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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 OSTI2013

Monte Carlo and analytical model predictions of leakage neutron exposures from passively scattered proton therapy

Purpose: Stray neutron radiation is of concern after radiation therapy, especially in children, because of the high risk it might carry for secondary cancers. Several previous studies predicted the stray neutron exposure from proton therapy, mostly using Monte Carlo simulations. Promising attempts to develop analytical models have also been reported, but these were limited to only a few proton beam energies. The purpose of this study was to develop an analytical model to predict leakage neutron equivalent dose from passively scattered proton beams in the 100-250-MeV interval.Methods: To develop and validate the analytical model, the authors used values of equivalent dose per therapeutic absorbed dose (H/D) predicted with Monte Carlo simulations. The authors also characterized the behavior of the mean neutron radiation-weighting factor, w{sub R}, as a function of depth in a water phantom and distance from the beam central axis.Results: The simulated and analytical predictions agreed well. On average, the percentage difference between the analytical model and the Monte Carlo simulations was 10% for the energies and positions studied. The authors found that w{sub R} was highest at the shallowest depth and decreased with depth until around 10 cm, where it started to increase slowly with depth. This was consistent among all energies.Conclusion: Simple analytical methods are promising alternatives to complex and slow Monte Carlo simulations to predict H/D values. The authors' results also provide improved understanding of the behavior of w{sub R} which strongly depends on depth, but is nearly independent of lateral distance from the beam central axis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Erratum: “Nonlinear modeling of forced magnetic reconnection in slab geometry with NIMROD” [Phys. Plasmas 24, 052508 (2017)]

The linear layer response time is evaluated inappropriately in Ref. 1. This affects analytic predictions for linear and quasilinear magnetic field penetration due to a resonant, externally applied field according to Eqs. (14) and (19) of Ref. 1, respectively. The linear layer response time for the visco-resistive regime is given according to Ref. 2 as $τ_{VR}$ =2.104$τ_{shA}$S$^{2/3}_{sh}$P$^{1/6}_{m}$, where $τ_{shA}$ ≡ $τ_A$/$k_ya$ is the local shear Alfvén time at the rational surface (called the local hydromagnetic time $τ_H$ in Ref. 2) and S sh =$Sk_ya$ is the local Lundquist number. However, Ref. 1 inadvertently omits the factor of $k_y$a in $τ_{shA}$, evaluating $τ_{VR}$ using the Alfvén time τA based on the sheared magnetic field strength at the computation boundary. The proper evaluation of $τ_{VR}$ results in a linear layer response time that is ($k_ya$) 1/3 =π -1/3 ≃0.68 shorter than that in Ref. 1. The value of the time $τ'_{VR}$ used to predict quasilinear equilibria according to Eq. (19) is shorter by the same factor. Using the correct value of $τ_{VR}$ has a direct effect on analytic predictions for the magnitude and phase of the linear penetrated field, consistent with Eq. (14) in Ref. 1. Figure 1 replaces Fig. 5 of Ref. 1, exhibiting even better agreement between NIMROD computations and analytic predictions. The highest percent difference in penetrated field magnitude is 11% and in phase is 8%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1990

Prediction of force and acceleration control spectra for Space Shuttle orbiter sidewall-mounted payloads

The development of force and acceleration control spectra for vibration testing of Space Shuttle (STS) orbiter sidewall-mounted payloads requiresreliable estimates of the sidewall apparent weight and free (i.e. unloaded) vibration during lift-off. The feasibility of analytically predicting these quantities has been investigated through the development and analysis of a finite element model of the STS cargo bay. Analytical predictions of the sidewall apparent weight were compared with apparent weight measurements made on OV-101, and analytical predictions of the sidewall free vibration response during lift-off were compared with flight measurements obtained from STS-3 and STS-4. These analysis suggest that the cargo bay finite element model has potential application for the estimation of force and acceleration control spectra for STS sidewall-mounted payloads.

Hipol, Philip J.↗
NASA NTRS1973

Apollo experience report: Spacecraft heating environment and thermal protection for launch through the atmosphere of the earth

The techniques that were used to define the aerothermodynamic environment of the Apollo spacecraft during the boost phase and to predict the structural temperatures of the spacecraft are discussed. The wind-tunnel and radiant-heating tests that were used to support the analytical predictions are discussed. The analytical predictions are discussed. The accuracy of the boost-phase heating-analysis techniques is shown by comparing the techniques with flight data. The analytical techniques for predicting heating characteristics and structural temperatures of the spacecraft were adequate for predicting the temperatures of the Apollo spacecraft during the boost phase.

Dotts, R. L.↗
NASA NTRS2018

On the Development of Shell Buckling Knockdown Factors for Stiffened Metallic Launch Vehicle Cylinders

From the 1920s to the early 1970s, many shell buckling experiments were conducted in an effort to understand the complex buckling behavior exhibited by thin-walled cylindrical shells, to provide data to correlate with new shell stability theories, and provide design guidelines. Typically, the experiments yielded buckling loads that were substantially lower than the corresponding analytical predictions, which were based on simplified linear bifurcation analyses of geometrically perfect shells with nominal dimensions and idealized boundary conditions. The seminal works by von Kármán and Tsien, by Donnell and Wan, and by Koiter identified small deviations from the idealized geometry of a shell, known as initial geometric imperfections, as a primary source of the discrepancy between corresponding analytical predictions and experimental results. However, the computational tools and capabilities at that time could not perform the nonlinear analyses needed to assess the effects of these imperfections on the buckling behavior of thin-walled shells. Thus, buckling design allowables were determined by establishing lower bounds to test data. Specifically, empirical design factors, that have become known as knockdown factors, were determined and were to be used in conjunction with linear bifurcation analyses for simply supported shells; that is, these empirical factors were used to "knock down" the value of the unconservative simplified analytical prediction. This approach to shell buckling design has proved satisfactory for most design purposes and remains prominent in industry practice, as evidenced by the extensive use of the NASA space vehicle design criteria and recommendations. Unfortunately, the current design guidelines have not been updated since they were first published in the late 1960s and may not be able to take full advantage of modern materials, precision manufacturing processes, and new structural concepts.

Hilburger, Mark W.↗
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
NASA NTRS1999

Noise Certification Predictions for FJX-2-Powered Aircraft Using Analytic Methods

Williams International Co. is currently developing the 700-pound thrust class FJX-2 turbofan engine for the general Aviation Propulsion Program's Turbine Engine Element. As part of the 1996 NASA-Williams cooperative working agreement, NASA agreed to analytically calculate the noise certification levels of the FJX-2-powered V-Jet II test bed aircraft. Although the V-Jet II is a demonstration aircraft that is unlikely to be produced and certified, the noise results presented here may be considered to be representative of the noise levels of small, general aviation jet aircraft that the FJX-2 would power. A single engine variant of the V-Jet II, the V-Jet I concept airplane, is also considered. Reported in this paper are the analytically predicted FJX-2/V-Jet noise levels appropriate for Federal Aviation Regulation certification. Also reported are FJX-2/V-Jet noise levels using noise metrics appropriate for the propeller-driven aircraft that will be its major market competition, as well as a sensitivity analysis of the certification noise levels to major system uncertainties.

Berton, Jeffrey J.↗
NASA NTRS2001

Testing of Densified Liquid Hydrogen Stratification in a Scale Model Propellant Tank

This paper describes a test program that was conducted at NASA to demonstrate the ability to load densified LH2 into a subscale propellant tank. This work was done through a collaborative effort between NASA Glenn Research Center and the Lockheed Martin Michoud Space Systems (LMMSS). The Multilobe tank, which was made from composite materials similar to that to be used on X-33, was formed from two lobes with a center septum. Test results are shown for data that was collected on filling the subscale tank with densified liquid hydrogen (DLH2) propellant that was produced at the NASA Plum Brook Station. Data is compared to analytical predictions. Data collected for this test series agrees well with analytical predictions of the environmental heat leak into the tank and the thermal stratification characteristics of the hydrogen propellant in the tank as it was filled with DLH2.

Jurns, John M.↗
DOE OSTI2019

Application of predictive data analytics to model daily hydrocarbon production using petrophysical, geomechanical, fiber-optic, completions, and surface data: A case study from the Marcellus Shale, North America

Predicting gas production from stimulated unconventional reservoirs has always been a challenge to oil and gas companies because the physics of fluid flow through such reservoirs is not well understood. On the other hand, modeling gas production from shale reservoirs is a complex multi-variate problem that requires proper integration of data from multiple disciplines, such as geology, petrophysics, geomechanics, completions, and surface-based measurements, etc. Here this study demonstrates the application of data-driven machine learning algorithms, integrating geoscientific, distributed acoustic sensing (DAS), distributed temperature sensing (DTS) fiber-optic, completions, flow scanner production log, and surface data to model daily gas production from a 28-stage stimulated horizontal well drilled in the Marcellus Shale of the Appalachian basin in North America. In addition, this study aims to utilize the data from a fiber-optic monitoring system, such as DAS and DTS to evaluate the well performance. We build supervised data-driven machine learning models using Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM) algorithms to understand the well performance and forecast the daily gas production. The study compares the forecasted results, tests the level of accuracy, and addresses different issues with these machine learning algorithms. A spatio-temporal database is constructed and used to perform sensitivity analyses to identify the key drivers for gas production. The results show that RF and ANN algorithms can be used to predict daily gas production with significantly high accuracy; however, RF algorithm is the best predictor in terms of highest accuracy (96%), less computational time and cost. Based on sensitivity analysis, it appears that Poisson's ratio, minimum horizontal stress, DAS, casing pressure, gamma log are the most important parameters to predict gas production.

02 PETROLEUM↗
NASA NTRS1980

Analysis of uncertainties in turbine metal temperature predictions

An analysis was conducted to examine the extent to which various factors influence the accuracy of analytically predicting turbine blade metal temperatures and to determine the uncertainties in these predictions for several accuracies of the influence factors. The advanced turbofan engine gas conditions of 1700 K and 40 atmospheres were considered along with those of a highly instrumented high temperature turbine test rig and a low temperature turbine rig that simulated the engine conditions. The analysis showed that the uncertainty in analytically predicting local blade temperature was as much as 98 K, or 7.6 percent of the metal absolute temperature, with current knowledge of the influence factors. The expected reductions in uncertainties in the influence factors with additional knowledge and tests should reduce the uncertainty in predicting blade metal temperature to 28 K, or 2.1 percent of the metal absolute temperature.

Stepka, F. S.↗
NASA NTRS1999

Aeroelastic Analysis of the NASA/ARMY/MIT Active Twist Rotor

Aeroelastic modeling procedures used in the design of a piezoelectric controllable twist helicopter rotor wind tunnel model are described. Two aeroelastic analysis methods developed for active twist rotor studies, and used in the design of the model blade, are described in this paper. The first procedure uses a simple flap-torsion dynamic representation of the active twist blade, and is intended for rapid and efficient control law and design optimization studies. The second technique employs a commercially available comprehensive rotor analysis package, and is used for more detailed analytical studies. Analytical predictions of hovering flight twist actuation frequency responses are presented for both techniques. Forward flight fixed system nP vibration suppression capabilities of the model active twist rotor system are also presented. Frequency responses predicted using both analytical procedures agree qualitatively for all design cases considered, with best correlation for cases where uniform blade properties are assumed.

Wilkie, W. Keats↗
DOE OSTI2023

Measurement of the angle between jet axes in pp collisions at $ \sqrt{s}$ = 5.02 TeV

This article reports measurements of the angle between differently defined jet axes in pp collisions at $\sqrt{s}$ = 5.02 TeV carried out by the ALICE Collaboration. Charged particles at midrapidity are clustered into jets with resolution parameters R = 0.2 and 0.4. The jet axis, before and after Soft Drop grooming, is compared to the jet axis from the Winner-Takes-All (WTA) recombination scheme. The angle between these axes, ΔR axis , probes a wide phase space of the jet formation and evolution, ranging from the initial high-momentum-transfer scattering to the hadronization process. The ΔR axis observable is presented for 20 < $p^{ch jet}_{T}$ < 100 GeV/c, and compared to predictions from the PYTHIA 8 and Herwig 7 event generators. The distributions can also be calculated analytically with a leading hadronization correction related to the non-perturbative component of the Collins-Soper-Sterman (CSS) evolution kernel. Comparisons to analytical predictions at next-to-leading-logarithmic accuracy with leading hadronization correction implemented from experimental extractions of the CSS kernel in Drell-Yan measurements are presented. The analytical predictions describe the measured data within 20% in the perturbative regime, with surprising agreement in the non-perturbative regime as well. These results are compatible with the universality of the CSS kernel in the context of jet substructure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1982

Empirical testing of an analytical model predicting electrical isolation of photovoltaic models

A major design requirement for photovoltaic modules is that the encapsulation system be capable of withstanding large DC potentials without electrical breakdown. Presented is a simple analytical model which can be used to estimate material thickness to meet this requirement for a candidate encapsulation system or to predict the breakdown voltage of an existing module design. A series of electrical tests to verify the model are described in detail. The results of these verification tests confirmed the utility of the analytical model for preliminary design of photovoltaic modules.

Garcia, A., III↗
NASA NTRS2000

Mechanically Induced g-Jitter from Space Station Rotary Joints

The mission of the International Space Station is to provide a working laboratory in orbit for research in engineering, life sciences, and microgravity. Among the microgravity disciplines that are preparing to utilize this international resource are materials processing, combustion, fluid dynamics, biotechnology, and fundamental physics. The Station promises to enable significant advances in each of these areas by making available a research facility in which gravitational and other accelerations, and their corresponding buoyancy and diffusion effects on various physical processes, are orders of magnitude lower than they are on Earth. In order to fulfill this promise, it is not enough for the Space Station to simply replicate a typical terrestrial scientific laboratory in orbit. Although an orbiting laboratory is free of most of the effects of gravitational acceleration by virtue of its free fall condition, it also produces structural vibration or jitter that can interfere with the processes under study. To ensure the quality of the acceleration environment and enable a successful mission, the Space Station Program has limited potential disturbances in two ways: first, by isolating the most sensitive payloads from the vehicle structure, and second, by quieting major disturbances at their sources. The first area, payload isolation, is implemented inside the pressurized modules at the rack level. Sub-rack level isolators have also been developed. This paper addresses the second area, disturbance source limits, for one of the major sources of mechanical noise on the Space Station: the Solar Alpha Rotary Joints. Due to the potential for large disturbances to the microgravity environment, an initial analytical prediction of rotary joint vibration output was made. Key components were identified and tested to validate the analytical predictions. Based on the component test results, the final vibration output of the joints was verified by a test on each fully assembled flight unit. This paper describes the Space Station microgravity requirements, the rotary joint hardware, and the disturbance producing aspects of joint operation. The test setup, instrumentation, test conditions, and results for the component level and system level measurements are described. An overall forcing function that describes the maximum torque imparted to the Station is created based on the test results, and these disturbances are shown to meet the applicable Space Station microgravity requirements.

Boucher, Robert L.↗
DOE OSTI2019

Convolution Lagrangian perturbation theory for biased tracers beyond general relativity

We compare analytic predictions for real and Fourier space two-point statistics for biased tracers from a variety of Lagrangian perturbation theory approaches against those from state of the art N-body simulations in f(R) Hu-Sawicki and the nDGP braneworld modified gravity theories. We show that the novel physics of gravitational collapse in scalar tensor theories with the chameleon or the Vainshtein screening mechanism can be effectively factored in with bias parameters analytically predicted using the peak-background split formalism when updated to include the environmental sensitivity of modified gravity theories as well as changes to the halo mass function. Here, we demonstrate that convolution Lagrangian perturbation theory (CLPT) and standard perturbation theory (SPT) approaches provide accurate analytic methods to predict the correlation function and power spectra, respectively, for biased tracers in modified gravity models and are able to characterize both the baryon acoustic oscillation, power-law, and small scale regimes needed for upcoming galaxy surveys such as DESI, Euclid, LSST and WFIRST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. Here, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Fluid manifold design for a solar energy storage tank

A design technique for a fluid manifold for use in a solar energy storage tank is given. This analytical treatment generalizes the fluid equations pertinent to manifold design, giving manifold pressures, velocities, and orifice pressure differentials in terms of appropriate fluid and manifold geometry parameters. Experimental results used to corroborate analytical predictions are presented. These data indicate that variations in discharge coefficients due to variations in orifices can cause deviations between analytical predictions and actual performance values.

Humphries, W. R.↗