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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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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Poster - 47: A parametrized prediction model of rectal toxicity in focal SBRT of low risk prostate cancer

There has been a recent trend towards watchful waiting in place of intervention for early stage prostate cancer (CaP). However, this approach can allow for disease progression, and subsequent whole-gland therapies such as prostatectomy and whole gland irradiation can result in functional deficits or rectal toxicities or both. A controversial alternative approach for this patient cohort is the use of focal therapy, where the treatment is focussed on an identified dominant index lesion (DIL). This work aims to investigate the treatment parameters for focal SBRT of the prostate under which clinically acceptable rectal NTCP levels can be achieved. For each of 25 low risk CaP patients, a hypothetical 2 cc DIL was modeled in the right-posterior quadrant of the prostate, and was used to build a PTV as the target for SBRT simulation. An SBRT prescriptions of 41 Gy and 37 Gy in 5 fractions were chosen, corresponding to the boost levels used in previous CaP dose escalation studies. DVH data were exported and used to calculate rectal NTCP values based on the Lyman-Kutcher-Burman (LKB) model using the QUANTEC reccommended model parameters. Rectal NTCP dependence on DIL-to-rectum separation, dose level, and DIL volume were investigated. The final goal of this ongoing work is to create a map of the maximum allowable prescription dose for a given patient geometry that achieves a clinically acceptable rectal NTCP level.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty

Bayesian inference provides a systematic framework for integration of data with mathematical models to quantify the uncertainty in the solution of the inverse problem. However, the solution of Bayesian inverse problems governed by complex forward models described by partial differential equations (PDEs) remains prohibitive with black-box Markov chain Monte Carlo (MCMC) methods. We present hIPPYlib-MUQ, an extensible and scalable software framework that contains implementations of state-of-the art algorithms aimed to overcome the challenges of high-dimensional, PDE-constrained Bayesian inverse problems. These algorithms accelerate MCMC sampling by exploiting the geometry and intrinsic low-dimensionality of parameter space via derivative information and low rank approximation. The software integrates two complementary open-source software packages, hIPPYlib and MUQ. hIPPYlib solves PDE-constrained inverse problems using automatically-generated adjoint-based derivatives, but it lacks full Bayesian capabilities. MUQ provides a spectrum of powerful Bayesian inversion models and algorithms, but expects forward models to come equipped with gradients and Hessians to permit large-scale solution. By combining these two complementary libraries, we created a robust, scalable, and efficient software framework that realizes the benefits of each and allows us to tackle complex large-scale Bayesian inverse problems across a broad spectrum of scientific and engineering disciplines. To illustrate the capabilities of hIPPYlib-MUQ, we present a comparison of a number of MCMC methods available in the integrated software on several high-dimensional Bayesian inverse problems. These include problems characterized by both linear and nonlinear PDEs, various noise models, and different parameter dimensions. The results demonstrate that large (~ 50×) speedups over conventional black box and gradient-based MCMC algorithms can be obtained by exploiting Hessian information (from the log-posterior), underscoring the power of the integrated hIPPYlib-MUQ framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Overland flow numerical model prediction, Lower Triangle Region in East River Watershed, Colorado, 3 days

This data package contains numerical simulation results of surface flow variables such as flow velocity and water depth in Lower Triangle Region in East River Watershed, Colorado. The surface flow is a consequence of a high intensity rainfall event with a total duration of 3 days, available at a resolution of 10 minutes. The results are computed on triangular multiresolution meshes with resolutions ranging from 10 meter to 80 meter. The data package also contains a simulation on a uniform triangular mesh with a resolution of 10 meter. The simulations consider surface flow only and neglect subsurface flow, infiltration, and evapotranspiration. The purpose of the data is to assess the quality of a mesh refinement strategy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Predictive Modeling of Aging and Degradation of Materials in Extreme Environments.

In order to accurately describe defect accumulation in heterogeneous microstructures and under complex irradiation conditions, simulation methods are needed that can explicitly account for the effect of nonhomogeneous microstructures on damage accumulation. In this project, an advanced simulation tool called spatially resolved stochastic cluster dynamics (SRSCD) is developed for this purpose. The proposed approach relies on solving spatially resolved coupled rate equations of standard cluster dynamics methods in a kinetic Monte Carlo scheme. Large-scale simulations of radiation damage in polycrystalline materials are enabled through several improvements made to this method, including a pseudo-adaptive meshing scheme for cascade implantation and implementation of this method in a synchronous parallel kinetic Monte Carlo framework.

36 MATERIALS SCIENCE↗