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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

2018 Research Highlight in Manufacturing: Global Value Chain and Manufacturing Analysis on Geothermal Power Plant Turbines

Currently, geothermal project developers customize the size of the power plant to fit the resource being developed. The turbine is designed and sized to optimize efficiency and resource utilization for electricity production; most often, other power plant components are then chosen to complement the turbine design. These custom turbine designs demand one-off manufacturing processes, which result in higher manufacturing setup costs, longer lead-times, and higher capital costs overall in comparison to larger-volume line manufacturing processes. In contrast, turbines produced in standard increments, manufactured in larger volumes, could result in lower costs per turbine. In this study, we developed a manufacturing cost model to identify requirements for equipment, facilities, raw materials, and labor. Then we used discounted cash flow (DCF) analysis to calculate the minimum sustainable price (MSP). We analyzed three different cases 1) 1 MWe geothermal ORC turboexpander 2) 5 MWe ORC turboexpander and 3) 20 MWe geothermal steam turbine, and calculated the cost of manufacturing the major components, such as the impellers/blades, shaft/rotor, nozzles, inlet guide lanes, disks, and casings. The results showed that MSP could highly vary between 893 $/kW and 30 $/kW based on turbine size, standardization and volume of manufacturing. The analysis also showed that the economy of scale applies both to the size of the turbine and the number manufactured in a single run. As an example, the unit price of a 5 MW standard design turbine could be 150 $/W cheaper than the custom design. Sensitivity analysis indicated that these savings come largely from reduced labor costs for design and engineering and manufacturing setup. In addition to manufacturing cost savings, there is a delivery time saving up to 10 months, which could have a positive effect to construction financing operation time. Another advantage of these standard turbines is their adaptability to different geothermal systems by operating at off-design conditions. We also modeled off-design turbine efficiencies within the commercially-favorable operating range of a standard ORC compared to custom-designed ORC equipment. Than we used these performance calculations and power output capacities in a discounted cash flow (DCF) analysis of plant operations and financing, thereby creating representative techno economic models of a total geothermal power plant. I this scope we used NREL's System Advisor model (SAM) and performed DCF analysis of standard and custom design turbines using results from IPSEpro over range of temperatures of interest. In this scope 63 different off-design cases were analyzed. This helped us to answer the question; 'Can today`s capital cost savings compensate the future revenue losses due to lower electricity generation?' The results showed that, the net capital cost savings for the modeled 5 MW case study may reach up to +$1,298,672. Also, the difference between NPV of standard design and custom design turbine could reach up to +$733,940. To sum up, when all the things mentioned above are taken into consideration, it can be said that, the standard turbines are competitive over a wide range of temperatures and flow rates and the give positive NPV differences for certain cases near their design point.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Dynamic Event Tree Analysis Through RAVEN

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (D-PRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed in a high modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and coupling with other application including the ones based on the MOOSE framework, developed by INL as well. RAVEN performs two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, RAVEN also models stochastic events, such as components failures, and performs uncertainty quantification. Such stochastic modeling is employed by using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This paper focuses on the first task and shows how it is possible to perform the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, the Dynamic PRA analysis, using Dynamic Event Tree, of a simplified pressurized water reactor for a Station Black-Out scenario is presented.

42 ENGINEERING↗
DOE OSTI2021

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

RBFNN-based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems

This paper presents a novel minimum entropy filter design for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. Motivated by stochastic distribution control, an output entropy model is developed using RBF neural network while the parameters of the model can be identified by the collected data. Based upon the presented model, the filtering problem has been investigated while the system dynamics have been represented. As the model output is the entropy of the estimation error, the optimal nonlinear filter is obtained based on the Lyapunov design which makes the model output minimum. Moreover, the entropy assignment problem has been discussed as an extension of the presented approach. To verify the presented design procedure, a numerical example is given which illustrates the effectiveness of the presented algorithm. The contributions of this paper can be included as 1) an output entropy model is presented using neural network; 2) a nonlinear filter design algorithm is developed as the main result and 3) a solution of entropy assignment problem is obtained which is an extension of the presented framework.

42 ENGINEERING↗
DOE OSTI2013

Defining Determinants and Dynamics and Cellulose Microfibril Biosynthesis, Assembly and Degredation OSP Number: 63079/A001

The central paradigm for converting plant biomass into soluble sugars for subsequent conversion to transportation fuels involves the enzymatic depolymerization of lignocellulosic plant cell walls by microbial enzymes. Despite decades of intensive research, this is still a relatively inefficient process, due largely to the recalcitrance and enormous complexity of the substrate. A major obstacle is still insufficient understanding of the detailed structure and biosynthesis of major wall components, including cellulose. For example, although cellulose is generally depicted as rigid, insoluble, uniformly crystalline microfibrils that are resistant to enzymatic degradation, the in vivo structures of plant cellulose microfibrils are surprisingly complex. Crystallinity is frequently disrupted, for example by dislocations and areas containing chain ends, resulting in “amorphous” disordered regions. Importantly, microfibril structure and the relative proportions of crystalline and non-crystalline disordered surface regions vary substantially and yet the molecular mechanisms by which plants regulate microfibril crystallinity, and other aspects of microfibril architecture, are still entirely unknown. This obviously has a profound effect on susceptibility to enzymatic hydrolysis and so this is a critical area of research in order to characterize and optimize cellulosic biomass degradation. The entire field of cell wall assembly, as distinct from polysaccharide biosynthesis, and the degree to which they are coupled, are relatively unexplored, despite the great potential for major advances in addressing the hurdle of biomass recalcitrance. Our overarching hypothesis was that identification of the molecular machinery that determine microfibril polymerization, deposition and structure will allow the design of more effective degradative systems, and the generation of cellulosic materials with enhanced and predictable bioconversion characteristics. Our experimental framework had been based on the idea that the most effective way to address this long standing and highly complex question is to adopt a broad ‘systems approach’. Accordingly, we assembled a multi-disciplinary collaborative team with collective expertise in plant biology and molecular genetics, polymer structure and chemistry, enzyme biochemistry and biochemical engineering. We used a spectrum of cutting edge technologies, including plant functional genomics, chemical genetics, live cell imaging, advanced microscopy, high energy X-ray spectroscopy and nanotechnology, to study the molecular determinants of cellulose microfibril structure. Importantly, this research effort was closely coupled with an analytical pipeline to characterize the effects of altering microfibril architecture on bioconversion potential, with the goal of generating predictive models to help guide the identification, development and implementation of new feedstocks. This project therefore spanned core basic science and applied research, in line with the goals of the program. Over the course of the project, accomplishments included: - Establishing platforms through reverse and forward genetics to identify and manipulate candidate genes that influence cellulose microfibril synthesis and structure in a model C3 grass, Brachypodium distachyon and a model C4 grass Setaria viridis; Identifying and characterizing the effects of a number of cellulose biosynthesis inhibitors (CBIs), and particularly those that target monocots with the aim of generating resistance loci; Developing protocols for the use of high energy X-ray diffraction (XRD) to study the structure and organization of cellulose microfibrils in plant walls, notably those in Arabidopsis and Brachypodium; Using the chemical and genetic based inhibition strategies to develop new mechanistic models of cellulose microfibril crystallization, and of how altering microfibril architecture influences digestibility.

09 BIOMASS FUELS↗
DOE OSTI2014

Soils Activity Mobility Study: Methodology and Application

This report presents a three-level approach for estimation of sediment transport to provide an assessment of potential erosion risk for sites at the Nevada National Security Site (NNSS) that are posted for radiological purposes and where migration is suspected or known to occur due to storm runoff. Based on the assessed risk, the appropriate level of effort can be determined for analysis of radiological surveys, field experiments to quantify erosion and transport rates, and long-term monitoring. The method is demonstrated at contaminated sites, including Plutonium Valley, Shasta, Smoky, and T-1. The Pacific Southwest Interagency Committee (PSIAC) procedure is selected as the Level 1 analysis tool. The PSIAC method provides an estimation of the total annual sediment yield based on factors derived from the climatic and physical characteristics of a watershed. If the results indicate low risk, then further analysis is not warranted. If the Level 1 analysis indicates high risk or is deemed uncertain, a Level 2 analysis using the Modified Universal Soil Loss Equation (MUSLE) is proposed. In addition, if a sediment yield for a storm event rather than an annual sediment yield is needed, then the proposed Level 2 analysis should be performed. MUSLE only provides sheet and rill erosion estimates. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) provides storm peak runoff rate and storm volumes, the inputs necessary for MUSLE. Channel Sediment Transport (CHAN-SED) I and II models are proposed for estimating sediment deposition or erosion in a channel reach from a storm event. These models require storm hydrograph associated sediment concentration and bed load particle size distribution data. When the Level 2 analysis indicates high risk for sediment yield and associated contaminant migration or when there is high uncertainty in the Level 2 results, the sites can be further evaluated with a Level 3 analysis using more complex and labor- and data-intensive methods. For the watersheds analyzed in this report using the Level 1 PSIAC method, the risk of erosion is low. The field reconnaissance surveys of these watersheds confirm the conclusion that the sediment yield of undisturbed areas at the NNSS would be low. The climate, geology, soils, ground cover, land use, and runoff potential are similar among these watersheds. There are no well-defined ephemeral channels except at the Smoky and Plutonium Valley sites. Topography seems to have the strongest influence on sediment yields, as sediment yields are higher on the steeper hill slopes. Lack of measured sediment yield data at the NNSS does not allow for a direct evaluation of the yield estimates by the PSIAC method. Level 2 MUSLE estimates in all the analyzed watersheds except Shasta are a small percentage of the estimates from PSIAC because MUSLE is not inclusive of channel erosion. This indicates that channel erosion dominates the total sediment yield in these watersheds. Annual sediment yields for these watersheds are estimated using the CHAN-SEDI and CHAN-SEDII channel sediment transport models. Both transport models give similar results and exceed the estimates obtained from PSIAC and MUSLE. It is recommended that the total watershed sediment yield of watersheds at the NNSS with flow channels be obtained by adding the washload estimate (rill and inter-rill erosion) from MUSLE to that obtained from channel transport models (bed load and suspended sediment). PSIAC will give comparable results if factor scores for channel erosion are revised towards the high erosion level. Application of the Level 3 process-based models to estimate sediment yields at the NNSS cannot be recommended at this time. Increased model complexity alone will not improve the certainty of the sediment yield estimates. Models must be calibrated against measured data before model results are accepted as certain. Because no measurements of sediment yields at the NNSS are available, model validation cannot be performed. This is also true for the models used in the Level 2 analyses presented in this study. The need to calibrate MUSLE to local conditions has been discussed. Likewise, the transport equations of CHAN-SEDI and CHAN-SEDII need to be calibrated against local data to assess their applicability under semi-arid conditions and for the ephemeral channels at the NNSS. Before these validations and calibration exercises can be undertaken, a long-term measured sediment yield data set must be developed. Development of long-term measured sediment yield data cannot be overemphasized. Long-term monitoring is essential for accurate characterization of watershed processes. It is recommended that a long-term monitoring program be set up to measure watershed erosion rates and channel sediment transport rates.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Design of a high efficiency 30 kW boost composite converter

An experimental 30 kW boost composite converter is described in this paper. The composite converter architecture, which consists of a buck module, a boost module, and a dual active bridge module that operates as a DC transformer (DCX), leads to substantial reductions in losses at partial power points, and to significant improvements in weighted efficiency in applications that require wide variations in power and conversion ratio. A comprehensive loss model is developed, accounting for semiconductor conduction and switching losses, capacitor losses, as well as dc and ac losses in magnetic components. Based on the developed loss model, the module and system designs are optimized to maximize efficiency at a 50% power point. Experimental results for the 30 kW prototype demonstrate 98.5%peak efficiency, very high efficiency over wide ranges of power and voltage conversion ratios, as well as excellent agreements between model predictions and measured efficiency curves.

42 ENGINEERING↗
DOE OSTI2023

Mechanism of structural colors in binary mixtures of nanoparticle-based supraballs

Inspired by structural colors in avian species, various synthetic strategies have been developed to produce noniridescent, saturated colors using nanoparticle assemblies. Nanoparticle mixtures varying in particle chemistry and size have additional emergent properties that affect the color produced. For complex multicomponent systems, understanding the assembled structure and a robust optical modeling tool can empower scientists to identify structure-color relationships and fabricate designer materials with tailored color. Here, we demonstrate how we can reconstruct the assembled structure from small-angle scattering measurements using the computational reverse-engineering analysis for scattering experiments method and use the reconstructed structure in finite-difference time-domain calculations to predict color. We successfully, quantitatively predict experimentally observed color in mixtures containing strongly absorbing nanoparticles and demonstrate the influence of a single layer of segregated nanoparticles on color produced. The versatile computational approach that we present is useful for engineering synthetic materials with desired colors without laborious trial-and-error experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $ \$ $6B annually (at $ \$ $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year), and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE) missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

42 ENGINEERING↗
DOE OSTI2022

Experimental realization of an extended Fermi-Hubbard model using a 2D lattice of dopant-based quantum dots

The Hubbard model is an essential tool for understanding many-body physics in condensed matter systems. Artificial lattices of dopants in silicon are a promising method for the analog quantum simulation of extended Fermi-Hubbard Hamiltonians in the strong interaction regime. However, complex atom-based device fabrication requirements have meant emulating a tunable two-dimensional Fermi-Hubbard Hamiltonian in silicon has not been achieved. Here, we fabricate 3 × 3 arrays of single/few-dopant quantum dots with finite disorder and demonstrate tuning of the electron ensemble using gates and probe the many-body states using quantum transport measurements. By controlling the lattice constants, we tune the hopping amplitude and long-range interactions and observe the finite-size analogue of a transition from metallic to Mott insulating behavior. We simulate thermally activated hopping and Hubbard band formation using increased temperatures. As atomically precise fabrication continues to improve, these results enable a new class of engineered artificial lattices to simulate interactive fermionic models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A Functional All-Hazard Approach to Critical Infrastructure Dependency Analysis

The critical infrastructures protection landscape is a vast and varied pattern of independent, but interconnected infrastructure systems that are essential to the function of our modern society. The U.S. policy on critical infrastructure protection has been continually evolving since the “President’s Commission on Critical Infrastructure Protection” was published in 1997. In response to these policies, federal, state, and local governments, along with research institutions, have invested a substantial amount of time and effort into identifying and analyzing critical infrastructure, their functions, and dependencies/interdependencies to better understand their vulnerabilities. To date, the ability to assess vulnerabilities, resiliency, and priorities for protecting interdependent critical infrastructure systems from an all-hazards perspective remains a difficult problem. In this paper we introduce the All-Hazards Analysis (AHA) methodology, which provides an integrated functional basis across infrastructure systems, through the implementation of a common language and a scalable level of decomposition to effectively evaluate the resilience of interconnected infrastructure systems. AHA models infrastructure systems as directed multidimensional graphs, which enable the evaluation of cross-sector interdependencies prior to, during, and after disruptive events. Finally, and by design, AHA enables the cross linking of data taxonomies to enable more effective data sharing, such as the National Critical Functions (NCF) and Infrastructure Data Taxonomy (IDT).

02 PETROLEUM↗
DOE OSTI2023

Thermodynamic modeling of double azeotropy systems

Azeotropes are boiling liquid mixtures which have identical vapor and liquid compositions. Known as polyazeotropy, azeotropic mixtures may exhibit more than one azeotrope at a given temperature or pressure. While the thermodynamic condition for azeotrope formation in vapor-liquid equilibrium is well established, thermodynamic modeling of mixtures exhibiting polyazeotropy remains challenging and prior modeling studies often failed to satisfactorily correlate the polyazeotropic behavior of associating systems. Based on the association NRTL model, this study presents successful correlation and analysis of double azeotrope formation for three diverse systems: strongly associating methanol + diethylamine binary, weakly associating ammonia + pentafluoroethane binary, and non-associating benzene + hexafluorobenzene binary systems. Furthermore, these systems exhibit polyazeotropic behavior as the temperature increases and approaches their Bancroft points.

42 ENGINEERING↗
DOE OSTI2018

Advanced trust region optimization strategies for glass box / black box models

We present an improved trust region filter (TRF) method for optimization of combined glass box/black box systems. Glass box systems refer to models that are easily expressed in an algebraic modeling language, providing cheap and accurate derivative information. By contrast, black box systems may be computationally expensive and derivatives are unavailable. The TRF method, as first introduced in our previous work (Eason and Biegler, AIChE J . 2016; 62:3124–3136), is able to handle hybrid systems containing both glass and black box components, which can frequently arise in chemical engineering, for example, when a multiphase reactor model is included in a flow sheet optimization problem. We discuss several recent modifications in the algorithm such as the sampling region, which maintains the algorithm's global convergence properties without requiring the trust region to shrink to zero in the limit. To benchmark the development of this optimization method, a test set of problems is generated based on modified problems from the CUTEr and COPS sets. The modified algorithm demonstrates improved performance using the test problem set. Finally, the algorithm is implemented within the Pyomo environment and demonstrated on a rigorous process optimization case study for carbon capture. © 2018 American Institute of Chemical Engineers AIChE J , 64: 3934–3943, 2018

Eason, John P.↗
DOE OSTI2025

Identification of short-range ordering motifs in semiconductors

Chemical short-range ordering is expected to be a key factor for tuning the electronic structure of semiconductors. However, experimental evidence of short-range ordering is still lacking due to the challenge of characterizing atomic-scale ordering motifs. Here, we determined the presence of short-range order in a ternary GeSiSn semiconductor system using advanced energy-filtered four-dimensional scanning transmission electron microscopy and large-scale atomistic models generated by a machine learning neuroevolution potential of first-principles accuracy. This approach revealed preferred ordering of different atomic species with the dominant occurrence of Si–Ge–Sn triplets. Our findings not only confirmed the presence of short-range order but also directly revealed the actual atomic structure, demonstrating the potential for informed atomic order–based band engineering as a third degree of freedom beyond composition and strain tuning.

Vogl, Lilian M. [University of California Berkeley↗
DOE OSTI2021

EKAT v.1.0

E3SM Kokkos Application Toolkit (EKAT) is a collection of C++, Fortran, and CMake utilities for providing a single implementation of common kernels based on the Kokkos programming model. The library contains utilities for vectorization, tridiagonal linear system solvers, and linear interpolation as well as some general-purpose utilities such as testing utilities, parameter lists, representation of physical units, and additional interfaces. The goal is to provide a centralized implementation for high-performance computing structures and common utilities that reduce code duplication and streamline maintenance efforts. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2022-1327 O

Bertagna, Luca↗
DOE OSTI2013

SciDAC-Center for Plasma Edge Simulation Report

The Common Component Architecture (CCA) effort is the embodiment of a long-range program of research and development into the formulation, roles, and use of component technologies in high-performance scientific computing. CCA components can interoperate with other components in a variety of frameworks, including SCIRun2 from the University of Utah. The SCIRun2 framework is also developing the ability to connect components from a variety of different models through a mechanism called meta-components. The meta component model operates by providing a plugin architecture for component models. Abstract components are manipulated and managed by the SCIRun2 framework, while concrete component models perform the actual work and communicate with each other directly. We will leverage the SCIRun2 framework and the Kepler system to orchestrate components in the Fusion Simulation Project (FSP) and to provide a CCA-based interface with Kepler. The groundwork for this functionality is being performed with the Scientific Data Management center. The SDM center is developing CCA-compliant interfaces for expressing and executing workflows and create workflow components based on SCIRun and Ptolemy (Kepler) execution engines, including development of uniform interfaces for selecting, starting, and monitoring scientific workflows. Accomplishments include Introduction to CCA and Simulation Software Systems, Introduction into SCIRun2 and Bridging within SCIRun2, CCALoop: A scalable design for a distributed component framework, and Combining Workflow methodologies with Component Architectures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

MO-E-18C-01: Open Access Web-Based Peer-To-Peer Training and Education in Radiotherapy

Purpose: Current training and education delivery models have limitations which result in gaps in clinical proficiency with equipment, procedures, and techniques. Educational and training opportunities offered by vendors and professional societies are by their nature not available at point of need or for the life of clinical systems. The objective of this work is to leverage modern communications technology to provide peer-to-peer training and education for radiotherapy professionals, in the clinic and on demand, as they undertake their clinical duties. Methods: We have developed a free of charge web site ( https://i.treatsafely.org ) using the Google App Engine and datastore (NDB, GQL), Python with AJAX-RPC, and Javascript. The site is a radiotherapy-specific hosting service to which user-created videos illustrating clinical or physics processes and other relevant educational material can be uploaded. Efficient navigation to the material of interest is provided through several RT specific search tools and videos can be scored by users, thus providing comprehensive peer review of the site content. The site also supports multilingual narration\translation of videos, a quiz function for competence assessment and a library function allowing groups or institutions to define their standard operating procedures based on the video content. Results: The website went live in August 2013 and currently has over 680 registered users from 55 countries; 27.2% from the United States, 9.8% from India, 8.3% from the United Kingdom, 7.3% from Brazil, and 47.5% from other countries. The users include physicists (57.4%), Oncologists (12.5%), therapists (8.2%) and dosimetrists (4.8%). There are 75 videos to date including English, Portuguese, Mandarin, and Thai. Conclusion: Based on the initial acceptance of the site, we conclude that this open access web-based peer-to-peer tool is fulfilling an important need in radiotherapy training and education. Site functionality should expand in the future to include document sharing and continuing education credits.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2012

A Soft-Switching Inverter for High-Temperature Advanced Hybrid Electric Vehicle Traction Motor Drives

The state-of-the-art hybrid electric vehicles (HEVs) require the inverter cooling system to have a separate loop to avoid power semiconductor junction over temperatures because the engine coolant temperature of 105°C does not allow for much temperature rise in silicon devices. The proposed work is to develop an advanced soft-switching inverter that will eliminate the device switching loss and cut down the power loss so that the inverter can operate at high-temperature conditions while operating at high switching frequencies with small current ripple in low inductance based permanent magnet motors. The proposed tasks also include high-temperature packaging and thermal modeling and simulation to ensure the packaged module can operate at the desired temperature. The developed module will be integrated with the motor and vehicle controller for dynamometer and in-vehicle testing to prove its superiority. This report will describe the detailed technical design of the soft-switching inverters and their test results. The experiments were conducted both in module level for the module conduction and switching characteristics and in inverter level for its efficiency under inductive and dynamometer load conditions. The performance will be compared with the DOE original specification.

33 ADVANCED PROPULSION SYSTEMS↗