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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Accelerance Decoupling: An Approach for Removing the Influence of the Test Stand from the Integrated Modal Test

The main objective for launch vehicle (LV) modal testing is to quantify the LV’s modal properties in the free-free state (post pad separation). However, given the size of most LV systems, free-free testing is a challenge and often not feasible. With this, a test stand, typically the launch pad itself, is introduced as the means of support. This shifts the challenge to developing robust numerical methods for removing the influence of the launch pad from the integrated system modal test. The Space Launch System (SLS) is no exception where the mobile launcher (ML) is used to support the vehicle for the integrated modal test (IMT). For the IMT, it is well understood from pre-test analysis with finite element models (FEMs) of the SLS and SLS coupled to ML that the ML has a significant influence on the SLS modal properties especially in the lower frequency range where the primary SLS bending modes exist. An accelerance decoupling (AD) method has been formulated for the purpose of “subtracting out” the influence of the ML from the IMT results. With AD, the SLS decoupled frequency response functions (FRFs) are directly extracted from the IMT FRFs. The subject approach is aimed to utilize measured data only and achieve a robust FRF decoupling scheme. AD is derived from a widely used coupling technique called “Receptance Coupling” (RC). The AD core equation reverses the RC process and utilizes a pair of auxiliary equations that enable the core equation to be resolved based on measured data only. In AD, the decoupled component FRFs are extracted from the coupled system FRFs with a transformation to remove the contribution of the “subtractive component”. This paper addresses the AD’s operational flexibility to resolve SLS free-free modal properties from coupled system measured data but also the possibility to include data from FEM if there is enough confidence in the FEM or if it is asserted that the effect to the final outcome is reasonable.

Accelerance decoupling↗
NASA NTRSDate not supplied

A Flexible Multibody Framework for Space Launch System Liftoff with Umbilical Separations

ICPSU clearances to the lifting SLS vehicle were computed utilizing a coupled system nonlinear simulation approach in order to accurately capture the impact of system flexibilities, SLS forcing functions, pad separation (twang), ML dynamics and timings • i.e., integrate ICPSU model into a SLS nonlinear Coupled Loads Analysis (CLA) This required utilizing a flexible multibody framework for the CLA and the integration of large displacements/rotations capabilities into that CLA • Gantry rotation • Disconnect and retraction of flexible hoses

Arya Majed↗
NASA NTRS2000

Validity of VO(2 max) in predicting blood volume: implications for the effect of fitness on aging

A multiple regression model was constructed to investigate the premise that blood volume (BV) could be predicted using several anthropometric variables, age, and maximal oxygen uptake (VO(2 max)). To test this hypothesis, age, calculated body surface area (height/weight composite), percent body fat (hydrostatic weight), and VO(2 max) were regressed on to BV using data obtained from 66 normal healthy men. Results from the evaluation of the full model indicated that the most parsimonious result was obtained when age and VO(2 max) were regressed on BV expressed per kilogram body weight. The full model accounted for 52% of the total variance in BV per kilogram body weight. Both age and VO(2 max) were related to BV in the positive direction. Percent body fat contributed <1% to the explained variance in BV when expressed in absolute BV (ml) or as BV per kilogram body weight. When the model was cross validated on 41 new subjects and BV per kilogram body weight was reexpressed as raw BV, the results indicated that the statistical model would be stable under cross validation (e.g., predictive applications) with an accuracy of +/- 1,200 ml at 95% confidence. Our results support the hypothesis that BV is an increasing function of aerobic fitness and to a lesser extent the age of the subject. The results may have implication as to a mechanism by which aerobic fitness and activity may be protective against reduced BV associated with aging.

NASA Discipline Cardiopulmonary↗
NASA NTRS2009

Benefits to the Simulation Training Community of a New ANSI Standard for the Exchange of Aero Simulation Models

The American Institute of Aeronautics Astronautics (AIAA) Modeling and Simulation Technical Committee is in final preparation of a new standard for the exchange of flight dynamics models. The standard will become an ANSI standard and is under consideration for submission to ISO for acceptance by the international community. The standard has some a spects that should provide benefits to the simulation training community. Use of the new standard by the training simulation community will reduce development, maintenance and technical refresh investment on each device. Furthermore, it will significantly lower the cost of performing model updates to improve fidelity or expand the envelope of the training device. Higher flight fidelity should result in better transfer of training, a direct benefit to the pilots under instruction. Costs of adopting the standard are minimal and should be paid back within the cost of the first use for that training device. The standard achie ves these advantages by making it easier to update the aerodynamic model. It provides a standard format for the model in a custom eXtensible Markup Language (XML) grammar, the Dynamic Aerospace Vehicle Exchange Markup Language (DAVE-ML). It employs an existing XML grammar, MathML, to describe the aerodynamic model in an input data file, eliminating the requirement for actual software compilation. The major components of the aero model become simply an input data file, and updates are simply new XML input files. It includes naming and axis system conventions to further simplify the exchange of information.

Hildreth, Bruce L.↗
NASA NTRS1998

Albumin infusion in humans does not model exercise induced hypervolaemia after 24 hours

We rapidly infused 234 +/- 3 mL of 5% human serum albumin in eight men while measuring haematocrit, haemoglobin concentration, plasma volume (PV), albumin concentration, total protein concentration, osmolality, sodium concentration, renin activity, aldosterone concentration, and atrial natriuretic peptide concentration to test the hypotheses that plasma volume expansion and plasma albumin content expansion will not persist for 24 h. Plasma volume and albumin content were expanded for the first 6 h after infusion (44.3 +/- 1.9-47.2 +/- 2.0 mL kg-1 and 1.9 +/- 0.1-2.1 +/- 0.1 g kg-1 at pre-infusion and 1 h, respectively, P < 0.05), but by 24 h plasma volume and albumin content decreased significantly from 1 h post-infusion and were not different from pre-infusion (44.8 +/- 1.9 mL kg-1 and 1.9 +/- 0.1 g kg-1, respectively). Plasma aldosterone concentration showed a significant effect of time over the 24 h after infusion (P < 0.05), and showed a trend to decrease at 2 h after infusion (167.6 +/- 32.5(-1) 06.2 +/- 13.4 pg mL-1, P = 0.07). These data demonstrate that a 6.8% expansion of plasma volume and 10.5% expansion of plasma albumin content by infusion does not remain in the vascular space for 24 h and suggest a redistribution occurs between the intravascular space and interstitial fluid space.

Non-NASA Center↗
NASA NTRS2009

Non-exercise Estimation of V02max Using a Dichotomy of Meeting or Not Meeting DHHS Physical Activity Recommendations

The physical activity guidelines (PAG) established by the US Dept. of Health and Human Services in 2008 is consistent with a rating of >/= 6 on the 11-point NASA Physical Activity Status Scale (PASS). Wier, et. al. developed non-exercise models for estimating VO2(sub max) from a combination of PASS, age, gender and either waist girth (WG) (R = 0.810, SEE= 4.799 ml/kg/min), %Fat (R = 0. 817, SEE = 4.716 ml/kg/min) or BMI (R = 0.802, SEE = 4.900 ml . kg-1. min -1 ). PURPOSE: to develop non-exercise models to estimate VO2max from age, gender, body composition (WG, %Fat, BMI) and PASS dichotomized at meets or does not meet the PAG (PAG-PASS), and to compare the accuracy of the PAG-PASS models with the models using the 11-point PASS. METHODS: 2417 men and 384 women were measured for VO2max by indirect calorimetry (RER >1.1); age (yr), gender by M = 1, W = 0; WG at the umbilicus; %fat by skin-folds, BMI by weight (kg) divided by height squared (m 2 ) , and PAGPASS by PASS < 6 = 0 and =/> 6 = 1. RESULTS: Three models were developed by multiple regression to estimate VO2(sub max) from age, gender, PAG-PASS and either WG (R = 0.790, SEE=5.019 ml/kg/min), %FAT (R= 0.080, SEE = 4.915 ml/kg/min) or BMI (R = 0.777, SEE = 5.162ml/kg/min). Cross-validation by the PRESS technique confirmed these statistics. Simple correlations between measured VO2(sub max) and estimates from the PAG-PASS models with WG, %Fat and BMI were 0.790, 0.800 and 0.777, minimally different from the correlations obtained with the PASS models (0.810, 0.810, and 0.802). PAG-PASS and PASS model constant errors were also similar: < 1 ml/kg/min for subsamples of age, gender, PASS and for VO2(sub max) between 30 and 50 ml/kg/min (70% of the sample) but > 1 ml/kg/min for VO2(sub max) <30 and >50 ml/kg/min. CONCLUSIONS: Non-exercise models using the combined effects of age, gender, body composition and the dichotomized PAG-PASS provide estimates of VO2(sub max) that are accurate for most adults, and the accuracy of these models are similar to previously published models using the 11-point PASS.

Wier, Larry T.↗
NASA NTRSDate not supplied

Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys

Ni-Ti based shape memory alloys (SMAs) have found wide-spread use in aerospace, automotive, biomedical, and commercial applications owing to their favorable properties and ease of operation. Especially important for many NASA applications is the ability to tune the martensitic transformation temperature of Ni-Ti alloys by varying the composition and processing conditions. Recently, researchers at NASA have compiled an extensive database of shape memory properties of materials, including over 8,000 multi-component Ni-Ti alloys containing 37 different alloying elements. Using this dataset, machine learning models are trained to predict transformation temperatures, hysteresis, and transformation strain with extremely small errors. These models are used to learn relationships between shape memory behavior and input parameters in the composition and processing space. ML predictions are validated through new experiments. The combination of an extensive dataset and accurate learning models, together, make our approach highly suitable for the rapid discovery of novel SMAs with targeted properties.

Shape Memory Alloys↗
NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗
NASA NTRSDate not supplied

Data-Driven Study of Shape Memory Behavior of Multi-Component Ni-Ti Alloys

Ni-Ti based shape memory alloys (SMAs) have found wide-spread use in aerospace, automotive, biomedical, and commercial applications owing to their favorable properties and ease of operation. Especially important for many NASA applications is the ability to tune the martensitic transformation temperature of Ni-Ti alloys by varying the alloy composition and processing conditions. Recently, researchers at NASA have compiled an extensive database of shape memory properties of materials, including over 8,000 multi-component Ni-Ti alloys containing 37 different alloying elements. Using this dataset, machine learning models are trained to predict transformation temperatures, hysteresis, and transformation strain with extremely low mean absolute errors. These models are used to learn relationships between shape memory behavior and input parameters in the composition and processing space. ML predictions are validated through new experiments. The combination of an extensive experimental dataset and accurate learning models, together, make our approach highly suitable for the rapid discovery and design of novel SMAs with targeted properties. We are not aware of any current approaches capable of predicting SMA transformation behavior over such a wide range of compositions and processing conditions.

Shape memory alloys↗
NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current health state of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗
NASA NTRS2012

Assessment of Mars Atmospheric Temperature Retrievals from the Thermal Emission Spectrometer Radiances

Motivated by the needs of Mars data assimilation. particularly quantification of measurement errors and generation of averaging kernels. we have evaluated atmospheric temperature retrievals from Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) radiances. Multiple sets of retrievals have been considered in this study; (1) retrievals available from the Planetary Data System (PDS), (2) retrievals based on variants of the retrieval algorithm used to generate the PDS retrievals, and (3) retrievals produced using the Mars 1-Dimensional Retrieval (M1R) algorithm based on the Optimal Spectral Sampling (OSS ) forward model. The retrieved temperature profiles are compared to the MGS Radio Science (RS) temperature profiles. For the samples tested, the M1R temperature profiles can be made to agree within 2 K with the RS temperature profiles, but only after tuning the prior and error statistics. Use of a global prior that does not take into account the seasonal dependence leads errors of up 6 K. In polar samples. errors relative to the RS temperature profiles are even larger. In these samples, the PDS temperature profiles also exhibit a poor fit with RS temperatures. This fit is worse than reported in previous studies, indicating that the lack of fit is due to a bias correction to TES radiances implemented after 2004. To explain the differences between the PDS and Ml R temperatures, the algorithms are compared directly, with the OSS forward model inserted into the PDS algorithm. Factors such as the filtering parameter, the use of linear versus nonlinear constrained inversion, and the choice of the forward model, are found to contribute heavily to the differences in the temperature profiles retrieved in the polar regions, resulting in uncertainties of up to 6 K. Even outside the poles, changes in the a priori statistics result in different profile shapes which all fit the radiances within the specified error. The importance of the a priori statistics prevents reliable global retrievals based a single a priori and strongly implies that a robust science analysis must instead rely on retrievals employing localized a priori information, for example from an ensemble based data assimilation system such as the Local Ensemble Transform Kalman Filter (LETKF).

Hoffman, Matthew J.↗
NASA NTRS2011

Pseudorandom Noise Code-Based Technique for Cloud and Aerosol Discrimination Applications

NASA Langley Research Center is working on a continuous wave (CW) laser based remote sensing scheme for the detection of CO2 and O2 from space based platforms suitable for ACTIVE SENSING OF CO2 EMISSIONS OVER NIGHTS, DAYS, AND SEASONS (ASCENDS) mission. ASCENDS is a future space-based mission to determine the global distribution of sources and sinks of atmospheric carbon dioxide (CO2). A unique, multi-frequency, intensity modulated CW (IMCW) laser absorption spectrometer (LAS) operating at 1.57 micron for CO2 sensing has been developed. Effective aerosol and cloud discrimination techniques are being investigated in order to determine concentration values with accuracies less than 0.3%. In this paper, we discuss the demonstration of a PN code based technique for cloud and aerosol discrimination applications. The possibility of using maximum length (ML)-sequences for range and absorption measurements is investigated. A simple model for accomplishing this objective is formulated, Proof-of-concept experiments carried out using SONAR based LIDAR simulator that was built using simple audio hardware provided promising results for extension into optical wavelengths. Keywords: ASCENDS, CO2 sensing, O2 sensing, PN codes, CW lidar

Campbell, Joel F.↗
NASA NTRS2011

Pseudorandom Noise Code-Based Technique for Thin Cloud Discrimination with CO2 and O2 Absorption Measurements

NASA Langley Research Center is working on a continuous wave (CW) laser based remote sensing scheme for the detection of CO2 and O2 from space based platforms suitable for ACTIVE SENSING OF CO2 EMISSIONS OVER NIGHTS, DAYS, AND SEASONS (ASCENDS) mission. ASCENDS is a future space-based mission to determine the global distribution of sources and sinks of atmospheric carbon dioxide (CO2). A unique, multi-frequency, intensity modulated CW (IMCW) laser absorption spectrometer (LAS) operating at 1.57 micron for CO2 sensing has been developed. Effective aerosol and cloud discrimination techniques are being investigated in order to determine concentration values with accuracies less than 0.3%. In this paper, we discuss the demonstration of a pseudo noise (PN) code based technique for cloud and aerosol discrimination applications. The possibility of using maximum length (ML)-sequences for range and absorption measurements is investigated. A simple model for accomplishing this objective is formulated, Proof-of-concept experiments carried out using SONAR based LIDAR simulator that was built using simple audio hardware provided promising results for extension into optical wavelengths.

Campbell, Joel F.↗
NASA NTRSDate not supplied

Digital Assistance for System Requirement Discovery and Analysis using Machine Learning Natural Language Processing Algorithm

NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future (see https://www.nasa.gov/uam-overview/ for more information). The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and machine learning natural language processing (ML NLP, or just NLP) capabilities. This presentation gives an overview of the NLP application within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

ATM↗
NASA NTRSDate not supplied

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗
NASA NTRSDate not supplied

Parameterization of Vertical Cloud Distribution from C3M and MERRA Data Using ML Method

Clouds play a key role in regulating the hydrological cycle and the Earth's radiative energy budget. However, global climate models (GCMs) with a horizontal grid spacing on the order of 100 km have limitations in representing sub-grid cloud dynamics with spatial scales on the order of 1 km, leading to potential uncertainties in cloud radiative feedback on the global scale. In our research, we will leverage the capabilities of Deep Machine Learning (DML) methods to construct parameterizations of sub-grid volumetric cloud fraction (VCF), which is the frequency of occurrence on a grid volume accumulated in the horizontal and vertical directions. Our investigation delves into the intricate relationship between VCF obtained from the NASA CALIPSO-CloudSat-CERES-MODIS (CCCM) satellite observation data and 3-D MERRA-2 reanalysis meteorological profiling data (e.g., wind, relative humidity, temperature). Through a comprehensive one-year data training utilizing the Sequence to Sequence DML method, we have successfully disentangled the complicated cloud formation dynamics across diverse meteorological conditions through a day-to-day analysis framework. Preliminary findings reveal promising statistical agreements in geographical and vertical distributions and seasonal variations of volumetric cloud fraction between ML prediction and satellite measurements. These results underscore the aptitude of our DML model to discern underlying cloud physical processes and accurately represent sub-grid cloud formation dynamics. Additionally, we have also employed trained neural network to analyze uncertainties arising from errors in meteorological data, further enhancing the robustness of our VCF parameterization.

Shan Zeng↗
NASA NTRSDate not supplied

Feasibility Study to Extract Artemis-1 Fixed Base Modes While Mounted on a Dynamically Active Mobile Launch Platform

There are several challenges associated with the scheduled integrated modal test (IMT)of the Space Launch System (SLS) Artemis-1flight vehicle mounted on the mobile launcher (ML). While the goal of the test is to characterize the Artemis-1, the inclusion of the ML as the support stand for the test means that the entire system must be well characterized. A considerable amount of effort and schedule will have to be devoted to understanding both the test stand (the ML) and the Artemis-1 flight vehicle, and there is a risk that the effort may not be completed in time for a successful launch. NASA has requested that alternative methods be investigated to generate test results that can remove the effects of the ML from the test. ATA Engineering, Inc., (ATA)was given a reduced model f theArtemis-1 flight vehicle’s IMT configuration containing the candidate set of accelerometers and interface degrees of freedom (DOF). The model was used to determine how well ATA’s fixed base correction technique is able to estimate fixed base modes from test data collected on the IMT. This paper presents the fixed base correction method results.

modal testing↗