Results for “variability prediction” — Page 38

NASA NTRS1980

Statistical fatigue of unnotched composite laminates

A general analytical, phenomenological model is developed for characterizing fatigue damage accumulation in unnotched composite laminates, and experimental results are used to evaluate the model. The fatigue model, which is developed for various types of cyclic loadings, is based on the assumption that the residual strength reduction is a monotonically decreasing function of the fatigue life. Parameters are determined from baseline test data consisting of one set of residual strength data, one set of static strength data, and one set of constant amplitude fatigue life data. Once the parameters are determined, the model is able to predict: (1) the statistical distribution of the fatigue life, and the residual strength under constant and variable amplitude loads, and (2) the effect of periodic proof tests, without any additional experimental data. Experimental test results show that predictions made from the present model agree quite well with the experimental results. The model is also applicable to fatigue of bonded or bolted composite joints.

Yang, J. N.↗

NASA NTRS2016

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Safe and efficient surface operations↗

NASA NTRS2001

Investigating Uncertainty in Predicting Carbon Dynamics in North American Biomes: Putting Support-Effect Bias in Perspective

A fundamental strategy in NASA's Earth Observing System's (EOS) monitoring of vegetation and its contribution to the global carbon cycle is to rely on deterministic, process-based ecosystem models to make predictions of carbon flux over large regions. These models are parameterized (that is, the input variables are derived) using remotely sensed images such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground measurements and interpolated maps. Since early applications of these models, investigators have noted that results depend partly on the spatial support of the input variables. In general, the larger the support of the input data, the greater the chance that the effects of important components of the ecosystem will be averaged out. A review of previous work shows that using large supports can cause either positive or negative bias in carbon flux predictions. To put the magnitude and direction of these biases in perspective, we must quantify the range of uncertainty on our best measurements of carbon-related variables made on equivalent areas. In other words, support-effect bias should be placed in the context of prediction uncertainty from other sources. If the range of uncertainty at the smallest support is less than the support-effect bias, more research emphasis should probably be placed on support sizes that are intermediate between those of field measurements and MODIS. If the uncertainty range at the smallest support is larger than the support-effect bias, the accuracy of MODIS-based predictions will be difficult to quantify and more emphasis should be placed on field-scale characterization and sampling. This talk will describe methods to address these issues using a field measurement campaign in North America and "upscaling" using geostatistical estimation and simulation.

Dungan, Jennifer L.↗

NASA NTRS1972

An analytical and experimental evaluation of shadow shields and their support members.

Review of the major results of an analytical and experimental program aimed at determining the potential effectiveness of shadow shields in minimizing radiant heat transfer into cryogenic propellant tanks on long-duration interplanetary missions, taking into account the thermal interaction between shields and their support structures. Analytical procedures are developed and applied that include the strut-shield interaction in predicting shadow shield performance. The results of experimental studies of the effects of configuration variables on the performance of a scale model of a shadow shield system are presented. The experimental results are used to verify the validity of analytical predictions. The results obtained show that: (1) shadow shields can be effective in reducing the heat transfer into cryogenic propellant tanks, and (2) the conductive heat transfer through supports can be reduced by selective surface coatings.

Stochl, R. J.↗

NASA NTRS1988

Correlations between optical/near-infrared and UV extinction parameters and the prediction of UV extinction from ground-based photometry

An analysis of the variability of absolute UV extinction, A lambda derived through the ratio total-to-selective extinction, R Av/E(B-V), for 31 lines of sight for which UV extinction parameters were derived, is presented. For these data, which sample a wide range of environments and which are characterized by R = 2.5 to 6.0, differences in the shapes of UV extinction curves are largely due to variations in optical/near-UV extinction through changes in R. From this, it is found that UV extinction curves can be reproduced with reasonable accuracy by simply knowing R. Detection of an A2175/E(B-V) = 10 + or - 1 mag implies that the total extinction at 2175 A can be estimated from E(B-V) alone.

Cardelli, Jason A.↗

NASA NTRS2000

First-Order Model Management With Variable-Fidelity Physics Applied to Multi-Element Airfoil Optimization

First-order approximation and model management is a methodology for a systematic use of variable-fidelity models or approximations in optimization. The intent of model management is to attain convergence to high-fidelity solutions with minimal expense in high-fidelity computations. The savings in terms of computationally intensive evaluations depends on the ability of the available lower-fidelity model or a suite of models to predict the improvement trends for the high-fidelity problem, Variable-fidelity models can be represented by data-fitting approximations, variable-resolution models. variable-convergence models. or variable physical fidelity models. The present work considers the use of variable-fidelity physics models. We demonstrate the performance of model management on an aerodynamic optimization of a multi-element airfoil designed to operate in the transonic regime. Reynolds-averaged Navier-Stokes equations represent the high-fidelity model, while the Euler equations represent the low-fidelity model. An unstructured mesh-based analysis code FUN2D evaluates functions and sensitivity derivatives for both models. Model management for the present demonstration problem yields fivefold savings in terms of high-fidelity evaluations compared to optimization done with high-fidelity computations alone.

Alexandrov, N. M.↗

NASA NTRS1995

Ion Acoustic Wave Frequencies and Onset Times During Type 3 Solar Radio Bursts

Conflicting interpretations exist for the low-frequency ion acoustic (S) waves often observed by ISEE 3 in association with intense Langmuir (L) waves in the source regions of type III solar radio bursts near 1 AU. Two indirect lines of observational evidence, as well as plasma theory, suggest they are produced by the electrostatic (ES) decay L yields L(PRIME) + S. However, contrary to theoretical predictions, an existing analysis of the wave frequencies instead favors the electromagnetic (EM) decays L yields T + S, where T denotes an EM wave near the plasma frequency. This conflict is addressed here by comparing the observed wave frequencies and onset times with theoretical predictions for the ES and EM decays, calculated using the time-variable electron beam and magnetic field orientation data, rather than the nominal values used previously. Field orientation effects and beam speed variations are shown analytically to produce factor-of-three effects, greater than the difference in wave frequencies predicted for the ES and EM decays; effects of similar magnitude occur in the events analyzed here. The S-wave signals are extracted by hand from a sawtooth noise background, greatly improving the association between S waves and intense L waves. Very good agreement exists between the time-varying predictions for the ES decay and the frequencies of most (but not all) wave bursts. The waves occur only after the ES decay becomes kinematically allowed, which is consistent with the ES decay proceeding and producing most of the observed signals. Good agreement exists between the EM decay's predictions and a significant fraction of the S-wave observations while the EM decay is kinematically allowed. The wave data are not consistent, however, with the EM decay being the dominant nonlinear process. Often the observed waves are sufficiently broadband to overlap simultaneously the frequency ranges predicted for the ES and EM decays. Coupling the dominance of the ES decay with this frequency overlap provides support for a previous suggestion that fundamental emission occurs when the EM decay is stimulated by the ES decay product waves. The periods in which the ES and EM decays produce observable S waves are consistent with the observed and (independently) predicted times of fundamental and harmonic radio emission. This supports interpretation of fundamental emission as stimulated EM decay and harmonic emission as the coalescence L + L(prime) yields T of beam-generated L waves and L(prime) waves produced by the ES decay, where T denotes an electromagnetic wave at twice the plasma frequency. Analysis of the electron beam data reveals that the time-varying beam speed is consistent with ballistic beam propagation with minimal energy loss, implying that the beam propagates in a state close to time- and volume-averaged marginal stability. This confirms a central tenet of the stochastic growth theory for type III bursts.

Cairns, Iver H.↗

NASA NTRS1988

Estimating forest biomass and volume using airborne laser data

An airborne pulsed laser system was used to obtain canopy height data over a southern pine forest in Georgia in order to predict ground-measured forest biomass and timber volume. Although biomass and volume estimates obtained from the laser data were variable when compared with the corresponding ground measurements site by site, the present models are found to predict mean total tree volume within 2.6 percent of the ground value, and mean biomass within 2.0 percent. The results indicate that species stratification did not consistently improve regression relationships for four southern pine species.

Nelson, Ross↗

NASA NTRS2006

Quiet High Speed Fan (QHSF) Flutter Calculations Using the TURBO Code

A scale model of the NASA/Honeywell Engines Quiet High Speed Fan (QHSF) encountered flutter wind tunnel testing. This report documents aeroelastic calculations done for the QHSF scale model using the blade vibration capability of the TURBO code. Calculations at design speed were used to quantify the effect of numerical parameters on the aerodynamic damping predictions. This numerical study allowed the selection of appropriate values of these parameters, and also allowed an assessment of the variability in the calculated aerodynamic damping. Calculations were also done at 90 percent of design speed. The predicted trends in aerodynamic damping corresponded to those observed during testing.

Bakhle, Milind A.↗

NASA NTRS2013

Northern Eurasian Heat Waves and Droughts

This article reviews our understanding of the characteristics and causes of northern Eurasian summertime heat waves and droughts. Additional insights into the nature of temperature and precipitation variability in Eurasia on monthly to decadal time scales and into the causes and predictability of the most extreme events are gained from the latest generation of reanalyses and from supplemental simulations with the NASA GEOS-5 AGCM. Key new results are: 1) the identification of the important role of summertime stationary Rossby waves in the development of the leading patterns of monthly Eurasian surface temperature and precipitation variability (including the development of extreme events such as the 2010 Russian heat wave), 2) an assessment of the mean temperature and precipitation changes that have occurred over northern Eurasia in the last three decades and their connections to decadal variability and global trends in SST, and 3) the quantification (via a case study) of the predictability of the most extreme simulated heat wave/drought events, with some focus on the role of soil moisture in the development and maintenance of such events. A literature survey indicates a general consensus that the future holds an enhanced probability of heat waves across northern Eurasia, while there is less agreement regarding future drought, reflecting a greater uncertainty in soil moisture and precipitation projections. Substantial uncertainties remain in our understanding of heat waves and drought, including the nature of the interactions between the short-term atmospheric variability associated with such extremes and the longer-term variability and trends associated with soil moisture feedbacks, SST anomalies, and an overall warming world.

SST↗

NASA NTRS2000

A Strip-Yield Model for Predicting the Growth of Part-Through Cracks Under Cyclic Loading

Flaws exist in aircraft structures due to manufacturing operations and material defects. Under variable amplitude cyclic loading, these flaws grow as part-through cracks reducing the residual strength of structural components. To meet damage tolerant design requirements, accurate flaw growth predictions are needed which account for continual changes in crack shape as well as crack growth retardation and acceleration. Predicting the growth of part-through cracks under cyclic loading using an innovative and computationally efficient model is the focus of the research summarized in this report. In this research effort, a slice synthesis methodology was developed and used to construct a modified strip-yield model for the part-through semi-elliptical surface flaw, enabling prediction of plasticity-induced closure along the crack front and subsequent fatigue crack growth under constant amplitude and variable amplitude loading. While modeling the plasticity-induced closure in a part-through flaw may be performed using three dimensional elastic-plastic finite element analysis, this type of effort is impractical from an engineering perspective. A modified strip-yield model similar to that used in FASTRAN for part-through flaws is a much needed engineering design tool, particularly when computational resources are limited.

Daniewicz, S. R.↗

NASA NTRS2002

Pulmonary venous flow determinants of left atrial pressure under different loading conditions in a chronic animal model with mitral regurgitation

BACKGROUND: The aim of our study was to quantitatively compare the changes and correlations between pulmonary venous flow variables and mean left atrial pressure (mLAP) under different loading conditions in animals with chronic mitral regurgitation (MR) and without MR. METHODS: A total of 85 hemodynamic conditions were studied in 22 sheep, 12 without MR as control (NO-MR group) and 10 with MR (MR group). We obtained pulmonary venous flow systolic velocity (Sv) and diastolic velocity (Dv), Sv and Dv time integrals, their ratios (Sv/Dv and Sv/Dv time integral), mLAP, left ventricular end-diastolic pressure, and MR stroke volume. We also measured left atrial a, x, v, and y pressures and calculated the difference between v and y pressures. RESULTS: Average MR stroke volume was 10.6 +/- 4.3 mL/beat. There were good correlations between Sv (r = -0.64 and r = -0.59, P <.01), Sv/Dv (r = -0.62 and r = -0.74, P <.01), and mLAP in the MR and NO-MR groups, respectively. Correlations were also observed between Dv time integral (r = 0.61 and r = 0.57, P <.01) and left ventricular end-diastolic pressure in the MR and NO-MR groups. In velocity variables, Sv (r = -0.79, P <.001) was the best predictor of mLAP in both groups. The sensitivity and specificity of Sv = 0 in predicting mLAP 15 mm Hg or greater were 86% and 85%, respectively. CONCLUSION: Pulmonary venous flow variables correlated well with mLAP under altered loading conditions in the MR and NO-MR groups. They may be applied clinically as substitutes for invasively acquired indexes of mLAP to assess left atrial and left ventricular functional status.

Pulmonary Veins/physiopathology↗

NASA NTRS1998

Simulation of Crack Propagation in Engine Rotating Components under Variable Amplitude Loading

The crack propagation life of tested specimens has been repeatedly shown to strongly depend on the loading history. Overloads and extended stress holds at temperature can either retard or accelerate the crack growth rate. Therefore, to accurately predict the crack propagation life of an actual component, it is essential to approximate the true loading history. In military rotorcraft engine applications, the loading profile (stress amplitudes, temperature, and number of excursions) can vary significantly depending on the type of mission flown. To accurately assess the durability of a fleet of engines, the crack propagation life distribution of a specific component should account for the variability in the missions performed (proportion of missions flown and sequence). In this report, analytical and experimental studies are described that calibrate/validate the crack propagation prediction capability ]or a disk alloy under variable amplitude loading. A crack closure based model was adopted to analytically predict the load interaction effects. Furthermore, a methodology has been developed to realistically simulate the actual mission mix loading on a fleet of engines over their lifetime. A sequence of missions is randomly selected and the number of repeats of each mission in the sequence is determined assuming a Poisson distributed random variable with a given mean occurrence rate. Multiple realizations of random mission histories are generated in this manner and are used to produce stress, temperature, and time points for fracture mechanics calculations. The result is a cumulative distribution of crack propagation lives for a given, life limiting, component location. This information can be used to determine a safe retirement life or inspection interval for the given location.

Bonacuse, P. J.↗

NASA NTRS1999

Simulation of Crack Propagation in Engine Rotating Components Under Variable Amplitude Loading

The crack propagation life of tested specimens has been repeatedly shown to strongly depend on the loading history. Overloads and extended stress holds at temperature can either retard or accelerate the crack growth rate. Therefore, to accurately predict the crack propagation life of an actual component, it is essential to approximate the true loading history. In military rotorcraft engine applications, the loading profile (stress amplitudes, temperature, and number of excursions) can vary significantly depending on the type of mission flown. To accurately assess the durability of a fleet of engines, the crack propagation life distribution of a specific component should account for the variability in the missions performed (proportion of missions flown and sequence). In this report, analytical and experimental studies are described that calibrate/validate the crack propagation prediction capability for a disk alloy under variable amplitude loading. A crack closure based model was adopted to analytically predict the load interaction effects. Furthermore, a methodology has been developed to realistically simulate the actual mission mix loading on a fleet of engines over their lifetime. A sequence of missions is randomly selected and the number of repeats of each mission in the sequence is determined assuming a Poisson distributed random variable with a given mean occurrence rate. Multiple realizations of random mission histories are generated in this manner and are used to produce stress, temperature, and time points for fracture mechanics calculations. The result is a cumulative distribution of crack propagation lives for a given, life limiting, component location. This information can be used to determine a safe retirement life or inspection interval for the given location.

P J Bonacuse↗

NASA NTRS1993

On the use of the noncentral chi-square density function for the distribution of helicopter spectral estimates

A probability density function for the variability of ensemble averaged spectral estimates from helicopter acoustic signals in Gaussian background noise was evaluated. Numerical methods for calculating the density function and for determining confidence limits were explored. Density functions were predicted for both synthesized and experimental data and compared with observed spectral estimate variability.

Garber, Donald P.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS1981

Predictability of the tropical atmosphere

An examination of the deterministic predictability for tropics and middle-latitudes separately indicates that the theoretical upper limit of deterministic predictability for low latitudes is shorter than that for middle latitudes. Variability of time averages in low latitudes is mainly determined by the location and intensity of the large-scale Hadley and Walker circulations. Since these are largely influenced by the slowly varying boundary conditions of sea surface temperature and soil moisture, and since synoptic instabilities are not strong enough to change drastically the large scale flow, there is larger potential for predictability of monthly and seasonal means in low latitudes. It is conjectured that for short and medium range deterministic prediction, a prescribed diabatic heating field due to moist convection may be more useful than their explicit calculation from the evolving flow.

Shukla, J.↗