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Results for “Data driven”
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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Development of interpretable, data-driven plasticity models with symbolic regression
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Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection
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Global Data-Driven Modeling of Wind Turbines in the Presence of Turbulence
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Industrial data-driven machine learning soft sensing for optimal operation of etching tools
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A data-driven reduced-order model for stiff chemical kinetics using dynamics-informed training
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Probing lattice defects in crystalline battery cathode using hard X-ray nanoprobe with data-driven modeling
Lattice defects, e.g., dislocations and grain boundaries, critically impact the properties of crystalline battery cathode materials. A longstanding challenge is to probe the meso–scale heterogeneity and evolution of lattice defects with sensitivity to atomic-scale details. Herein, we tackle this issue with a unique combination of X-ray nanoprobe diffractive imaging and advanced machine learning techniques. The domains with different lattice defect configuration within a single-crystalline LiCoO2 cathode particle are faithfully revealed using our approach. We further visualize the rearrangement of grain boundaries and local crystallinity upon mild thermal annealing. Furthermore, these results pave a direct way to the understanding of crystalline battery materials’ response under external stimuli with high fidelity, which provides valuable empirical guidance to defect-engineering strategies for improving the cathode materials against aggressive battery operation.
Data-driven spatial modeling of global long-term urban land development: The SELECT model
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Interleaving physics- and data-driven models for power system transient dynamics
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Data-driven time series reconstruction for modern power systems research
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A data-driven sensor placement approach for detecting voltage violations in distribution systems
Stochastic fluctuations in power injections from distributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. Here, in this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits. We formulate a bilevel optimization problem that minimizes the number of sensors and avoids false sensor alarms in the upper level while ensuring detection of any voltage violations in the lower level. This problem is challenging due to the nonlinearity of the power flow equations and the presence of binary variables. Accordingly, we employ recently developed conservative linear approximations of the power flow equations that overestimate or underestimate the voltage magnitudes. By replacing the nonlinear power flow equations with conservative linear approximations, we can ensure that the resulting sensor locations and thresholds are sufficient to identify any constraint violations. Additionally, we apply various problem reformulations to significantly improve computational tractability while simultaneously ensuring an appropriate placement of sensors. Lastly, we improve the quality of the results via an approximate gradient descent method that adjusts the sensor thresholds. We demonstrate the effectiveness of our proposed method for several test cases, including a system with multiple switching configurations.
Smart parts: Data-driven model order reduction for nonlinear mechanical assemblies
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A cost-based criterion for implementing data-driven disruption predictors
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Full field reservoir modeling of shale assets using advanced data-driven analytics
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Data-driven stochastic AC-OPF using Gaussian process regression
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