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MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service
Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.
Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks
Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.
Analytical Expression for DC Link Capacitor Current in a Cascaded H-Bridge Multi-Level Active Front-End Converter
Medium-voltage grid-tied systems often use a cascaded H-bridge multi-level active front-end. In this converter, dc link bus capacitors play an important role in stabilizing the converter and enabling both active and reactive power injections. The present work provides analytical expressions for the capacitor current, which are essential for optimizing system design (especially capacitor size vs. lifetime). Then, the expression is incorporated into the grid connected bidirectional power system model. Consequently, this work contributes to the guiding principles to choose accurate dc link capacitor ratings against grid-side power delivery requirements. The analytical results have been validated with detailed simulations and hardware results.
A Big Data Analytics Framework for HPC Log Data: Three Case Studies Using the Titan Supercomputer Log
Reliability, availability and serviceability (RAS) logs of high performance computing (HPC) resources, when closely investigated in spatial and temporal dimensions, can provide invaluable information regarding system status, performance, and resource utilization. These data are often generated from multiple logging systems and sensors that cover many components of the system. The analysis of these data for finding persistent temporal and spatial insights faces two main difficulties: the volume of RAS logs makes manual inspection difficult and the unstructured nature and unique properties of log data produced by each subsystem adds another dimension of difficulty in identifying implicit correlation among recorded events. To address these issues, we recently developed a multi-user Big Data analytics framework for HPC log data at Oak Ridge National Laboratory (ORNL). This paper introduces three in-progress data analytics projects that leverage this framework to assess system status, mine event patterns, and study correlations between user applications and system events. We describe the motivation of each project and detail their workflows using three years of log data collected from ORNL's Titan supercomputer.
Design sensitivity derivatives for isoparametric elements by analytical and semi-analytical approaches
This paper presents an analytical approach for incorporating design sensitivity calculations directly into the finite element analysis. The formulation depends on the implicit differentiation approach and requires few additional calculations to obtain the design sensitivity derivatives. In order to evaluate this approach, it is compared with the semi-analytical approach which is based on commonly used finite difference formulations. Both approaches are implemented to calculate the design sensitivities for continuum and structural isoparametric elements. To demonstrate the accuracy and robustness of the developed analytical approach compared to the semi-analytical approach, some test cases using different structural and continuum element types are presented.
Numerical simulation of the hot-tail runaway electron production mechanism using CQL3D and comparison with Smith–Verwichte analytical model
Abstract The hot-tail mechanism of runaway electron (RE) production (Harvey et al 2000 Phys. Plasmas 7 4590) is the primary source of RE in the case of rapidly cooling tokamak plasma. Quantifying this mechanism is very important as it can provide most of the post-thermal-quench (TQ) current, or a seed current for the secondary source of RE through the avalanche mechanism. An analytic model which omits pitch-angle scattering is often used in literature for estimating the hot-tail RE density (Smith and Verwichte 2008 Phys. Plasmas 15 072502). In the present study, we use the CQL3D bounce-averaged Fokker–Planck code (Harvey and McCoy 1992 Proc. IAEA Technical Committee Meeting on Advances in Simulation and Modeling of Thermonuclear Plasmas p 527) to test the limits of validity of the model. In particular, we examine the cases of Z = 1 and Z = 18 ions, for sets of different initial temperature, density, electric field and the characteristic time of temperature decay. We show that for Z = 1 plasma, the ratio of RE density computed by CQL3D to that estimated from the model is within 0.6–6.0 in studied cases. For the Z = 18 case, this factor is systematically a much smaller number, typically 0.02–0.6. We suggest a simple correction to the model that narrows down the range of this ratio to 0.3–3.8 in all of the cases, including Z = 1 and Z = 18 plasmas.
Experimental and analytical study of the longitudinal aerodynamic characteristics of analytically and empirically designed Strake-wing configurations at subcritical speeds
Sixteen analytically and empirically designed strakes have been tested experimentally on a wing-body at three subcritical speeds in such a way as to isolate the strake-forebody loads from the wing-afterbody loads. Analytical estimates for these longitudinal results are made using the suction analogy and the augmented vortex lift concepts. The synergistic data are reasonably well estimated or bracketed by the high- and low-angle-of-attack vortex lift theories over the Mach number range and up to maximum lift or strake-vortex breakdown over the wing. Also, the strake geometry is very important in the maximum lift value generated and the lift efficiency of a given additional area. Increasing size and slenderness ratios are important is generating lift efficiently, but similar efficiency can also be achieved by designing a strake with approximately half the area of the largest gothic strake tested. These results correlate well with strake-vortex-breakdown observations in the water tunnel.
Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations
Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.
LLNL ES&H Analytical Services and Instrumentation Analytical Laboratory (ALAB) (CY2022 Management Review Report)
"An annual review shall be conducted of the analytical activities to ensure continuing suitability, adequacy, and effectiveness." (ALAB-01 Rev 6 ALQAP section 6.11.2) "The laboratory management shall review its management system at planned intervals, in order to ensure its continuing suitability, adequacy and effectiveness..."(ISO/IEC 17025:2017 Section 8.9.1) "In accordance with a predetermined schedule and procedure, the laboratory's top management shall periodically conduct a review of the laboratory's management system and testing and/or calibration activities to ensure their continuing suitability and effectiveness, and to introduce necessary changes or improvements. "(TNI Standard EL-V1M2-2016-Rev2.1:Quality Systems General Requirements)
Improved analytic longitudinal response analysis for axisymmetric launch vehicles. Volume I - Linear analytic model
Improved analytic longitudinal response analysis for axisymmetric launch vehicles - linear model
Analytical investigation of turbine erosion phenomena. Volume I - Assembly of analytical model of wet vapor turbine blade erosion Interim technical report
Construction and application of analytical model of wet vapor turbine blad erosion
Increasing accessibility to deep learning-based analytics for space biology: pretrained models, transfer learning, and analytics platform development
Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.
Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO 2 ] and methane [CH 4 ]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. Furthermore, this approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO 2 and CH 4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).
Determination of Biogenic Content by 14 C in fuels using Liquid Scintillation Analysis Laboratory Analytical Procedure (Laboratory Analytical Procedure)
This procedure covers the determination of the percent 14 C in biogenic gasoline, jet fuel, and diesel range hydrocarbons prepared from distillation of hydrotreated pyrolysis oils and blended with fossil derived hydrocarbons (e.g. fossil diesel, gasoline, jet, or toluene). Either an internal 14C standard or a color quench curve is used to correct for potential signal quenching effects. Recent publications provide some background of direct LSC methods used for determination of biogenic fuel content.
Analytical Determination of Radioactive Strontium and Cesium by Thermal Ionization Mass Spectrometry
Thermal ionization mass spectrometry (TIMS) has been evaluated for the detection of the radioactive isotopes of strontium and cesium. The instrument was investigated for the analysis of isotopic ratios of 89Sr to 90Sr and 135Cs to 137Cs in the presence of atomic isobars (Y and Zr for Sr analysis and Ba for Cs analysis). Signicant discrimination using TIMS was found for both Sr and Cs through evaluation using stable isotopes. Fusion procedures with different flux were evaluated for preparation of sample matrices. Chemical separations were developed and used after fusion to isolate the Sr and Cs from a prepared background matrix (i.e., Montana Soil). A graded approach, increasing in stable background isotopes, was performed to monitor the chemistry and instrument response. A thermal neutron irradiation was performed to produce a sample that contained the radioisotopes of Sr and Cs. Even though the irradiated sample was not a certified standard it does provide accurate expectation values via published, well understood cumulative fission yield data. The results presented in this work correlate extremely well with the published data. Furthermore, a significant decrease in uncertainty (by a factor of 5 { 10) was found for the isotopic ratios measured with TIMS compared to the published values.