GPU-based Parallel Computing Method For Contact And Impact Problems Of Automotive Body

Finite element (FE) simulation of contact and impact process is an important partof the automotive CAE technology. It is widely applied to engineering problems, suchas car crash simulation and sheet metal forming simulation. This kind of simulationusually involves material nonlinearity, geometric nonlinearity and nonlinear boundaryconditions. Due to these three kinds of nonlinearity, the FE analysis of contact andimpact problems faced with enormous computations and low computing efficiency.Therefore, there is a very strong demand for parallel computing in practicalapplications. Nowadays, the most common parallel computing methods are based onthe coarse-grained parallel domain decomposition strategy, and use CPU-basedcomputer network as the computing hardware. In these traditional parallel computingmethods, computation efficiency is directly related to the number of computing nodes.Furthermore, in practice, more complex programming and expensive hardware arerequired for more computing nodes. Therefore, they are not cost effective for bothindividual and business.Modern graphics processor unit (GPU) has developed into a kind of multi-coreprocessors with highly internal parallelism, and its float point processing ability ismuch higher than CPUs at the same period. In the meantime, the appearance ofprogrammable shaders brings several general computing characteristics for GPU.Nowadays, general-purpose computing on GPU (GPGPU) becomes to a novel andeffective methods for general large data processing and numerical simulations. Theearly GPGPU needed to use high-level shading languages to code, such as Cg. Severalresearchers have tried to use early GPGPU to improve computing efficiency, but theseGPU-based FE codes cannot meet the demands of requirements in accuracy andefficiency. This is mainly due to the limited of double float support and the datatransfer efficiency. Later, an efficient and intuitive GPGPU program developmenttools named compute unified device architecture (CUDA) is presented by NVIDIA.CUDA brings an efficient way for GPGPU with low computing cost and generalprogramming language.In this paper, a GPU-based parallel strategy for explicit FE computing with a fullfine-grain parallel contact algorithm is presented to meet the demands of engineeringapplications. And, the high performance parallel computing of automotive body crashsimulation and sheet forming simulation on normal personal computer with a CUDA-capable device are realized. The main research content and result are asfollows:(1) A GPU-based parallel explicit FE computing platform with independentintellectual property rights based on the characteristics of explicit scheme andlightweight threads parallel computing model of GPU is presented (Patent PendingNumber:201210266435.1). The main advantage of this platform is constructed threekinds of one-to-one mapping relationship between CUDA thread and computingobject, including thread-to-element, thread-to-node and thread-to-freedom. Compareto the coarse-grained parallel FE algorithm based on grid partition technology, thefine-grained parallel strategy can enhance calculation efficiency without anypre-treatment processes and boundary data processes. Therefore, the most parts ofexplicit FE calculation processes involving nodal speed computing and displacementcomputing can mapped to GPU computing to achieve high efficient.(2) The nodal force assembling on fine-grained parallel platform has long been adifficult subject. This paper proposed a pre-index strategy to realized parallelassembling on GPU with few additional works. In the meantime, parallel strategiesfor two kinds of shell element including Belytschlko-Tsay (BT) shell element andEdged-based smoothed triangular (EST) shell element are presented based on theabove parallel computing platform. Parallel reduction method is introduced tocalculate all kinds of single variables, such as global time step. Finally, an entireparallelized explicit FE iterative process based on GPU is proposed, which can obtainan optimal computational efficiency by reduce the data transfers between CPU andGPU. The numerical examples for nonlinear shell structures show that this methodcan greatly improve the computational efficiency with the same computing results ofserial computing on CPU. For example, about37times speedup obtained by GTX580GPU compare to I7CPU for an elastic-plastic large deformation problem with18.5million degrees of freedom.(3) During a FE analysis of contact problem, the time consumption of contactalgorithm usually occupies more than70%of the total computation time. Therefore,an entire GPU-based parallel contact algorithm is proposed in this paper, includingparallel hierarchy-territory contact-searching algorithm (HITA) and two kinds ofparallel contact force calculation algorithms involve parallel penalty function methodparallel defense node algorithm. HITA is an efficient contact-searching algorithm andespecially suitable for complex problems contain self-contact phenomenon.Furthermore, the computing independence of contact segments searching in the same hierarchy is suited for GPU parallel computing. Firstly, this paper proposed severaltechnical means to realize the parallel search of test pair on GPU, including thread tosegment mapping scheme, the GPU-based sort method and the technology of improvethe size of thread granularity. Secondly, in contact pair searching phase, a mappingrelationship between thread and test pair is presented to achieve the parallel searchingin the same hierarchy. And, a store strategy based on sort is used to realize efficientdata transfer between higher-level hierarchies and lower-level hierarchies. In thecontact force calculation phase, fine-grained parallel strategy based on thread tocontact pair mapping is present to parallel computing contact force, and atomicoperation is used to contact force scatter. Based on the above mentioned algorithms, aGPU-based contact process simulation software named CPS-GPU (SoftwareRegistered Number:2011SR001966) is developed based on the self-developed serialcontact process simulation software DYSI3D. The numerical examples alsodemonstrate that this software can get highly accuracy and efficiency. For example,about20times speedup can obtain by using GTX580graphics card to calculate aBody in White (BIW) crash model with17million degrees of freedom.(4) This paper presents a complete GPU parallel computing method to acceleratethe FE analysis of sheet metal forming process. According to the requirement of highcomputing accuracy for material flow in sheet forming simulation, a parallelcomputing method for shell element with complex material constitutive andfriction-considering contact force computation are proposed. In the meantime, theway to parallel a simple contact algorithm integrated in the self-developed sheet metalforming simulation software CADEMII is studied. Firstly, the wide broad searchmethod used in real-time collision detection is introduction to test pair searchingduring the pre-contact searching. Secondly, a parallel contact pair update method afterpre-contact searching is proposed based on the information of adjacent contactsegments. Finally, a GPU-based sheet metal forming parallel computing softwarenamed CADEM-GPU (Software Registered Number:2010SR052426) is developedbased on CADEMII. To extend the computing efficiency and practicability of thissoftware, several usefully technologies such as data asynchronous transfer methodand real-time display technology based on OpenGL are added. Numerical examplesshow that more than20times speedup can be obtained by using GTX460graphics tocalculate sheet metal FE model with tens of thousands of elements.