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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Investigating Cryogenic Pump Lifetime and Exploring Alternative Designs

Cryogenic pumps are necessary for the operation of several neutrino experiments at Fermilab where some of the primary components of the detectors are liquid argon and liquid nitrogen. The pumps currently in use are centrifugal pumps with a small vortex impeller, manufactured by Barber Nichols. This pump has an average lifetime of 8000 hours. Such a short lifetime means that each pump requires maintenance multiple times a year, which is not optimal. The goal of this project was to determine what aspects of the current pump design could be modified to improve the lifetime of the pump such that it would only require maintenance once a year. In this poster I present on the different areas of design improvement that were identified.

Baakkonen, Katie↗
DOE OSTI2025

Selective capture and recovery of uranium oxide colloids from aqueous soil suspensions using high gradient magnetic filtration

High Gradient Magnetic Filtration (HGMF) is a promising method for the selective capture and recovery of uranium oxide from surface soils. To date, however, magnetic filtration of uranium oxide has only been demonstrated at a proof-of-principle scale using relatively small filters (<5 cm 3 ) at low flowrates (<60 mL/min). Here, to explore the efficacy of magnetic filtration of uranium oxide at a larger scale, a newly designed HGMF apparatus that is more than an order of magnitude larger than our earlier filters (106 cm 3 ) was designed, fabricated, and tested at relatively high flowrates. Filtration experiments were performed using aqueous uranium oxide particle suspensions with Arizona Road Dust (ARD) as a soil simulant. At a flowrate of 125 mL/min, the apparatus’ uranium capture rate was exceptionally high (96 %), but selectivity was poor due to the high rate of capture for diamagnetic soil constituents (e.g., 77 % for silicon). All particles were captured at a lower rate when the flowrate was increased to 250 mL/min, but uranium selectivity was significantly increased due to the more substantial reduction in diamagnetic particle capture (i.e., capture rate of 77 % and 15 % for uranium and silicon, respectively). When backwashing the apparatus at the same flowrates used during filtration experiments, the rate of uranium recovery tended to be fairly low. Nevertheless, higher flowrates (1 L/min) and sonication were both shown to be highly effective methods of increasing uranium recovery. Magnetic field simulations were also performed to investigate potential optimizations to the design of the apparatus. These simulations showed that the intensity of the applied magnetic field could be increased by increasing the thickness of the steel magnetic housing. Additionally, stochastic trajectory simulations were performed to investigate the potential mechanisms of particle capture.

HGMF↗
DOE OSTI2023

A data compression and optimal galaxy weights scheme for Dark Energy Spectroscopic Instrument and weak lensing data sets

Combining different observational probes, such as galaxy clustering and weak lensing, is a promising technique for unveiling the physics of the Universe with upcoming dark energy experiments. The galaxy redshift sample from the Dark Energy Spectroscopic Instrument (DESI) will have a significant overlap with major ongoing imaging surveys specifically designed for weak lensing measurements: the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper Suprime-Cam (HSC) survey. In this work, we analyse simulated redshift and lensing catalogues to establish a new strategy for combining high-quality cosmological imaging and spectroscopic data, in view of the first-year data assembly analysis of DESI. In a test case fitting for a reduced parameter set, we employ an optimal data compression scheme able to identify those aspects of the data that are most sensitive to cosmological information and amplify them with respect to other aspects of the data. We find this optimal compression approach is able to preserve all the information related to the growth of structures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Ground heat exchanger design tool with rowwise placement of boreholes

Simulation-based design tools have been used since the late 1980s for designing ground heat exchangers (GHE) used with ground source heat pump (GSHP) systems. The ground heat exchanger simulations used in these tools rely on thermal response functions known as g-functions. Because of the significant computational burden in computing g-functions for even a single configuration, the design tools have relied on libraries of pre-computed g-functions. These g-functions were available for standard configuration shapes, such as lines, rectangles, open rectangles, L-shapes, and U-shapes. Standard shapes are often sub-optimal. For any building on a site, the available land may preclude use of a standard shape. For large GSHP systems with significantly imbalanced annual heat rejection and extraction loads, large rectangular fields may experience significant heat build-up (or heat draw-down) in the interior of the field. This paper describes a new ground heat exchanger design tool capable of automatically selecting and sizing both standard and irregular configurations. The focus of this paper is a method for creating, selecting and sizing irregular configurations where the available land area and "no-go" zones are described as irregular polygons.

Spitler, Jeffery↗
NASA NTRS2011

TAMU: Blueprint for A New Space Mission Operations System Paradigm

The Transferable, Adaptable, Modular and Upgradeable (TAMU) Flight Production Process (FPP) is a System of System (SOS) framework which cuts across multiple organizations and their associated facilities, that are, in the most general case, in geographically disperse locations, to develop the architecture and associated workflow processes of products for a broad range of flight projects. Further, TAMU FPP provides for the automatic execution and re-planning of the workflow processes as they become operational. This paper provides the blueprint for the TAMU FPP paradigm. This blueprint presents a complete, coherent technique, process and tool set that results in an infrastructure that can be used for full lifecycle design and decision making during the flight production process. Based on the many years of experience with the Space Shuttle Program (SSP) and the International Space Station (ISS), the currently cancelled Constellation Program which aimed on returning humans to the moon as a starting point, has been building a modern model-based Systems Engineering infrastructure to Re-engineer the FPP. This infrastructure uses a structured modeling and architecture development approach to optimize the system design thereby reducing the sustaining costs and increasing system efficiency, reliability, robustness and maintainability metrics. With the advent of the new vision for human space exploration, it is now necessary to further generalize this framework to take into consideration a broad range of missions and the participation of multiple organizations outside of the MOD; hence the Transferable, Adaptable, Modular and Upgradeable (TAMU) concept.

Ruszkowski, James T.↗
DOE OSTI2016

Gas Gun Model and Comparison to Experimental Performance of Pipe Guns Operating with Light Propellant Gases and Large Cryogenic Pellets

Injection of multiple large (~10 to 30 mm diameter) shattered pellets into ITER plasmas is presently part of the scheme planned to mitigate the deleterious effects of disruptions on the vessel components. To help in the design and optimize performance of the pellet injectors for this application, a model referred to as “the gas gun simulator” has been developed and benchmarked against experimental data. The computer code simulator is a Java program that models the gas-dynamics characteristics of a single-stage gas gun. Following a stepwise approach, the code utilizes a variety of input parameters to incrementally simulate and analyze the dynamics of the gun as the projectile is launched down the barrel. Using input data, the model can calculate gun performance based on physical characteristics, such as propellant-gas and fast-valve properties, barrel geometry, and pellet mass. Although the model is fundamentally generic, the present version is configured to accommodate cryogenic pellets composed of H2, D2, Ne, Ar, and mixtures of them and light propellant gases (H2, D2, and He). The pellets are solidified in situ in pipe guns that consist of stainless steel tubes and fast-acting valves that provide the propellant gas for pellet acceleration (to speeds ~200 to 700 m/s). The pellet speed is the key parameter in determining the response time of a shattered pellet system to a plasma disruption event. The calculated speeds from the code simulations of experiments were typically in excellent agreement with the measured values. With the gas gun simulator validated for many test shots and over a wide range of physical and operating parameters, it is a valuable tool for optimization of the injector design, including the fast valve design (orifice size and volume) for any operating pressure (~40 bar expected for the ITER application) and barrel length for any pellet size (mass, diameter, and length). Key design parameters and proposed values for the pellet injectors for the ITER disruption mitigation systems are discussed.

Combs, S. K.↗
DOE OSTI2019

Recent advances in machine learning towards multiscale soft materials design

The multiscale design of soft materials requires an ensemble of computational techniques spanning quantum-chemistry to molecular dynamics to continuum modeling. The recent emergence of machine-learning (ML) and modern optimization algorithms has accelerated material property prediction, as well as stimulated the development of hybrid ML/molecular modeling methodologies capable of providing physical insights unobtainable from purely physics-based modeling and intuition. Such hybrid techniques also have important ramifications for the ML-enhanced interpretation of results from simulations and experiments alike. Leveraging ML techniques for the design of chemical or morphological structures based on a target property or functionality represents an exciting goal for the general area of soft materials, including polymers, liquid crystals, colloids, or biomolecules, to name a few representative classes of systems. Furthermore, we provide a perspective on recent work using ML techniques of relevance for the multiscale design of soft materials and outline potential future directions of interest to the soft materials community.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Traditional trial-and-error methods for materials discovery are inefficient to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multi-step chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a flexible approach that allows tailoring the machine learning decision-making to suit the practical constraints in individual high-throughput experimental platforms, followed by performing resource-efficient yield optimization using available open-source Python libraries.

Tamura, Clara [Univ. of Washington, Seattle, WA (U↗
DOE OSTI2025

Reinforcement Learning for In-Spill Optimization of the Mu2e Resonant Extraction: Compensating Non-Stationarity

We present design considerations and challenges for the fast machine learning component of a third-order resonant beam extraction regulation system being commissioned to deliver steady beam rates to the mu2e experiment at Fermilab. Dedicated quadrupoles drive the tune toward the 29/3 resonance each spill, extracting beam at kV multiwire septa. The overall Spill Regulation System consists of (1) a “slow” process using ~100-spill averages to adjust the base quad ramp infrequently, (2) a feedforward harmonic content compensator, and (3) the “fast” ML agent reacting during each ongoing spill with on-the-fly additive corrections to the sum of (1) and (2). We have demonstrated improved beam-rate steadying for a fast ML agent compared to a PID controller using a quasi-physical spill simulation, and demonstrated distillation of that simulation into a predictive surrogate model. Current work includes a data-and-training pipeline to generate data-aware surrogates with real-world dynamics, even as the dynamics shift unpredictably. The surrogates are to act as RL environments against which to train our fast ML control agents before deploying them on FPGA in the live system. Further current efforts focus on modeling and controlling beam loss around the storage ring, understanding additional available hardware inputs to the model, and the interplay of these with beam-steadying performance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Design of a Critical Experiment to Validate Yttrium Hydride at Varying Temperatures

As microreactors become an emerging nuclear energy source for remote applications, designers are looking at the use of high temperature moderator material to reduce the fuel mass in the core by up to a factor of two. The microreactor community is in constant search of the best moderator material to retain hydrogen at high temperatures while optimizing neutronic performance. Recent simulation work has shown that materials which have a high and low Z component are optimal for this application. Material properties studies have further shown that yttrium hydride maintains hydrogen and stays as a solid until well over the needed temperatures, and thus retains hydrogen. A critical experiment is planned at the National Criticality Experiments Research Center (NCERC) to test the reactivity behavior of yttrium hydride at standard and elevated temperatures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2018

Transition Delay via Vortex Generators in a Hypersonic Boundary Layer at Flight Conditions

The potential of realizable, stationary streaks undergoing non-modal growth to stabilize a hypersonic boundary-layer flow and, subsequently, delay the laminar-turbulent transition onset, is studied via numerical computations. The geometry and flow conditions are selected to match a relevant trajectory location from the ascent phase of the HIFiRE-1 flight experiment, namely, a 7-degree half-angle cone with 2.5 mm nose radius, freestream Mach number of 5.30, freestream unit Reynolds number equal to 13.42 x 10(exp 6)/m, and wall-to-adiabatic temperature ratio of approximately 0.35 over most of the test article. This paper investigates flow modifications induced by wall-mounted vortex generators (VGs), followed by an analysis of the modal instability of the perturbed, streaky boundary-layer flow. Results are presented both for a single array of VGs that was designed on the basis of optimal growth theory and for a VG configuration involving two separate arrays with opposite orientations that ware designed to provide staged control of flow instabilities while simultaneously reducing the amplification of streak instabilities resulting from the control devices. Earlier research had shown that the onset of transition during the HIFiRE-1 flight experiment, which did not include any control devices, correlated with an amplification factor of N = 14.7 for the planar Mack modes. If one assumes that the transition N -factor is not affected by the introduction of the VGs, then the control configurations based on a single array of VGs and two separate arrays would result in a transition delay of 17% and 40%, respectively. These findings suggest a passive flow control s to induce streaks that would delay transition in hypersonic boundary dominated by Mack-mode instabilities.

Incompressible boundary layer↗
DOE OSTI2023

Understanding the fusion yield dependencies in OMEGA DT-layered implosion experiments using a physics-based statistical mapping model

Improving the performance of inertial confinement fusion implosions requires physics models that can accurately predict the response to changes in the experimental inputs. Good predictive capability has been demonstrated for the fusion yield using a statistical mapping of simulated outcomes to experimental data [Gopalaswamy et al., Nature 565(771), 581–586 (2019)]. In this paper, a physics-based statistical mapping approach is used to extract and quantify all the major sources of degradation of fusion yield for direct-drive implosions on the OMEGA laser. Here, the yield is found to be dependent on the age of the deuterium tritium fill, the ℓ = 1 asymmetry in the implosion core, the laser beam-to-target size ratio, and parameters related to the hydrodynamic stability. A controlled set of experiments were carried out where only the target fill age was varied while keeping all other parameters constant. The measurements were found to be in excellent agreement with the fill age dependency inferred using the mapping model. In addition, a new implosion design was created, guided by the statistical mapping model by optimizing the trade-offs between increased laser energy coupling at larger target size and the degradations caused by the laser beam-to-target size ratio and hydrodynamic instabilities. When experimentally performed, an increased fusion yield was demonstrated in targets with larger diameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Autonomous Output‐Oriented Aerosol Jet Printing Enabled by Hybrid Machine Learning

Additive manufacturing (AM) is rapidly revolutionizing modern manufacturing with recent progress in advanced printing methods and improved properties of printed materials. However, traditional AM methods are limited by their input‐oriented nature, which demands tedious trial‐and‐error tuning of printing parameters to achieve desired output properties. Here, in this work, an output‐oriented artificial intelligence‐integrated AM (AIAM) method is reported that enables an user to specify desired output properties while the printer autonomously discovers the optimal input printing parameters by integrating hybrid machine learning models and in situ measurements. Based on a predictive mapping between the input printing parameters and the output properties of interests established with <20 experiments designed by active learning, inverse design tasks are performed to intelligently generate the printing parameter settings that lead to desired outcomes using reinforcement learning. This method is demonstrated by autonomous aerosol jet printing (AJP) of conductive polymer films and achieving user‐defined electrical resistances with an ultralow error of 3.7%. The AIAM method, with its output‐oriented nature, holds the potential to significantly improve the autonomy, predictability, efficiency, and accessibility of the AM processes, which will unlock new possibilities in the autonomous and intelligent printing of a broad range of functional materials and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2014

PuReMD-GPU: A reactive molecular dynamics simulation package for GPUs

We present an efficient and highly accurate GP-GPU implementation of our community code, PuReMD, for reactive molecular dynamics simulations using the ReaxFF force field. PuReMD and its incorporation into LAMMPS (Reax/C) is used by a large number of research groups worldwide for simulating diverse systems ranging from biomembranes to explosives (RDX) at atomistic level of detail. The sub-femtosecond time-steps associated with ReaxFF strongly motivate significant improvements to per-timestep simulation time through effective use of GPUs. This paper presents, in detail, the design and implementation of PuReMD-GPU, which enables ReaxFF simulations on GPUs, as well as various performance optimization techniques we developed to obtain high performance on state-of-the-art hardware. Comprehensive experiments on model systems (bulk water and amorphous silica) are presented to quantify the performance improvements achieved by PuReMD-GPU and to verify its accuracy. In particular, our experiments show up to 16× improvement in runtime compared to our highly optimized CPU-only single-core ReaxFF implementation. PuReMD-GPU is a unique production code, and is currently available on request from the authors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Report on Characterization and Processing of MDD Powder

Uranium oxide powers most civilian nuclear reactors worldwide. A large infrastructure based on a well-established technology is in place to support this strategic component of the energy industry. Because uranium oxide fuels are used so ubiquitously, it is expected that ceramic fuel pellets will continue to be used. A better understanding of the properties of the starting materials, the processing methods used to fabricate fuel pellets and how the properties of pellets change in service, are important aspects being studied via experiments, models and simulations. A close integration of these approaches is essential if we are to find new ways to optimize both the fuel composition and structure for the purpose of improving performance, e.g., designed microstructures, reducing process losses, e.g. by net shape sintering, and enabling reprocessing of used fuel; e.g., incorporation of transuranics. Ceramic oxide fuel pellets are typically cold pressed and sintered from a powder feedstock. Consequently, a complete understanding of pellet fabrication requires a thorough knowledge of the process from powder synthesis through quality control and acceptance. In this study, uranium oxide powder synthesized by Modified Direct Denitration (MDD) is evaluated. Use of powders synthesized by novel, simplified approaches such as MDD are both a challenge and an opportunity. The MDD synthesis process offers an opportunity to simplify the fabrication process potentially reducing process losses. MDD also provides a simple path to incorporate transuranics from used fuel reprocessing with minimal handling. The challenge is to demonstrate and ultimately prove the reliability and reproducibility of simplified processing with the performance of fuel pellets experiencing in-pile service. This report summarizes a processing study of uranium oxide pellets made from MDD uranium oxide.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. These include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Scaling Study of Microbially-Enhanced Methane Production From Coal (Mecbm): Optimizing Nutrient Delivery For Maximized Methane Production

Microbially-enhanced coalbed methane (MECBM) recovery is a novel method to stimulate gas production in CBM reservoirs. We first develop suitable nutrient recipes for native coals from sites each in the Illinois Basin and in the San Juan Basin. The optimal concentrations for statistically significant parameters are identified and the methane yields at laboratory scale are obtained (Part I & II). We then demonstrate the importance of replicating truly in-situ conditions for bio- gasification studies by investigating methane productivity in a core holder simulating in-situ conditions. Based on the experimental results, the resulting gas production correlates positively with the permeability of the samples. We examine the deliverability of engineered microbial solutions to promote coal bioconversion in coals and demonstrate that greater than milli-darcy permeability is required under in-situ stress conditions (Part III). This constraint is supplemented by imaging and a fractal-based characterization approach to evaluate changes in the physical properties of coal due to bio-gasification. High resolution SEM imaging is used to observe swelling of the coal matrix with sub-micron wide continuous pores observed to persist even after microbial treatment (IV). We explore the potential of generating, propping-open and sustaining the functionality of hydraulic fractures in coal using different fracturing fluids (Part V). The alteration of the nano-pore structure due to MECBM treatment is examined by using small angle X-ray scattering (SAXS) to define the in-situ porosity evolution as a proxy to infer permeability evolution (Part VI). Coupled hydro-mechanical and bio-gasification modeling is applied to determine the effectiveness of nutrient delivery by hydraulic fracturing and its field implementation. Compared with both the unstimulated and hydro-shear-dilated cases of the natural fracture network, hydraulic fracturing demonstrates a significant improvement to enhance nutrient delivery. A preliminary analysis of field-scale methane yield is completed with consideration of the combined influence of permeability evolution, solute transport and microbial growth (Part VII). Developing nutrient solutions for Illinois Basin and San Juan Basin (Task 2): To enhance methane release from coal through bio-gasification, suitable nutrient solutions are needed to stimulate microbial activities toward coal depolymerization and conversion to biogas. Specific for bituminous coal in the Illinois basin, a nutrient recipe that can be used to enhance coal bio- gasification in situ is developed. Based on our previous experience working with Illinois coal and the characteristics of the formation water and the indigenous microbial community, four parameters, Fe powder, methanol, ethanol, and trace minerals are optimized through using a Box-Behnken design. The optimal condition predicted by the models is: Fe-powder at 74 mM, methanol at 97.91 mM, ethanol at 100 mM, and trace minerals at 100%. Under these conditions, the predicted methane yield and content is 1,417.35 ft 3 /ton and 80.7%, respectively. These results are then validated by experimental studies. In addition, each added component is evaluated in terms of its contribution to methane generated. Specifically, the role of coal in the bio-gasification process is studied against two other solid materials. Experimental data from different solid matrices prove that coal is indeed degraded by the studied microbial community.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1972

The ORNL-SNAP shielding program

The effort in the ORNL-SNAP shielding program is directed toward the development and verification of computer codes using numerical solutions to the transport equation for the design of optimized radiation shields for SNAP power systems. A brief discussion is given for the major areas of the SNAP shielding program, which are cross-section development, transport code development, and integral experiments. Detailed results are presented for the integral experiments utilizing the TSF-SNAP reactor. Calculated results are compared with experiments for neutron and gamma-ray spectra from the bare reactor and as transmitted through slab shields.

Mynatt, F. R.↗