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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Turbulent mixing in supercritical jets: effect of compressibility factor and inflow condition

Fuel injection and turbulent mixing at supercritical pressures determines ignition and combustion in numerous engineering applications. Flow evolution under such conditions is characterized by strong non-linear coupling between dynamics, transport coefficients, and thermodynamics. Experimental studies observe that the jets injected at supercritical pressures exhibit significantly different dynamics from the jets at subcritical conditions, owing to the lack of distinct liquid and gas phases in supercritical state. Thus, the averaged flow quantities such as the potential core length, jet spatial growth rate and velocity decay profiles differ in the two conditions, resulting in different mixed-fluid distributions. In this study, turbulent jet direct numerical simulations (DNS) are performed to examine the variations in flow statistics between injection of Nitrogen (N₂) in Nitrogen (N₂) at both subcritical (perfect-gas) and supercritical conditions. In all cases, isothermal round jets at Reynolds number (Re_{D}), based on jet diameter (D) and jet orifice velocity (U₀), of 5000 are considered. For mixing analyses, a passive scalar transported with the flow is examined.

Sharan, Nek↗
NASA NTRSDate not supplied

Wall-Modeled Large-Eddy Simulations of Jet Noise in Flight Conditions

A campaign of wall-modeled large-eddy simulations (WMLES) using structured curvilinear overlapping grids has been performed with the Launch Ascent and Vehicle Aerodynamics (LAVA) computational fluid dynamics (CFD) software to predict jet noise for single-stream axisymmetric round jets. The simulations address the new Prediction Uncertainty Reduction (PUR) technical challenge within the context of NASA’s Commercial Supersonic Technology (CST) project. The focus of this effort is to generate a simulation database for single-stream axisymmetric round nozzles at several operating points both for static (no ambient co-flow), and in-flight (M ͚ =0.3 co-flow) conditions. The operating conditions range in jet exit Mach number from 0.38 to 1.1 with nozzle temperature ratios (NTR) from 0.84 to 2.7. The effect of the flight-stream on far-field noise sound spectra is assessed. Comparison of LES predictions to microphone array measurements demonstrate good agreement within the resolved frequency range. A dip in the predicted low frequency noise spectra for observers between 120° and 145° is observed. This dip is smaller for lower Mach numbers and seems to be correlated to the Mach wave radiation angle. The Mach 1.1 jet shows broadband-shock associated noise. While the onset of BBSN appears to be captured correctly in WMLES, some differences in its magnitude and the prominent frequency at which it occurs persist between the experiment and the simulations. The effect of the outer nozzle boundary layer state created by the co-flow is assessed and shows to be important for accurate comparisons with experiments. A change of 2.5dB between a slip-wall condition and a artificially thickened turbulent boundary layer was observed. In addition, simulations were performed with an alternative nozzle geometry that includes a internal plug and has twice the nozzle exit diameter. These two configurations resulted in very comparable spectra which is consistent with experimental observations. A generally stronger deviation from experimental results is observed for in-flight cases compared to static conditions. The applicability and correct usage of acoustic analogies used for far-field propagation with strong turbulent co-flows needs to be investigated more systematically using canonical problems to improve comparisons between experiments and WMLES. This is especially true for coherent noise sources seen in BBSN.

CST↗
NASA NTRSDate not supplied

Air Data Probe Anomalies in Flight through Measured High Ice Water Content Conditions

High concentrations of ice crystals in convective storms have caused anomalous air temperature and airspeed readings during commercial and research flight operations. These anomalies occur when ice crystals are ingested in the heated probe inlet, melt or partially melt to liquid water, and then refreeze or remain in a liquid state depending on the probe heat and cloud conditions. In pitot probes, the refreezing may cause complete blockage of the total pressure, which causes airspeed anomalies. In total air temperature probes, the melted ice water may flow near the temperature sensing element and cause the total air temperature reading to approach 0 degree Celsius. During the High Ice Water Content (HIWC) RADAR and HIWC-2022 flight campaigns and the Convective Process Experiment (CPEX-CV) flight campaign, a total of 71 anomalies were recorded on the NASA DC-8 pitot probes when subjected to specific flight and cloud conditions. Similarly, a research TAT probe mounted near the pitot probes had 19 anomalies. This paper presents analyses of the measured natural conditions that led to these TAT and pitot anomalies, identifies two types of pitot probe anomalies, applies a concentration factor to estimate local TWC conditions near the TAT and pitot probes, and identifies the static air temperature and pressure altitude where the anomalies occurred on the Part 33 Appendix D envelope.

Aircraft Icing↗
NASA NTRSDate not supplied

Air Data Probe Anomalies in Flight through Measured High Ice Water Content Conditions

High concentrations of ice crystals in convective storms have caused anomalous air temperature and airspeed readings during commercial and research flight operations. These anomalies occur when ice crystals are ingested in the heated probe inlet, melt or partially melt to liquid water, and then refreeze or remain in a liquid state depending on the probe heat and cloud conditions. In pitot probes, the refreezing may cause complete blockage of the total pressure, which causes airspeed anomalies. In total air temperature probes, the melted ice water may flow near the temperature sensing element and cause the total air temperature reading to approach 0 degree Celsius. During the High Ice Water Content (HIWC) RADAR and HIWC-2022 flight campaigns and the Convective Process Experiment (CPEX-CV) flight campaign, a total of 71 anomalies were recorded on the NASA DC-8 pitot probes when subjected to specific flight and cloud conditions. Similarly, a research TAT probe mounted near the pitot probes had 19 anomalies. This paper presents analyses of the measured natural conditions that led to these TAT and pitot anomalies, identifies two types of pitot probe anomalies, applies a concentration factor to estimate local TWC conditions near the TAT and pitot probes, and identifies the static air temperature and pressure altitude where the anomalies occurred on the Part 33 Appendix D envelope.

Aircraft Icing↗
NASA NTRS2025

Rheology of Lunar Regolith Simulant Under Varying Gravitational Conditions

Understanding structure-property-process relationship aka rheology of regolith in varying gravity conditions is critical for exploration and future missions. In this work, a framework for studying rheology of lunar regolith simulant in varying gravity conditions (Terrestrial, Lunar and Martian) is described theoretically/numerically, where the driving force of flow of simulants was gravitational acceleration. In the analysis particle-particle interaction forces were included. It was found that the dynamic behavior of granular material is extremely sensitive to external conditions by virtue of the myriad of forces present between particle grains. The results obtained were validated against results obtained in earth and Lunar gravity conditions. The theoretical/numerical and experimental results showed that the complex interaction of these forces can drastically change the dynamics of the material, which is not captured by standard design rules, generally used in industry, for variable gravity applications.

Simulation↗
NASA NTRSDate not supplied

Understanding Relationships Between Satellite, Model, and Ground-Based Surface Temperature Characterizations From Overcast to Clear Conditions in Support of Satellite Remote Sensing of Clouds and Radiation

Accurate and consistent global estimates of cloud coverage and their properties are fundamental to long-term Earth radiation budget (ERB) monitoring efforts like the Clouds and the Earth’s Radiant Energy System (CERES) project. Cloud detection algorithms often apply thresholding approaches to identify where clouds occur by comparing satellite-measured radiances with those that are expected under cloud-free conditions. In addition, once a cloud is detected, the derivation of cloud optical and microphysical properties also requires knowledge of the background radiances below the cloud. In the infrared, knowledge of the surface emissivity and the expected skin temperature under both cloudy and cloud-free conditions is needed. These traits are generally well known over the oceans. Over land, however, comparisons between satellite-derived land surface temperature (LST) with that characterized in numerical weather analyses reveal large differences in many parts of the world, often exceeding 5 K, which can lead to significant satellite cloud detection and cloud property retrieval errors. Furthermore, clouds have a dramatic influence on the LST, and therefore characterization of that model parameter also depends on the capability of the model to accurately resolve clouds. Thus, the LST characterized in models is, at times, a poor approximation for what would otherwise be observed, thereby impeding accurate satellite cloud retrievals. As a result, we seek to develop a more robust method for estimating the LST required for satellite cloud characterizations. This effort is accomplished through a combination of surface emission/air temperature relationship studies in all-sky conditions using ground measurement stations, along with deep neural network (DNN) estimates of expected LST under overcast and cloud-free conditions. We demonstrate that substituting DNN-predicted LST for that generated by numerical models can mitigate model-inherent diurnal dependencies and reduce overall bias and uncertainty relative to satellite/ground observations by 0.5–4 K and 0.5–2 K, respectively. It is expected that this work will lead to improved satellite cloud retrievals that enhance ERB monitoring efforts.

B Scarino↗
NASA NTRS2024

A Diversity of Temperature and Pressure Conditions Recorded by Zircon within Suevite from Ries Crater, Germany

The temperature and pressure conditions experienced by rocks during an impact event can be constrained using petrologic and microstructural analysis and is crucial to providing ground truth to the impact cratering process. Suevite is a polymict, impact melt-bearing breccia, specific to Ries crater in Germany. There are competing models for suevite formation and emplacement, such as clastic flows pushed out of the crater rim or ejecta plume fallback. Knowledge of the temperature and pressure pathways recorded by grains within the suevite can help distinguish between these and other models. The accessory phase zircon (ZrSiO4) and its high-pressure polymorph reidite are particularly useful in such circumstances as they are highly refractory minerals that can record the high-temperature and/or high-pressure conditions of an impact event. Here we present evidence for a wide array of temperature and pressure conditions recorded in zircon grains within a single thin section of suevite. Zircons in this study range from unshocked to highly shocked (>53 GPa), and record temperatures more than 1673 °C. These findings confirm previous studies concluding that suevites contain material exposed to very diverse pressure and temperature conditions during initial shock compression and excavation but do not, as a whole, experience extreme temperatures (>1673 °C) or pressures (>30 GPa).

mineralogy↗
NASA NTRSDate not supplied

Quantifying the Sensitivity of Condition Incidence Parameters in the Evidence Library

One approach to quantifying spaceflight risk at NASA makes use event driven probabilistic techniques. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is such a tool that estimates medical risk metrics via simulation and enables optimization of medical resources subject to mission constraints [1]. Previous analyses have informed medical set composition, exercise countermeasures, and water intake, where each analysis quantifies the risk associated with proposed variations in system design. As future mission profiles extend beyond Low-Earth Orbit (LEO) and lengthen in duration, understanding these risks and contributing factors is critical. MEDPRAT employs Monte Carlo sampling techniques to simulate missions and track the occurrence of medical events. These events follow fault-tree-like progressions through levels of severity and mitigation via medical treatment to many possible outcomes and these are reported throughout the mission. Making this possible, are the medical databases that contain evidence gathered by the Human Research Program (HRP). Quantifying the impact of uncertainty or variability in the input data is an important step in evaluating the credibility of modeling and simulation results. In this work, we investigate the sensitivity of medical risk metrics with respect to the condition incidence parameters within the Evidence Library (EL) [2] as the medical database input for MEDPRAT. The medical conditions, contained in the EL, are equipped with incidence rates that describe the likelihood that the condition will occur. These incidence rates reflect historical spaceflight data or when appropriate, terrestrial data. In this presentation, we will explore how uncertainty in these rates propagate to the medical risk described by MEDPRAT. These results identify the conditions and parameters with the largest contribution to medical risks.

Ian Lim↗
DOE OSTI2025

Extended FFT-based micromechanical formulation to consider general non-periodic boundary conditions

Here, this paper presents a new approach for applying non-periodic boundary conditions in the context of FFT-based methods to solve micromechanical problems in heterogeneous solids. The domain of the original problem is extended to satisfy the periodicity requirements at the boundary of the extended domain. The velocity constraint on the boundary of the original domain is replaced by a corresponding constraint on the velocity gradient in the extended volume, and a two-level augmented Lagrangian method is used to enforce the constraint. The proposed method is implemented as an extension of the large-strain elasto-viscoplastic FFT-based (LS-EVPFFT) model of Zecevic et al. (2022). The proposed method is verified in the cases of fully imposed velocity boundary conditions and mixed velocity/traction-free boundary conditions. The accuracy and convergence of the method are studied next, followed by applications to bending and indentation of polycrystals that illustrate the extended capabilities of the proposed formulation.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Extending Explicit Guidance Methods to Higher Dimensions, Additional Conditions, and Higher Order Integration

Guidance functions play critical roles in autonomy to steer vehicles and aircraft to the intended target or destination. Explicit guidance (E Guidance) solves the two-point boundary value problem with initial and final conditions for position and velocity. The original formulation of E Guidance involves translational acceleration commands with a direct relationship to time, and it is possible to modify E Guidance for rotational acceleration. Other extensions for E Guidance include higher dimensions, additional conditions, and higher-order integration of the linearly independent E Guidance functions. The most promising extension involves higher-order integration of the E Guidance functions, but it may be physically impractical by initially moving away from the target. This paper provides a brief overview of some methods that extend E Guidance to higher dimensions, utilize additional conditions, or perform higher-order integration, and if they satisfy the two-point boundary value problem.

explicit guidance↗
NASA NTRSDate not supplied

Extending Explicit Guidance Methods to Higher Dimensions, Additional Conditions, and Higher Order Integration

Guidance functions play critical roles in autonomy to steer vehicles and aircraft to the intended target or destination. Explicit guidance (E Guidance) solves the two-point boundary value problem with initial and final conditions for position and velocity. The original formulation of explicit guidance involves translational acceleration commands with a direct relationship to time, and it is possible to modify E Guidance for rotational acceleration. Other extensions for E Guidance include higher dimensions, additional conditions, and higher-order integration of the linearly independent E Guidance functions. The most promising extension involves higher-order integration of the E Guidance functions, but it may be physically impractical by initially moving away from the target. This paper provides a brief overview of some methods that extend E Guidance to higher dimensions, utilize additional conditions, or perform higher-order integration, and if they satisfy the two-point boundary value problem.

explicit guidance↗
DOE OSTI2025

Joint Modeling of Wind Speed and Wind Direction Through a Conditional Approach

Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is therefore crucial to accurately estimate the joint probability distribution of wind speed and direction. In this work, we develop a conditional approach to model these two variables, where the joint distribution is decomposed into the product of the marginal distribution of wind direction and the conditional distribution of wind speed given wind direction. To accommodate the circular nature of wind direction, a von Mises mixture model is used; the conditional wind speed distribution is modeled as a directional dependent Weibull distribution via a two-stage estimation procedure, consisting of a directional binned Weibull parameter estimation, followed by a harmonic regression to estimate the dependence of the Weibull parameters on wind direction. A Monte Carlo simulation study indicates that our method outperforms two other approaches in estimation efficiency: one that utilizes periodic spline quantile regression and another that generates data from the commonly used Abe-Ley distribution for cylindrical data. We illustrate our method by using the output from a regional climate model to investigate how the joint distribution of wind speed and direction may change under some future climate scenarios. Our method indicates significant changes in the variation of wind speed with respect to some directions.

17 WIND ENERGY↗
DOE OSTI2024

A high efficiency rooftop air conditioning system using multi-speed compressors

This study delineates a meticulous exploration of technologies to enhance the energy efficiency of rooftop air conditioning units, employing the DOE/ORNL heat pump design model for comprehensive engineering design and optimization. A baseline rooftop air conditioning unit, featuring a 13 ton (45.7 kW) cooling capacity and a 17.9 integrated energy efficiency ratio, served as the point of departure for substantive efficiency enhancements. Key modifications included the consolidation of two refrigerant circuits into one, integrating three parallel 2-stage (dual-speed) compressors, fan replacements with high-efficiency substitutes. Notably, a lower global warming potential refrigerant, R452B, was evaluated as a substitute for R-410A, demonstrating better performance in the lab prototype. Further, the achieved measured integrated energy efficiency ratio of 21.4 in the lab prototype surpassed the baseline integrated energy efficiency ratio. Comparative evaluations between R410A and R452B indicated heightened efficiency with the latter, showcasing a lab-demonstrated integrated energy efficiency ratio of 22.4 at the rated capacity of 13.8 ton (48.5 kW) and 23.9 integrated energy efficiency ratio at the rated capacity of 10 ton (35.2 kW). This research underscores the successful development of a rigorous, energy efficient rooftop air conditioning unit prototype with noteworthy environmental and economic implications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Effect of seasonal anoxia on geochemical cycling in a stratified pond: Comparison to cooler pond conditions 40 years ago

Seasonal stratification in temperate lakes deeper than a few meters creates favorable conditions for pronounced vertical redox zones, often resulting in anaerobic hypolimnions and significant geochemical changes. Here, this study examined thermocline formation and trace element behavior in a seasonally stratified pond amid rising air temperatures. Over two years, data were collected from Pond B at the US Department of Energy Savannah River Site in Aiken, South Carolina. Pond B, a man-made monomictic reservoir, received cooling water from a nuclear reactor from 1961 to 1964. Strong thermal stratification forms a distinct thermocline in May and progresses downward until November. Compared to the 1980s, this study shows a delayed onset and extended duration of stratification. The prolonged summer stratification reduces deep water oxygen replenishment, extending hypoxic conditions. Trace and major elements sampled in the water column revealed strong correlations between As, Fe, and Mn profiles, with concentrations increasing by 1–2 orders of magnitude in the anaerobic hypolimnion. This period captured the seasonal transition from winter mixing to summer stratification to fall overturn. Under anoxic conditions, Fe(III) reduces to Fe(II) in the sediment, releasing dissolved iron into the water column. The extended anoxic periods likely promoted arsenic release from sediments. Prolonged anoxia may enhance arsenic mobilization and solubility in the lake. This study illustrates how climate-induced changes in seasonal stratification of contaminated waters can convert contaminant sinks into sources, offering insights into the cycling of arsenic and other dissolved ions in stratified lakes and their implications for water quality management.

Anoxic conditions↗
DOE OSTI2026

Machine Learning–Based Condition Monitoring of a Circulating Water System of a Canadian Nuclear Plant

With the need to maintain long-term reliable energy using nuclear power plants, there is an underlying demand to ensure that the maintenance of plant components and systems is also done in an efficient and cost-effective manner. One way to achieve this is by moving from time-based maintenance to condition-based maintenance. The research presented in this paper focuses on applying statistical and machine-learning-based methods to capture anomalies within data for fault detection to further develop into condition monitoring. This paper focuses on system data for a circulating water system (CWS) of a pressurized heavy-water reactor for detecting anomalies. The different methodologies used for detecting and capturing anomalies in the CWS data are matrix profile, density-based spatial clustering of applications with noise (DBSCAN), and support vector machines (SVMs). Matrix profile and DBSCAN are used to distinguish between normal data and anomalous data. This paper presents a hybrid method using DBSCAN and SVM when a portion of the data is used for DBSCAN to generate clusters. This portion of data is then used to train the SVM along with the clusters generated by DBSCAN as output. SVM is then tested on unseen data as a predictive tool, which can work in real time to categorize data points as either normal or anomalous. This paper presents results that show the high accuracies of DBSCAN and SVM in capturing anomalies within the data for a CWS for fault detection. Thus, the maintenance plan would be focused on component condition rather than a time-based schedule by switching to an automated system to identify and predict faults within a CWS.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Wall conditioning effects of boron powder injection in KSTAR with a tungsten divertor

Boron powder dropped into KSTAR plasmas decreased the radiated power, core electron density, and Z eff , indicating that the ablated and redeposited boron powder helped to condition the plasma-facing surfaces. Visible line emission of oxygen and tungsten were also reduced by 50% (80%) with boron injection into H-mode (L-mode) discharges that used the new KSTAR tungsten monoblock lower divertor. Dynamic particle balance analysis found a negligible difference in the inferred wall pumping rate during the steady portion of the discharges. It is inferred that the observed conditioning effects were principally caused by a reduction in intrinsic impurities as opposed to a reduction in wall recycling. These results are in qualitative agreement with low-Z injected powder experiments across many fusion devices, confirming the utility of low-Z powder injection as a real-time wall conditioning tool.

boron powder injection↗
DOE OSTI2026

Geologic Seawater Air Conditioning (GeoSWAC) System: Resource Assessment and Techno-Economic Evaluation in Puerto Rico

Puerto Rico's hot, humid climate drives a high and persistent demand for cooling, straining an aging and fuel-dependent energy grid while increasing peak electricity loads. Much of this challenge stems from inefficient air-conditioning systems operating in buildings without passive cooling design, making cooling both costly and vulnerable to disruption - especially during hurricanes. To address these issues, researchers evaluated a new alternative called Geologic Seawater Air Conditioning (GeoSWAC), which uses inland wells connected to naturally cold, deep seawater. By avoiding long offshore pipelines and energy-intensive refrigeration cycles, GeoSWAC can cut electricity use by 75-90%, reduce environmental impacts, and operate more reliably during power outages. A case study at the University of Puerto Rico's Rio Piedras campus found that GeoSWAC could deliver significantly lower levelized costs of cooling, reduced operational expenses, and improved water conservation compared to the campus's existing chilled-water plant. The system offers key advantages - including access to a constant cold heat sink, low pumping requirements, and enhanced resilience - for coastal regions such as Puerto Rico, the broader Caribbean, and Florida. However, its effectiveness depends heavily on site-specific geological conditions, such as subsurface connectivity and aquifer characteristics, which require detailed investigation. Future research should refine hydrogeological models, conduct performance and economic analyses, and address regulatory and permitting frameworks to support broader adoption of this promising technology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Loss of Control Detection for Commercial Transport Aircraft Using Conditional Variational Autoencoders

This work describes a detector for the loss of control condition of a commercial transport in flight. The detector has a belief state defined by the latent variable stochastic modeling of a conditional variational autoencoder (CVAE) constructed with bidirectional recurrent layers. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining loss-of-control (LOC) for a commercial transport. We use the thresholds for these quantitative metrics to define a condition vector for training the CVAE. First, we demonstrate through experimentation that reconstruction probability is an accurate indicator that the vehicle has shifted to an LOC state. Second, we introduce a technique for inferring that the vehicle is approaching a flight state change by measuring a shift in the sampling distributions of the CVAE latent space. The sampling distributions for flight observations that are approaching envelope limits are localized to external areas of the latent space. We provide an analysis of its applicability to flight data from NASA’s dynamically-scaled generic transport model (GTM) aircraft.

Loss of control↗