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The present study aims to perform inverse analysis of a conjugate heat transfer problem including conduction and forced convection via the quasi-Newton method. The inverse analysis is defined for a heat source that is surrounded by a solid medium which is exposed to a free stream in external flow.

The objective of the inverse design problem is finding temperature distribution of the heat source as thermal boundary condition to establish a prescribed temperature along the interface of solid body and fluid. This problem is a simplified version of thermal-based ice protection systems in which the formation of ice is avoided by maintaining the interface of fluid and solid at a specified temperature.

The effects of the different pertinent parameters such as Reynolds number, interface temperature and thermal conductivity ratio of fluid and solid mediums are analyzed.

This paper fulfils the analysis to study how thermal based anti-icing system can be used with different heat source shapes.

The purpose of this paper is to model radiation ovens and to propose a method capable of designing this kind of oven for the paint curing application. Providing a uniform cure condition on the body, especially bodies of complicated geometries, needs accurately‐designed ovens. An algorithm with high speed and high convergence capability is the most serious requirement for designing ovens of this kind.

In this paper, the state of the art in dynamic optimisation of radiation paint cure ovens is reviewed and a novel objective function, based on paint cure window, is proposed to be applied for designing radiation ovens. It has been shown that the proper definition of the objective function in such problems makes the mathematical model more robust and hence facilitates the convergence of the design iterations.

The computational results provide some information regarding the design space topology and show that the proposed objective function speeds up the convergence of the design procedure by an order of magnitude as compared to the currently used industrial‐standard objective function.

Determination of curing condition is an important requirement for designing a new oven or changing the working condition of an existing oven. In this research, a practical method is proposed for improving design procedure of cure ovens to make the method both time and cost efficient. The method is specifically implemented on paint cure ovens.

The quality of cured paint is usually a prominent issue that directly takes influence from the curing condition of ovens. For the complex geometries of curing body in commonly designed ovens, some areas are not properly cured or may be burnt. This issue is a significant defect in coating industry. Designing ovens with the proposed method in this paper guaranties the provided curing condition by the oven and therefore the curing quality.

Oblique impacts which occur in many situations in mineral industries leads to material removal and fail of mechanical parts. Studies will be helpful in optimal design to have minimum machine malfunctions.

In the present work, the Hertz-Di Maio Di Renzo nonlinear model of contact is used to simulate the impact phenomenon as a micro-sliding process. The modified Archard equation is used to evaluate wear over the impact. The wear coefficient is evaluated by a pin-disk machine. An impact-wear tester is used to validate the model results.

The measurements indicate an increase in surface hardness because of the several impacts. It is considered in the wear predictive model.

The model predictions compared with the experimental data, obtained from the impact-wear tester, show that the model well predicts the impact wear and can be used as a predictive tool to study the practical design problems and to explain some phenomena associated with the percussive impact.

The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model.

The model is developed with flight velocity, ambient temperature, liquid water content, median volumetric diameter and icing time taken as inputs and icing thickness given as outputs. To enhance the icing prediction accuracy, the model involves geometrical constraints into the loss function. Then the model is trained according to icing samples of 2D NACA0012 airfoil acquired by numerical simulation.

The results show that the involvement of geometrical constraints effectively enhances the prediction accuracy of ice shape, by weakening the appearance of fluctuation features. After training, the airfoil icing prediction model can be used for quickly predicting airfoil icing.

This work involves geometrical constraints in airfoil icing prediction model. The proposed model has reasonable capability in the fast assessment of aircraft icing.

The purpose of this study is to reduce the application of petroleum in automobile paint industry by replacing it with bio-based castor oil along with nano fillers to synthesize automobile base coat (BC).

Bio-based polyurethane (PU) coating applicable in automobile BC was synthesized by using modified castor oil incorporated with nano silica (NS) and titanium-based pigment particles. The influential characteristics of the coating was studied by carrying out cross-cut tape test, abrasion resistance, pencil hardness, lap-shear, thermo gravimetric analysis, differential scanning calorimetry, dynamic mechanical analysis and acid, alkali and oil resistance tests.

Incorporation of NS particles, along with titanium-based pigment particles in optimized ratio into the paint matrix, increases the mechanical, chemical and oil resistance properties and hydrophobicity of the BC, and the findings are compared with the petro-based commercial BC.

There is no significant improvement in thermal properties of the paint matrix, and it is less thermally stable than the commercial BC.

The paint developed through this study provides a simple and practical solution to reduce the petro-based feed-stock in automobile paint industry.

The current work which reports the use of ecofriendly PU BC for automobile paint applications is novel and findings of this study are original.

This paper aims to discuss the inverse problems that arise in various practical heat transfer processes. The purpose of this paper is to provide an identification method for predicting the internal boundary conditions for thermal analysis of mechanical structure. A few examples of heat transfer systems are given to illustrate the applicability of the method and the challenges that must be addressed in solving the inverse problem.

In this paper, the thermal network method and the finite difference method are used to model the two-dimensional heat conduction inverse problem of the tube structure, and the heat balance equation is arranged into an explicit form for heat load prediction. To solve the matrix ill-conditioned problem in the process of solving the inverse problem, a Tikhonov regularization parameter selection method based on the inverse computation-contrast-adjustment-approach was proposed.

The applicability of the proposed method is illustrated by numerical examples for different dynamically varying heat source functions. It is proved that the method can predict dynamic heat source with different complexity.

The modeling calculation method described in this paper can be used to predict the boundary conditions for the inner wall of the heat transfer tube, where the temperature sensor cannot be placed.

This paper presents a general method for the direct prediction of heat sources or boundary conditions in mechanical structure. It can directly obtain the time-varying heat flux load and thtemperature field of the machine structure.

The purpose of this paper is to incorporate prior knowledge in the artificial neural network (ANN) model for the prediction of continuous cooling transformation (CCT) diagram of steel, so that the model predictions become valid from materials engineering point of view.

Genetic algorithm (GA) is used in different ways for incorporating system knowledge during training the ANN. In case of training, the ANN in multi-objective optimization mode, with prediction error minimization as one objective and the system knowledge incorporation as the other, the generated Pareto solutions are different ANN models with better performance in at least one objective. To choose a single model for the prediction of steel transformation, different multi-criteria decision-making (MCDM) concepts are employed. To avoid the problem of choosing a single model from the non-dominated Pareto solutions, the training scheme also converted into a single objective optimization problem.

The prediction results of the models trained in multi and single objective optimization schemes are compared. It is seen that though conversion of the problem to a single objective optimization problem reduces the complexity, the models trained using multi-objective optimization are found to be better for predicting metallurgically justifiable result.

ANN is being used extensively in the complex materials systems like steel. Several works have been done to develop ANN models for the prediction of CCT diagram. But the present work proposes some methods to overcome the inherent problem of data-driven model, and make the prediction viable from the system knowledge.

The purpose of this paper is to improve the lattice Boltzmann method’s ability to simulate a microflow under constant heat flux.

Develop the thermal lattice Boltzmann method based on double population of hydrodynamic and thermal distribution functions.

The buoyancy forces, caused by gravity, can change the hydrodynamic properties of the flow. As a result, the gravity term was included in the Boltzmann equation as an external force, and the equations were rewritten under new conditions.

To the best of the authors’ knowledge, the current study is the first attempt to investigate mixed-convection heat transfer in an inclined microchannel in a slip flow regime.

The purpose of this study to investigate the high temperature tribological performances of CrN and CrAlN coatings on AISI H13 steel, which was beneficial to improve the wear resistance of hot work molds.

Arc ion plating was used to deposit the CrN and CrAlN coatings on AISI H13 steel, and the tribological performances of CrN and CrAlN coatings were evaluated using a ball-on-plate wear tester.

The average coefficients of friction and wear rate of CrAlN coating in the normal wear period are 0.33 and 5.34 × 10^–9 mm³·N^–1·mm^–1, respectively, which are lower than those of CrN coating, exhibiting that the outstanding friction reduction. The formations of Cr and Al oxides during the wear process are the main factor in enhancing the tribological performance of CrAlN coating.

CrN and CrAlN coatings were applied for hot work molds, and their tribological performances were comparatively investigated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0359/

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease which has become a public health concern, whose growing prevalence has been reported as around 33.9% in Iran. As oxidative stress plays a crucial role in the pathogenesis of NAFLD, antioxidant compounds such as quercetin could ameliorate the side effect of oxidative stress. The aim of the current study was to assess the effect of quercetin on lipid profile, liver enzymes and inflammatory indices in NAFLD patients.

In a randomized, double-blind, placebo-controlled trial conducted as a pilot study, 90 patients with NAFLD were supplemented with either a quercetin or a placebo capsule twice daily (500 mg) for 12 weeks. Both groups were advised to follow an energy-balanced diet with physical activity recommendations. Blood sample was obtained for laboratory parameters at baseline and the end of week 12.

At the end of the follow-up, quercetin group had significantly greater reduction in anthropometric parameters, cholesterol (−15 ± (−41, 0.00) in Q group versus −1± (−8, 2) in control group, p = 0.004), TG (−56.7 ± 22.7) in Q group versus −13.4 ± 27.7 in control group, p = 0.04), and tumor necrosis factor-α (TNF-α) (−49.5 ± (−99, 21) in Q group versus −5 ± (−21, 0.30) in the control group, p < 0.0001) compared to the control group. However, changes in fatty liver grade, liver enzymes, as well as high density lipoprotein-cholesterol and high-sensitivity C-reactive protein were not significantly different between the two groups.

To the best of the authors’ knowledge, this was the first study which assessed the effect of quercetin supplementation on liver enzymes, lipid profile and inflammatory indices of NAFLD patients as a double-blind placebo-controlled pilot study.