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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Resistance of Silicon Nitride Turbine Components to Erosion and Hot Corrosion/oxidation Attack

Silicon nitride turbine components are under intensive development by AlliedSignal to enable a new generation of higher power density auxiliary power systems. In order to be viable in the intended applications, silicon nitride turbine airfoils must be designed for survival in aggressive oxidizing combustion gas environments. Erosive and corrosive damage to ceramic airfoils from ingested sand and sea salt must be avoided. Recent engine test experience demonstrated that NT154 silicon nitride turbine vanes have exceptional resistance to sand erosion, relative to superalloys used in production engines. Similarly, NT154 silicon nitride has excellent resistance to oxidation in the temperature range of interest - up to 1400 C. Hot corrosion attack of superalloy gas turbine components is well documented. While hot corrosion from ingested sea salt will attack silicon nitride substantially less than the superalloys being replaced in initial engine applications, this degradation has the potential to limit component lives in advanced engine applications. Hot corrosion adversely affects the strength of silicon nitride in the 850 to 1300 C range. Since unacceptable reductions in strength must be rapidly identified and avoided, AlliedSignal and the NASA Lewis Research Center have pioneered the development of an environmental life prediction model for silicon nitride turbine components. Strength retention in flexure specimens following 1 to 3300 hour exposures to high temperature oxidation and hot corrosion has been measured and used to calibrate the life prediction model. Predicted component life is dependent upon engine design (stress, temperature, pressure, fuel/air ratio, gas velocity, and inlet air filtration), mission usage (fuel sulfur content, location (salt in air), and times at duty cycle power points), and material parameters. Preliminary analyses indicate that the hot corrosion resistance of NT154 silicon nitride is adequate for AlliedSignal's initial engine applications. Protective coatings and/or inlet air filtration may be required to achieve required ceramic component lives in more aggressive environments.

Strangmen, Thomas E.↗
DOE OSTI2025

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Recombinant protein production (RPP) is central to biotechnology, where recombinant proteins are used as either end products or catalysts in the synthesis of chemicals, fuels, and materials. Among the major cost drivers, culture medium plays a pivotal role in determining protein yield and quality. This review presents a comprehensive perspective on the critical stages of “smart” culture medium optimization: planning, screening, modeling, optimization, and validation. In the planning stage, we examine the nutritional and energetic roles of medium components, including carbon, nitrogen, amino acids, salts, and trace metals, and their impacts on culture parameters such as pH, oxidative state, and osmolality. We highlight the variability in trace metal content due to water sources, culture vessels, and raw materials, which can substantially influence RPP. The screening stage covers Design of Experiments (DoE) approaches, assessing their theoretical basis, implementation, and limitations. For modeling, we describe methods that integrate experimental data to develop predictive models for smart medium formulation. Model-based optimization strategies can then be employed to select optimal media compositions for a given application. The validation stage aims to evaluate model predictions and provide feedback for model training and refinement. Finally, we survey mechanistic and artificial intelligence/machine learning (AI/ML)-driven models as integrated, transformational tools for predictive modeling of bioprocess conditions, nutrient availability, cellular metabolism, and protein quality, with the goal of optimizing culture media to enhance protein yields while reducing costs and environmental impact. We conclude by addressing the challenges of translating laboratory-scale medium optimization to industrial-scale settings and exploring future AI/ML-driven approaches that may overcome current bottlenecks and accelerate medium design for RPP. Overall, this review provides a unified framework for advancing smart medium design in RPP.

Artificial Intelligence/Machine Learning (AI/ML)↗
DOE OSTI2026

REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model’s prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Saklani, Shanu [Indian Institute of Technology, Ka↗
NASA NTRS1982

A three-part geometric model to predict the radar backscatter from wheat, corn, and sorghum

A model to predict the radar backscattering coefficient from crops must include the geometry of the canopy. Radar and ground-truth data taken on wheat in 1979 indicate that the model must include contributions from the leaves, from the wheat head, and from the soil moisture. For sorghum and corn, radar and ground-truth data obtained in 1979 and 1980 support the necessity of a soil moisture term and a leaf water term. The Leaf Area Index (LAI) is an appropriate input for the leaf contribution to the radar response for wheat and sorghum, however the LAI generates less accurate values for the backscattering coefficient for corn. Also, the data for corn and sorghum illustrate the importance of the water contained in the stalks in estimating the radar response.

Ulaby, F. T.↗
DOE OSTI2025

A mixture parameterized biologically based dosimetry model to predict body burdens of polycyclic aromatic hydrocarbons in developmental zebrafish toxicity assays

Polycyclic aromatic hydrocarbons (PAHs) are a group of environmental toxicants found ubiquitously as complex mixtures in human-impacted environments. Developmental zebrafish exposures have been used widely to study PAH toxicity, but most studies report nominal exposure concentrations. Nominal exposure concentrations can be unreliable dose metrics due to differences in toxicant bioavailability resulting from disparate exposure methodologies and chemical properties. Toxicokinetic modeling can predict toxicant tissue doses to facilitate comparison between exposures of different chemicals, methodologies, and biological models. We parameterize a biologically based dosimetry model for developmental zebrafish toxicity assays for 9 PAHs. The model was optimized with measurements from media, tissue, and plastic plate walls throughout a static developmental exposure to a mixture of 10 PAHs of high abundance within the Portland Harbor Superfund Site. Plate binding, volatilization, zebrafish permeability, and tissue—media partitioning coefficients vary widely between PAHs. Model predictions accounted for 83% and 54% of 48 hpf body burdens within a factor of 2 resulting from exposures to mixtures and individual PAHs, respectively. Accounting for solubility significantly improves model performance. Competition for active sites in metabolizing enzymes may change biotransformation kinetics between individual PAH and mixture exposures. Area under the curve estimations of concentrations in zebrafish resulted in altered hazard rankings from nominal exposure concentrations. Future work will be oriented to generalizing the model to other PAHs. This PAH dosimetry model improves the interpretability of developmental zebrafish toxicity assays by providing time-resolved body burdens from nominal exposure concentrations.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS1992

A simplified generic cryostat thermal model for predicting cryogen mass and lifetime

This study presents a simple generic cryostat thermal model developed for predicting cryogen mass and the lifetime of cryogenic space telescopes. The model is based on a lumped parameter representation of eight nodes and over 35 conductors. The major telescope Dewar components represented as nodes are the main cryogen tank, the three vapor-cooled shields, the outer shell, barrel baffle, and the barrel baffle heat exchanger. The input to the model consists of the vapor-cooled shield, support straps and instrument cable geometry, and the cryostat heat loads from the instrument and through the aperture. The predictions from the model were compared with those from the more detailed models of IRAS, COBE, and SIRTF. Correlation of the helium flow rates of the IRAS and COBE Dewars was 12 percent above the actual flight or ground test data and for SIRTF it was less than 2 percent of the prediction from the detailed model.

Birur, G. C.↗
DOE OSTI2026

Revisiting the validity of eddy viscosity models for predicting airflow over water waves

In this study, we revisit the validity of eddy viscosity models for predicting wave-induced airflow disturbances over ocean surface waves. We first derive a turbulence curvilinear model for the phase-averaged Navier–Stokes equations, extending the work of Cao, Deng & Shen (2020 J. Fluid Mech. 901, A27), by incorporating turbulence stress terms previously neglected in the linearised viscous curvilinear model. To verify our formulation, we perform a priori tests by numerically solving the model using mean wind and turbulence stress profiles from large-eddy simulations (LES) of airflow over waves across various wave ages. Results show that including turbulence stress terms improves wave-induced airflow predictions compared with the previous viscous curvilinear model. We further show that using a standard mixing-length eddy viscosity yields inaccurate predictions at certain wave ages, as it fails to capture wave-induced turbulence, which fundamentally differs from mean shear-driven turbulence. The LES data show that accurate representations of wave-induced stresses require a complex-valued eddy viscosity. The maximum magnitude of this eddy viscosity scales as ∼𝑢 𝜏 ⁢𝜁 𝑖𝑛𝑛𝑒𝑟 , where 𝑢 𝜏 is the friction velocity and 𝜁 𝑖𝑛𝑛𝑒𝑟 is the inner-layer thickness, the height at which the eddy-turnover time matches the wave advection time scale. This scaling aligns with the prediction by Belcher & Hunt (1993 J. Fluid Mech. 251, 109–148). Overall, the findings demonstrate that traditional eddy viscosity models are inadequate for capturing wave-induced turbulence. More sophisticated turbulence models are essential for the accurate prediction of airflow disturbances and form drag in wind–wave interaction models.

16 TIDAL AND WAVE POWER↗
NASA NTRS2013

The Relation Between Atmospheric Humidity and Temperature Trends for Stratospheric Water

We analyze the relation between atmospheric temperature and water vapor-a fundamental component of the global climate system-for stratospheric water vapor (SWV). We compare measurements of SWV (and methane where available) over the period 1980-2011 from NOAA balloon-borne frostpoint hygrometer (NOAA-FPH), SAGE II, Halogen Occultation Experiment (HALOE), Microwave Limb Sounder (MLS)/Aura, and Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) to model predictions based on troposphere-to-stratosphere transport from ERA-Interim, and temperatures from ERA-Interim, Modern Era Retrospective-Analysis (MERRA), Climate Forecast System Reanalysis (CFSR), Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC), HadAT2, and RICHv1.5. All model predictions are dry biased. The interannual anomalies of the model predictions show periods of fairly regular oscillations, alternating with more quiescent periods and a few large-amplitude oscillations. They all agree well (correlation coefficients 0.9 and larger) with observations for higherfrequency variations (periods up to 2-3 years). Differences between SWV observations, and temperature data, respectively, render analysis of the model minus observation residual difficult. However, we find fairly well-defined periods of drifts in the residuals. For the 1980s, model predictions differ most, and only the calculation with ERA-Interim temperatures is roughly within observational uncertainties. All model predictions show a drying relative to HALOE in the 1990s, followed by a moistening in the early 2000s. Drifts to NOAA-FPH are similar (but stronger), whereas no drift is present against SAGE II. As a result, the model calculations have a less pronounced drop in SWV in 2000 than HALOE. From the mid-2000s onward, models and observations agree reasonably, and some differences can be traced to problems in the temperature data. These results indicate that both SWV and temperature data may still suffer from artifacts that need to be resolved in order to answer the question whether the large-scale flow and temperature field is sufficient to explain water entering the stratosphere.

Fueglistaler, S.↗
NASA NTRS2007

Spectroscopic Detection of COClF in the Tropical and Mid-Latitude Lower Stratosphere

We report retrievals of COClF (carbonyl chlorofluoride) based on atmospheric chemistry experiment (ACE) solar occultation spectra recorded at tropical and mid-latitudes during 2004-2005. The COClF molecule is a temporary reservoir of both chlorine and fluorine and has not been measured previously by remote sensing. A maximum COClF mixing ratio of 99.7+/-48.0 pptv (10(exp -12) per unit volume, 1 sigma) is measured at 28km for tropical and subtropical occultations (latitudes below 20deg in both hemispheres) with lower mixing ratios at both higher and lower altitudes. Northern hemisphere mid-latitude mixing ratios (30-50degN) resulted in an average profile with a peak mixing ratio of 51.7+/-32.1 pptv, 1 sigma, at 27 km, also decreasing above and below that altitude. We compare the measured average profiles with the one reported set of in situ lower stratospheric mid-latitude measurements from 1986 and 1987, a previous two-dimensional (2-D) model calculation for 1987 and 1993, and a 2-D-model prediction for 2004. The measured average tropical profile is in close agreement with the model prediction; the northern mid-latitude profile is also consistent, although the peak in the measured profile occurs at a higher altitude (2.5-4.5km offset) than in the model prediction. Seasonal average 2-D-model predictions of the COClF stratospheric distribution for 2004 are also reported.

Rinsland, Curtis P.↗
NASA NTRS2009

A Model for Predicting Thermoelectric Properties of Bi2Te3

A parameterized orthogonal tight-binding mathematical model of the quantum electronic structure of the bismuth telluride molecule has been devised for use in conjunction with a semiclassical transport model in predicting the thermoelectric properties of doped bismuth telluride. This model is expected to be useful in designing and analyzing Bi2Te3 thermoelectric devices, including ones that contain such nano - structures as quantum wells and wires. In addition, the understanding gained in the use of this model can be expected to lead to the development of better models that could be useful for developing other thermoelectric materials and devices having enhanced thermoelectric properties. Bi2Te3 is one of the best bulk thermoelectric materials and is widely used in commercial thermoelectric devices. Most prior theoretical studies of the thermoelectric properties of Bi2Te3 have involved either continuum models or ab-initio models. Continuum models are computationally very efficient, but do not account for atomic-level effects. Ab-initio models are atomistic by definition, but do not scale well in that computation times increase excessively with increasing numbers of atoms. The present tight-binding model bridges the gap between the well-scalable but non-atomistic continuum models and the atomistic but poorly scalable ab-initio models: The present tight-binding model is atomistic, yet also computationally efficient because of the reduced (relative to an ab-initio model) number of basis orbitals and flexible parameterization of the Hamiltonian.

Lee, Seungwon↗
NASA NTRS2006

An Energy Balance Model to Predict Chemical Partitioning in a Photosynthetic Microbial Mat

Studies of biosignature formation in photosynthetic microbial mat communities offer potentially useful insights with regards to both solar and extrasolar astrobiology. Biosignature formation in such systems results from the chemical transformation of photosynthetically fixed carbon by accessory microorganisms. This fixed carbon represents a source not only of reducing power, but also energy, to these organisms, so that chemical and energy budgets should be coupled. We tested this hypothesis by applying an energy balance model to predict the fate of photosynthetic productivity under dark, anoxic conditions. Fermentation of photosynthetically fixed carbon is taken to be the only source of energy available to cyanobacteria in the absence of light and oxygen, and nitrogen fixation is the principal energy demand. The alternate fate for fixed carbon is to build cyanobacterial biomass with Redfield C:N ratio. The model predicts that, under completely nitrogen-limited conditions, growth is optimized when 78% of fixed carbon stores are directed into fermentative energy generation, with the remainder allocated to growth. These predictions were compared to measurements made on microbial mats that are known to be both nitrogen-limited and populated by actively nitrogen-fixing cyanobacteria. In these mats, under dark, anoxic conditions, 82% of fixed carbon stores were diverted into fermentation. The close agreement between these independent approaches suggests that energy balance models may provide a quantitative means of predicting chemical partitioning within such systems - an important step towards understanding how biological productivity is ultimately partitioned into biosignature compounds.

Hoehler, Tori M.↗
DOE OSTI2025

Mapping tree height in complex terrain of northern China using ultra-high-resolution images

Tree height is a key parameter for estimating forest biomass and carbon sequestration. In recent years, notable progress has been made in mapping tree height using satellite imagery. However, existing tree height products show low accuracy in mountainous and complex terrains, and few studies typically addressed tree height estimations in mountain areas. This study examines the Mentougou district of Beijing, China, characterized by complex terrain and mountainous landscapes. We analyzed two methods for estimating tree height: one using only spectral features and another combining spectral features with topographic factors (elevation, slope, aspect). We used 3-m resolution PlanetScope 8-band multispectral imagery, with 710 field-measured individual tree heights averaged to obtain 471 pixel-level tree height values as ground-truth, to develop tree height prediction models using eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Gradient Boosting Machine (GBM) models. The results show that the XGBoost model consistently presented the highest accuracy for both methods evaluated. Specifically, the XGBoost model that combined spectral data with elevation and slope variables with an R² of 0.75 and an RMSE of 2.69 m. Using the XGBoost model, we generated the tree height map for the Mentougou area at 3 m resolution, showing tree heights ranging from 0.5 to 30.4 m, and the model’s prediction error standard deviations ranged from 2.50 to 4.71 m, indicating reliable performance across varied terrain. Additionally, we compared and evaluated the global tree height products, identifying limitations in the accuracy within complex terrains. This study demonstrates the potential for accurately predicting tree heights by combining high-resolution multispectral satellites with a terrain factor modeling approach.

Complex terrain↗
NASA NTRS2004

CDF and PDF Comparison Between Humacao, Puerto Rico and Florida

The knowledge of the atmospherics phenomenon is an important part in the communication system. The principal factor that contributes to the attenuation in a Ka band communication system is the rain attenuation. We have four years of tropical region observations. The data in the tropical region was taken in Humacao, Puerto Rico. Previous data had been collected at various climate regions such as desserts, template area and sub-tropical regions. Figure 1 shows the ITU-R rain zone map for North America. Rain rates are important to the rain attenuation prediction models. The models that predict attenuation generally are of two different kinds. The first one is the regression models. By using a data set these models provide an idea of the observed attenuation and rain rates distribution in the present, past and future. The second kinds of models are physical models which use the probability density functions (PDF).

Gonzalez-Rodriguez, Rosana↗
NASA NTRSDate not supplied

Updates to the Predictive Materials Modeling Software Tools

Updates on NASA's efforts to build predictive material models from the micro-scale to the macro-scale are presented. To complement the mission design cycle process and reduce the need for extensive testing, NASA is developing modeling and simulation tools that enable characterizing material properties and response to hot plasma experienced during atmospheric entry. The PuMA (microstructure analysis), PATO (macroscale material response), SPARTA (direct simulation Monte Carlo) and ARCHeS (arc heater modeling) codes are described. A range of applications, encompassing calculating effective material properties, study of high shear boundary layer flow over woven materials, new model of silicone-based coatings, new model of mechanical erosion, as well as approach to loose multi-physics coupling, are presented.

material modeling↗
NASA NTRS2020

A Statistical Model to Predict the Extratropical Transition of Tropical Cyclones

This paper introduces a logistic regression model for the extratropical transition(ET) of tropical cyclones in the North Atlantic and the Western North Pacific, using elastic net regularization to select predictors and estimate coefficients.Predictors are chosen from the 1979-2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to two days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the Western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic, and 92% of those in the Western North Pacific. 90% of transition time errors are less than 24 h. Select examples of the model's performance on individual storms illustrate its strengths and weaknesses.Two versions of the model are presented: an "operational model" that may provide baseline guidance for operational forecasts, and a "hazard model"that can be integrated into statistical TC risk models. As instantaneous diagnostics for tropical/extratropical status, both models' zero lead time predictions perform about as well as the widely used Cyclone Phase Space (CPS) in the Western North Pacific and better than the CPS in the North Atlantic, and predict the timings of the transitions better than CPS in both basins.

Melanie Bieli↗
NASA NTRS2017

CEDAR-GEM Challenge for Systematic Assessment of Ionosphere/Thermosphere Models in Predicting TEC During the 2006 December Storm Event

In order to assess current modeling capability of reproducing storm impacts on total electron content (TEC), we considered quantities such as TEC, TEC changes compared to quiet time values, and the maximum value of the TEC and TEC changes during a storm. We compared the quantities obtained from ionospheric models against ground-based GPS TEC measurements during the 2006 AGU storm event (14-15 December 2006) in the selected eight longitude sectors. We used 15 simulations obtained from eight ionospheric models, including empirical, physics-based, coupled ionosphere-thermosphere, and data assimilation models. To quantitatively evaluate performance of the models in TEC prediction during the storm, we calculated skill scores such as RMS error, Normalized RMS error (NRMSE), ratio of the modeled to observed maximum increase (Yield), and the difference between the modeled peak time and observed peak time. Furthermore, to investigate latitudinal dependence of the performance of the models, the skill scores were calculated for five latitude regions. Our study shows that RMSE of TEC and TEC changes of the model simulations range from about 3 TECU (total electron content unit, 1 TECU = 1016 el m2) (in high latitudes) to about 13 TECU (in low latitudes), which is larger than latitudinal average GPS TEC error of about 2 TECU. Most model simulations predict TEC better than TEC changes in terms of NRMSE and the difference in peak time, while the opposite holds true in terms of Yield. Model performance strongly depends on the quantities considered, the type of metrics used, and the latitude considered.

Shim, J. S.↗
DOE OSTI2025

Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids

Ionic liquids (ILs) are a novel class of solvents that have attracted significant attention due to their unique and tunable properties. Among their physiochemical characteristics, surface tension plays a critical role in various industrial applications including electrolytes, heat transfer fluids, and separation processes. However, because of the exploratory nature of IL design and the vast combinatorial space of possible anion–cation pairs, the experimental determination of these properties is often impractical, being both time-consuming and costly. To overcome these challenges, computational approaches are increasingly employed to develop accurate predictive models that can accelerate IL discovery and design. In this study, we present two deep learning (DL) models for predicting the surface tension of ILs across a broad temperature range at a constant pressure. The models use simplified molecular input line entry system, SMILES, representations of ILs to extract molecular features as inputs. Both DL models demonstrate excellent agreement with experimental data, achieving an R 2 value of 0.990 and a root-mean-square error of 0.792 mN/m. In conclusion, these results offer valuable insights for the rapid screening and rational design of ILs with tailored surface tension values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1978

A global model for rain attenuation prediction

A model was developed for the prediction of rain attenuation statistics for 8 rain rate climate regions spanning all land regions from the equator to the poles. The model uses empirically determined rain rate distributions for each climate region and an empirical model to estimate path averaged rain rate for given values of point rain rate. Attenuation distribution observations were compared with model predictions. Good agreement was obtained for earth-space paths at 0.1 percent of the year. At the extremes, 0.01 and 1 percent of the year the observations were generally within the model estimate bounds but the agreement is not as good as at 0.1 percent of the year. The discrepancies are attributed to the limited duration of the attenuation observations and to the limited number of rain rate distribution observations.

Crane, R. K.↗