An Efficient Parallel Simulation Method for Posterior Inference on Paths of Markov Processes

2014 ◽  
Author(s):  
Matthias Held ◽  
Marcel Omachel
Author(s):  
Moresh J. Wankhede ◽  
Neil W. Bressloff ◽  
Andy J. Keane

Computational fluid dynamics (CFD) simulations to predict and visualize the reacting flow dynamics inside a combustor require fine resolution over the spatial and temporal domain, making them computationally very expensive. The traditional time-serial approach for setting up a parallel combustor CFD simulation is to divide the spatial domain between computing nodes and treat the temporal domain sequentially. However, it is well known that spatial domain decomposition techniques are not very efficient especially when the spatial dimension (or mesh count) of the problem is small and a large number of nodes are used, as the communication costs due to data parallelism becomes significant per iteration. Hence, temporal domain decomposition has some attraction for unsteady simulations, particularly on relatively coarse spatial meshes. The purpose of this study is two-fold: (i), to develop a time-parallel CFD simulation method and apply it to solve the transient reactive flow-field in a combustor using an unsteady Reynolds-averaged Navier Stokes (URANS) formulation in the commercial CFD code FLUENT™ and (ii) to investigate its benefits relative to a time-serial approach and its potential use for combustor design optimization. The results show that the time-parallel simulation method correctly captures the unsteady combustor flow evolution but, with the applied time-parallel formulation, a clear speed-up advantage, in terms of wall-clock time, is not obtained relative to the time-serial approach. However, it is clear that the time-parallel simulation method provides multiple stages of transient combustor flow-field solution data whilst converging towards a final converged state. The availability of this resulting data could be used to seed multiple levels of fidelity within the framework of a multi-fidelity co-Kriging based design optimization strategy. Also, only a single simulation would need to be setup from which multiple fidelities are available.


Author(s):  
Shirin Kordnoori ◽  
Hamidreza Mostafaei ◽  
Shaghayegh Kordnoori ◽  
Mohammadmohsen Ostadrahimi

Semi-Markov processes can be considered as a generalization of both Markov and renewal processes. One of the principal characteristics of these processes is that in opposition to Markov models, they represent systems whose evolution is dependent not only on their last visited state but on the elapsed time since this state. Semi-Markov processes are replacing the exponential distribution of time intervals with an optional distribution. In this paper, we give a statistical approach to test the semi-Markov hypothesis. Moreover, we describe a Monte Carlo algorithm able to simulate the trajectories of the semi-Markov chain. This simulation method is used to test the semi-Markov model by comparing and analyzing the results with empirical data. We introduce the database of Network traffic which is employed for applying the Monte Carlo algorithm. The statistical characteristics of real and synthetic data from the models are compared. The comparison between the semi-Markov and the Markov models is done by computing the Autocorrelation functions and the probability density functions of the Network traffic real and simulated data as well. All the comparisons admit that the Markovian hypothesis is rejected in favor of the more general semi Markov one. Finally, the interval transition probabilities which show the future predictions of the Network traffic are given.


2019 ◽  
Vol 147 ◽  
pp. 283-287 ◽  
Author(s):  
Xiukai Zhao ◽  
Lei Lyu ◽  
Chen Lyu ◽  
Cun Ji

2021 ◽  
Vol 1971 (1) ◽  
pp. 012070
Author(s):  
Xin Kang ◽  
Rui Wang ◽  
Zhicheng Lv ◽  
Baiyu Li ◽  
Weihua Mou

2014 ◽  
Vol 522-524 ◽  
pp. 1187-1191
Author(s):  
Hong Tao Hou ◽  
Qun Li ◽  
Chao Wang ◽  
Qiang Chang ◽  
Wei Ping Wang

In this paper, we proposed a parallel simulation method for performance analysis of the Global Navigation Satellite System (GNSS) based on simulation model portability 2(SMP2) and service-oriented modeling method. GNSS is a space engineering system with a large-scale and complex structure, and the proposed method can be used to construct large complex simulation systems to gain the reusability, composability and interoperability of heterogeneous simulation resources. Firstly, the method including the conceptual framework, system architecture and system engineering process is introduced. Then the parallel model development, composition and schedule method are detailed respectively. Finally, a distributed M&S environment based on service-oriented SMP2 is designed, and an example of navigation system volume simulation is given to validate the whole method.


Author(s):  
Lu Zhao ◽  
Tu Zhen-biao ◽  
Wei Jia-ning ◽  
Zhu Yu-tong ◽  
Liu Tong-lin ◽  
...  

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