High solar irradiance during pre-monsoon conditions in tropical regions increases photovoltaic energy generation potential but simultaneously elevates module operating temperature, which adversely affects electrical performance and long-term reliability. Crystalline silicon photovoltaic modules exhibit a negative temperature coefficient of approximately −0.4 to −0.5 %/°C, making thermal management an important consideration for sustained operation. This study experimentally investigates the influence of selective spectral filtering on the thermal and electrical performance of 20 W monocrystalline and polycrystalline photovoltaic modules under natural outdoor conditions in Pune, India (18.9876°N, 73.9651°E). Spectral bands of 400–450 nm, 450–570 nm, 570–750 nm, and 750–1100 nm were evaluated and compared with full-spectrum exposure at 1000 W/m². The 570–750 nm spectral band transmitted 436 W/m² and produced a maximum output power of 15.49 W from the monocrystalline module. Under this condition, a band-limited photon utilization efficiency of 25% was obtained when referenced to the transmitted irradiance. However, this metric differs from conventional photovoltaic conversion efficiency, which is defined using total incident solar irradiance. When referenced to the full incident irradiance of 1000 W/m², the corresponding system-level efficiency was approximately 11.06%, compared with 14.06% under full-spectrum exposure. Selective filtering reduced module surface temperature by approximately 10°C, corresponding to an estimated power retention benefit of about 4.5% based on temperature coefficient analysis. Monocrystalline modules exhibited superior spectral response and lower recombination sensitivity than polycrystalline modules. The results indicate that selective spectral filtering functions primarily as a thermal mitigation and spectral optimization strategy rather than a power enhancement technique. The findings are based on a short-term experimental campaign conducted under pre-monsoon conditions in Pune and should be interpreted within the context of the prevailing seasonal and atmospheric conditions.
Domestic heat pump water heaters offer an energy-efficient solution for hot water production in tropical countries. This study evaluates the energy and exergy performance as well as the environmental impact of R-152a as a replacement for R-134a in a domestic heat pump water heater. The evaluation uses a thermodynamic modelling approach under fixed operating conditions, with an evaporator temperature of 10°C, a condenser temperature of 50°C, and a water heating capacity of 1.6 m³. The results indicate that R-152a achieved an actual coefficient of performance (COP) of 6.25, which was 4.3% higher than that of R-134a at 5.99. R-152a also required a lower refrigerant mass flow rate. The mass flow rate decreased from 7.595 g/s for R-134a to 4.555 g/s for R-152a, which represents a reduction of approximately 40%. The exergy analysis further showed that R-152a consistently exhibited higher exergy efficiency than R-134a across the investigated ranges of evaporator temperature and compressor efficiency. This result indicates that R-152a has a lower thermodynamic irreversibility. From an environmental perspective, R-152a also showed a much lower direct Total Equivalent Warming Impact (TEWI). Its direct TEWI was 195.3 kg CO2-eq, which was 91.3% lower than that of R-134a at 2252.25 kg CO2-eq. This reduction is mainly related to the lower Global Warming Potential (GWP) of R-152a, which is 124, compared with 1430 for R-134a. The results demonstrate that R-152a is a promising low-GWP alternative to R-134a for domestic heat pump water heaters, offering improved thermodynamic performance, higher exergy efficiency, and substantially reduced environmental impact. However, its flammability must be carefully addressed through proper safety measures before practical implementation.
Supersonic ejectors are widely used in refrigeration systems due to their potential to improve system performance. However, their performance is highly sensitive to geometric parameters, requiring both a comprehensive understanding of internal flow behavior and systematic investigation. In this study, a CFD model validated against experimental data reported in the literature is used to investigate and optimize ejector performance under fixed operating conditions with particular attention to internal flow characteristics. The effects of key geometric parameters are analyzed using single-factor analysis, followed by a multi-factor analysis using an orthogonal test method. According to the CFD results, the optimized configuration increases the entrainment ratio from 0.442 to 0.526, representing an 18.8% improvement. The critical back pressure decreases from 217.2 to 206.2 kPa, corresponding to a 5.04% reduction, indicating a trade-off between entrainment performance and the stable operating range. This study highlights the importance of linking performance to flow structure, providing a more physically grounded basis for ejector analysis and optimization.
In this study, alternative refrigerants are evaluated under a total of sixteen different operating conditions covering both heating and cooling modes by using multi-criteria decision-making methods. Seven refrigerants are compared based on the coefficient of performance as the thermodynamic performance criterion, global warming potential as the environmental impact, and flammability classification as the safety criterion. Criterion weights are determined using the Shannon Entropy, CRITIC, and equal weighting methods, and the ranking procedures are carried out using the TOPSIS, VIKOR, and EDAS techniques. Based on the sixteen operating scenarios and the three evaluation criteria considered (COP, GWP, and flammability), R1234ze(E) is the most suitable alternative under all methods and operating conditions, achieving an average COP of approximately 3.28 together with a global warming potential of only 1 compared with 1430 for R134a, corresponding to a GWP reduction of more than 99%; R134a is consequently ranked among the least favorable options in most scenarios due to its high environmental impact, despite its competitive average COP of approximately 3.22. Despite its high global warming potential, R134a ranked among the top alternatives in some evaluation scenarios when entropy-based weighting and the TOPSIS method were applied. However, the EDAS method penalized R134a regardless of the weighting approach, indicating a higher sensitivity to unfavorable environmental indicators. The EDAS method reflects this trade-off more strongly than the other methods by effectively discriminating against alternatives with high GWP values, making it more suitable for identifying balanced working fluids within a candidate pool.
Increasing the demand for greater efficiency and sustainability in corrugated plate heat exchangers has driven comprehensive research to address the drawbacks of conventional designs. This review summarizes the latest developments in enhancing plate heat exchanger performance, with a focus on plate surface geometry and working-fluid effects. The most important geometric details for thermo-hydraulic properties, such as chevron angle, corrugation pattern, plate spacing, and corrugation depth and pitch, are systematically examined with respect to their thermo-hydraulic effects. The review also covers surface coatings, including potential contributions to fouling mitigation and pressure drop reduction, as well as persistent issues related to durability, health, and environmental impact, along with their associated costs. The review also discusses the application of nanofluids as working fluids, explaining that while they can improve heat transfer, they also entail drawbacks such as increased viscosity, pressure drop, and unknown long-term effects on plate integrity. In addition to designs and material strategies, the review focuses on how they incorporate sustainable and renewable energies into plate heat exchangers to reduce dependence on fossil fuels and their negative impact on the environment, as well as reducing operational costs. Also, cutting-edge data-driven techniques, including machine learning, deep learning, and artificial intelligence, are presented as promising extensions to other CFD techniques to develop and enhance predictive models while reducing computational costs and time. Finally, several research gaps are identified, including insufficient attention to unsteady and oscillatory flow conditions, highlighting areas that require further investigation.
