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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

The Subseasonal Experiment (SubX): A Multi-Model Subseasonal Prediction Experiment

SubX is a multi-model subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced seventeen years of retrospective (re-) forecasts and more than a year of weekly real-time forecasts. The re-forecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation three weeks ahead of time in specific regions. The SubX multi-model ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden-Julian Oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated with skillful predictions of the MJO four weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones which can help emergency management and aid organizations to plan for disasters. (Capsule Summary) A research to operations project in service of developing better operational subseasonal forecasts.

Precipitation↗
DOE OSTI2022

Physics-informed machine learning modeling for predictive control using noisy data

Due to the occurrence of over-fitting at the learning phase, the modeling of chemical processes via artificial neural networks (ANN) by using corrupted data (i.e., noisy data) is an ongoing challenge. Therefore, this work investigates the effect of both Gaussian and non-Gaussian noise on the performance of process-structure based recurrent neural networks (RNN) models, which take the form of partially-connected RNN models in this work, that are used to approximate a class of multi-input-multi-outputs nonlinear systems. Furthermore, two different techniques, specifically Monte Carlo dropout and co-teaching, are utilized in the development of partially-connected RNN models. Here, these two techniques are employed to reduce the over-fitting in ANNs when noisy data is used in the training process and, hence, to improve the open-loop accuracy as well as the closed-loop performance under a Lyapunov-based model predictive controller (MPC). Aspen Plus Dynamics, a well-known high-fidelity process simulator, is used to simulate a large-scale chemical process application in order to demonstrate the anticipated improvements in both open-loop approximation and closed-loop controller performance in the presence of Gaussian and non-Gaussian noise in the data set using physics-informed RNNs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Climate Modeling and Prediction at NSIPP

The talk will review modeling and prediction efforts undertaken as part of NASA's Seasonal to Interannual Prediction Project (NSIPP). The focus will be on atmospheric model results, including its use for experimental seasonal prediction and the diagnostic analysis of climate anomalies. The model's performance in coupled experiments with land and atmosphere models will also be discussed.

Suarez, Max↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
NASA NTRS2008

Multiplexed Predictive Control of a Large Commercial Turbofan Engine

Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model.

Richter, hanz↗
NASA NTRS2003

Modeling beta-adrenergic control of cardiac myocyte contractility in silico

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology.

Non-NASA Center↗
NASA NTRSDate not supplied

Using Long-Short Term Memory Models to Predict Solar Active Regions Emergence

We train Long Short-Term Memory (LSTM) models that predict the formation of active regions (ARs), the main source of eruptive solar activity. Using the Doppler shift velocity, the continuum intensity and the magnetic field full-disk maps from SDO/HMI we have created time-series datasets of acoustic power and magnetic flux which are used to train Long Short-Term Memory (LSTM) models on predicting decreases in continuum intensity 12 hours in advance. Testing of the models' performance was done on data from 5 ARs, unseen from the model during training. The model predicted the emergence of AR11726, AR13165 and AR13179, 10, 29 and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet parts of the solar disc, showing the ability of the model to capture acoustic power anomalies and predict continuum intensity variations. This work sets the foundations for the very first ML-aided prediction of solar ARs.

SMD↗
NASA NTRSDate not supplied

1st AIAA CFD Transition Modeling and Prediction Workshop: OVERFLOW Results for the SST and Langtry-Menter Models

This paper reports the results of CFD simulations of the test cases for the AIAA 1stCFDTransition Modeling and Prediction Workshop held at the AIAA Scitech Forum on 21-22January, 2021 using the NASA solver OVERFLOW. The Langtry-Menter transition model was used in combination with the Shear Stress Transport (SST) turbulence model. The testcases include the ERCOFTAC zero-pressure-gradient flat plate cases T3A and T3B with by-pass transition, the natural laminar flow NLF(1)-0416 airfoil, the DLR inclined 6:1 prolate spheroid, and the NASA Common Research Model with wing design optimized for natural laminar flow transition (CRM-NLF). OVERFLOW load and transition predictions obtained using best practices are compared with experiments and prior simulations. Additional results are provided for modified turbulence model boundary specifications to improve alignment to reference data and assess model sensitivity.

ARMD↗
NASA NTRS1996

A Comparison of Tension and Compression Creep in a Polymeric Composite and the Effects of Physical Aging on Creep Behavior

Experimental and analytical methods were used to investigate the similarities and differences of the effects of physical aging on creep compliance of IM7/K3B composite loaded in tension and compression. Two matrix dominated loading modes, shear and transverse, were investigated for two load cases, tension and compression. The tests, run over a range of sub-glass transition temperatures, provided material constants, material master curves and aging related parameters. Comparing results from the short-term data indicated that although trends in the data with respect to aging time and aging temperature are similar, differences exist due to load direction and mode. The analytical model used for predicting long-term behavior using short-term data as input worked equally as well for the tension or compression loaded cases. Comparison of the loading modes indicated that the predictive model provided more accurate long term predictions for the shear mode as compared to the transverse mode. Parametric studies showed the usefulness of the predictive model as a tool for investigating long-term performance and compliance acceleration due to temperature.

Gates, Thomas S.↗
NASA NTRSDate not supplied

Aeroacoustic Characterization of Optimum Hovering Rotors using Artificial Neural Networks

This work illustrates the use of artificial neural network modeling in the aerodynamic and aeroacoustic characterization of optimum hovering rotors over a broad range of design and operating conditions. Design of Experiments was used to create input feature spaces over eight input factors: the number of rotor blades, rotor radius, rotor rotation rate, design thrust condition, collective pitch, airfoil camber, the location of maximum camber, and the airfoil thickness. A low-fidelity tool chain was then used at the discrete data points defined by the designed input feature spaces to analytically design optimum hovering rotors and simulate aerodynamic and aeroacoustic quantities. This allowed for the generation of data sets over which to train and test the artificial neural network prediction models. Prediction models were trained over the data sets for the actual thrust generated by the rotor, power loading, tonal thickness and loading noise at the fundamental blade passage frequency, and broadband self-noise at seventeen one-third octave bands between 1 kHz and 40 kHz. These prediction models were validated by testing over data previously unseen by the models to quantify their capability for generalization to new data within the design feature space. The models were then used to study the effect each input feature had on the aeroacoustics and aerodynamics of optimum hovering rotors and physical insights were concluded to further explain the effect of each input. This characterization study showed that tonal noise and power loading were most sensitive to the number of rotor blades and the rotor rotation rate and that broadband noise was most sensitive to collective pitch and the design thrust condition.

Christopher S Thurman↗
DOE OSTI2017

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require an elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.

42 ENGINEERING↗
NASA NTRS1980

Review and developments of dissemination models for airborne carbon fibers

Dissemination prediction models were reviewed to determine their applicability to a risk assessment for airborne carbon fibers. The review showed that the Gaussian prediction models using partial reflection at the ground agreed very closely with a more elaborate diffusion analysis developed for the study. For distances beyond 10,000 m the Gaussian models predicted a slower fall-off in exposure levels than the diffusion models. This resulting level of conservatism was preferred for the carbon fiber risk assessment. The results also showed that the perfect vertical-mixing models developed herein agreed very closely with the diffusion analysis for all except the most stable atmospheric conditions.

Elber, W.↗
DOE OSTI2022

Optimized Machine Learning Model for Predicting Groundwater Contamination

The use of physical models to predict groundwater contaminant movement remains technically challenging due to the complexity of the phenomena, the heterogeneity of key parameters in nature, and the presence of poorly defined interactive and feedback processes. New approaches to address these challenges are needed. In this study, we evaluate various Artificial Intelligence (AI)-based approaches to understand a hexavalent chromium (Cr(VI)) plumes located on the U.S. Department of Energy’s (DOE) Hanford Site in Richland, WA. The groundwater monitoring dataset used in this study included data from the 100 Area along the Columbia River and included data collected between 2010 to 2019. This study investigates the most prominent contaminant, Cr(VI), with the Extreme Gradient Boosting (XGBoost) machine learning model. The XGBoost models were compared with optimized versions using an Empirical Bayes Search Cross-Validation technique for better prediction. The optimized XGBoost model yielded an R^2 value of 0.99 on the training set and 0.85 on the testing set, whereas XGBoost without optimization yielded a value of 0.83 on the training set and 0.85 on the testing set. This paper provides an overview of a computational method for groundwater contamination modeling that shows promise for improving current remediation efforts.

Mazumdar, Hirak↗
NASA NTRS2001

Reusable Solid Rocket Motor Nozzle Joint-4 Thermal Analysis

This study provides for development and test verification of a thermal model used for prediction of joint heating environments, structural temperatures and seal erosions in the Space Shuttle Reusable Solid Rocket Motor (RSRM) Nozzle Joint-4. The heating environments are a result of rapid pressurization of the joint free volume assuming a leak path has occurred in the filler material used for assembly gap close out. Combustion gases flow along the leak path from nozzle environment to joint O-ring gland resulting in local heating to the metal housing and erosion of seal materials. Analysis of this condition was based on usage of the NASA Joint Pressurization Routine (JPR) for environment determination and the Systems Improved Numerical Differencing Analyzer (SINDA) for structural temperature prediction. Model generated temperatures, pressures and seal erosions are compared to hot fire test data for several different leak path situations. Investigated in the hot fire test program were nozzle joint-4 O-ring erosion sensitivities to leak path width in both open and confined joint geometries. Model predictions were in generally good agreement with the test data for the confined leak path cases. Worst case flight predictions are provided using the test-calibrated model. Analysis issues are discussed based on model calibration procedures.

Clayton, J. Louie↗
NASA NTRS2012

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

Daigle, Matthew J.↗
DOE OSTI2018

Combination of searches for heavy resonances decaying into bosonic and leptonic final states using 36 fb - 1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

Searches for new heavy resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons, are presented using a data sample corresponding to 36.1 fb -1 of pp collisions at $ \sqrt{s}=13 $ TeV collected during 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting bosonic decay modes in the qqqq, ννqq, ℓνqq, ℓℓqq, ℓνℓν, ℓℓνν, ℓνℓℓ, ℓℓℓℓ, qqbb, ννbb, ℓνbb, and ℓℓbb final states are combined, searching for a narrow-width resonance. Likewise, analyses selecting the leptonic ℓν and ℓℓ final states are also combined. These two sets of analyses are then further combined. No significant deviation from the Standard Model predictions is observed. Three benchmark models are tested: a model predicting the existence of a new heavy scalar singlet, a simplified model predicting a heavy vector-boson triplet, and a bulk Randall-Sundrum model with a heavy spin-2 Kaluza-Klein excitation of the graviton. Cross section limits are set at the 95% confidence level using an asymptotic approximation and are compared with predictions for the benchmark models. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The data exclude a heavy vector-boson triplet with mass below 5.5 TeV in a weakly coupled scenario and 4.5 TeV in a strongly coupled scenario, as well as a Kaluza-Klein graviton with mass below 2.3 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Searches for heavy diboson resonances in pp collisions at $ \sqrt{s}=13 $ TeV with the ATLAS detector

Searches for new heavy resonances decaying to WW, WZ, and ZZ bosons are presented, using a data sample corresponding to 3.2 fb -1 of pp collisions at s=13 TeV colle cted with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting ννqq, ℓνqq, ℓℓqq and qqqq final states are combined, searching for an arrow-width resonance with mass between 500 and 3000 GeV. The discriminating variable is either an invariant mass or a transverse mass. No significant deviations from the Standard Model predictions are observed. Three benchmark models are tested: a model predicting the existence of a new heavy scalar singlet, a simplified model predicting a heavy vector-boson triplet, and a bulk Randall-Sundrum model with a heavy spin-2 graviton. Cross-section limits are set at the 95% confidence level and are compared to theoretical cross-section predictions for a variety of models. The data exclude a scalar singlet with mass below 2650 GeV, a heavy vector-boson triplet with mass below 2600 GeV, and a graviton with mass below 1100 GeV. These results significantly extend the previous limits set using pp collisions at √s=8 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2012

Impact of Land Model Calibration on Coupled Land-Atmosphere Prediction

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry and wet land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystem in NASA's Land Information System (LIS-OPT/UE). The impact of the calibration on the a) spinup of the land surface used as initial conditions, and b) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. Changes in ambient weather and land-atmosphere coupling are evaluated along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Results indicate that the offline calibration leads to systematic improvements in land-PBL fluxes and near-surface temperature and humidity, and in the process provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction.

Santanello, Joseph A., Jr.↗