Global Sensitivity Analysis Using the Ultra-Low Resolution E3SM to Investigate Parametric Uncertainty in Arctic Climate
Abstract not provided.
Publications
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Jump to search filtersSimultaneous Direct Time Fitting of a Multi-Mode Response to Determine the Instantaneous Frequency and Damping
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SDynPy: A Structural Dynamics Python Library
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Richardson extrapolation for small diatomic molecules on the QSCOUT device
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Carrier Dynamics of Polar, Semipolar, and Nonpolar InGaN/GaN LEDs Measured by Small-Signal Electroluminescence
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Scintillating Nanoguide Containing Organic Glass Fluorophores
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Geometric Mechanics Formulations of Momentum-Based Descriptions for Fluids Using Bundle-Valued Differential Forms
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Nonlinear Material Characterization in Dynamic Testing: Part 1- Experiments
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DataSEA: Mature, modern data management enabling sustainable data strategy
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A Review of Using Transfer Path Analysis Methods to Derive Multi-Axis Vibration Environments
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Hyper-differential sensitivity analysis with respect to model discrepancy
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The Quantum and Classical Streaming Complexity of Quantum and Classical Max-Cut
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Developing data-driven approaches to design and discovery for extreme physics on the Z Machine
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Design and Evaluation of Vertical GaN PN Diode Junction Termination Extensions in High Temperature Environments
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Enhancing Confidence in a Software Ecosystem through Complementary Layers of Software Testing
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Stability and Breakdown of Acid-Induced Hydrated Silicate Gels and Metal Silicate Hydrates under Geothermal Conditions: A Preliminary Laboratory Study
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Notes on Regression Analysis for Radar Parameter Estimation
A fundamental task of radar, beyond merely detecting a target, is to estimate some parameters associated with it. For example, this might include range, direction, velocity, etc. In any case, multiple measurements, often noisy, need to be processed to yield a ‘best estimate’ of the parameter. A common mathematical method for doing so is called “Regression” analysis. The goal is to minimize the expected squared error in the estimate. Even when alternate algorithms are considered, the least squared-error regression analysis is the benchmark against which alternatives are compared.
Comparative Failure Analysis of Components Exposed to Multi-Axis & Single-Axis Vibration Testing
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DeCARbonizing your Commute
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Structural evolution of Gadolinium under dynamic compression
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Progress in interband cascade lasers with advanced waveguides
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Efficient Proximal Subproblem Solvers for An Inexact Nonsmooth Trust-Region Method
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A Bayesian Framework for Coupling Optimal Experimental Design and Optimal Control
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Adaptive Randomized Sketching for Dynamic Nonsmooth Optimization
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Transformer Analysis with ANSYS Maxwell and COMSOL
This is an extension of work described by Rodriguez et al. (2021). It continues analyses of a generic transformer design by Wes Greenwood. In this report, we summarize that work and add comparable results using the ANSYS Maxwell software (henceforward, “ANSYS”), and with COMSOL . We found the ANSYS and COMSOL calculations of inductance agreed well with previous results for simplified coils in air, and with a ferromagnetic core. We then describe the ANSYS and COMSOL approach and show results for a full transformer model based on magnetic field analyses. Finally, we present electrostatic analyses of E field enhancement, once again resolving individual wires. The purpose is to assess the electrostatic fields in order to locate where electric breakdown is likely to originate. We found the maximum enhancement between the secondary and either the primary or the tertiary at the end of the windings with a large potential difference.
Results 3751–3775 of 99,299