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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Geostatistical Mapping of Salinity Conditioned on Borehole Logs, Montebello Oil Field, California

We present a geostatistics-based stochastic salinity estimation framework for the Montebello Oil Field that capitalizes on available total dissolved solids (TDS) data from groundwater samples as well as electrical resistivity (ER) data from borehole logging. Data from TDS samples (n = 4924) was coded into an indicator framework based on falling below four selected thresholds (500, 1000, 3000, and 10,000 mg/L). Collocated TDS-ER data from the surrounding groundwater basin were then employed to produce a kernel density estimator to establish conditional probabilities for ER data (n = 8 boreholes) falling below the selected TDS thresholds within the Montebello Oil Field area. Directional variograms were estimated from these indicator coded data, and 500 TDS realizations from conditional indicator simulation were generated for the subsurface region above the Montebello Oil Field reservoir. Simulations were summarized as 3D maps of median TDS, most likely salinity class, and probability for exceeding each of the specified TDS thresholds. Results suggested TDS was below 500 mg/L in most of the study area, with a trend toward higher values (500 to 1000 mg/L) to the southwest; consistent with the average regional groundwater flow direction. Discrete localized zones of TDS greater than 1000 mg/L were observed, with one of these zones in the greater than 10,000 mg/L range; however, these areas were not prevalent. The probabilistic approach used here is adaptable and is readily modified to include additional data and types and can be employed in time-lapse salinity modeling through Bayesian updating.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Validation of Light Water Reactor Ex-Core Calculations with VERA

The Consortium for Advanced Simulation of Light Water Reactors (CASL) Virtual Environment for Reactor Applications (VERA) is a reactor simulation software. It offers unique capabilities by combining high-fidelity in-core radiation transport with temperature feedback by using MPACT (a deterministic neutron transport code) and COBRA-TF (a thermal-hydraulic code) with follow-on, fixed-source transport calculations using the Shift Monte Carlo code to calculate ex-core quantities of interest. In these coupled calculations, MPACT provides Shift with the fission source for follow-on ex-core calculations. These ex-core simulations can be set up to calculate detector responses, as well as the flux and fluence in ex-core regions of interest, such as the reactor pressure vessel, nozzle, and irradiated capsules. A Watts Bar Nuclear Plant Unit 1 (WBN1) ex-core model was developed, as described in this paper, and this model was used to perform coupon calculations. The results for the coupon flux calculations show close agreement with the reference values for cycle 1 produced by the two-dimensional Discrete Ordinates Transport (DORT) code and presented in a BWXT Services Inc. report. However, differences in the results (10%) seen in cycles 2 and 3 and the reasons for these differences are discussed in this paper. The VERA WBN1 model was also used to perform a vessel fluence calculation for cycle 1. Additionally, a collaboration between CASL and Duke Energy led to the first code-to-code validation of VERA for reactor ex-core applications that used a model for the Shearon Harris reactor. Results from this collaboration show excellent agreement between VERA and the Monte Carlo N-Particle Transport Code for the detector response calculations. The work performed under this collaboration is also detailed in this paper.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2008

High Speed Solution of Spacecraft Trajectory Problems Using Taylor Series Integration

Taylor series integration is implemented in a spacecraft trajectory analysis code-the Spacecraft N-body Analysis Program (SNAP) - and compared with the code s existing eighth-order Runge-Kutta Fehlberg time integration scheme. Nine trajectory problems, including near Earth, lunar, Mars and Europa missions, are analyzed. Head-to-head comparison at five different error tolerances shows that, on average, Taylor series is faster than Runge-Kutta Fehlberg by a factor of 15.8. Results further show that Taylor series has superior convergence properties. Taylor series integration proves that it can provide rapid, highly accurate solutions to spacecraft trajectory problems.

Scott, James R.↗
NASA NTRS1997

Pentium Pro inside: A treecode at 430 Gigaflops on ASCI Red - 1

As an entry for the 1997 Gordon Bell performance prize, we present results from two methods of solving the gravitational N-body problem on the Intel Teraflops system at Sandia National Laboratory (ASCI Red). The first method, an O(N2) algorithm, obtained 635 Gigaflops for a 1 million particle problem on 6800 Pentium Pro processors. The second solution method, a tree-code which scales as O(N log N), sustained 170 Gigaflops over a continuous 9.4 hour period on 4096 processors, integrating the motion of 322 million mutually interacting particles in a cosmology simulation, while saving over 100 Gigabytes of raw data. Additionally, the tree-code sustained 430 Gigaflops on 6800 processors for the first 5 time-steps of that simulation. This tree-code solution is approximately 105 times more efficient than the O(N2) algorithm for this problem. As an entry for the 1997 Gordon Bell price/performance prize, we present two calculations from the disciplines of astrophysics and fluid dynamics. The simulations were performed on two 16 Pentium Pro processor Beowulf-class computers (Loki and Hyglac) constructed entirely from commodity personal computer technology, at a cost of roughly $50k each in September, 1996. The price of an equivalent system in August 1997 is less than $30. At Los Alamos, Loki performed a gravitational tree-code N-body simulation of galaxy formation using 9.75 million particles, which sustained an average of 879 Mflops over a ten day period, and produced roughly 10 Gbytes of raw data.

Warren, M. S.↗
DOE OSTI2025

Multi-Objective Optimization of Uranium Target Assembly–3: A Comparison of Genetic and Traditional Methods

Commonly produced as a byproduct of uranium fission, 99 Mo is a key medical isotope that is in high demand in the United States. An international goal is to switch from medical isotope production technologies that require highly enriched uranium to medical isotope production technologies that require only low-enriched uranium. Niowave Inc. is contributing to this goal by developing an accelerator-driven subcritical assembly called the Uranium Target Assembly (UTA). This work compares the performance of Dakota’s Multi-Objective Genetic Algorithm (MOGA) against traditional sensitivity analysis in the neutronic optimization of the UTA-3 system. The design objectives are k-eigenvalue (k eff ) and natural uranium fission power, which are directly correlated with the amount of 99 Mo produced. Dakota:MOGA did not perform as well as human engineering ingenuity in optimization studies with high numbers of input parameters, such as fuel rod type selection and fuel rod placement. However, Dakota:MOGA did outperform traditional sensitivity analysis in optimization studies with fewer than 20 parameters and revealed the degree to which each parameter influences the optimal design space for k eff and natural uranium fission power (to a lesser extent). As the design model became more complex in the final stage of design, the computational resources required to calculate the design objective values in the Monte Carlo N-Particle transport code from selected input parameter combinations limited Dakota:MOGA’s performance, and, unfortunately, human intervention was required to discern the optimal design space. In conclusion, future work will attempt to reduce computational resource constraints by incorporating areduced-order neutronics model into the optimization cycle.

Accelerator-driven systems↗
DOE OSTI2025

Development of the first relativistic electron loss probe with pitch and energy resolution in the DIII-D tokamak

A relativistic electron probe has been developed in the DIII-D tokamak, capable of simultaneously resolving pitch angles and energies of runaway electrons (REs) for the first time. Due to the relativistic speeds of REs, their gyro-orbit size becomes comparable to those of fast deuterium with energies in the tens of keV range. This allows for the measurement of RE strike images on a phosphor plane, with their orbits being deflected by the Lorentz force as they pass through a pinhole aperture. The strike positions correspond to the energies and pitch of the incident REs. Monte Carlo N-Particle Transport Code has shown that an ultra-thin phosphor coating significantly reduces the energy deposition from γ-rays, while allowing a much greater deposition from REs, minimizing the background noise. Finally, the novel system, developed for the DIII-D tokamak, is expected to provide unprecedented insights into the phase-space dynamics of REs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

ALPHANSO: Open-source modeling of (α, n) neutron source terms

Applications ranging from nuclear safeguards to dark matter detection require accurate predictions of neutron yields and energy spectra produced by (α, n) reactions. Legacy tools like SOURCES-4C remain widely used despite significant limitations, including outdated nuclear data, missing target nuclides, and restricted accessibility. Here, we present ALPHANSO, an open-source Python package for calculating (α, n) neutron source terms. ALPHANSO incorporates modern nuclear data libraries and formats covering all naturally occurring target nuclides and provides a transparent, modular framework for updating or extending the data as new evaluations are released. Comparison with an updated version of SOURCES-4A, NeuCBOT, and experimental measurements across a range of elements and materials shows that ALPHANSO reproduces neutron yields and spectra in good agreement with experimental data and state-of-the-art (α, n) calculations. These results demonstrate that ALPHANSO is a reliable, accessible, and modern alternative to legacy (α, n) source term codes such as SOURCES-4C. Its open-source design and modular data handling make it readily extensible to future evaluated nuclear data and low-background applications.

(α, n) reactions↗
DOE OSTI2022

Polyethylene density uncertainty in the simulation of neutron detectors

Simulations with the Monte Carlo N-Particle® (MCNP®) code are frequently used to study neutron coincidence counters for international safeguards. Reducing sources of uncertainty enables more accurate simulations, which reduces the need for expensive or impossible measurements. Accurately specifying the density of polyethylene is crucial for accurate simulations. The density of polyethylene ranges from 0.88 to 0.97 g/cm 3 and high-density polyethylene (HDPE) ranges from about 0.944 to 0.965 g/cm 3 . The density of a specific detector should be measured exactly for the best simulations. Here, the density of the blue/white Active Well Coincidence Counter (AWCC) bulk polyethylene was measured to be 0.9594 +- 0.0013 g/cm 3 at 1-sigma confidence. The density was calculated from repeated measurements of the various polyethylene dimensions for volume, and weighing the polyethylene for the mass. The AWCC neutron detection efficiency was simulated for each polyethylene density. The relative range of efficiencies (3σ) was 3.4% for unspecified polyethylene, 0.9% for HDPE, and 0.3% for the measured density. The impact of modeling small features such as screws was also studied and was found to be a negligible 0.2%. Measuring polyethylene density can reduce its role in uncertainty from one of the largest to one of the smallest contributors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Comparison of MCNP thermal scattering laws to inelastic neutron scattering data

Neutron transport computations employ thermal scattering laws (TSLs) to account for the influence of material structure and collective excitations at low energy (≲ 10 eV). Recent work has been undertaken to improve TSL accuracy and expand the number of available materials in the Evaluated Nuclear Data File (ENDF). However, comparison of TSL to measured data is often sparse. In this work, we present a method to evaluate TSL accuracy in the Monte Carlo N-Particle (MCNP) code by simulating inelastic neutron scattering (INS) measurement and comparing to data. The method is briefly described with example application to the recently released ENDF-VIII TSL library for polyethylene as well as a cryogenic PE TSL we are separately developing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Monte Carlo N-Particle forward modeling for density reconstruction of double shell capsule radiographs

In the Double Shell Inertial Confinement Fusion concept, characterizing the shape asymmetry of imploding metal shells is vital for understanding energy-efficient compression and radiative losses of the thermonuclear fuel. The Monte Carlo N-Particle MCNP® code forward models radiography of Double Shell capsule implosions using the Advanced Radiographic Capability at the National Ignition Facility. A procedure is developed for using MCNP to reconstruct density profiles from the radiograph image intensity. For a given Double Shell imploding target geometry, MCNP radiographs predict image contrast, which can help guide experimental design. Finally, in future work, the calculated MCNP synthetic radiographs will be compared with experimental radiographs to determine the radial and azimuthal density profiles of the Double Shell capsules.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Benchmark between antenna code TOPICA, RAPLICASOL and Petra-M for the ICRH ITER antenna

ITER will be equipped with three plasma heating systems: neutral beam (NB), electron cyclotron (EC), and ion cy-clotron resonance heating (ICRH). The latter consists of two identical ICRH antennas to deliver 20 MW to the plasma (baseline, upgradable to 40 MW). ICRH will play a crucial role in the ignition and sustainment of burning plasmas in ITER. A high fidelity and robust modeling effort to understand the interaction of the IC waves with the scrape-off-layer (SOL) plasma is a very important aspect. Among the main important research topics, we have the assessment of the antenna loading for different plasma scenarios, the role of the lower hybrid resonance in front of the antenna and how to include it in our models, and the RF sheath boundary conditions to evaluate the antenna impurity generation. In this work, we tackle the first of these by reporting on ICRF simulations employing the Petra-M code, which is an electromagnetic simulation tool for modeling RF wave propagation based on MFEM [http://mfem.org] for the ITER ICRH antenna. Moreover, a benchmark between the well tested antenna codes TOPICA, RAPLI-CASOL, which is based on COMSOL [www.comsol.com], and the Petra-M code is also presented. In conclusion, S- and Z-matrices and wave electric field are compared showing an excellent agreement among these codes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations

Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high- delity simulations of radiation transport within an urban domain. The model is constructed to employ a source location (x, y, z) as input and return the estimated count rate for a set of speci ed detector locations. Because MCNP simulations are computationally expensive, we develop e cient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes (GP) and neural networks (NN) that we train and verify using the MCNP simulations. The trained surrogate models provide an e cient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo (MCMC) algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Activation Analysis in Preparation for a Tungsten Irradiation Experiment at LANSCE

To organize the safe handling of activated material, knowing the residual dose rates is crucial. Here, in this work, we present the pre-experiment activation analysis for an experiment in which tungsten blocks are irradiated by 800-MeV protons. In this analysis, we use the Monte Carlo N-Particle (MCNP) code for radiation transport, Attila4MC for unstructured mesh generation, and Activation in Accelerator Radiation Environments (AARE), including CINDER2008, for activation analysis. If the tungsten blocks must be removed within a day after the experiment, then exposure to personnel entering the room must be reduced. One exposure-reduction strategy is to add carbon steel shielding around the tungsten blocks, efficiently reducing the dose from the activated tungsten. However, the shielding becomes activated itself during irradiation: 56Mn is the dominant contributor for short decay times. The actual schedule at the time of the experiment allowed sufficient cool-off time for the tungsten in the room so that additional shielding was not necessary. A less rigorous comparison of the calculated values with the post-experiment measurements showed reasonable agreement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

CADIS and FW-CADIS Variance Reduction in Gamma Transport for Predicting Prompt Forensics Signatures

The goal of prompt nuclear forensics is to determine the characteristics of a nuclear detonation based on the signatures available almost immediately after the explosion. An important characteristic is the reaction time history (RTH), a measure of the device’s rate of neutron multiplication. The RTH can be estimated by observation of the gamma radiation emitted from the detonation, which can be detected directly or observed indirectly as Teller light. Gamma transport simulations used to predict these radiation fields are often modeled stochastically using the Monte Carlo N-Particle (MCNP) code, which can be a computationally demanding task due to the number of particle histories needed to achieve statistical convergence. In an attempt to improve the efficiency of these calculations, we evaluate two variance reduction techniques: Consistent Adjoint-Driven Importance Sampling (CADIS) and Forward-Weighted Consistent Adjoint-Driven Importance Sampling (FW-CADIS). These methods use a deterministically calculated adjoint flux to create weight windows and source biasing that guide MCNP sampling. We study the utility of CADIS and FW-CADIS for their use in MCNP gamma transport for nuclear forensics prediction simulations. Furthermore, the results demonstrate that both CADIS and FW-CADIS improve the accuracy for forensics-focused simulations, with CADIS being most beneficial in direct detection and FW-CADIS being ideal for computing a global Teller light source.

CADIS↗
DOE OSTI2024

Montepy

Montepy is an object-oriented Python interface for MCNP input files. It excels at reading, editing, creating, and writing MCNP input files. The Monte Carlo N Particle (MCNP) code is a Monte Carlo transport solver for simulating many different types of radiation problems and is developed by Los Alamos National Laboratory. Montepy creates an object-oriented interface for the MCNP input file to allow for efficient automated modifications to large, complex simulations.

Gale, Micah [Idaho National Laboratory (INL), Idah↗
DOE OSTI2025

hiposa v0.1.0

Fast construction of hierarchical N-dimensional experimental design. The software allows construction of sampling strategies that are self-avoiding, tunable in resolution in N dimensions. This code is a drop-in replacement for seeding optimization runs often performed using uniform random sampling. The hierarchical approach allows for a rapid, top down method for construction the most effective design that drives towards a global optimum.

Zwart, PetrusH [Lawrence Berkeley National Laborat↗
DOE OSTI2021

Dose Coefficient Calculation for Use in Dosimetry Assessment of a Fission-Based Weapon

In the event of a fission-based weapon or improvised nuclear device (IND) detonation, dose coefficients can be harnessed to provide dose assessments for defense, emergency preparedness, and consequence management, as well as to prospectively inform the assessment of radiation biomarkers and development of medical prophylaxis countermeasures for defense and homeland security stakeholders and decision-makers. Although dose coefficients have previously been calculated for this group, they would apply specifically to the studied population, the 1945 Japanese cohort, after which their anthropomorphic computational phantoms were modeled. For this reason, applications to other populations may be limited, and instead, an assessment of a more standardized population is desired. We employed a series of computational human phantoms representing international reference individuals: UF/NCI voxel phantom series containing newborn, 1-, 5-, 10-, 15-, and 35-year-old males and females. Irradiation of the phantoms was simulated using the Monte Carlo N-Particle transport code to determine organ dose coefficients under four idealized irradiation geometries at three distances from the detonation hypocenter at Hiroshima and Nagasaki using DS02 free-in-air prompt neutron and photon fluence spectra. Through these simulations, age-specific dose coefficients were determined for individual organs. Various articulated PIMAL stylized phantoms were simulated as well to estimate the effect of body posture on dose coefficients and determine the effect of posture on dosimetric estimation and reconstruction. Results additionally demonstrate that 137 Cs and the Watt fission spectra are not ideal general surrogate sources for fission weapons, which may be considered for experimental testing of medical countermeasures. Supplementary data provided tabulates the compilation of organ dose-rate coefficients in this study.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗