Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Monte Carlo method”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Neutronics Calculation Advances at Los Alamos: Manhattan Project to Monte Carlo

The history and advances of neutronics calculations at Los Alamos during the Manhattan Project through the present are reviewed. Substantial improvements to neutron diffusion methods and the invention of both the Monte Carlo neutron transport methods in 1947 and deterministic discrete ordinates Sn in 1953 were all made at Los Alamos just after the Manhattan Project. We briefly summarize early simpler and more approximate neutronics methods and then describe the need to better predict neutronics behavior through consideration of theoretical equations, models and algorithms, experimental measurements, and available computing capabilities and their limitations. This paper briefly covers key advances in deterministic methods during the Manhattan Project. These capabilities, coupled with increasing postwar defense needs and the invention of electronic computing with the Electronic Numeric Integrator and Computer, known as ENIAC, and the Mathematical Analyzer Numerical Integrator and Automatic Computer Model, known as MANIAC, led to the creation of Monte Carlo and deterministic discrete ordinates neutronics transport methods. We note the important role that the scientific comradery between the Los Alamos scientists played in the process. This paper briefly covers the early methods, algorithms, computers, and electronic and women pioneers that enabled Monte Carlo to spread to all areas of science. We focus heavily on these early developments and the subsequent creation of the MCNP® code, advances in its associated nuclear data, and its applications to problems of national defense at Los Alamos.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo

We present results and discuss methods for computing the melting temperature of dense molecular hydrogen using a machine learned model trained on quantum Monte Carlo data. In this newly trained model, we emphasize the importance of accurate total energies in the training. We integrate a two phase method for estimating the melting temperature with estimates from the Clausius–Clapeyron relation to provide a more accurate melting curve from the model. We make detailed predictions of the melting temperature, solid and liquid volumes, latent heat, and internal energy from 50 to 180 GPa for both classical hydrogen and quantum hydrogen. At pressures of roughly 173 GPa and 1635 K, we observe molecular dissociation in the liquid phase. Here, we compare with previous simulations and experimental measurements.

08 HYDROGEN↗
NASA NTRSDate not supplied

Evaluation of CFD as a Surrogate for Wind Tunnel Testing - Experimental Uncertainty Quantification

A series of wind tunnel tests is being performed at the Unitary Plan Wind Tunnel at Langley Research Center to assess the validity of using computational fluid dynamics (CFD) as a surrogate for wind tunnel testing. In order to make proper comparisons, uncertainties in CFD results and experimental data must be well understood. The material presented will highlight the methods, assumptions, and elemental inputs used to achieve experimental uncertainty estimates for several variables of interest.The work performed to date has focused on gaining insight into random uncertainty via statistical analysis of repeat data and systematic uncertainty via Monte Carlo propagation analysis. These methods were combined using a second-order Monte Carlo propagation, resulting in a probability box (P-box) for each variable and at all conditions of interest. Figure 1 provides an example of one such P-box, showing the experimental uncertainty in a locally determined dynamic pressure (QC10) for one of many tunnel conditions evaluated during the Flow Survey experiment. This particular condition (Condition 27) has nominal set points of Mach number = 3.85, Reynolds number = 3x10(exp 6) ft(exp -1), total pressure = 5160 psfa, and total temperature = 150°F. The uncertainty in dynamic pressure at this condition is now well defined by this plot. Armed with this experimental data uncertainty, meaningful comparisons can be made with computational results, when their associated uncertainties are also considered.

Heather P Houlden↗
DOE OSTI2023

Accelerating Markov Chain Monte Carlo sampling with diffusion models

Global fits of physics models require efficient methods for exploring high-dimensional and/or multimodal posterior functions. We introduce a novel method for accelerating Markov Chain Monte Carlo (MCMC) sampling by pairing a Metropolis-Hastings algorithm with a diffusion model that can draw global samples with the aim of approximating the posterior. We briefly review diffusion models in the context of image synthesis before providing a streamlined diffusion model tailored towards low-dimensional data arrays. We then present our adapted Metropolis-Hastings algorithm which combines local proposals with global proposals taken from a diffusion model that is regularly trained on the samples produced during the MCMC run. Our approach leads to a significant reduction in the number of likelihood evaluations required to obtain an accurate representation of the Bayesian posterior across several analytic functions, as well as for a physical example based on a global fit of parton distribution functions. Our method is extensible to other MCMC techniques, and we briefly compare our method to similar approaches based on normalising flows. A code implementation can be found at https://github.com/NickHunt-Smith/MCMC-diffusion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Wavefunction matching for solving quantum many-body problems

Ab initio calculations have an essential role in our fundamental understanding of quantum many-body systems across many subfields, from strongly correlated fermions to quantum chemistry and from atomic and molecular systems to nuclear physics. One of the primary challenges is to perform accurate calculations for systems where the interactions may be complicated and difficult for the chosen computational method to handle. Here we address the problem by introducing an approach called wavefunction matching. Wavefunction matching transforms the interaction between particles so that the wavefunctions up to some finite range match that of an easily computable interaction. This allows for calculations of systems that would otherwise be impossible owing to problems such as Monte Carlo sign cancellations. We apply the method to lattice Monte Carlo simulations of light nuclei, medium-mass nuclei, neutron matter and nuclear matter. We use high-fidelity chiral effective field theory interactions and find good agreement with empirical data. These results are accompanied by insights on the nuclear interactions that may help to resolve long-standing challenges in accurately reproducing nuclear binding energies, charge radii and nuclear-matter saturation in ab initio calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Piecewise interaction picture density matrix quantum Monte Carlo

The density matrix quantum Monte Carlo (DMQMC) set of methods stochastically samples the exact N-body density matrix for interacting electrons at finite temperature. We introduce a simple modification to the interaction picture DMQMC (IP-DMQMC) method that overcomes the limitation of only sampling one inverse temperature point at a time, instead allowing for the sampling of a temperature range within a single calculation, thereby reducing the computational cost. At the target inverse temperature, instead of ending the simulation, we incorporate a change of picture away from the interaction picture. The resulting equations of motion have piecewise functions and use the interaction picture in the first phase of a simulation, followed by the application of the Bloch equation once the target inverse temperature is reached. We find that the performance of this method is similar to or better than the DMQMC and IP-DMQMC algorithms in a variety of molecular test systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Methods for System-Level Multidisciplinary Uncertainty Analysis of Low-Boom Flight Vehicles

Current research supporting NASA’s Commercial Supersonic Technology project is focused on the efficient prediction of uncertainty in sonic boom loudness generated by low-boom aircraft concepts. This paper focuses on research incorporating aircraft trim and aerostructural analysis into a multidisciplinary system-level uncertainty analysis. This enables the modeling of a steady-state representation of a point in the uncertainty space, simulating the vehicle as it would be flown. This approach also enables multiple uncertain parameters defining the configuration of the vehicle to be reduced to three: Mach number, altitude, and aircraft weight. To demonstrate this methodology, a case study exploring a conceptual low-boom supersonic aircraft is performed. Two different approaches are used to model the interactions between nearfield pressure signature analysis and sonic boom propagation, and their performance is evaluated in terms of accuracy and computational expense. One method uses a set of local surrogate models to generate a large number of nearfield signatures and perform Monte Carlo analysis. This method is found to produce, at a lower expense, uncertainty metrics that are comparable to the second method, in which uncertainty metrics are computed based on loudness metric values obtained directly from simulated nearfield signatures.

UQ↗
NASA NTRSDate not supplied

Method for System-Level Multidisciplinary Uncertainty Analysis of Low-Boom Flight Vehicles

Current research supporting NASA’s Commercial Supersonic Technology project is focused on the efficient prediction of uncertainty in sonic boom loudness generated by low-boom aircraft concepts. This paper focuses on research incorporating aircraft trim and aerostructural analysis into a multidisciplinary system-level uncertainty analysis. This enables the modeling of a steady-state representation of a point in the uncertainty space, simulating the vehicle as it would be flown. This approach also enables multiple uncertain parameters defining the configuration of the vehicle to be reduced to three: Mach number, altitude, and aircraft weight. To demonstrate this methodology, a case study exploring a conceptual low-boom supersonic aircraft is performed. Two different approaches are used to model the interactions between nearfield pressure signature analysis and sonic boom propagation, and their performance is evaluated in terms of accuracy and computational expense. One method uses a set of local surrogate models to generate a large number of nearfield signatures and perform Monte Carlo analysis. This method is found to produce, at a lower expense, uncertainty metrics that are comparable to the second method, in which uncertainty metrics are computed based on loudness metric values obtained directly from simulated nearfield signatures.

Supersonics↗
NASA NTRS2018

Realistic Covariance Generation for the GPM Spacecraft

We present several different methods for generating realistic predictive covariance, including Monte-Carlo simulations and more direct linear methods which require the addition of process noise. The Monte-Carlo simulation starts with an epoch uncertainty sample basis and propagates each trial to a time of interest in the future. The variance-covariance of the state elements as well as other higher order sample statistics can be readily computed from the propagated sample. While this method preserves the nonlinear effects on the propagated uncertainty, it is computationally intensive as a statistically significant sample size must be considered in the propagation process. Moreover, if the epoch covariance is optimistic, this can result in an underestimation of the prediction error. Another method is to propagate a state sensitivity matrix simultaneously with the satellite state, which allows the epoch covariances to be propagated forward in a linear fashion. This method does not preserve the non-linearity of the satellite state uncertainty but is much less computationally intensive. The propagated state covariance is the scaled to represent the realistic level of GPM state uncertainty via a "e-tuning process." The tuning process generates an inflation factor based on the observed error statistics of the predictive satellite trajectories when compared to the definitive ones. Difference tuning strategies are considered and compared via Goodness-of-Fit method testing for the Gaussian properties of the scaled covariance.

prediction error↗
NASA NTRS2010

Using MathCad to Evaluate Exact Integral Formulations of Spacecraft Orbital Heats for Primitive Surfaces at Any Orientation

With the advent of high speed computing Monte Carlo ray tracing techniques has become the preferred method for evaluating spacecraft orbital heats. Monte Carlo has its greatest advantage where there are many interacting surfaces. However Monte Carlo programs are specialized programs that suffer from some inaccuracy, long calculation times and high purchase cost. A general orbital heating integral is presented here that is accurate, fast and runs on MathCad, a generally available engineering mathematics program. The integral is easy to read, understand and alter. The integral can be applied to unshaded primitive surfaces at any orientation. The method is limited to direct heating calculations. This integral formulation can be used for quick orbit evaluations and spot checking Monte Carlo results.

Pinckney, John↗
DOE OSTI2020

Identification and Quantification of Beta-Emitters in Waste Packages by Segmented Gamma-Scanning - 20114

A commonly applied method for the non-destructive characterization of the radioactive inventory of waste packages (e.g. 200-l drums) is segmented gamma-scanning. The gamma-emitting inventory is identified via their characteristic lines in the measured spectra. The corresponding net peak areas allow for quantification applying appropriate models. The identification and quantification of beta-emitters present in the waste package require further evaluation. While some of them emit gamma-rays, too, which can be used for identification and quantification, pure beta-emitters contribute to the gamma-spectra only by their Bremsstrahlung-spectra. {sup 32}P, {sup 90}Sr and {sup 90}Y are typical representatives of these isotopes. A method is developed for identification and quantification of these pure beta-emitters in radioactive waste packages. Origin of the procedure is the evaluation of the measured gamma-spectrum. After the identification of all lines present in the spectrum (characteristic gamma-lines, single- and double-escape-lines, X-ray-lines, annihilation peak etc.) this information is used together with calibration data of the detection system and further a-priori information like matrix composition, cross-section data from databases etc. to simulate the spectra without the Bremsstrahlung-components by an appropriate method. This may be Monte Carlo simulations or, like in our case, a computer code taking into account mathematical descriptions of the physical effects like photo effect, (multiple) Compton scattering, backscattering etc. and the attenuation effects of the waste matrix. In any case, the resulting spectrum must simulate the real spectrum without its Bremsstrahlung-component as good as possible. Subtracting the simulated from the measured spectrum then results in the Bremsstrahlung-components caused by the beta-emitters. Prior to evaluation, a set of Bremsstrahlung-spectra is calculated and stored for different beta-emitters in different matrices using the Bethe-Heitler equation. This data set can be extended for new conditions, if necessary, and is allocated in a data base for future application. Applying a non-linear optimization using the generalized reduced gradient method for comparing the calculated with the derived Bremsstrahlung-spectra results in the identification of the beta-emitter and a factor, which is proportional to its activity. The proportionality is determined by previous calibration measurements. The application of the method is demonstrated by measurements on a 200-l mock-up drum and a real waste package. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Space Policy Directive-1 has led to NASA partnerships with commercial entities on procurement which includes the development of the Human Landing System (HLS) [1]. With the goal of delivering human crew to the lunar surface by 2024, system uncertainties become an important obstacle to the maturation of multiple new, driving technologies and mission concepts of the HLS program. As unmitigated uncertainties have previously led to failed development programs, these risks and their impacts must be understood and handled to ensure program success [2]. Sources of uncertainty include novel engine designs and configurations, increased reliance on cryogenic fluid management(CFM), and refueling technologies—which propagate as high-level performance metrics such as overall propellant mass and engine performance. Also, the occurrence of operational uncertainties—e.g. launch conditions or need to abort during the mission—can cause cascading effects on the rest of the mission that are difficult to definitively quantify, and are outside the scope of control. These concrete examples and other occurrences can be categorized as either epistemic or aleatory uncertainties.Epistemic uncertainty arises due to a lack of knowledge and can be alleviated with design and program maturation. Aleatory uncertainty is due to the inherent randomness of the system and cannot be directly reduced, unlike epistemic uncertainty. Robust design and probabilistic methods can compensate for aleatory effects. A taxonomy of uncertainty is referred to for this work [3]. In this paper, a probabilistic methodology to handle uncertainties has been demonstrated on a three-element HLS concept [1, 4], which allows tracking of current best estimates of the concept and assessment of concept design robustness against uncertainties. A sample case has been completed for this abstract, and an expansion on the methodology will be included in the final paper. This methodology has two key parts: first, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. This abstract will cover the basics of the approach used, and further details and justifications will be in the final paper.The mission profile associated with this three-element concept (Fig 1) was modeled as a set of mission events that facilitated mass changes, idles, or spacecraft maneuvers. The mission profile scope starts with each element’s NRHO orbit insertion and aggregation and ends at post-sortie rendezvous with Orion. More detail on the mission profile will be in the final paper. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as the physics framework to model the HLS architecture for applying the probabilistic methodology [5, 6]. Specifically, a parametric representation of the lander, ascent, and transfer elements and the mission profile of each element was established, with vehicle and mission parameters available as inputs to allow for a dynamic model. Each vehicle stage was modeled with high-level performance metrics, using Isp and propellant mass fraction (PMF) to remain parametric. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission profile events. These parameters were frozen at their nominal values for the purposes of baselining architecture performance and sizing the vehicle appropriately based on reference documentation [1]. Range estimating—a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities—is traditionally used with Mass Equipment Lists (MELs), but has been adapted with operational parameters as well as vehicle parameters in theDYREQT model to capture mission uncertainty alongside vehicle uncertainty [7, 3]. This method was selected due to its application and insight on a system from a bottom-up perspective, independence from historical rules of thumb, and ability to generate sensitivities based on design decisions and uncertainties. As a sample case for the abstract, the boiloff rates of the vehicle elements and the loiter times during the mission (simulating launch time variations and changing window of opportunities) were used with range estimating to provide preliminary results. To perform the range estimation portion of this methodology (depicted in Fig. 3, further details in final paper), the DYREQT model was sampled using a Design of Experiments (DoE) to efficiently explore the architecture design space with respect to the sample set of uncertainty parameters; 5,000 cases via Latin Hypercube Sampling were computed on the DYREQT architecture model. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Range estimating was applied to the surrogates instead of the actual models, which saves computational expense due to the bulk of cases needed for the Monte Carlo simulation as part of range estimating. Uncertainty parameters were sampled independently from triangular distributions using the DoE ranges as ‘min’ and ‘max’, and the nominal value as ‘most likely’. Based engineering intuition, some uncertainty parameters are correlated—e.g. if the main propellant has a high boil-off rate, the oxidizer should follow suit as both are related to CFM technology.While a Monte Carlo simulation samples all inputs as independent, the results would show model correlations; thus, it is efficient to sample the inputs as correlated. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo. A table for the DoE ranges and probability distribution parameters is shown in Table 1, and more details on Correlated Monte Carlo Simulations will be discussed in the final paper. The model’s resulting DoE showed that multivariate polynomial equations fit via least squares method captured its behavior accurately for the sample case. For the Correlated Monte Carlo Simulation, a positive correlation between fuel and oxidizer boiloff rates was used as a demonstration. 10,000 cases were computed with the surrogates and the launched masses for each vehicle element was collated. The results can be displayed in a probability density function (PDF), showing the impact of the uncertainty parameters chosen. Integrating the PDFs will yield a cumulative distribution function (CDF) that shows the cumulative probability of a given value on the x-axis. For the sample case, the elements’ launch mass margin was calculated and represented in as CDFs, as a demonstrated representation of figures of merit for the HLS concept. For the lander and ascent elements, the NRHO mass insertion limit is 16t; the transfer element has a limit of 30t [1]. It can be seen with Figure 2 that this probabilistic methodology can provide insight into mass margin with respect to the uncertainties being modeled. Currently, the results show that the lander (descent) vehicle element has the most restrictive design space; it is the only element to show a 10% probability of negative margin. Further analysis on the Monte Carlo results will show sensitivities for driving constraints and parameters for architecture feasibility, which can lead to establishing potential mission rules.The combination of range estimating with a parametric architecture model for HLS demonstrated the capability of this probabilistic methodology in a sample case. As the HLS development progresses, this methodology has the potential for keeping current best estimates of architecture performance for awarded concepts due to the flexibility in DYREQT’s modeling framework and its parametric nature. Concept maturation and increased epistemic knowledge can be injected into the model probabilistic modeling, and thus continue to track probability of mission success.

Stephanie Y Zhu↗
NASA NTRS1989

Monte Carlo computations of F-region incoherent radar spectra at high latitudes and the use of a simple method for non-Maxwellian spectral calculations

Monte Carlo simulations of ion velocity distributions in the high-latitude F region have been performed in order to improve the calculation of incoherent radar spectra in the auroral ionosphere. The results confirm that when the ion temperature becomes large due to frictional heating in the presence of collisions with the neutral background constituent, F region spectra evolve from a normal double hump, to a triple hump, to a spectrum with a single maximum. An empirical approach is developed to overcome the inadequacy of the Maxwellian assumption for the case of radar aspect angles of between 30 and 70 deg.

Kikuchi, K.↗
DOE OSTI2022

Unstructured mesh-based multi-physics reactor transient simulation with iMC code

In this study, we present a time-dependent Monte Carlo simulation result about a complex fuel-loaded light water reactor system with thermal-hydraulic feedback using the KAIST iMC code. The intra-pin temperature distribution is calculated based on Monte Carlo-tallied detailed power distribution and the finite element heat transfer method. The temperature effect of fuel, absorber, and coolant are adequately reflected into the dynamic response by the on-the-fly Doppler broadened cross-section correction. For the time-dependent Monte Carlo transport simulation, the predictor-corrector quasi-static Monte Carlo method is used for this study. We tested the coupled analysis framework with a 17-by-17 fuel assembly loaded with the centrally-shielded burnable absorber (CSBA) fuel element. The temperature feedback effect on the material cross-section and the moderator density incurred negative reactivity in response to the external positive reactivity insertion, showing the inherent stability of the reactor system. The pin-wise power and temperature change during the system transient are presented and discussed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Calorimeter with Bayesian unfolding of spectra of high-flux broadband x rays

We report the development of a multipurpose differential x-ray calorimeter with a broad energy bandwidth. The absorber architecture is combined with a Bayesian unfolding algorithm to unfold high energy x-ray spectra generated in high-intensity laser–matter interactions. Particularly, we show how to extract absolute energy spectra and how our unfolding algorithm can reconstruct features not included in the initial guess. The performance of the calorimeter is evaluated via Monte Carlo generated data. The method accuracy to reconstruct electron temperatures from bremsstrahlung is shown to be 5% for electron temperatures from 1 to 50 MeV. We study bremsstrahlung generated in solid target interaction showing an electron temperature of 0.56 ± 0.04 MeV for a 700 μm Ti titanium target and 0.53 ± 0.03 MeV for a 50 μm target. We investigate bremsstrahlung from a target irradiated by laser-wakefield accelerated electrons showing an endpoint energy of 551 ± 5 MeV, inverse Compton generated x rays with a peak energy of 1.1 MeV, and calibrated radioactive sources. The total energy range covered by all these sources ranges from 10 keV to 551 MeV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Moderate-resolution K-band Spectroscopy of Substellar Companion κ Andromedae b

We present moderate-resolution (R ∼ 4000) K-band spectra of the “super-Jupiter,” κ Andromedae b. The data were taken with the OSIRIS integral field spectrograph at Keck Observatory. The spectra reveal resolved molecular lines from H{sub 2}O and CO, and are compared to a custom PHOENIX atmosphere model grid appropriate for young planetary-mass objects. We fit the data using a Markov chain Monte Carlo forward-modeling method. Using a combination of our moderate-resolution spectrum and low-resolution, broadband data from the literature, we derive an effective temperature of T {sub eff} = 1950–2150 K, a surface gravity of logg=3.5--4.5, and a metallicity of [M/H] = −0.2–0.0. These values are consistent with previous estimates from atmospheric modeling and the currently favored young age of the system (<50 Myr). We derive a C/O ratio of 0.70{sub −0.24}{sup +0.09} for the source, broadly consistent with the solar C/O ratio. This, coupled with the slightly subsolar metallicity, implies a composition consistent with that of the host star, and is suggestive of formation by a rapid process. The subsolar metallicity of κ Andromedae b is also consistent with predictions of formation via gravitational instability. Further constraints on formation of the companion will require measurement of the C/O ratio of κ Andromedae A. We also measure the radial velocity of κ Andromedae b for the first time, with a value of −1.4 ± 0.9 km s{sup −1} relative to the host star. We find that the derived radial velocity is consistent with the estimated high eccentricity of κ Andromedae b.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

The physics of irradiation of biological matter by ion beams

In radiobiology, predicting the evolution of irradiated biological matter is nowadays an active field of research to identify DNA lesions or to adapt the radiotherapeutic protocols in radiation oncology. In this context, the numerical methods, based on Monte Carlo track-structure simulations, represent the most suitable and powerful tools for understanding the radiobiological damages induced by ionizing particles. Here, we report the theoretical differential and total cross sections, computed within the quantum mechanical continuum distorted wave-eikonal initial state (CDW-EIS) method, for ion impact on water vapor and DNA nucleobases. These cross sections have been used to build up the input database for the homemade Monte Carlo track-structure TILDA-V. A comparison between the theoretical predictions and the available experimental data is presented. Micro-dosimetry results obtained with TILDA-V are also reported.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS1980

Comparison of quick-look plume heating calculations and Monte Carlo direct simulation

A quick-look method has been developed for estimating convective heat flux due to plume impingement in the transition flow regime on a concave surface surrounding a rocket nozzle. Comparison with the flowfield and heat fluxes computed by the Monte Carlo Direct Simulation Method show the quick-look method to be conservative. Assumptions regarding the nature of the flowfield based on engineering judgment were shown to be essentially valid. Further, the assumption of free molecular impingement was shown to be non-conservative for portions of the surface. Potential refinements of the quick-look method are discussed.

Guernsey, C. S.↗