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Computational fluid dynamics (CFD)-based wear predictions are computationally expensive to evaluate, even with a high-performance computing infrastructure. Thus, it is difficult to provide accurate local wear predictions in a timely manner. Data-driven approaches provide a more computationally efficient way to approximate the CFD wear predictions without running the actual CFD wear models. In this paper, a machine learning (ML) approach, termed WearGP, is presented to approximate the 3D local wear predictions, using numerical wear predictions from steady-state CFD simulations as training and testing datasets. The proposed framework is built on Gaussian process (GP) and utilized to predict wear in a much shorter time. The WearGP framework can be segmented into three stages. At the first stage, the training dataset is built by using a number of CFD simulations in the order of O(102). At the second stage, the data cleansing and data mining processes are performed, where the nodal wear solutions are extracted from the solution database to build a training dataset. At the third stage, the wear predictions are made, using trained GP models. Two CFD case studies including 3D slurry pump impeller and casing are used to demonstrate the WearGP framework, in which 144 training and 40 testing data points are used to train and test the proposed method, respectively. The numerical accuracy, computational efficiency and effectiveness between the WearGP framework and CFD wear model for both slurry pump impellers and casings are compared. It is shown that the WearGP framework can achieve highly accurate results that are comparable with the CFD results, with a relatively small size training dataset, with a computational time reduction on the order of 105 to 106.

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TYPE Journal Article YEAR 2019

DOI OSTI Scopus

Constraining preheat energy deposition in MagLIF experiments with multi-frame shadowgraphy

Physics of Plasmas

Harvey-Thompson, Adam J.; Geissel, Matthias G.; Jennings, Christopher A.; Weis, Matthew R.; Laros, James H.; Fein, Jeffrey R.; Ampleford, David A.; Chandler, Gordon A.; Glinsky, Michael E.; Hahn, K.D.; Hansen, Stephanie B.; Harding, Eric H.; Knapp, Patrick K.; Paguio, R.R.; Perea, L.; Peterson, Kyle J.; Porter, John L.; Rambo, Patrick K.; Robertson, Grafton K.; Rochau, G.A.; Schwarz, Jens S.; Shores, Jonathon S.; Sinars, Daniel S.; Slutz, Stephen A.; Smith, Gary L.; Smith, Ian C.; Speas, Christopher S.; Whittemore, K.; Woodbury, D.

A multi-frame shadowgraphy diagnostic has been developed and applied to laser preheat experiments relevant to the Magnetized Liner Inertial Fusion (MagLIF) concept. The diagnostic views the plasma created by laser preheat in MagLIF-relevant gas cells immediately after the laser deposits energy as well as the resulting blast wave evolution later in time. The expansion of the blast wave is modeled with 1D radiation-hydrodynamic simulations that relate the boundary of the blast wave at a given time to the energy deposited into the fuel. This technique is applied to four different preheat protocols that have been used in integrated MagLIF experiments to infer the amount of energy deposited by the laser into the fuel. The results of the integrated MagLIF experiments are compared with those of two-dimensional LASNEX simulations. The best performing shots returned neutron yields ∼40-55% of the simulated predictions for three different preheat protocols.

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TYPE Journal Article YEAR 2019

DOI OSTI Scopus