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At least 793 records · Page 44

Probing Complex Free-Radical Reaction Pathways of Fuel Model Compounds

Fossil (e.g. coal) and renewable (e.g. woody biomass) organic energy resources have received considerable attention as possible sources of liquid transportation fuels and commodity chemicals. Knowledge of the reactivity of these complex materials has been advanced through fundamental studies of organic compounds that model constituent substructures. In particular, an improved understanding of thermochemical reaction pathways involving free-radical intermediates has arisen from detailed experimental kinetic studies and, more recently, advanced computational investigations. In this presentation, we will discuss our recent investigations of the fundamental pyrolysis pathways of model compounds that represent key substructures in the lignin component of woody biomass with a focus on molecules representative of the dominant beta-O-4 aryl ether linkages. Additional mechanistic insights gleaned from DFT calculations on the kinetics of key elementary reaction steps will also be presented, as well as a few thoughts on the significant contributions of Jim Franz to this area of free radical chemistry.

01 COAL, LIGNITE, AND PEAT↗

Accelerating Satellite Image Based Large-Scale Settlement Detection with GPU

Computer vision algorithms for image analysis are often computationally demanding. Application of such algorithms on large image databases\---- such as the high-resolution satellite imagery covering the entire land surface, can easily saturate the computational capabilities of conventional CPUs. There is a great demand for vision algorithms running on high performance computing (HPC) architecture capable of processing petascale image data. We exploit the parallel processing capability of GPUs to present a GPU-friendly algorithm for robust and efficient detection of settlements from large-scale high-resolution satellite imagery. Feature descriptor generation is an expensive, but a key step in automated scene analysis. To address this challenge, we present GPU implementations for three different feature descriptors\-- multiscale Historgram of Oriented Gradients (HOG), Gray Level Co-Occurrence Matrix (GLCM) Contrast and local pixel intensity statistics. We perform extensive experimental evaluations of our implementation using diverse and large image datasets. Our GPU implementation of the feature descriptor algorithms results in speedups of 220 times compared to the CPU version. We present an highly efficient settlement detection system running on a multiGPU architecture capable of extracting human settlement regions from a city-scale sub-meter spatial resolution aerial imagery spanning roughly 1200 sq. kilometers in just 56 seconds with detection accuracy close to 90\%. This remarkable speedup gained by our vision algorithm maintaining high detection accuracy clearly demonstrates that such computational advancements clearly hold the solution for petascale image analysis challenges.

Patlolla, Dilip Reddy↗

Rolling Element Bearing Stiffness Matrix Determination (Presentation)

Current theoretical bearing models differ in their stiffness estimates because of different model assumptions. In this study, a finite element/contact mechanics model is developed for rolling element bearings with the focus of obtaining accurate bearing stiffness for a wide range of bearing types and parameters. A combined surface integral and finite element method is used to solve for the contact mechanics between the rolling elements and races. This model captures the time-dependent characteristics of the bearing contact due to the orbital motion of the rolling elements. A numerical method is developed to determine the full bearing stiffness matrix corresponding to two radial, one axial, and two angular coordinates; the rotation about the shaft axis is free by design. This proposed stiffness determination method is validated against experiments in the literature and compared to existing analytical models and widely used advanced computational methods. The fully-populated stiffness matrix demonstrates the coupling between bearing radial, axial, and tilting bearing deflections.

17 WIND ENERGY↗

Recovery Act. Development and Validation of an Advanced Stimulation Prediction Model for Enhanced Geothermal Systems

This research project aims to develop and validate an advanced computer model that can be used in the planning and design of stimulation techniques to create engineered reservoirs for Enhanced Geothermal Systems. The specific objectives of the proposal are to; Develop a true three-dimensional hydro-thermal fracturing simulator that is particularly suited for EGS reservoir creation; Perform laboratory scale model tests of hydraulic fracturing and proppant flow/transport using a polyaxial loading device, and use the laboratory results to test and validate the 3D simulator; Perform discrete element/particulate modeling of proppant transport in hydraulic fractures, and use the results to improve understand of proppant flow and transport; Test and validate the 3D hydro-thermal fracturing simulator against case histories of EGS energy production; and Develop a plan to commercialize the 3D fracturing and proppant flow/transport simulator. The project is expected to yield several specific results and benefits. Major technical products from the proposal include; A true-3D hydro-thermal fracturing computer code that is particularly suited to EGS; Documented results of scale model tests on hydro-thermal fracturing and fracture propping in an analogue crystalline rock; Documented procedures and results of discrete element/particulate modeling of flow and transport of proppants for EGS applications; and Database of monitoring data, with focus of Acoustic Emissions (AE) from lab scale modeling and field case histories of EGS reservoir creation.

15 GEOTHERMAL ENERGY↗

How DARHT Works - the World's Most Powerful X-ray Machine

The Dual Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory is an essential scientific tool that supports Stockpile Stewardship at the Laboratory. The World's most powerful x-ray machine, it's used to take high-speed images of mock nuclear devices - data that is used to confirm and modify advanced computer codes in assuring the safety, security, and effectiveness of the U.S. nuclear deterrent.

43 PARTICLE ACCELERATORS↗

Using Advanced Modeling to Accelerate the Scale-Up of Carbon Capture Technologies

Carbon capture and storage (CCS) is one of many approaches that are critical for significantly reducing domestic and global CO2 emissions. The U.S. Department of Energy’s Clean Coal Technology Program Plan envisions 2nd generation CO2 capture technologies ready for demonstration-scale testing around 2020 with the goal of enabling commercial deployment by 2025 [1]. Third generation technologies have a similarly aggressive timeline. A major challenge is that the development and scale-up of new technologies in the energy sector historically takes up to 15 years to move from the laboratory to pre-deployment and another 20 to 30 years for widespread industrial scale deployment. In order to help meet the goals of the DOE carbon capture program, the Carbon Capture Simulation Initiative (CCSI) was launched in early 2011 to develop, demonstrate, and deploy advanced computational tools and validated multi-scale models to reduce the time required to develop and scale up new carbon capture technologies. The CCSI Toolset (1) enables promising concepts to be more quickly identified through rapid computational screening of processes and devices, (2) reduces the time to design and troubleshoot new devices and processes by using optimization techniques to focus development on the best overall process conditions and by using detailed device-scale models to better understand and improve the internal behavior of complex equipment, and (3) provides quantitative predictions of device and process performance during scale up based on rigorously validated smaller scale simulations that take into account model and parameter uncertainty[2]. This article focuses on essential elements related to the development and validation of multi-scale models in order to help minimize risk and maximize learning as new technologies progress from pilot to demonstration scale.

Miller, David↗

Advanced modeling to accelerate the scale up of carbon capture technologies

In order to help meet the goals of the DOE carbon capture program, the Carbon Capture Simulation Initiative (CCSI) was launched in early 2011 to develop, demonstrate, and deploy advanced computational tools and validated multi-scale models to reduce the time required to develop and scale-up new carbon capture technologies. This article focuses on essential elements related to the development and validation of multi-scale models in order to help minimize risk and maximize learning as new technologies progress from pilot to demonstration scale.

20 FOSSIL-FUELED POWER PLANTS↗

Recovery Act. Development and Validation of an Advanced Stimulation Prediction Model for Enhanced Geothermal System

The research project aims to develop and validate an advanced computer model that can be used in the planning and design of stimulation techniques to create engineered reservoirs for Enhanced Geothermal Systems. The specific objectives of the proposal are to: 1) Develop a true three-dimensional hydro-thermal fracturing simulator that is particularly suited for EGS reservoir creation. 2) Perform laboratory scale model tests of hydraulic fracturing and proppant flow/transport using a polyaxial loading device, and use the laboratory results to test and validate the 3D simulator. 3) Perform discrete element/particulate modeling of proppant transport in hydraulic fractures, and use the results to improve understand of proppant flow and transport. 4) Test and validate the 3D hydro-thermal fracturing simulator against case histories of EGS energy production. 5) Develop a plan to commercialize the 3D fracturing and proppant flow/transport simulator. The project is expected to yield several specific results and benefits. Major technical products from the proposal include: 1) A true-3D hydro-thermal fracturing computer code that is particularly suited to EGS, 2) Documented results of scale model tests on hydro-thermal fracturing and fracture propping in an analogue crystalline rock, 3) Documented procedures and results of discrete element/particulate modeling of flow and transport of proppants for EGS applications, and 4) Database of monitoring data, with focus of Acoustic Emissions (AE) from lab scale modeling and field case histories of EGS reservoir creation.

15 GEOTHERMAL ENERGY↗

Systems Engineering Building Advances Power Grid Research

Researchers and industry are now better equipped to tackle the nation’s most pressing energy challenges through PNNL’s new Systems Engineering Building – including challenges in grid modernization, buildings efficiency and renewable energy integration. This lab links real-time grid data, software platforms, specialized laboratories and advanced computing resources for the design and demonstration of new tools to modernize the grid and increase buildings energy efficiency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Argonne News Brief: Cancer’s Big Data Problem

Data is pouring into the hands of cancer researchers, thanks to improvements in imaging, models and understanding of genetics. But they’ll need a lot of help—and some powerful supercomputers—to translate this data into better, more personalized treatment for cancer patients. A new initiative called the Joint Design of Advanced Computing Solutions for Cancer, which taps four different national laboratories, is poised to help.

60 APPLIED LIFE SCIENCES↗

Beam Dynamics Simulations in Project X RFQ with CST Studio Suite

Typically the RFQs are designed using the Parmteq, DesRFQ and other similar specialized codes, which produces the files containing the field and geometrical parameters for every cell. The beam dynamic simulations with these analytical fields a re, of course, ideal realizations of the designed RFQs. The new advanced computing capabilities made it possible to simulate beam and even dark current in the realistic 3D electromagnetic fields in the RFQs that may reflect cavity tuning, presence of tune rs and couplers, RFQ segmentation etc. The paper describes the utilization of full 3D field distribution obtained with CST Studio Suite for beam dynamic simulations using both PIC solver of CST Particle Studio and the beam dynamic code TRACK.

43 PARTICLE ACCELERATORS↗

Thermal Stratification Analysis for Sodium Fast Reactors

The sodium fast reactor (SFR) is the most mature reactor concept of all the generation-IV nuclear systems and is a promising reactor design that is currently under development by several organizations. The majority of sodium fast reactor designs utilize a pool type arrangement which incorporates the primary coolant pumps and intermediate heat exchangers within the sodium pool. These components typically protrude into the pool thus reducing the risk and severity of a loss of coolant accidents. To further ensure safe operation under even the most severe transients a more comprehensive understanding of key thermal hydraulic phenomena in this pool is desired. One of the key technology gaps identified for SFR safety is determining the extent and the effects of thermal stratification developing in the pool during postulated accident scenarios such as a protected or unprotected loss of flow incident. In an effort to address these issues, detailed flow models of transient stratification in the pool during an accident can be developed. However, to develop the calculation models, and ensure they can reproduce the underlying physics, highly spatially resolved data is needed. This data can be used in conjunction with advanced computational fluid dynamic calculations to aid in the development of simple reduced dimensional models for systems codes such as SAM and SAS4A/SASSYS-1.

Schneider, James↗

Thermal Stratification Analysis for Sodium Fast Reactors

The sodium fast reactor (SFR) is the most mature reactor concept of all the generation-IV nuclear systems and is a promising reactor design that is currently under development by several organizations. The majority of sodium fast reactor designs utilize a pool type arrangement which incorporates the primary coolant pumps and intermediate heat exchangers within the sodium pool. These components typically protrude into the pool thus reducing the risk and severity of a loss of coolant accidents. To further ensure safe operation under even the most severe transients a more comprehensive understanding of key thermal hydraulic phenomena in this pool is desired. One of the key technology gaps identified for SFR safety is determining the extent and the effects of thermal stratification developing in the pool during postulated accident scenarios such as a protected or unprotected loss of flow incident. In an effort to address these issues, detailed flow models of transient stratification in the pool during an accident can be developed. However, to develop the calculation models, and ensure they can reproduce the underlying physics, highly spatially resolved data is needed. This data can be used in conjunction with advanced computational fluid dynamic calculations to aid in the development of simple reduced dimensional models for systems codes such as SAM and SAS4A/SASSYS-1.

Schneider, James↗

What is Machine Learning?

Have you ever been unsure what people mean when they talk about 'machine learning'? What machines? And what are they learning? This video is for you! For more information about Machine Learning at LLNL and other Data Science projects, visit the LLNL Data Science Institute website at data-science.llnl.gov

3-D PRINTING↗

Data Mining Critical Infrastructure Systems: Models and Tools

Critical infrastructures (CIs) such as power, water, transportation, and telecommunication are highly complex interacting systems that are vital to national security, economy and public life. They play an important role in several core urban computing challenges. Advances in computing resources and techniques have led to enormous progress in developing intelligent frameworks for analyzing these large heterogeneous systems. In this article, we survey state-of-the-art and foundational work in this upcoming area from a data mining perspective. We discuss basic concepts of CIs, their properties, impacts on them due to natural or human-caused disturbances and different computational methodologies used for modeling and understanding their behavior. We also discuss recent work that specifically deals with two critical sectors of CIs, namely power and transportation systems. Lastly, we also describe several existing tools and methods that are used to facilitate decision making for domain operators, enable efficient and faster disaster response for federal and state agencies and help improve the security and resiliency of these CIs.

97 MATHEMATICS AND COMPUTING↗

Measuring pristine air to reveal pollution's impact

Data collected in flights above the Amazon rainforest shows a greater human impact on the atmosphere than previously thought. Researchers spent hours packed aboard a steamy Gulfstream-1 research aircraft as it zig-zagged between pristine air over the Amazon rainforest and polluted air nearby. It was like a trip through time, as scientists weaved between the two vastly different settings, snagging air samples characteristic of today’s industrial environment as well as samples of unpolluted air, like that before the industrial age.

54 ENVIRONMENTAL SCIENCES↗

MACHINE LEARNING BASED CHEMICAL EXPLOSIVE MODE ANALYSIS (ML-CEMA)

The software consists of a Machine Learning based Chemical Explosive Mode Analysis (ML-CEMA) tool for advanced computational flame diagnostics. CEMA, originally based on eigen-analysis of the local thermochemical system, is capable of identifying reaction fronts and limit phenomena such as auto-ignition and extinction in practical combustion systems, such as internal combustion engines and gas turbine combustors. However, the original CEMA is computationally expensive for large reaction mechanisms that are typically needed to describe fuel chemistry of practical large-hydrocarbon fuels. This novel ML-CEMA tool employs a ML technique to accelerate the eigen-analysis of the basic CEMA approach by orders of magnitude, thus making it suitable for practical fuels. In ML-CEMA,zero-dimensional (0D) reactors and one-dimensional (1D) premixed flames are first used to generate a large number of data points for neural network based ML training. The trained ML model is then used to perform CEMA prediction. This ML-CEMA tool has been demonstrated in canonical 0D and 1D configurations as well as highly-transient three-dimensional spray flames exhibiting multi-mode turbulent combustion, showing promising results. ML-CEMA, as a standalone tool, can be used for computationally-efficient diagnostics of massive datasets generated from both experiments and simulations. For example, based on spatially resolved measurements of a small set of reactive scalars(such as temperature, hydroxyl radical and formaldehyde), ML-CEMA can effectively identify flame fronts and rare events. ML-CEMA also provides a robust online or offline flame feature detection tool. When used for on-the-fly simulations, ML-CEMA further enables zone-adaptive combustion modeling, in which the predicted eigenvalue is used as a robust mode indicator for judicious assignment of locally-valid combustion models. This ML-CEMA based zone-adaptive model can lead to substantial computational cost savings when used for large-scale simulations of multi-mode combustion systems. Third Party Code Web Page to Download Code Web Page Location of Third Party License

Xu, Chao↗

Economic and Environmental Assessment of Biological Conversions of Agile BioFoundry (ABF) Bio-Derived Chemicals

Bio-derived chemicals are an essential part of the growing bioeconomy. They possess the potential to boost a new domestic bioproduct industry, improve the sustainability of integrated biomanufacturing, and reduce U.S. dependence on fossil energy. The Agile BioFoundry (ABF) consortium, a Department of Energy (DOE)-sponsored collaboration, is investigating biobased pathways to produce advantaged products including advanced biofuels, fuel intermediates, and bioproducts. ABF is integrating advanced computational tools for biological engineering, process design and data analysis, and economic/sustainability modeling tools into a comprehensive and dynamic platform for biomanufacturing of microbes and using them to produce key metabolic intermediates or beachhead molecules which may be derivatized into several distinct bioproducts of industrial interest. In this presentation, we first discuss a methodology to select a single exemplar product molecule to represent each beachhead pathway based on similarities with other end-molecule options (yields/titers/rates, fermentation operation mode, oxygen requirements, general separations challenges, etc.), in order to maintain a reasonable number of cases for rigorous process simulation. We then use techno-economic analysis (TEA) and life-cycle analysis (LCA) to highlight sensitivities and trends around economic performance and environmental footprint, reflecting examples for two selected ABF technology pathways to bio-derived chemicals: 1) adipic acid production via muconic acid fermentation from mixed sugars with Pseudomonas putida and 2) cineole via geranyl diphosphate with Rhodosporidium toruloides. We present multi-variable scan plots highlighting key drivers on costs and greenhouse gas emissions in order to identify the parameter space in which each pathway could ultimately achieve economic and environmental sustainability meeting or exceeding commodity product benchmarks, thus prioritizing future R&D focus areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗