Browsing by Author "Kishore, Ravi Anant"

Now showing 1 - 9 of 9
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    3D printed graphene-based self-powered strain sensors for smart tires in autonomous vehicles
    Maurya, Deepam; Khaleghian, Seyedmeysam; Sriramdas, Rammohan; Kumar, Prashant; Kishore, Ravi Anant; Kang, Min-Gyu; Kumar, Vireshwar; Song, Hyun-Cheol; Lee, Seul-Yi; Yan, Yongke; Park, Jung-Min (Jerry); Taheri, Saied; Priya, Shashank (2020-10-26)
    The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability. Designing efficient sensors for smart tires for autonomous vehicles remains a challenge. Here, the authors present a tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis.
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    Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator
    Kishore, Ravi Anant; Mahajan, Roop L.; Priya, Shashank (MDPI, 2018-08-24)
    Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.
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    Efficient Direct-Drive Small-Scale Low-Speed Wind Turbine
    Kishore, Ravi Anant; Marin, Anthony; Priya, Shashank (De Gruyter, 2014)
    There is growing need for the green, reliable, and cost-effective power solution for the expanding wireless microelectronic devices. In many scenarios, these needs can be met through a small-scale wind energy portable turbine (SWEPT) that operates near ground level where wind speed is of the order of few meters per second. SWEPT is a three-bladed, 40 cm rotor diameter, direct-drive, horizontal-axis wind turbine that has very low cut-in wind speed of 1.7 m/s. It operates in a wide range of wind speeds between 1.7 m/s and 10 m/s and produces rated power output of 1 W at wind speed of 4.0 m/s. The wind turbine is capable of producing electrical power up to 9.8 W at wind speed of 10 m/s. The maximum efficiency of SWEPT was found to be around 21% which makes it one of the most efficient wind turbines reported at the small scale and low wind speed. These advancements open many new opportunities for embedding and utilizing wireless and portable devices.
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    Low-grade Thermal Energy Harvesting and Waste Heat Recovery
    Kishore, Ravi Anant (Virginia Tech, 2018-12-14)
    Low-grade heat, either in the form of waste heat or natural heat, represents an extremely promising source of renewable energy. A cost-effective method for recovering the low-grade heat will have a transformative impact on the overall energy scenario. Efficiency of heat engines deteriorates with decrease in hot-side temperature, making low-grade heat recovery complex and economically unviable using the current state-of-the-art technologies, such as Organic Rankine cycle, Kalina cycle and Stirling engine. In this thesis, a fundamental breakthrough is achieved in low-grade thermal energy harvesting using thermomagnetic and thermoelectric effects. This thesis systematically investigates two different mechanisms: thermomagnetic effect and thermoelectric effect to generate electricity from the low-grade heat sources available near ambient temperature to 200°C. Using thermomagnetic effect, we demonstrate a novel ultra-low thermal gradient energy recovery mechanism, termed as PoWER (Power from Waste Energy Recovery), with ambient acting as the heat sink. PoWER devices do not require an external heat sink, bulky fins or thermal fluid circulation and generate electricity on the order of 100s μW/cm3 from heat sources at temperatures as low as 24°C (i.e. just 2°C above the ambient) to 50°C. For the high temperature range of 50-200°C, we developed the unique low fill fraction thermoelectric generators that exhibit a much better performance than the commercial modules when operated under realistic conditions such as constant heat flux boundary condition and high thermally resistive environment. These advancements in thermal energy harvesting and waste heat recovery technology will have a transformative impact on renewable energy generation and in reducing global warming.
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    Optimization of segmented thermoelectric generator using Taguchi and ANOVA techniques
    Kishore, Ravi Anant; Sanghadasa, Mohan; Priya, Shashank (Springer Nature, 2017-12-01)
    Recent studies have demonstrated that segmented thermoelectric generators (TEGs) can operate over large thermal gradient and thus provide better performance (reported efficiency up to 11%) as compared to traditional TEGs, comprising of single thermoelectric (TE) material. However, segmented TEGs are still in early stages of development due to the inherent complexity in their design optimization and manufacturability. In this study, we demonstrate physics based numerical techniques along with Analysis of variance (ANOVA) and Taguchi optimization method for optimizing the performance of segmented TEGs. We have considered comprehensive set of design parameters, such as geometrical dimensions of p-n legs, height of segmentation, hot-side temperature, and load resistance, in order to optimize output power and efficiency of segmented TEGs. Using the state-of-the-art TE material properties and appropriate statistical tools, we provide near-optimum TEG configuration with only 25 experiments as compared to 3125 experiments needed by the conventional optimization methods. The effect of environmental factors on the optimization of segmented TEGs is also studied. Taguchi results are validated against the results obtained using traditional full factorial optimization technique and a TEG configuration for simultaneous optimization of power and efficiency is obtained.
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    A Review on Low-Grade Thermal Energy Harvesting: Materials, Methods and Devices
    Kishore, Ravi Anant; Priya, Shashank (MDPI, 2018-08-14)
    Combined rejected and naturally available heat constitute an enormous energy resource that remains mostly untapped. Thermal energy harvesting can provide a cost-effective and reliable way to convert available heat into mechanical motion or electricity. This extensive review analyzes the literature covering broad topical areas under solid-state low temperature thermal energy harvesting. These topics include thermoelectricity, pyroelectricity, thermomagneticity, and thermoelasticity. For each topical area, a detailed discussion is provided comprising of basic physics, working principle, performance characteristics, state-of-the-art materials, and current generation devices. Technical advancements reported in the literature are utilized to analyze the performance, identify the challenges, and provide guidance for material and mechanism selection. The review provides a detailed analysis of advantages and disadvantages of each energy harvesting mechanism, which will provide guidance towards designing a hybrid thermal energy harvester that can overcome various limitations of the individual mechanism.
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    Small-scale Wind Energy Portable Turbine (SWEPT)
    Kishore, Ravi Anant (Virginia Tech, 2013-05-24)
    Large Scale Wind Turbines (LSWTs) have been extensively examined for decades but very few studies have been conducted on the small scale wind turbines (SSWTs) especially for the applications near ground level where wind speed is of order of few meters per second. This study provides the first systematic effort towards design and development of SSWTs (rotor diameter<50 cm) targeted to operate at low wind speeds (<5 m/s). An inverse design and optimization tool based on Blade Element Momentum theory is proposed. The utility and efficacy of the tool was validated by demonstrating a 40 cm diameter small-scale wind energy portable turbine (SWEPT) operating in very low wind speed range of 1 m/s-5 m/s with extremely high power coefficient. In comparison to the published literature, SWEPT is one of the most efficient wind turbines at the small scale and very low wind speeds with the power coefficient of 32% and overall efficiency of 21% at its rated wind speed of 4.0 m/s. It has very low cut-in speed of 1.7 m/s. Wind tunnel experiments revealed that SWEPT has rated power output of 1 W at 4.0 m/s, and it is capable of producing power output up to 9.3 W at wind speed of 10 m/s. The study was further extended to develop a piezoelectric wind turbine which operates below 2.0 m/s wind speed. The piezoelectric wind turbine of overall dimension of 100mm x 78mm x 65mm is capable of producing peak electric power of about 450 microwatt at the rated wind speed of 1.9 m/s.
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    Thermo-Magneto-Electric Generator Arrays for Active Heat Recovery System
    Chun, Jinsung; Song, Hyun-Cheol; Kang, Min-Gyu; Kang, Han Byul; Kishore, Ravi Anant; Priya, Shashank (Springer Nature, 2017-02-01)
    Continued emphasis on development of thermal cooling systems is being placed that can cycle low grade heat. Examples include solar powered unmanned aerial vehicles (UAVs) and data storage servers. The power efficiency of solar module degrades at elevated temperature, thereby, necessitating the need for heat extraction system. Similarly, data centres in wireless computing system are facing increasing efficiency challenges due to high power consumption associated with managing the waste heat. We provide breakthrough in addressing these problems by developing thermo-magneto-electric generator (TMEG) arrays, composed of soft magnet and piezoelectric polyvinylidene difluoride (PVDF) cantilever. TMEG can serve dual role of extracting the waste heat and converting it into useable electricity. Near room temperature second-order magnetic phase transition in soft magnetic material, gadolinium, was employed to obtain mechanical vibrations on the PVDF cantilever under small thermal gradient. TMEGs were shown to achieve high vibration frequency at small temperature gradients, thereby, demonstrating effective heat transfer.
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    Ultra-high performance wearable thermoelectric coolers with less materials
    Kishore, Ravi Anant; Nozariasbmarz, Amin; Poudel, Bed; Sanghadasa, Mohan; Priya, Shashank (Springer Nature, 2019-04-16)
    Thermoelectric coolers are attracting significant attention for replacing age-old cooling and refrigeration devices. Localized cooling by wearable thermoelectric coolers will decrease the usage of traditional systems, thereby reducing global warming and providing savings on energy costs. Since human skin as well as ambient air is a poor conductor of heat, wearable thermoelectric coolers operate under huge thermally resistive environment. The external thermal resistances greatly influence thermoelectric material behavior, device design, and device performance, which presents a fundamental challenge in achieving high efficiency for on-body applications. Here, we examine the combined effect of heat source/sink thermal resistances and thermoelectric material properties on thermoelectric cooler performance. Efficient thermoelectric coolers demonstrated here can cool the human skin up to 8.2 degrees C below the ambient temperature (170% higher cooling than commercial modules). Cost-benefit analysis shows that cooling over material volume for our optimized thermoelectric cooler is 500% higher than that of the commercial modules.