Aerodynamic optimisation of a hypersonic reentry vehicle based on solution of the Boltzmann–BGK equation and evolutionary optimisation

Evans, Ben; Walton, Sean

doi:10.1016/j.apm.2017.07.024

Cited by 13 publications

(17 citation statements)

References 27 publications

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“…The simplification made by Bhatnager, Gross and Krook (BGK) in the Boltzmann equation assumes that the departures of the flow from thermodynamic equilibrium are small. A further assumption deduced from their conclusion is that the effect of the molecular collisions in a non-equilibrium fluid, forces the non-equilibrium molecular velocity distribution back toward a state of equilibrium, at a rate proportional to the molecular collision frequency [10]. The Boltzmann-BGK equation may be written as…”

Section: Boltzmann-bgk Equationmentioning

confidence: 99%

“…The reader will now be given a brief explanation of some of the key aspects of the Boltzmann-BGK solver developed by Evans, for a more detailed explanation, see [9,10].…”

Section: Boltzmann-bgk Solvermentioning

confidence: 99%

“…The p-space domain is discretised into a linear, discontinuous, triangle element, unstructured mesh ( Figure 2). There are multiple advantages to using a discretisation technique of this nature [9,10] but it is specifically utilised for rarefied hypersonic flow because of the shock capturing properties. For further discretisation techniques, the reader is directed to [28,29].…”

Section: Physical Space Discretisationmentioning

confidence: 99%

“…Due to the molecular variables of a high speed and altitude fluid differing from continuum molecular variables, the use of a more fundamental physical description must be adopted. The Boltzmann equation has been utilised in several research studies [9][10][11][12][13][14][15][16][17], all highlighting the potential of a novel Boltzmann solver due to its accurate use in both rarefied atmospheric conditions at hypersonic speed and more typical, low-speed atmospheric conditions. However, to the authors knowledge, no other Boltzmann-BGK based hypersonic CFD solver has been utilised for space shuttle geometry optimisation except that of Evans, et al [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Computational Aerodynamic Optimisation of a 2D Space-Shuttle Geometry

Thomas¹,

Benjamin²

2019

Int J Astronaut Aeronautical Eng

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The need for cost minimisation in astronautical engineering, to help make human kind an interplanetary species, is not just limited to the flight and manufacturing of spacecraft. Hypersonic aerodynamic optimisation has been an expensive and time-consuming necessity since the beginning of the Apollo missions. A Boltzmann-BGK solver developed at Swansea University is utilised in this research. Firstly, a study was undertaken to determine how the results of an arbitrary simulation would vary depending on the number of nodes in the velocity space mesh, with the higher nodal number producing a more realistic temperature distribution. The best nodal configuration found was then used to find the best nose geometry for a space shuttle during hypersonic re-entry and ascent. An extension on the geometric variables (an increase in the space-shuttle length) was then simulated to evaluate how this changed drag results compared to an identical nose geometry with a shorter length; the results were found to be identical. Additionally, a study was undertaken to evaluate the effect of extending the outer boundary of the simulation from a half boundary to a full boundary encompassing the entire geometry, focusing on the temperature results, with a focus on the rear of the space shuttle. The full boundary extension was found to only change the temperature distribution at the rear of the geometry, the front portion of the geometry had an identical temperature distribution to the previous half-boundary simulation. Finally, the rear of the fully extended boundary simulation was altered to have a rounded rear, this was found to reduce the temperature distribution protruding from the rear.

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Section: Boltzmann-bgk Equationmentioning

confidence: 99%

“…The reader will now be given a brief explanation of some of the key aspects of the Boltzmann-BGK solver developed by Evans, for a more detailed explanation, see [9,10].…”

Section: Boltzmann-bgk Solvermentioning

confidence: 99%

Section: Physical Space Discretisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computational Aerodynamic Optimisation of a 2D Space-Shuttle Geometry

Thomas¹,

Benjamin²

2019

Int J Astronaut Aeronautical Eng

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“…Evolutionary algorithm use has been steadily increasing in the number of published papers corresponding to an increasing number of applications over the past 20 years [1][2][3][4][5][6][7][8]. Originating as an alternative to traditional mathematical optimization techniques, the techniques now span across almost every discipline to include data compression, traveling salesmen, image processing, and more importantly for spacecraft: control theory [9][10][11][12][13], system identification [14 -19], and trajectory optimization [20][21][22][23][24][25]. Randomly searching a solution space to perform a global optimization routine can be computationally expensive and time consuming.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Cooper¹,

Smeresky²

2020

Advances in Spacecraft Attitude Control

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Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification.

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Evolutionary Computation for Adaptive Quantum Device Design

et al. 2021

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As noisy intermediate‐scale quantum (NISQ) devices grow in number of qubits, determining good or even adequate parameter configurations for a given application, or for device calibration, becomes a cumbersome task. An evolutionary algorithm is presented here which allows for the automatic tuning of the parameters of any arrangement of coupled qubits, to perform a given task with high fidelity. The algorithm's use is exemplified with the generation of schemes for the distribution of quantum states and the design of multi‐qubit gates. The algorithm is demonstrated to converge very rapidly, yielding unforeseeable designs of quantum devices that perform their required tasks with excellent fidelities. Given these promising results, practical scalability, and application versatility, the approach has the potential to become a powerful technique to aid the design and calibration of NISQ devices.

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Aerodynamic optimisation of a hypersonic reentry vehicle based on solution of the Boltzmann–BGK equation and evolutionary optimisation

Cited by 13 publications

References 27 publications

Computational Aerodynamic Optimisation of a 2D Space-Shuttle Geometry

Computational Aerodynamic Optimisation of a 2D Space-Shuttle Geometry

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Evolutionary Computation for Adaptive Quantum Device Design

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