performance of thermosyphon
Economic Indicators of the College Station-Bryan MSA, July 2024
No full textThe Business-Cycle Index increased 0.3% from April 2024 to May 2024. The local unemployment rate for May 2024 was unchanged from the April value of 3.1%. Local nonfarm employment increased by 0.2% from April to May 2024. Inflation-adjusted taxable sales decreased by 3.2% from April 2024 to May 2024. Inflation-adjusted quarterly wage payments increased by 2.8% in the fourth quarter of 2023 compared to the previous quarter. Texas A&M had the highest total enrollment and engineering enrollment among selected MSAs that house large universities
Resilient Behavior of Chemically Stabilized Geomaterials and Assessment Of Moisture-Susceptible Durability
No full textThe resilient modulus (MR) is a key parameter for the characterization of the natural subgrade soils. The locally available subgrade soils often fail to perform satisfactorily under the desired traffic or environmental loads due to low resiliency to repetitive stresses. In such cases, soil stabilization is often recommended as an economical alternative to increase the moduli of subgrade soils. Traditional calcium-based stabilizers are commonly used by several transportation and defense agencies for routine stabilization works. Non-traditional ���organic synthetic polymer��� stabilizer is also rapidly gaining traction for ground improvement. Resilient moduli characterization of treated soils at higher compaction energy is needed to understand their resilient behavior.
The moisture-susceptible durability of chemically treated subgrade soils is important for pavement design life, and an exhaustive understanding of the seasonal transitional behavior is required parallelly with the recommendation of a rational moisture-based durability test method. As the durability of the treated layers is directly related to the moisture ingress and egress during the service life of pavements, it is critical to investigate the behavior of untreated and treated subgrade soils at different post-compaction moisture contents.
A detailed experimental study, from soil characterization tests to engineering tests, were conducted on nine types of soils ranging between sand, silt, and clay, and 3 types of stabilizers such as cement, lime, and vinyl acetate ethylene (VAE) copolymer. Two types of stabilizers were considered for each soil. This study developed a comprehensive database of resilient modulus and model parameters of various resilient moduli characterization models