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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Observing System Simulations for Small Satellite Formations Estimating Bidirectional Reflectance

The bidirectional reflectance distribution function (BRDF) gives the reflectance of a target as a function of illumination geometry and viewing geometry, hence carries information about the anisotropy of the surface. BRDF is needed in remote sensing for the correction of view and illumination angle effects (for example in image standardization and mosaicing), for deriving albedo, for land cover classification, for cloud detection, for atmospheric correction, and other applications. However, current spaceborne instruments provide sparse angular sampling of BRDF and airborne instruments are limited in the spatial and temporal coverage. To fill the gaps in angular coverage within spatial, spectral and temporal requirements, we propose a new measurement technique: Use of small satellites in formation flight, each satellite with a VNIR (visible and near infrared) imaging spectrometer, to make multi-spectral, near-simultaneous measurements of every ground spot in the swath at multiple angles. This paper describes an observing system simulation experiment (OSSE) to evaluate the proposed concept and select the optimal formation architecture that minimizes BRDF uncertainties. The variables of the OSSE are identified; number of satellites, measurement spread in the view zenith and relative azimuth with respect to solar plane, solar zenith angle, BRDF models and wavelength of reflection. Analyzing the sensitivity of BRDF estimation errors to the variables allow simplification of the OSSE, to enable its use to rapidly evaluate formation architectures. A 6-satellite formation is shown to produce lower BRDF estimation errors, purely in terms of angular sampling as evaluated by the OSSE, than a single spacecraft with 9 forward-aft sensors. We demonstrate the ability to use OSSEs to design small satellite formations as complements to flagship mission data. The formations can fill angular sampling gaps and enable better BRDF products than currently possible.

small satellite↗
NASA NTRS2018

New Crop Selection

For extended space flight, reliable food supplies are a necessity. Most of the food products consumed by astronauts today are stored for flight via freeze drying. Fresh food is needed to supplement known national deficiencies in the stored food diet (Cooper et al.). This is so because stored foods can lose nutritional value. Fresh food is the answer to the nutritional demands of space flight. Kennedy Space Center's Utilization and Life Sciences Office (UB-A), under the Exploration Research and Technology Program (UB), conducts research on plant growth and development under International Space Station (ISS) conditions. UB-A analyzes the growth responses of leafy greens in microgravity and through the manipulation of environmental conditions (CO2 levels, light intensity, relative humidity, and water delivery). By manipulating growing conditions researchers can optimize food production using minimal/restricted resources. The New Crop Selection experiments are testing the suitability of leafy crops to ISS conditions. Results from this study showed that 'Dragoon' Lettuce and 'Red Russian' Kale have the largest fresh mass.

Bhuiyan, Ruqayah H.↗
DOE OSTI2023

ML-ACCEPT: Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters

This project, ML-ACCEPT, took a significant step toward achieving the first objective of the DIFFERENTIATE program: to help engineers to more rapidly and cost-effectively consider a wider range of more novel concepts before selecting an engineering-optimal architecture for high-fidelity detailed design and evaluation. Compared to existing methods, the ML-ACCEPT project investigated innovative technologies to make the design of power converters more cost-effective and time-efficient by i) integrating recent breakthroughs in ML, power electronics, simulation software, and optimization to research, develop, and developed a suite of ML-enhanced hypothesis generation tools for power converter design; and ii) facilitating the integration of the proposed software tools into existing power-converter design work-flows. Some existing tools for power-converter design have a certain level of intelligence to aid the design process, but to our knowledge none of them has ML-enhanced capability of automating the electrical circuit design process by selecting, configuring, and tailoring the individual components that enable available resources (e.g., high-voltage DC power) to be converted to the desired output (e.g., lower-voltage DC power with a desired voltage ripple), subject to application-specific (e.g., plasma generation and automotive applications) thermal and packaging considerations. The state-of-the-art circuit design of power converters is still heavily reliant on human experts to select the optimal topology and search for design parameters with human’s experience and intuitions, which can be very time-consuming, inefficient, and labor intensive. The team is composed of University of Michigan-Dearborn (UM-Dearborn), Lawrence Livermore National Laboratory (LLNL), Oak Ridge National Laboratory (ORNL), Modelon Inc., and lighthouse customers.

42 ENGINEERING↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗
DOE OSTI2022

Viterbi decoding of CRES signals in Project 8

Abstract Cyclotron radiation emission spectroscopy (CRES) is a modern approach for determining charged particle energies via high-precision frequency measurements of the emitted cyclotron radiation. For CRES experiments with gas within the fiducial volume, signal and noise dynamics can be modelled by a hidden Markov model. We introduce a novel application of the Viterbi algorithm in order to derive informational limits on the optimal detection of cyclotron radiation signals in this class of gas-filled CRES experiments, thereby providing concrete limits from which future reconstruction algorithms, as well as detector designs, can be constrained. The validity of the resultant decision rules is confirmed using both Monte Carlo and Project 8 data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

Machine Learning↗
DOE OSTI2020

Gaussian Process Optimization of Sensitivity-Based Similarity Metrics between New Nuclear Applications and New/Existing Benchmarks

Confidently designing safe, new nuclear criticality experiments requires expert judgement, which could take years of experience. Sensitivity/uncertainty (S/U) analysis can be utilized by less experienced individuals to conservatively estimate uncertainties in important parameters, such as k eff , in newly proposed nuclear applications. This type of analysis relies on matching new nuclear applications with existing benchmark experiments. The Whisper-1.1 software package included in MCNP6.2 ®1 contains more than 1,100 International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmarks. These benchmarks however rarely match new nuclear applications. The number of benchmarks available to match a given set of materials or geometric configurations varies significantly. Furthermore, recently performed benchmark experiments may not have had enough time to be properly documented and published. Benchmarks are vital for determining the accuracy of nuclear data and can assist nuclear physics and evaluators in improving nuclear data libraries. Exhaustively exploring the parameter space using simulations with software such as MCNP is too computationally expensive. In this work, Gaussian process optimization was implemented to reduce the number of simulations needed for optimization over multiple parameters. This optimization scheme was designed to selectively generate new benchmarks with high sensitivity-based similarity metrics to user-defined nuclear applications. Two benchmark models of spherically nested shells containing plutonium, uranium, tantalum, and water were used in the optimization to match an application containing plutonium plates, stacked in a tantalum reflector, surrounded by water.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Heat Exchanger Design Optimization for Mitigation of Diesel Engine Exhaust Fouling

Abstract The design optimization of a diesel exhaust-coupled heat and mass exchanger that drives a 2.71 kW cooling capacity absorption heat pump is presented in this study. Fouling layer thermal resistance and pressure drops from single-tube experiments are used to develop a thermodynamic, heat transfer, and pressure drop model for the exhaust-coupled desorber. A parametric study is performed to select a desorber design that meets system performance while minimizing footprint. Experimental heat duties and pressure drops are within 10% and 3%, respectively, of the model predictions. Thus, large data sets from single-tube experiments with representative geometries are successful in accounting for fouling effects at the component level. Desorber design optimization based on this approach ensures continued heat pump performance after fouling. This study, along with the single-tube experiments, presents a systematic approach to design exhaust-coupled heat exchangers while considering the effects of fouling. These results are applicable for a wide range of waste-heat recovery applications, and this method can be extended to different geometries and operating conditions.

Energy & Fuels↗
DOE OSTI2020

Medium amplitude parallel superposition (MAPS) rheology. Part 2: Experimental protocols and data analysis

An experimental protocol is developed to directly measure the new material functions revealed by medium amplitude parallel superposition (MAPS) rheology. This protocol measures the medium amplitude response of a material to a simple shear deformation composed of three sine waves at different frequencies, revealing a rich dataset consisting of up to 19 measurements of the third-order complex modulus at distinct three-frequency coordinates. We discuss how the choice of input frequencies influences the features of the MAPS domain studied by the experiment. A polynomial interpolation method for reducing the bias of measured values from spectral leakage and reducing variance due to noise is discussed, including a derivation of the optimal range of amplitudes for the input signal. This leads to the conclusion that conducting the experiment in a stress-controlled fashion possesses a distinct advantage to the strain-controlled mode. The experimental protocol is demonstrated through measurements of the MAPS response of a model complex fluid: a surfactant solution of wormlike micelles. The resulting dataset is indeed large and feature-rich, while still acquired in a time comparable to similar medium amplitude oscillatory shear (MAOS) experiments. We demonstrate that the data represent measurements of an intrinsic material function by studying its internal consistency, compatibility with low-frequency predictions for Coleman–Noll simple fluids, and agreement with data obtained via MAOS amplitude sweeps. Finally, the data are compared to predictions from the corotational Maxwell model to demonstrate the power of MAPS rheology in determining whether a constitutive model is consistent with a material’s time-dependent response.

Lennon, Kyle R. (ORCID:0000000212515461)↗
NASA NTRS1989

Acceleration, gamma, and theta guidance for abort landing in a windshear

This paper is concerned with the guidance of abort landing trajectories in a windshear. First, optimal trajectories are determined by minimizing the peak value of the altitude drop. Then, two guidance schemes, approximating the optimal trajectors, are developed: acceleration guidance (based on the relative acceleration) and gamma guidance (based on the absolute path inclination). From numerical experiments, it appears that both the acceleration guidance and the gamma guidance yield trajectories that are close to the optimal trajectory. In addition, a theta guidance scheme (modified constant pitch guidance) is developed that is superior to the constant pitch guidance in terms of the altitude loss and the survival capability in severe windshears.

Miele, A.↗
NASA NTRS1992

Microgravity vibration isolation: Optimal preview and feedback control

In order to achieve adequate low-frequency vibration isolation for certain space experiments an active control is needed, due to inherent passive-isolator limitations. Proposed here are five possible state-space models for a one-dimensional vibration isolation system with a quadratic performance index. The five models are subsets of a general set of nonhomogeneous state space equations which includes disturbance terms. An optimal control is determined, using a differential equations approach, for this class of problems. This control is expressed in terms of constant, Linear Quadratic Regulator (LQR) feedback gains and constant feedforward (preview) gains. The gains can be easily determined numerically. They result in a robust controller and offers substantial improvements over a control that uses standard LQR feedback alone.

Hampton, R. D.↗
DOE OSTI2022

Thermodynamically Driven Synthetic Optimization for Cation-Disordered Rock Salt Cathodes

Relating the synthesis conditions of materials to their functional performance has long been an experience-based trial-and-error process. However, this methodology is not always efficient in identifying an appropriate protocol and can lead to overlooked opportunities for the performance optimization of materials through simple modifications of the synthesis process. In this work, we systematically track the structural evolution in the synthesis of a representative disordered rocksalt (a promising next-generation Li-ion cathode material) at the scale of both the long-range crystal structure and the short-range atomic structure using various in situ and ex situ techniques, including transmission electron microscopy, X-ray diffraction, and pair distribution function analysis. Further, an optimization strategy is proposed for the synthesis protocol, leading to a remarkably enhanced capacity (specific energy) of 313 mAh g –1 (987 Wh kg –1 ) at a low rate (20 mA g –1 ), with a capacity of more than 140 mAh g –1 retained even at a very high cycling rate of 2,000 mA g –1 . This strategy is further rationalized using ab initio calculations, and important opportunities for synthetic optimization demonstrated in this study are highlighted.

36 MATERIALS SCIENCE↗
NASA NTRS1999

Measuring Tropospheric Winds from Space Using a Coherent Doppler Lidar Technique

The global measurement of tropospheric wind profiles has been cited by the operational meteorological community as the most important missing element in the present and planned observing system. The most practical and economical method for obtaining this measurement is from low earth orbit, utilizing a Doppler lidar (laser radar) technique. Specifically, this paper will describe the coherent Doppler wind lidar (CDWL) technique, the design and progress of a current space flight project to fly such a system on the Space Shuttle, and plans for future flights of similar instruments. The SPARCLE (SPAce Readiness Coherent Lidar Experiment) is a Shuttle-based instrument whose flight is targeted for March, 2001. The objectives of SPARCLE are three-fold: Confirm that the coherent Doppler lidar technique can measure line-of-sight winds to within 1-2 m/s accuracy; Collect data to permit validation and improvement of instrument performance models to enable better design of future missions; and Collect wind and backscatter data for future mission optimization and for atmospheric studies. These objectives reflect the nature of the experiment and its program sponsor, NASA's New Millennium Program. The experiment is a technology validation mission whose primary purpose is to provide a space flight validation of this particular technology. (It should be noted that the CDWL technique has successfully been implemented from ground-based and aircraft-based platforms for a number of years.) Since the conduct of the SPARCLE mission is tied to future decisions on the choice of technology for free-flying, operational missions, the collection of data is intrinsically tied to the validation and improvement of instrument performance models that predict the sensitivity and accuracy of any particular present or future instrument system. The challenges unique to space flight for an instrument such as SPARCLE and follow-ons include: Obtaining the required lidar sensitivity from the long distance of orbit height to the lower atmosphere; Maintaining optical alignments after launch to orbit, and during operations in "microgravity"; Obtaining pointing knowledge of sufficient accuracy to remove the speed of the spacecraft (and the rotating Earth) from the measurements; Providing sufficient power (not a problem on the Shuttle) and cooling to the instrument. The paper will describe the status and challenges of the SPARCLE project, the value of obtaining wind data from orbit, and will present a roadmap to future instruments for scientific research and operational meteorology.

Miller, Timothy L.↗
NASA NTRS2002

The Efficacy of Using Synthetic Vision Terrain-Textured Images to Improve Pilot Situation Awareness

The General Aviation Element of the Aviation Safety Program's Synthetic Vision Systems (SVS) Project is developing technology to eliminate low visibility induced General Aviation (GA) accidents. SVS displays present computer generated 3-dimensional imagery of the surrounding terrain on the Primary Flight Display (PFD) to greatly enhance pilot's situation awareness (SA), reducing or eliminating Controlled Flight into Terrain, as well as Low-Visibility Loss of Control accidents. SVS-conducted research is facilitating development of display concepts that provide the pilot with an unobstructed view of the outside terrain, regardless of weather conditions and time of day. A critical component of SVS displays is the appropriate presentation of terrain to the pilot. An experimental study is being conducted at NASA Langley Research Center (LaRC) to explore and quantify the relationship between the realism of the terrain presentation and resulting enhancements of pilot SA and performance. Composed of complementary simulation and flight test efforts, Terrain Portrayal for Head-Down Displays (TP-HDD) experiments will help researchers evaluate critical terrain portrayal concepts. The experimental effort is to provide data to enable design trades that optimize SVS applications, as well as develop requirements and recommendations to facilitate the certification process. In this part of the experiment a fixed based flight simulator was equipped with various types of Head Down flight displays, ranging from conventional round dials (typical of most GA aircraft) to glass cockpit style PFD's. The variations of the PFD included an assortment of texturing and Digital Elevation Model (DEM) resolution combinations. A test matrix of 10 terrain display configurations (in addition to the baseline displays) were evaluated by 27 pilots of various backgrounds and experience levels. Qualitative (questionnaires) and quantitative (pilot performance and physiological) data were collected during the experimental runs. This paper focuses on the experimental set-up and final physiological results of the TP-HDD simulation experiment. The physiological measures of skin temperature, heart rate, and muscle response, show a decreased engagement (while using the synthetic vision displays as compared to the baseline conventional display) of the sympathetic and somatic nervous system responses which, in turn, indicates a reduced level of mental workload. This decreased level of workload is expected to enable improvement in the pilot's situation and terrain awareness.

Uenking, Michael D.↗
DOE OSTI2020

A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models

Bayesian experimental design (BED) aims at designing an experiment to maximize the information gathering from the collected data. The optimal design is usually achieved by maximizing the mutual information (MI) between the data and the model parameters. When the analytical expression of the MI is unavailable, e.g.,having implicit models with intractable data distributions, a neural network-based lower bound of the MI was recently proposed and a gradient ascent method was used to maximize the lower bound [1]. However, the approach in [1] requires a pathwise sampling path to compute the gradient of the MI lower bound with respect to the design variables, and such a pathwise sampling path is usually inaccessible for implicit models. In this work, we propose a hybrid gradient approach that leverages recent advances in variational MI estimator and evolution strategies (ES)combined with black-box stochastic gradient ascent (SGA) to maximize the MI lower bound. This allows the design process to be achieved through a unified scalable procedure for implicit models without sampling path gradients. Several experiments demonstrate that our approach significantly improves the scalability of BED for implicit models in high-dimensional design space.

Zhang, Jiaxin↗
DOE OSTI2023

Constraining light thermal inelastic dark matter with NA64

A vector portal between the Standard Model and the dark sector is a predictive and compelling framework for thermal dark matter. Through co-annihilations, models of inelastic dark matter (iDM) and inelastic Dirac dark matter (i2DM) can reproduce the observed relic density in the MeV to GeV mass range without violating cosmological limits. In these scenarios, the vector mediator behaves like a semi-visible particle, evading traditional bounds on visible or invisible resonances, and uncovering new parameter space to explain the muon (g–2) anomaly. By means of a more inclusive signal definition at the NA64 experiment, we place new constraints on iDM and i2DM using a missing energy technique. With a recast-based analysis, we contextualize the NA64 exclusion limits in parameter space and estimate the reach of the newly collected and expected future NA64 data. Our results motivate the development of an optimized search program for semi-visible particles, in which fixed-target experiments like NA64 provide a powerful probe in the sub-GeV mass range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1988

Hormone purification by isoelectric focusing in space

The objective of the program was the definition and development of optimal methods for electrophoretic separations in microgravity. The approach is based on a triad consisting of ground based experiments, mathematical modeling and experiments in microgravity. Zone electrophoresis is a rate process, where separation is achieved in uniform buffers on the basis of differences in electrophoretic mobilities. Optimization and modeling of continuous flow electrophoresis mainly concern the hydrodynamics of the flow process, including gravity dependent fluid convection due to density gradients and gravity independent electroosmosis. Optimization of focusing requires a more complex model describing the molecular transport processes involved in electrophoresis of interacting systems. Three different focusing instruments were designed, embodying novel principles of fluid stabilization. Fluid stability was achieved by: (1) flow streamlining by means of membrane elements in combination with rapid fluid recycling; (2) apparatus rotation in combination with said membrane elements; and (3) shear stress induced by rapid recycling through a narrow gap channel.

Bier, M.↗
NASA NTRS1988

Computational design of an experimental laser-powered thruster

An extensive numerical experiment, using the developed computer code, was conducted to design an optimized laser-sustained hydrogen plasma thruster. The plasma was sustained using a 30 kW CO2 laser beam operated at 10.6 micrometers focused inside the thruster. The adopted physical model considers two-dimensional compressible Navier-Stokes equations coupled with the laser power absorption process, geometric ray tracing for the laser beam, and the thermodynamically equilibrium (LTE) assumption for the plasma thermophysical and optical properties. A pressure based Navier-Stokes solver using body-fitted coordinate was used to calculate the laser-supported rocket flow which consists of both recirculating and transonic flow regions. The computer code was used to study the behavior of laser-sustained plasmas within a pipe over a wide range of forced convection and optical arrangements before it was applied to the thruster design, and these theoretical calculations agree well with existing experimental results. Several different throat size thrusters operated at 150 and 300 kPa chamber pressure were evaluated in the numerical experiment. It is found that the thruster performance (vacuum specific impulse) is highly dependent on the operating conditions, and that an adequately designed laser-supported thruster can have a specific impulse around 1500 sec. The heat loading on the wall of the calculated thrusters were also estimated, and it is comparable to heat loading on the conventional chemical rocket. It was also found that the specific impulse of the calculated thrusters can be reduced by 200 secs due to the finite chemical reaction rate.

Jeng, San-Mou↗