Nanocomposites of GO/D-Mannitol Assisted Thermoelectric Power Generator for Transient Waste Heat Recovery

Jame, Lourdu; Perumal, Suresh; Moorthy, Manojkumar; Kumar, G. Joselin Retna

doi:10.1155/2022/2460364

Cited by 2 publications

(1 citation statement)

References 28 publications

(35 reference statements)

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“…With an increase in 2 International Journal of Energy Research the heating power, a decrease in the thermal resistance of the heat pipe was observed with higher heat-to-electric conversion efficiency. Jame et al [30] investigated enhancing the conversion efficiency of thermoelectric generators and to absorb the unsteady state heat. Their work shows the role of nanostructured graphite (G) and graphene oxide (GO) decorated phase change material (PCM) of D-mannitol connected with the hot side of the thermoelectric module on the improvement of conversion efficiency and TEG power output.…”

Section: Introductionmentioning

confidence: 99%

Investigation on the Heat-to-Power Generation Efficiency of Thermoelectric Generators (TEGs) by Harvesting Waste Heat from a Combustion Engine for Energy Storage

Ragupathi

Barik

2023

International Journal of Energy Research

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The present work explores a novel method of waste heat recovery (WHR) from the internal combustion (IC) engine and power generation using heat from hot exhaust gas (HEG) and thermoelectric generator (TEG). This system uses TEGs of aluminium oxide (Al2O3), bismuth telluride (Bi2Te3), lead telluride (PbTe3), and silicon germanium (SiGe) for the investigation of WHR and power generation. The experiments were conducted using a 5.9 kW diesel engine as a source of heat and a heat exchanger (HE) with fins. The parameters such as variation in temperature, rate of heat recovery, rate of power generation, and conversion efficiency of TEGs were analyzed by varying the load on the engine from 0% to 100% with steps of 25% and different zones of the heat exchanger, namely, zone 1, zone 2, zone 3, and zone 4. During the experiment, it was observed that zone 2 gives a high-temperature difference compared to zone 1, zone 3, and zone 4. TEG of bismuth telluride (Bi2Te3) increases the heat recovery rate overall compared to Al2O3, PbTe3, and SiGe throughout the load spectrum and operating conditions concerning different zones. The maximum power generation by TEGs of Al2O3, Bi2Te3, PbTe3, and SiGe was found to be 6.35 W, 7.18 W, 6.41 W, and 5.93 W at zone 2 while the engine was operated at 75% of load, respectively. Bi2Te3 gives the highest conversion efficiency compared to other TEGs irrespective of the working zones. The thermal energy-to-electrical energy conversion efficiency was maximum of about 7.8% given by the TEG of Bi2Te3 at zone 2 while the engine was operated at 75% of the load.

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Section: Introductionmentioning

confidence: 99%

Investigation on the Heat-to-Power Generation Efficiency of Thermoelectric Generators (TEGs) by Harvesting Waste Heat from a Combustion Engine for Energy Storage

Ragupathi

Barik

2023

International Journal of Energy Research

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Thermal Degradation Studies and Machine Learning Modelling of Nano-Enhanced Sugar Alcohol-Based Phase Change Materials for Medium Temperature Applications

Kottala

Chigilipalli

Mukuloth³

et al. 2023

Energies

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Thermogravimetric analysis (TGA) was utilised to compare the thermal stability of pure phase change material (D-mannitol) to that of nano-enhanced PCM (NEPCM) (i.e., PCM containing 0.5% and 1% multiwalled carbon nanotubes (MWCNT)). Using model-free kinetics techniques, the kinetics of pure PCM and NEPCM degradation were analysed. Three different kinetic models such as Kissinger-Akahira-Sunose (KAS), the Flynn-Wall-Ozawa (FWO), and the Starink were applied to assess the activation energies of the pure and nano-enhanced PCM samples. Activation energies for pure PCM using the Ozawa, KAS, and Starink methods ranged from 71.10–77.77, 79.36–66.87, and 66.53–72.52 kJ/mol, respectively. NEPCM’s (1% MWCNT) activation energies ranged from 76.59–59.11, 71.52–52.28, and 72.15–53.07 kJ/mol. Models of machine learning were utilised to predict the degradation of NEPCM samples; these included linear regression, support vector regression, random forests, gaussian process regression, and artificial neural network models. The mass loss of the sample functioned as the output parameter, while the addition of nanoparticles weight fraction, the heating rate, and the temperature functioned as the input parameters. Experiment-based TGA data can be accurately predicted using the created machine learning models.

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Nanocomposites of GO/D-Mannitol Assisted Thermoelectric Power Generator for Transient Waste Heat Recovery

Cited by 2 publications

References 28 publications

Investigation on the Heat-to-Power Generation Efficiency of Thermoelectric Generators (TEGs) by Harvesting Waste Heat from a Combustion Engine for Energy Storage

Investigation on the Heat-to-Power Generation Efficiency of Thermoelectric Generators (TEGs) by Harvesting Waste Heat from a Combustion Engine for Energy Storage

Thermal Degradation Studies and Machine Learning Modelling of Nano-Enhanced Sugar Alcohol-Based Phase Change Materials for Medium Temperature Applications

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