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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

TU-CD-304-03: Dosimetric Verification and Preliminary Comparison of Dynamic Wave Arc for SBRT Treatments

Purpose: To evaluate the potential dosimetric benefits and verify the delivery accuracy of Dynamic Wave Arc, a novel treatment delivery approach for the Vero SBRT system. Methods: Dynamic Wave Arc (DWA) combines simultaneous movement of gantry/ring with inverse planning optimization, resulting in an uninterrupted non-coplanar arc delivery technique. Thirteen SBRT complex cases previously treated with 8–10 conformal static beams (CRT) were evaluated in this study. Eight primary centrally-located NSCLC (prescription dose 4×12Gy or 8×7.5Gy) and five oligometastatic cases (2×2 lesions, 10×5Gy) were selected. DWA and coplanar VMAT plans, partially with dual arcs, were generated for each patient using identical objective functions for target volumes and OARs on the same TPS (RayStation, RaySearch Laboratories). Dosimetric differences and delivery time among these three planning schemes were evaluated. The DWA delivery accuracy was assessed using the Delta4 diode array phantom (ScandiDos AB). The gamma analysis was performed with the 3%/3mm dose and distance-to-agreement criteria. Results: The target conformity for CRT, VMAT and DWA were 0.95±0.07, 0.96±0.04 and 0.97±0.04, while the low dose spillage gradient were 5.52±1.36, 5.44±1.11, and 5.09±0.98 respectively. Overall, the bronchus, esophagus and spinal cord maximum doses were similar between VMAT and DWA, but highly reduced compared with CRT. For the lung cases, the mean dose and V20Gy were lower for the arc techniques compares with CRT, while for the liver cases, the mean dose and the V30Gy presented slightly higher values. The average delivery time of VMAT and DWA were 2.46±1.10 min and 4.25±1.67 min, VMAT presenting shorter treatment time in all cases. The DWA dosimetric verification presented an average gamma index passing rate of 95.73±1.54% (range 94.2%–99.8%). Conclusion: Our preliminary data indicated that the DWA is deliverable with clinically acceptable accuracy and has the potential to further improve the plan quality. This collaborative work was supported by the Flemish government through the Hercules foundation and corporate funding from BrainLab AG. The first and the sixth author are financially supported by Brainlab AG. The other authors have no conflict of interest.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-378: Evaluation of Dose-Volume Variability and Parameters Between Prostate IMRT and VMAT Plans

Purpose: This study compared the rectal dose-volume consistency, equivalent uniform dose (EUD) and normal tissue complication probability (NTCP) in prostate intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Methods: For forty prostate IMRT and fifty VMAT patients treated using the same dose prescription (78 Gy/39 fraction) and dose-volume criteria in inverse planning optimization, the rectal EUD and NTCP were calculated for each patient. The rectal dose-volume consistency, showing the variability of dose-volume histogram (DVH) among patients, was defined and calculated based on the deviation between the mean and corresponding rectal DVH. Results: From both the prostate IMRT and VMAT plans, the rectal EUD and NTCP were found decreasing with the rectal volume. The decrease rates for the IMRT plans (EUD = 0.47 × 10{sup −3} Gy cm{sup −3} and NTCP = 3.94 × 10{sup −2} % cm{sup −3}) were higher than those for the VMAT (EUD = 0.28 × 10{sup −3} Gy cm{sup −3} and NTCP = 2.61 × 10{sup −2} % cm{sup −3}). In addition, the dependences of the rectal EUD and NTCP on the dose-volume consistency were found very similar between the prostate IMRT and VMAT plans. This shows that both delivery techniques have similar variations of the rectal EUD and NTCP on the dose-volume consistency. Conclusion: Dependences of the dose-volume consistency on the rectal EUD and NTCP were compared between the prostate IMRT and VMAT plans. It is concluded that both rectal EUD and NTCP decreased with an increase of the rectal volume. The variation rates of the rectal EUD and NTCP on the rectal volume were higher for the IMRT plans than VMAT. However, variations of the rectal dose-volume consistency on the rectal EUD and NTCP were found not significant for both delivery techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Design, tuning, and blackbox optimization of laser systems

Chirped pulse amplification (CPA) and subsequent nonlinear optical (NLO) systems constitute the backbone of myriad advancements in semiconductor manufacturing, communications, biology, defense, and beyond. Accurately and efficiently modeling CPA+NLO-based laser systems is challenging because of the complex coupled processes and diverse simulation frameworks. Our modular start-to-end model unlocks the potential for exciting new optimization and inverse design approaches reliant on data-driven machine learning methods, providing a means to create tailored CPA+NLO systems unattainable with current models. To demonstrate this new, to our knowledge, technical capability, we present a study on the LCLS-II photo-injector laser, representative of a high-power and spectro-temporally non-trivial CPA+NLO system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Robust multiresonant nonlocal metasurfaces by rational design

Abstract Dielectric metasurfaces supporting optical resonances have become a promising platform for quantum and nonlinear optics. However, resonant metasurfaces remain limited in their capacity to independently control the behavior of many distinct resonances despite efforts in computational optimization and inverse design. In this work, we overcome longstanding limitations by introducing a generalized rational design paradigm based on symmetry. Specifically, we use symmetry-broken metasurfaces with periodic “quadromer” lattices comprised of four nanostructures per unit cell to enable extensive control of multiple optical resonances. The rationally designed metasurfaces are readily fabricable, and we experimentally demonstrate metasurfaces that support up to four high Q-factor resonances with deliberately chosen free-space polarizations, spectral separations, and mode profiles. Our design paradigm may unlock new applications for multiresonant metasurfaces in quantum and nonlinear optics, optical sensing, and augmented reality displays.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Physics-based modeling and data analytics [Slides]

This presentation contains a summary of ongoing work within the physics-based modeling and data analytics work package within the Nuclear Materials Discovery and Qualification initiative (NMDQi). Topics include work on MOOSE-based crystal plasticity, molecular dynamics modeling of recombination in metals and alloys, the MOOSE Stochastic Tools Module, and machine learning and atomistic modeling to predict thermo-kinetic properties of nuclear structural materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

RAYLEIGH QUOTIENT ITERATION IN 3D, DETERMINISTIC NEUTRON TRANSPORT

Today’s “grand challenge” neutron transport problems require 3-D meshes with billions of cells, hundreds of energy groups, and accurate quadratures and scattering expansions. Leadership-class computers provide platforms on which high-fidelity fluxes can be calculated. However, appropriate methods are needed that can use these machines effectively. Such methods must be able to to use hundreds of thousands of cores and have good convergence properties. Rayleigh quotient iteration (RQI) is an eigenvalue solver that has been added to the SN code Denovo to address convergence. Rayleigh quotient iteration is an optimal shifted inverse iteration method that should converge in fewer iterations than the more common power method and other shifted inverse iteration methods for many problems of interest. Denovo’s RQI uses a new multigroup Krylov solver for the fixed source solutions inside every iteration that allows parallelization in energy in addition to space and angle. This Krylov solver has been shown to scale successfully to 200,000 cores: for example one test problem scaled from 69,120 cores to 190,080 cores with 98% efficiency. This paper shows that RQI works for some small problems. However, the Krylov method upon which it relies does not always converge because RQI creates ill-conditioned systems. This result leads to the conclusion that preconditioning is needed to allow this method to be applicable to a wider variety of problems.

Slaybaugh, R. N.↗
DOE OSTI2012

Rayleigh Quotient Iteration in 3D, Deterministic Neutron Transport

Today's "grand challenge" neutron transport problems require 3-D meshes with billions of cells, hundreds of energy groups, and accurate quadratures and scattering expansions. Leadership-class computers provide platforms on which high-fidelity fluxes can be calculated. However, appropriate methods are needed that can use these machines effectively. Such methods must be able to use hundreds of thousands of cores and have good convergence properties. Rayleigh quotient iteration (RQI) is an eigenvalue solver that has been added to the Sn code Denovo to address convergence. Rayleigh quotient iteration is an optimal shifted inverse iteration method that should converge in fewer iterations than the more common power method and other shifted inverse iteration methods for many problems of interest. Denovo's RQI uses a new multigroup Krylov solver for the fixed source solutions inside every iteration that allows parallelization in energy in addition to space and angle. This Krylov solver has been shown to scale successfully to 200,000 cores: for example one test problem scaled from 69,120 cores to 190,080 cores with 98% efficiency. This paper shows that RQI works for some small problems. However, the Krylov method upon which it relies does not always converge because RQI creates ill-conditioned systems. This result leads to the conclusion that preconditioning is needed to allow this method to be applicable to a wider variety of problems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Direct and Inverse Problems of Thermomechanics Concerning the Optimization and Identification of the Thermal Stressed State of Deformed Solids

We describe the main stages of the development of investigations originated by Prof. Vihak in the fields of the theory and methods of optimal control over the thermal processes and thermal stressed state of deformed solids, theory of inverse problems of heat conduction and thermomechanics, and the mathematical methods in the mechanics of deformable solids. We indicate the existence of close relationship between the obtained theoretical results and the applied problems of thermal power engineering motivating investigations of this kind. We describe a significant contribution made by the researchers of the Pidstryhach Institute for Applied Problems in Mechanics and Mathematics of the Ukrainian National Academy of Sciences to the development of investigations in these fields. The prospects of further development of these studies are analyzed and some facts from the life and creative activity of Prof. V. М. Vihak are presented.

42 ENGINEERING↗
DOE OSTI2021

Reinforcement learning in discrete action space applied to inverse defect design

Abstract Reinforcement learning (RL) algorithms that include Monte Carlo Tree Search (MCTS) have found tremendous success in computer games such as Go, Shiga and Chess. Such learning algorithms have demonstrated super-human capabilities in navigating through an exhaustive discrete action search space. Motivated by their success in computer games, we demonstrate that RL can be applied to inverse materials design problems. We deploy RL for a representative case of the optimal atomic scale inverse design of extended defects via rearrangement of chalcogen (e.g. S) vacancies in 2D transition metal dichalcogenides (e.g. MoS 2 ). These defect rearrangements and their dynamics are important from the perspective of tunable phase transition in 2D materials i.e. 2H (semi-conducting) to 1T (metallic) in MoS 2 . We demonstrate the ability of MCTS interfaced with a reactive molecular dynamics simulator to efficiently sample the defect phase space and perform inverse design—starting from randomly distributed S vacancies, the optimal defect rearrangement of defects corresponds a line defect of S vacancies. We compare MCTS performance with evolutionary optimization i.e. genetic algorithms and show that MCTS converges to a better optimal solution (lower objective) and in fewer evaluations compared to GA. We also comprehensively evaluate and discuss the effect of MCTS hyperparameters on the convergence to solution. Overall, our study demonstrates the effectives of using RL approaches that operate in discrete action space for inverse defect design problems.

42 ENGINEERING↗
DOE OSTI2018

Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems

We develop a framework for goal-oriented optimal design of experiments (GOODE) for large-scale Bayesian linear inverse problems governed by PDEs. This framework differs from classical Bayesian optimal design of experiments (ODE) in the following sense: we seek experimental designs that minimize the posterior uncertainty in the experiment end-goal, e.g. a quantity of interest (QoI), rather than the estimated parameter itself. This is suitable for scenarios in which the solution of an inverse problem is an intermediate step and the estimated parameter is then used to compute a QoI. In such problems, a GOODE approach has two benefits: the designs can avoid wastage of experimental resources by a targeted collection of data, and the resulting design criteria are computationally easier to evaluate due to the often low-dimensionality of the QoIs. We present two modified design criteria, A-GOODE and D-GOODE, which are natural analogues of classical Bayesian A- and D-optimal criteria. We analyze the connections to other ODE criteria, and provide interpretations for the GOODE criteria by using tools from information theory. Then, we develop an efficient gradient-based optimization framework for solving the GOODE optimization problems. Additionally, we present comprehensive numerical experiments testing the various aspects of the presented approach. The driving application is the optimal placement of sensors to identify the source of contaminants in a diffusion and transport problem. As a result, we enforce sparsity of the sensor placements using an L1-norm penalty approach, and propose a practical strategy for specifying the associated penalty parameter.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Adjoint DSMC for nonlinear Boltzmann equation constrained optimization

Applications for kinetic equations such as optimal design and inverse problems often involve finding unknown parameters through gradient-based optimization algorithms. Based on the adjoint-state method, we derive two different frameworks for approximating the gradient of an objective functional constrained by the nonlinear Boltzmann equation. While the forward problem can be solved by the DSMC method, it is difficult to efficiently solve the high-dimensional continuous adjoint equation obtained by the “optimize-then-discretize” approach. This challenge motivates us to propose an adjoint DSMC method following the “discretize-then-optimize” approach for Boltzmann-constrained optimization. We also analyze the properties of the two frameworks and their connections. Here, several numerical examples are presented to demonstrate their accuracy and efficiency.

97 MATHEMATICS AND COMPUTING↗