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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Target tracking via recursive Bayesian state estimation in cognitive radar networks

To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of radars, including consideration of waveform design, path planning, and radar selection, which are separately but not jointly taken into account in existing work. The tracking procedure, built on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), is formulated as two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, an illustrative example introduces a specific scenario in 2-D space. Simulation results based on the scenario demonstrate that the proposed framework can accurately track the target under the management of the network of radars.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Selective recovery of silver from waste low-temperature co-fired ceramic and valorization through silver nanoparticle synthesis

Highlights: • Silver rich LTCC an e-waste has been recycled and valorized. • Selectively, Ag was leached and recovered as nanopowder. • The process can circulate the Ag within the industry in a close loop. • Brings back the material to production stream and address the circular economy. • Can be part of cradle-to-cradle technology and lower the futuristic carbon economy. - Abstract: Considering the value of silver metal and silver nanoparticles, the waste generated during manufacturing of low temperature co-fired ceramic (LTCC) were recycled through the simple yet cost effective process by chemical-metallurgy. Followed by leaching optimization, silver was selectively recovered through precipitation. The precipitated silver chloride was valorized though silver nanoparticle synthesis by a simple one-pot greener synthesis route. Through leaching-precipitation optimization, quantitative selective recovery of silver chloride was achieved, followed by homogeneous pure silver nanoparticle about 100 nm size were synthesized. The reported recycling process is a simple process, versatile, easy to implement, requires minimum facilities and no specialty chemicals, through which semiconductor manufacturing industry can treat the waste generated during manufacturing of LTCC and reutilize the valorized silver nanoparticles in manufacturing in a close loop process. Our reported process can address issues like; (i) waste disposal, as well as value-added silver recovery, (ii) brings back the material to production stream and address the circular economy, and (iii) can be part of lower the futuristic carbon economy and cradle-to-cradle technology management, simultaneously.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1982

Use of optimization to predict the effect of selected parameters on commuter aircraft performance

The relationships between field length and cruise speed and aircraft direct operating cost were determined. A gradient optimizing computer program was developed to minimize direct operating cost (DOC) as a function of airplane geometry. In this way, the best airplane operating under one set of constraints can be compared with the best operating under another. A constant 30-passenger fuselage and rubberized engines based on the General Electric CT-7 were used as a baseline. All aircraft had to have a 600 nautical mile maximum range and were designed to FAR part 25 structural integrity and climb gradient regulations. Direct operating cost was minimized for a typical design mission of 150 nautical miles. For purposes of C sub L sub max calculation, all aircraft had double-slotted flaps but with no Fowler action.

Wells, V. L.↗
DOE OSTI2026

Selection of a Pair of Experiments to Optimally Reduce Uncertainty in Targeted Nuclear Data

We propose a novel process to select a pair of differential and integral experiments that best reduce uncertainties in targeted 239 ⁢Pu nuclear data while compressing the current nuclear data pipeline from 20 to 3 years. 239⁢ Pu nuclear data are poorly understood for neutrons in the intermediate energy range due to sparsity and uncertainty in historical experiments. New experiments targeting this range will enable better understanding of these nuclear data, but choosing the ideal experiments to conduct is challenging. Beginning with a prior distribution represented by samples of nuclear data generated from theory, generalized least squares adjustments are made to incorporate data from historical experiments. To quantify potential uncertainty reduction obtainable from a pair of candidate experiments, we compute the D-optimality criterion of the posterior covariance of intermediate energy range nuclear data compared to the equivalent covariance after additional adjustment to the pair of candidate experiments. Repeating the process for each of many candidate pairs facilitates the final selection. Results support 63⁢ Cu total cross section measurements for differential experiments and alumina and alumina/graphite configurations for integral experiments. This analysis enables choosing differential and integral experiments to be executed concurrently while shortening decision times relative to the current nuclear data pipeline.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2002

Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

Sobieszczanski-Sobieski, Jaroslaw↗
NASA NTRS2004

Launch Vehicle Propulsion Parameter Design Multiple Selection Criteria

The optimization tool described herein addresses and emphasizes the use of computer tools to model a system and focuses on a concept development approach for a liquid hydrogen/liquid oxygen single-stage-to-orbit system, but more particularly the development of the optimized system using new techniques. This methodology uses new and innovative tools to run Monte Carlo simulations, genetic algorithm solvers, and statistical models in order to optimize a design concept. The concept launch vehicle and propulsion system were modeled and optimized to determine the best design for weight and cost by varying design and technology parameters. Uncertainty levels were applied using Monte Carlo Simulations and the model output was compared to the National Aeronautics and Space Administration Space Shuttle Main Engine. Several key conclusions are summarized here for the model results. First, the Gross Liftoff Weight and Dry Weight were 67% higher for the design case for minimization of Design, Development, Test and Evaluation cost when compared to the weights determined by the minimization of Gross Liftoff Weight case. In turn, the Design, Development, Test and Evaluation cost was 53% higher for optimized Gross Liftoff Weight case when compared to the cost determined by case for minimization of Design, Development, Test and Evaluation cost. Therefore, a 53% increase in Design, Development, Test and Evaluation cost results in a 67% reduction in Gross Liftoff Weight. Secondly, the tool outputs define the sensitivity of propulsion parameters, technology and cost factors and how these parameters differ when cost and weight are optimized separately. A key finding was that for a Space Shuttle Main Engine thrust level the oxidizer/fuel ratio of 6.6 resulted in the lowest Gross Liftoff Weight rather than at 5.2 for the maximum specific impulse, demonstrating the relationships between specific impulse, engine weight, tank volume and tank weight. Lastly, the optimum chamber pressure for Gross Liftoff Weight minimization was 2713 pounds per square inch as compared to 3162 for the Design, Development, Test and Evaluation cost optimization case. This chamber pressure range is close to 3000 pounds per square inch for the Space Shuttle Main Engine.

Shelton, Joey Dewayne↗
DOE OSTI2019

Achieving High Selectivity for Alkyne Hydrogenation at High Conversions with Compositionally Optimized PdAu Nanoparticle Catalysts in Raspberry Colloid-Templated SiO 2

Improving the selectivity for catalytic hydrogenation of alkynes is a key step in upgrading feedstocks for olefin polymerization. Herein, dilute Pd x Au 1-x alloy nanoparticles embedded in raspberry colloidtemplated silica (x = 0.02, 0.04, and 0.09) are demonstrated to be highly active and selective for the gas-phase hydrogenation of 1-hexyne, exhibiting higher selectivity than pure Pd at high conversion. The conversion of 1-hexyne remains high even for the very low amounts of Pd in Pd 0.02 Au 0.98 . These catalysts are highly resistant to sintering—addressing a long-standing challenge in the use of Au-based catalysts. Clear evidence is presented that the addition of the second hydrogen to the halfhydrogenated intermediate is the rate-limiting step, and that the stability of the half-hydrogenated intermediate of the alkyne is higher than the half-hydrogenated alkene which explains the high selectivity even at high conversions. Moreover, of the three compositions investigated, optimum selectivity and activity is observed for the nanoparticles containing 4% Pd. The apparent activation energy for production of 1-hexene from 1-hexyne is measured to be 38 kJ mol -1 for the Pd 0.04 Au 0.96 catalysts, which is ~14 kJ mol -1 lower than for pure Pd. The hydrogenation is completely, but reversibly, suppressed by adding CO to the reactant mixture, indicating that the Pd centers are the active sites for reaction. The method of templating used in preparation of the catalysts is highly customizable and versatile. As a result, this study demonstrates that the composition of the nanoparticles as defined by the dilution ratio of Pd in Au and by the method used to make the supported catalyst are important tunable parameters that can be used to optimize activity and selectivity of bimetallic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

An Intelligent System for Automatic Selection of DC-DC Converter Topology with Optimal Design

In this paper, we present an intelligent system that has the capabilities of automatically selecting topology classes and optimizing circuit parameters of DC-DC power converters for a given design specification. The system, Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters (ML-ACCEPT), uses a hybrid of machine learning technologies, decision tree inference, reinforcement learning and deep neural networks. The system gives high accurate recommendations of design topology classes and computationally efficient results in optimizing power efficiencies in power converter design.

Xue, Lincoln↗
DOE OSTI2019

High-Resolution Mass Spectrometry Quantification: Impact of Differences in Data Processing of Centroid and Continuum Data

High-resolution mass spectrometry (HRMS) in full scan mode acquires all ions present in the sample of interest offering a lot of qualitative information. This, in combination with the improved performance of the new generation HRMS systems, triggers more (bio) analysts to switch from triple quad MS systems to HRMS for quantitative analysis. Quantitative processing of HRMS data is performed based on narrow mass extraction windows rather than on nominal mass product ion chromatograms (SRM or MRM). Optimal processing of HRMS data requires different considerations and software tools and can have an impact on data processing and final results. The selection of centroid versus continuum/profile data for processing, selection of the optimal narrow mass extraction window, using theoretical versus measured accurate mass for the extraction of the ion chromatograms as well as differences in calculations and data handling residing in the different vendor software packages are tackled in the presented manuscript. These differences are illustrated on HRMS data acquired for the same plasma samples on three different platforms, i.e., a Sciex QToF, a Waters QToF, and a Thermo Orbitrap system, and processed in four different software packages, i.e., Sciex Analyst® TF, Waters Masslynx, Waters Unifi, and Thermo Xcalibur. The impact of these differences on quantitative HRMS performance was evaluated on calibration curves of eight small molecule compounds in plasma using four different ways of processing. Simple guidelines are provided for the selection of an optimal mass extraction window for continuum and centroided data. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Optimal Communication Topology Determination and Sensor Selection for Independent Airspace Surveillance

The paper presents an approach to sensors selection and network topology determination for independent airspace surveillance with maximum outcome and minimum cost using ground based distributed sensing, computing and communication network infrastructure. The selection criteria includes minimum estimation error, maximum airspace coverage, minimum communication time and power consumption while guaranteeing the system observability and providing in-time quality information to a monitoring observer. The developed algorithm uses multi-objective optimization strategy taking into account trade-offs between conflicting objectives and relaxations for in time implementation. It is implemented utilizing graph theoretic tools. The approach is validated in a desktop simulation environment using synthetic sensors data generated for a simulated multi-vehicle flight scenario in the selected regional airspace.

Distributed sensing↗
NASA NTRSDate not supplied

On-Orbit Performance of the NOAA-21 Advanced Technology Microwave Sounder (ATMS)

The Advanced Technology Microwave Sounder (ATMS) instrument provides sounding measurements of Earth’s atmosphere to collect temperature and water vapor data for NOAA’s Joint Polar Satellite System (JPSS) program. ATMS is a total-power passive microwave radiometer with 22 channels spanning a frequency range from 22 to 183 GHz. A general description of the ATMS instrument is detailed in [1]. Three ATMS units are currently on-orbit. The Suomi National Polar-orbiting Partnership (SNPP) unit was launched in 2011 and the NOAA-20 (previously JPSS-1) unit was launch in 2017. Both SNPP and NOAA-20 ATMS units are currently operational and the data is used for numerical weather prediction (NWP) models. The NOAA-21 (formerly JPSS-2) ATMS unit was launched on November 10, 2022 from Vandenberg Space Force Base in California [2]. The ATMS instrument began generating radiance data on November 21, 2022. At the time of this abstract NOAA-21 ATMS is currently completing on-orbit commissioning and checkout activities; it is expected that the commissioning activities will be complete by the IGARSS 2023 conference. This paper will focus on the on-orbit performance of the NOAA-21 ATMS instrument based on measurements and activities from the commissioning period. Comparisons will be made to specifications and to expected performance based on pre-launch test activities. Pre-launch JPSS-2 (NOAA-21) ATMS performance is detailed in [4]. Additionally, the NOAA-21 performance will be compared to the on-orbit performance of the SNPP and NOAA-21 ATMS units. The post-launch performance of SNPP ATMS is detailed in [1][5][6][7]. The post-launch performance of NOAA-20 ATMS is detailed in [5][7][8]. On-orbit performance is evaluated through data collects from nominal mission operations as well as specific post-launch tests and spacecraft maneuvers. Nominal mission operations can be used to evaluate parameters such as instrument thermal stability, Noise Equivalent Delta Temperature (NEDT)/radiometric sensitivity, geolocation, inter-channel noise correlation, striping, and radiometric bias and stability. Shortly after instrument activation, the instrument thermal stability as measured from onboard temperature sensors is evaluated to demonstrate stable and steady-state behavior. Methods for computing NEDT are described in [9][10][11]. Passive geolocation measurements are performed using the coastline inflection point (CIP) method, described in [8][12]. The striping index, used as a metric to quantify striping, is the ratio of along-track variance to cross-track variance of the observed brightness temperature [4][8][14]. Inter-channel noise correlation has been previously reported for SNPP and NOAA-20 [1][8]. Dedicated post-launch tests and spacecraft maneuvers are utilized during the commissioning phase for further evaluation of on-orbit instrument performance. These activities provide information on the optimal space view selection, noise power spectral density (PSD), gain stability, scan bias, interference from onboard transmitters, reflector emissivity, active geolocation, and radiometric bias and stability detection methods. The optimal space view selection is used to determine which of ATMS space view sectors is preferred for minimizing contamination of the space view [1]. Point and stare testing is used to generate noise PSD and gain stability information, as described in [1][8]. Spacecraft roll and pitch maneuvers allow the ATMS to view different zones, including deep space, and the data can be used to provide information on scan biases and antenna sidelobe contamination [1][8]. The pitch maneuver can also be used to assess interference from onboard Ka-band transmitters and reflector emissivity [8]. Active geolocation is evaluated as a method for geolocation, described in [15].

Edward Kim↗
NASA NTRSDate not supplied

Optimal Communication Topology Construction and Sensor Selection for Independent Airspace Surveillance

The paper presents an approach with no estimation feedback to sensors selection and communication network topology computation for independent airspace surveillance with maximum outcome and minimum cost using ground based distributed sensing, computing and communication network infrastructure. The selection criteria includes maximum airspace coverage with minimal resources, minimum communication time and power consumption while guaranteeing the system observability and providing in-time high quality information to both stationary and mobile users. The developed algorithms use multi-objective optimization strategy taking into account trade-offs between conflicting objectives and are implemented using off-the-shelf computational tools. The algorithms are validated in a desktop simulation environment using synthetic sensors data generated for a simulated multi-vehicle flight scenario in the selected regional airspace and parameters of a notional wireless communication network.

Distributed sensing↗
DOE OSTI2026

Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization

We develop a new methodology to select scenarios of DER adoption most critical for distribution grids. Anticipating risks of future voltage and line flow violations due to additional PV adopters is central for utility investment planning but continues to rely on deterministic or ad hoc scenario selection. We propose a highly efficient search framework based on multi-objective Bayesian Optimization. We treat underlying grid stress metrics as computationally expensive black-box functions, approximated via Gaussian Process surrogates and design an acquisition function based on probability of scenarios being Pareto-critical across a collection of line- and bus-based violation objectives. Our approach provides a statistical guarantee and offers an order of magnitude speed-up relative to a conservative exhaustive search. Case studies on realistic feeders with 200-400 buses demonstrate the effectiveness and accuracy of our approach.

Mulkin, Olivier↗
DOE OSTI2023

Stochastic optimization of a uranium oxide reaction mechanism using plasma flow reactor measurements

Abstract In this work, a coupled Monte Carlo Genetic Algorithm (MCGA) approach is used to optimize a gas phase uranium oxide reaction mechanism based on plasma flow reactor (PFR) measurements. The PFR produces a steady Ar plasma containing U, O, H, and N species with high temperature regions (3000–5000 K) relevant to observing UO formation via optical emission spectroscopy. A global kinetic treatment is used to model the chemical evolution in the PFR and to produce synthetic emission signals for direct comparison with experiments. The parameter space of a uranium oxide reaction mechanism is then explored via Monte Carlo sampling using objective functions to quantify the model-experiment agreement. The Monte Carlo results are subsequently refined using a genetic algorithm to obtain an experimentally corroborated set of reaction pathways and rate coefficients. Out of 12 reaction channels targeted for optimization, four channels are found to be well constrained across all optimization runs while another three channels are constrained in select cases. The optimized channels highlight the importance of the OH radical in oxidizing uranium in the PFR. This study comprises a first step toward producing a comprehensive experimentally validated reaction mechanism for gas phase uranium molecular species formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1981

Optimal maneuvers at supersonic speeds in a vertical plane

This paper presents several optimal maneuvers of supersonic aircraft in a vertical plane. The general dimensionless equations of motion are derived and the computation of the optimal trajectories are carried out using the aerodynamics and engine characteristics of a light-weight fighter called the supercruiser. Because of the normalizing of the control variables, namely, the load factor and the thrust-to-weight ratio, the results applied to any supersonic aircraft. The optimality of the singular thrust control and the optimal junction of different subarcs are discussed. The proposed method of computing the optimal trajectory is very efficient and makes explicit the selection of the optimal control. The technique should be useful for performance assessment of supersonic aircraft with potential for implementation of onboard flight control system.

Vinh, N. X.↗
NASA NTRS1992

Approximation, abstraction and decomposition in search and optimization

In this paper, I discuss four different areas of my research. One portion of my research has focused on automatic synthesis of search control heuristics for constraint satisfaction problems (CSPs). I have developed techniques for automatically synthesizing two types of heuristics for CSPs: Filtering functions are used to remove portions of a search space from consideration. Another portion of my research is focused on automatic synthesis of hierarchic algorithms for solving constraint satisfaction problems (CSPs). I have developed a technique for constructing hierarchic problem solvers based on numeric interval algebra. Another portion of my research is focused on automatic decomposition of design optimization problems. We are using the design of racing yacht hulls as a testbed domain for this research. Decomposition is especially important in the design of complex physical shapes such as yacht hulls. Another portion of my research is focused on intelligent model selection in design optimization. The model selection problem results from the difficulty of using exact models to analyze the performance of candidate designs.

Ellman, Thomas↗
NASA NTRS2014

Selection of Thermal Worst-Case Orbits via Modified Efficient Global Optimization

Efficient Global Optimization (EGO) was used to select orbits with worst-case hot and cold thermal environments for the Stratospheric Aerosol and Gas Experiment (SAGE) III. The SAGE III system thermal model changed substantially since the previous selection of worst-case orbits (which did not use the EGO method), so the selections were revised to ensure the worst cases are being captured. The EGO method consists of first conducting an initial set of parametric runs, generated with a space-filling Design of Experiments (DoE) method, then fitting a surrogate model to the data and searching for points of maximum Expected Improvement (EI) to conduct additional runs. The general EGO method was modified by using a multi-start optimizer to identify multiple new test points at each iteration. This modification facilitates parallel computing and decreases the burden of user interaction when the optimizer code is not integrated with the model. Thermal worst-case orbits for SAGE III were successfully identified and shown by direct comparison to be more severe than those identified in the previous selection. The EGO method is a useful tool for this application and can result in computational savings if the initial Design of Experiments (DoE) is selected appropriately.

Moeller, Timothy M.↗