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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Logistic Risk Model for the Unique Effects of Inherent Aerobic Capacity on (+)G(sub z) Tolerance Before and After Simulated Weightlessness

Small sample size (n less than 1O) and inappropriate analysis of multivariate data have hindered previous attempts to describe which physiologic and demographic variables are most important in determining how long humans can tolerate acceleration. Data from previous centrifuge studies conducted at NASA/Ames Research Center, utilizing a 7-14 d bed rest protocol to simulate weightlessness, were included in the current investigation. After review, data on 25 women and 22 men were available for analysis. Study variables included gender, age, weight, height, percent body fat, resting heart rate, mean arterial pressure, Vo(sub 2)max and plasma volume. Since the dependent variable was time to greyout (failure), two contemporary biostatistical modeling procedures (proportional hazard and logistic discriminant function) were used to estimate risk, given a particular subject's profile. After adjusting for pro-bed-rest tolerance time, none of the profile variables remained in the risk equation for post-bed-rest tolerance greyout. However, prior to bed rest, risk of greyout could be predicted with 91% accuracy. All of the profile variables except weight, MAP, and those related to inherent aerobic capacity (Vo(sub 2)max, percent body fat, resting heart rate) entered the risk equation for pro-bed-rest greyout. A cross-validation using 24 new subjects indicated a very stable model for risk prediction, accurate within 5% of the original equation. The result for the inherent fitness variables is significant in that a consensus as to whether an increased aerobic capacity is beneficial or detrimental has not been satisfactorily established. We conclude that tolerance to +Gz acceleration before and after simulated weightlessness is independent of inherent aerobic fitness.

Ludwig, David A.↗
NASA NTRS1989

Evaluation of the heat transfer module (FAHT) of Failure Analysis Nonlinear Thermal And Structural Integrated Code (FANTASTIC)

The heat transfer module of FANTASTIC Code (FAHT) is studied and evaluated to the extend possible during the ten weeks duration of this project. A brief background of the previous studies is given and the governing equations as modeled in FAHT are discussed. FAHT's capabilities and limitations based on these equations and its coding methodology are explained in detail. It is established that with improper choice of element size and time step FAHT's temperature field prediction at some nodes will be below the initial condition. The source of this unrealistic temperature prediction is identified and a procedure is proposed for avoiding this phenomenon. It is further shown that the proposed procedure will converge to an accurate prediction upon mesh refinement. Unfortunately due to lack of time FAHT's ability to accurately account for pyrolysis and surface ablation has not been verified. Therefore, at the present time it can be stated with confidence that FAHT can accurately predict the temperature field for a transient multi-dimensional, orthotropic material with directional dependence, variable property, with nonlinear boundary condition. Such a prediction will provide an upper limit for the temperature field in an ablating decomposing nozzle liner. The pore pressure field, however, will not be known.

Keyhani, Majid↗
NASA NTRS1973

A direct procedure for partitioning scanning workload in control display system design

Experimental eye scanning measurements were made from simulated instrument approaches in a flight-like cockpit representing a contemporary jet transport. The procedure for predicting the partition of the pilot's scanning workload required for monitoring and controlling a task with status displays and a flight director was simplified. When there is but a single director control display, iteration in the preliminary design computations is eliminated. The preliminary design computations are based on predictions of closed-loop input-correlated errors in displayed variables with respect to the trimmed flight values. Methods for predicting multiloop error coherence, and for correcting the predicted partition of scanning workload when the pilot's scanning remnant contribution is significant are included.

Clement, W. F.↗
NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.

prognostics↗
NASA NTRSDate not supplied

A Path to Improving Simulated Properties of Low Clouds over the Beaufort Sea using Airborne In Situ Observations of Subgrid-Scale Variability

Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. One of the main reasons for this problem are the possible unrealistic assumptions that models/reanalyses make about the subgrid-scale (SGS) variability of meteorological properties, as well as the relationship between SGS variability and grid-scale (GS) cloud properties. We utilize cloud and thermodynamic data of low level (primarily) liquid clouds collected from two aircraft campaigns conducted over the Beaufort Sea to better understand and characterize this problem. Examining data from the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign and the 1998 First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE) reveals that GS cloud water variability is closely related with SGS distribution of total water (i.e. water vapor + cloud water). We investigate two related approaches to prediction of GS cloud properties from SGS variability: the critical saturation ratio method, and the critical relative humidity method. We find significant correlation between GS cloud water and SGS supersaturation when the critical saturation ratio is set at 100%, as well as a notable relationship between GS cloud water and the width of the SGS total water distribution. Critical relative humidity also compares well with GS cloud water. However, we also find that the assumptions of a static critical saturation ratio of 100% to be unrealistic, as well as a fixed SGS distribution width. Empirical calculations from the ARISE data show a large sensitivity of these SGS variables to GS relative humidity, and so a SGS parameterization allowing them to vary according to GS thermodynamic properties may result in more realistic GS cloud water values.

J. Brant Dodson↗
NASA NTRS1972

Two-dimensional finite-element temperature variance analysis

The finite element method is extended to thermal analysis by forming a variance analysis of temperature results so that the sensitivity of predicted temperatures to uncertainties in input variables is determined. The temperature fields within a finite number of elements are described in terms of the temperatures of vertices and the variational principle is used to minimize the integral equation describing thermal potential energy. A computer calculation yields the desired solution matrix of predicted temperatures and provides information about initial thermal parameters and their associated errors. Sample calculations show that all predicted temperatures are most effected by temperature values along fixed boundaries; more accurate specifications of these temperatures reduce errors in thermal calculations.

Heuser, J. S.↗
NASA NTRS2005

Revealing Relationships among Relevant Climate Variables with Information Theory

The primary objective of the NASA Earth-Sun Exploration Technology Office is to understand the observed Earth climate variability, thus enabling the determination and prediction of the climate's response to both natural and human-induced forcing. We are currently developing a suite of computational tools that will allow researchers to calculate, from data, a variety of information-theoretic quantities such as mutual information, which can be used to identify relationships among climate variables, and transfer entropy, which indicates the possibility of causal interactions. Our tools estimate these quantities along with their associated error bars, the latter of which is critical for describing the degree of uncertainty in the estimates. This work is based upon optimal binning techniques that we have developed for piecewise-constant, histogram-style models of the underlying density functions. Two useful side benefits have already been discovered. The first allows a researcher to determine whether there exist sufficient data to estimate the underlying probability density. The second permits one to determine an acceptable degree of round-off when compressing data for efficient transfer and storage. We also demonstrate how mutual information and transfer entropy can be applied so as to allow researchers not only to identify relations among climate variables, but also to characterize and quantify their possible causal interactions.

Knuth, Kevin H.↗
NASA NTRS1994

Maximum predictive power and the superposition principle

In quantum physics the direct observables are probabilities of events. We ask how observed probabilities must be combined to achieve what we call maximum predictive power. According to this concept the accuracy of a prediction must only depend on the number of runs whose data serve as input for the prediction. We transform each probability to an associated variable whose uncertainty interval depends only on the amount of data and strictly decreases with it. We find that for a probability which is a function of two other probabilities maximum predictive power is achieved when linearly summing their associated variables and transforming back to a probability. This recovers the quantum mechanical superposition principle.

Summhammer, Johann↗
NASA NTRS1951

Analytical investigation of fully developed laminar flow in tubes with heat transfer with fluid properties variable along the radius

Relations were analytically obtained for the prediction of radial distributions of velocity and temperature for fully developed laminar flow of gases and of liquid metals in tubes with fluid properties variable along the radius. The relations are applicable to both heating and cooling of the fluid. By use of the relations for velocity and temperature distributions, relations were obtained among Nusselt number, friction parameter, and ratio of wall to bulk temperature. The Nusselt number and friction parameter were found to be independent of Reynolds number and Prandtl number. The effects of ratio of wall to bulk temperature on Nusselt number and friction parameter could be eliminated by evaluating the fluid properties at specified temperatures in the fluid.

Deissler, Robert G↗
NASA NTRS2021

Investigation of several proxies to estimate sulfuric acid concentration in volcanic plume conditions

Sulfuric acid (H2SO4) is commonly accepted as a key precursor for atmospheric new particle formation (NPF). However, direct measurements of [H2SO4] remain challenging, thus preventing the determination of this important quantity, and, consequently, a complete understanding of its contribution to the NPF process. Several proxies have been developed to bridge the gaps, but their ability to predict [H2SO4] in very specific conditions such as those encountered in volcanic plumes (including in particular high sulphur dioxide mixing ratios) has not been evaluated so far. In this context, the main objective of the present study was to develop new proxies for daytime [H2SO4] in volcanic plume conditions and compare their performance to that of the proxies available in the literature. In specific, the data collected at Maïdo during the OCTAVE 2018 campaign, in the volcanic eruption plume of the Piton de la Fournaise, were first used to derive seven proxies based on the knowledge of sulphur dioxide (SO2) mixing ratio, global radiation, condensation sink (CS) and relative humidity (RH). In three of the seven proxies (F1–F3), all variables were given equal weight in the prediction of [H2SO4], while adjusted powers were allowed for the different variables in the other four proxies (A1–A4). Proxies A1–A4 were overall found to perform better compared to F1–F3, with, in specific, improved predictive ability for [H2SO4] > 2 × 108 per cu.cm. The CS was observed to play an important role in regulating [H2SO4], while, in contrast, the inclusion of RH did not improve the predictions. A last expression accounting for an additional sink term related to cluster formation, S1, was also tested and showed a very good predictive ability over the whole range of measured [H2SO4]. The newly developed proxies were in a second step further evaluated using airborne measurements performed in the passive degassing plume of Etna during the STRAP 2016 campaign. Increased correlations between observed and predicted [H2SO4] were obtained when the dependence of predicted [H2SO4] over CS was the lowest, and when the dependence over [SO2] was concurrently the highest. The best predictions were finally retrieved by the simple formulation of F2 (in which [SO2] and radiation alone were assumed to explain the variations of [H2SO4] with equal contributions), with a pre factor adapted to the STRAP data. All in all, our results illustrate the fairly good capacity of the proxy available in the literature to describe [H2SO4] in volcanic plume conditions, but highlight at the same time the benefit of the newly developed proxies for the prediction of the highest concentrations ([H2SO4] > 2–3 × 108 per cu.cm). Also, the contrasting behaviours of the new proxies in the two investigated datasets indicate that in volcanic plumes like in other environments, the relevance of a proxy can be affected by changes in environmental conditions, and that location specific coefficients do logically improve the predictions.

new particle formation↗
NASA NTRS2019

Application of a Temperature-Dependent Load Prediction Method to a RUAG Six-Component Block-Type Balance

Temperature-dependent data of a RUAG six-component block-type balance was analyzed to assess the accuracy of two load prediction methods for temperature-dependent balance data. The supplied data was prepared for the analysis by splitting it into calibration and check load data subsets. The first calibration data subset was obtained at a temperature of 294 Kelvin. The second calibration data subset was obtained at a temperature of 315 Kelvin. A subset of 38 points was extracted from the second data set and used as check loads so that the accuracy of the two load prediction methods could be tested. First, the Iterative Method in combination with an extended independent and dependent variable set was used for the balance load prediction. This approach fits electrical outputs as a function of loads and the temperature and, afterwards, constructs a load iteration scheme from the regression coefficients so that loads can be predicted from outputs and the temperature during a wind tunnel test. The Non-Iterative Method was also used for the load prediction. This alternate method can more easily be implemented in a data system as loads are directly fitted as a function of electrical outputs and the temperature. Analysis results for the axial force are only discussed in the paper as similar results were obtained for the other five load components. Results for both methods clearly show that the cross-product term constructed from either a primary gage load or a primary gage output and the temperature explains the majority of the temperature-dependent part of the predicted balance load. This term models the temperature dependent nature of the gage sensitivity. Therefore, it is recommended to apply primary gage loadings at different temperatures during a balance calibration whenever temperature effects need to be described. These loadings will contain information about the temperature-dependent nature of the gage sensitivities that can be quantified by related cross-product terms in regression models of the data.

strain-gage balance↗
NASA NTRS2014

Pan-Continental Droughts in North America over the Last Millennium

Regional droughts are common in North America, but pan-continental droughts extending across multiple regions, including the 2012 event, are rare relative to single-region events. Here, the tree-ring-derived North American Drought Atlas is used to investigate drought variability in four regions over the last millennium, focusing on pan-continental droughts. During the Medieval Climate Anomaly (MCA), the central plains (CP), Southwest (SW), and Southeast (SE) regions experienced drier conditions and increased occurrence of droughts and the Northwest (NW) experienced several extended pluvials. Enhanced MCA aridity in the SW and CP manifested as multidecadal megadroughts. Notably, megadroughts in these regions differed in their timing and persistence, suggesting that they represent regional events influenced by local dynamics rather than a unified, continental-scale phenomena. There is no trend in pan-continental drought occurrence, defined as synchronous droughts in three or more regions. SW, CP, and SE (SW+CP+SE) droughts are the most common, occurring in 12 percent of all years and peaking in prevalence during the twelfth and thirteenth centuries; patterns involving three other regions occur in about 8 percent of years. Positive values of the Southern Oscillation index (La Nina conditions) are linked to SW, CP, and SE (SW+CP+SE) droughts and SW, CP, and NW (SW+CP+NW) droughts, whereas CP, NW, and SE (CP+NW+SE) droughts are associated with positive values of the Pacific decadal oscillation and Atlantic multidecadal oscillation. While relatively rare, pan-continental droughts are present in the paleo record and are linked to defined modes of climate variability, implying the potential for seasonal predictability. Assuming stable drought teleconnections, these events will remain an important feature of future North American hydroclimate, possibly increasing in their severity in step with other expected hydroclimate responses to increased greenhouse gas forcing.

anomalies↗
NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and automation, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Tong, Michael T.↗
NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and data science, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Core Size↗
NASA NTRS2004

Degree Of Diminution In Vagal-Cardiac Activity Predicts Sudden Death In Familial Dysautonomia When Resting Tachycardia Is Absent

Patients with familial dysautonomia (FD) have an increased risk of sudden death, but sensitive and specific predictors of sudden death in FD are lacking. Methods. We recorded 10-min resting high-fidelity 12-lead ECGs in 14 FD patients and in 14 age/gender-matched healthy subjects and studied 25+ different heart rate variability (HRV) indices for their ability to predict sudden death in the FD patients. Indices studied included those from 4 "nonlinear" HRV techniques (detrended fluctuation analysis, approximate entropy, correlation dimension, and PoincarC analyses). The predictive value of PR, QRS, QTc and JTc intervals, QT dispersion (QTd), beat-to-beat QT and PR interval variability indices (QTVI and PRVI) and 12- lead high frequency QRS ECG (150-250 Hz) were also studied. FD patients and controls (C) differed (Pless than 0.0l) with respect to 20+ of the HRV indices (FD less than C) and with respect to QTVI and PRVI (FDBC) and HF QRS- related root mean squared voltages (FDBC) and reduced amplitude zone counts (FD less than C). They differed less with respect to PR intervals (FD less than C) and JTc intervals (FD greater than C) (P less than 0.05 for both) and did not differ at all with respect to QRS and QTc intervals and to QTd. Within 12 months after study, 2 of the 14 patients succumbed to sudden cardiac arrest. The best predictor of sudden death was the degree of diminution in HRV vagal-cardiac (parasympathetic) parameters such as RMSSD, the SDl of Poincare plots, and HF spectral power. Excluding the two FD patients who had resting tachycardia (HR greater than 100, which confounds traditional HRV analyses), the following criteria were independently 100% sensitive and 100% specific for predicting sudden death in the remaining 12 FD patients during spontaneous breathing: RMSSD less than 13 ms and/or PoincarC SD1 less than 9 ms. In FD patients without supine tachycardia, the degree of diminution in parasympathetic HRV parameters (by high-fidelity ECG) predicts incipient death.

Schlegel, T. T.↗
NASA NTRS1972

Investigation of the utility of mean square approximation systems and in system response predictions

A method is presented for estimating the variability of a system's natural frequencies arising from the variability of the system's parameters. The only information required to obtain the estimates is the member variability, in the form of second order properties, and the natural frequencies and mode shapes of the mean system. Several examples are worked out in detail to illustrate how the method is applied.

Bogdanoff, J. L.↗
NASA NTRS2003

Predicting Modes of the Unsteady Vorticity Field near the Trailing Edge of a Blade

Progress on predicting modes of the unsteady velocity/vorticity field of a turbulent boundary layer from Reynolds stress statistics is described. Prediction of these modes, that provide the source terms for trailing edge noise predictions in aircraft engine fans and other configurations, will allow for the first time detailed viscous flow effects to be included in such noise calculations. The key accomplishments of this work in FY02 are: (1) The development of a Matlab code for the prediction of modes in two- and three-dimensional boundary layers, previously applied to plane wakes; (2) Predictions with the code using a constant lengthscale formulation in a fully developed turbulence channel flow. Comparison of these boundary layer predictions with available DNS simulation results; and (3) Formulation of an improved model using a variable lengthscale proportional to mixing length. Turbulent channel flow predictions and comparison with DNS results. This work is being carried out in continuous communication and collaboration with the Glegg research group at Florida Atlantic University, which will be incorporating mode predictions into engine noise calculations.

Devenport, William J.↗
NASA NTRSDate not supplied

Reliability-Based Damage Tolerance Framework for Achieving Highly Reusable Launch Systems

The United States Space Force and NASA are exploring approaches that enable rapid launch response via highly reusable launch systems. One hindrance to achieving high reusability is the inability to meet damage tolerance requirements. To this end, a framework for applying reliability-based damage tolerance to space structures is presented. The reliability-based approach employs probabilistic distributions of key input variables such as material properties, geometry, loads, inspection probability, and defect characteristics to predict hardware reliability for the planned mission life. The probabilistic variables needed to perform the reliability assessments are defined and recommendations on how to collect the required data are provided. Several example problems illustrate the implementation and execution of this approach.

Leland Shimizu↗