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Abstract Dimethyl ether appears to be a better choice among various diesel alternatives due to its high cetane number and sootless combustion. However, the physical and chemical properties of dimethyl ether are very different from those of diesel. The physical properties influence spray formation and atomization characteristics, while chemical properties determine combustion and emission formation characteristics. Thus, fuel's physical and chemical properties significantly determine engine performance and emissions. In the present work, spray combustion and emission formation characteristics of n-heptane, dimethyl ether, and their blends (10, 25, and 50% dimethyl ether in n-heptane) were numerically studied in a constant volume chamber. Results show that the n-heptane spray combustion has the highest heat release rate with an intense premix combustion phase, whereas dimethyl ether spray combustion has the lowest heat release rate and shortest premix combustion phase. The magnitude of the premixed phase and heat release rate decreases with the increase in dimethyl ether mass fraction in the blends. Soot, carbon monoxide (CO), unburned hydrocarbon (UHC), and nitric oxide (NO) emissions decreased with the increase in the dimethyl ether mass fraction in the blends and were lowest for the dimethyl ether.

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.

Abstract Wellbore pressure gradient in gas wells is significant in designing deliquification technologies and optimizing production. At present, no model has yet to be established specifically for gas wells at a wide gas flowrate range. When calculating pressure gradient in a specific gas field, engineers must evaluate these widely used models and get the best performance model at a certain range. To establish a more comprehensive model in horizontal gas wells, an experimental study was conducted to investigate the flow behavior of liquid-gas two-phase flow at different gas and liquid velocities and inclined angles in a 50 mm visual pipe. The evaluation of these widely used models against the experimental data shows that no model can predict liquid holdup at different gas velocity ranges, and huge deviations due to several reasons can be observed. After conducting a comprehensive analysis, a new liquid holdup correlation was proposed based on the Mukherjee–Brill model by correlating from the experimental results, which have parametric ranges closer to the production of gas wells. This new model adopts a new dimensionless gas velocity number to characterize flow similarities and better scale up pressure from the experiment to the gas wells. By validating against experimental data and field data, the results indicate that the new two-phase flow model has stable performance and can accurately predict pressure gradients at different ranges of pressure and gas/liquid velocities.

Abstract Heat pipes and two-phase thermosyphons are highly efficient heat transfer devices utilizing continuous evaporation and condensation of working fluid for two-phase heat transport in closed systems. Because of the nearly isothermal and fully passive phase-change heat transfer mechanism, heat pipes and thermosyphons have found many applications in nuclear engineering, space technologies, and other energy systems. High-temperature heat pipes are used in nuclear microreactors to remove fission power from the primary system and are coupled with power conversion systems or process heat applications. Modeling of the two-phase flow phenomena inside a heat pipe is essential to its design and safety analysis. In this study, a comprehensive one-dimensional two-phase three-field flow model has been developed for the analysis of heat pipes in normal operation conditions and transients. The conservation or field equations of mass, momentum, and energy were developed for the liquid film, vapor, and droplet. In addition, constitutive models or correlations were reviewed thoroughly and provided for the closure of the three-field equations. Specific constitutive equations regarding interfacial mass and heat transfer at two interfaces, namely film-gas interface and gas-droplet interface, were reviewed for droplet entrainment and deposition rates as well as film and droplet evaporation rates. Additionally, mechanistic correlations of annular flow film thickness were recommended for the modeling of the thermosyphons without a wick as a critical constitutive correlation. Furthermore, experimental data needs from new experiments using a prototype working fluid or surrogate fluids for the model validation of high-temperature heat pipes in microreactors were recommended for future research.

Accurate predictions of fuel spray behavior and mixture formation in simulations of direct-injection spark-ignition (DISI) engines are fundamental to ensure proper description of all subsequent processes including ignition, combustion, and emissions. In this work, the spray evolution in a single-cylinder optical DISI engine was studied experimentally and numerically with the goal of enabling predictive computational fluid dynamics (CFD) modeling of in-cylinder sprays. The authors explored a wide range of operating conditions characterized by several fuel injection temperatures and engine speeds, using a well-characterized nine-component gasoline surrogate known as PACE-20. The effect of flash boiling and intake crossflow on the spray is discussed, with a focus on evaluating the ability of the spray models to capture highly transient spray behavior. In the experiments, the fuel temperature was varied between 20°C and 80°C, allowing for non-flash- to flash-boiling transition to emerge with enhanced flashing intensity at the highest temperatures. Spray collapse resulted in vapor-rich regions, owing to the locally lower inertia of the fluid. Varying the engine speed from 650 to 1950 rpm promoted increasingly more turbulent in-cylinder crossflow which interacted with the spray during the injection event and resulted in enhanced spray dispersion. The CFD model was able to capture the spray morphology transition at different fuel temperatures and engine speeds adequately. It is shown that the spray breakup model could capture the transitional spray behavior induced by flash boiling atomization and intake flow via proper initialization of the spray cone angle and calibration of the spray models’ constants.

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.