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Climate change and global warming are the visible consequences of the increased amount of carbon dioxide (CO 2 ) in the atmosphere. Among the various sources of anthropogenic CO 2 emission, the diesel engine has a significant contribution. The development of a reliable system to efficiently minimize CO 2 emissions from diesel engines to the safest level is lacking in the open literature. Therefore, a comprehensive multidisciplinary approach has been applied in this paper to investigate the efficacy of the post-combustion carbon capture (PCC) process for the diesel engine. The experiments have been performed on the exhaust of a direct injection diesel engine at five different brake powers with blends of aqueous ammonia (AQ_NH 3 ), monoethanolamine (MEA), N , N -dimethylethanolamine (DMEA), and 1-ethyl-3-methylimidazolium tetrafluoroborate (C 2 mim BF 4 ) ionic liquid (IL) as an absorbent for CO 2 capture. The reaction mechanism of these absorbent with CO 2 are also studied by the geometrical, energetical, MESP, frontier molecular orbitals, and NBO analysis using the first-principles density functional theory (DFT) calculations. The maximum CO 2 absorption efficiency of almost 97% was achieved for the blend consisting of 67% of AQ_NH 3 and 33% of MEA. Moreover, AQ_MEA and blend of AQ_NH 3 , DMEA, and C 2 mim BF 4 ionic liquid showed 96% and 94% CO 2 absorption efficiency, respectively.

In the context of wire electrical discharge machining (WEDM), determining the fraction of thermal energy transferred to the workpiece (f c ) is crucial for numerical modelling. This information is necessary to anticipate material removal mechanisms and understand thermal behaviour. In this study, two metaphor-less Rao algorithms are modified to solve the inverse heat conduction problem (IHCP) for the estimation of f c during the WEDM process without knowing any prior information on the transient functional form of f c . These two algorithms are compared in terms of accuracy and convergence speed. The Rao-1 algorithm stands out with high accuracy and rapid convergence. To evaluate the algorithm applicability in estimating f c , the following cases are considered: (1) a numerical investigation with artificial Gaussian error in simulated temperature readings and (2) a real-time experiment on WEDM setup with varying discharge currents. The RMS error between the actual and estimated value of fc with SS-304 material during numerical investigation is found to be 562 W/m which is just 0.008 times of heat source. Real-time experiments reveal that the discharge current is directly proportional to the total energy supplied by the wire as well as f c . The f c values estimated by the proposed inverse algorithm with various discharge currents fall within the range of 15–18%, aligning with the existing literature. This shows the proposed methodology is accurate and can be extended to incorporate other machining processes.

A closed-loop pulsating heat pipe (CLPHP) can provide effective and adaptable thermal solutions for various applications. This work presents extensive experimental studies on CLPHP to enhance thermal performance using nanofluid. The experimental studies are conducted using two different heat transfer fluids: deionized (DI) water and a nanofluid (Al2O3-DI water with 0.1 mass/% nanoparticles). Parametric studies are performed with different combinations of filling ratios (FR) and heat input values. To analyze the experimental data, an in-house Python library named PyPulseHeatPipe is developed, which facilitates statistical analysis, data visualization, and process data for machine learning from raw experimental data. Furthermore, the experimental datasets are used to train various machine learning (ML) models, including random forest regressor (RFR), extreme gradient boosting regressor, gradient boosting regressor, support vector machine, and K-nearest neighbors (KNN) to determine the thermal parameters for a given CLPHP. These models precisely predict the thermal performance of CLPHP using two novel approaches. The first approach predicts thermal resistance under given thermal properties such as evaporator temperature, pressure, FR, heat input, and heat transfer fluid, while the second approach predicts thermal parameters such as evaporator temperature, pressure, and heat input to achieve the desired thermal resistance. For the first approach, the RFR model performs the best among the trained ML models, with the lowest root mean square error (RMSE) of 0.0175 and the highest goodness of fit, with R2 score and R2-adjusted (R2-adj.) of 0.9873 and 0.9872, respectively. For the second approach, the KNN model achieves the highest goodness of fit (R2-adj.) for evaporator temperature, pressure, and heat input values of around 0.9889, 0.9524, and 0.8149, respectively. This study establishes a foundation for the more efficient thermal design of CLPHP in various engineering systems by integrating experimental research with data-driven solutions through ML.

A closed-loop pulsating heat pipe (CLPHP) can provide effective and adaptable thermal solutions for various applications. This work presents extensive experimental studies on CLPHP to enhance thermal performance using nanofluid. The experimental studies are conducted using two different heat transfer fluids: deionized (DI) water and a nanofluid (Al 2 O 3 -DI water with 0.1 mass/% nanoparticles). Parametric studies are performed with different combinations of filling ratios (FR) and heat input values. To analyze the experimental data, an in-house Python library named PyPulseHeatPipe is developed, which facilitates statistical analysis, data visualization, and process data for machine learning from raw experimental data. Furthermore, the experimental datasets are used to train various machine learning (ML) models, including random forest regressor (RFR), extreme gradient boosting regressor, gradient boosting regressor, support vector machine, and K-nearest neighbors (KNN) to determine the thermal parameters for a given CLPHP. These models precisely predict the thermal performance of CLPHP using two novel approaches. The first approach predicts thermal resistance under given thermal properties such as evaporator temperature, pressure, FR, heat input, and heat transfer fluid, while the second approach predicts thermal parameters such as evaporator temperature, pressure, and heat input to achieve the desired thermal resistance. For the first approach, the RFR model performs the best among the trained ML models, with the lowest root mean square error (RMSE) of 0.0175 and the highest goodness of fit, with R 2 score and R 2 -adjusted ( R 2 -adj.) of 0.9873 and 0.9872, respectively. For the second approach, the KNN model achieves the highest goodness of fit ( R 2 -adj.) for evaporator temperature, pressure, and heat input values of around 0.9889, 0.9524, and 0.8149, respectively. This study establishes a foundation for the more efficient thermal design of CLPHP in various engineering systems by integrating experimental research with data-driven solutions through ML.

A closed-loop pulsating heat pipe (CLPHP) can provide effective and adaptable thermal solutions for various applications. This work presents extensive experimental studies on CLPHP to enhance thermal performance using nanofluid. The experimental studies are conducted using two different heat transfer fluids: deionized (DI) water and a nanofluid (Al.sub.2O.sub.3-DI water with 0.1 mass/% nanoparticles). Parametric studies are performed with different combinations of filling ratios (FR) and heat input values. To analyze the experimental data, an in-house Python library named PyPulseHeatPipe is developed, which facilitates statistical analysis, data visualization, and process data for machine learning from raw experimental data. Furthermore, the experimental datasets are used to train various machine learning (ML) models, including random forest regressor (RFR), extreme gradient boosting regressor, gradient boosting regressor, support vector machine, and K-nearest neighbors (KNN) to determine the thermal parameters for a given CLPHP. These models precisely predict the thermal performance of CLPHP using two novel approaches. The first approach predicts thermal resistance under given thermal properties such as evaporator temperature, pressure, FR, heat input, and heat transfer fluid, while the second approach predicts thermal parameters such as evaporator temperature, pressure, and heat input to achieve the desired thermal resistance. For the first approach, the RFR model performs the best among the trained ML models, with the lowest root mean square error (RMSE) of 0.0175 and the highest goodness of fit, with R.sup.2 score and R.sup.2-adjusted (R.sup.2-adj.) of 0.9873 and 0.9872, respectively. For the second approach, the KNN model achieves the highest goodness of fit (R.sup.2-adj.) for evaporator temperature, pressure, and heat input values of around 0.9889, 0.9524, and 0.8149, respectively. This study establishes a foundation for the more efficient thermal design of CLPHP in various engineering systems by integrating experimental research with data-driven solutions through ML.

Purpose – The purpose of this paper is to develop a numerical model for estimating the unknown boundary heat flux in a parallel plate channel for the case of a hydrodynamically and thermally developing laminar flow. Design/methodology/approach – The conjugate gradient method (CGM) is used to solve the inverse problem. The momentum equations are solved using an in-house computational fluid dynamics (CFD) source code. The energy equations along with the adjoint and sensitivity equations are solved using the finite volume method. Findings – The effects of number of measurements, distribution of measurements and functional form of unknown flux on the accuracy of estimations are investigated in this work. The prediction of boundary flux by the present algorithm is found to be quite reasonable. Originality/value – It is noticed from the literature review that study of inverse problem with hydrodynamically developing flow has not received sufficient attention despite its practical importance. In the present work, a hydrodynamically and thermally developing flow between two parallel plates is considered and unknown transient boundary heat flux at the upper plate of a parallel plate channel is estimated using CGM.

This article provides a comprehensive review of the recent literature on various natural fiber and inorganic filler-based polymer composites used in rotational molding (RM). The RM has grown in prominence in various essential applications in recent years. Different industries are working to create lighter components, especially in the automobile and aerospace industries, to improve fuel efficiency and reduce costs. Polymer matrix composites are lightweight, recyclable, corrosion-resistant, and cost-effective. Nonetheless, they are likewise limited in terms of strength, to overcome the polymer’s obvious limitations natural fibers and inorganic particle fillers are often added to polymer composites in RM to improve their stiffness and strength and expand their uses. This necessitates a comprehensive study of the various materials available for rotational molding and their influence on the mechanical properties of composites. The variety of materials used in rotational molding is examined and recent advancements are highlighted in the first section. The second section of the discussion focuses on various materials used in rotational molding, their properties, and their advantages and disadvantages. The third section of the paper is dedicated to examining the relationship between the molecular weight of the material and the resulting crystallinity and mechanical properties of blended composites. The fourth section, which comes next, is about mixing natural fibers and inorganic filler with the base resin and their effect on the mechanical properties of a roto-molded product and also discusses the effect of fillers on the flow, void, and viscosity. The final section of the paper discusses several factors that can affect the properties of composites, including the particle size of natural and inorganic fillers, the heating and cooling of the mold, aging and degradation, and the rheology of the composite material. Past literature depicts that the mechanical properties of composite increase when the particle size gets smaller for both natural filler and inorganic particulate filler. This literature review has led to the following conclusion: to develop highly efficient particulate composites that can be greatly aided by careful selection of the base resin, additives, and parameter characterization.

An experimental study was performed on Oscillating Heat Pipe (OHP) with closed loop structure. The capillary tube made up of copper of this OHP has inner diameter of 2.15 mm and thickness of wall is 1 mm. Different set of experiments were performed with filling ratios ranging from 30% to 100%, heating power ranging from 115 to 430 W at evaporator section and free convection condition at condenser section. The water has been considered as a working fluid. The thermal resistance was calculated for measuring the thermal performance of OHP. The changes in thermal resistance with heat input for various filling ratios has been carried out. The thermal resistance achieved lowest for 70 % filling ratio.

Climate change and global warming are the visible consequences of the increased amount of carbon dioxide (CO ) in the atmosphere. Among the various sources of anthropogenic CO emission, the diesel engine has a significant contribution. The development of a reliable system to efficiently minimize CO emissions from diesel engines to the safest level is lacking in the open literature. Therefore, a comprehensive multidisciplinary approach has been applied in this paper to investigate the efficacy of the post-combustion carbon capture (PCC) process for the diesel engine. The experiments have been performed on the exhaust of a direct injection diesel engine at five different brake powers with blends of aqueous ammonia (AQ_NH ), monoethanolamine (MEA), N,N-dimethylethanolamine (DMEA), and 1-ethyl-3-methylimidazolium tetrafluoroborate (C mim BF ) ionic liquid (IL) as an absorbent for CO capture. The reaction mechanism of these absorbent with CO are also studied by the geometrical, energetical, MESP, frontier molecular orbitals, and NBO analysis using the first-principles density functional theory (DFT) calculations. The maximum CO absorption efficiency of almost 97% was achieved for the blend consisting of 67% of AQ_NH and 33% of MEA. Moreover, AQ_MEA and blend of AQ_NH , DMEA, and C mim BF ionic liquid showed 96% and 94% CO absorption efficiency, respectively.

Chemical looping combustion (CLC) is the most reliable carbon capture technology for curtailing CO2 insertion into the atmosphere. This paper presents the cold flow simulation results necessary to understand the hydrodynamic viability of the fast-fluidized bed air reactor. Hematite is selected as an oxygen carrier due to its easy availability and active nature during the reactions. The dense discrete phase model (DDPM) approach using the commercial software Ansys Fluent is applied in the simulation. An accurate and stable solution is achieved using the second-order upwind numerical scheme. A pressure difference of 150 kPa is obtained between the outlet and inlet of the selected air reactor, which is necessary for the movement of the particle. The stable circulating rate of hematite is achieved after 28 s of particle injection inside the air reactor. The results have been validated from the experimental results taken from the literature.