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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A combined phase contrast imaging and heterodyne interferometer system for multiscale fluctuation measurements (invited)

In this work, a novel combined diagnostic capable of measuring multiscale density fluctuations that extend from magnetohydrodynamic (MHD) to the lower range of electron temperature gradient turbulence has been designed, installed, and operated at DIII-D. The combined diagnostic was constructed by adding a heterodyne interferometer to the pre-existing phase contrast imaging (PCI) system, both of which measure line-integrated electron density fluctuations. The port-space footprint is minimized via use of a single 10.6 μm probe beam. With temporal bandwidths in excess of 1 MHz, the PCI measures high-k (1.5 cm -1 < |k R | ≤ 25 cm -1 ) fluctuations with sensitivity 3×10 13 m -2 /$\sqrt{kHz}$, while the interferometer simultaneously measures low-k (|k R | < 5 cm -1 ) fluctuations with sensitivity 3×10 14 m -2 /$\sqrt{kHz}$. The intentional mid-k overlap has been empirically verified with sound-wave calibrations and allows quantitative investigation of multiscale effects that are predicted to be significant in the reactor-relevant T e ~ T i regime. Lastly, via correlation with the primary DIII-D interferometer, the toroidal mode numbers of core-localized MHD can be measured.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Contrastive Machine Learning with Gamma Spectroscopy Data Augmentations for Detecting Shielded Radiological Material Transfers

Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

The LLNL nuclear data infrastructure for the GNDS data format

The next generation of nuclear data infrastructure tools at the Livermore National Laboratory (LLNL) consists of pipeline of codes that read and process nuclear data from evaluated files saved in the new GNDS (Generalised Nuclear Data Structure) nuclear data format. The processing code FUDGE (For Updating Data and Generating Evaluations) is at the front-end of this pipeline as it reads and process the evaluated data for use in downstream transport codes. FUDGE is Python based with C and C++ extensions for computationally intensive tasks. As is the case for the evaluated data, the processed output is also saved in the GNDS format and the GIDI+ API is provided as the interface between the processed data and the transport codes. GIDI+ is a C++ based suite of codes and it includes GIDI (General Interaction Data Interface), a library for reading and writing GNDS data, and MCGIDI which is the cross section lookup, and reaction and product distribution sampling interface between Monte Carlo transport codes and the GNDS data. GIDI provides methods for easy access to the multi-group processed GNDS data and this is demonstrated through its implementation in ARDRA, the LLNL deterministic transport code. The evaluation and sampling methods in MCGIDI are available as both CPU and GPU methods which facilitates the use of MCGIDI in both traditional CPU-based as well as the next generation mixed model computational architectures. This is demonstrated through the GIDI+ implementation in MERCURY, the LLNL Monte Carlo transport code. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Dehalogenation reactions between halide salts and phosphate compounds

Reactions between phosphoric acid [H 3 PO 4 ] or ammonium hydrogen phosphates [i.e., NH 4 H 2 PO 4 , (NH 4 ) 2 HPO 4 ] and halide salts can be used to dehalogenate (remove halides from) salt-based waste streams, where the process of removing halides yields products that have more efficient disposal pathways for repository storage. In this context, the term efficiency is defined as higher waste loadings and simplified immobilization processes with potential for recycle of certain salt components (e.g., 37 Cl as H 37 Cl or NH 4 37 Cl). The main streams identified for these processes are nuclear wastes generated during electrochemical reprocessing of used nuclear fuel as well as used halide salts from molten salt reactor operation. The potential byproducts of these reactions are fairly consistent across the range of halide species (i.e., F, Cl, Br, I) where the most common are hydrogen halides [e.g., HCl (g) ] or ammonium halides (e.g., NH 4 Cl). However, trihalide compounds (e.g., NCl 3 ), nitrogen triiodide ammine adducts [NI 3 ·(NH 3 ) x ], and ammonium triiodide (NH 4 I 3 ) are also possible. Several of these byproducts (i.e., NCl 3 , NBr 3 , NI 3 , and NH 4 I 3 ) are shock-sensitive contact explosives so their production in these processes must be tracked and carefully controlled, which includes methods of immediate neutralization upon production such as direct transport to a caustic scrubber for dissolution. Several benefits arise from utilizing H 3 PO 4 as the phosphate additive during dehalogenation reactions for making iron phosphate waste forms including more oxidized iron (higher Fe 3+ :Fe 2+ ratios), higher chemical durabilities, and the avoidance of trihalides, but the byproducts are hydrogen halides, which are corrosive and require special handling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Development of an Innovative High-Thermal Conductivity UO2 Ceramic Composites Fuel Pellets with Carbon Nano-Tubes Using Spark Plasma Sintering

Uranium dioxide (UO2) is the most common fuel material in commercial nuclear power reactors. Despite its numerous advantages such as high melting point, good high-temperature stability, good chemical compatibility with cladding and coolant, and resistance to radiation, it suffers from low thermal conductivity that can result in large temperature gradients within the UO2 fuel pellet, causing it to crack and release fission gases. Thermal swelling of the pellets also limits the lifetime of UO2 fuel in the reactor. To mitigate these problems, we propose to develop novel UO2 fuel with uniformly distributed carbon nanotubes (CNTs) that can provide high-conductivity thermal pathways and can eliminate fuel cracking and fission gas release due to high temperatures. CNTs have been investigated extensively for the past decade to explore their unique physical properties and many potential applications. CNTs have high thermal conductivity (6600 W/mK for an individual single- walled CNT and >3000 W/mK for an individual multi-walled CNT) and high temperature stability up to 2800°C in vacuum and about 750°C in air. These properties make them attractive candidates in preparing nano-composites with new functional properties. The objective of the proposed research is to develop high thermal conductivity of UO2–CNT composites without affecting the neutronic property of UO2 significantly. The concept of this goal is to utilize a rapid sintering method (5–15 min) called spark plasma sintering (SPS) in which a mixture of CNTs and UO2 powder are used to make composites with different volume fractions of CNTs. Incorporation of these nanoscale materials plays a fundamentally critical role in controlling the performance and stability of UO2 fuel. We will use a novel in situ growth process to grow CNTs on UO2 particles for rapid sintering and develop UO2-CNT composites. This method is expected to provide a uniform distribution of CNTs at various volume fractions so that a high thermally conductive UO2-CNT composite is obtained with a minimal volume fraction of CNTs. The mixtures are sintered in the SPS facility at a range of temperatures, pressures, and time durations so as to identify the optimal processing conditions to obtain the desired microstructure of sintered UO2-CNT pellets. The second objective of the proposed work is to identify the optimal volume fraction of CNTs in the microstructure of the composites that provides the desired high thermal conductivity yet retaining the mechanical strength required for efficient function as a reactor fuel. We will systematically study the resulting microstructure (grain size, porosity, distribution of CNTs, etc.) obtained at various SPS processing conditions using optical microscopy, scanning electron microscopy (SEM), and transmission electron microscope (TEM). We will conduct indentation hardness measurements and uniaxial strength measurements as a function of volume fraction of CNTs to determine the mechanical strength and compare them to the properties of UO2. The fracture surfaces will be studied to determine the fracture characteristics that may relate to the observed cracking during service. Finally, we will perform thermal conductivity measurements on all the composites up to 1000° C. This study will relate the microstructure, mechanical properties, and thermal properties at various volume fractions of CNTs. The overall intent is to identify optimal processing conditions that will provide a well-consolidated compact with optimal microstructure and thermo-mechanical properties. The deliverables include: (1) fully characterized UO2-CNT composite with optimal CNT volume fraction and high thermal conductivity and (2) processing conditions for production of UO2-CNT composite pellets using SPS method.

Subhash, Ghatu↗
DOE OSTI2014

Determination of the Sensitivity of the Antineutrino Probe for Reactor Core Monitoring

This paper presents a feasibility study of the use of the detection of reactor-antineutrinos (ν{sup ¯}{sub e}) for non proliferation purpose. To proceed, we have started to study different reactor designs with our simulation tools. We use a package called MCNP Utility for Reactor Evolution (MURE), initially developed by CNRS/IN2P3 labs to study Generation IV reactors. The MURE package has been coupled to fission product beta decay nuclear databases for studying reactor antineutrino emission. This method is the only one able to predict the antineutrino emission from future reactor cores, which don't use the thermal fission of {sup 235}U, {sup 239}Pu and {sup 241}Pu. It is also the only way to include off-equilibrium effects, due to neutron captures and time evolution of the fission product concentrations during a reactor cycle. We will present here the first predictions of antineutrino energy spectra from innovative reactor designs (Generation IV reactors). We will then discuss a summary of our results of non-proliferation scenarios involving the latter reactor designs, taking into account reactor physics constraints.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

New In-Situ and Operando Facilities for Catalysis Science at NSLS-II: The Deployment of Real-Time, Chemical, and Structure-Sensitive X-ray Probes

The start of operations at the National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory heralded a new beginning for photon-science-based research capabilities in catalysis. This new facility builds on many years of pioneering work that was conducted at the NSLS synergistically by many scientists from academia, government labs, and industry. Over several decades, numerous discoveries in catalysis were driven through the emergence of an arsenal of tools at the NSLS that exploited the power of emerging X-ray methods encompassing scattering, spectroscopy, and imaging. Thus, in-situ and operando methodologies that coupled reactor environments directly with advanced analytical techniques paved a rapid path towards realizing an improved fundamental understanding at the frontiers of chemical science challenges of the day.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Ultrasonic Link Model Development

The objective of this project is to develop and demonstrate methods for transmission of information in nuclear facilities across physical barriers as acoustic/elastic guided waves along existing in-place metal piping infrastructure. Acoustic communication (AC) system hardware and software layers for efficient and resilient transfer of data are being developed,with the goal of performing experimental demonstration of information transmission in a representative environment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

MFIX-Exa: Performance prediction of multiphase energy conversion devices

MFIX-Exa targets the high-fidelity CFD-DEM model in which particles are unresolved by the fluid grid (typically using a fluid mesh approximately twice the particle diameter) but discrete particle dynamics including collisions are resolved with a simple linear-spring dashpot model. The fluid field is solved with a modern low Mach number formation of a cell-centered, nodal-pressure approximate projection method using a Godunov scheme . Physics capabilities include: complex geometries via embedded boundaries (EBs), open and closed system ideal gas equations of state, species transport and heterogeneous chemi- cal reactions. MFIX-Exa is built on the AMReX software framework, the ECP Block-Structured Adaptive Mesh Refinement (AMR) Co-Design Center, which allows the code to be portable and performant.Further integration into the ECP ecosystem includes HYPRE linear solvers and Ascent for in situ visualization. Current project efforts focus on scaling up realistic simulations to the KPP of the challenge problem: NETs 50kW chemical looping reactor, shakedown and scaling on JSLE and OLCF TDS systems and reaching for any remaining performance improvements.

Musser, Jordan↗
DOE OSTI2017

Microstructural Characterization of Irradiated U{sub 0.17}ZrH{sub 1.6} Using Ultrasonic Techniques

In recent years, there has been an increased level of effort to understand the changes in microstructure that occur due to irradiation of nuclear fuel. The primary driver for this increased effort is the potential for designing new fuels that are safer and more reliable, in turn enabling new and improved reactor technologies. Much of the data on microstructural change in irradiated fuels is generated through a host of post irradiation examination techniques such as optical microscopy (OM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) to determine grain structure, porosity, crack geometry, etc. in irradiated fuels. Such 'traditional' examination techniques were recently used to characterize a novel new fuel consisting of U{sub 0.17}ZrH{sub 1.6} pellets bonded to zircaloy-2 cladded with lead-bismuth eutectic before and after irradiation. In addition to the conventional suite of post-irradiation examination tools, several nondestructive interrogation techniques exist which bring the potential for in-situ assessment of microstructural changes. One such method, ultrasonic examination, was tested on each of the samples. This paper describes the results of this examination. Ultrasonic measurements have a long and successful history of use for nondestructive materials characterization, including nondestructive evaluation (NDE) of degradation and damage, microstructure characterization, and measurement of physical parameters for process control. Ultrasonic NDE is critical to the nuclear power industry's in-service inspection program for maintaining pressure boundary integrity, and is being actively investigated for PIE of fuels. Ultrasonic measurements, typically performed at 10 MHz or higher, performed post irradiation show that fuel microstructural parameters, such as porosity and grain size, can be correlated to ultrasonic velocity. The objective of this examination was to assess the feasibility of using ultrasonic measurements on irradiated fuel to determine sensitivity to microstructural changes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Comparison of Surrogate Models with Physical Models for Dynamic Probabilistic Risk Analysis Using the RAVEN Code

Dynamic probabilistic risk analysis (DPRA) has emerged in the nuclear industry as an effective tool to identify points of weakness in nuclear reactors and create a comprehensive approach to quantifying risk. While DPRA allows for detailed analysis, it usually requires thousands of runs due to the necessity of exploring low probability, high consequence events, which can be computationally prohibitively difficult to assess. Risk Analysis Virtual Environment (RAVEN) is a software package being developed under the Nuclear Energy Advanced Modeling and Simulation program at the Idaho National Laboratory. It is able to manage complex control logic to drive a RELAP-7 simulation and to handle sampling strategies including Monte Carlo, Latin Hypercube, Grid and Adaptive samplers. By performing a thorough investigation of the possibility space of an accident scenario with RELAP-7, RAVEN is able to identify consequences of accident scenarios that have not been encountered. The most computational resource intensive step of a RAVEN analysis is running the RELAP-7 code modeling the physical system behavior. Using surrogate models to represent the outcomes of more complex simulations rather than the full model can greatly reduce the computation effort required. In situations where the uncertainty distributions of a model are unknown, or when there are multiple sets of uncertainty distributions, DPRA generally requires rerunning the physical model for each set of uncertainty distributions used. By taking an unbiased sample of the physical model and creating a surrogate model from the results, the surrogate model can be sampled under a different set of uncertainty distributions to provide equivalent results. This paper presents an example of this approach. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Addition of gaseous olefin to produce boiler fuel and stability studies

As a renewable liquid fuel, bio‐oil produced from fast pyrolysis of biomass is receiving increasing attention. However, bio‐oil has some negative properties, such as polymerization over time, high water content, high viscosity, and low energy value. It must be upgraded to be used as a fuel. The objective of this study was to develop an upgrading method by esterification and olefination reactions to produce bio‐oil with lower water content and higher energy value for combustion as a boiler fuel. Loblolly pine wood was pyrolyzed at a feed rate of ∼7 kg/h in a Mississippi State University proprietary auger reactor to produce bio‐oil with 65% yield. Upgrading reactions were performed in a high‐pressure batch autoclave. Bio‐oil was reacted with alcohols and gaseous olefins in the presence of a heterogeneous catalyst to produce upgraded bio‐oil. The influence of reaction conditions such as reaction time, reaction temperature, and catalyst content were evaluated. Physical and chemical properties such as acid value, higher heating value, water content, and optimal conditions were identified. The result of olefination shows improved bio‐oil quality and increased energy density. Accelerated aging tests were also performed. © 2014 American Institute of Chemical Engineers Environ Prog, 2014 34: 933–941, 2015

Tao, Jingming↗
DOE OSTI2014

Optimization Method to Branch and Bound Large SBO State Spaces Under Dynamic Probabilistic Risk Assessment via use of LENDIT Scales and S2R2 Sets

Traditional Probabilistic Risk Assessment (PRA) methods have been developed and are quite effective in evaluating risk associated with complex systems, but lack the capability to evaluate complex dynamic systems. These time and energy scales associated with the transient may vary as a function of transition time to a different physical state. Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems, while complete, results in issues associated with combinatorial explosion. In order to address the combinatorial complexity arising from the number of possible state configurations and discretization of transition times, a characteristic scaling metric (LENDIT – length, energy, number, distribution, information and time) is proposed as a means to describe systems uniformly and thus provide means to describe relational constraints expected in the dynamics of a complex (coupled) systems. Thus when LENDIT is used to characterize four sets – ‘state, system, resource and response’ (S2R2) – describing reactor operations (normal and off-normal), LENDIT and S2R2 in combination have the potential to ‘branch and bound’ the state space investigated by DPRA. In this paper we introduce the concept of LENDIT scales and S2R2 sets applied to a branch-and-bound algorithm and apply the methods to a station black out transient (SBO).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

SAM Finite Volume Method Development Status Update: GCR Application, Restart, and MultiApp

The System Analysis Module (SAM) is being developed as a modern system analysis code for advanced non-light-water-reactor safety analysis under the U.S. DOE NEAMS program. Previous feasibility studies have demonstrated that a staggered-grid finite volume method (SG-FVM), implemented under the MOOSE framework, can deliver more than an order of magnitude speedup over the existing continuous Galerkin finite element method (CG-FEM) solver for liquid-cooled, incompressible but thermally expandable flow systems. This work extends the previous effort to compressible, gas-cooled reactor applications, where pressure couples directly into the mass equation adding additional nonlinearity into the equation system. New code capabilities are implemented for pebble bed high-temperature gas-cooled reactor (PB-HTGR) analysis, including a pebble bed CoreChannel component, built-in pebble bed effective thermal conductivity model and channel-to-channel crossflow model. The capabilities are tested, benchmarked, and demonstrated for problems with increased level of model and physical complexities, including the HTTU effective thermal conductivity test, the SANA passive cooling test, and a demonstration case using the GPBR200 reactor design covering steady-state operation, DLOFC and PLOFC transients. Across all cases, the SG-FVM solver demonstrated strong robustness and efficiency, and the solutions agree well with reference results and data. The finding of this work proves that SG-FVM is a viable and efficient solver pathway for compressible, gas-cooled reactor system analysis in SAM. In addition, work has been done to successfully support SAM-FVM recover/restart code feature that is essential to reactor safety analysis applications, and MultiApp code feature that is essential to multi-scale and multi-physics simulations. In summary, this work continued from previous feasibility studies, and further demonstrated that the SG-FVM will serve as a strong foundation for SAM’s advanced solver algorithm for future deployment.

Zou, Ling↗
DOE OSTI2024

Collaborative Research Proposal: Time Resolved Optical Emissions Spectroscopy and Laser Induced Fluorescence Spectroscopy of Nanosecond Pulsed Discharges in a Gas-Liquid Water Film Reactor

Electrical discharge plasma formed in contact with liquid water is of interest for a wide range of applications in chemical, biomedical, agricultural, electrical, and materials science and engineering. Such plasma reactors are of very timely importance since they also have significant disinfection capability by inactivating bacteria, viruses and other pathogens. Many types of plasma sources including those driven by AC, DC, RF, microwave, and pulsed electrical power supplies coupled to a wide range of different electrode configurations and reactor designs have been developed and explored which contact the plasma with liquid water. Recent roadmaps have recommended that further work is needed to develop our understanding of the fundamental chemical and physical process which occur at the interface of non-thermal plasma with liquid water solutions in order to advance this large diversity of applications which ultimately depend upon efficient production of key reactive chemical species. There is a wide range of interacting factors that affect the chemical reactions that occur in the plasma, in the liquid phase, and at the interface. These factors ultimately govern the key reactive chemical species formed and used in the various applications and they include a) the reactor design and input parameters, b) the discharge and transport processes, c) the plasma properties, and d) the resulting chemical reactions. For pulsed discharges, the power supply design and output parameters control the applied voltage, frequency, rise time, and width (duration) of the applied pulses. The reactor design involves specification of the gas-liquid contacting methods, electrode gap distance, reactor volume and shape, and gas and liquid flow rates achievable. The gas and liquid compositions as well as the liquid properties such as pH and conductivity are also of key importance in determination of the resulting chemical reactions. In addition, the important plasma properties include plasma gas temperature, electron density, electron energy (distribution), and size of the plasma channels which are all affected by the discharge and transport properties. Many studies have focused on specific aspects of these various processes. Of particular important and relevance to the proposed work is the utilization of nanosecond pulses to generate plasma in gas-liquid systems, liquid bubbles, underwater, and in gases. Recent advances in nanosecond pulses provide significant advantages in utilization with liquid water. For example, fast rise time and short pulses are less sensitive to water conductivity, such short pulses may provide advantages in fast temporal quenching of the plasma, and fundamental analysis of pulse properties, including pulse shape and width, may be facilitated by investigation of single filamentary fast pulses. Many studies have also dealt with the role of the gas composition on formation of reactive oxygen species (ROS) (i.e., hydroxyl radicals – ·OH, hydrogen peroxide – H 2 O 2 , various atomic oxygen species, ozone-O 3 , hydroperoxyl radicals HO 2 ·) and reactive nitrogen species (RNS) (i.e., nitrogen oxides – NO, NO 2 , N 2 O - collectively termed NO X , nitrite-NO 2 - , nitrate-NO 3 - , peroxynitrite-ONOO - ). The hydroxyl radical is the critical species in many chemical oxidation reactions for chemical degradation of toxic compounds in water and gases and for synthesis of some compounds. The mixture of various nitrogen oxide species is important for many biochemical and biological processes involved in biomedical and agricultural applications including disinfection and fertilizer production. The present proposal focuses on determination of the effects of time resolved electron density and hydroxyl radicals on plasma chemical reactions through collaboration with the Princeton Collaborative Low Temperature Plasma Research Facility (PCRF) at the Princeton Plasma Physics Laboratory (PPPL). In order to further investigate the role of the plasma generated electrons and hydroxyl radicals on the overall formation of the key species including hydrogen peroxide, hydroxyl radicals, and nitrogen oxides, the proposed work, thus seeks to determine high resolution time resolved electron density by optical emissions spectroscopy and time resolved hydroxyl radicals using laser induced fluorescence in the nanosecond discharge reactor. The combination of data on electron density and hydroxyl radical concentration will be utilized in the present work to more fully characterize the chemical reaction processes in this system and to advance the design, development, and operation of such chemical reactors for a wide range of applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Parallel Monte Carlo Methods for Heterogeneous Hardware Computer Systems Using GPUs and Coprocessors: Recent Development of ARCHER Code - Paper 54

Traditional methods of accelerating Monte Carlo (MC) calculations typically use CPUs as the computing unit. The parallelism is based upon multithreading on a multi-core processor, and message passing interface (MPI) across many processors and nodes. In recent years, the hardware accelerators have been introduced by the high performance computing industry as an alternative approach to achieve massive parallelism. Hardware accelerators have the advantage of high Floating-point Operations Per Second (FLOPS) and high energy efficiency. Examples include graphics processing units (GPUs) and coprocessors. Compute-intensive programs can be off-loaded to them to be concurrently executed by hundreds or thousands of active threads. Existing production MC codes, however, cannot be directly compiled or executed on the new platforms. It is therefore necessary to rewrite or redevelop the code using the hardware accelerator specific programming models and APIs. Several studies have been conducted on using GPUs to accelerate MC calculations in reactor analysis and medical physics. In this paper, we present the recent progress at Rensselaer Polytechnic Institute (RPI) in developing a new parallel MC code called ARCHER (Accelerated Radiation-transport Computations in Heterogeneous Environments) for coupled photon-electron transport simulations. The code is designed as a test bed to evaluate the efficacy of heterogeneous computing systems for low-cost MC-based radiation dosimetric calculations. The code has three variants corresponding to three parallel hardware platforms: ARCHER-CPU (multi-core CPU), ARCHER-GPU (Nvidia GPU) and ARCHER-COP (Intel Xeon Phi coprocessor). The code has been used for several applications, including CT imaging dosimetry, radiotherapy dosimetry and radiation shielding design. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Communication: Control of chemical reactions using electric field gradients

We examine theoretically a new idea for spatial and temporal control of chemical reactions. When chemical reactions take place in a mixture of solvents, an external electric field can alter the local mixture composition, thereby accelerating or decelerating the rate of reaction. The spatial distribution of electric field strength can be non-trivial and depends on the arrangement of the electrodes producing it. In the absence of electric field, the mixture is homogeneous and the reaction takes place uniformly in the reactor volume. When an electric field is applied, the solvents separate and the reactants are concentrated in the same phase or separate to different phases, depending on their relative miscibility in the solvents, and this can have a large effect on the kinetics of the reaction. This method could provide an alternative way to control runaway reactions and to increase the reaction rate without using catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Chalcogenide Glass-Capped Fiber-Optic Sensor for Real-Time Temperature Monitoring in Extreme Environments

We demonstrate a novel chalcogenide glass (ChG)-capped optical fiber temperature sensor capable of operating within harsh environment. The sensor architecture utilizes the heat-induced phase change (amorphous-to-crystalline) property of ChGs, which rapidly (80–100 ns) changes the optical properties of the material. The sensor response to temperature variation around the phase change of the ChG cap at the tip of the fiber provides abrupt changes in the reflected power intensity. This temperature is indicative of the temperature at the sensing node. We present the sensing performance of six different compositions of ChGs and a method to interpret the temperature profile between 440 °C and 600 °C in real-time using an array structure. The unique radiation-hardness property of ChGs makes the devices compatible with high-temperature and high-radiation environments, such as monitoring the cladding temperature of Light Water (LWR) or Sodium-cooled Fast (SFR) reactors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗