Search NASA⌕ Search

SEARCH · Search NASA

Results for “optimization algorithm”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Comparison of external beam treatment techniques for T1-2, N0, M0 glottic cancers

The purpose of this study was to compare 4 different external beam radiation therapy treatment techniques for the treatment of T1-2, N0, M0 glottic cancers: traditional lateral beams with wedges (3D), 5-field intensity-modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), and proton therapy. Treatment plans in each technique were created for 10 patients using consistent planning parameters. The photon treatment plans were optimized using Philips Pinnacle{sub 3} v.9 and the IMRT and VMAT plans used the Direct Machine Parameter Optimization algorithm. The proton treatment plans were optimized using Varian Eclipse Proton v.8.9. The prescription used for each plan was 63 Gy in 28 fractions. The contours for spinal cord, right carotid artery, left carotid artery, and normal tissue were created with respect to the patient's bony anatomy so that proper comparisons of doses could be made with respect to volume. An example of the different isodose distributions will be shown. The data collection for comparison purposes includes: clinical treatment volume coverage, dose to spinal cord, dose to carotid arteries, and dose to normal tissue. Data comparisons will be displayed graphically showing the maximum, mean, median, and ranges of doses.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS1988

Algorithm for in-flight gyroscope calibration

An optimal algorithm for the in-flight calibration of spacecraft gyroscope systems is presented. Special consideration is given to the selection of the loss function weight matrix in situations in which the spacecraft attitude sensors provide significantly more accurate information in pitch and yaw than in roll, such as will be the case in the Hubble Space Telescope mission. The results of numerical tests that verify the accuracy of the algorithm are discussed.

Davenport, P. B.↗
DOE OSTI2012

Multi-core and many-core shared-memory parallel raycasting volume rendering optimization and tuning

Given the computing industry trend of increasing processing capacity by adding more cores to a chip, the focus of this work is tuning the performance of a staple visualization algorithm, raycasting volume rendering, for shared-memory parallelism on multi-core CPUs and many-core GPUs. Our approach is to vary tunable algorithmic settings, along with known algorithmic optimizations and two different memory layouts, and measure performance in terms of absolute runtime and L2 memory cache misses. Our results indicate there is a wide variation in runtime performance on all platforms, as much as 254% for the tunable parameters we test on multi-core CPUs and 265% on many-core GPUs, and the optimal configurations vary across platforms, often in a non-obvious way. For example, our results indicate the optimal configurations on the GPU occur at a crossover point between those that maintain good cache utilization and those that saturate computational throughput. This result is likely to be extremely difficult to predict with an empirical performance model for this particular algorithm because it has an unstructured memory access pattern that varies locally for individual rays and globally for the selected viewpoint. Our results also show that optimal parameters on modern architectures are markedly different from those in previous studies run on older architectures. In addition, given the dramatic performance variation across platforms for both optimal algorithm settings and performance results, there is a clear benefit for production visualization and analysis codes to adopt a strategy for performance optimization through auto-tuning. These benefits will likely become more pronounced in the future as the number of cores per chip and the cost of moving data through the memory hierarchy both increase.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Multi-core and Many-core Shared-memory Parallel Raycasting Volume Rendering Optimization and Tuning

Given the computing industry trend of increasing processing capacity by adding more cores to a chip, the focus of this work is tuning the performance of a staple visualization algorithm, raycasting volume rendering, for shared-memory parallelism on multi-core CPUs and many-core GPUs. Our approach is to vary tunable algorithmic settings, along with known algorithmic optimizations and two different memory layouts, and measure performance in terms of absolute runtime and L2 memory cache misses. Our results indicate there is a wide variation in runtime performance on all platforms, as much as 254% for the tunable parameters we test on multi-core CPUs and 265% on many-core GPUs, and the optimal configurations vary across platforms, often in a non-obvious way. For example, our results indicate the optimal configurations on the GPU occur at a crossover point between those that maintain good cache utilization and those that saturate computational throughput. This result is likely to be extremely difficult to predict with an empirical performance model for this particular algorithm because it has an unstructured memory access pattern that varies locally for individual rays and globally for the selected viewpoint. Our results also show that optimal parameters on modern architectures are markedly different from those in previous studies run on older architectures. And, given the dramatic performance variation across platforms for both optimal algorithm settings and performance results, there is a clear benefit for production visualization and analysis codes to adopt a strategy for performance optimization through auto-tuning. These benefits will likely become more pronounced in the future as the number of cores per chip and the cost of moving data through the memory hierarchy both increase.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Optimizing Medical Kits for Spaceflight

The Integrated Medical Model (IMM) is a probabilistic model that estimates medical event occurrences and mission outcomes for different mission profiles. IMM simulation outcomes describing the impact of medical events on the mission may be used to optimize the allocation of resources in medical kits. Efficient allocation of medical resources, subject to certain mass and volume constraints, is crucial to ensuring the best outcomes of in-flight medical events. We implement a new approach to this medical kit optimization problem. METHODS We frame medical kit optimization as a modified knapsack problem and implement an algorithm utilizing a dynamic programming technique. Using this algorithm, optimized medical kits were generated for 3 different mission scenarios with the goal of minimizing the probability of evacuation and maximizing the Crew Health Index (CHI) for each mission subject to mass and volume constraints. Simulation outcomes using these kits were also compared to outcomes using kits optimized..RESULTS The optimized medical kits generated by the algorithm described here resulted in predicted mission outcomes more closely approached the unlimited-resource scenario for Crew Health Index (CHI) than the implementation in under all optimization priorities. Furthermore, the approach described here improves upon in reducing evacuation when the optimization priority is minimizing the probability of evacuation. CONCLUSIONS This algorithm provides an efficient, effective means to objectively allocate medical resources for spaceflight missions using the Integrated Medical Model.

Optimization↗
DOE OSTI2013

Provably optimal parallel transport sweeps on regular grids

We have found provably optimal algorithms for full-domain discrete-ordinate transport sweeps on regular grids in 3D Cartesian geometry. We describe these algorithms and sketch a 'proof that they always execute the full eight-octant sweep in the minimum possible number of stages for a given P{sub x} x P{sub y} x P{sub z} partitioning. Computational results demonstrate that our optimal scheduling algorithms execute sweeps in the minimum possible stage count. Observed parallel efficiencies agree well with our performance model. An older version of our PDT transport code achieves almost 80% parallel efficiency on 131,072 cores, on a weak-scaling problem with only one energy group, 80 directions, and 4096 cells/core. A newer version is less efficient at present-we are still improving its implementation - but achieves almost 60% parallel efficiency on 393,216 cores. These results conclusively demonstrate that sweeps can perform with high efficiency on core counts approaching 10{sup 6}. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Explainable Neural Architecture Search (XNAS)

Code for the paper Learning Interpretable Models Through Multi-Objective Neural Architecture Search by Zachariah Carmichael, Tim Moon, and Sam Ade Jacobs. Monumental advances in deep learning have led to unprecedented achievements across a multitude of domains. While the performance of deep neural networks is indubitable, the architectural design and interpretability of such models are nontrivial. Research has been introduced to automate the design of neural network architectures through neural architecture search (NAS). Recent progress has made these methods more pragmatic by exploiting distributed computation and novel optimization algorithms. However, there is little work in optimizing architectures for interpretability. To this end, we propose a multiobjective distributed NAS framework that optimizes for both task performance and introspection. We leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable AI (XAI) techniques to reward architectures that can be better comprehended by humans. The framework is evaluated on several image classification datasets. We demonstrate that jointly optimizing for introspection ability and task error leads to more disentangled architectures that perform within tolerable error.

Carmichael, ZachariahJ↗
DOE OSTI2023

Enhancing flexibility for climate change using seasonal energy storage (aquifer thermal energy storage) in distributed energy systems

Long-term energy storage is expected to play a vital role in the deep decarbonization of building energy sectors, while enhancing the flexibility of buildings to withstand future climate variations. However, it is challenging to design distributed multi-energy systems (DMES) while taking into account the uncertainties introduced by climate change, since stochastic optimization of such systems is difficult. The present study introduces a stochastic optimization model to address this bottleneck, taking into account DMES including aquifer thermal energy storage (ATES) as the long-term thermal storage. For the first time, a novel optimization algorithm links ATES with the DMES optimization model with the support of a simplified geotechnical model. Subsequently, a case study was conducted, focusing on a residential district in Chicago where the impact of future climate condition, energy demand, and solar and wind energy potentials were evaluated using Weather Research and Forecasting (WRF) data (up to 2080) and the EnergyPlus model. The study revealed that ATES is an attractive way to improve the renewable energy penetration level and minimize the dependence on fossil fuels with reasonable support from the grid to assist the fluctuations in both demand and generation. Furthermore, ATES notably reduces fuel consumption and dependence while greatly enhancing the flexibility of the energy system to withstand fluctuations in demand and renewable energy generation brought by future climate variations. These qualities will make ATES an important part of distributed energy systems, even though it is not currently the lowest cost alternative due to lack of technology maturity. Furthermore, the design platform introduced in the present study can be used to design DMES enhancing flexibility to accommodate future climate variations.

25 ENERGY STORAGE↗
DOE OSTI2011

Optimizing entangling quantum gates for physical systems

Optimal control theory is a versatile tool that presents a route to significantly improving figures of merit for quantum information tasks. We combine it here with the geometric theory for local equivalence classes of two-qubit operations to derive an optimization algorithm that determines the best entangling two-qubit gate for a given physical setting. We demonstrate the power of this approach for trapped polar molecules and neutral atoms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Feedback Optimization of Incentives for Distribution Grid Services

Energy prices and net power injection limitations regulate the operations in distribution grids and typically ensure that operational constraints are met. Nevertheless, unexpected or prolonged abnormal events could undermine the grid's functioning. During contingencies, customers could contribute effectively to sustaining the network by providing services. Herein this paper proposes an incentive mechanism that promotes users' active participation by essentially altering the energy pricing rule. The incentives are modeled via a linear function whose parameters can be computed by the system operator (SO) by solving an optimization problem. Feedback-based optimization algorithms are then proposed to seek optimal incentives by leveraging measurements from the grid, even in the case when the SO does not have a full grid and customer information. Numerical simulations on a standard testbed validate the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

WE-E-17A-07: Patient-Specific Mathematical Neuro-Oncology: Biologically-Informed Radiation Therapy and Imaging Physics

Purpose: To demonstrate a method of generating patient-specific, biologically-guided radiation therapy (RT) plans and to quantify and predict response to RT in glioblastoma. We investigate the biological correlates and imaging physics driving T2-MRI based response to radiation therapy using an MRI simulator. Methods: We have integrated a patient-specific biomathematical model of glioblastoma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated RT optimization to construct individualized, biologically-guided plans. Patient-individualized simulations of the standard-of-care and optimized plans are compared in terms of several biological metrics quantified on MRI. An extension of the PI model is used to investigate the role of angiogenesis and its correlates in glioma response to therapy with the Proliferation-Invasion-Hypoxia- Necrosis-Angiogenesis model (PIHNA). The PIHNA model is used with a brain tissue phantom to predict tumor-induced vasogenic edema, tumor and tissue density that is used in a multi-compartmental MRI signal equation for generation of simulated T2- weighted MRIs. Results: Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized RT plans would have a significant impact on delaying tumor progression, with Days Gained increases from 21% to 105%. For the T2- MRI simulations, initial validation tests compared average simulated T2 values for white matter, tumor, and peripheral edema to values cited in the literature. Simulated results closely match the characteristic T2 value for each tissue. Conclusion: Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for RT generated biologically-guided doses that decreased normal tissue dose and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma. Simulated T2-MRI is shown to be consistent with known physics of MRI and can be used to further investigate biological drivers of imaging-based response to RT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

PyBioNetFit

Systems biology models written in the BioNetGen language often contain parameters that must be fit based on experimental data. PyBioNetFit is a software tool designed to fit parameters of BioNetGen models. The user is expected to provide a BioNetGen model that contains unknown parameters, along with one or more text files containing experimental data. PyBioNetFit will perform a series of calls to the BioNetGen simulator using different parameter sets, to identify the parameter set that best fits the data. The parameter search is dictated by the user's choice of one of several metaheuristic optimization algorithms, including differential evolution, particle swarm optimization, scatter search, and simulated annealing. The software supports parallelized computing on multi-core computers and on computing clusters. We expect this software to be a useful tool for researchers designing BioNetGen models in collaboration with experimentalists.

Mitra, Eshan↗
DOE OSTI2022

Harmonized Automatic Relay Mitigation of Nefarious Intentional Events (HARMONIE) - Special Protection Scheme (SPS)

The harmonized automatic relay mitigation of nefarious intentional events (HARMONIE) special protection scheme (SPS) was developed to provide adaptive, cyber-physical response to unpredictable disturbances in the electric grid. The HARMONIE-SPS methodology includes a machine learning classification framework that analyzes real time cyber-physical data and determines if the system is in normal conditions, cyber disturbance, physical disturbance, or cyber-physical disturbance. This classification then informs response, if needed and/or suitable, and included cyber-physical corrective actions. Beyond standard power system mitigations, a few novel approaches were developed that included a consensus algorithm-based relay voting scheme, an automated power system triggering condition and corrective action pairing algorithm, and a cyber traffic routing optimization algorithm. Both the classification and response techniques were tested within a newly integrated emulation environment composed of a real-time digital simulator (RTDS) and SCEPTRE™. This report details the HARMONIE-SPS methodology, highlighting both the classification and response techniques, and the subsequent testing results from the emulation environment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimized Test Section for Helium Flow Loop Experiment

Phase 3 of this project uses the knowledge and techniques gained from Phase 1 and Phase 2. The first outcome of Phase 3 is a new test section which can be manufactured and tested in the Helium Flow Loop Experiment to verify the results of the optimization study. The second outcome of Phase 3 is analyzing the features of this design and determining its performance under simulated TBM conditions: heat flux, pressure, mass flow rate, one-sided and volumetric heating. This will be accomplished by first using the results from the Phase 1 analyses and then applying the optimization strategy from Phase This phase uses the STAR-CCM+ Design Manager tool to apply the optimization strategy to the Helium Flow Loop Experiment (HFLE). The optimization algorithm used by the Design Manager tool is Simultaneous Hybrid Exploration that is Robust, Progressive, and Adaptive (SHERPA search algorithm). In addition to demonstrating the optimization features, a simple parameter sweep is also included in this phase. This report details the applied single objective optimization.

42 ENGINEERING↗
DOE OSTI2026

Three-Dimensional Grid Visualization for Planning Activities: A Dubai Case Study

National Laboratory of the Rockies (NLR), in collaboration with the Dubai Electricity and Water Authority (DEWA) and Infra-X, has undertaken the Energy Visualization Analysis Project. The aim of this project is to enhance analytical and 3D visualization capabilities for distribution network planning and renewable energy integration. As modern grid continues to evolve with large-scale solar PV deployment and emerging distributed energy resources (DERs), the ability to effectively analyze, visualize, and communicate complex grid behaviors has become increasingly critical. The project focuses on developing empirical use cases based on real distribution feeder data and engineering workflows, ensuring the outcomes are directly aligned with operational environment. Through time-series power flow simulations and nodal hosting capacity analysis, the study quantifies the impacts of high PV penetration on voltage and thermal limits within representative 11 kV feeders. These analyses identify specific nodes and conditions where DER integration challenges arise. Furthermore, a Battery Energy Storage System (BESS) optimization algorithm was applied to determine the optimal size and placement of storage systems that can mitigate network constraints and enhance hosting capacity. The comparative results between base-case and BESS-augmented scenarios clearly demonstrate improvements in network stability and load management efficiency. In parallel, the NLR team developed an immersive 3D visualization framework, enabling interactive exploration of grid simulations using commodity head-mounted display (HMD) systems. This framework transforms conventional 2D simulation data into spatially intuitive visual environments - allowing engineers to analyze feeder conditions, PV hosting potential, and BESS effects in real time. This report represents the first foundational phase in establishing a visualization-driven analytical ecosystem. It provides a methodological foundation for data integration, visualization architecture, and simulation-based decision support, paving the way for large-scale adoption of immersive visualization across DEWA's Smart Grid Initiative, R&D activities, and future network resilience studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1991

Performance optimization of helicopter rotor blades

As part of a center-wide activity at NASA Langley Research Center to develop multidisciplinary design procedures by accounting for discipline interactions, a performance design optimization procedure is developed. The procedure optimizes the aerodynamic performance of rotor blades by selecting the point of taper initiation, root chord, taper ratio, and maximum twist which minimize hover horsepower while not degrading forward flight performance. The procedure uses HOVT (a strip theory momentum analysis) to compute the horse power required for hover and the comprehensive helicopter analysis program CAMRAD to compute the horsepower required for forward flight and maneuver. The optimization algorithm consists of the general purpose optimization program CONMIN and approximate analyses. Sensitivity analyses consisting of derivatives of the objective function and constraints are carried out by forward finite differences. The procedure is applied to a test problem which is an analytical model of a wind tunnel model of a utility rotor blade.

Walsh, Joanne L.↗
NASA NTRS1993

Algorithms For Segmentation Of Complex-Amplitude SAR Data

Several algorithms implement improved method of segmenting highly speckled, high-resolution, complex-amplitude synthetic-aperture-radar (SAR) digitized images into regions, within each backscattering characteristics similar or homogeneous from place to place. Method provides for approximate, deterministic solution by two alternative algorithms almost always converging to local minimums: one, Iterative Conditional Modes (ICM) algorithm, which locally maximizes posterior probability density of region labels; other, Maximum Posterior Marginal (MPM) algorithm, which maximizes posterior marginal density of region labels at each pixel location. ICM algorithm optimizes reconstruction of underlying scene. MPM algorithm minimizes expected number of misclassified pixels, possibly better in remote sensing of natural scenes.

Rignot, Eric J. M.↗
NASA NTRS1993

An analytical/numerical correlation study of the multiple concentric cylinder model for the thermoplastic response of metal matrix composites

The utility of a recently developed analytical micromechanics model for the response of metal matrix composites under thermal loading is illustrated by comparison with the results generated using the finite-element approach. The model is based on the concentric cylinder assemblage consisting of an arbitrary number of elastic or elastoplastic sublayers with isotropic or orthotropic, temperature-dependent properties. The elastoplastic boundary-value problem of an arbitrarily layered concentric cylinder is solved using the local/global stiffness matrix formulation (originally developed for elastic layered media) and Mendelson's iterative technique of successive elastic solutions. These features of the model facilitate efficient investigation of the effects of various microstructural details, such as functionally graded architectures of interfacial layers, on the evolution of residual stresses during cool down. The available closed-form expressions for the field variables can readily be incorporated into an optimization algorithm in order to efficiently identify optimal configurations of graded interfaces for given applications. Comparison of residual stress distributions after cool down generated using finite-element analysis and the present micromechanics model for four composite systems with substantially different temperature-dependent elastic, plastic, and thermal properties illustrates the efficacy of the developed analytical scheme.

Pindera, Marek-Jerzy↗