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Results for “Twinning”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Characterizing Approximation Methods for Digital Twins in Scientific Computing.
Abstract not provided.
Combining In-situ Diagnostics and Data Analytics for Discovery of Process-Structure-Property Relationships in AM parts – A Step Toward Digital Twins
In-situ additive manufacturing (AM) diagnostic tools (e.g., optical/infrared imaging, acoustic, etc.) already exist to correlate process anomalies to printed part defects. This current work aimed to augment existing capabilities by: 1) Incorporating in-situ imaging w/ machine learning (ML) image processing software (ORNL- developed "Peregrine") for AM process anomaly detection 2) Synchronizing multiple in-situ sensors for simultaneous analysis of AM build events 3) Correlating in-situ AM process data, generated part defects and part mechanical properties The key R&D question investigated was to determine if these new combined hardware/software tools could be used to successfully quantify defect distributions for parts build via SNL laser powder bed fusion (LPBF) machines, aiming to better understand data-driven process-structure-property- performance relationships. High resolution optical cameras and acoustic microphones were successfully integrated in two LPBF machines and linked to the Peregrine ML software. The software was successfully calibrated on both machines and used to image hundreds of layers of multiple builds to train the ML software in identifying printed part vs powder. The software's validation accuracy to identify this aspect increased from 56% to 98.8% over three builds. Lighting conditions inside the chamber were found to significantly impact ML algorithm predictions from in-situ sensors, so these were tailored to each machine's internal framework. Finally, 3D part reconstructions were successfully generated for a build from the compressed stack of layer-wise images. Resolution differences nearest and furthest from the optical camera were discussed. Future work aims to improve optical resolution, increase process anomalies identified, and integrate more sensor modalities.
One Dimensional Penstock Flow Models for Hydropower Digital Twin
This report details the inelastic, unsteady, one-dimensional flow equations that are used to model flow through a penstock in a hydropower facility. The flow model is coupled to a mechanistic turbine model to compute the rotation rate of the turbine, which is dependent on the flow rate, gravitational head, and various parameters of the machine, such as inlet vane angle. Results are given for an example hydropower facility in which the turbine is subjected to a specified time-varying load, and the results on the flow rate are shown. Future applications for the flow model are also briefly reviewed.
MLOps for Digital Twins
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Superconducting Twin Axis Cavity ¿ Development and Applications
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Elastic Flow Modeling for Hydropower Digital Twins
This report details the elastic, unsteady, one-dimensional flow equations that are used to model flow through a penstock in a hydropower facility. The elastic flow model is accurate even in cases of fast transients, long penstock length, and high gravitation head. The compressibility of the water and elasticity of the pipe walls are explicitly accounted for in the developed models so that the water hammer phenomena can be accurately captured. The elastic flow model is coupled to a mechanistic turbine model to resolve the dynamic feedbacks between the elastic water column and the turbine rotation rate. A finite volume method is employed to solve the governing equations, and the method is shown to have low numerical diffusion in sharp gradient phenomena, such as those encountered when simulating water hammer. The method is applied to single dimensional linear advection benchmark problem and numerical results are compared with analytic results. This is followed on by an application to a full hydropower system with a coupled turbine. The elastic flow model results are compared against an inelastic model.
Understanding Building Energy Use in Boise-Twin Falls: Basic Building Stock Characterization
This report is part of a publication series that focuses on approximately 100 different local geographies, or "clusters." Each report provides commercial and multifamily building characteristic and energy data for a local geography, with the intention of helping policymakers at the city, county and state levels better understand building energy use. Specifically, this report breaks down the building stock in the counties shown in Figure 1 by building type, size, energy consumption, and emissions.
Synthetic threat injection using digital twin informed augmentation .
Abstract not provided.
How to Train Your Digital Twin: Practical Deep Learning Approaches to ModelingAs-built Components.
Abstract not provided.
Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds.
Abstract not provided.
Reduced order modeling with boosting Barlow Twins self supervised learning for contact problem in a compressible hyperelastic material.
Abstract not provided.
Epistemic uncertainty-aware Barlow twins reduced order modeling for nonlinear contact problems.
Abstract not provided.
Predictive digital twin of geologic CO2 storage and plume evolution.
Abstract not provided.