Utilizing GPUs to Accelerate Turbomachinery CFD Codes

GPU computing has established itself as a way to accelerate parallel codes in the high performance computing world. This work focuses on speeding up APNASA, a legacy CFD code used at NASA Glenn Research Center, while also drawing conclusions about the nature of GPU computing and the requirements to make GPGPU worthwhile on legacy codes. Rewriting and restructuring of the source code was avoided to limit the introduction of new bugs. The code was profiled and investigated for parallelization potential, then OpenACC directives were used to indicate parallel parts of the code. The use of OpenACC directives was not able to reduce the runtime of APNASA on either the NVIDIA Tesla discrete graphics card, or the AMD accelerated processing unit. Additionally, it was found that in order to justify the use of GPGPU, the amount of parallel work being done within a kernel would have to greatly exceed the work being done by any one portion of the APNASA code. It was determined that in order for an application like APNASA to be accelerated on the GPU, it should not be modular in nature, and the parallel portions of the code must contain a large portion of the code's computation time

Production Level CFD Code Acceleration for Hybrid Many-Core Architectures

In this work, a novel graphics processing unit (GPU) distributed sharing model for hybrid many-core architectures is introduced and employed in the acceleration of a production-level computational fluid dynamics (CFD) code. The latest generation graphics hardware allows multiple processor cores to simultaneously share a single GPU through concurrent kernel execution. This feature has allowed the NASA FUN3D code to be accelerated in parallel with up to four processor cores sharing a single GPU. For codes to scale and fully use resources on these and the next generation machines, codes will need to employ some type of GPU sharing model, as presented in this work. Findings include the effects of GPU sharing on overall performance. A discussion of the inherent challenges that parallel unstructured CFD codes face in accelerator-based computing environments is included, with considerations for future generation architectures. This work was completed by the author in August 2010, and reflects the analysis and results of the time

Semi‐automatic porting of a large‐scale CFD code to multi‐graphics processing unit clusters

A typical large‐scale CFD code based on adaptive, edge‐based finite‐element formulations for the solution of compressible and incompressible flow is taken as a test bed to port such codes to graphics hardware (graphics processing units, GPUs) using semi‐automatic techniques. In previous work, a GPU version of this code was presented, in which, for many run configurations, all mesh‐sized loops required throughout time stepping were ported. This approach simultaneously achieves the fine‐grained parallelism required to fully exploit the capabilities of many‐core GPUs, completely avoids the crippling bottleneck of GPU–CPU data transfer, and uses a transposed memory layout to meet the distinct memory access requirements posed by GPUs. The present work describes the next step of this porting effort, namely to integrate GPU‐based, fine‐grained parallelism with Message‐Passing‐Interface‐based, coarse‐grained parallelism, in order to achieve a code capable of running on multi‐GPU clusters. This is carried out in a semi‐automated fashion: the existing Fortran–Message Passing Interface code is preserved, with the translator inserting data transfer calls as required. Performance benchmarks indicate up to a factor of 2 performance advantage of the NVIDIA Tesla M2050 GPU (Santa Clara, CA, USA) over the six‐core Intel Xeon X5670 CPU (Santa Clara, CA, USA), for certain run configurations. In addition, good scalability is observed when running across multiple GPUs. The approach should be of general interest, as how best to run on GPUs is being presently considered for many so‐called legacy codes

Semi‐automatic porting of a large‐scale Fortran CFD code to GPUs

The development of automatic techniques to port a substantial portion of FEFLO, a general‐purpose legacy CFD code operating on unstructured grids, to run on GPUs is described. FEFLO is a typical adaptive, edge‐based finite element code for the solution of compressible and incompressible flows, which is primarily written in Fortran 77 and has previously been ported to vector, shared memory parallel and distributed memory parallel machines. Owing to the large size of FEFLO and the likelihood of human error in porting, as well as the desire for continued development within a single codebase, a specialized Python script, based on FParser (Int. J. Comput. Sci. Eng. 2009; 4 :296–305), was written to perform automated translation from the OpenMP‐parallelized edge and point loops to GPU kernels implemented in CUDA, along with GPU memory management. The results of verification benchmarks and performance indicate that performances achieved by such a translator can rival those of codes rewritten by specialists. The approach should be of general interest, as how best to run on GPUs is being presently considered for many so‐called legacy codes

Theoretical and numerical methods for predicting ship-wave impact generated sea spray

Spray generated by ships traveling in cold oceans often leads to topside ice accretion, which can be dangerous to vessels. To develop a full methodology of goal based design for ice accretion there are two critical knowledge gaps, both of which are complex to close, and require new methods and techniques. One is a comparison of ice accretion rates for different structures in the same icing conditions. The second knowledge gap is validation data that compares predicted ice growth rates for all types of ship and offshore structures against observed values. Estimation of the spray flux is a first step in predicting icing accumulation. The amount of spray water, the duration of exposure to the spray, and the frequency at which the spray is generated are all important parameters in estimating the spray flux. Most existing spray flux formulae are based on field observations from small fishing vessels. They consider meteorological and oceanographic parameters but neglect the vessel behavior. Ship heave and pitch motions, together with ship speed and heading relative to the waves, determine the frequency of spray events. Thus the existing formulae are not generally applicable to different sizes and types of vessels. The current study develops simple methods to quantify spray properties in terms that can be applied to vessels of any size or type, which consequently addresses the first knowledge gap. Formulae to estimate water content and spray duration are derived based on principles of energy conservation and dimensional analysis. To estimate spray frequency considering ship motions, a theoretical model is proposed. The model inputs are restricted to ship’s principal particulars, operating conditions, and environmental conditions. Wave-induced motions are estimated using semi-empirical analytical expressions. A novel spray threshold is developed to separate deck wetness frequency from spray frequency. Spray flux estimates are validated against full-scale field measurements available in the open literature and reasonable agreement was obtained. The complex interaction between the structure and a multi-phase fluid, including spray are not fully understood. Limitations of field measurements and model experiments encourage the use of numerical simulation to understand the formation of such spray. In this study, full-scale simulation models of wave-generated sea spray are also developed by implementing a smooth particle hydrodynamics (SPH) method. A three-dimensional (3D) numerical wave tank equipped with a flap-type wave maker and a wave absorber is created to produce regular waves of various heights and steepness. A full-scale medium-size fishing vessel (MFV) is modeled to impact waves in head sea conditions at various forward speeds. Moving ship dynamics with three degree-of-freedom (3-DOF) in waves are resolved instead of mimicking a relative ship speed. The resultant spray water amount is measured using a numerical collection box and compared against field measurements and the theoretical model, where a reasonable agreement is found. The model is able to distinguish between green water and spray water. A multi-phase two-dimensional (2D) simulation is also performed that demonstrates the role of wind in the fragmentation of water sheets into droplets and their distributions over the deck. The simulation results indicate energy released from a surging ship significantly contributes to the generation of spray. An investigation was also performed to explore means to speed up the computationally intensive SPH simulations. A comparison with a traditional CPU (central processing unit) clusters with GPU (graphics processing unit) was performed where GPUs demonstrated faster executions. All the SPH simulations were run on GPUs

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

The progress made in accelerating simulations of fluid flow using GPUs, and the challenges that remain, are surveyed. The review first provides an introduction to GPU computing and programming, and discusses various considerations for improved performance. Case studies comparing the performance of CPU- and GPUbased solvers for the Laplace and incompressible Navier–Stokes equations are performed in order to demonstrate the potential improvement even with simple codes. Recent efforts to accelerate CFD simulations using GPUs are reviewed for laminar, turbulent, and reactive flow solvers. Also, GPU implementations of the lattice Boltzmann method are reviewed. Finally, recommendations for implementing CFD codes on GPUs are given and remaining challenges are discussed, such as the need to develop new strategies and redesign algorithms to enable GPU acceleration.Keywords: Graphics processing unit (GPU), Reactive flow, Computational fluid dynamics (CFD), Laminar flows, Turbulent flow, CUD

Development of an Unsteady Aeroelastic Solver for the Analysis of Modern Turbomachinery Designs

Developers of aircraft gas turbine engines continually strive for greater efficiency and higher thrust-to-weight ratio designs. To meet these goals, advanced designs generally feature thin, low aspect airfoils, which offer increased performance but are highly susceptible to flow-induced vibrations. As a result, High Cycle Fatigue (HCF) has become a universal problem throughout the gas turbine industry and unsteady aeroelastic computational models are needed to predict and prevent these problems in modern turbomachinery designs. This research presents the development of a 3D unsteady aeroelastic solver for turbomachinery applications. To accomplish this, a well established turbomachinery Computational Fluid Dynamics (CFD) code called Corsair is loosely coupled to the commercial Computational Structural Solver (CSD) Ansys® through the use of a Fluid Structure Interaction (FSI) module. Significant modifications are made to Corsair to handle the integration of the FSI module and improve overall performance. To properly account for fluid grid deformations dictated by the FSI module, temporal based coordinate transformation metrics are incorporated into Corsair. Wall functions with user specified surface roughness are also added to reduce fluid grid density requirements near solid surfaces. To increase overall performance and ease of future modifications to the source code, Corsair is rewritten in Fortran 90 with an emphasis on reducing memory usage and improving source code readability and structure. As part of this effort, the shared memory data structure of Corsair is replaced with a distributed model. Domain decomposition of individual grids in the radial direction is also incorporated into Corsair for additional parallelization, along with a utility to automate this process in an optimal manner based on user input. This additional parallelization helps offset the inability to use the fine grain mp-threads parallelization in the original code on non-distributed memory architectures such as the PC Beowulf cluster used for this research. Conversion routines and utilities are created to handle differences in grid formats between Corsair and the FSI module. The resulting aeroelastic solver is tested using two simplified configurations. First, the well understood case of a flexible cylinder in cross flow is studied with the natural frequency of the cylinder set to the shedding frequency of the Von Karman streets. The cylinder is self excited and thus demonstrates the correct exchange of energy between the fluid and structural models. The second test case is based on the fourth standard configuration and demonstrates the ability of the solver to predict the dominant vibrational modes of an aeroelastic turbomachinery blade. For this case, a single blade from the fourth standard configuration is subjected to a step function from zero loading to the converged flow solution loading in order to excite the structural modes of the blade. These modes are then compared to those obtained from an in vacuo Ansys® analysis with good agreement between the two

New strategies for the aerodynamic design optimization of aeronautical configurations through soft-computing techniques

Premio Extraordinario de Doctorado de la UAH en 2013Lozano Rodríguez, Carlos, codir.This thesis deals with the improvement of the optimization process in the aerodynamic design of aeronautical configurations. Nowadays, this topic is of great importance in order to allow the European aeronautical industry to reduce their development and operational costs, decrease the time-to-market for new aircraft, improve the quality of their products and therefore maintain their competitiveness. Within this thesis, a study of the state-of-the-art of the aerodynamic optimization tools has been performed, and several contributions have been proposed at different levels: -One of the main drawbacks for an industrial application of aerodynamic optimization tools is the huge requirement of computational resources, in particular, for complex optimization problems, current methodological approaches would need more than a year to obtain an optimized aircraft. For this reason, one proposed contribution of this work is focused on reducing the computational cost by the use of different techniques as surrogate modelling, control theory, as well as other more software-related techniques as code optimization and proper domain parallelization, all with the goal of decreasing the cost of the aerodynamic design process. -Other contribution is related to the consideration of the design process as a global optimization problem, and, more specifically, the use of evolutionary algorithms (EAs) to perform a preliminary broad exploration of the design space, due to their ability to obtain global optima. Regarding this, EAs have been hybridized with metamodels (or surrogate models), in order to substitute expensive CFD simulations. In this thesis, an innovative approach for the global aerodynamic optimization of aeronautical configurations is proposed, consisting of an Evolutionary Programming algorithm hybridized with a Support Vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size, geometry parameterization sensitivity and techniques for design of experiments are discussed and the potential of the proposed approach to achieve innovative shapes that would not be achieved with traditional methods is assessed. -Then, after a broad exploration of the design space, the optimization process is continued with local gradient-based optimization techniques for a finer improvement of the geometry. Here, an automated optimization framework is presented to address aerodynamic shape design problems. Key aspects of this framework include the use of the adjoint methodology to make the computational requirements independent of the number of design variables, and Computer Aided Design (CAD)-based shape parameterization, which uses the flexibility of Non-Uniform Rational B-Splines (NURBS) to handle complex configurations. The mentioned approach is applied to the optimization of several test cases and the improvements of the proposed strategy and its ability to achieve efficient shapes will complete this study

New strategies for the aerodynamic design optimization of aeronautical configurations through soft-computing techniques

Premio Extraordinario de Doctorado de la UAH en 2013Lozano Rodríguez, Carlos, codir.This thesis deals with the improvement of the optimization process in the aerodynamic design of aeronautical configurations. Nowadays, this topic is of great importance in order to allow the European aeronautical industry to reduce their development and operational costs, decrease the time-to-market for new aircraft, improve the quality of their products and therefore maintain their competitiveness. Within this thesis, a study of the state-of-the-art of the aerodynamic optimization tools has been performed, and several contributions have been proposed at different levels: -One of the main drawbacks for an industrial application of aerodynamic optimization tools is the huge requirement of computational resources, in particular, for complex optimization problems, current methodological approaches would need more than a year to obtain an optimized aircraft. For this reason, one proposed contribution of this work is focused on reducing the computational cost by the use of different techniques as surrogate modelling, control theory, as well as other more software-related techniques as code optimization and proper domain parallelization, all with the goal of decreasing the cost of the aerodynamic design process. -Other contribution is related to the consideration of the design process as a global optimization problem, and, more specifically, the use of evolutionary algorithms (EAs) to perform a preliminary broad exploration of the design space, due to their ability to obtain global optima. Regarding this, EAs have been hybridized with metamodels (or surrogate models), in order to substitute expensive CFD simulations. In this thesis, an innovative approach for the global aerodynamic optimization of aeronautical configurations is proposed, consisting of an Evolutionary Programming algorithm hybridized with a Support Vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size, geometry parameterization sensitivity and techniques for design of experiments are discussed and the potential of the proposed approach to achieve innovative shapes that would not be achieved with traditional methods is assessed. -Then, after a broad exploration of the design space, the optimization process is continued with local gradient-based optimization techniques for a finer improvement of the geometry. Here, an automated optimization framework is presented to address aerodynamic shape design problems. Key aspects of this framework include the use of the adjoint methodology to make the computational requirements independent of the number of design variables, and Computer Aided Design (CAD)-based shape parameterization, which uses the flexibility of Non-Uniform Rational B-Splines (NURBS) to handle complex configurations. The mentioned approach is applied to the optimization of several test cases and the improvements of the proposed strategy and its ability to achieve efficient shapes will complete this study