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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1 , a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

Flow properties in expansion tube with helium, argon, air, and CO2

Test flow velocities from 5 to 7 km/sec were generated in a 6-in. expansion tube using helium, argon, air, and CO2 test gases. Pitot pressure profiles across the flow at the test section are presented for the four test gases, and measured flow quantities are compared to computer predicted values. Comparison of predicted and measured flow quantities suggests the expansion to be near thermochemical equilibrium for all test gases and implies the existence of a totally reflected shock at the secondary diaphragm. Argon, air, and CO2 flows were observed to attenuate while traversing the acceleration section, whereas no attenuation was observed for helium.

Miller, C. G.↗
NASA NTRS1993

An examination of the aerodynamic moment on rotor blade tips using flight test data and analysis

The analysis CAMRAD/JA is used to model two aircraft, a Puma with a swept-tip blade and a UH-60A Black Hawk. The accuracy of the analysis in predicting the torsion loads is assessed by comparing the predicted loads with measurements from flight tests. The influence of assumptions in the analytical model is examined by varying model parameters and comparing the predicted results to baseline values for the torsion loads. Flight test data from a research Puma are used to identify the source of torsion loads. These data indicate that the aerodynamic section moment in the region of the blade tip dominates torsion loading in high-speed flight. Both the aerodynamic section moment at the blade tip and the pitch-link loads are characterized by large positive (nose-up) moments in the first quadrant with rapid reversal of load so that the moment is negative in the second quadrant. Both the character and magnitude of this loading are missed by the CAMRAD/JA analysis.

Maier, Thomas H.↗
NASA NTRS1991

Forward flight trim calculation and frequency response validation of a high-order helicopter simulation model

This paper describes a new trim procedure, that includes the calculation of the steady-state response of the rotor blades, and that is applicable to straight flight and steady coordinated turns. This paper also describes the results of a validation study for a high order linearized model of helicopter flight dynamics, that includes rotor, inflow, and actuator dynamics. The model is obtained by numerical perturbations of a nonlinear, blade element type mathematical model. Predicted responses are compared with flight test data for two values of flight speed. The comparison is carried out in the frequency domain. Numerical simulations show that the trim algorithm is very accurate, and preserves the periodicity of the aircraft states. The results also indicate that the predictions of the linearized model are in good agreement with flight test data, especially at medium and high frequencies.

Kim, Frederick D.↗
NASA NTRS1996

Life test results for the advanced very high resolution radiometer scanner

The following paper reports the results obtained during a 3.33-year life test on the TIROS Advanced Very High Resolution Radiometer/3 (AVHRR/3) Scanner. The bearing drag torque and lubricant loss over life will be compared to predicted values developed through modeling. The condition of the lubricant at the end of the test will be described and a theory presented to explain the results obtained. The differences (if any) in the predicted and measured values of drag torque and lubricant loss will be discussed and possible reasons for these examined.

Lenz, James↗
DOE OSTI2021

Mongooses ( Urva auropunctata ) as reservoir hosts of Leptospira species in the United States Virgin Islands, 2019–2020

During 2019–2020, the Virgin Islands Department of Health investigated potential animal reservoirs of Leptospira spp., the bacteria that cause leptospirosis. In this cross-sectional study, we investigated Leptospira spp. exposure and carriage in the small Indian mongoose (Urva auropunctata, syn: Herpestes auropunctatus), an invasive animal species. This study was conducted across the three main islands of the U.S. Virgin Islands (USVI), which are St. Croix, St. Thomas, and St. John. We used the microscopic agglutination test (MAT), fluorescent antibody test (FAT), real-time polymerase chain reaction (lipl32 rt-PCR), and bacterial culture to evaluate serum and kidney specimens and compared the sensitivity, specificity, positive predictive value, and negative predictive value of these laboratory methods. Mongooses (n = 274) were live-trapped at 31 field sites in ten regions across USVI and humanely euthanized for Leptospira spp. testing. Bacterial isolates were sequenced and evaluated for species and phylogenetic analysis using the ppkgene. Anti-Leptospira spp. antibodies were detected in 34% (87/256) of mongooses. Reactions were observed with the following serogroups: Sejroe, Icterohaemorrhagiae, Pyrogenes, Mini, Cynopteri, Australis, Hebdomadis, Autumnalis, Mankarso, Pomona, and Ballum. Of the kidney specimens examined, 5.8% (16/270) were FAT-positive, 10% (27/274) were culture-positive, and 12.4% (34/274) were positive by rt-PCR. Of the Leptospira spp. isolated from mongooses, 25 were L. borgpetersenii, one was L. interrogans, and one was L. kirschneri. Positive predictive values of FAT and rt-PCR testing for predicting successful isolation of Leptospira by culture were 88% and 65%, respectively. The isolation and identification of Leptospira spp. in mongooses highlights the potential role of mongooses as a wildlife reservoir of leptospirosis; mongooses could be a source of Leptospira spp. infections for other wildlife, domestic animals, and humans.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1982

Thermoelastohydrodynamic analysis of an oil pumping ring seal

The selection and evaluation of the numerical methods used in the thermoelastohydrodynamic analysis of an oil pumping ring seal are described, and the results from test problems used to demonstrate the validity of the implementing computer program are presented. A sampling of predicted pumping ring performance values is compared with existing experimental data. Agreement between prediction and test performance is obtained, yielding detailed information concerning fluid and solid parameters.

Smith, P. J.↗
NASA NTRS1978

A method for predicting full scale buffet response with rigid wind tunnel model fluctuating pressure data. Volume 2: Power spectral densities for method assessment

The predicted upper and lower bounds power spectra for all of the cases and response items given in Volume 1 are plotted. The flight test power spectra are shown on each prediction plot for the nominal value of angle of attack that most closely agrees with the flexible angle for the prediction. The flight test and prediction conditions are given in tabular form for all cases considered.

Cunningham, A. M., Jr.↗
DOE OSTI2021

Predicting bulge to total luminosity ratio of galaxies using deep learning

ABSTRACT We present a deep learning model to predict the r-band bulge-to-total luminosity ratio (B/T) of nearby galaxies using their multiband JPEG images alone. Our Convolutional Neural Network (CNN) based regression model is trained on a large sample of galaxies with reliable decomposition into the bulge and disc components. The existing approaches to estimate the B/T ratio use galaxy light-profile modelling to find the best fit. This method is computationally expensive, prohibitively so for large samples of galaxies, and requires a significant amount of human intervention. Machine learning models have the potential to overcome these shortcomings. In our CNN model, for a test set of 20 000 galaxies, 85.7 per cent of the predicted B/T values have absolute error (AE) less than 0.1. We see further improvement to 87.5 per cent if, while testing, we only consider brighter galaxies (with r-band apparent magnitude <17) with no bright neighbours. Our model estimates the B/T ratio for the 20 000 test galaxies in less than a minute. This is a significant improvement in inference time from the conventional fitting pipelines, which manage around 2–3 estimates per minute. Thus, the proposed machine learning approach could potentially save a tremendous amount of time, effort, and computational resources while predicting B/T reliably, particularly in the era of next-generation sky surveys such as the Legacy Survey of Space and Time (LSST) and the Euclid sky survey which will produce extremely large samples of galaxies.

Grover, Harsh (ORCID:0000000321338142)↗
DOE OSTI2021

Defense Waste Processing Facility Nitric-Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by Savannah River National Laboratory (SRNL) from 2011 to 2016. The goal of this work was to develop empirical correlation models to predict these values from measurable variables from the chemical process so that these quantities could be predicted a-priori from the sludge or simulant composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) state of the glass from the Defense Waste Processing Facility (DWPF) melter. This report summarizes the work on these correlations based on the aforementioned data. Previous work on these correlations was documented in a technical report covering data from 2011-2015. This current report supersedes this previous report. Further refinement of the models as additional data are collected is recommended. The glass REDOX depends on the concentrations of nitrate and manganese (oxidants), and of glycolate, formate, oxalate, carbon, and antifoam (reductants) in the melter feed. The waste sludge contains nitrite, nitrate, manganese (Mn), and oxalate. Virtually all of the nitrite is converted to nitrate or NO+NO 2 +N 2 O gases in the CPC. The portion of the nitrite converted to nitrate increases the amount of nitrate in the sludge. The amount of glycolate in the final melter feed depends on the amount of the glycolic acid feed that is destroyed. Similarly, the amounts of formate and oxalate formed during the decomposition of glycolic acid are required. The material balance on carbon was found to not close in most cases. Generally, there was less carbon at the end of testing compared to the inputs. The most uncertain product variable was glycolate, so material balances were performed where the glycolate concentration was adjusted, usually upward, to close the balance. Correlation versus the original, as-measured, data was generally poor, but correlation against the material balance adjusted values was greatly improved. It was also shown that the correlation of the measured REDOX versus the predicted REDOX was much better when the material balance adjusted glycolate values were used. Three data series were primarily used during the regressions of the data; these series were 1) Sludge Batch 9 NG flowsheet simulant runs NG51-62 (SB9-NG); 2) Scaled Runs + Bounding Hydrogen Runs (SR+BH); and 3) Runs GN43-50 and 57 (43-50,57). The glycolate destruction was found to correlate with acid stoichiometry (AS), percent reducing acid (PRA), and for some data series, headspace to simulant volume ratio (HSV), mercury (Hg), and nitrate. Although glycolate destruction for pairs of data series (e.g., [SB9-NG] and [SR+BH]) were found to depend on HSV, the combination of all three data series was not found to have significant dependence on this variable. The best model for glycolate destruction depended on AS, nitrate, and Hg. This model predicted the product glycolate compositions of the data to within 92-106%. The conversion of glycolate to formate was high when noble metals and Hg were not present, with values up to 100%. When noble metals and Hg were present, this conversion ranged from zero to 7%, and was dependent on AS. Lower AS gave higher conversions to formate. The conversion to oxalate was found to depend on the AS and the initial concentration of nitrite. An alternative fit versus AS and the form of ruthenium (Ru) used is a possible alternative. This fit was somewhat less statistically significant. This second model predicts that more oxalate is formed when Ru-nitrosyl nitrate is used rather than Ru chloride. The conversion of glycolate to oxalate ranged from zero to 6%. The conversion of nitrite to nitrate depended primarily on AS and PRA, with HSV and Hg being significant when these variables were varied. For multiple series of data, nitrite was also needed to SRNL-STI-2017-00172 5HYLVLRQ viL distinguish between data series, and the effect of HSV became insignificant. The best model for nitrite to nitrate conversion depended on AS, PRA, nitrite, and Hg. The 95% confidence intervals on the predicted values of glycolate destruction, glycolate to oxalate conversion, and nitrite to nitrate conversion were used to determine the uncertainty in the predicted REDOX when starting with only the composition of the sludge, AS, and PRA. Using the 95% confidences on an individual value (that is the confidence in getting a particular value for one single test as opposed to what the mean would be for multiple tests), the uncertainty in the predicted REDOX was calculated. The uncertainty in the actual product composition glycolate, oxalate, formate, and nitrate concentrations translated to an uncertainty in the REDOX value of ±0.1,which is approximately the uncertainty claimed in the REDOX model itself.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1981

Pressure distributions and shock shapes for 12.84 deg/7 deg on-axis and bent-nose biconics in air at Mach 6

Pressure distributions and shock shapes on a spherically blunted, 12.84 deg /7 deg on axis biconic and a spherically blunted, 12.84 deg/7 deg bent nose biconic at Mach 6 in air were measured. The angle of attack, referenced to the axis of aft cone, was varied from 0 deg to 25 deg in nominal 5 deg increments. Two values of free stream Reynolds number based on model length were tested. Predictions from simple, theories and from a supersonic, three dimensional, external invsicid code (STEIN) are compared with measured values. Predicted STEIN shock shapes and windward pressures are in agreement with measured values for both biconics over the present range of angle of attack.

Miller, C. G., III↗
DOE OSTI2025

Geothermal well testing pressure prediction by using a hybrid transformer model system: FORGE well use case

Geothermal has huge potential to become an indispensable component in achieving the goal of sustainable energy economy, given its capability to provide consistent baseload power to the electric grid. Injection tests are crucial in geothermal energy system as they naturally help to evaluate reservoir properties, understand fluid flow and even enhance reservoir performance. In this research, we developed a hybrid model system that integrates machine learning (ML) regression, a physics-based mathematical model, and transformer deep learning. Trained and validated using FORGE injection test dataset, this system can forecast the pressure variations both upward and downward over time. The pressure prediction achieved prediction accuracy within 3-6% variance of true pressure values. The system can significantly save time and reduce costs by testing only a few cycles and then using model predictions for further analysis, instead of conducting additional real injection cycle tests. The developed model system also holds promise for designing injection test processes and maintaining well production in geothermal energy. Presented at the IMAGE ‘25 Conference led by Shell.

FORGE↗
DOE OSTI2024

Predicting the tensile and compressive modulus of electrospun fiber mat‐reinforced hydrogels using the Halpin–Tsai equations

Abstract The reinforcement of mechanically‐weak hydrogels to yield composites with increased stiffness, strength, or toughness is a well‐established approach. In particular, introducing electrospun nanofibers into hydrogels is a common strategy for biomedical applications, as the resulting hierarchical structure mimics biology and allows for control over fiber diameter and alignment and tuning of mechanical properties. However, further study of the link between the constituent materials and the mechanical properties of the composite is uncommon. One potential model to understand the mechanical properties of fiber‐reinforced hydrogels involves the Halpin–Tsai equations, which relate the modulus values of the fibers and hydrogel matrix and the fiber volume fraction, to the modulus of the composite. To assess the application of this model to fiber‐reinforced hydrogels, predicted values were compared with experimental values from mechanical testing of a poly(ethylene glycol) (PEG) matrix reinforced with an electrospun polycaprolactone (PCL) fiber mat. Although the equations described these systems well in tension, providing a facile approach to identify a fiber volume fraction that will achieve a desired modulus, the Halpin–Tsai approach was less successful under compression. This study motivates additional investigation of the role of structural features of hydrogel composites in determining mechanical properties to enable design of materials for specific applications.

Polymer Science↗
DOE OSTI2021

DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

Abstract Background Driven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction. Results To explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist’s real-value distance prediction is 0.896 Å 2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å 2 of DeepDist’s multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist’s multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment. Conclusions DeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1977

A fixed tilt solar collector employing reversible vee-trough reflectors and vacuum tube receivers for solar heating and cooling systems

The usefulness of vee-trough concentrators in improving the efficiency and reducing the cost of collectors assembled from evacuated tube receivers was studied in the vee-trough/vacuum tube collector (VTVTC) project. The VTVTC was analyzed rigorously and various mathematical models were developed to calculate the optical performance of the vee-trough concentrator and the thermal performance of the evacuated tube receiver. A test bed was constructed to verify the mathematical analyses and compare reflectors made out of glass, Alzak and aluminized FEP Teflon. Tests were run at temperatures ranging from 95 to 180 C. Vee-trough collector efficiencies of 35 to 40% were observed at an operating temperature of about 175 C. Test results compared well with the calculated values. Predicted daily useful heat collection and efficiency values are presented for a year's duration of operation temperatures ranging from 65 to 230 C. Estimated collector costs and resulting thermal energy costs are presented. Analytical and experimental results are discussed along with a complete economic evaluation.

Selcuk, M. K.↗
NASA NTRS1979

Space Shuttle SRM development

The successful static test of the fourth Development Space Shuttle Solid Rocket Motor (SRM) in February 1979 concluded the development testing phase of the SRM Project. Qualification and flight motors are currently being fabricated, with the first qualification motor to be static tested. Delivered thrust-time traces on all development motors were very close to predicted values, and both specific and total impulse exceeded specification requirements. 'All-up' static tests conducted with a solid rocket booster equipment on development motors achieved all test objectives. Transportation and support equipment concepts have been proven, baselining is complete, and component reusability has been demonstrated. Evolution of the SRM transportation support equipment, and special test equipment designs are reviewed, and development activities discussed. Handling and processing aspects of large, heavy components are described.

Brinton, B. C.↗
NASA NTRS1990

Cryogenic temperature effects on sting-balance deflections in the National Transonic Facility

An investigation was conducted at the National Transonic Facility (NTF) to document the change in sting-balance deflections from ambient to cryogenic temperatures. Space limitations in some NTF models do not allow the use of on-board angle of attack instrumentation. In order to obtain angle of attack data, pre-determined sting-balance bending data must be combined with arc sector angle measurements. Presently, obtaining pretest sting-balance data requires several cryogenic cycles and cold loadings over a period of several days. A method of reducing the calibration time required is to obtain only ambient temperature sting-balance bending data and correct for changes in material properties at cryogenic temperatures. To validate this method, two typical NTF sting-balance combinations were tested. The test results show excellent agreement with the predicted values and the repeatability of the data was 0.01 degree.

Popernack, Thomas G., Jr.↗
NASA NTRS1990

An improved approach for flight readiness assessment

An improved methodology for quantitatively evaluating failure risk for a spaceflight system in order to assess flight readiness is presented. This methodology is of particular value when information relevant to failure prediction, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is limited. In this approach, engineering analysis models that characterize specific failure modes based on the physics and mechanics of the failure phenomena are used in a prescribed probabilistic structure to generate a failure probability distribution that is modified by test and flight experience in a Bayesian statistical procedure. The probabilistic structure and statistical methodology are generally applicable to any failure mode for which quantitative engineering analysis can be employed to characterize the failure phenomenon and are particularly well suited for use under the constraints on information availability that are typical of such spaceflight systems as the Space Shuttle and planetary spacecraft.

Moore, N. R.↗