A Distributed Computing Method for Building Group Disaster Simulation Based on Finite Particle Method
A distributed computing and building complex technology, applied in computing, complex mathematical operations, instruments, etc., can solve the problems such as the inability to efficiently carry out earthquake damage simulation, the difficulty of solving stiffness matrix, and the low efficiency of large-scale computing. performance, improve computing speed, improve computing scale and computing efficiency
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Embodiment 1
[0094] like figure 1 As shown, this embodiment provides a distributed computing method for catastrophe simulation of building groups based on the finite particle method, including the following steps:
[0095] S1. Data related to the structure of the building group is transmitted to the main control node for preparation and startup of distributed computing;
[0096] S2. The master control node distributes the data related to the structure of the building group to the execution processes on the slave nodes in the cluster through the high-speed network;
[0097] S3. Execute the calculation of particle displacement and unit internal force on the data related to the structure of the building group from the execution process on the node;
[0098] S4. The execution process on the slave node submits the calculation result back to the master control node, and the master control node updates the resultant force and resultant moment of each mass point in the building complex structure....
Embodiment 2
[0161] The specific calculation method steps of this embodiment are the same as those of Embodiment 1, the difference is that in this embodiment, specific Spark is used as a distributed parallel computing framework for calculation, YARN is used as a cluster resource manager, and the specific distributed computing system structure and management Flowchart such as image 3 As shown, the user first submits the calculation task of the urban building group through the Spark Yarn Client. After receiving the task, the Resource Manager selects a Node Manager in the cluster to allocate the Container, starts the Application Master process in the Container, initializes the SparkContext in the Application Master, and then the Application The Master applies for a Container from the Resource Manager, and after applying for the Container, notifies the Node Manager to start the Executor process in the obtained Container. SparkContext assigns Task to Executor, and Executor sends running status...
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