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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Regional Relative Risk, a Physics-Based Metric for Characterizing Airborne Infectious Disease Transmission

Airborne infectious disease transmission events occur over a wide range of spatial scales and can be an important means of disease transmission. Physics- and biology-based models can assist in predicting airborne transmission events, overall disease incidence, and disease control strategy efficacy. We describe a new theory that extends current approaches for the case in which an individual is infected by a single airborne particle, including the scenario in which numerous infectious particles are present in the air but only one causes infection. A single infectious particle can contain more than one pathogenic microorganism and be physically larger than the pathogen itself. This approach allows robust relative risk estimates even when there is wide variation in (i) individual exposures and (ii) the individual response to that exposure (the pathogen dose-response function can take any mathematical form and vary by individual). Based on this theory, we propose the regional relative risk—a new metric, distinct from the traditional relative risk metric, that compares the risk between two regions. In theory, these regions can range from individual rooms to large geographic areas. In this paper, we apply the regional relative risk metric to outdoor disease transmission events over spatial scales ranging from 50 m to 20 km, demonstrating that in many common cases minimal input information is required to use the metric. Also, we demonstrate that the model predictions are consistent with data from prior outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as transmission within indoor environments. This work provides context for (i) the initial stages of an airborne disease outbreak and (ii) larger-scale disease spread, including unexpected low-probability disease “sparks” that potentially affect remote populations, a key practical issue in controlling airborne disease outbreaks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Finite Element Model Development and Validation for Aircraft Fuselage Structures

The ability to extend the valid frequency range for finite element based structural dynamic predictions using detailed models of the structural components and attachment interfaces is examined for several stiffened aircraft fuselage structures. This extended dynamic prediction capability is needed for the integration of mid-frequency noise control technology. Beam, plate and solid element models of the stiffener components are evaluated. Attachment models between the stiffener and panel skin range from a line along the rivets of the physical structure to a constraint over the entire contact surface. The finite element models are validated using experimental modal analysis results. The increased frequency range results in a corresponding increase in the number of modes, modal density and spatial resolution requirements. In this study, conventional modal tests using accelerometers are complemented with Scanning Laser Doppler Velocimetry and Electro-Optic Holography measurements to further resolve the spatial response characteristics. Whenever possible, component and subassembly modal tests are used to validate the finite element models at lower levels of assembly. Normal mode predictions for different finite element representations of components and assemblies are compared with experimental results to assess the most accurate techniques for modeling aircraft fuselage type structures.

Buehrle, Ralph D.↗
DOE OSTI2022

Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models

Representation of soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. The magnitude of this uncertainty can be reduced by accurate representation of environmental controllers of SOC stocks in ESMs. In this study, we used data of environmental factors, field SOC observations, ESM projections and machine learning approaches to identify dominant environmental controllers of SOC stocks and derive functional relationships between environmental factors and SOC stocks. Our derived functional relationships predicted SOC stocks with similar accuracy as the machine learning approach. We used the derived relationships to benchmark the coupled model intercomparison project phase six ESM representation of SOC stocks. We found divergent environmental control representation in ESMs in comparison to field observations. Representation of SOC in ESMs can be improved by including additional environmental factors and representing their functional relationships with SOC consistent with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Synthetic Tuning of Exciton–Phonon Coupling in Janus WS 2(1-x) Se 2x Monolayers Revealed by Resonant Raman Excitation Spectroscopy for Optoelectronic Applications

Janus monolayers, such as WSSe, have broken out-of-plane symmetry and an intrinsic dipole moment, impacting exciton transport, lifetime, and phonon interactions while imbuing piezoelectric, photocatalytic, and Rashba spin-splitting properties to transition metal dichalcogenides (TMDs). The new properties of this atomically thin material can be used for optoelectronic device applications. As TMDs are converted into Janus monolayers, e.g., top selenization of WS2 to WSSe, the bandgap and structure smoothly evolve, impacting not only the formation of excitons but also their complex interactions with different phonon modes. Resonant Raman excitation profiles (REPs) are uniquely well-suited to reveal both excitonic transitions and exciton–phonon coupling. Here, the resonant REPs of $A^{'}_{1}$ WS 2 and A 1 WSSe modes are measured to understand the strength of their coupling with the A, B, and C excitonic bands of a WS2 monolayer throughout its stepwise transformation into Janus WSSe by pulsed laser deposition (PLD) of energetic selenium species. In situ Raman spectroscopy during deposition is used to controllably prepare stable intermediate Janus structures, WS 2(1-x) Se 2x (0 ≤ x ≤ 0.5), for ex situ measurement of their resonant REPs. As x increases, REPs reveal not only pronounced excitonic bands that gradually shift toward lower photon energies but also strong, mode-selective exciton–phonon coupling. First-principles resonant Raman simulations independently predict this spectral behavior and are shown capable of matching the spectrally broadened, experimentally observed REP profiles in this model system, indicating their strong predictive capability for future experiments. The combination of controlled synthesis, REP characterization, and predictive theory employed here demonstrates a powerful pathway to understand and ultimately tune exciton–phonon interactions for future quantum optical devices.

Janus monolayers↗
DOE OSTI2022

Evaluation of Turbulence and Dispersion in Multiscale Atmospheric Simulations over Complex Urban Terrain during the Joint Urban 2003 Field Campaign

Abstract This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment. Significance Statement Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments.

42 ENGINEERING↗
DOE OSTI2023

Neural network model of neutral beam injection in the EAST tokamak to enable fast transport simulations

The neutral beam injection (NBI) system in EAST produces energetic neutral particles, which collide with electrons and ions in tokamak plasmas and heat the plasmas through Coulomb collisions. Moreover, it drives a non-inductive source of current, due to the charge-exchange collision between neutral particles and ions, and injects toroidal torque, which generates a toroidal rotation of the plasma. The effect caused by the NBI system, such as plasma heating, current drive, total neutron rate, momentum transfer, and shine-through, are modeled by a comprehensive module called NUBEAM. However, NUBEAM is computationally intensive since it relies on Monte Carlo methods. In this work, a neural network model has been developed as a surrogate model for NUBEAM in EAST. The database for neural-network model training, validation and testing is generated by running TRANSP for experimental discharges from recent EAST campaigns (after the latest NBI upgrade) while using the NUBEAM module. Simulation results illustrate that the trained neural network has the capability of replicating the predictions made by NUBEAM while demanding a significantly shorter execution time. Finally, these results indicate that surrogate models like the one proposed in this work could enable fast transport simulations for EAST after integrating them into a control-oriented predictive code such as COTSIM.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling

Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant’s NO x emission rates is performed, directly comparing each modeling method’s performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NO x emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. Finally, these efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method’s performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems.

42 ENGINEERING↗
NASA NTRS2016

Investigating Mars South Residual CO2 Cap with a Global Climate Model

The CO2 cycle is one of the three controlling climate cycles on Mars. One aspect of the CO2 cycle that is not yet fully understood is the existence of a residual CO2 ice cap that is offset from the south pole. Previous investigations suggest that the atmosphere may control the placement of the south residual cap (e.g., Colaprete et al., 2005). These investigations show that topographically forced stationary eddies in the south during southern hemisphere winter produce colder atmospheric temperatures and increased CO2 snowfall over the hemisphere where the residual cap resides. Since precipitated CO2 ice produces higher surface albedos than directly deposited CO2 ice, it is plausible that CO2 snowfall resulting from the zonally asymmetric atmospheric circulation produces surface ice albedos high enough to maintain a residual cap only in one hemisphere. The goal of the current work is to further evaluate Colaprete et al.'s hypothesis by investigating model-predicted seasonally varying snowfall patterns in the southern polar region and the atmospheric circulation components that control them.

CO2 cycle↗
NASA NTRS1977

Prediction of pilot-aircraft stability boundaries and performance contours

Control-theoretic pilot models can provide important new insights regarding the stability and performance characteristics of the pilot-aircraft system. Optimal-control pilot models can be formed for a wide range of flight conditions, suggesting that the human pilot can maintain stability if he adapts his control strategy to the aircraft's changing dynamics. Of particular concern is the effect of sub-optimal pilot adaptation as an aircraft transitions from low to high angle-of-attack during rapid maneuvering, as the changes in aircraft stability and control response can be extreme. This paper examines the effects of optimal and sub-optimal effort during a typical 'high-g' maneuver, and it introduces the concept of minimum-control effort (MCE) adaptation. Limited experimental results tend to support the MCE adaptation concept.

Stengel, R. F.↗
NASA NTRS1979

The ionosphere of Saturn - Predictions for Pioneer 11

Model calculations indicate that the lower ionosphere of Saturn is controlled by photochemical processes, with basic features similar to the Jovian ionosphere. The scale height of the upper ionosphere is large (about 3350 km). A peak electron density of about 100,000/cu cm 2250 km above a 10 to the 19th per cu cm reference level is expected assuming an eddy coefficient at the homopause of 1.3 million sq cm/sec and a relatively hot exosphere at 1300 K.

Waite, J. H., Jr.↗
NASA NTRS1982

An analytical prediction of pilot ratings utilizing human pilot model

In order to analytically predict pilot ratings, an evaluation method of a manual control system which consists of an aircraft and a human pilot, is proposed and examined. The method is constructed upon the assumptions that the control mission determines the critical frequency the pilot should bring to his focus, and that the degree of closed-loop stability and the human compensation necessary to attain the stability determine the human subjective evaluation of the system. As a result, a simple evaluation chart is introduced. The chart enables prediction of the subjective evaluation, if the controlled element dynamics and the mission are given. The chart is in good accord with almost all of the existing results of pilot ratings. This method has the following advantages: (1) simplicity, in a sense that the method needs to evaluate only two typical controlled element parameters, namely, the gain slope and the phase at the critical control frequency; (2) applicability to unstable controlled elements; (3) predictability of controllability limits of manual control; (4) possibility of estimating human compensatory dynamics.

Tanaka, K.↗
NASA NTRS1986

Solar wind control of the polar-cap voltage

The results of using various models to predict polar-cap potentials are reviewed. Quantitative predictions of merging rate, and hence of polar-cap voltage, based on subsolar merging models are reviewed, and tests of dayside merging models and their results are reexamined. Predictions and tests of cusp merging models are discussed. Two new empirical formulas for the dependence of the asymptotic polar-cap potential on the IMF are presented, and predictions of the dependence of dayside and lobe merging voltages on the IMF are derived from the antiparallel merging hypothesis.

Reiff, Patricia H.↗
DOE OSTI2018

Search for black holes and sphalerons in high-multiplicity final states in proton-proton collisions at $ \sqrt{s}=13 $ TeV

A search in energetic, high-multiplicity final states for evidence of physics beyond the standard model, such as black holes, string balls, and electroweak sphalerons, is presented. The data sample corresponds to an integrated luminosity of 35.9 fb$^{-1}$ collected with the CMS experiment at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV in 2016. Standard model backgrounds, dominated by multijet production, are determined from control regions in data without any reliance on simulation. No evidence for excesses above the predicted background is observed. Model-independent 95% confidence level upper limits on the cross section of beyond the standard model signals in these final states are set and further interpreted in terms of limits on semiclassical black hole, string ball, and sphaleron production. In the context of models with large extra dimensions, semiclassical black holes with minimum masses as high as 10.1 TeV and string balls with masses as high as 9.5 TeV are excluded by this search. Results of the first dedicated search for electroweak sphalerons are presented. An upper limit of 0.021 is set on the fraction of all quark-quark interactions above the nominal threshold energy of 9 TeV resulting in the sphaleron transition.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Development and Validation of a Dynamic Response Model for a Cold Flow Circulating Fluidized Bed

A dynamic response model has been created of the Cold Flow Circulating Fluidized Bed at NETL in Morgantown, WV. The model is considered dynamic, as opposed to steady state, because it is used to predict the transient responses of gas and solids flows throughout the unit as process conditions vary. The model was built in ProTrax with Advanced Continuous Simulation Language (ACSL) custom code. Dynamic experiments, varying both gas and solids flows, in the CFB unit have been used to validate the accuracy of the model. The advantage of this model is its utility in predicting gas hold up and solids flow throughout the unit during transitions of process conditions. Accurate predictions of these phenomena are important in process control and optimization. The dynamic experiments used in validating the model include modulations of the system pressure, move air (solids flow), and riser gas velocity with step and sinusoidal changes.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1989

Comparison of the measured and predicted response of the Earth Radiation Budget Experiment active cavity radiometer during solar observations

The Earth Radiation Budget Experiment consists of an array of radiometric instruments placed in earth orbit by the National Aeronautics and Space Administration to monitor the longwave and visible components of the earth's radiation budget. Presented is a dynamic electrothermal model of the active cavity radiometer used to measure the earth's total radiative exitance. Radiative exchange is modeled using the Monte Carlo method and transient conduction is treated using the finite element method. Also included is the feedback circuit which controls electrical substitution heating of the cavity. The model is shown to accurately predict the dynamic response of the instrument during solar calibration.

Mahan, J. R.↗
DOE OSTI2017

Effects of Substituents on the S N 2 Free Energy of Activation for α-O-4 Lignin Model Compounds

Current methods for processing lignocellulosic biomass fail to take full advantage of the phenolic chemicals available in lignin, the third most common polymer on the planet. The complexity of the lignin polymer structure, the lack of knowledge of the mechanisms by which it breaks down, and the difficulties in predicting and controlling the reaction product distribution make improvement in processing methods challenging. The use of model compounds, which contain only a few key features of the native polymer, allow the reactivity of the overall polymer to be investigated more simply by isolating key elements. In our previous work, we examined the mechanism of acidolysis for two α-O-4 lignin model compounds, benzylphenyl ether (BPE) and 1-(phenoxyethyl)benzene (PEB). In the present work, we examine the rate-limiting step of this mechanism, a nucleophilic attack on the α-carbon commensurate with protonation of the ether oxygen, for other α-O-4 model compounds that contain features more reminiscent of native lignin, including compounds based on native model dimers. The effects of individual substituents and combinations of substituents are also examined in order to determine their contributions to the reactivity of native lignin dimers. Simple relationships are examined between ground-state properties and reactivity. The results of these calculations show that the effects of substituents on the reactivity of BPE-based compounds are quite different from those of PEB-based compounds. While reasonable correlations can be found for BPE reactivity and properties across substituents, PEB shows much less predictable reactivity except among small subsets of substituents. Overall, the trends observed here provide useful information about the reactivity of α-O-4 bonds in lignin during acidolysis as a function of the local chemical environment.

Pelzer, Adam W.↗
NASA NTRS1974

Research study on stabilization and control modern sampled-data control theory

The methods of continuous and discrete describing function analysis were applied to predicting the existence of self-sustained oscillations in the single-axis model of the large space telescope system with nonlinear control moment gyroscope friction characteristics. It is shown that the stability equations may be solved by a numerical-iterative technique using the describing function analysis, instead of the usual graphical methods. The numerical method is found to be effective in leading to a convergent solution rapidly, with an appropriate guess of the initial condition.

Kuo, B. C.↗