ABSTRACTThis paper presents a comprehensive numerical investigation of the performance of pulsating heat pipes (PHPs) within nuclear reactor cooling systems. A volume of fluid (VOF) method was used to simulate the complex multiphase flow, providing detailed insights into fluid distribution, phase interactions, and temperature variations under different operating conditions. The simulations revealed distinct phase separation and convective flow patterns that enhance heat transfer efficiency, which is critical for optimizing thermal management in nuclear reactors. Additionally, artificial neural network (ANN) models were employed to predict volume fractions and wall temperatures, achieving high accuracy with R2 values of 0.99 and 0.98, respectively, and low mean absolute errors (MAE). The ANN models also reduced computational time by 90% compared to traditional numerical simulations. These findings highlight the potential of PHPs to improve heat transfer in nuclear systems and demonstrate the practicality of ANN models for real‐time thermal optimization. The research contributes to enhancing the safety and efficiency of nuclear reactor cooling systems, with broader implications for thermal management across various engineering applications.

ABSTRACTThe inspiration for this study originates from a recognized research gap within the broader collection of studies on nanofluids, with a specific focus on their interactions with different surfaces and boundary conditions (BCs). The primary purpose of this research is to use an artificial neural network to examine the combination of Alumina‐Engine oil‐based nanofluid flow subject to electro‐magnetohydrodynamic effects, within a porous medium, and over a stretching surface with an impermeable structure under convective BCs. The flow model incorporates Thermophoresis and Brownian motion directly from Buongiorno's model. Accounting for the porous medium's effect, the model integrates the Forchheimer number (depicting local inertia) and the porosity factor developed in response to the presence of the porous medium. The conversion of governing equations into non‐linear ordinary differential systems is achieved by implementing transformations. A highly non‐linear ordinary differential system's final system is solved using a numerical scheme (Runge–Kutta fourth‐order). Findings indicate that the porosity factor positively impacts the skin friction and the momentum boundary layer. The influence suggests an increment in the frictional force and a decline in the velocity profile. The volume fraction, Prandtl number, and magnetic number significantly impact the flow profiles. The skin friction data is tabulated with some physical justifications.

ABSTRACTWinglets are an extended angled or vertical projected at the wing tips used to reduce the drag encountered during the flight of an aircraft. The main aim of this research was to study the effects of winglets on NACA 4412 airfoil at 15° angle of attack. The simulation was done on the basis of the aerodynamic properties such as lift (CL), drag (CD), and lift/drag (CL/CD) ratio for both with and without the winglets at various cant angles. The designing was carried out in ANSYS Design Modeler for both with and without winglet. Further, the meshing part was again carried out in ANSYS Mesh. K‐Epsilon (two equation) turbulence model is used for the simulation at the inlet speed of 100 m/s, since it is the most common model used to simulate the mean flow characteristics for high turbulent conditions. Further, the cant angle has been optimized to get the maximum coefficient of lift using Nelder Mead, Genetic Algorithm, and Genetic Algorithm with ANN optimization techniques.

Abstract Turbulence significantly influences fluid flow topology optimization, and this has already been verified under the incompressible flow regime. However, the same cannot be said about the compressible flow regime, in which the density field now affects and couples all of the fluid flow and turbulence equations and makes obtaining the adjoint model, which is necessary for topology optimization, extremely difficult. Up to now, the turbulence phenomenon has still not been considered in compressible flow topology optimization, which is what is being proposed and analyzed here. Rather than being based in the Reynolds‐Averaged Navier–Stokes (RANS) equations which are defined only for incompressible flow, the equations are now based on the Favre‐Averaged Navier–Stokes (FANS) equations, which are the counterpart of the RANS equations for compressible flow and feature different dependencies and terms. The compressible turbulence model being considered is the compressible version of the Spalart–Allmaras model, which differs from the usual Spalart–Allmaras model, since now there are some new spatially varying density and specific heat terms that depend on the primal variables and that act over some of the turbulence terms of the overall model. The adjoint equations are obtained by using an automatic differentiation scheme through a coupled software platform. The optimization algorithm is IPOPT, and some examples are presented to show the effect of turbulence in the compressible flow topology optimization.

Abstract Granular flow problems characterized by large deformations are widespread in various applications, including coastal and geotechnical engineering. The paper deals with the application of a rigid‐perfectly plastic two‐phase model extended by the Drucker–Prager yield criterion to simulate granular media with a finite volume flow solver (FV). The model refers to the combination of a Bingham fluid and an Eulerian strain measure to assess the failure region of granular dam slides. A monolithic volume‐of‐fluid (VoF) method is used to distinguish between the air and granular phases, both governed by the incompressible Navier–Stokes equations. The numerical framework enables modeling of large displacements and arbitrary shapes for large‐scale applications. The displayed validation and verification focuses on the rigid‐perfectly plastic material model for noncohesive and cohesive materials with varying angles of repose. Results indicate a good agreement of the predicted soil surface and strain results with experimental and numerical data.

SummaryWeighted compact nonlinear schemes (WCNSs) are a popular family of high‐resolution shock‐capturing schemes for simulating compressible flows, of which the nonlinear interpolation procedure is dominant for the performance. In this work, a simplified weighting strategy is introduced for the nonlinear interpolation procedure. Firstly, an equivalent weighting formulation of WCNS is presented by explicitly including the whole‐point stencil into the set of candidate stencils. Secondly, motivated by the reorganization of WCNS, the WCNS‐CU6 scheme is achieved in a more straightforward way. Thirdly, by introducing a TENO selection procedure in the framework of WCNS‐CU6‐Simplified, a TCNS6‐Simplified scheme is proposed, the resolution of which is comparable with the excellent TENO6 scheme, while the computational cost is much lower. The simplified schemes exhibit more outstanding, at least comparable, fidelity than the original schemes, however, with superior characteristics in terms of efficiency and simplicity. A variety of benchmark test problems are studied to demonstrate the behaviour of the simplified weighting strategy.

Abstract In this article, the spatial local discontinuous Galerkin (LDG) approximation coupled with the temporal implicit‐explicit Runge–Kutta (RK) evolution for the micropolar fluid equations are adopted to construct the discretization method. To avoid the incompressibility constraint, the artificial compressibility strategy method is used to convert the micropolar fluid equations into the Cauchy–Kovalevskaja type equations. Then the LDG method based on the modal expansion and the implicit‐explicit RK method are properly combined to construct the expected third‐order method. Theoretically, the unconditionally stable of the fully discrete method are derived in multidimensions for triangular meshs. And the numerical experiments are given to verify the theoretical and effectiveness of the presented methods.

SummaryThis paper presents a new WENO‐Z scheme (WENO‐MZ) that incorporates a mapping function to enhance the weights of the less smooth sub‐stencils. The mapping function uses an innovative approach to modify the weight ratio of the less smooth sub‐stencil to the smooth stencil. In addition, we present the WENO‐MD scheme, which is a further development of the WENO‐MZ scheme that incorporates a modifier function. The WENO‐MD scheme shows improvements over the WENO‐MZ scheme by achieving an improved optimal order at critical points in higher orders and by increasing the proportion of less smooth sub‐stencils. Theoretical and numerical experiments have shown that the newly developed methods have improved shock capture capabilities and resolution compared to WENO‐JS, WENO‐Z, WENO‐M, WENO‐D, and WENO‐AIM, and also lead to significant computational time savings compared to WENO‐M and WENO‐AIM.

Abstract Researchers can incorporate uncertainties in computational fluid dynamics (CFD) that go beyond the inaccuracies caused by numerical discretization thanks to stochastic simulations. This study confirms the validity of current stochastic modeling tools by providing examples of stochastic simulations in conjunction with numerical solutions for incompressible flows. A numerical technique for solving deterministic and stochastic models is developed in this work. Our approach employs the Euler‐Maruyama method for stochastic modeling, representing a stochastic version of the third‐order explicit‐implicit scheme. For the deterministic model, the scheme is third‐order accurate. The consistency and stability of the constructed scheme are provided in the mean square sense. The scheme is the predictor–corrector type that is built on two time levels. Moreover, a mathematical model of the Casson nanofluid flow with variable thermal conductivity is given with the effect of the chemical reaction. The appropriate transformations are used to condense the set of partial differential equations (PDEs) down to one that is dimensionless. The scheme is applied for the deterministic and stochastic models of dimensionless flow problems. The velocity profile's deterministic and stochastic behavior are shown using contour plots. Results show that growing values of the thermal mixed convection parameter enhance the velocity profile. This article presents the progress made in stochastic computational fluid dynamics (SCFD) and highlights the energy‐related aspects of our discoveries. Our computational approach and stochastic modeling techniques provide new insights into the energy properties of Casson nanofluid flow, specifically regarding the variability of thermal conductivity and chemical processes. Our objective is to clarify the complex interaction of these factors on energy dynamics. This article presents a contemporary summary of the latest SCFD advancements. Additionally, it highlights potential directions for future research and unresolved issues that require attention from the members of the field of computational mathematics.