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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 1,387 records · Page 77

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NASA NTRS1985

Piloted simulation of an algorithm for onboard control of time-optimal intercept

A piloted simulation of algorithms for onboard computation of trajectories for time-optimal intercept of a moving target by an F-8 aircraft is described. The algorithms, use singular perturbation techniques, generate commands in the cockpit. By centering the horizontal and vertical needles, the pilot flies an approximation to a time-optimal intercept trajectory. Example simulations are shown and statistical data on the pilot's performance when presented with different display and computation modes are described.

Price, D. B.↗

NASA NTRSDate not supplied

An Optimal Estimation Aerosol Retrieval Algorithm for Lidar-only and Combined Lidar Polarimeter Remote Sensors

Global measurements of aerosol vertical profile, composition, concentration, and size distribution are very important due to aerosol impacts on air quality, climate, clouds, and ocean ecosystems. We have developed a lidar-only and combined lidar and polarimeter algorithm to retrieve vertically-resolved profiles of aerosol microphysical properties. The new retrieval system is modular in design. It consists of three modules, a vertically-resolved aerosol profile retrieval module for lidar data, an aerosol and cloud/ocean retrieval module for polarimeter data, and a combined retrieval module for both lidar and polarimeter measurements. In addition to performing optimal estimation retrievals on various data sources, we have designed the system so that it can be used to carry out aerosol retrieval performance trade studies for various lidar and polarimeter configurations. For example, the retrieval system can take inputs from attenuated backscatter lidar at two wavelengths (e.g. CALIPSO like instrument), aerosol backscattering measured at two wavelengths and and aerosol extinction measured at one wavelength (HSRL-1), or aerosol backscattering measured at three wavelengths and aerosol extinction measured at two wavelengths (HSRL-1) (HSRL-2). We have applied the lidar-only algorithm to both simulated data and various field campaign data (DISCOVER-AQ, CHARMS, TCAP, and ORACLES). For polarimeter-only modules, we have done the same for SABOR, TCAP, NAAMES, and ORACLES data. We have applied the combined retrieval algorithm to ORACLES data. The simulated retrieval studies show that the combined lidar+polarimeter retrieval provides much higher information content relative to their individual counterparts for retrieving effective radius, particle concentrations, and absorption properties.

Xu Liu↗

NASA NTRS2004

A Decision-Theoretic Approach to Autonomous Planetary Rover Control

The report discusses the: Decentralized Control of Markov Decision Processes. Study the complexity of decentralized control of Markov decision processes, and develop algorithms for finding optimal control policies. Scheduling Contract Algorithms. Develop an optimal method for scheduling runs of a contract anytime algorithm (one that takes the deadline as input) in situations where the deadline is unknown, multiple problem instances must be solved, and a multi-processor machine is available. Planetary Rover Control as a Markov Decision Process.Use the Markov decision process framework to formalize and solve problems in planetary rover control. Adaptive Peer Selection. Use reinforcement learning to maximize the expected down-load speed for a client in a peer-to-peer file sharing system.

Zilberstein, Shlomo↗

NASA NTRS2001

Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm

A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.

Holst, Terry L.↗

NASA NTRS2010

Overview and Software Architecture of the Copernicus Trajectory Design and Optimization System

The Copernicus Trajectory Design and Optimization System represents an innovative and comprehensive approach to on-orbit mission design, trajectory analysis and optimization. Copernicus integrates state of the art algorithms in optimization, interactive visualization, spacecraft state propagation, and data input-output interfaces, allowing the analyst to design spacecraft missions to all possible Solar System destinations. All of these features are incorporated within a single architecture that can be used interactively via a comprehensive GUI interface, or passively via external interfaces that execute batch processes. This paper describes the Copernicus software architecture together with the challenges associated with its implementation. Additionally, future development and planned new capabilities are discussed. Key words: Copernicus, Spacecraft Trajectory Optimization Software.

Williams, Jacob↗

NASA NTRS2012

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.

Zaychik, Kirill B.↗

NASA NTRS2004

Evolutionary Multiobjective Design Targeting a Field Programmable Transistor Array

This paper introduces the ISPAES algorithm for circuit design targeting a Field Programmable Transistor Array (FPTA). The use of evolutionary algorithms is common in circuit design problems, where a single fitness function drives the evolution process. Frequently, the design problem is subject to several goals or operating constraints, thus, designing a suitable fitness function catching all requirements becomes an issue. Such a problem is amenable for multi-objective optimization, however, evolutionary algorithms lack an inherent mechanism for constraint handling. This paper introduces ISPAES, an evolutionary optimization algorithm enhanced with a constraint handling technique. Several design problems targeting a FPTA show the potential of our approach.

global optimization↗

NASA NTRS1995

Optimal Full Information Synthesis for Flexible Structures Implemented on Cray Supercomputers

This paper considers an algorithm for synthesis of optimal controllers for full information feedback. The synthesis procedure reduces to a single linear matrix inequality which may be solved via established convex optimization algorithms. The computational cost of the optimization is investigated. It is demonstrated the problem dimension and corresponding matrices can become large for practical engineering problems. This algorithm represents a process that is impractical for standard workstations for large order systems. A flexible structure is presented as a design example. Control synthesis requires several days on a workstation but may be solved in a reasonable amount of time using a Cray supercomputer.

Lind, Rick↗

NASA NTRSDate not supplied

Comparison of Multisine Peak Factor Minimization Algorithms for Aircraft System Identification(Presentation)

Two phase-optimized multisine peak factor minimization algorithms are presented and evaluated. The first algorithm minimizes peak factor by iteratively clipping the peaks of generated multisine signals. The second algorithm optimizes peak factor indirectly through minimization of an approximation of the infinity norm of the multisine. Algorithm performance was evaluated as a function of different signal properties, including the number of harmonics, harmonic spacing, and number of snow harmonics (extra harmonics included for further reduction of the peak factor). The two algorithms are compared against results obtained by minimizing peak factor directly using a simplex algorithm, which has been a common approach when designing phase-optimized multisines for system identification flight tests. Sample results show that the clipping and infinity norm algorithms produced multisine signals with comparable peak factors that were lower than that of the simplex algorithm. However, the clipping algorithm runs an order of magnitude faster than the other two algorithms, which also makes it practical to repeat the algorithm multiple times to achieve even lower peak factors.

flight test↗

DOE OSTI2026

Accelerating Traction Motor Optimization Design with AI Surrogate Models

The advancement of artificial intelligence systems enables the use of data-driven physics-based surrogate models to explore design spaces rapidly and deeply for engineering projects. This work presents a surrogate model workflow that accelerates electric traction motor design optimization by replacing finite element analysis (FEA) with an artificial neural network (ANN) and using this model in a genetic algorithm for design optimization. A baseline interior permanent-magnet motor is parameterized and sampled to generate FEA-labeled training data, after which a feed-forward ANN predicts key outputs (e.g., loss components and weight). The validated surrogate enables genetic-algorithm optimization and deep search over the design space without new FEA runs, producing Pareto-optimal trade-offs between weight and losses and set of optimized designs for rapid downselection of manufacturable motor designs.

Ribeiro, Pedro [ORNL] (ORCID:0009000921026641)↗

DOE OSTI2016

SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT protocol parameters with an average DSC of 0.86 ± 0.04 (range: 0.80–0.99).

60 APPLIED LIFE SCIENCES↗

NASA NTRS2002

Pre-Hardware Optimization of Spacecraft Image Processing Algorithms and Hardware Implementation

Spacecraft telemetry rates and telemetry product complexity have steadily increased over the last decade presenting a problem for real-time processing by ground facilities. This paper proposes a solution to a related problem for the Geostationary Operational Environmental Spacecraft (GOES-8) image data processing and color picture generation application. Although large super-computer facilities are the obvious heritage solution, they are very costly, making it imperative to seek a feasible alternative engineering solution at a fraction of the cost. The proposed solution is based on a Personal Computer (PC) platform and synergy of optimized software algorithms, and reconfigurable computing hardware (RC) technologies, such as Field Programmable Gate Arrays (FPGA) and Digital Signal Processors (DSP). It has been shown that this approach can provide superior inexpensive performance for a chosen application on the ground station or on-board a spacecraft.

Kizhner, Semion↗

DOE OSTI2019

Stochastic Optimization for Grid Resilience. FY18 Final Technical Report

The optimal restoration problem lies at the foundation of the evaluation and improvement of resilience in power systems. In this report we present a scalable decomposition algorithm for solving this problem for realistic power systems. The algorithm has been developed in three major research threads, corresponding to the chapters in the present report. Chapter 1 studies mixed-integer programming (MIP) formulations for the requirement that each electrical island that appears after a recon guration must have at least one energized generator. We provide three alternative MIP formulations for this restriction, show their equivalence, and prove that two of them are stronger in terms of their linear programming relaxation than the formulation most commonly used in the power systems literature. Since the time to solve MIPs can vary signi cantly between equivalent formulations, we also present computational experiments on IEEE test systems for the Intentional Controlled Islanding (ICI) and the Black Start Allocation (BSA) problems. We observe that a polynomially separable, exponential in size, strong formulation yields the best performance for the BSA problem and exhibits a comparable performance to a linear in size, weak formulation for the ICI problem. Chapter 2 presents a methodology to generate mixed-integer linear approximations of the AC power ow equations governing lines, transformers (including tap regulators and phase shifters), shunt and series compensators, and consumption responsive to voltage. Our approximations are capable of capturing limitations for power grids under abnormal or uncommon operating conditions where common assumptions used in power ow relaxations may not hold. Particularly, the approximation captures overvoltages under almost no load, which is a major concern when energizing lines after a blackout. The method constructs piecewise linear approximations of the non-linear power ow equations by minimizing the squared approximation error over a subset of the feasible domain. We test the method using a realistic database for the Chilean system (1548 buses, 1114 lines, 564 transformers). The resulting approximations yield root mean square errors in the order of 0.05pu and are successfully applied to solve a transmission switching instance for the Chilean system. Chapter 3 presents our algorithm for optimal restoration. The algorithm works by partitioning the problem into a master problem and a slave problem. The master problem optimizes over energization sequences of generators, buses and branches, for the entire restoration horizon, while respecting the energization requiments studied in Chapter 1. The slave problem veri es that there exist power ow solutions supporting the energization sequences proposed by the master problem, adding cuts to the master if an infeasible sequence is detected.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Energy Minimization of Discrete Protein Titration State Models Using Graph Theory

There are several applications in computational biophysics which require the optimization of discrete interacting states; e.g., amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial-time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of maximum flow-minimum cut graph analysis. The interaction energy graph, a graph in which vertices (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein, and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial-time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered.

energy minimization↗

DOE OSTI2024

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Demers, Steven M. E. (ORCID:0000000192213246)↗

DOE OSTI2022

An agent-based blackboard system for multi-objective optimization

In the field of multi-objective optimization, there are a multitude of algorithms from which to choose. Each algorithm has strengths and weaknesses associated with the mechanics for finding the Pareto front. Recently, researchers have begun to examine how multi-agent environments can be used to help solve multi-objective optimization problems. In this work, we propose a multi-objective optimization algorithm based on a multi-agent blackboard system (MABS). The MABS framework allows for multiple agents to read and write pertinent optimization problem data to a central blackboard agent. Agents can stochastically search the design space, use previously discovered solutions to explore local optima, or update and prune the Pareto front. A centralized blackboard framework allows the optimization problem to be solved in a cohesive manner and permits stopping, restarting, or updating the optimization problem. The MABS framework is tested against three alternative optimization algorithms across a suite of engineering design problems and typically outperforms the other algorithms in discovering the Pareto front. A parallelizability study is performed where we find that the MABS is able to evaluate a set number of designs, which require an evaluation time ranging from 0 to 300 seconds, quicker than a traditional optimization algorithm: this fact becomes more apparent the longer it takes to evaluate a design. To provide context for the benefits provided by MABS, a real-world nuclear engineering design problem is examined. MABS is used to examine the placement of experiments in a nuclear reactor, where we are able to evaluate hundreds of configurations for experimental placement while maintaining a strict set of safety constraints.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks

Hyperparameters employed by deep learning (DL) methods play a substantial role in the performance and reliability of these methods in practice. Unfortunately, finding performance-optimizing hyperparameter settings is a notoriously difficult task. Hyperparameter search methods typically have limited production-strength implementations or do not target scalability within a highly parallel machine, portability across different machines, experimental comparison between different methods, and tighter integration with workflow systems. In this paper, we present DeepHyper, a Python package that provides a common interface for the implementation and study of scalable hyperparameter search methods. It adopts the Balsam workflow system to hide the complexities of running large numbers of hyperparameter configurations in parallel on high-performance computing (HPC) systems. We implement and study asynchronous model-based search methods that consist of sampling a small number of input hyperparameter configurations and progressively fitting surrogate models over the input-output space until exhausting a user-defined budget of evaluations. We evaluate the efficacy of these methods relative to approaches such as random search, genetic algorithms, Bayesian optimization, and hyperband on DL benchmarks on CPU- and GPU-based HPC systems.

Balaprakash, Prasanna↗

DOE OSTI2020

Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization

Abstract Within density functional theory (DFT), adding a Hubbard U correction can mitigate some of the deficiencies of local and semi-local exchange-correlation functionals, while maintaining computational efficiency. However, the accuracy of DFT+U largely depends on the chosen Hubbard U values. We propose an approach to determining the optimal U parameters for a given material by machine learning. The Bayesian optimization (BO) algorithm is used with an objective function formulated to reproduce the band structures produced by more accurate hybrid functionals. This approach is demonstrated for transition metal oxides, europium chalcogenides, and narrow-gap semiconductors. The band structures obtained using the BO U values are in agreement with hybrid functional results. Additionally, comparison to the linear response (LR) approach to determining U demonstrates that the BO method is superior.

Chemistry↗