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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Screening of Li-Based Solid Electrolytes Using Bond-Valence Methods and Graph Neural Networks

Li-based solid-state electrolyte (Li-SSE) materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for Li-SSE materials using a combination of bond-valence methods and graph neural networks. We demonstrate the screening approach with a dataset containing tens of thousands of Li-containing compounds. Furthermore, we combine the machine-learning screening procedure with an isovalent substitution scheme to generate and screen additional Li SSE candidates beyond existing databases. Finally, we discuss relative importances of geometric and bond-valence quantities in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li-SSE materials.

Materials discovery↗
NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗
NASA NTRS1994

Higher-Order Neural Networks Applied to 2D and 3D Object Recognition

A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.

Spirkovska, Lilly↗
NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗
NASA NTRS2019

European Service Module - Structural Test Article (E-STA) Building Block Test Approach and Model Correlation Observations

The Orion European Service Module - Structural Test Article (E-STA) underwent sine vibration testing in 2016 using the Mechanical Vibration Facility (MVF) multi-axis shaker system at NASA Glenn Research Center’s (GRC) Plum Brook Station (PBS) Space Power Facility (SPF). The main objective was to verify the structural integrity of the European Service Module (ESM) under sine sweep dynamic qualification vibration testing. A secondary objective was to perform a fixed-base modal survey, while E-STA was still mounted to MVF, in order to achieve a test correlate the finite element model (FEM). To facilitate the E-STA system level correlation effort, a building block test approach was implemented. Modal tests were performed on two major subassemblies, the crew module/launch abort structure (CM/LAS) and the crew module adapter (CMA) mass simulators. These subassembly FEMs were individually correlated and then integrated into the E-STA FEM prior to the start of the E-STA sine vibration test. This paper summarizes the modal testing and model correlation efforts of both of these subassemblies and how the building block approach assisted in the overall correlation of the E-STA FEM. This paper will also cover modeling practices that should be avoided, recommended instrumentation positioning on complex structures, and the importance of the FEM geometrically matching CAD in sufficient detail in order to adequately replicate internal load paths. The goal of this paper is to inform the reader of the hard earned lessons learned and pitfalls to avoid when applying a building block test approach.

Winkel, James P.↗
NASA NTRS1994

Passive mapping and intermittent exploration for mobile robots

An adaptive state space architecture is combined with diktiometric representation to provide the framework for designing a robot mapping system with flexible navigation planning tasks. This involves indexing waypoints described as expectations, geometric indexing, and perceptual indexing. Matching and updating the robot's projected position and sensory inputs with indexing waypoints involves matchers, dynamic priorities, transients, and waypoint restructuring. The robot's map learning can be opganized around the principles of passive mapping.

Engleson, Sean P.↗
NASA NTRS2003

Precision Agriculture: Changing the Face of Farming

To a large extent our work has grown out of the remote sensing technology and conceptual framework developed by geologists. For example the drive to look at the physics of reflectance and atmospheric corrections is rooted in work done in the early 1980s by the United States Geological Survey and NASA. Our work on emissivity and thermal behavior of plants pulls on research done using the Thermal Infrared Multispectral Scanner, an instrument originally conceived for geologic applications. Even our ability to geometrically map the airborne imagery onto the globe was explicitly developed because of need to map sediment flow patterns in along the coast of Louisiana. Growing from this base we have learned much in the last few years and believe our integration of geologic remote sensing with the other fields of expertise was a wise investment. Clearly none of the specialties alone could develop, let alone test, the basic approach we are now finding so powerful. This is the path that will ultimately give the information needed by the farmer. We also recognize how small a portion of the total problem has been solved. Having developed the basic logic, built proto-type tools and performed initial tests everything else remains to be done. And problems, scientific and practical, are everywhere. We have not established sensitivities. We have not robustly segregated the contributions of crop residue, soil moisture, shadows, plant and soil to the energy leaving the surface. What we do is extremely expensive and difficult. It is experimental in methodology and uses research oriented tools. We are constantly alive to the practicality of moving our results into commercial applications. We know another airborne instrument will have to be available. Atmospheric parameters will have to be measured automatically. The software will have to be re-written for speed. At times the list of problems seems endless. But the potential is also enormous. Agriculture is a huge portion of our economy. Just a one percent increase in efficiency is a $2,000,000 change. We all depend on farmers, literally for the bread we eat. No other activity of man even has an impact on the land that farming has. If application of precision agriculture can nelp farmers manage their land better, we all may benefit.

Rickman, D.↗
NASA NTRSDate not supplied

Machine-Learned Committor Functions for Reactive Molecular Dynamics

Reactive molecular dynamics (MD) is a powerful tool for atomistic-scale modeling of a diverse range of chemical processes. However, scaling these simulations to large systems and long times scales remains a challenge because of the complexity of the potential energy function required. The authors previously developed a heuristic approach, called REACTER, that incorporates reactivity in MD simulations in a less general but much more computationally efficient manner. REACTER uses standard, fixed valence force fields as the underlying potentialenergy surface for describing all interatomic interactions but adds a procedure for enforcing user-defined reactions that occur when certain geometric constraints on relative atomic positions are satisfied. Further, these bonding changes can be accepted or rejected with a probability related tothe local thermal energy. This work seeks to generalize this approach by replacing the set of user defined geometric constraints and energetic criteria with a committor function that specifies the probability of a reaction occurring on the basis of the local atomic configuration. The committor function is a useful mathematical tool for modeling rare events but, unfortunately, is very difficult to compute for realistic systems in a general way. This work describes a method for approximating the committor function using a machine learning approach, specifically a deep neural network trained with data from reactive MD and DFT-based dynamics simulations. This network is coupled to the existing REACTER protocol, as implemented in the LAMMPS MD package, and used to make on-the-fly predictions of reaction probabilities without the more extensive user input previously required. The new method is demonstrated using the polymerization of polystyrene as a case study. Although very dependent on the quality and quantity of training data, machine-learned committor functions show promise as a method for incorporating reaction probability from higher level calculations into highly scalable MD simulations.

polymer simulations↗
NASA NTRS2023

NASA Passive Thermal Control Engineering Guidebook

The NASA Passive Thermal Control Engineering Guidebook provides recommendations, including best practices and lessons learned, related to the passive thermal control engineering discipline. Topics include analysis (including documentation and review), thermal hardware (design/selection, vendors, and integration), testing, and flight operations. The passive thermal discipline includes thermal control and thermal protection systems. The passive thermal control discipline, which is addressed in this Guidebook, is broad and covers internal and external systems, component passive thermal analysis, vehicle on-orbit attitude timeline analysis, integrated thermal analysis, various thermal control apparatus (heaters and controls, coatings, blankets and insulations, isolators, geometric design for view factors, materials emissivity/absorptivity properties), passive cooling of avionics, purge, vent, and drain for vehicle cavities and compartments, thermal model development and correlation, thermal cycle and thermal-vacuum testing. Topics related to thermal protection systems (TPS) are not within the scope of this document. The Guidebook provides a consolidated reference for early career as well as experienced engineers embarking on a new task. Leveraging the experience of the group minimizes the learning curve that exists at the start of new projects, reduces the risk of repeating mistakes, and improves the organization’s ability to deliver high-quality products efficiently. The intent of this document is to provide guidance based on collective experience, and is not intended impose constraints on project work. Best practices can vary substantially and it is important to apply judgment wisely based on careful consideration of the specific design and requirements. Implementation of the methods described will vary from project to project and should be applied in conjunction with the judgment of the thermal engineers performing and reviewing the work. The recommendations in this document are not intended to replace program, project, branch, Center, or NASA requirements and/or policies.

thermal↗
NASA NTRS2023

NASA Passive Thermal Control Engineering Guidebook

The NASA Passive Thermal Control Engineering Guidebook provides recommendations, including best practices and lessons learned, related to the passive thermal control engineering discipline. Topics include analysis (including documentation and review), thermal hardware (design/selection, vendors, and integration), testing, and flight operations. The passive thermal discipline includes thermal control and thermal protection systems. The passive thermal control discipline, which is addressed in this Guidebook, is broad and covers internal and external systems, component passive thermal analysis, vehicle on-orbit attitude timeline analysis, integrated thermal analysis, various thermal control apparatus (heaters and controls, coatings, blankets and insulations, isolators, geometric design for view factors, materials emissivity/absorptivity properties), passive cooling of avionics, purge, vent, and drain for vehicle cavities and compartments, thermal model development and correlation, thermal cycle and thermal-vacuum testing. Topics related to thermal protection systems (TPS) are not within the scope of this document. The Guidebook provides a consolidated reference for early career as well as experienced engineers embarking on a new task. Leveraging the experience of the group minimizes the learning curve that exists at the start of new projects, reduces the risk of repeating mistakes, and improves the organization’s ability to deliver high-quality products efficiently. The intent of this document is to provide guidance based on collective experience, and is not intended impose constraints on project work. Best practices can vary substantially and it is important to apply judgment wisely based on careful consideration of the specific design and requirements. Implementation of the methods described will vary from project to project and should be applied in conjunction with the judgment of the thermal engineers performing and reviewing the work. The recommendations in this document are not intended to replace program, project, branch, Center, or NASA requirements and/or policies.

thermal↗
NASA NTRS2013

Lessons Learned in the Selection and Development of Test Cases for the Aeroelastic Prediction Workshop: Rectangular Supercritical Wing

The Aeroelastic Prediction Workshop brought together an international community of computational fluid dynamicists as a step in defining the state of the art in computational aeroelasticity. The Rectangular Supercritical Wing (RSW) was chosen as the first configuration to study due to its geometric simplicity, perceived simple flow field at transonic conditions and availability of an experimental data set containing forced oscillation response data. Six teams performed analyses of the RSW; they used Reynolds-Averaged Navier-Stokes flow solvers exercised assuming that the wing had a rigid structure. Both steady-state and forced oscillation computations were performed by each team. The results of these calculations were compared with each other and with the experimental data. The steady-state results from the computations capture many of the flow features of a classical supercritical airfoil pressure distribution. The most dominant feature of the oscillatory results is the upper surface shock dynamics. Substantial variations were observed among the computational solutions as well as differences relative to the experimental data. Contributing issues to these differences include substantial wind tunnel wall effects and diverse choices in the analysis parameters.

Heeg, Jennifer↗
NASA NTRS2007

A Parametric Geometry Computational Fluid Dynamics (CFD) Study Utilizing Design of Experiments (DOE)

Design of Experiments (DOE) was applied to the LAS geometric parameter study to efficiently identify and rank primary contributors to integrated drag over the vehicles ascent trajectory in an order of magnitude fewer CFD configurations thereby reducing computational resources and solution time. SME s were able to gain a better understanding on the underlying flowphysics of different geometric parameter configurations through the identification of interaction effects. An interaction effect, which describes how the effect of one factor changes with respect to the levels of other factors, is often the key to product optimization. A DOE approach emphasizes a sequential approach to learning through successive experimentation to continuously build on previous knowledge. These studies represent a starting point for expanded experimental activities that will eventually cover the entire design space of the vehicle and flight trajectory.

Rhew, Ray D.↗
NASA NTRS2010

Vehicle Sketch Pad: a Parametric Geometry Modeler for Conceptual Aircraft Design

The conceptual aircraft designer is faced with a dilemma, how to strike the best balance between productivity and fidelity? Historically, handbook methods have required only the coarsest of geometric parameterizations in order to perform analysis. Increasingly, there has been a drive to upgrade analysis methods, but these require considerably more precise and detailed geometry. Attempts have been made to use computer-aided design packages to fill this void, but their cost and steep learning curve have made them unwieldy at best. Vehicle Sketch Pad (VSP) has been developed over several years to better fill this void. While no substitute for the full feature set of computer-aided design packages, VSP allows even novices to quickly become proficient in defining three-dimensional, watertight aircraft geometries that are adequate for producing multi-disciplinary meta-models for higher order analysis methods, wind tunnel and display models, as well as a starting point for animation models. This paper will give an overview of the development and future course of VSP.

Hahn, Andrew S.↗
NASA NTRSDate not supplied

Machine Learning Algorithms for Aerosol and Cloud Detection Using CATS on the ISS

Clouds and aerosols are one of the largest uncertainties in understanding and forecasting the Earth’s changing climate system. The type and height of aerosols are important factors in determining the top-of-atmosphere (TOA) radiation budget, either direct reflection of solar radiation back to space and/or absorption of solar radiation. In addition to their impact on the Earth’s climate system, aerosols near the surface from wildfires, man-made pollution events, and dust storms are hazardous to human health. The phase and height of clouds also play a critical role in determining the role of clouds in the Earth’s climate system. Cirrus clouds in the upper troposphere can induce a significant daytime TOA warming effect, while liquid water clouds near the surface cause a large corresponding cooling effect. Lidar measurements provide accurate vertically resolved information about clouds and aerosols, including complex multi-layer scenes where passive sensors are challenged and at night, when passive sensors are unable to measure cloud and aerosol properties. The Cloud-Aerosol Transport System (CATS) is a lidar instrument that operated for 33 months on the International Space Station (ISS) at the 1064 nm wavelength to measure attenuated total backscatter and depolarization ratio. These fundamental measurements are used to derive “vertical feature mask” cloud and aerosol products, including layer top/base heights, layer geometrical thickness, aerosol type, and cloud phase. While space-based lidar systems like CATS provide cloud and aerosol vertical distributions that improve our understanding of the climate system, averaging of the daytime data from these sensors is required, at the expense of spatial resolution, to improve the daytime signal-to noise (SNR) and thus atmospheric layer detection. This presentation shows results from machine learning (ML) techniques that, when applied to CATS data: 1. improve the 1064 nm SNR 2. enable detection of atmospheric features during daytime with a horizontal resolution of 350 m or 5 km (compared to the 60 km required for standard CATS data products) 3. increase the number of atmospheric layers detected in the CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination, cloud phase, and aerosol typing compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. The ML tools described in this paper can facilitate the development of smaller, low-cost lidar systems in the future and enable real-time accessibility of lidar data products from future lidar systems for monitoring and forecasting of hazardous events.

John Yorks↗
NASA NTRS2003

Historical Review of Uncommanded Lateral-Directional Motions at Transonic Conditions

This paper presents the results of a survey of past experiences with uncommanded lateral-directional motions at transonic speeds during specific military aircraft programs. The effort was undertaken to provide qualitative and quantitative information on past airplane programs that might be of use to the participants in the joint NASA/Navy/Air Force Abrupt Wing Stall (AWS) Program. The AWS Program was initiated because of the experiences of the F/A-l8E/F development program, during which unexpected, severe wing-drop motions were encountered by preproduction aircraft at transonic conditions. These motions were judged to be significantly degrading to the primary mission requirements of the aircraft. Although the problem was subsequently solved for the production version of the F/A-l8E/F, a high-level review panel emphasized the poor understanding of such phenomena and issued a strong recommendation to: "Initiate a national research effort to thoroughly and systematically study the wing drop phenomena." A comprehensive, cooperative NASA/Navy/Air Force AWS Program was designed to respond to provide the required technology requirements. As part of the AWS Program, a work element was directed at a historical review of wing-drop experiences in past aircraft development programs at high subsonic and transonic speeds. In particular, information was requested regarding: specific aircraft configurations that exhibited uncommanded motions and the nature of the motions; geometric characteristics of the air- planes; flight conditions involved in occurrences; relevant data, including wind-tunnel, computational, and flight sources; figures of merit used for analyses; and approaches used to alleviate the problem. An attempt was also made to summarize some of the more important lessons learned from past experiences, and to recommend specific research efforts. In addition to providing technical information to assist the AWS research objectives, the study produced fundamental information regarding the historical challenge of uncommanded lateral-directional motions at transonic conditions and the associated aerodynamic phenomena.

Chambers, Joseph R.↗
NASA NTRS2003

Historical Review of Uncommanded Lateral-Directional Motions At Transonic Conditions (Invited)

This paper presents the results of a survey of past experiences with uncommanded lateral-directional motions at transonic speeds during specific military aircraft programs. The effort was undertaken to provide qualitative and quantitative information on past airplane programs that might be of use to the participants in the joint NASA/Navy/Air Force Abrupt Wing Stall (AWS) Program. The AWS Program was initiated because of the experiences of the F/A-18E/F development program, during which unexpected, severe wing-drop motions were encountered by preproduction aircraft at transonic conditions. These motions were judged to be significantly degrading to the primary mission requirements of the aircraft. Although the problem was subsequently solved for the production version of the F/A-l8E/F, a high-level review panel emphasized the poor understanding of such phenomena and issued a strong recommendation to: Initiate a national research effort to thoroughly and systematically study the wing drop phenomena. A comprehensive, cooperative NASA/Navy/Air Force AWS Program was designed to respond to provide the required technology requirements. As part of the AWS Program, a work element was directed at a historical review of wing-drop experiences in past aircraft development programs at high subsonic and transonic speeds. In particular, information was requested regarding: specific aircraft configurations that exhibited uncommanded motions and the nature of the motions; geometric characteristics of the air- planes; flight conditions involved in occurrences; relevant data, including wind-tunnel, computational, and flight sources; figures of merit used for analyses; and approaches used to alleviate the problem. An attempt was also made to summarize some of the more important lessons learned from past experiences, and to recommend specific research efforts. In addition to providing technical information to assist the AWS research objectives, the study produced fundamental information regarding the historical challenge of uncommanded lateral-directional motions at transonic conditions and the associated aerodynamic phenomena.

Chambers, Joseph R.↗
NASA NTRSDate not supplied

Climatology of Global Precipitation Measurement Mission Precipitation Regimes and Implications for Global Estimates of Vertical Winds

The Global Precipitation Measurement (GPM) mission Validation Network (VN) framework leverages over 118 ground-based polarimetric Doppler radars to validate a large subset of precipitation measurements and retrievals from the GPM Dual-frequency Precipitation Radar (DPR). Recently, GPM DPR reflectivity profiles within the VN have been classified according to their convective regime using unsupervised machine learning techniques. The archetypal regimes are stratiform, convective, mixed stratiform-convective (e.g., transition regions), and “other” (e.g., peripheral regions of light precipitation). Subcategories within these four primary regimes vary according to the characteristic depth of included reflectivity profiles, resulting in 12 main GPM DPR precipitation profile categories. Polarimetry of ground-based Doppler radars in the VN offers additional insights into the types of precipitation, while pairs of radars positioned near each other enable retrieval of vertical winds via dual-Doppler analysis. Geometrically matched to the DPR reflectivity profiles in the GPM VN, these ground-based data and retrievals contribute more detailed characterization of the distinct kinematic and microphysical structures associated with each of the 12 DPR precipitation regimes. DPR reflectivity profiles linked with wind in the VN are restricted to GPM overpasses of proximal radar pairs that allow dual-Doppler analysis. Although a limited subset of DPR profiles in the VN are matched with vertical motion, agreement between the reflectivity structures paired with wind data and those of the greater DPR dataset in the VN suggest that estimates of vertical motion may be inferred in regions without ground-based measurements. We present a climatology of the 12 convective regimes identified within the DPR VN dataset as well as early efforts to estimate the kinematic and microphysical structures of precipitation profiles within the greater GPM DPR dataset by applying machine learning techniques. Precipitation data paired with global estimates of vertical winds from these efforts offer early insight to and support upcoming missions to retrieve convective mass flux, including the Investigation of Convective Updrafts (INCUS) in the Tropics and the global Atmosphere Observing System (AOS).

Precipitation↗
NASA NTRS2013

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Hurricanes↗