Evaluation of the Potential of Nanofluids Containing Different Ag Nanoparticle Size Distributions for Enhanced Solar Energy Conversion in Hybrid Photovoltaic-thermal (PVT) Applications

Hybridising photovoltaic and photothermal technologies into a single system that can simultaneously deliver heat and power represents one of the leading strategies for generating clean energy at more affordable prices. In a hybrid photovoltaic-thermal (PVT) system, the capability to modulate the thermal and electrical power output is significantly influenced by the spectral properties of the heat transfer fluid utilised. In this study, we report on one of the first experimental evaluations of the capability of a multimodal silver nanofluid containing various particle shapes and particle sizes to selectively modulate the solar energy for PVT applications. The diverse set of particle properties led up to a 50.4% enhancement in the solar energy absorbed by the nanofluid over the 300 nm – 550 nm spectral region, where silicon is known to exhibit poor photovoltaic conversion performances. This improved substantially the absorption of solar energy, with an additional 18 – 129 W m-2 of thermal power being generated by the PVT system. Along with the advancements made in the thermal power output of the PVT system, a decrease of 4.7 – 36.6 W m-2 in the electrical power generated by the photovoltaic element was noted. Thus, for every ~11 W m-2 increase of thermal power achieved through the addition of the nanoparticles, a reduction of ~3 W m-2 in the ability to generate clean electricity was sustained by the PVT. Despite the energy trade-offs involved under the conditions of the nanofluid, the PVT system cumulatively harvested 405 W m-2 of solar energy, which amounts to a total conversion efficiency of 45%. Furthermore, the economics of the additional energy harvested through merging of the two systems was found to reach an enhancement of 77% under certain European conditions.

Cu-Ni-Based Alloys from Nanopowders as Potent Thermoelectric Materials for High-Power Output Applications

A new approach for the development of thermoelectric materials, which focuses on a high-power factor instead of a large figure of merit zT, has drawn attention in recent years. In this context, the thermoelectric properties of Cu-Ni-based alloys with a very high electrical conductivity, a moderate Seebeck coefficient, and therefore a high power factor are presented as promising low-cost alternative materials for applications aiming to have a high electrical power output. The Cu-Ni-based alloys are prepared via an arc melting process of metallic nanopowders. The heavy elements tin and tungsten are chosen for alloying to further improve the power factor while simultaneously reducing the high thermal conductivity of the resulting metal alloy, which also has a positive effect on the zT value. Overall, the samples prepared with low amounts of Sn and W show an increase in the power factor and figure of merit zT compared to the pure Cu-Ni alloy. These results demonstrate the potential of these often overlooked metal alloys and the utilization of nanopowders for thermoelectric energy conversion.

Optimum Design and Performance Simulation of a Multi-device Integrated System Based on a Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cells (PEMFCs) based on syngas are a promising technology for electric vehicle applications. To increase the fuel conversion efficiency, the low-temperature waste heat from the PEMFC is absorbed by a refrigerator. The absorption refrigerator provides cool air for the interior space of the vehicle. Between finishing the steam reforming reaction and flowing into the fuel cell, the gases release heat continuously. A Brayton engine is introduced to absorb heat and provide a useful power output. A novel thermodynamic model of the integrated system of the PEMFC, refrigerator, and Brayton engine is established. Expressions for the power output and efficiency of the integrated system are derived. The effects of some key parameters are discussed in detail to attain optimum performance of the integrated system. The simulation results show that when the syngas consumption rate is 4.0 × 10−5 mol s−1cm−2, the integrated system operates in an optimum state, and the product of the efficiency and power density reaches a maximum. In this case, the efficiency and power density of the integrated system are 0.28 and 0.96 J s−1 cm−2, respectively, which are 46% higher than those of a PEMFC.

Energy and Environmental Analysis of a Solar Evacuated Tube Heat Pipe Integrated Thermoelectric Generator Using IoT

This paper investigates the solar evacuated tube heat pipe system (SEHP) coupled with a thermoelectric generator (TEG) using the internet of things (IoT). The TEGs convert heat energy into electricity through the Seebeck effect that finds application in the waste heat recovery process for the generation of power. The present work deals with the theoretical study on solar evacuated tube heat pipe integrated TEG and it is validated experimentally using with and without parabolic trough concentrating collector. And the carbon credit of the TEG system is determined to find its potential in the environmental aspect. Also, the boost type converter is used to raise the power output by increasing the voltage from the TEG for rural electrifications. However, it is found that the maximum power output due to the influence of the parabolic trough concentrator results in increased efficiency when compared with the non-concentrating SEHP-TEG system. The TEG output power can be boosted up to a maximum of 5.98 V using a power electronic boost converter. Besides, the recorded real sensor data with Arduino is implemented in the experimental process for automatic remote monitoring of the temperature.

Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

This paper deals with the impact of uncertain input parameters on the electrical power generation of galloping-based piezoelectric energy harvester (GPEH). A distributed parameter model for the system is derived and solved by using Newmark beta numerical integration technique. Nonlinear systems tend to behave in a completely different manner in response to a slight change in input parameters. Due to the complex manufacturing process and various technical defects, randomness in system properties is inevitable. Owing to the presence of randomness within the system parameters, the actual power output differs from the expected one. Therefore, stochastic analysis is performed considering uncertainty in aerodynamic, mechanical, and electrical parameters. A polynomial neural network (PNN) based surrogate model is used to analyze the stochastic power output. A sensitivity analysis is conducted and highly influenced parameters to the electric power output are identified. The accuracy and adaptability of the PNN model are established by comparing the results with Monte Carlo simulation (MCS). Further, the stochastic analyses of power output are performed for various degrees of randomness and wind velocities. The obtained results showed that the influence of the electromechanical coefficient on power output is more compared to other parameters.