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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 271 records · Page 15

PROTEUS: Machine Learning Driven Resilience for Extreme-scale Systems

The objective of this project is to design, develop, and evaluate scalable software to enhance resilience, data checkpointing, program restart, and analysis. The proposed tasks are to 1) develop scalable machine learning techniques to learn temporal change patterns in a scalable and in-situ manner, and to minimize data movement and maximize learning locally closest to data; 2) design a concise data representation and indexing mechanism to capture the distribution of changes in data that can guarantee point-wise user-defined tolerable errors while reducing the data storage requirements by an order of magnitude or more; 3) develop data reduction techniques as library modules; 4) exploit local SSD for minimizing data movement in storage hierarchy; 5) develop anomaly detection algorithms that can predict corruptions based on learning of emerging patterns; 6) develop software libraries to be incorporated within widely used data formats and APIs; and 7) evaluate the proposed software using DOE scientific applications. The outcomes of the proposed work are to satisfy many synergistic data reduction and resilience requirements for large-scale data intensive applications executed on extreme-scale computing systems. The developed mechanism for error-bound data approximation is directly applicable to existing scientific applications. Through machine learning from historical events and change distribution, this work will enable anomaly detection for DOE computer facility.

97 MATHEMATICS AND COMPUTING↗

Light Water Reactor Sustainability Program: Use of Time Distributions to Predict Operator Procedure Performance in Dynamic Human Reliability Analysis

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework affords software capable of conducting human reliability analysis (HRA) using a dynamic approach built around operating procedures (OPs) from nuclear power plants (NPPs). Previous HUNTER reports document the development of this software tool, the coupling of HUNTER to the simulator code, the collection of operator performance data by using simulators to calibrate HUNTER models, and linking HUNTER to probabilistic risk assessment (PRA) software. The present report largely addresses two topics. The first is a new function in HUNTER called the HUNTER Procedure Performance Predictor (P3). HUNTER P3 uses HUNTER’s built in Monte Carlo tools featuring human performance variability to identify potential error traps in procedures. The second topic is time distribution analysis to generate time inputs for dynamic HRA. The current analysis was performed to investigate time distributions for task primitives, which are the minimum task unit of analysis used in dynamic HRA modeling. Using the time distribution data, the elapsed time for human actions in an extended loss of AC power (ELAP) scenario is then investigated. Time data and prediction are essential for modeling procedure performance.

99 GENERAL AND MISCELLANEOUS↗

Integrated Analyses of Multiple Worldwide Aerosol Mass Spectrometer Datasets for Improved Understanding of Aerosol Sources and Processes and for Comparison with Global Models

The AMS is the only current instrument that provides real-time, quantitative, and size-resolved data on submicron non-refractory aerosol species with a time resolution of a few minutes or better. The AMS field data are multidimensional and massive, containing extremely rich information on aerosol chemistry, microphysics and dynamics—basic information that is required to evaluate and quantify the radiative climate forcing of atmospheric aerosols. The high time resolution of the AMS data also reveals details of aerosol dynamic variations that are vital to understanding the physico-chemical processes of atmospheric aerosols that govern aerosol properties relevant to the climate. There are two primary objectives of this 3-year project. Our first objective is to perform highly integrated analysis of dozens of AMS datasets acquired from various urban, forested, coastal, marine, mountain peak, and rural/remote locations around the world and synthesize and inter-compare results with a focus on the sources and the physico-chemical processes that govern aerosol properties relevant to aerosol climate forcing. Our second objective is to support our collaboration with global aerosol modelers, in which we will supply the size-resolved aerosol composition and temporal variation data (via a public web interface) and our analysis results for use in model testing and validation and for translation of the rich AMS database into model constraints that can improve climate forcing simulations. Several prominent global aerosol modelers have expressed enthusiastic support for this collaboration. The specific tasks that we propose to accomplish include 1) to develop, validate, and apply multivariate analysis techniques for improved characterization and source apportionment of organic aerosols; 2) to evaluate aerosol source regions and relative contributions based on back-trajectory integration (PSCF method); 3) to summarize and synthesize submicron aerosol information, including composition, concentration, size distribution and (inferred) shape and mixing state in various environments and their regional and seasonal variations within the context of regional and global modeling; and 4) to quantitatively evaluate important processes in various atmospheric environments and during different seasons, focusing on acid-catalyzed SOA formation, new particle growth, and photochemical processes of atmospheric organic aerosols (i.e., SOA production and POA oxidation). We will also examine the correlations and compile the ratios between important pairs of aerosol and gas phase species using region-specific and season-specific correlations and as a function of photochemical age and compare them with the ratios produced by various models. To enable our collaborations with the modelers, we will supply (via a public web interface) AMS data and our analysis results for use in model testing and validation and facilitate the use of the AMS information to constrain calculations of radiative forcing. Model output and AMS measurements and derived parameters will be compared with a focus on regional variability of model/measurement discrepancies and their causes. Finally we will share results, insights and data mining algorithms through peer-reviewed publications, presentations/tutorials at conferences/workshops, and web dissemination of analysis results and in-house developed software packages.

58 GEOSCIENCES↗

GlyQ-IQ: Glycomics Quintavariate-Informed Quantification with High-Performance Computing and GlycoGrid 4D Visualization

Dense LC-MS datasets have convoluted extracted ion chromatograms with multiple chromatographic peaks that cloud the differentiation between intact compounds with their overlapping isotopic distributions, peaks due to insource ion fragmentation, and noise. Making this differentiation is critical in glycomics datasets because chromatographic peaks correspond to different intact glycan structural isomers. The GlyQ-IQ software is targeted chromatography centric software designed for chromatogram and mass spectra data processing and subsequent glycan composition annotation. The targeted analysis approach offers several key advantages to LC-MS data processing and annotation over traditional algorithms. A priori information about the individual target’s elemental composition allows for exact isotope profile modeling for improved feature detection and increased sensitivity by focusing chromatogram generation and peak fitting on the isotopic species in the distribution having the highest intensity and data quality. Glycan target annotation is corroborated by glycan family relationships and in source fragmentation detection. The GlyQ-IQ software is developed in this work (Part 1) and was used to profile N-glycan compositions from human serum LC-MS Datasets. The companion manuscript GlyQ-IQ Part 2 discusses developments in human serum N-glycan sample preparation, glycan isomer separation, and glycan electrospray ionization. A case study is presented to demonstrate how GlyQ-IQ identifies and removes confounding chromatographic peaks from high mannose glycan isomers from human blood serum. In addition, GlyQ-IQ was used to generate a broad N-glycan profile from a high resolution (100K/60K) nESI-LS-MS/MS dataset including CID and HCD fragmentation acquired on a Velos Pro Mass spectrometer. 101 glycan compositions and 353 isomer peaks were detected from a single sample. 99% of the GlyQ-IQ glycan-feature assignments passed manual validation and are backed with high resolution mass spectra and mass accuracies less than 7 ppm.

Environmental Molecular Sciences Laboratory↗

Simulation and big data challenges in tuning building energy models

EnergyPlus is the flagship building energy simulation software used to model whole building energy consumption for residential and commercial establishments. A typical input to the program often has hundreds, sometimes thousands of parameters which are typically tweaked by a buildings expert to “get it right”. This process can sometimes take months. “Autotune” is an ongoing research effort employing machine learning techniques to automate the tuning of the input parameters for an EnergyPlus input description of a building. Even with automation, the computational challenge faced to run the tuning simulation ensemble is daunting and requires the use of supercomputers to make it tractable in time. In this paper, we describe the scope of the problem, particularly the technical challenges faced and overcome, and the software infrastructure developed/in development when taking the EnergyPlus engine, which was primarily designed to run on desktops, and scaling it to run on shared memory supercomputers (Nautilus) and distributed memory supercomputers (Frost and Titan). The parametric simulations produce data in the order of tens to a couple of hundred terabytes. We describe the approaches employed to streamline and reduce bottlenecks in the workflow for this data, which is subsequently being made available for the tuning effort as well as made available publicly for open-science.

Sanyal, Jibonananda↗

Sci—Fri PM: Dosimetry—06: Commissioning of a 3D patient specific QA system for hypofractionated prostate treatments

The objective of this work is to commission the 6MV-SRS beam model in COMPASS (v2.1, IBA-Dosimetry) and validate its use for patient specific QA of hypofractionated prostate treatments. The COMPASS system consists of a 2D ion chamber array (MatriXX{sup Evolution}), an independent gantry angle sensor and associated software. The system can either directly calculate or reconstruct (using measured detector responses) a 3D dose distribution on the patient CT dataset for plan verification. Beam models are developed and commissioned in the same manner as a beam model is commissioned in a standard treatment planning system. Model validation was initially performed by comparing both COMPASS calculations and reconstructions to measured open field beam data. Next, 10 hypofractionated prostate RapidArc plans were delivered to both the COMPASS system and a phantom with ion chamber and film inserted. COMPASS dose distributions calculated and reconstructed on the phantom CT dataset were compared to the chamber and film measurements. The mean (± standard deviation) difference between COMPASS reconstructed dose and ion chamber measurement was 1.4 ± 1.0%. The maximum discrepancy was 2.6%. Corresponding values for COMPASS calculation were 0.9 ± 0.9% and 2.6%, respectively. The average gamma agreement index (3%/3mm) for COMPAS reconstruction and film was 96.7% and 95.3% when using 70% and 20% dose thresholds, respectively. The corresponding values for COMPASS calculation were 97.1% and 97.1%, respectively. Based on our results, COMPASS can be used for the patient specific QA of hypofractionated prostate treatments delivered with the 6MV-SRS beam.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DIORAMA Guide to Building from Source

This document will describe the process for building the Distributed Infrastructure Offering Real- Time Access to Modeling and Analysis (DIORAMA) software from source. There are many packages included in DIORAMA depending on phenomenology and sensor suite. Each developed package depends on several other packages. The dependency graph in figure 1 shows the complicated dependencies of each package. Because of these dependencies the packages must be built in the order outlined in this guide.

97 MATHEMATICS AND COMPUTING↗

Requirements Description of GAMSOR and GAMSRC

This report presents the modeling and simulation capabilities of Argonne National Laboratory’s GAMSOR and GAMSRC code that is used in present reactor analysis activities. These capabilities will then be used to establish the set of verification tasks necessary to verify GAMSOR for use on commercial projects. A similar approach was taken for the PERSENT, REBUS and DIF3D software packages. The GAMSOR program is a modified version of DIF3D used to obtain the coupled neutron gamma flux distributions for a given reactor system. GAMSOR was first developed in the 1980s and its work has progressed in parallel with DIF3D throughout its various development phases. While GAMSOR is compatible with DIF3D-FD and DIF3D-Nodal, DIF3D-VARIANT is most commonly used today and will be the focus of the present verification effort. As covered in [1], GAMSOR was built to provide more accurate heating distributions by explicitly solving for both the gamma and neutron heating distributions. Without this consideration, gamma heat is normally assumed to be deposited locally at the site of gamma production. This is acceptable for modeling some phenomena because the total energy production is preserved, but it can lead to inaccurate approximations of fluence-dependent behaviors such as fuel performance, swelling, cladding embrittlement, radiation induced creep, and power peaking. The outputs from GAMSOR are contained in multiple intermediate and final output files: FIXSRC, VARSRC, GTFLUX, GHFLUX, PWDINT, NPDINT, and GPDINT. DIF3D normally produces a PWDINT file containing power density results, with the assumption that gamma rays are absorbed where they are emitted (i.e., energy deposition is local with no modeled transport). With GAMSOR, neutron power density (NPDINT) and gamma power density (GPDINT) are both produced, with their summed power densities replacing the results in PWDINT for total heating (i.e., recoverable) power density.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Simulation and Big Data Challenges in Tuning Building Energy Models

EnergyPlus is the flagship building energy simulation software used to model whole building energy consumption for residential and commercial establishments. A typical input to the program often has hundreds, sometimes thousands of parameters which are typically tweaked by a buildings expert to get it right . This process can sometimes take months. Autotune is an ongoing research effort employing machine learning techniques to automate the tuning of the input parameters for an EnergyPlus input description of a building. Even with automation, the computational challenge faced to run the tuning simulation ensemble is daunting and requires the use of supercomputers to make it tractable in time. In this proposal, we describe the scope of the problem, the technical challenges faced and overcome, the machine learning techniques developed and employed, and the software infrastructure developed/in development when taking the EnergyPlus engine, which was primarily designed to run on desktops, and scaling it to run on shared memory supercomputers (Nautilus) and distributed memory supercomputers (Frost and Titan). The parametric simulations produce data in the order of tens to a couple of hundred terabytes.We describe the approaches employed to streamline and reduce bottlenecks in the workflow for this data, which is subsequently being made available for the tuning effort as well as made available publicly for open-science.

Sanyal, Jibonananda↗

Tessera: Open source software for accelerated data science

Extracting useful, actionable information from data can be a formidable challenge for the safeguards, nonproliferation, and arms control verification communities. Data scientists are often on the “front-lines” of making sense of complex and large datasets. They require flexible tools that make it easy to rapidly reformat large datasets, interactively explore and visualize data, develop statistical algorithms, and validate their approaches—and they need to perform these activities with minimal lines of code. Existing commercial software solutions often lack extensibility and the flexibility required to address the nuances of the demanding and dynamic environments where data scientists work. To address this need, Pacific Northwest National Laboratory developed Tessera, an open source software suite designed to enable data scientists to interactively perform their craft at the terabyte scale. Tessera automatically manages the complicated tasks of distributed storage and computation, empowering data scientists to do what they do best: tackling critical research and mission objectives by deriving insight from data. We illustrate the use of Tessera with an example analysis of computer network data.

Sego, Landon H.↗

KAPPA: A PACKAGE FOR SYNTHESIS OF OPTICALLY THIN SPECTRA FOR THE NON-MAXWELLIAN κ-DISTRIBUTIONS BASED ON THE CHIANTI DATABASE

Non-Maxwellian κ-distributions have been detected in the solar transition region and in flares. These distributions are characterized by a high-energy tail and a near-Maxwellian core and are known to have a significant impact on the resulting optically thin spectra arising from collisionally dominated astrophysical plasmas. We developed the KAPPA package for the synthesis of such line and continuum spectra. The package is based on the freely available CHIANTI database and software, and can be used in a similar manner. Ionization and recombination rates together with the ionization equilibria are provided for a range of κ values. Distribution-averaged collision strengths for excitation are obtained using an approximate method for all transitions in all ions available within CHIANTI. The validity of this approximate method is tested through a comparison with direct calculations. Typical precisions of better than 5% are found with all cases being within 10%. Tools for the calculation of synthetic line and continuum intensities are provided and described. Examples of the synthetic spectra and SDO/AIA responses to emission for the κ-distributions are given.

79 ASTRONOMY AND ASTROPHYSICS↗

Fitting FFT-derived spectra: Theory, tool, and application to solar radio spike decomposition

Spectra derived from fast Fourier transform (FFT) analysis of time-domain data intrinsically contain statistical fluctuations whose distribution depends on the number of accumulated spectra contributing to a measurement. The tail of this distribution, which is essential for separating the true signal from the statistical fluctuations, deviates noticeably from the normal distribution for a finite number of accumulations. In this paper, we develop a theory to properly account for the statistical fluctuations when fitting a model to a given accumulated spectrum. The method is implemented in software for the purpose of automatically fitting a large body of such FFT-derived spectra. We apply this tool to analyze a portion of a dense cluster of spikes recorded by our FASR Subsystem Testbed instrument during a record-breaking event that occurred on 2006 December 6. The outcome of this analysis is briefly discussed.

79 ASTRONOMY AND ASTROPHYSICS↗

Comprehensive Solutions for Integration of Solar Resources into Grid Operations

The need for proactive closed-loop integration of uncertainty information into system operations and probability-based controls is widely recognized, but rarely implemented in system operations. Proactive integration for this project means that the information concerning expected uncertainty ranges for net load and balancing requirements, including required balancing capacity, ramping and ramp duration characteristics, will be fed back into the generation commitment and dispatch algorithms to modify their performance so that potential shortages of these characteristics can be prevented. This basic, yet important, premise is the motivating factor for this project. The achieved project goal is to demonstrate the benefit of such a system. The project quantifies future uncertainties, predicts additional system balancing needs including the prediction intervals for capacity and ramping requirements of future dispatch intervals, evaluates the impacts of uncertainties on transmission including the risk of overloads and voltage problems, and explores opportunities for intra-hour generation adjustments helping to provide more flexibility for system operators. The resulting benefits culminate in more reliable grid operation in the face of increased system uncertainty and variability caused by solar power. The project identifies that solar power does not require special separate penetration level restrictions or penalization for its intermittency. Ultimately, the collective consideration of all sources of intermittency distributed over a wide area unified with the comprehensive evaluation of various elements of balancing process, i.e. capacity, ramping, and energy requirements, help system operators more robustly and effectively balance generation against load and interchange. This project showed that doing so can facilitate more solar and other renewable resources on the grid without compromising reliability and control performance. Efforts during the project included developing and integrating advanced probabilistic solar forecasts, including distributed PV forecasts, into closed –loop decision making processes. Additionally, new uncertainty quantifications methods and tools for the direct integration of uncertainty and variability information into grid operations at the transmission and distribution levels were developed and tested. During Phase 1, project work focused heavily on the design, development and demonstration of a set of processes and tools that could reliably and efficiently incorporate solar power into California’s grid operations. In Phase 2, connectivity between the ramping analysis tools and market applications software were completed, multiple dispatch scenarios demonstrated a successful reduction of overall uncertainty and an analysis to quantify increases in system operator reliability, and the transmission and distribution system uncertainty prediction tool was introduced to system operation engineers in a live webinar. The project met its goals, the experiments prove the advancements to methods and tools, when working together, are beneficial to not only the California Independent System Operator, but the benefits are transferable to other system operators in the United States.

14 SOLAR ENERGY↗

Flexible Pilot Jobs Framework for Distributed High Throughput Computing

Experimental particle physics has been at the forefront of analyzing the world’s largest datasets for decades. The high-energy physics (HEP) community was among the first to develop suitable software and computing tools for this purpose. GlideinWMS is a Glidein-based workload management system whose purpose is to provide experiments like CMS at CERN, DUNE at Fermilab, and others, a way to access and efficiently use vast amounts of computing resources. This system wants to provide a simple way to submit jobs to a set of computing resources, that will be provided to users behind the scenes. Glideins are the pilot jobs executed on the worker nodes at the grid sites, performing operations such as hardware detection, environment setup, and error handling. After all these operations, they will launch the actual user job. Many grid sites are supported, such as shared clusters, Google CE, and AWS. My internship aimed to design and code a flexible pilot jobs framework that will replace the one used by GlideinWMS, developing a modular and flexible skeleton of the Glidein and adding further functionalities. My project also focused on the application of machine learning techniques as support to this management system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Simulation of Internal Combustion Engines with High-Performance Computing Tools

Traditional Lagrangian spray modeling approaches for internal combustion engines are highly griddependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was developed and implemented by Senecal et al. (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against nonevaporating, evaporating, and reacting data from the literature. The current numerical study focusses on further demonstration of the grid-convergent modeling approach for simulating a single-cylinder Cat® compression ignition engine. The simulated injector is characterized with a nominal nozzle exit diameter of 259 μm. Simulations using various minimum grid sizes (ranging from 125 μm to 1000 μm) are compared for engine performance and emissions parameters of interest such as pressure, heat release rate, ignition delay, NOx, HC, and soot emissions. The peak cell-count for the highest resolution simulation was on the order of 34 million. These computationally expensive simulations were facilitated at a high-performance computing facility at Argonne National Laboratory. METIS load-balancing algorithm was developed and implemented in Converge code for the simulations. Scaling studies were also performed. The validity of previously recommended grid settings (ASME-ICEF2012-92043) for accuracy/runtime trade-off is further assessed. Efficacy of a simplified combustion model is also compared against a detailed chemical kinetics based combustion model.

33 ADVANCED PROPULSION SYSTEMS↗

MOLNs: A Cloud Platform for Interactive, Reproducible, and Scalable Spatial Stochastic Computational Experiments in Systems Biology Using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. In conclusion, MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

97 MATHEMATICS AND COMPUTING↗

An Advanced Network and distributed Storage Laboratory (ANDSL) for Data Intensive Science (Final Report)

The original intent of this project was to build and operate an Advanced Network and Distributed Storage Laboratory (ANDSL) for Data Intensive Science that will prepare the Open Science Grid (OSG) community for a new generation of wide area communication capabilities operating at a 100Gb rate. The plan was to engage a team of four dedicated scientists that will design, develop and operate a state of the art laboratory that will be fully integrated into the organizational and operational infrastructure of the OSG. The ANDSL was planned to include host-based hardware and software required to profile, emulate and study real-life distributed storage workloads at rates of 100 Gbps. The laboratory was designed to utilize the Department Of Energy’s Advanced Network Initiative (ANI) 100GbTestbed as connections to the fabric become available so that OSG stakeholders can use it to profile real-life data intensive science applications as well as experiment with advanced storage and data management technologies. The goal of the original program of work was to close the adoption gap, decreasing the time between availability of production 100Gbps links and the end-to-end use of such a network to enable scientific discovery.

100Gb networks↗

A differentiable simulation package for performing inference of synchrotron-radiation-based diagnostics

The direction of particle accelerator development is ever-increasing beam quality, currents and repetition rates. This poses a challenge to traditional diagnostics that directly intercept the beam due to the mutual destruction of both the beam and the diagnostic. An alternative approach is to infer beam parameters non-invasively from the synchrotron radiation emitted in bending magnets. However, inferring the beam distribution from a measured radiation pattern is a complex and computationally expensive task. To address this challenge we present SYRIPY ( SYnchrotron Radiation In PYthon ), a software package intended as a tool for performing inference of synchrotron-radiation-based diagnostics. SYRIPY has been developed using PyTorch , which makes it both differentiable and able to leverage the high performance of GPUs, two vital characteristics for performing statistical inference. The package consists of three modules: a particle tracker, Lienard–Wiechert solver and Fourier optics propagator, allowing start-to-end simulation of synchrotron radiation detection to be carried out. SYRIPY has been benchmarked against SRW , the prevalent numerical package in the field, showing good agreement and up to a 50× speed improvement. Finally, we have demonstrated how SYRIPY can be used to perform Bayesian inference of beam parameters using stochastic variational inference.

43 PARTICLE ACCELERATORS↗