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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Assessment of Low-Level Pu Isotope Ratio Measurements Using Multicollector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS/MS) Equipped with a Pre-Mass Filter

We present an initial investigation into the performance of a multicollector inductively coupled plasmamass spectrometer equipped with a pre-mass filter (Neoma MC-ICP-MS/MS) for making plutonium (Pu) isotope ratio measurements on solutions containing low level (i.e., pg mL –1 ) Pu concentrations. This assessment was achieved by comparison of the 240 Pu/ 239 Pu, 241 Pu/ 239 Pu, and 242 Pu/ 239 Pu ratios attained over a one month period on the MC-ICP-MS/MS with the long-term (∼1 year) performance observed on the predecessor MC-ICP-MS (Neptune Plus) instrument each equipped with an equipped with an APEXΩ desolvating nebulizer for repeated measurements of certified reference materials from New Brunswick Program Office (NBL PO) CRM 136a and CRM 137. The MC-ICP-MS/MS performance of repeated measurement of CRM 136a (n = 20) resulted in mean values of 240 Pu/ 239 Pu = 0.1448 ± 0.0006, 241 Pu/ 239 Pu = 0.00371 ± 0.00006, and 242 Pu/ 239 Pu = 0.00682 ± 0.00006 (k = 2). The CRM 137 (n = 20), analyzed during the same analytical sessions, produced mean values for 240 Pu/ 239 Pu = 0.2414 ± 0.0006, 241 Pu/ 239 Pu = 0.00464 ± 0.00007, and 242 Pu/ 239 Pu = 0.0157 ± 0.0001 (k = 2). These results closely align with the certificate values for CRM 136a and CRM 137 and are within the k = 2 envelopes defined by the long-term performance of the traditional MC-ICP-MS approach (Neptune Plus). Examination of the performance of the various Pu isotope ratios as a function of total Pu content revealed accurate results (<3% relative difference, or RD) above ∼50 fg total Pu. The results presented here demonstrate the capability of the MC-ICP-MS/MS making accurate and precise low level Pu isotopic measurements. While the intent of this work was not to investigate the functionality of the collision cell, the pre-mass filter was employed. Future studies are warranted to investigate the entire capability of the MC-ICP-MS/MS collision cell and pre-cell mass filter optimization for performing low level Pu isotope measurements, even in mixed matrix samples.

CRM↗
DOE OSTI2024

Dataset For: A Guide to Residential Energy Storage and Rooftop Solar: State Net Metering Policies and Utility Rate Tariff Structures

Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by introducing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies facing the challenge of above-average electric utility rates. This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit. Suggested modifications include: location, household load profile, rate tariff structure, and renewable energy system design.

14 SOLAR ENERGY↗
DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong correlation between this feature and both endpoints. Conclusion: This pilot study shows that it is feasible to predict dose-volume endpoints based on patient-specific anatomic features. The developed methodology can potentially help to identify patients at risk for higher OAR doses, thus improving the efficiency of treatment planning. R01-184173.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Enhanced Performance Fast Reactors with Engineered Passive Safety Systems

This report summarizes the work and results of NEUP Project #15-8251, "Enhanced Performance Fast Reactors with Engineered Passive Safety System," performed from 2015-2018 at UC Berkeley and Argonne National Laboratory. This project concerns the incorporation of a novel engineered passive safety system, called the Autonomous Reactivity Control (ARC) system, into Sodium-cooled Fast Reactors (SFRs) to improve their transient and economic performance. The first part of this project consisted of fleshing out the basic principles and the manufacturing process for an SFR fuel assembly with the ARC system installed, with the details provided in high-quality, step-by-step CAD drawings. Once the detailed design is laid out, high-fidelity simulations were performed to optimize the dimensions of important ARC system components and to quantify the standalone time-response of ARC systems in a typical SFR environment. These high-fidelity simulations were used to create reduced-order models of ARC system response which could be incorporated into the SAS4A/SASSYS-1 whole-core transient models. In order to accurately account for the time-response of ARC systems in whole-core transient simulations, modifications were made to the SAS4A/SASSYS-1 transient code. These new capabilities were tested and incorporated into the production version of the code, and have been used extensively in this analysis. Before performing whole-core transient calculations, the two reference cores selected for this study to cover a wide design space were simulated and characterized. The two reference cores, (1) a medium-sized oxide-fueled Advanced Burner Reactor and (2) a large metal-fueled Breed-and-Burn core, challenge conventional means of achieving passive safety. Because the two cores vary substantially, simulating the ARC system in both allows for conclusions to be drawn about the applicability of ARC systems in different SFRs. Once both cores were fully characterized, transient calculations were performed to understand the complicated interplay between the ARC system and other inherent reactivity feedback mechanisms. By performing parametric studies of the transient behavior, ARC system designs that best improve transient performance in each core were identified. Once transient improvements were established, the transient calculations were extended to improve reactor economics through design modifications. Upon establishing the potential benefits of ARC system inclusion, the study examines the negative neutronic implications of incorporating ARC systems into the reference cores. The study concludes by discussing a number of knowledge gaps that exist which may have important implications for ARC system implementation. Ultimately, the results of this project indicate that ARC systems may provide an effective means to improve transient performance in certain types of systems. The transient simulations in the oxide Advanced Burner Reactor showed that significant margin to coolant boiling and fuel melting could be gained through the use of a properly designed ARC system, thus improving inherent safety. While oscillatory behavior was induced in the transient response for certain combinations of ARC system parameters, the oscillation-free design space that can be effectively used to improve performance was found to be large, and the additional margin was demonstrated to allow economical improvements to the core design. Conversely, difficulties were encountered in the Breed-and-Burn core due to fundamental aspects of the core design. The combination of a particularly strong positive coolant void feedback and the use of metallic fuel allowed for diverging oscillations to manifest in response to certain transient initiators, and in the end it was found that the current ARC system design could not simultaneously enable passive safety in all transients examined. In order for complete passive safety to be achieved, a mechanism for slowing the reverse actuation of ARC systems is necessary. Regardless, the standard ARC system was able to improve the performance of two out of the three examined transients by increasing the margin to boiling in Loss of Heat Sink transients and avoiding boiling in Transient Overpower accidents. Additionally, no significant barriers to implementation were found, although the issue of tritium production should be closely monitored in any core incorporating ARC systems. Still, a number of technology gaps exist, and these are discussed at the conclusion of the report to recommend directions for future work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Evaluating sealability of blended smart polymer and fiber additive for geothermal drilling with the effect of fracture opening size

Geothermal formations often contain extensive fracture networks. These fracture networks contribute to the significant loss of drilling fluids during geothermal drilling. Multiple loss circulation materials (LCM) such as fiber, granules, and pills have been proposed to tackle this problem but with only limited success. Recent advances in materials science have led to the development of thermoset shape memory polymers (SMP) to address the lost circulation problem. In this paper, we evaluate a thermoset SMP performance in sealing near wellbore fractures of different sizes in geothermal wells. The SMP performance was assessed using granite disks and cylindrical granite cores having fracture sizes of 1000 μm and 3000 μm. A static filtration test was performed using cedar fiber, CaCO 3 , and SMP. Results showed cedar fiber performed better than the CaCO 3 ., reducing fluid loss by 89% and improving sealing pressure by 200 psi. A novel dynamic testing unit that allows for high-temperature testing under flowing conditions was used in this study. The analysis showed that 3% by weight SMP and fiber blends could bridge and plug the 1000 μm fracture. For a larger fracture of 3000 μm width, there was a need to increase the weight concentration of the SMP to 6% to plug the fracture opening effectively. We showed the influence of key parameters such as the type of LCM, concentration, and particle size distribution in optimizing the performance of drilling fluid loss treatment.

02 PETROLEUM↗
DOE OSTI2016

Enhancing the light extraction efficiency of AlGaN deep ultraviolet light emitting diodes by using nanowire structures

The performance of conventional AlGaN deep ultraviolet light emitting diodes has been limited by the extremely low light extraction efficiency (<10%), due to the unique transverse magnetic (TM) polarized light emission. Here, we show that, by exploiting the lateral side emission, the extraction efficiency of TM polarized light can be significantly enhanced in AlGaN nanowire structures. Using the three-dimensional finite-difference time domain simulation, we demonstrate that the nanowire structures can be designed to inhibit the emission of guided modes and redirect trapped light into radiated modes. A light extraction efficiency of more than 70% can, in principle, be achieved by carefully optimizing the nanowire size, nanowire spacing, and p-GaN thickness.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Light Water Reactor Sustainability Program A Reference Plan for Control Room Modernization: Planning and Analysis Phase

The U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) program is collaborating with a U.S. nuclear utility to bring about a systematic fleet-wide control room modernization. To facilitate this upgrade, a new distributed control system (DCS) is being introduced into the control rooms of these plants. The DCS will upgrade the legacy plant process computer and emergency response facility information system. In addition, the DCS will replace an existing analog turbine control system with a display-based system. With technology upgrades comes the opportunity to improve the overall human-system interaction between the operators and the control room. To optimize operator performance, the LWRS Control Room Modernization research team followed a human-centered approach published by the U.S. Nuclear Regulatory Commission. NUREG-0711, Rev. 3, Human Factors Engineering Program Review Model (O’Hara et al., 2012), prescribes four phases for human factors engineering. This report provides examples of the first phase, Planning and Analysis. The three elements of Planning and Analysis in NUREG-0711 that are most crucial to initiating control room upgrades are: • Operating Experience Review: Identifies opportunities for improvement in the existing system and provides lessons learned from implemented systems. • Function Analysis and Allocation: Identifies which functions at the plant may be optimally handled by the DCS vs. the operators. • Task Analysis: Identifies how tasks might be optimized for the operators. Each of these elements is covered in a separate chapter. Examples are drawn from workshops with reactor operators that were conducted at the LWRS Human System Simulation Laboratory HSSL and at the respective plants. The findings in this report represent generalized accounts of more detailed proprietary reports produced for the utility for each plant. The goal of this LWRS report is to disseminate the technique and provide examples sufficient to serve as a template for other utilities’ projects for control room modernization.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Performance Portability of HPC Discovery Science Software: Fusion Energy Turbulence Simulations at Extreme Scale

As HPC R&D moves forward on a variety of “path to exascale” architectures today, an associated objective is to demonstrate performance portability of discovery-science-capable software. Important application domains, such as Magnetic Fusion Energy (MFE), have improved modelling of increasingly complex physical systems -- especially with respect to reducing “time-to-solution” as well as “energy to solution.” The emergence of new insights on confinement scaling in MFE systems has been aided significantly by efficient software capable of harnessing powerful supercomputers to carry out simulations with unprecedented resolution and temporal duration to address increasing problem sizes. Specifically, highly scalable particle-in-cell (PIC) programing methodology is used in this paper to demonstrate how modern scientific applications can achieve efficient architecture-dependent optimizations of performance scaling and code portability for path-to-exascale platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Unusual Strain Effect of Pt-Based L10 Face-Centered Tetragonal Core in Core/Shell Nanoparticles for Oxygen Reduction Reaction

Here, nanoparticles with low-Pt content core and a few-layer Pt skin are attractive catalysts toward the oxygen reduction reaction (ORR) not only for its low cost, but also because its activity can be enhanced by judiciously choosing the core alloy. Achieving optimal ORR performance would require fine tuning of the core composition and structure. Previous work studying the enhancement effects has primarily focused on core alloys with a cubic structure, (i.e. disordered alloy or L12 ordered structure) which limits the tuning to composition alone. In this work, using ab initio calculations, we have systemically investigated a new class of Pt0.5M0.5 (M=V, Cr, Fe, Co, Ni and Cu) core alloy that has a face-centered tetragonal L10 intermetallic structure. We have calculated the adsorption energies of O, OH, and OOH on various Pt skins and the underlying tetragonal structured alloys, which allows us to not only predict the optimal number of pure Pt skin layers but also tune the activity of the catalysts toward the peak of the ORR volcano plot. More importantly, using adsorption energies on intermediate structures, we are able to decompose the enhancement factor into ligand, normal and shear strain effects, and reveal the significant contribution of the shear strain that is only possible with a tetragonal core but not a cubic one. Our results point to a new direction in designing tetragonal structured intermetallic core-shell nanoparticles for ORR applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

The EIC accelerator: design highlights and project status

The design of the electron-ion collider (EIC) at Brookhaven National Laboratory is well underway, aiming at a peak electron-proton luminosity of 10e+34 cm^-1·sec^-1. This high luminosity, the wide center-of-mass energy range from 29 to 141 GeV (e-p) and the high level of polarization require innovative solutions to maximize the performance of the machine, which makes the EIC one of the most challenging accelerator projects to date. The complexity of the EIC will be discussed, and the project status and plans will be presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control

Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

SU-E-T-621: Planning Methodologies for Cancer of the Anal Canal: Comparing IMRT, Rapid Arc, and Pencil Beam Scanning Proton Beam

Purpose: To evaluate planning methods for anal canal cancer and compare the results of 9-field Intensity Modulated Radiotherapy (IMRT), Volumetric Modulated Arc Therapy (Varian, RapidArc), and Proton Pencil Beam Scanning (PBS). Methods: We generated plans with IMRT, RapidArc (RA) and PBS for twenty patients for both initial phase including nodes and cone down phase of treatment using Eclipe (Varian). We evaluated the advantage of each technique for each phase. RA plans used 2 to 4 arcs and various collimator orientations. PBS used two posterior oblique fields. We evaluated the plans comparing dose volume histogram (DVH), locations of hot spots, and PTV dose conformity. Results: Due to complex shape of target, for RA plans, multiple arcs (>2) are required to achieve optimal PTV conformity. When the PTV exceeds 15 cm in the superior-inferior direction, limitations of deliverability start to dominate. The PTV should be divided into a superior and an inferior structure. The optimization is performed with fixed jaws for each structure and collimator set to 90 degrees for the inferior PTV. Proton PBS plans show little advantage in small bowel sparing when treating the nodes. However, PBS plan reduces volumetric dose to the bladder at the cost of higher doses to the perineal skin. IMRT plans provide good target conformity, but they generate hot spots outside of the target volume. Conclusion: When using one planning technique for entire course of treatment, Multiple arc (>2) RA plans are better as compared to IMRT and PBS plans. When combining techniques, RA for the initial phase in combination with PBS for the cone down phase results in the most optimal plans.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Final Technical Report

The Department of Energy is interested in technologies that support the sustainable production of fuels, chemicals, and other bioproducts from plant biomass, to offset the nation’s reliance on fossil resources. The plant cell wall of energy crops provides the largest reservoir of raw materials for bioproducts. However, the widespread use of plant cell walls is hampered by their complexity and resistance to breakdown. To improve the productivity and cost-effectiveness of using energy crops to generate bioproducts, the fundamental problem of deconstructing plant cell walls must be addressed. This project developed and evaluated an innovative genetic modification technology to produce strategically designed enzymes that specifically accumulate in the plant cell wall. The resulting enzyme-engineered energy crops are expected to grow normally under natural conditions but break down more quickly and easily under high temperature during the production of biobased products. As such, this plant cell wall targeting enzyme engineering effort will reduce the cost of plant cell wall deconstruction and ultimately improve the economics of bioproducts. The overall objective of this project is to develop and evaluate the in-planta enzyme engineering technology to reduce lignocellulose deconstruction cost. The concept was first validated using tobacco plant, a model plant system that is typically used in lab testing for initial concept validation. Then the enzyme optimization was validated using switchgrass, the energy crop to be used to produce bioproducts. There are three specific objectives in this Phase I project: (1) validate the enzyme optimization concept using tobacco plant, a model plant system. (2) validate the enzyme optimization concept using switchgrass. (3) techno-economic analysis (TEA) for further scale-up application. By the end of this project, in-planta enzyme engineering was validated in both tobacco and switchgrass plants, with improved enzyme activity and saccharification efficiency. The in-planta enzyme engineering in Tabacco didn’t have a significant impact on plant growth and development. Transgenic tobacco plants with in-planta cellulose degrading enzymes showed higher biomass digestibility than wild type. Gene construction and transformation in switchgrass was much longer than expected, which delayed the research progress. Besides, in-planta engineering of lignin degrading enzyme is more challenging than cellulose degrading enzyme, in terms of expression detection. Expression of lignin degrading enzyme and cellulose degrading enzyme improved biomass yield and saccharification efficiency of switchgrass, respectively. It is promising to express both genes in switchgrass for optimized overall performance. According to the results of TEA, switchgrass biomass production cost is mainly attributed to by fertility and harvesting. Biomass production profit can increase up to 10-fold depending on biomass price. The PHA production profit is also sensitive to the biomass price. The proposed technology could potentially reduce the biomass deconstruction cost from 33% to 9% of PHA revenue, making the biomass-based PHA competitive to petroleum-based polymers even in case of relatively high biomass price of biomass. Therefore, cultivation of the genetically engineered self-deconstruction switchgrass for Polyhydroxyalkanoate (PHA) production could benefit switchgrass grower and PHA producer with attractive profits for both sectors. This new enzyme optimization approach will be beneficial for bioindustries that use energy crops as feedstocks. It will improve the economic viability of converting energy crops to renewable products that support a sustainable society and helps address the Nation’s long-term strategic needs for renewable products and reduction of reliance on fossil resources.

42 ENGINEERING↗
DOE OSTI2018

PERFORMANCE MODELING OF A NOVEL “SMART” MAGNETIC PARTICLE-EMBEDDED PCM LAYER FOR THERMAL MANAGEMENT SYSTEMS

Developing stable and environmentally friendly phase change materials (PCM) has been an active area of research due to many applications, such as industrial energy storage system, cooling/heating applications in buildings, and thermal management for electronics equipment as well as for batteries and photovoltaic modules. However, previous studies confirmed that most PCMs suffer a serious disadvantage of low thermal conductivity. A novel design of PCM layer is investigated here for thermal management application. The novel PCM layer is comprised of a PCM and magnetic particles coated on the shell. When a magnetic field is applied in the thermal management system, due to the magnetic of the particles, PCM layer are attached to the heat source to absorb heat, which significantly improves the heat transfer between PCM and the heat source. The melting temperature of the PCM and Curie temperature of the magnetic particles are carefully selected to optimize the performance and to ensure materials compatibility. A numerical study is conducted to reveal the heat transfer and performance improvement of the PCM layer comparing to the conventional PCMs.

Zhang, Mingkan↗
DOE OSTI2015

Development of an X-Ray Talbot-Lau Moire Deflectometer of Fast Density Profile Measurements of Dense Plasmas Generated by Beam Target Interactions

For the first time an x-ray Talbot-Lau moiré deflectometer is being developed that will use a flash tube source and fast detector for dynamic density gradient measurements. In Talbot-Lau moiré deflectometry, an x-ray grating makes an mage of itself on a second grating (the Talbot effect) to produce a moiré pattern on a detector. The test object is placed between these gratings, with variations in index of refraction changing the pattern. A third grating in front of an incoherent x-ray source produces an array of coherent sources. With a 150 kV x-ray flash tube as the source, the gratings are placed in a glancing angle setup for performance at ~60 keV. The detector is a gated CCD with a fast scintillator for x-ray conversion. This diagnostic, designed for the Dual-Axis Radiographic Hydrodynamic Test facility (DARHT) at Los Alamos National Laboratory, measures the density profile of dense plasma plumes ejected from beam-target interactions. DARHT has two high-current, pulsed, inductive linear electron accelerators with bremsstrahlung targets at the end of each beam line to create 2-D radiographic images of hydrodynamic tests. One multipulse accelerator has up to four beam pulses striking the same target within 2 μs. Computer simulations that model target evolution and ejected material between pulses are used to design these targets for optimal radiographic performance; the x-ray deflectometer will directly measure density gradients in the ejected plumes and provide the first experimental constraints to these models. During the first year, currently underway, the diagnostic systems are being designed. In year two, the flash tube and fast detector will be deployed at DARHT for radiographic imaging while the deflectometer is built and tested on the bench with a continuous source. In year three, the fast deflectometer will be installed on DARHT and density measurements will be performed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

MPMC and MCMD: Free High-Performance Simulation Software for Atomistic Systems

Advancements in parallel computing and hardware have allowed computational exploration of chemical systems of interest with unprecendented accuracy and efficiency. The typical development of molecular simulation software is initially inspired by a particular scientific inquiry. The softwares presented herein, MPMC (Massively Parallel Monte Carlo) and MCMD (Monte Carlo/Molecular Dynamics) were born out of a pursuit to simulate condensed phase physical and chemical interactions in porous materials. MPMC first began in 2005 and has been used for dozens of published results in the literature but has not yet been introduced in a standalone paper. MCMD is a more recent expansion and re-write with some published work, focused on adding molecular dynamics algorithms for transport and other time-dependent properties of chemical systems. Each software functions as a standalone with some unique and other overlapping features. Here, a driving aim of this work is to consider periodic, long-range polarization effects in classical simulation, and both codes are optimized to perform such calculations. Sample results will be presented which highlight methodically that inclusion of explicit polarization produces simulation results with predictive power which in general are in greater agreement with experiment than non-polarizable analogous simulations.

97 MATHEMATICS AND COMPUTING↗