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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

An empirical propellant response function for combustion stability predictions

An empirical response function model was developed for ammonium perchlorate propellants to supplant T-burner testing at the preliminary design stage. The model was developed by fitting a limited T-burner data base, in terms of oxidizer size and concentration, to an analytical two parameter response function expression. Multiple peaks are predicted, but the primary effect is of a single peak for most formulations, with notable bulges for the various AP size fractions. The model was extended to velocity coupling with the assumption that dynamic response was controlled primarily by the solid phase described by the two parameter model. The magnitude of velocity coupling was then scaled using an erosive burning law. Routine use of the model for stability predictions on a number of propulsion units indicates that the model tends to overpredict propellant response. It is concluded that the model represents a generally conservative prediction tool, suited especially for the preliminary design stage when T-burner data may not be readily available. The model work included development of a rigorous summation technique for pseudopropellant properties and of a concept for modeling ordered packing of particulates.

Hessler, R. O.↗
NASA NTRS1989

Mach 5 inlet CFD and experimental results

An experimental research program was conducted in the NASA Lewis Research Center 10 ft. by 10 ft. supersonic wind tunnel. The two-dimensional inlet model was designed to study the Mach 3.0 to 5.0 speed range for an 'over-under' turbojet plus ramjet propulsion system. The model was extensively instrumented to provide both analytical code validation data as well as inlet performance information. Support studies for the program include flow field predictions with both three-dimensional parabolized Navier-Stokes (PNS) and three-dimensional full Navier-Stokes (FNS) analytical codes. Analytical predictions and experimental results are compared.

Weir, Lois J.↗
DOE OSTI2022

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Two-dimensional Navier-Stokes heat transfer analysis for rough turbine blades

A quasi-three-dimensional thin-layer Navier-Stokes analysis was used to predict heat transfer to rough surfaces. Comparisons are made between predicted and experimental heat transfer for turbine blades and flat plates of known roughness. The effect of surface roughness on heat transfer was modeled using a mixing length approach. The effect of near-wall grid spacing and convergence criteria on the accuracy of the heat transfer predictions are examined. An eddy viscosity mixing length model having an inner and outer layer was used. A discussion of the appropriate model for the crossover between the inner and outer layers is included. The analytic results are compared with experimental data for both flat plates and turbine blade geometries. Comparisons between predicted and experimental heat transfer showed that a modeling roughness effects using a modified mixing length approach results in good predictions of the trends in heat transfer due to roughness.

Boyle, R. J.↗
NASA NTRS1991

Two-dimensional Navier-Stokes heat transfer analysis for rough turbine blades

A quasi-three-dimensional thin-layer Navier-Stokes analysis was used to predict heat transfer to rough surfaces. Comparisons are made between predicted and experimental heat transfer for turbine blades and flat plates of known roughness. The effect of surface roughness on heat transfer was modeled using a mixing length approach. The effect of near-wall grid spacing and convergence criteria on the accuracy of the heat transfer predictions are examined. An eddy viscosity mixing length model having an inner and outer layer was used. A discussion of the appropriate model for the crossover between the inner and outer layers is included. The analytic results are compared with experimental data for both flat plates and turbine blade geometries. Comparisons between predicted and experimental heat transfer showed that a modeling roughness effects using a modified mixing length approach results in good predictions of the trends in heat transfer due to roughness.

Boyle, R. J.↗
NASA NTRS1975

Cold air study of the effect on turbine stator blade aerodynamic performance of coolant ejection from various trailing edge slot geometries. 2: Comparison of experimental and analytical results

Experimentally determined efficiencies of turbine stator blades having trailing-edge coolant ejection are compared with efficiencies predicted from two previously published approximate analytical methods. The experimental results were obtained from two-dimensional data with the temperature of the primary and coolant flows both being nearly ambient. Data from five stator blade configurations having different slotted trailing-edge geometries were included in the comparison. The two analytical methods gave results which agreed reasonably well with experimental results. An average of the absolute values of differences between experimental and predicted efficiencies for all five blade configurations showed that one method gave average efficiency differences which were about 1.3 percent different than experimental efficiencies, while the other method gave average efficiency differences that were about 0.7 percent different than experimental. However, in some instances, maximum differences of as much as 4 percent occurred. A comparison between experimental and analytical results indicated that the ratio of trailing-edge slot width to trailing-edge thickness influences the measured efficiencies to a greater extent than is accounted for by either analytical model.

Prust, H. W., Jr.↗
DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗
NASA NTRS2011

Demagnetization Tests Performed on a Linear Alternator for a Stirling Power Convertor

Demagnetization temperature of a linear alternator (LA) can be accurately predicted through an analytical Maxwell model. The M-H characteristics of the alternator magnets must be known. Vendor data are given for cube-shaped magnets, and the shape of a LA magnet may affect its magnetic properties. At GRC, M-H data are directly measured for each LA magnet. This method was validated using TDC alternator tests on the Alternator Test Rig. The analytical Maxwell modeling was utilized on a different style linear alternator to predict demagnetization temperatures for the Advanced Stirling Convertor.

Geng, Steven M.↗
NASA NTRS1986

Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements

A review is presented of numerical models which were developed to interpret thermal IR data and to identify the governing parameters and surface energy fluxes recorded in the images. Analytic, predictive, diagnostic and empirical models are described. The limitations of each type of modeling approach are explored in terms of the error sources and inherent constraints due to theoretical or measurement limitations. Sample results of regional-scale soil moisture or evaporation patterns derived from the Heat Capacity Mapping Mission and GOES satellite data through application of the predictive model devised by Carlson (1981) are discussed. The analysis indicates that pattern recognition will probably be highest when data are collected over flat, arid, sparsely vegetated terrain. The soil moisture data then obtained may be accurate to within 10-20 percent.

Carlson, T. N.↗
NASA NTRS2014

Contra-Rotating Open Rotor Tone Noise Prediction

Reliable prediction of contra-rotating open rotor (CROR) noise is an essential element of any strategy for the development of low-noise open rotor propulsion systems that can meet both the community noise regulations and cabin noise limits. Since CROR noise spectra exhibit a preponderance of tones, significant efforts have been directed towards predicting their tone content. To that end, there has been an ongoing effort at NASA to assess various in-house open rotor tone noise prediction tools using a benchmark CROR blade set for which significant aerodynamic and acoustic data have been acquired in wind tunnel tests. In the work presented here, the focus is on the nearfield noise of the benchmark open rotor blade set at the cruise condition. Using an analytical CROR tone noise model with input from high-fidelity aerodynamic simulations, tone noise spectra have been predicted and compared with the experimental data. Comparisons indicate that the theoretical predictions are in good agreement with the data, especially for the dominant tones and for the overall sound pressure level of tones. The results also indicate that, whereas the individual rotor tones are well predicted by the combination of the thickness and loading sources, for the interaction tones it is essential that the quadrupole source is also included in the analysis.

Propeller Noise↗
NASA NTRS1981

Comparison of analytical and experimental supersonic aerodynamic characteristics of a forward control missile

Techniques to predict the aerodynamic characteristics of slender cruciform missiles have been developed and are constantly being updated and improved. This paper presents comparisons between analytical and experimental supersonic aerodynamic data for a class of canard-controlled missile configurations similar to the sidewinder missile. Three aerodynamic prediction computer codes, including program MISSILE2, a recently improved version of program MISSILE, are evaluated by comparison with the test data to assess their accuracy. The major emphasis is placed on the roll control characteristics. In addition, tail span optimization, longitudinal and lateral control, induced roll, and missile roll orientation effects are addressed.

Allen, J. M.↗
NASA NTRS1972

Experiment and analysis of directional effects on radiant heat transfer.

Critical examination of the validity of the commonly used simplified models for predicting local and overall radiant heat interchange among real surfaces. The spectral local irradiation was measured at wavelengths of 3.08 and 4.51 microns in a configuration consisting of three plane parallel surfaces of finite extent. The test surfaces were gold with mechanical roughness and temperature varying from 0.02 to 7.1 microns and 77 to 760 K, respectively. Comparisons between experimental data and predictions for six different models ranging from the simple diffuse to the most detailed diffuse-plus-specular-directional-property model with specular component calculated according to Beckmann are presented. It is shown that the calculation of the overall irradiation using appropriate constant-property models agrees well with the experimental data to within combined experimental and analytical uncertainty. In general, it is concluded that for accurate prediction of local irradiation the directional characteristics of the surfaces must be considered.

Toor, J. S.↗
NASA NTRS2011

Effect of Data Reduction and Fiber-Bridging on Mode I Delamination Characterization of Unidirectional Composites

Reliable delamination characterization data for laminated composites are needed for input in analytical models of structures to predict delamination onset and growth. The double-cantilevered beam (DCB) specimen is used to measure fracture toughness, GIc, and strain energy release rate, GImax, for delamination onset and growth in laminated composites under mode I loading. The current study was conducted as part of an ASTM Round Robin activity to evaluate a proposed testing standard for Mode I fatigue delamination propagation. Static and fatigue tests were conducted on specimens of IM7/977-3 and G40-800/5276-1 graphite/epoxies, and S2/5216 glass/epoxy DCB specimens to evaluate the draft standard "Standard Test Method for Mode I Fatigue Delamination Propagation of Unidirectional Fiber-Reinforced Polymer Matrix Composites." Static results were used to generate a delamination resistance curve, GIR, for each material, which was used to determine the effects of fiber-bridging on the delamination growth data. All three materials were tested in fatigue at a cyclic GImax level equal to 90% of the fracture toughness, GIc, to determine the delamination growth rate. Two different data reduction methods, a 2-point and a 7-point fit, were used and the resulting Paris Law equations were compared. Growth rate results were normalized by the delamination resistance curve for each material and compared to the nonnormalized results. Paris Law exponents were found to decrease by 5.4% to 46.2% due to normalizing the growth data. Additional specimens of the IM7/977-3 material were tested at 3 lower cyclic GImax levels to compare the effect of loading level on delamination growth rates. The IM7/977-3 tests were also used to determine the delamination threshold curve for that material. The results show that tests at a range of loading levels are necessary to describe the complete delamination behavior of this material.

Murri, Gretchen B.↗
DOE OSTI2021

Perspectives on Machine Learning-assisted Plasma Medicine: Towards Automated Plasma Treatment

Cold atmospheric plasmas (CAPs) have shown great promise for medical applications through their synergistic chemical, electrical, and thermal effects, which can induce therapeutic outcomes. However, safe and reproducible plasma treatment of complex biological surfaces poses a major hurdle to the widespread adoption of CAPs for medical applications. Predictive modeling of the mutual interactions between the plasma and biological surfaces and, thus, systematic approaches to quantify and predict plasma treatment outcomes remain largely elusive due to the lack of mechanistic understanding of plasma-surface interactions that can span across vastly different length-and time-scales. In addition, real-time sensing capabilities in biomedical CAP devices are often limited, which can be detrimental to plasma treatment due to the intrinsic plasma and surface variability during the treatment, as well as sensitivity to external perturbations. All of these challenges can make reproducible and effective plasma treatment of biological surfaces difficult to realize, which is further compounded by errors due to human operation of hand-held CAP devices. Machine learning and data-driven approaches can be particularly useful in addressing these challenges in three major ways: (i) data-driven modeling of hard-to-model plasma-surface interactions and plasma treatment outcomes; (ii) learning data analytics for plasma and surface diagnostics in real-time; and (iii) developing predictive controllers that enable reliable and effective CAP treatments. Furthermore, this paper discusses the promise of machine learning to accelerate plasma medicine research in these areas, toward machine learning-assisted and automated CAP treatment of complex biological surfaces.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1975

Input guide for computer programs to generate thermodynamic data for air and Freon CF4

FORTRAN computer programs were developed to calculate the thermodynamic properties of Freon 14 and air for isentropic expansion from given plenum conditions. Thermodynamic properties for air are calculated with equations derived from the Beattie-Bridgeman nonstandard equation of state and, for Freon 14, with equations derived from the Redlich-Quang nonstandard equation of state. These two gases are used in scale model testing of model rocket nozzle flow fields which requires simulation of the prototype plume shape with a cold flow test approach. Utility of the computer programs for use in analytical prediction of flow fields is enhanced by arranging card or tape output of the data in a format compatible with a method-of-characteristics computer program.

Tevepaugh, J. A.↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗