Mohammad Hassan Djavareshkian

To address the environmental degradation of solar panels, we developed a durable, multifunctional nanocomposite coating. The coating integrates TiO₂, ZnO, and SiO₂ nanoparticles into a silicone resin, which is applied using a scalable spray-deposition technique to achieve self-cleaning, UV protection, and thermal management. Key performance metrics were significantly improved: the coating created a superhydrophobic surface (Water Contact Angle > 145°), provided strong UV absorption (> 0.87 a.u.) while retaining high visible light transmittance (> 95 %), and increased thermal emissivity from 71 % to 84.9 %. In field tests, coated panels consistently outperformed uncoated panels, delivering up to 59 % higher power output and reducing surface temperatures by up to 8.6 %. The novelty lies in the strategic integration of three complementary nanoparticles to provide comprehensive environmental protection in a single, robust layer. This work demonstrates a practical, scalable solution to improve the efficiency and long-term durability of solar panels in real-world applications.

Axisymmetric flows present unique numerical challenges absent in planar configurations, with bow shocks positioned closer to bodies, compressing stabilizing subsonic layers and promoting carbuncle instabilities. This study conducts the first systematic evaluation of three AUSM-family schemes (AUSM[ ]UP, SLAU, AUSM[ ]M) across inviscid and viscous axisymmetric flows spanning transonic to supersonic regimes. Four benchmarks are examined: supersonic sphere ([ ]), convergent–divergent nozzle, mixed-compression inlet ([ ]) and transonic bump ([ ]). AUSM[ ]M emerges superior for high-speed axisymmetric applications, uniquely suppressing carbuncle instability on the sphere while achieving 65.8% improvement in post-shock Mach accuracy and 12% shock stand-off error vs AUSM[ ]UP’s 28.5%. While AUSM[ ]M incurs a higher computational cost in simpler flows (requiring 54% more CPU time for the sphere case), this expense is justified by its superior capability to suppress carbuncle instabilities and ensure numerical robustness, a trade-off that becomes advantageous in complex configurations where it achieves faster convergence. For the complex inlet, AUSM[ ]M converges 21.3% faster with optimal total-pressure recovery (3.7% error). It captures shock-induced separation most accurately, predicting separation length within 2.75% on the bump. Conversely, AUSM[ ]UP exhibits severe shock instabilities, while SLAU consistently fails to resolve strong shocks despite excellent low-Mach performance. Results establish AUSM[ ]M as the preferred scheme for axisymmetric simulations where shock resolution and numerical stability are paramount.

Unmanned aerial vehicles (UAVs) with flying wing designs offer unique advantages in terms of maneuverability and fuel efficiency, but achieving precise control over these tailless aircraft can be challenging. This study investigates the potential of notch leading edge (NLG) technology to improve the effectiveness of control surfaces in flying wing UAVs. NLG refers to a design feature where notches or depressions are strategically incorporated along the leading edge of the wings. This seemingly minor modification can significantly impact the aircraft′s aerodynamic performance and controllability. The present research leverages numerical methods to meticulously examine the influence of NLG placement on a crucial parameter: the roll torque coefficient. This coefficient quantifies the effectiveness of control surfaces, such as split drag rudder, in yawing moments, essential for maneuvering the aircraft. The investigation adopts a parametric approach, meticulously varying the dimensions and positions of the NLG along the wing span. This comprehensive analysis reveals a fascinating relationship between NLG placement and control surface efficacy. The study convincingly demonstrates that positioning the NLG closer to the wing root, where the wing meets the fuselage, yields superior results compared to placements near the wingtip.

The increasingly prominent function of unmanned aerial vehicles (UAVs) across fields ranging from logistics and surveillance to farming and research calls for ongoing innovation in their flight efficiency. This study probes the effect of making strategic alterations in the designs of UAVs’ wings, including the implementation of dogtooth shapes, on such key aerodynamic factors as lift, drag, and stalling behavior. This work concentrates on the range of the Reynolds number of 600,000 and lower. With the noted applications in mind, the research examines aerodynamic optimization strategies within the regime of the low Reynolds number. Additionally, the research examines the key function of control surfaces in maintaining the stability and controllability of UAVs across different flying conditions. The said control surfaces complement the optimized wing design in controlling flight with utmost precision, thus enabling safe and efficient flight. Through examination of the interaction between wing design adjustments and control surfaces, this work seeks to provide useful insight for UAV designers. The research findings have the potential for leading the way in the design of next‐generation UAVs with outstanding aerodynamic functionality, thus performing a greater array of operations with greater effectiveness.

Three preconditioning methods suggested by Eriksson, Choi, Merkel, and Turkel have been utilized within a 2D upwind Euler flow solver designed for unstructured grids. These strategies efficiently address the complexities of steady inviscid flows at low Mach numbers. The conservative formulations of the preconditioning matrices are rigorously derived. This implementation enables a more accurate evaluation of high‐gradient flows. Extensive simulations are conducted on various flow scenarios, including flows over the NACA0012 airfoil, a multi‐element three‐element airfoil, and a smooth bump with varying Mach numbers, to validate the effectiveness of the aforementioned preconditioning strategies. Compared to the non‐preconditioned approach, the results demonstrate significant accuracy and convergence speed improvements for all three preconditioning methods. These strategies exhibit remarkable efficiency for low Mach and incompressible flows. Among the three approaches, the Turkel preconditioner stands out with its optimal condition number, leading to superior performance. For low Mach numbers, convergence is accelerated by up to 88%, while at transonic speeds, it still achieves a notable 38% increase in convergence speed. Additionally, the preconditioning techniques preserve solution accuracy near challenging stagnation points. This study establishes a unified conservative framework for assessing preconditioning approaches and highlights their ability to resolve the complex fluid physics of low Mach number flows on unstructured grids. The findings underscore the significance of employing such strategies to enhance accuracy and computational efficiency in evaluating high‐gradient flows.

This paper introduces NAUSM+M+AUFS (New Improved Advection Upstream Splitting Method Plus Artificially Upstream Flux Vector Splitting), a novel hybrid computational scheme for simulating compressible flows on triangular grids. The AUSM+M (Improved Advection Upstream Splitting Method) method is enhanced through two key modifications to boost numerical stability and robustness in high Mach number and hypersonic flows. The first modification redefines the interfacial numerical sound velocity, reducing shock anomalies and improving shock-capturing by integrating velocity and characteristic sound speed parameters. The second modification addresses the insufficiency of the pressure flux dissipation term at supersonic speeds by introducing a formulation that increases dissipation proportionally to the Mach number, thereby enhancing performance in high-speed flows. These enhancements constitute the NAUSM+M method. The NAUSM+M+AUFS scheme combines the strengths of NAUSM+M and AUFS (Artificially Upstream Flux Vector Splitting) methods, particularly in overcoming the limitations of NAUSM+M in handling shock instabilities and the carbuncle phenomenon on structure triangular grids. A dynamic switching function adjusts the weighting between NAUSM+M and AUFS, optimizing accuracy and stability based on local flow conditions. Numerical tests demonstrate that NAUSM+M+AUFS significantly outperforms AUSM+M, NAUSM+M, and AUFS, effectively eliminating the carbuncle phenomenon and providing smooth shock wave contours. In steady flow analysis, the new hybrid method achieves convergence speeds comparable to AUFS and shows 15% to 45% superior convergence accelerating than AUSM+M, depending on the convergence rate. In addition, in steady flow analysis, the accuracy of NAUSM+M+AUFS is 46% better than that of AUFS. This approach represents a significant advancement, offering a robust, accurate, and efficient solution for high-speed aerodynamic simulations, with broad applicability across various compressible flow challenges.

This paper introduces a novel platform that integrates three preconditioning matrices based on conservative variables: the Turkel, Choi–Merkle and Onur matrices. The platform aims to compare these matrices in terms of accuracy and robustness by investigating their performance in solving three distinct and challenging high-gradient laminar flow problems: (i) Bi-Plane NACA0012 airfoil, (ii) lid-driven flow in a square cavity and (iii) flow in a planar T-junction. These problems serve as new and challenging test cases to accurately determine the abilities of the preconditioning matrices. The preconditioning matrices are applied to evaluate the numerical solutions, and their performance in complex flow fields is assessed in terms of accuracy and efficiency. By solving these flow problems, the effectiveness of the preconditioning matrices is thoroughly analyzed. By integrating these preconditioning matrices into a single platform, this paper significantly contributes to the field. The approach enables a direct and meaningful comparison of the performance of the Turkel, Choi–Merkle and Onur matrices in solving these new and challenging laminar flow problems. While all three matrices demonstrate comparable accuracy in predicting flow characteristics like pressure coefficients, Turkel’s method shows superior convergence acceleration across the almost test cases due to alpha parameter and modifications of the momentum equations. In the external flow case, Turkel converges 22–53% faster than the other matrices by adjusting momentum terms with an alpha parameter. For the internal lid-driven cavity case, Turkel again accelerates convergence up to 38% over Choi–Merkle as Reynolds and Mach numbers increase. However, Onur’s method stalls at high Reynolds/Mach numbers. At low values of Reynolds and Mach numbers, Onur reduces computational cost by 17%. Finally, for the T-junction case, Turkel and Choi–Merkle perform almost identically, decreasing CPU time by 55% vs Onur, which lacks convergence robustness. While the preconditioning matrices have similar accuracy, Turkel offers the best convergence improvement by accounting for momentum effects. Onur works well at low Reynolds/Mach numbers but shows limitations at higher values. The selection of the optimal preconditioning matrix should be based on whether momentum or viscosity dominates in the flow field, as well as the intensity of the viscosity gradient rate.

This study presents an innovative implicit–explicit time-stepping algorithm based on a first-order temporal accuracy method, addressing challenges in simulating all-regimes of fluid flows. The algorithm's primary focus is on mitigating stiffness inherent in the density-based “Roe” method, pivotal in finite volume approaches employing unstructured meshes. The objective is to comprehensively evaluate the method's efficiency and robustness, contrasting it with the explicit fourth-order Runge–Kutta method. This evaluation encompasses simulations across a broad spectrum of Mach numbers, including scenarios of incompressible and compressible flow. The scenarios investigated include the Sod Riemann problem to simulate compressible Euler equations, revealing the algorithm's versatility, and the low Mach number Riemann problem to analyze system stiffness in incompressible flow. Additionally, Navier–Stokes equations are employed to study viscous and unsteady flow patterns around stationary cylinders. The study scrutinizes two time-stepping algorithms, emphasizing accuracy, stability, and computational efficiency. The results demonstrate the implicit–explicit Runge–Kutta algorithm's superior accuracy in predicting flow discontinuities in compressible flow. This advantage arises from the semi-implicit nature of the equations, reducing numerical errors. The algorithm significantly enhances accuracy and stability for low Mach number Riemann problems, addressing increasing stiffness as Mach numbers decrease. Notably, the algorithm optimizes computational efficiency for both low Mach number Riemann problems and viscous flows around cylinders, reducing computational costs by 38%–68%. The investigation extends to a two dimensional hypersonic inviscid flow over cylinder and double Mach reflection case, showcasing the method's proficiency in capturing complex and hypersonic flow behavior. Overall, this research advances the understanding of time discretization techniques in computational fluid dynamics, offering an effective approach for handling a wide range of Mach numbers while improving accuracy and efficiency.

In the present study, the effects of the wing fence on the wing tip vortices and control surfaces located at the tip of the wing in a flying wing aircraft have been investigated using a numerical method. For the size of the fences, the average dimensions extracted from the wing tip vortices at different angles of attack are used. The basic determining parameter is the rolling torque coefficient, which is tried to be shown by employing a parametric study of the flow behavior in different situations of fence placement. These effects on the rolling torque of the aircraft are measured due to the presence of the split drag rudder control system. In this study, the fences were installed at three different heights and three different positions along the length of the wing, which were investigated at angles of attack of 7 to 16 degrees. The next stage of the research is to design the dimensions of the fence using the single-objective optimization method (a method to find the best solution for a problem with a specific goal). The designing of the fences at three points based on the dimensions of the wing tip vortex is carried out with the computational fluid dynamics (CFD) method (CFD is a computational method that uses physical laws to predict the behavior of fluids.). The aim of this research is to achieve the best design that converges to an optimal solution with minimum time and cost (CFD solution is long). However, CFD analysis requires a lot of computational time. To address this challenge, we employed a hybrid learning model comprising the radial basis function (RBF), a type of artificial neural network, and Kriging, a Gaussian process-based interpolation technique. The dataset for training the hybrid model was obtained from numerical solutions of CFD simulations involving a fence placed at various locations on the wing. Additionally, a genetic algorithm was employed as the optimization method in all instances where it was required. Using the power of machine learning techniques helped us identify the optimal placement of the fence to prevent it from being engulfed by the vortex and to optimize the utilization of the split drag system, yielding significant improvements.