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The purpose of this paper is to provide an overview of the computational progress in the development of hydrogen-fired gas turbines. This review aims to identify suitable combustion models, appropriate NO_x chemistry mechanisms and NO_x emission levels for effective utilization of hydrogen as an alternative fuel in gas turbines.

Hydrogen is recognized as a potential alternative fuel for achieving exceptionally low emissions in gas turbines. The developments in conventional, trapped vortex combustor and micromix combustors are discussed, along with various computational models aimed at accurately predicting combustion and emission characteristics. The results of numerical simulations were then discussed with emphasis on their role in optimizing the combustor geometry.

Computational studies that were used to optimize the combustor geometry to reduce NO_x emissions and the flashback phenomenon are discussed. To retrofit existing gas turbines for hydrogen fuel, minor modifications that are required were discussed by analyzing extensive literature. The influence of key design and geometrical parameters on NO_x emissions and the appropriate selection of combustion models for numerical simulations in optimizing various combustion systems are elaborated.

The review emphasizes the computational studies in the progress of hydrogen-fired gas turbine developments. The previous reviews were primarily focused on the combustion technologies for hydrogen-fired gas turbines. This comprehensive review focuses on the key design parameters, flame structure, selection of combustion models, combustion efficiency improvement and impact of parametric studies on NO_x formation of various combustion systems, in particular hydrogen combustion for gas turbine applications.

This study aims to numerically analyse a full-scale burner across a wide range of operating pressure conditions and determine the effect of swirl velocity on flame stabilization, flame holding and combustion performance.

This study uses a numerical analysis approach to investigate a three-dimensional full-scale burner. Modified governing equations are used to determine the effect of swirl velocity on flame stabilization and flame holding. The GR-Mech 3.0 chemical reaction mechanism is used to predict the combustion process. To validate the model, a grid independence study is performed.

The study reveals that swirl velocity enhances flame stability, resulting in better combustion rates. As the swirl velocity increases, higher flame temperatures are observed due to high convective heat recirculation. The heat transfer coefficient and high radiative extinction coefficient are found to vary based on fuel swirl velocity. The mass fraction of CH₄ and CO emphasizes the role of swirl velocity on flame structure. Increasing velocity potentially improves combustion by delaying the process, leading to better combustion and lower emissions.

The findings of this study contribute to the understanding of swirl-stabilized combustion and can guide the development of advanced combustion technologies, making it a valuable addition to the existing combustion field.

This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis.

Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families.

From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy.

The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.

The purpose of the paper is to analyse the performances of closures and compressibility corrections classically used in turbulence models when applied to highly-compressible turbulent boundary layers (TBLs) over flat plates.

A direct numerical simulation (DNS) database of TBLs, covering a wide range of thermodynamic conditions, is presented and exploited to perform a priori analyses of classical and recent closures for turbulent models. The results are systematically compared to the “exact” terms computed from DNS.

The few compressibility corrections available in the literature are not found to capture DNS data much better than the uncorrected original models, especially at the highest Mach numbers. Turbulent mass and heat fluxes are shown not to follow the classical gradient diffusion model, which was shown instead to provide acceptable results for modelling the vibrational turbulent heat flux.

The main originality of the present paper resides in the DNS database on which the a priori tests are conducted. The database contains some high-enthalpy simulations at large Mach numbers, allowing to test the performances of the turbulence models in the presence of both chemical dissociation and vibrational relaxation processes.

Wall-modeled large eddy simulation (LES) is a practical tool for solving wall-bounded flows with less computational cost by avoiding the explicit resolution of the near-wall region. However, its use is limited in flows that have high non-equilibrium effects like separation or transition. This study aims to present a novel methodology of using high-fidelity data and machine learning (ML) techniques to capture these non-equilibrium effects.

A precursor to this methodology has already been tested in Radhakrishnan et al. (2021) for equilibrium flows using LES of channel flow data. In the current methodology, the high-fidelity data chosen for training includes direct numerical simulation of a double diffuser that has strong non-equilibrium flow regions, and LES of a channel flow. The ultimate purpose of the model is to distinguish between equilibrium and non-equilibrium regions, and to provide the appropriate wall shear stress. The ML system used for this study is gradient-boosted regression trees.

The authors show that the model can be trained to make accurate predictions for both equilibrium and non-equilibrium boundary layers. In example, the authors find that the model is very effective for corner flows and flows that involve relaminarization, while performing rather ineffectively at recirculation regions.

Data from relaminarization regions help the model to better understand such phenomenon and to provide an appropriate boundary condition based on that. This motivates the authors to continue the research in this direction by adding more non-equilibrium phenomena to the training data to capture recirculation as well.

The study aims to explore how Soret and Dufour diffusions, thermal radiation, joule heating and magnetohydrodynamics (MHD) affect the flow of hybrid nanofluid (Al₂O₃-SiO₂/water) over a porous medium using a mobile slender needle.

To streamline the analysis, the authors apply appropriate transformations to change the governing model of partial differential equations into a group of ordinary differential equations. Following this, the authors analyze the transformed equations using the homotopy analysis method within Mathematica software, leading to the derivation of analytical solutions. This study investigates how changing values for porous medium, MHD, Soret and Dufour numbers and thermal radiation influence concentration, temperature and velocity profiles. In addition, the research assesses the effects on local Sherwood number, skin friction and Nusselt number.

In this investigation, the authors explore the movement of a needle away from its origin ( ε > 0). As the magnetic and porous medium parameters increase, there is a correspondence decrease in the velocity profile. Simultaneously, an increase in the Dufour number and thermal radiation parameter yields to a higher temperature profile, whereas arise in the Soret number results in an enhanced concentration profile. Furthermore, growth in the magnetic field parameter is correlated with a reduction in skin friction, Nusselt and Sherwood numbers. In addition, an examination of the data reveals that an escalation in the thermal radiation parameter is associated with an elevation in the Nusselt number. Moreover, an elevation in the Dufour number results in an augmentation in the Nusselt number.

These results have practical applications across diverse fields, including heat transfer enhancement, energy conversion systems, advanced manufacturing and material processing.

This study is distinctive in its investigation of the flow of hybrid nanofluid (Al₂O₃-SiO₂/water) over a slender, moving needle. The analysis includes joule heating, MHD, porous medium, thermal radiation and considering the effects of Soret and Dufour.

This study aims to investigate the influence mechanism of thermal-mechanical deformations on the CO₂ mixture gases dry gas seal (DGS) flow field and compare the deformation characteristics and sealing performance between two-way and one-way thermal-fluid-solid coupling models.

The authors established a two-way thermal-fluid-solid coupling model by using gas film thickness as the transfer parameter between the fluid and solid domain, and the model was solved using the finite difference method and finite element method. The thermal-mechanical deformations of the sealing rings, the influence of face deformation on the flow field and sealing performance were obtained.

Thermal-mechanical deformations cause a convergent gap between the two sealing end faces, resulting in an increase in the gas film thickness, but a decrease in the gas film temperature and sealing ring temperature. The axial relative deformations of rotating and stationary ring end faces caused by mechanical and thermal loads in the two-way coupling model are less than those in the one-way coupling (OWC) model, and the gas film thickness and leakage rate are larger than those in the OWC model, whereas the gas film stiffness is the opposite.

This paper provides a theoretical support and reference for the operational stability and structural optimization design of CO₂ mixture gases DGS under high-pressure and high-speed operation conditions.

The investigation concentrated on studying a distinct category of tubular heat exchanger that uses swirling airflow over tube bundle maintained at constant heat flux. Swirl flow is achieved using a novel perforated baffle plate with rectangular openings and multiple adjustable opposite-oriented saw-tooth flow deflectors. These deflectors were strategically placed at the inlet of the heat exchanger to create a swirling flow downstream.

The custom-built axial flow heat exchanger consists of three baffle plates arranged longitudinally supporting tube bundle maintained at constant heat flux. The baffle plate equipped with saw-tooth flow deflector of various geometry represented by space height ratio(e/h). Next, ambient air was then directed over the tube bundle at varying Reynolds number and the effect of baffle spacing (PR), Space height ratio (e/h) and inclination angle(a) of deflectors on performance of heat exchanger was experimentally analyzed.

The heat transfer augmentation of heat exchanger for given operating condition is strongly dependent on geometry, inclination angle of deflector and baffle spacing.

An average improvement of 1.42 times in thermal enhancement factor was observed with inclination angle of 30°, space height ratio of 0.4 and a pitch ratio of 1.2 when compared to a heat exchanger without a baffle plate under similar operating conditions.

This paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications.

An extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis.

The performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature.

Techniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed.

The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.