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The performance of a bladeless Tesla turbine is closely tied to momentum diffusion, kinetic energy transfer and wall shear stress generation on its rotating disks. The surface roughness adds complexity of flow analysis in such a domain. This paper aims to assess the effect of roughness on flow structures and the application of roughness models in flow cross sections with submillimeter height, including both stationary and rotating walls.

This research starts with the examination of flow over a rough flat plate, and then proceeds to study flow within minichannels, evaluating the effect of roughness on flow characteristics. An in-house test stand validates the numerical solutions of minichannel. Finally, flow through the minichannel with corotating walls was analyzed. The k-ω SST turbulent model and Aupoix's roughness method are used for numerical simulations.

The findings emphasize the necessity of considering the constricted dimensions of the flow cross section, thereby improving the alignment of derived results with theoretical estimations. Moreover, this study explores the effects of roughness on flow characteristics within the minichannel with stationary and rotating walls, offering valuable insights into this intricate phenomenon, and depicts the appropriate performance of chosen roughness model in studied cases.

The originality of this investigation is the assessment and validation of flow characteristics inside minichannel with stationary and corotating walls when the roughness is implemented. This phenomenon, along with the effect of roughness on the transportation of kinetic energy to the rough surface of a minichannel in an in-house test setup, is assessed.

This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.

In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.

The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.

The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.

The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.

This paper aims to present a comprehensive analysis of conjugate heat transfer phenomena occurring within the developing region of square ducts under both isothermal and isoflux boundary conditions. The study involves a rigorous numerical investigation, using advanced computational methods to simulate the complex heat exchange interactions between solid structures and surrounding fluid flows. The results of this analysis provide valuable insights into the heat transfer characteristics of such systems and contribute to a deeper understanding of fluid–thermal interactions in duct flows.

The manuscript outlines a detailed numerical methodology, combining computational fluid dynamics and finite element analysis, to accurately model the conjugate heat transfer process. This approach ensures both the thermal behaviour of the solid walls and the fluid flow dynamics are well captured.

The results presented in the manuscript reveal significant variations in heat transfer characteristics for isothermal and isoflux boundary conditions. These findings have implications for optimizing heat exchangers and enhancing thermal performance in various engineering applications.

The insights gained from this study have the potential to influence the design and optimization of heat exchange systems, contributing to advancements in energy efficiency and engineering practices.

The research introduces a novel approach to study conjugate heat transfer in square ducts, particularly focusing on the developing region. This unique perspective offers fresh insights into heat transfer mechanisms that were previously not thoroughly explored.

The purpose of this paper is to investigate the effects of different surface structures, dimensional parameters and cavitation models on the lubrication characteristics of water-lubricated journal bearings.

In this paper, the coupling iteration method of ANSYS and MATLAB is established to calculate the journal orbits of water-lubricated bearing, and the differences between the journal orbits of the smoothed and the textured water-lubricated bearings are compared and analyzed, and the effects of different bearing materials, L/D ratios and clearance ratios on the lubrication performance of water-lubricated bearings are investigated. The effects of different cavitation models on the static equilibrium position and whirling trajectory of water-lubricated bearings are compared.

The results show that when the surface texture is distributed in the upper bearing or the bearing elastic modulus decreases, the bearing stability increases. Considering shear cavitation and noncondensing gas, the rotor journal orbits amplitude decreases at high speed with low clearance ratio. A water film test rig for water-lubricated bearings is built to measure the full-circle water film pressure of water-lubricated journal bearings, and the experimental results are compared with the simulation results, which are in good agreement.

The findings provide a theoretical basis for optimizing the structure of water-lubricated bearings.

Flow induced by rotating disks is of great practical importance in several engineering applications such as rotating heat exchangers, turbine disks, pumps and many more. The present research has been freshly displayed regarding the implementation of an engine oil-based Casson tri-hybrid nanofluid across a rotating disk in mass and heat transferal developments. The purpose of this study is to contemplate the attributes of the flowing tri-hybrid nanofluid by incorporating porosity effects and magnetization and velocity slip effects, viscous dissipation, radiating flux, temperature slip, chemical reaction and activation energy.

The articulated fluid flow is described by a set of partial differential equations which are converted into one set of higher-order ordinary differential equations (ODEs) by using convenient conversions. The numerical solution of this transformed set of ODEs has been spearheaded by using the effectual bvp4c scheme.

The acquired results show that the heat transmission rate for the Casson tri-hybrid nanofluid is intensified by, respectively, 9.54% and 11.93% when compared to the Casson hybrid nanofluid and Casson nanofluid. Also, the mass transmission rate for the Casson tri-hybrid nanofluid is augmented by 1.09% and 2.14%, respectively, when compared to the Casson hybrid nanofluid and Casson nanofluid.

The current investigation presents an educative response on how the flow profiles vary with changes in the inevitable flow parameters. As per authors’ knowledge, no such scrutinization has been carried out previously; therefore, our results are novel and unique.

This study aims to explore the thermal energy diffusion and flow features of a hybrid nanofluid in a thin film. In particular, the focus is to elicit the impact of shape factor in the backdrop of a magnetic field. The hybrid nanofluid is the amalgamation of various shaped nanoscale particles of copper and alumina in water.

The equations of motion and energy are modeled using the Tiwari–Das model. The differential equations governing the physics of the designed model have been obtained by the application of scaling analysis. To achieve quantitative outcomes, Runge–Kutta–Fehlberg numerical code along with shooting techniques is used. Validation of the derived outcomes with available data in literature reveals a greater accuracy of the numerical procedure used in this investigation.

The dynamics of the slender nano liquid film is explored eliciting the impact of various flow parameters. The rate of energy transport of the Cu-Al₂O₃/ water with blade-shaped nanoparticle, at a fixed Prandtl number (=2) is enhanced by 14.7% compared to that evaluated with spherical particles. The presence of hybrid nanoparticles has an affirmative impact in boosting the rate of heat transfer (RHT). The temperature and the rate of thermal diffusion of the hybrid nanofluid are more prominent than those of the Cu-H₂O case. The numerical outcomes of this investigation are collated with the already published works as a limiting case and are found to be in good agreement.

The adopted methodology helped to obtain the results of the present problem. To the best of authors’ knowledge, it can be shown that the originality of the work with the table of comparison. There is a good agreement between present outcomes with the existed results.

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.

The purpose of this paper is to develop a load detection method for domestic induction cooktops. The solution aims to minimize its impact in the converter power transmission while enabling the estimation of the equivalent electrical parameters of the load. This method is suitable for a multi-output resonant inverter topology with shared power devices.

The considered multi-output converter presents power devices that are shared between several loads. Thus, applying load detection methods in the literature requires a halt in the power transfer to ensuring safe operation. The proposed method uses a complementary short-voltage pulse to excite the induction heating (IH) coil without stopping the power transfer to the remaining IH loads. With the current through the coil and the analytical equations, the equivalent inductance and resistance of the load is estimated. The precision of the method has been evaluated by simulation, and experimental results are provided.

The measurement of the current through the induction coil as a response to a short-time single-pulse voltage variation provides enough information to estimate the load equivalent parameters, allowing to differentiate between no-load, non-suitable IH load and suitable IH load situations.

The proposed method provides a solution for load detection without requiring additional circuitry. It aims for low power transmission to the load and ensures zero-voltage switching and reduced peak current even in no-load cases. Moreover, the proposed solution is extensible to less complex converters, as the half bridge.

Analyzing and reducing entropy generation is useful for enhancing the thermodynamic performance of engineering systems. This study aims to explore how polymers and nanoparticles in the presence of Lorentz forces influence the fluid behavior and heat transfer characteristics to lessen energy loss and entropy generation.

The dispersion model is initially used to examine the behavior of polymer additives over a magnetized surface. The governing system of partial differential equations (PDEs) is subsequently reduced through the utilization of similarity transformation techniques. Entropy analysis is primarily performed through the implementation of numerical computations on a non-Newtonian polymeric FENE-P model.

The numerical simulations conducted in the presence of Lorentz forces provide significant insights into the consequences of adding polymers to the base fluid. The findings suggest that such an approach minimizes entropy in the flow region. Through the utilization of polymer-MHD (magnetohydrodynamic) interactions, it is feasible to reduce energy loss and improve the efficiency of the system.

This study’s primary motivation and novelty lie in examining the significance of polymer additives as agents that reduce entropy generation on a magnetic surface. The author looks at how nanofluids affect the development of entropy and the loss of irreversibility. To do this, the author uses the Lorentz force, the Soret effect and the Dufour effect to minimize entropy. The findings contribute to fluid mechanics and thermodynamics by providing valuable insights for engineering systems to increase energy efficiency and conserve resources.

This study aims to investigate whether consumers and small businesses can transition from disposable to reusable coffee cups, using a community social marketing intervention, led by a Social Purpose Organisation.

An emergent case study approach using multiple sources of data developed an in-depth, multifaceted, real-world context evaluation of the intervention. The methodology draws on citizen science “messy” data collection involving multiple, fragmented sources.

Moving from single-use cups to reusables requires collective commitment by retailers, consumers and policymakers, despite the many incentives and penalties applied to incentivise behaviour change. Difficult post-COVID economics, austerity and infrastructure gaps are undermining both reusable acceptance and interim solutions to our dependence upon disposables.

Although the non-traditional methodology rendered gaps and omissions in the data, the citizen science was democratising and inclusive for the community.

Our practical contribution evaluates a whole community intervention setting to encourage reusable cups, integrating multiple stakeholders, in a non-controllable, non-experimental environment in contrast to previous research. This paper demonstrates how small community grants can foster impactful collaborative partnerships between an SPO and researchers, facilitate knowledge-exchange beyond the initial remit and provide a catalyst for possible future impact and outcomes.

To assess the impact at both the outcome and the process level of the intervention, we use Pawson and Tilley’s realist evaluation theory – the Context Mechanism Outcome framework. The methodological contribution demonstrates the process of citizen science “messy” data collection, likely to feature more frequently in future social science research addressing climate change and sustainability challenges.