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The Direct Simulation Monte Carlo (DSMC) method, introduced by Graeme Bird over five decades ago, has become a crucial statistical particle-based technique for simulating low-density gas flows. Its widespread acceptance stems from rigorous validation against experimental data. This study focuses on four validation test cases known for their complex shock–boundary and shock–shock interactions: (a) a flat plate in hypersonic flow, (b) a Mach 20.2 flow over a 70-degree interplanetary probe, (c) a hypersonic flow around a flared cylinder, and (d) a hypersonic flow around a biconic. Part A of this paper covers the first two cases, while Part B will discuss the remaining cases. These scenarios have been extensively used by researchers to validate prominent parallel DSMC solvers, due to the challenging nature of the flow features involved. The validation requires meticulous selection of simulation parameters, including particle count, grid density, and time steps. This work evaluates the SPARTA (Stochastic Parallel Rarefied-gas Time-Accurate Analyzer) kernel's accuracy against these test cases, highlighting its parallel processing capability via domain decomposition and MPI communication. This method promises substantial improvements in computational efficiency and accuracy for complex hypersonic vehicle simulations. [ABSTRACT FROM AUTHOR]

In the past decade, there has been significant progress in the development, testing, and production of vehicles capable of achieving hypersonic speeds. This area of research has garnered immense interest due to the transformative potential of these vehicles. Part B of this paper initially explores the current state of hypersonic vehicle development and deployment, as well as the propulsion technologies involved. At next, two additional test cases, used for the evaluation of DSMC code SPARTA are analyzed: a Mach 12.4 flow over a flared cylinder and a Mach 15.6 flow over a 25/55-degree biconic. These (2D-axisymmetric) test cases have been selected as they are tailored for the assessment of flow and heat transfer characteristics of present and future hypersonic vehicles, for both their external and internal aerodynamics. These test cases exhibit (in a larger range compared to the test cases presented in Part A of this work) shock–boundary and shock–shock interactions, which can provide a fair assessment of the SPARTA DSMC solver accuracy, in flow conditions which characterize hypersonic flight and can adequately test its ability to qualitatively and quantitatively capture the complicated physics behind such demanding flows. This validation campaign of SPARTA provided valuable experience for the correct tuning of the various parameters of the solver, especially for the use of adequate computational grids, thus enabling its subsequent application to more complicated three-dimensional test cases of hypersonic vehicles. [ABSTRACT FROM AUTHOR]

This work investigates the aerodynamic performance of a hypersonic waverider designed to operate at Mach 7, focusing on optimizing its design through advanced computational methods. Utilizing the Direct Simulation Monte Carlo (DSMC) method, the three-dimensional flow field around the specifically designed waverider was simulated to understand the shock wave interactions and thermal dynamics at an altitude of 90 km. The computational approach included detailed meshing around the vehicle's critical leading edges and the use of three-dimensional iso-surfaces of the Q-criterion to map out the shock and vortex structures accurately. Additional simulation results demonstrate that the waverider achieved a lift–drag ratio of 2.18, confirming efficient aerodynamic performance at a zero-degree angle of attack. The study's findings contribute to the broader understanding of hypersonic flight dynamics, highlighting the importance of precise computational modeling in developing vehicles capable of operating effectively in near-space environments. [ABSTRACT FROM AUTHOR]

An evolutionary algorithm based framework, a combination of modified breeder genetic algorithms incorporating characteristics of classic genetic algorithms, is utilized to design an offline/online path planner for unmanned aerial vehicles (UAVs) autonomous navigation. The path planner calculates a curved path line with desired characteristics in a three--dimensional (3-D) rough terrain environment, represented using B-Spline curves, with the coordinates of its control points being the evolutionary algorithm artificial chromosome genes. Given a 3-D rough environment and assuming flight envelope restrictions, two problems are solved: i) UAV navigation using an offline planner in a known environment, and, ii) UAV navigation using an online planner in a completely unknown environment. The offline planner produces a single B-Spline curve that connects the starting and target points with a predefined initial direction. The online planner, based on the offline one, is given on-board radar readings which gradually produces a smooth 3-D trajectory aiming at reaching a predetermined target in an unknown environment; the produced trajectory consists of smaller B-Spline curves smoothly connected with each other. Both planners have been tested under different scenarios, and they have been proven effective in guiding an UAV to its final destination, providing near-optimal curved paths quickly and efficiently. Index Terms--3-D path planning, B-splines, evolutionary algorithms, navigation, UAV.