• Energy Research
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Abstract The accurate information of the thermal stresses and temperature in isotropic elastic solids is the key for many engineering applications. At present the classical linear coupled theory of thermoelasticity deduced with the assumptions of small temperature changes is widely used to solve the thermoelastic problems in engineering. In this paper, to describe the thermoelastic behavior in isotropic solids undergoing large temperature changes more accurately, the novel coupled models of thermoelasticity and the corresponding finite element models have been presented explicitly and validated by experimental measurement. The effect of large temperature changes on the solutions of thermoelastic problems is discussed. For the heat transfer process, if the isotropic elastic solids will expand when heated and contract when cooled and the condition d E E d T · σ i j E − δ i j 1 − 2 ν α 0 can be met in the context of small deformations, the effect of large temperature changes can be regarded as increasing the specific heat. The proposed models are applied to solve two thermoelastic problems. From the obtained numerical results, the effect of large temperature changes will increase with the amplitude of temperature change and may be considerably even when the temperature changes slowly.

Abstract Double and added double stage organic Rankine cycle systems are configured to recover exhaust gas waste heat of dual fuel engines. To evaluate the performance of the models proposed here, energy, exergy and economic analyses are performed. Several working fluids are evaluated for recommendation for double and added double stage organic Rankine cycle systems. In the double stage organic Rankine cycle, cycle 1 and cycle 2 are connected in parallel. Working fluids R123, R141b, and R601 are used in cycle 1, and R245fa, R236ea, and R1233zd in cycle 2. In the double stage organic Rankine cycle, the working fluid combinations of R601-R1233zd, R601-R245fa and R123-R245fa show better performance when considering power, heat transfer area and payback period, which are 1760 kW, 2108.9 m2 and 4.21 year, respectively for R601-R245fa. In the added double stage organic Rankine cycle, cycle 1 and cycle 2 are connected in two stages and cycle 1 and cycle 3 in parallel. The net power of the working fluid combinations of R123-R245fa and R123-R1233zd are 1799 kW and 1782 kW, respectively, which are higher than those of the others. Further, for R123-R245fa, the heat transfer area and payback period are 3352 m2 and 6.20 year, respectively, which is better compared to those of other working fluid combinations.

Abstract Newer generation sources and loads are posing new challenges to the conventional power system protection schemes. Adaptive and intelligent protection methodology, based on advanced measurement techniques and intelligent fault diagnosis such as machine learning (ML), is found to be useful to meet these challenges. A large number of research works are reported on ML-based power system fault diagnosis. However, ML techniques are evolving at a very fast pace, and an inclusive, as well as state-of-the-art review on ML-based power system fault diagnosis, is not available in the literature. Given this need and growing trend towards ML, the study presented in this paper aims to provide a comprehensive review of ML-based power system fault diagnosis. At first, efforts have been made to enlist the issues present in conventional fault diagnosis which led to the popularity of ML techniques. Also, a baseline framework and workflow for ML-based fault diagnosis are presented. Next, various unsupervised and supervised learning techniques have been discussed separately which have been used by several researchers for fault diagnosis. The discussion throughout is supported with tabulated facts for fault detection, classification and localization works with techniques used, different simulation tools used, and their application system. The advantages and disadvantages of all the techniques of fault diagnosis have also been discussed which will help the readers in the selection of techniques for their research. A brief review of reinforcement learning and transfer learning is also given as they are gaining popularity in power system-related studies and have the potential to be used for fault diagnosis. Finally, the research trends, some key issues, and directions for future research have been highlighted.

Microalgae are promising sources of valuable compounds: carotenoids, polyunsaturated fatty acids, lipids, etc. To overcome the feasibility challenge due to low yield and attain commercial potential, researchers merge technologies to enhance algal bioprocess. In this context, nanomaterials are attractive for enhancing microalgal bioprocessing, from cultivation to downstream extraction. Nanomaterials enhance biomass and product yields (mainly lipid and carotenoids) through improved nutrient uptake and stress tolerance during cultivation. They also provide mechanistic insights from recent studies. They also revolutionize harvesting via nano-induced sedimentation, flocculation, and flotation. Downstream processing benefits from nanomaterials, improving extraction and purification. Special attention is given to cost-effective extraction, showcasing nanomaterial integration, and providing a comparative account. The review also profiles nanomaterial types, including metallic nanoparticles, magnetic nanomaterials, carbon-based nanomaterials, silica nanoparticles, polymers, and functionalized nanomaterials. Challenges and future trends are discussed, emphasizing nanomaterials' role in advancing sustainable and efficient microalgal bioprocessing, unlocking their potential for bio-based industries.

Abstract The present work attempts to devise an efficient method utilizing an on-grid photovoltaic-thermal heat pump water heater (PV-THPWH) integrated with a real-time variable frequency controller to achieve the goal of energy-efficient buildings. The prime focus is to reduce the grid's dependence on the compressor's energy-intensive operation by employing a feedback-controlled variable frequency drive (VFD). Additionally, the possibilities involved with addressing the electrical and thermal energy requirements of an energy-efficient building was investigated utilizing the proposed system. R-32 refrigerant in the photovoltaic-thermal (PV-T) evaporator coils of the heat pump assembly help to cool the photovoltaic (PV) panel while delivering the absorbed heat in the condenser to heat water contained inside the storage tank. Outdoor experiments and theoretical investigations of the combined system were carried out to appraise the dynamic behavior under varying solar irradiation and ambient temperature conditions. The observations conveyed that the PV-THPWH system succeeded in reducing the PV panel operating temperature by 25%, which resulted in a 20% increment in PV power output. Also, the performance indicators, such as the instantaneous energy efficiency and instantaneous PV efficiency, were found to increase by 15% and 34%, respectively, resulting in an average coefficient of performance of 6.4. For a clear sky day, the recorded total PV energy output was 4.67 units, while the VFD compressor consumption was 3.42 units, and the surplus 1.25 units were sent to the grid. Furthermore, the economic analysis reported a payback period of 2.3 years for the developed PV-THPWH system.

Abstract Date biomass is a carbon-rich renewable resource that can be considered a potential carbon-rich substrate for energy generation over anaerobic digestion (AD). However, due to its complex nature, appropriate pretreatment is necessary to achieve a higher methane yield. Hence, the current study was envisioned to evaluate the influence of three different pretreatment strategies, namely acid, alkali, and hydrothermal pretreatment on biochemical methane potential (BMP) of seven diverse sorts of Algerian date biomass, namely Pedicels, Fibrilium, Petiole, Fruit bunch, Spath, Palm, and its mixture. Among all the pretreatment conditions, alkaline pretreatment highly influenced the lignin composition of date biomass and showed higher BMP. Among all sorts of biomass, higher BMP was detected through Palm as 295.9 mL CH4/g-TS, whereas the lowest BMP values were recorded with Petiole as 226.74 mL CH4/g-TS. Among all the experimental variations, ammonium pretreated Palm biomass documented the highest substrate conversion efficiency (63.80%), which correlates well with the observed higher BMP values. Nevertheless, there was a very marginal improvement in BMP detected in the case of other pretreatment strategies compared to alkaline pretreatment. This might be due to the efficacy of the applied pretreatment method on delignification of date biomass.