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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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Sensitivity to a frequency-dependent circular polarization in an isotropic stochastic gravitational wave background
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Systematic study of the stochastic gravitational-wave background due to stellar core collapse
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Augmenting collider searches and enhancing discovery potentials through stochastic jet grooming
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Self-Averaging Stochastic Kohn-Sham Density-Functional Theory
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Breaking the Theoretical Scaling Limit for Predicting Quasiparticle Energies The Stochastic GW Approach
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Dependence of Perpendicular Viscosity on Magnetic Fluctuations in a Stochastic Topology
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Integrated Stochastic Weather and Production Simulation Modeling
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Toward Using Surrogates to Accelerate Solution of Stochastic Electricity Grid Operations Problems.
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Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids
This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flow equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.
Optimal Sizing of Behind-the-Meter Energy Storage with Stochastic Load and PV Generation for Islanded Operation
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Stochastic Gain Degradation in III-V Heterojunction Bipolar Transistors due to Single Particle Displacement Damage
As device dimensions decrease single displacement effects are becoming more important. We measured the gain degradation in III-V Heterojunction Bipolar Transistors due to single particles using a heavy ion microbeam. Two devices with different sizes were irradiated with various ion species ranging from oxygen to gold to study the effect of the irradiation ion mass on the gain change. From the single steps in the inverse gain (which is proportional to the number of defects) we calculated Cumulative Distribution Functions to help determine design margins. The displacement process was modeled using the Marlowe Binary Collision Approximation (BCA) code. The entire structure of the device was modeled and the defects in the base-emitter junction were counted to be compared to the experimental results. While we found good agreement for the large device, we had to modify our model to reach reasonable agreement for the small device.
A stochastic model for tumor geometry evolution during radiation therapy in cervical cancer
Purpose: To develop mathematical models to predict the evolution of tumor geometry in cervical cancer undergoing radiation therapy. Methods: The authors develop two mathematical models to estimate tumor geometry change: a Markov model and an isomorphic shrinkage model. The Markov model describes tumor evolution by investigating the change in state (either tumor or nontumor) of voxels on the tumor surface. It assumes that the evolution follows a Markov process. Transition probabilities are obtained using maximum likelihood estimation and depend on the states of neighboring voxels. The isomorphic shrinkage model describes tumor shrinkage or growth in terms of layers of voxels on the tumor surface, instead of modeling individual voxels. The two proposed models were applied to data from 29 cervical cancer patients treated at Princess Margaret Cancer Centre and then compared to a constant volume approach. Model performance was measured using sensitivity and specificity. Results: The Markov model outperformed both the isomorphic shrinkage and constant volume models in terms of the trade-off between sensitivity (target coverage) and specificity (normal tissue sparing). Generally, the Markov model achieved a few percentage points in improvement in either sensitivity or specificity compared to the other models. The isomorphic shrinkage model was comparable to the Markov approach under certain parameter settings. Convex tumor shapes were easier to predict. Conclusions: By modeling tumor geometry change at the voxel level using a probabilistic model, improvements in target coverage and normal tissue sparing are possible. Our Markov model is flexible and has tunable parameters to adjust model performance to meet a range of criteria. Such a model may support the development of an adaptive paradigm for radiation therapy of cervical cancer.
Risk Reduction of CO 2 Storage with Stochastic Simulations
The purpose of this project was to create an efficient and flexible tool based on principal component analysis (PCA) for a generation of differentiable realizations of porosity and permeability fields. Of particular interest were data which had significant connectivity between patches of the same rock type, which is called “binary”. The efficiency requirement needed a so called kernel PCA, and the binary images were found with the method called optimization based PCA (OPCA) for which interpretation is provided. The ability to honor given data at particular locations is also incorporated. The tool worked very well and efficiently for two dimensional (2-D) simulations. Furthermore, the tool was applied to a three-dimensional (3-D) dataset and determined that conditioning can be used to maintain the connectivity between vertical layers.