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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Comparison of Analytical and Numerical Performance Predictions for an International Space Station Node 3 Internal Active Thermal Control System Regenerative Heat Exchanger

The complexity of International Space Station (ISS) systems modeling often necessitates the concurrence of various dissimilar, parallel analysis techniques to validate modeling. This was the case with a feasibility and performance study of the ISS Node 3 Regenerative Heat Exchanger (RHX). A thermo-hydraulic network model was created and analyzed in SINDA/FLUINT. A less complex, closed form solution of the systems dynamics was created using an Excel Spreadsheet. The purpose of this paper is to provide a brief description of the modeling processes utilized, the results and benefits of each to the ISS Node 3 RHX study.

Wise, Stephen A.↗
NASA NTRS2007

Structural Test and Analysis of a Hybrid Inflatable Antenna

NASA is developing ultra-lightweight structures technology for communication antennas for space missions. One of the research goals is to evaluate the structural characteristics of inflatable and rigidizable antennas through test and analysis. Being able to test and analyze the structural characteristics of a full scale antenna is important to enable the simulation of various mission scenarios to determine system performance in space. Recent work completed to evaluate a Hybrid Inflatable Antenna concept will be discussed. Tests were completed on a 2-m prototype to optimize its static shape and identify its modal dynamics that are important for analytical model validation. These test results were used to evaluate a preliminary finite element model of the antenna, and this model development and correlation activity is also described in the paper.

Gaspar, James L.↗
DOE OSTI2018

Numerical Model Development and Validation for the WECCOMP Control Competition: Preprint

This paper details the development and validation of a numerical model of the Wavestar wave energy converter (WEC) in WEC-Sim. This numerical model was developed in support of the WEC Control Competition (WECCCOMP), an open competition with the objective of maximizing WEC power production through innovative control strategies. WECCCOMP has two stages: numerical implementation of control strategies, and experimental implementation. The work presented in this paper is for support of the stage one numerical implementation, where contestants are provided a WEC-Sim model of the Wavestar device to develop their control algorithms.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

“Max-tech FHR”: What is the maximum technically achievable water delivery capacity from a single 120 V plug?

Electrifying water heating will require affordable, direct drop-in replacements for existing gas water heaters that meet consumer expectations for hot water delivery. For many replacement scenarios, affordability requires a replacement option with like-for-like physical product dimensions, and existing consumer expectations presume a large delivery capacity from a small water heater. This work explores the technical potential for hot water delivery capacity for a heat pump water heater (HPWH) powered from a common 120 V plug. Delivery capability in this work is quantified by two metrics: the First Hour Rating (FHR) defined in the Code of Federal Regulations, and a real-world draw profile that resulted in hot water runout during field studies of 120 V HPWHs. The work focuses on the common “tall-and-slim” gas water heater product category, with external product dimensions of 20 inch diameter and 60 inch height. Using a simulation model in DOE/ORNL Heat Pump Design Model, validated by experimental baseline testing of a commercially available 120 V HPWH model, several measures were evaluated to maximize the delivery capacity of a 120 V HPWH. The 120 V HPWH was constrained by 1) external product dimensions of 20 in diameter and 60 inch height, 2) electrical power of 1.5 kW (85% of the available power from a dedicated 120 V, 15 A circuit), and 3) commercially-available components. The measures implemented included a large compressor, a pumped loop with plate heat exchanger to maximize tank stratification, enlarged heat exchangers, and relatively thin insulation to maximize the water storage volume.

Gluesenkamp, Kyle↗
NASA NTRS2005

Active Aeroelastic Wing Aerodynamic Model Development and Validation for a Modified F/A-18A

A new aerodynamic model has been developed and validated for a modified F/A-18A used for the Active Aeroelastic Wing (AAW) research program. The goal of the program was to demonstrate the advantages of using the inherent flexibility of an aircraft to enhance its performance. The research aircraft was an F/A-18A with wings modified to reduce stiffness and a new control system to increase control authority. There have been two flight phases. Data gathered from the first flight phase were used to create the new aerodynamic model. A maximum-likelihood output-error parameter estimation technique was used to obtain stability and control derivatives. The derivatives were incorporated into the National Aeronautics and Space Administration F-18 simulation, validated, and used to develop new AAW control laws. The second phase of flights was used to evaluate the handling qualities of the AAW aircraft and the control law design process, and to further test the accuracy of the new model. The flight test envelope covered Mach numbers between 0.85 and 1.30 and dynamic pressures from 600 to 1250 pound-force per square foot. The results presented in this report demonstrate that a thorough parameter identification analysis can be used to improve upon models that were developed using other means. This report describes the parameter estimation technique used, details the validation techniques, discusses differences between previously existing F/A-18 models, and presents results from the second phase of research flights.

Cumming, Stephen B.↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗
DOE OSTI2022

Redshifts of radio sources in the Million Quasars Catalogue from machine learning

ABSTRACT With the aim of using machine learning techniques to obtain photometric redshifts based upon a source’s radio spectrum alone, we have extracted the radio sources from the Million Quasars Catalogue. Of these, 44 119 have a spectroscopic redshift, required for model validation, and for which photometry could be obtained. Using the radio spectral properties as features, we fail to find a model which can reliably predict the redshifts, although there is the suggestion that the models improve with the size of the training sample. Using the near-infrared–optical–ultraviolet bands magnitudes, we obtain reliable predictions based on the 12 503 radio sources which have all of the required photometry. From the 80:20 training–validation split, this gives only 2501 validation sources, although training the sample upon our previous SDSS model gives comparable results for all 12 503 sources. This makes us confident that SkyMapper, which will survey southern sky in the u, v, g, r, i, z bands, can be used to predict the redshifts of radio sources detected with the Square Kilometre Array. By using machine learning to impute the magnitudes missing from much of the sample, we can predict the redshifts for 32 698 sources, an increase from 28 to 74 per cent of the sample, at the cost of increasing the outlier fraction by a factor of 1.4. While the ‘optical’ band data prove successful, at this stage we cannot rule out the possibility of a radio photometric redshift, given sufficient data which may be necessary to overcome the relatively featureless radio spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Grid Modeling and Hosting Capacity Analysis

An interface with the OpenDSS distribution grid simulator, facilitating users in calibrating and validating models. Additionally, three distinct tools have been developed: PV Hosting Capacity: This tool allows users to determine the additional amount of photovoltaic (PV) generation that can be integrated into the system without breaching operational constraints. EV Hosting Capacity: This tool focuses on identifying the capacity for incorporating extra electric vehicle (EV) load into the system without surpassing operational limitations. Project Impact Analysis Tool: This tool is designed to assess and report all potential grid violations associated with a specific project, providing valuable insights into its impact on the distribution grid.

Poudel, Shiva↗
DOE OSTI2018

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including "canopy greenness", processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the "greenness rising" and end of the "greenness falling" stages. Lastly, the database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

General multi-group macroscopic modeling for thermo-chemical non-equilibrium gas mixtures

This paper opens a new door to macroscopic modeling for thermal and chemical non-equilibrium. In a game-changing approach, we discard conventional theories and practices stemming from the separation of internal energy modes and the Landau-Teller relaxation equation. Instead, we solve the fundamental microscopic equations in their moment forms but seek only optimum representations for the microscopic state distribution function that provides converged and time accurate solutions for certain macroscopic quantities at all times. The modeling makes no ad hoc assumptions or simplifications at the microscopic level and includes all possible collisional and radiative processes; it therefore retains all non-equilibrium fluid physics. We formulate the thermal and chemical non-equilibrium macroscopic equations and rate coefficients in a coupled and unified fashion for gases undergoing completely general transitions. All collisional partners can have internal structures and can change their internal energy states after transitions. The model is based on the reconstruction of the state distribution function. The internal energy space is subdivided into multiple groups in order to better describe non-equilibrium state distributions. The logarithm of the distribution function in each group is expressed as a power series in internal energy based on the maximum entropy principle. The method of weighted residuals is applied to the microscopic equations to obtain macroscopic moment equations and rate coefficients succinctly to any order. The model’s accuracy depends only on the assumed expression of the state distribution function and the number of groups used and can be self-checked for accuracy and convergence. We show that the macroscopic internal energy transfer, similar to mass and momentum transfers, occurs through nonlinear collisional processes and is not a simple relaxation process described by, e.g., the Landau-Teller equation. Unlike the classical vibrational energy relaxation model, which can only be applied to molecules, the new model is applicable to atoms, molecules, ions, and their mixtures. Numerical examples and model validations are carried out with two gas mixtures using the maximum entropy linear model: one mixture consists of nitrogen molecules undergoing internal excitation and dissociation and the other consists of nitrogen atoms undergoing internal excitation and ionization. Results show that the original hundreds to thousands of microscopic equations can be reduced to two macroscopic equations with almost perfect agreement for the total number density and total internal energy using only one or two groups. We also obtain good prediction of the microscopic state populations using 5-10 groups in the macroscopic equations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Stellar granulation as the source of high-frequency flicker in Kepler light curves

A large fraction of cool, low-mass stars exhibit brightness fluctuations that arise from a combination of convective granulation, acoustic oscillations, magnetic activity, and stellar rotation. Much of the short-timescale variability takes the form of stochastic noise, whose presence may limit the progress of extrasolar planet detection and characterization. In order to lay the groundwork for extracting useful information from these quasi-random signals, we focus on the origin of the granulation-driven component of the variability. We apply existing theoretical scaling relations to predict the star-integrated variability amplitudes for 508 stars with photometric light curves measured by the Kepler mission. We also derive an empirical correction factor that aims to account for the suppression of convection in F-dwarf stars with magnetic activity and shallow convection zones. So that we can make predictions of specific observational quantities, we performed Monte Carlo simulations of granulation light curves using a Lorentzian power spectrum. These simulations allowed us to reproduce the so-called flicker floor (i.e., a lower bound in the relationship between the full light-curve range and power in short-timescale fluctuations) that was found in the Kepler data. The Monte Carlo model also enabled us to convert the modeled fluctuation variance into a flicker amplitude directly comparable with observations. When the magnetic suppression factor described above is applied, the model reproduces the observed correlation between stellar surface gravity and flicker amplitude. Observationally validated models like these provide new and complementary evidence for a possible impact of magnetic activity on the properties of near-surface convection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

The Double-edged Sword of Data-driven Super-Resolution: Adversarial Super-resolution Models

Data-driven super-resolution (SR) methods are often integrated into imaging pipelines as preprocessing steps to improve downstream tasks such as classification and detection. However, these SR models introduce a previously unexplored attack surface into imaging pipelines. In this paper, we present AdvSR, a framework demonstrating that adversarial behavior can be embedded directly into SR model weights during training, requiring no access to inputs at inference time. Unlike prior attacks that perturb inputs or rely on backdoor triggers, AdvSR operates entirely at the model level. By jointly optimizing for reconstruction quality and targeted adversarial outcomes, AdvSR produces models that appear benign under standard image quality metrics while inducing downstream misclassification. We evaluate AdvSR on three SR architectures (SRCNN, EDSR, SwinIR) paired with a YOLOv11 classifier and demonstrate that AdvSR models can achieve high attack success rates with minimal quality degradation. These findings highlight a new model-level threat for imaging pipelines, with implications for how practitioners source and validate models in safety-critical applications.

Sullivan, Haley [ORNL] (ORCID:0000000274069217)↗
DOE OSTI2014

Comparison of the Accuracy and Speed of Transient Mobile A/C System Simulation Models: Preprint

The operation of air conditioning (A/C) systems is a significant contributor to the total amount of fuel used by light- and heavy-duty vehicles. Therefore, continued improvement of the efficiency of these mobile A/C systems is important. Numerical simulation has been used to reduce the system development time and to improve the electronic controls, but numerical models that include highly detailed physics run slower than desired for carrying out vehicle-focused drive cycle-based system optimization. Therefore, faster models are needed even if some accuracy is sacrificed. In this study, a validated model with highly detailed physics, the 'Fully-Detailed' model, and two models with different levels of simplification, the 'Quasi-Transient' and the 'Mapped- Component' models, are compared. The Quasi-Transient model applies some simplifications compared to the Fully-Detailed model to allow faster model execution speeds. The Mapped-Component model is similar to the Quasi-Transient model except instead of detailed flow and heat transfer calculations in the heat exchangers, it uses lookup tables created with the Quasi-Transient model. All three models are set up to represent the same physical A/C system and the same electronic controls. Speed and results of the three model versions are compared for steady state and transient operation. Steady state simulated data are also compared to measured data. The results show that the Quasi-Transient and Mapped-Component models ran much faster than the Fully-Detailed model, on the order of 10- and 100-fold, respectively. They also adequately approach the results of the Fully-Detailed model for steady-state operation, and for drive cycle-based efficiency predictions

02 PETROLEUM↗
DOE OSTI2019

Analysis of the Effect of Vehicle Platooning on the Optimal Control of a Heavy Duty Engine Thermal System

One promising method for reducing fuel consumption and emissions, particularly in heavy duty trucks, is platooning. As the distance between vehicles decreases, the following vehicles will experience less aerodynamic drag on the front of the vehicle. However, reducing the velocity of the air contacting the front of the vehicle could have adverse effects on the temperature of the engine. To compensate for this effect, the energy consumption of the engine cooling system might increase, ultimately limiting the overall improvements obtained with platooning. Understanding the coupling between drag reduction and engine cooling load requirement is key for successfully implementing platooning strategies. Additionally, in a Connected and Automated Vehicle (CAV) environment, where information of the future engine load becomes available, the operation of the cooling system can be optimized in order to achieve the maximum fuel consumption reduction. In this paper, a control-oriented physics-based model for the engine cooling loop of a Volvo engine is developed and validated against road data. Starting from the validated model, an optimal control problem for the coolant system is formulated considering the tradeoff between the tracking of the engine temperature setpoint and the corresponding fuel consumption under different trailing distances. To compare the coolant system performance, Dynamic Programming (DP) is used to determine the global optimal solution for the coolant system actuator. The coupling between optimal cooling system operation and reduction in ram air are evaluated by comparing the results obtained from the DP under different platooning conditions against the unrestricted scenario. In addition, the paper analyzes the changes in the tradeoff between fuel consumption and setpoint tracking for different vehicle distances. This analysis will provide useful insight on the sensitivity of the coolant system controller calibration to the platoon distance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1984

Time series modeling of human operator dynamics in manual control tasks

A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency responses of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that has not been previously modeled to demonstrate the strengths of the method.

Biezad, D. J.↗
NASA NTRS1984

Time Series Modeling of Human Operator Dynamics in Manual Control Tasks

A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency response of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that was previously modeled to demonstrate the strengths of the method.

Biezad, D. J.↗