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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

RENEWABLE ENERGY AND ENVIRONMENTAL SUSTAINABILITY USING BIOMASS FROM DAIRY AND BEEF ANIMAL PRODUCTION

The Texas Panhandle is regarded as the "Cattle Feeding Capital of the World", producing 42% of the fed beef cattle in the United States within a 200-mile radius of Amarillo generating more than 5 million tons of feedlot manure /year. Apart from feedlots, the Bosque River Region in Erath County, just north of Waco, Texas with about 110,000 dairy cattle in over 250 dairies, produces 1.8 million tons of manure biomass (excreted plus bedding) per year. While the feedlot manure has been used extensively for irrigated and dry land crop production, most dairies, as well as other concentrated animal feeding operations (CAFO's), the dairy farms utilize large lagoon areas to store wet animal biomass. Water runoff from these lagoons has been held responsible for the increased concentration of phosphorus and other contaminates in the Bosque River which drains into Lake Waco—the primary source of potable water for Waco's 108,500 people. The concentrated animal feeding operations may lead to land, water, and air pollution if waste handling systems and storage and treatment structures are not properly managed. Manure-based biomass (MBB) has the potential to be a source of green energy at large coal-fired power plants and on smaller-scale combustion systems at or near confined animal feeding operations. Although MBB particularly cattle biomass (CB) is a low quality fuel with an inferior heat value compared to coal and other fossil fuels, the concentration of it at large animal feeding operations can make it a viable source of fuel. The overall objective of this interdisciplinary proposal is to develop environmentally benign technologies to convert low-value inventories of dairy and beef cattle biomass into renewable energy. Current research expands the suite of technologies by which cattle biomass (CB: manure, and premature mortalities) could serve as a renewable alternative to fossil fuel. The work falls into two broad categories of research and development. Category 1 – Renewable Energy Conversion. This category addressed mostly in volume I involves developing. Thermo-chemical conversion technologies including cofiring with coal, reburn to reduce nitrogen oxide (NO, N2O, NOx, etc.) and Hg emissions and gasification to produce low-BTU gas for on-site power production in order to extract energy from waste streams or renewable resources. Category 2 – Biomass Resource Technology. This category, addressed mostly in Volume II, deals with the efficient and cost-effective use of CB as a renewable energy source (e.g. through and via aqueous-phase, anaerobic digestion or biological gasification). The investigators formed an industrial advisory panel consisting fuel producers (feedlots and dairy farms) and fuel users (utilities), periodically met with them, and presented the research results; apart from serving as dissemination forum, the PIs used their critique to red-direct the research within the scope of the tasks. The final report for the 5 to 7 year project performed by an interdisciplinary team of 9 professors is arranged in three volumes: Vol. I (edited by Kalyan Annamalai) addressing thermo-chemical conversion and direct combustion under Category 1 and Vol. II and Vol. III ( edited by J M Sweeten) addressing biomass resource Technology under Category 2. Various tasks and sub-tasks addressed in Volume I were performed by the Department of Mechanical Engineering (a part of TEES; see Volume I), while other tasks and sub-tasks addressed in Volume II and IIII were conducted by Texas AgriLife Research at Amarillo; the TAMU Biological & Agricultural Engineering Department (BAEN) College Station; and West Texas A&M University (WTAMU) (Volumes II and III). The three volume report covers the following results: fuel properties of low ash and high ash CB (particularly DB) and MB (mortality biomass and coals, non-intrusive visible infrared (NVIR) spectroscopy techniques for ash determination, dairy energy use surveys at 14 dairies in Texas and California, cofiring of low quality CB with high quality coal, emission results and ash fouling behavior, using CB as reburn fuel for NOx and Hg reduction, gasification of fuels to produce low quality gases, modeling of reburn, pilot scale test results, synthesis of engineering characterization, geographical mapping, a transportation cost study to determine potential handling and transportation systems for co-firing with coal at regional coal-fired power plants, software analyses for the design of off-site manure, pre-processing and storage systems for a typical dairy farm or beef cattle feedlot, recursive production functions/systems models for both cattle feedlots, systems modeling, stocks and flows of energy involved in the CAFO system, feedback from an Industry Advisory Committee (IAC) to the investigators on project direction and task emphasis and economics of using CB as cofiring and reburn fuel.

09 BIOMASS FUELS↗
DOE OSTI2012

RENEWABLE ENERGY AND ENVIRONMENTAL SUSTAINABILITY USING BIOMASS FROM DAIRY AND BEEF ANIMAL PRODUCTION

The Texas Panhandle is regarded as the "Cattle Feeding Capital of the World", producing 42% of the fed beef cattle in the United States within a 200-mile radius of Amarillo generating more than 5 million tons of feedlot manure/year. Apart from feedlots, the Bosque River Region in Erath County, just north of Waco, Texas with about 110,000 dairy cattle in over 250 dairies, produces 1.8 million tons of manure biomass (excreted plus bedding) per year. While the feedlot manure has been used extensively for irrigated and dry land crop production, most dairies, as well as other concentrated animal feeding operations (CAFO's), the dairy farms utilize large lagoon areas to store wet animal biomass. Water runoff from these lagoons has been held responsible for the increased concentration of phosphorus and other contaminates in the Bosque River which drains into Lake Waco -- the primary source of potable water for Waco's 108,500 people. The concentrated animal feeding operations may lead to land, water, and air pollution if waste handling systems and storage and treatment structures are not properly managed. Manure-based biomass (MBB) has the potential to be a source of green energy at large coal-fired power plants and on smaller-scale combustion systems at or near confined animal feeding operations. Although MBB particularly cattle biomass (CB) is a low quality fuel with an inferior heat value compared to coal and other fossil fuels, the concentration of it at large animal feeding operations can make it a viable source of fuel. The overall objective of this interdisciplinary proposal is to develop environmentally benign technologies to convert low-value inventories of dairy and beef cattle biomass into renewable energy. Current research expands the suite of technologies by which cattle biomass (CB: manure, and premature mortalities) could serve as a renewable alternative to fossil fuel. The work falls into two broad categories of research and development. Category 1 -- Renewable Energy Conversion. This category addressed mostly in volume I involves developing. Thermo-chemical conversion technologies including cofiring with coal, reburn to reduce nitrogen oxide (NO, N2O, NOx, etc.) and Hg emissions and gasification to produce low-BTU gas for on-site power production in order to extract energy from waste streams or renewable resources. Category 2 -- Biomass Resource Technology. This category, addressed mostly in Volume II, deals with the efficient and cost-effective use of CB as a renewable energy source (e.g. through and via aqueous-phase, anaerobic digestion or biological gasification). The investigators formed an industrial advisory panel consisting fuel producers (feedlots and dairy farms) and fuel users (utilities), periodically met with them, and presented the research results; apart from serving as dissemination forum, the PIs used their critique to re-direct the research within the scope of the tasks. The final report for the 5 to 7 year project performed by an interdisciplinary team of 9 professors is arranged in three volumes: Vol. I (edited by Kalyan Annamalai) addressing thermo-chemical conversion and direct combustion under Category 1 and Vol. II and Vol. III ( edited by J M Sweeten) addressing biomass resource Technology under Category 2. Various tasks and sub-tasks addressed in Volume I were performed by the Department of Mechanical Engineering (a part of TEES; see Volume I), while other tasks and sub-tasks addressed in Volume II and IIII were conducted by Texas AgriLife Research at Amarillo; the TAMU Biological & Agricultural Engineering Department (BAEN) College Station; and West Texas A&M University (WTAMU) (Volumes II and III). The three volume report covers the following results: fuel properties of low ash and high ash CB (particularly DB) and MB (mortality biomass and coals, non-intrusive visible infrared (NVIR) spectroscopy techniques for ash determination, dairy energy use surveys at 14 dairies in Texas and California, cofiring of low quality CB with high quality coal, emission results and ash fouling behavior, using CB as reburn fuel for NOx and Hg reduction, gasification of fuels to produce low quality gases, modeling of reburn, pilot scale test results, synthesis of engineering characterization, geographical mapping, a transportation cost study to determine potential handling and transportation systems for co-firing with coal at regional coal-fired power plants, software analyses for the design of off-site manure, pre-processing and storage systems for a typical dairy farm or beef cattle feedlot, recursive production functions/systems models for both cattle feedlots, systems modeling, stocks and flows of energy involved in the CAFO system, feedback from an Industry Advisory Committee (IAC) to the investigators on project direction and task emphasis and economics of using CB as cofiring and reburn fuel.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2013

Mathematical and Computational Tools for Predictive Simulation of Complex Coupled Systems under Uncertainty

Methods and algorithms are developed to enable the accurate analysis of problems that exhibit interacting physical processes with uncertainties. These uncertainties can pertain either to each of the physical processes or to the manner in which they depend on each others. These problems are cast within a polynomial chaos framework and their solution then involves either solving a large system of algebraic equations or a high dimensional numerical quadrature. In both cases, the curse of dimensionality is manifested. Procedures are developed for the efficient evaluation of the resulting linear equations that advantage of the block sparse structure of these equations, resulting in a block recursive Schur complement construction. In addition, embedded quadratures are constructed that permit the evaluation of very high-dimensional integrals using low-dimensional quadratures adapted to particular quantities of interest. The low-dimensional integration is carried out in a transformed measure space in which the quantity of interest is low-dimensional. Finally, a procedure is also developed to discover a low-dimensional manifold, embedded in the initial high-dimensional one, in which scalar quantities of interest exist. This approach permits the functional expression of the reduced space in terms of the original space, thus permitting cross-scale sensitivity analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Automation of The Guiding Center Expansion

We report on the use of the recently-developed Mathematica package VEST (Vector Einstein Summation Tools) to automatically derive the guiding center transformation. Our Mathematica code employs a recursive procedure to derive the transformation order-by-order. This procedure has several novel features. (1) It is designed to allow the user to easily explore the guiding center transformation’s numerous nonunique forms or representations. (2) The procedure proceeds entirely in cartesian position and velocity coordinates, thereby producing manifestly gyrogauge invariant results; the commonly-used perpendicular unit vector fields e 1 , e 2 are never even introduced. (3) It is easy to apply in the derivation of higher-order contributions to the guiding center transformation without fear of human error. Our code therefore stands as a useful tool for exploring subtle issues related to the physics of toroidal momentum conservation in tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

DC buffering and floating current for a high voltage IMB application

An interface technique for the latest generation of the Impedance Measurement Box (IMB) has been conceived to enable measurement of impedance spectra for battery modules up to 300V. A 300V capable or higher IMB is an enabling technology for in-situ diagnostics within electric vehicle charging stations or battery back-ups within power distribution sub-stations. It is possible that the existing IMB can be adapted via a 300V interface module to a test battery with voltage significantly greater than 50V. Recently a new concept was conceived for the calibration, algorithm and electronics of the IMB. That algorithm and calibration for that concept have been physically validated. The principal feature of the new electronics is the floating current source excitation of the battery under test. The single ended current excitation of the battery under test, used in the 50V IMB, requires that the negative terminal of the test battery must be the analog ground for the IMB. The new floating current technique allows the test battery to be fully high impedance isolated for a measurement. That isolation will improve IMB noise immunity and enable interrogation of cells internal to a battery module. All these techniques still use the same rapid concept for impedance measurement with the IMB. The purpose of this disclosure is to provide an overview of the analytical validation for three concepts to interface the floating current excitation to a high voltage battery. Recursive simulation models were used in different test scenarios to validate the various new concepts. The analysis will show that it is possible to interface the floating signal current to obtain an impedance measurement on a high voltage test battery. Additionally, the analysis will investigate stress seen by electronics while testing a 300V battery.

25 ENERGY STORAGE↗
DOE OSTI2014

Parallel Block Structured Adaptive Mesh Refinement on Graphics Processing Units

Block-structured adaptive mesh refinement is a technique that can be used when solving partial differential equations to reduce the number of zones necessary to achieve the required accuracy in areas of interest. These areas (shock fronts, material interfaces, etc.) are recursively covered with finer mesh patches that are grouped into a hierarchy of refinement levels. Despite the potential for large savings in computational requirements and memory usage without a corresponding reduction in accuracy, AMR adds overhead in managing the mesh hierarchy, adding complex communication and data movement requirements to a simulation. In this paper, we describe the design and implementation of a native GPU-based AMR library, including: the classes used to manage data on a mesh patch, the routines used for transferring data between GPUs on different nodes, and the data-parallel operators developed to coarsen and refine mesh data. We validate the performance and accuracy of our implementation using three test problems and two architectures: an eight-node cluster, and over four thousand nodes of Oak Ridge National Laboratory’s Titan supercomputer. Our GPU-based AMR hydrodynamics code performs up to 4.87× faster than the CPU-based implementation, and has been scaled to over four thousand GPUs using a combination of MPI and CUDA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

User Manual for the PROTEUS Mesh Tools

This report describes the various mesh tools that are provided with the PROTEUS code giving both descriptions of the input and output. In many cases the examples are provided with a regression test of the mesh tools. The most important mesh tools for any user to consider using are the MT_MeshToMesh.x and the MT_RadialLattice.x codes. The former allows the conversion between most mesh types handled by PROTEUS while the second allows the merging of multiple (assembly) meshes into a radial structured grid. Note that the mesh generation process is recursive in nature and that each input specific for a given mesh tool (such as .axial or .merge) can be used as “mesh” input for any of the mesh tools discussed in this manual.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

SchemaOnRead Manual

SchemaOnRead provides tools for implementing schema-on-read including a single function call (e.g., schemaOnRead("filename")) that reads text (TXT), comma separated value (CSV), raster image (BMP, PNG, GIF, TIFF, and JPG), R data (RDS), HDF5, NetCDF, spreadsheet (XLS, XLSX, ODS, and DIF), Weka Attribute-Relation File Format (ARFF), Epi Info (REC), Pajek network (PAJ), R network (NET), Hypertext Markup Language (HTML), SPSS (SAV), Systat (SYS), and Stata (DTA) files. It also recursively reads folders (e.g., schemaOnRead("folder")), returning a nested list of the contained elements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Highlights of X-Stack ExM Deliverable Swift/T

Swift/T is a key success from the ExM: System support for extreme-scale, many-task applications1 X-Stack project, which proposed to use concurrent dataflow as an innovative programming model to exploit extreme parallelism in exascale computers. The Swift/T component of the project reimplemented the Swift language from scratch to allow applications that compose scientific modules together to be build and run on available petascale computers (Blue Gene, Cray). Swift/T does this via a new compiler and runtime that generates and executes the application as an MPI program. We assume that mission-critical emerging exascale applications will be composed as scalable applications using existing software components, connected by data dependencies. Developers wrap native code fragments using a higherlevel language, then build composite applications to form a computational experiment. This exemplifies hierarchical concurrency: lower-level messaging libraries are used for fine-grained parallelism; highlevel control is used for inter-task coordination. These patterns are best expressed with dataflow, but static DAGs (i.e., other workflow languages) limit the applications that can be built; they do not provide the expressiveness of Swift, such as conditional execution, iteration, and recursive functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Generalization of mixed multiscale finite element methods with applications

Many science and engineering problems exhibit scale disparity and high contrast. The small scale features cannot be omitted in the physical models because they can affect the macroscopic behavior of the problems. However, resolving all the scales in these problems can be prohibitively expensive. As a consequence, some types of model reduction techniques are required to design efficient solution algorithms. For practical purpose, we are interested in mixed finite element problems as they produce solutions with certain conservative properties. Existing multiscale methods for such problems include the mixed multiscale finite element methods. We show that for complicated problems, the mixed multiscale finite element methods may not be able to produce reliable approximations. This motivates the need of enrichment for coarse spaces. Two enrichment approaches are proposed, one is based on generalized multiscale finte element metthods (GMsFEM), while the other is based on spectral element-based algebraic multigrid (rAMGe). The former one, which is called mixed GMsFEM, is developed for both Darcy’s flow and linear elasticity. Application of the algorithm in two-phase flow simulations are demonstrated. For linear elasticity, the algorithm is subtly modified due to the symmetry requirement of the stress tensor. The latter enrichment approach is based on rAMGe. The algorithm differs from GMsFEM in that both of the velocity and pressure spaces are coarsened. Due the multigrid nature of the algorithm, recursive application is available, which results in an efficient multilevel construction of the coarse spaces. Stability, convergence analysis, and exhaustive numerical experiments are carried out to validate the proposed enrichment approaches. iii

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

User Manual for the PROTEUS Mesh Tools

PROTEUS is built around a finite element representation of the geometry for visualization. In addition, the PROTEUS-SN solver was built to solve the even-parity transport equation on a finite element mesh provided as input. Similarly, PROTEUS-MOC and PROTEUS-NEMO were built to apply the method of characteristics on unstructured finite element meshes. Given the complexity of real world problems, experience has shown that using commercial mesh generator to create rather simple input geometries is overly complex and slow. As a consequence, significant effort has been put into place to create multiple codes that help assist in the mesh generation and manipulation. There are three input means to create a mesh in PROTEUS: UFMESH, GRID, and NEMESH. At present, the UFMESH is a simple way to generate two-dimensional Cartesian and hexagonal fuel assembly geometries. The UFmesh input allows for simple assembly mesh generation while the GRID input allows the generation of Cartesian, hexagonal, and regular triangular structured grid geometry options. The NEMESH is a way for the user to create their own mesh or convert another mesh file format into a PROTEUS input format. Given that one has an input mesh format acceptable for PROTEUS, we have constructed several tools which allow further mesh and geometry construction (i.e. mesh extrusion and merging). This report describes the various mesh tools that are provided with the PROTEUS code giving both descriptions of the input and output. In many cases the examples are provided with a regression test of the mesh tools. The most important mesh tools for any user to consider using are the MT_MeshToMesh.x and the MT_RadialLattice.x codes. The former allows the conversion between most mesh types handled by PROTEUS while the second allows the merging of multiple (assembly) meshes into a radial structured grid. Note that the mesh generation process is recursive in nature and that each input specific for a given mesh tool (such as .axial or .merge) can be used as “mesh” input for any of the mesh tools discussed in this manual.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

High-Throughput Combinatorial Development of High-Entropy Alloys For Light-Weight Structural Applications

The primary limitation of today’s lightweight structural alloys is that specific yield strengths (SYS) higher than 200MPa x cc/g (typical value for titanium alloys) are extremely difficult to achieve. This holds true especially at a cost lower than 5dollars/kg (typical value for magnesium alloys). Recently, high-entropy alloys (HEA) have shown promising SYS, yet the large composition space of HEA makes screening compositions complex and time-consuming. Over the course of this 2-year project we started from 150 billion compositions and reduced the number of potential low-density (<5g/cc), low-cost (<5dollars/kg) high-entropy alloy (LDHEA) candidates that are single-phase, disordered, solid-solution (SPSS) to a few thousand compositions. This was accomplished by means of machine learning to guide design for SPSS LDHEA based on a combination of recursive partitioning, an extensive, experimental HEA database compiled from 24 literature sources, and 91 calculated parameters serving as phenomenological selection rules. Machine learning shows an accuracy of 82% in identifying which compositions of a separate, smaller, experimental HEA database are SPSS HEA. Calculation of Phase Diagrams (CALPHAD) shows an accuracy of 71-77% for the alloys supported by the CALPHAD database, where 30% of the compiled HEA database is not supported by CALPHAD. In addition to machine learning, and CALPHAD, a third tool was developed to aid design of SPSS LDHEA. Phase diagrams were calculated by constructing the Gibbs-free energy convex hull based on easily accessible enthalpy and entropy terms. Surprisingly, accuracy was 78%. Pursuing these LDHEA candidates by high-throughput experimental methods resulted in SPSS LDHEA composed of transition metals (e.g. Cr, Mn, Fe, Ni, Cu) alloyed with Al, yet the high concentration of Al, necessary to bring the mass density below 5.0g/cc, makes these materials hard and brittle, body-centered-cubic (BCC) alloys. A related, yet multi-phase BCC alloy, based on Al-Cr-Fe-Ni, shows compressive strain >10% and specific compressive yield strength of 229 MPa x cc/g, yet does not show ductility in tensile tests due to cleavage. When replacing Cr in Al-Cr-Fe-based 4- and 5-element LDHEA with Mn, hardness drops 2x. Combined with compression test results, including those on the ternaries Al-Cr-Fe and Al-Mn-Fe suggest that Al-Mn-Fe-based LDHEA are still worth pursuing. These initial results only represent one compressive stress-strain curve per composition without any property optimization. As such, reproducibility needs to be followed by optimization to show their full potential. When including Li, Mg, and Zn, single-phase Li-Mg-Al-Ti-Zn LDHEA has been found with a specific ultimate compressive strength of 289MPa x cc/g. Al-Ti-Mn-Zn showed a specific ultimate compressive strength of 73MPa x cc/g. These initial results after hot isostatic pressing (HIP) of the ball-milled powders represent the lower end of what is possible, since no secondary processing (e.g. extrusion) has been performed to optimize strength and ductility. Compositions for multi-phase (e.g. dual-phase) LDHEA were identified largely by automated searches through CALPHAD databases, while screening for large face-centered-cubic (FCC) volume fractions, followed by experimental verification. This resulted in several new alloys. Li-Mg-Al-Mn-Fe and Mg-Mn-Fe-Co ball-milled powders upon HIP show specific ultimate compressive strengths of 198MPa x cc/g and 45MPa x cc/g, respectively. Several malleable quarternary Al-Zn-based alloys have been found upon arc/induction melting, yet with limited specific compressive yield strength (<75 MPa x cc/g). These initial results are all without any optimization for strength and/or ductility. High-throughput experimentation allowed us to triple the existing experimental HEA database as published in the past 10 years in less than 2 years which happened at a rate 10x higher than previous methods. Furthermore, we showed that high-throughput thin-film combinatorial methods can be used to get insight in isothermal phase diagram slices. Although it is straightforward to map hardness as a function of composition for sputtered, thin-film, compositional gradients by nano-indentation and compare the results to micro-indentation on bulk samples, the simultaneous impact of composition, roughness, film density, and microstructure on hardness requires monitoring all these properties as a function of location on the compositional gradient, including dissecting the impact of these 4 factors on the hardness map. These additional efforts impact throughput significantly. This work shows that a lot of progress has been made over the years in predicting phase formation that aids the discovery of new alloys, yet that a lot of work needs to be done to predict phases more accurately for LDHEA, whether done by CALPHAD or by other means. More importantly, more work needs to be done to predict mechanical properties of novel alloys, like yield strength, and ductility. Furthermore, this work shows that there is a need for the generation of an empirical alloy database covering strategic points in a multi-dimensional composition space to allow for faster and more accurate predictive interpolations to identify the oasis in the dessert more quickly. Finally, this work suggests that it is worth pursuing a ductile alloy with a SYS > 300 MPa x cc/g in a mass density range of 6-7 g/cc, since the chances for a single-phase or majority-phase FCC increase significantly. Today’s lightweight steels are in this density range.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A JSON to CMake tool

As part of the conversion of the MCNP®testing system to CTest, a tool was needed to generate CMake variables from JSON formatted data structure. The system was first implemented using the JSON to CMake parser which with some slight modifications provided data useful for the MCNP CTest testing system. Upon further evaluation, we found it to be slow when setting up the 1600 tests during the CMake configuration step. Not only was it slow, it did not validate the JSON files during configuration allowing for unexpected behavior. Upon some web searching we came across a simple recursive python example that would provide the same capability needed by our testing system. This python example was effectively wrapped into a single python script that was used to convert the JSON data into CMake variables. This tool is attached as json_to_cmake.py and is listed in the appendix. In essence, the tool provides single variables for each value defined within the JSON file. The name for the variable is constructed by using the associated object names and array indicies needed to deference the variable itself. In the following sections, examples are provided to better describe how this is done.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Operational Planning Strategy for a Sequim Bay Underwater UXO Test Site (SBU2)

The Department of Defense (DoD) has identified more than 400 underwater sites that are potentially contaminated with munitions. Most areas are in shallow water (0-35 m) where the munitions pose a threat to human health and the environment. The Strategic Environmental Research and Development Program (SERDP) and Environmental Security Technology Certification Program (ESTCP) held a workshop in 2018 to establish the requirements, framework, protocols, responsibilities, and time lines for development of a series of underwater UXO Standardized Demonstration sites (“test beds”) for the purpose of testing the efficacy of sensors, equipment, and technologies to detect UXO. The workshop recommended a phased approach to building test beds to capitalize on: 1) incorporation of lessons learned from existing DoD funded projects, 2) leveraging existing underwater test bed environments funded by other programs, and 3) supporting recursive learning from early phases of the test bed development (i.e. efficiently optimizing through a scaling-up approach). This strategy document provides the framework to establish a working operational plan for the Sequim Bay Underwater UXO (SBU2) Test Bed (Phase II). Details of the plan are dependent on earlier phases of the effort as shown in Figure 1. Results from Phase I are being evaluated, and when complete, will inform the next iteration of this plan. As such, it should be considered a living document that will be informed by the lessons learned and updated as new information becomes known and requirements are refined. This approach will allow SBU2 to evolve to meet the needs of the remediation community.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Genetic Algorithm for Hyperparameter Optimization in Gaussian Process Modeling

A genetic algorithm is developed and applied to optimize hyperparameters of convolutional recursively determined dual neural network-Gaussian process (NNGP) kernels. As a specific application of the combined GPNN-GA algorithm, it is applied to image classification in publicly available data of Hyper Suprime-Cam Subaru Strategic Program. Matthews correlation coefficient is calculated based on results of binary star-galaxy classification and used as a fitting function of the GA module of the algorithm. The simulation results confirm significant improvement of the classification accuracy with optimized hyperparameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Learning Hidden Structure in Multi-Fidelity Information Sources for Efficient Uncertainty Quantification (LDRD 218317)

This report summarizes the work done under the Laboratory Directed Research and Development (LDRD) project entitled "Learning Hidden Structure in Multi-Fidelity Information Sources for Efficient Uncertainty Quantification". In this project we investigated multi-fidelity strategies for fusing data from information sources of varying cost and accuracy. Most existing strategies exploit hierarchical relationships between models, for example that occur when different models are generated by refining a numerical discretization parameter. In this work we focused on encoding the relationships between information sources using directed acyclic graphs. The multi-fidelity networks can have general structure and represent a significantly greater variety of modeling relationships than recursive networks used in the current state literature. Numerical results show that a non-hierarchical multi-fidelity Monte Carlo strategy can reduce the cost of estimating uncertainty in predictions of a model of plasma expanding in a vacuum by almost two orders of magnitude.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Cooperative Research and Development Agreement With Georgetown University Report: National Institutes of Health, National Center for Advancing Translational Sciences Clinical and Translational Science Award

Oak Ridge National Laboratory (ORNL) is participating with Georgetown University (GU) as a subrecipient in response to the National Institutes of Health (NIH), National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Award (CTSA) (U54 Clinical Trial Optional) funding opportunity announcement. This Cooperative Research and Development Agreement is put in to place to facilitate the development and implementation of clinical interventions that demonstrably improve human health is currently a complex, recursive, and inefficient process that leads to delays of years or decades before discoveries in biomedical research result in health benefits for patients and communities. NCATS conducts and supports research in the science of translation, to discover the mechanistic and operational principles of the intervention development and dissemination process, thereby providing the scientific foundation for improvements in translational efficiency that will accelerate the realization of interventions that improve human health. Under NCATS’ leadership, the CTSA Program supports a national network of medical research institutions called hubs. GU is the lead institution in one of the NIH hubs that was created as a result of a previous NIH CTSA. The missions of the GU have historically included the advancement of health through research in the clinical and biomedical sciences, the education of future leaders in medical and nursing practice and academia, and the provision of compassionate and scientifically competent patient care and service to the Washington, DC community and the nation. GU is the lead institution for the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS), a multi-institutional partnership of medical research institutions forged from a desire to promote clinical research and translational science. Through multiple collaborations among these institutions, GHUCCTS is transforming clinical research and translational science in order to bring new scientific advances to health care. Oak Ridge National Laboratory is the Department of Energy's (DOE) largest science and energy laboratory. Managed since April 2000 by a partnership of the University of Tennessee and Battelle, ORNL was established in 1943 as a part of the secret Manhattan Project to pioneer a method for producing and separating plutonium. During the 1950s and 1960s, ORNL became an international center for the study of nuclear energy and related research in the physical and life sciences. With the creation of DOE in the 1970s, ORNL's mission broadened to include a variety of energy technologies and strategies. Today the laboratory supports the nation with a peacetime science and technology mission that is just as important as, but very different from, its role during the Manhattan Project. ORNL is home to the world's premier center for high performance supercomputing to enable scientific discovery. ORNL has extensive expertise in various areas of computer science that are uniquely situated to support GU. Additionally, ORNL’s leading computational user facilities present a unique opportunity to leverage the largest scale machines for open science in support of the stated mission of the NCATS CTSA. ORNL's partnership with GU will offer unparalleled opportunity in data analytics, deep-learning, artificial intelligence, and urban dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

TEMPI: An Interposed MPI Library with Canonical Representation of MPI Datatypes [Poster]

TEMPI provides a transparent non-contiguous data-handling layer compatible with various MPIs. MPI Datatypes are a powerful abstraction for allowing an MPI implementation to operate on non-contiguous data. CUDA-aware MPI implementations must also manage transfer of such data between the host system and GPU. The non-unique and recursive nature of MPI datatypes mean that providing fast GPU handling is a challenge. The same noncontiguous pattern may be described in a variety of ways, all of which should be treated equivalently by an implementation. This work introduces a novel technique to do this for strided datatypes. Methods for transferring non-contiguous data between the CPU and GPU depends on the properties of the data layout. This work shows that a simple performance model can accurately select the fastest method. Unfortunately, the combination of MPI software and system hardware available may not provide sufficient performance. The contributions of this work are deployed on OLCF Summit through an interposer library which does not require privileged access to the system to use

97 MATHEMATICS AND COMPUTING↗