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Results for “Optimization algorithms”

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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

WE-AB-303-07: Computational Monitoring Framework of Four-Dimensional Dose Distributions During Treatment Time Based On a 2D/3D Registration with Adaptive Transformation Parameters in Lung Stereotactic Body Radiotherapy

Purpose: The aim of this study was to develop a computational framework for monitoring four-dimensional (4D) dose distributions during treatment time based on a 2D/3D registration with adaptive transformation parameters (ATPs) in lung stereotactic body radiotherapy (SBRT). Methods: The 4D dose distributions during the SBRT were calculated by applying a pencil beam convolution (PBC) algorithm to simulated 4D-computed tomography (CT) images during treatment time (hereinafter referred to as “treatment” 4D-CT images). The “treatment” 4D-CT images were derived by deforming 3D planning CT images by using transformation parameters so that 2D planning portal dose images (PDIs) can resemble 2D dynamic clinical PDIs in each frame, which were derived from electronic portal imaging device (EPID) images. The transformation parameters were optimized by the Levenberg- Marquardt (LM) algorithm. The optimized transformation parameters in a certain frame were adaptively employed as initial transformation parameters for optimization of the parameters in the consecutive frame. Gamma pass rates (3mm/3%) between dynamic clinical PDIs and dynamic “treatment” PDIs derived from the “treatment” 4D-CT images were measured for comparing the proposed frameworks without and with the ATPs. These proposed frameworks were applied to the EPID dynamic images (40 frames) of two patients with lung cancer, who underwent the SBRT. Results: Gamma pass rates without and with the ATPs were 83.01±4.42% and 89.31±3.18% on average, respectively (p<0.05). Moreover, percentage errors between prescribed doses and estimated doses at an isocenter by the proposed frameworks without and with the ATPs were 10.67±5.66% and 6.34±0.47% on average, respectively (p<0.05). Conclusion: The proposed framework with ATPs could be useful to ensure the quality of the SBRT by monitoring 4D dose distributions during treatment time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Large gradients via correlation in random parameterized quantum circuits

Scaling of variational quantum algorithms to large problem sizes requires efficient optimization of random parameterized quantum circuits. For such circuits with uncorrelated parameters, the presence of exponentially vanishing gradients in cost function landscapes is an obstacle to optimization by gradient descent methods. In this work, we prove that reducing the dimensionality of the parameter space by utilizing circuit modules containing spatially or temporally correlated gate layers can allow one to circumvent the vanishing gradient phenomenon. Here, examples are drawn from random separable circuits and asymptotically optimal variational versions of Grover's algorithm based on the quantum alternating operator ansatz. In the latter scenario, our bounds on cost function variation imply a transition between vanishing gradients and efficient trainability as the number of layers is increased toward $\mathcal{O}\left({2}^{n/2}\right)$, the optimal oracle complexity of quantum unstructured search.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

H ∞ Control for Energy Dispatch in Autonomous Nanogrid With Communication Delays

This paper proposes an optimal controller and estimator for energy dispatch to balance the power supply and demand considering communication delays. The proposed algorithm involves modeling an autonomous nanogrid (ANG) consisting of distributed energy resources, energy storage systems, loads, an $H$ ∞ controller with a reference power modulation technique, and a state estimator. The ANG was developed to express the dynamic supply-demand energy balance of a nanogird system. Reference power modulation was designed to generate the desired ESS power based on the imbalanced energy. Random communication delays were modeled using a stochastic variable satisfying the Bernoulli random binary distribution. The optimal $H$ ∞ controller and estimator were developed using a linear matrix inequality approach to exponentially stabilize the closed-loop system. Simulations were performed using real daily demand forecasts obtained from the Korea Meteorological Administration to demonstrate the effectiveness of the proposed real-time optimization algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

TH-AB-202-09: Direct-Aperture Optimization for Combined MV+kV Dose Planning in Fluoroscopic Real-Time Tumor-Tracking Radiation Therapy

Purpose: Real-time kV fluoroscopic tumor tracking has the benefit of direct tumor position monitoring. However, there is clinical concern over the excess kV imaging dose cost to the patient when imaging in continuous fluoroscopic mode. This work addresses this specific issue by proposing a combined MV+kV direct-aperture optimization (DAO) approach to integrate the kV imaging beam into a treatment planning such that the kV radiation is considered as a contributor to the overall dose delivery. Methods: The combined MV+kV DAO approach includes three algorithms. First, a projected Quasi-Newton algorithm (L-BFGS) is used to find optimized fluence with MV+kV dose for the best possible dose distribution. Then, Engel’s algorithm is applied to optimize the total number of monitor units and heuristically optimize the number of apertures. Finally, an aperture shape optimization (ASO) algorithm is applied to locally optimize the leaf positions of MLC. Results: Compared to conventional DAO MV plans with continuous kV fluoroscopic tracking, combined MV+kV DAO plan leads to a reduction in the total number of MV monitor units due to inclusion of kV dose as part of the PTV, and was also found to reduce the mean and maximum doses on the organs at risk (OAR). Compared to conventional DAO MV plan without kV tracking, the OAR dose in the combined MV+kV DAO plan was only slightly higher. DVH curves show that combined MV+kV DAO plan provided about the same PTV coverage as that in the conventional DAO plans without kV imaging. Conclusion: We report a combined MV+kV DAO approach that allows real time kV imager tumor tracking with only a trivial increasing on the OAR doses while providing the same coverage to PTV. The approach is suitable for clinic implementation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Multi-agent voltage control in distribution systems using GAN-DRL-based approach

Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. Further, this paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios.

14 SOLAR ENERGY↗
DOE OSTI2019

HyperSpace

Machine learning (ML) models often contain numerous hyperparameters, free parameters that must be set before the models can be trained. As the number of model hyperparameters increases, their optimization becomes significantly more challenging as we face a combinatorial increase in potential model configurations. Similarly, there is an increased chance that our models’ hyperparameters interact in complex ways. HyperSpace allows a user to optimize complex machine learning algorithms based on their hyperparameters. HyperSpace works by parallelizing parameter search spaces, running Bayesian model based optimization (SMBO) over each of these spaces in parallel. It was designed to be as minimally invasive as possible such that very little change to existing code will be needed to get a user started.

Ramanathan, Arvind↗
DOE OSTI2014

Sci—Thur AM: YIS - 07: Design and production of 3D printed bolus for electron radiation therapy

This is a proof-of-concept study demonstrating the capacity for modulated electron radiation therapy (MERT) using 3D printed bolus. Previous reports have involved bolus design using an electron pencil beam model and fabrication using a milling machine. In this study, an in-house algorithm is presented that optimizes the dose distribution with regard to dose coverage, conformity and homogeneity within planning target volume (PTV). The algorithm uses calculated result of a commercial electron Monte Carlo dose calculation as input. Distances along ray lines from distal side of 90% isodose to distal surface of PTV are used to estimate the bolus thickness. Inhomogeneities within the calculation volume are accounted for using coefficient of equivalent thickness method. Several regional modulation operators are applied to improve dose coverage and uniformity. The process is iterated (usually twice) until an acceptable MERT plan is realized, and the final bolus is printed using solid polylactic acid. The method is evaluated with regular geometric phantoms, anthropomorphic phantoms and a clinical rhabdomyosarcoma pediatric case. In all cases the dose conformity is improved compared to that with uniform bolus. The printed boluses conform well to the surface of complex anthropomorphic phantoms. For the rhabdomyosarcoma patient, the MERT plan yields a reduction of mean dose by 38.2% in left kidney relative to uniform bolus. MERT using 3D printed bolus appears to be a practical, low cost approach to generating optimized bolus for electron therapy. The method is effective in improving conformity of prescription isodose surface and in sparing immediately adjacent normal tissues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

NO-LESS: Near optimal curtailment strategy selection for net load balancing in micro grids

The integration of renewable energy sources such as solar PVs into micro grids has increased in recent years. However, the intermittent and unpredictable nature of renewable energy generation makes supply uncertain, in turn requiring continuous net load balancing to ensure grid stability. One technique to achieve net load balancing is curtailment, however, selection of the optimal set of curtailment strategies to achieve a targeted curtailment is NP-hard. State-of-the-art curtailment selection techniques approach this problem by either developing computationally expensive optimal curtailment strategies or reduce accuracy to generate sub-optimal solutions quickly. In this work, we develop NO-LESS: a Near OptimaL CurtailmEnt Strategy Selection algorithm. NO-LESS is a novel Fully Polynomial Time Approximation Scheme (FPTAS) which determines bounded (near optimal) curtailment strategies in a small amount of time while simultaneously satisfying practical constraints on strategy switching overhead and curtailment fairness. We perform both theoretical analysis and practical evaluation to show that NO-LESS is a scalable solution for performing net load balancing through curtailment strategy selection in Micro Grids.

14 SOLAR ENERGY↗
DOE OSTI2019

Scalable Algorithms for MPI Intergroup Allgather and Allgatherv

MPI intergroup collective communication defines message transfer patterns between two disjoint groups of MPI processes. Such patterns occur in coupled applications, and in modern scientific application workflows, mostly with large data sizes. However, current implementations in MPI production libraries adopt the “root gathering algorithm”, which does not achieve optimal communication transfer time. In this paper, we propose algorithms for the intergroup Allgather and Allgatherv communication operations under single-port communication constraints. Here we implement the new algorithms using MPI point-to-point and standard intra-communicator collective communication functions. We evaluate their performance on the Cori supercomputer at NERSC. Using message sizes per compute node ranging from 64KBytes to 8MBytes, our experiments show significant performance improvements of up to 23.67 times on 256 compute nodes compared with the implementations of production MPI libraries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer

We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for sparse reconstruction. On a test dataset, the dependent variable is initialized to its average value and then a sparse reconstruction of the combined vector is obtained in which the dependent variable is typically shifted closer to its true value, as in a standard inpainting or denoising task. Here, a quantum annealer, which can presumably exploit a fully entangled initial state to better explore the complex energy landscape, is used to solve the highly non-convex sparse coding optimization problem. The regression algorithm is demonstrated for a lattice quantum chromodynamics simulation data using a D-Wave 2000Q quantum annealer and good prediction performance is achieved. The regression test is performed using six different values for the number of fully connected logical qubits, between 20 and 64. The scaling results indicate that a larger number of qubits gives better prediction accuracy.

97 MATHEMATICS AND COMPUTING↗