Acceleration of supersonic/hypersonic reactive CFD simulations via heterogeneous CPU-GPU supercomputing

The numerical study of reactive flows subjected to supersonic conditions is accelerated by the co-design of a novel strategy to integrate finite-rate chemistry by an adaptive multi-block ODE algebra solver for Graphical Processing Units (GPU), that is coupled to a parallel, shock-capturing Finite-Volume reactive flow solver running on CPUs. The resulting GPGPU solver is validated on Large Eddy Simulations (LES) of a scramjet configuration, whose experimental measurements are available from the literature. It is demonstrated that the proposed method significantly accelerates the solution of reactive CFD computations with Direct Integration of the finite-rate chemistry

HPC-enabling technologies for high-fidelity combustion simulations

With the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end. }The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the CoEC project, grant agreement No. 952181 and the CoE RAISE project grant agreement no. 951733.Peer ReviewedPostprint (published version

Lattice Boltzmann modeling for shallow water equations using high performance computing

The aim of this dissertation project is to extend the standard Lattice Boltzmann method (LBM) for shallow water flows in order to deal with three dimensional flow fields. The shallow water and mass transport equations have wide applications in ocean, coastal, and hydraulic engineering, which can benefit from the advantages of the LBM. The LBM has recently become an attractive numerical method to solve various fluid dynamics phenomena; however, it has not been extensively applied to modeling shallow water flow and mass transport. Only a few works can be found on improving the LBM for mass transport in shallow water flows and even fewer on extending it to model three dimensional shallow water flow fields. The application of the LBM to modeling the shallow water and mass transport equations has been limited because it is not clearly understood how the LBM solves the shallow water and mass transport equations. The project first focuses on studying the importance of choosing enhanced collision operators such as the multiple-relaxation-time (MRT) and two-relaxation-time (TRT) over the standard single-relaxation-time (SRT) in LBM. A (MRT) collision operator is chosen for the shallow water equations, while a (TRT) method is used for the advection-dispersion equation. Furthermore, two speed-of-sound techniques are introduced to account for heterogeneous and anisotropic dispersion coefficients. By selecting appropriate equilibrium distribution functions, the standard LBM is extended to solve three-dimensional wind-driven and density-driven circulation by introducing a multi-layer LB model. A MRT-LBM model is used to solve for each layer coupled by the vertical viscosity forcing term. To increase solution stability, an implicit step is suggested to obtain stratified flow velocities. Numerical examples are presented to verify the multi-layer LB model against analytical solutions. The model’s capability of calculating lateral and vertical distributions of the horizontal velocities is demonstrated for wind- and density- driven circulation over non-uniform bathymetry. The parallel performance of the LBM on central processing unit (CPU) based and graphics processing unit (GPU) based high performance computing (HPC) architectures is investigated showing attractive performance in relation to speedup and scalability

Multi-objective optimisation methods applied to aircraft techno-economic and environmental issues

Engineering methods that couple multi-objective optimisation (MOO) techniques with high fidelity computational tools are expected to minimise the environmental impact of aviation while increasing the growth, with the potential to reveal innovative solutions. In order to mitigate the compromise between computational efficiency and fidelity, these methods can be accelerated by harnessing the computational efficiency of Graphic Processor Units (GPUs). The aim of the research is to develop a family of engineering methods to support research in aviation with respect to the environmental and economic aspects. In order to reveal the non-dominated trade-o_, also known as Pareto Front(PF), among conflicting objectives, a MOO algorithm, called Multi-Objective Tabu Search 2 (MOTS2), is developed, benchmarked relative to state-of-the-art methods and accelerated by using GPUs. A prototype fluid solver based on GPU is also developed, so as to simulate the mixing capability of a microreactor that could potentially be used in fuel-saving technologies in aviation. By using the aforementioned methods, optimal aircraft trajectories in terms of flight time, fuel consumption and emissions are generated, and alternative designs of a microreactor are suggested, so as to assess the trade-offs between pressure losses and the micro-mixing capability. As a key contribution to knowledge, with reference to competitive optimisers and previous cases, the capabilities of the proposed methodology are illustrated in prototype applications of aircraft trajectory optimisation (ATO) and micromixing optimisation with 2 and 3 objectives, under operational and geometrical constraints, respectively. In the short-term, ATO ought to be applied to existing aircraft. In the long-term, improving the micro-mixing capability of a microreactor is expected to enable the use of hydrogen-based fuel. This methodology is also benchmarked and assessed relative to state-of-the-art techniques in ATO and micro-mixing optimisation with known and unknown trade-offs, whereas the former could only optimise 2 objectives and the latter could not exploit the computational efficiency of GPUs. The impact of deploying on GPUs a micro-mixing _ow solver, which accelerates the generation of trade-off against a reference study, and MOTS2, which illustrates the scalability potential, is assessed. With regard to standard analytical function test cases and verification cases in MOO, MOTS2 can handle the multi-modality of the trade-o_ of ZDT4, which is a MOO benchmark function with many local optima that presents a challenge for a state-of-the-art genetic algorithm for ATO, called NSGAMO, based on case studies in the public domain. However, MOTS2 demonstrated worse performance on ZDT3, which is a MOO benchmark function with a discontinuous trade-o_, for which NSGAMO successfully captured the target PF. Comparing their overall performance, if the shape of the PF is known, MOTS2 should be preferred in problems with multi-modal trade-offs, whereas NSGAMO should be employed in discontinuous PFs. The shape of the trade-o_ between the objectives in airfoil shape optimisation, ATO and micro-mixing optimisation was continuous. The weakness of MOTS2 to sufficiently capture the discontinuous PF of ZDT3 was not critical in the studied examples … [cont.]

A GPU Accelerated Discontinuous Galerkin Conservative Level Set Method for Simulating Atomization

abstract: This dissertation describes a process for interface capturing via an arbitrary-order, nearly quadrature free, discontinuous Galerkin (DG) scheme for the conservative level set method (Olsson et al., 2005, 2008). The DG numerical method is utilized to solve both advection and reinitialization, and executed on a refined level set grid (Herrmann, 2008) for effective use of processing power. Computation is executed in parallel utilizing both CPU and GPU architectures to make the method feasible at high order. Finally, a sparse data structure is implemented to take full advantage of parallelism on the GPU, where performance relies on well-managed memory operations. With solution variables projected into a kth order polynomial basis, a k+1 order convergence rate is found for both advection and reinitialization tests using the method of manufactured solutions. Other standard test cases, such as Zalesak's disk and deformation of columns and spheres in periodic vortices are also performed, showing several orders of magnitude improvement over traditional WENO level set methods. These tests also show the impact of reinitialization, which often increases shape and volume errors as a result of level set scalar trapping by normal vectors calculated from the local level set field. Accelerating advection via GPU hardware is found to provide a 30x speedup factor comparing a 2.0GHz Intel Xeon E5-2620 CPU in serial vs. a Nvidia Tesla K20 GPU, with speedup factors increasing with polynomial degree until shared memory is filled. A similar algorithm is implemented for reinitialization, which relies on heavier use of shared and global memory and as a result fills them more quickly and produces smaller speedups of 18x.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Hindered diffusion of nanoparticles

Brownian theory provides us with a powerful tool which can be used to delve into a microscopic world of molecules, cells and nanoparticles, that was originally presumed to be beyond our reach. Consequently, modeling the inherent dynamics of a system through a Brownian transport equation is of relevance to several real-word problems that involve nanoparticles including, the transport and mitigation of particulate matter (PM) generated though fossil fuel combustion and nanocarrier mediated drug delivery. Experimentally forecasting these systems is challenging due to the simultaneous prevalence of disparate length and time scales in them. Correspondingly, an in-silico driven assessment at such nanoscales can complement existing experimental techniques.Hence, in this thesis, a novel multiphase direct numerical simulation (DNS) framework is proposed to address the transport at these nanoscales. A coupled Langevin-immersed boundary method (LaIBM), that solves the fluid as an Eulerian field and the particle in a Lagrangian basis, is developed in this thesis. This framework is unique in its capability to include the resolved instantaneous hydrodynamics around the Brownian nanoparticle (without the need for an a-priori determination of the relevant mobility tensors) into the particle (Langevin) equation of motion. The performance of this technique is established and validated using well-established theoretical bases including the well-known theories for unbounded and hindered diffusion (wherein hydrodynamic interactions mediated by the fluid such as particle-particle or particle-wall influence the governing dynamics) of Brownian particles in a liquid. Correspondingly, it is shown that directional variations in mean-squared displacements, velocity auto-correlation functions and diffusivities of the Brownian nanoparticle correspond well with these standard theoretical bases. Moreover, since the resolved flow around the particle is inherently available in the proposed DNS method, the nature of the hydrodynamic resistances (on the particle) including the inherent anisotropies and correlated inter-particle interactions (mediated by the fluid) are further identified and shown to influence particle mobility. Furthermore, this framework is also extended towards Brownian transport in a rarefied gas using first order models to account for the non-continuum effects. Thus, the utility of this novel method is established in both colloids and aerosols, thereby aiding in modeling the transport of a fractal shaped PM (in the latter) and a spherical nanocarrier in a micro-channel (in the former)