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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning models for segmentation and classification of cyanobacterial cells

Abstract Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.

Huffine, Clair A.↗
DOE OSTI2023

CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms

Computational singular perturbation (CSP) is a method to analyze dynamical systems. It targets the decoupling of fast and slow dynamics using an alternate linear expansion of the right-hand side of the governing equations based on eigenanalysis of the associated Jacobian matrix. This representation facilitates diagnostic analysis, detection and control of stiffness, and the development of simplified models. For this work, we have implemented CSP in a C++ open-source library CSPlib using the Kokkos parallel programming model to address portability across diverse heterogeneous computing platforms, i.e., multi/many-core CPUs and GPUs. We describe the CSPlib implementation and present its computational performance across different computing platforms using several test problems. Specifically, we test the CSPlib performance for a constant pressure ignition reactor model on different architectures, including IBM Power 9, Intel Xeon Skylake, and NVIDIA V100 GPU. The size of the chemical kinetic mechanism is varied in these tests. As expected, the Jacobian matrix evaluation, the eigensolution of the Jacobian matrix, and matrix inversion are the most expensive computational tasks. When considering the higher throughput characteristic of GPUs, GPUs performs better for small matrices with higher occupancy rate. CPUs gain more advantages from the higher performance of well-tuned and optimized linear algebra libraries such as OpenBLAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Compilation of PRF Canyon Floor Pan Sample Analysis Results

On September 28, 2015, debris collected from the PRF (236-Z) canyon floor, Pan J, was observed to exhibit chemical reaction. The material had been transferred from the floor pan to a collection tray inside the canyon the previous Friday. Work in the canyon was stopped to allow Industrial Hygiene to perform monitoring of the material reaction. Canyon floor debris that had been sealed out was sequestered at the facility, a recovery plan was developed, and drum inspections were initiated to verify no additional reactions had occurred. On October 13, in-process drums containing other Pan J material were inspected and showed some indication of chemical reaction, limited to discoloration and degradation of inner plastic bags. All Pan J material was sealed back into the canyon and returned to collection trays. Based on the high airborne levels in the canyon during physical debris removal, ETGS (Encapsulation Technology Glycerin Solution) was used as a fogging/lock-down agent. On October 15, subject matter experts confirmed a reaction had occurred between nitrates (both Plutonium Nitrate and Aluminum Nitrate Nonahydrate (ANN) are present) in the Pan J material and the ETGS fixative used to lower airborne radioactivity levels during debris removal. Management stopped the use of fogging/lock-down agents containing glycerin on bulk materials, declared a Management Concern, and initiated the Potential Inadequacy in the Safety Analysis determination process. Additional drum inspections and laboratory analysis of both reacted and unreacted material are planned. This report compiles the results of many different sample analyses conducted by the Pacific Northwest National Laboratory on samples collected from the Plutonium Reclamation Facility (PRF) floor pans by the CH2MHill’s Plateau Remediation Company (CHPRC). Revision 1 added Appendix G that reports the results of the Gas Generation Rate and methodology. The scope of analyses requested by CHPRC includes the determination of common anions, gamma spectrometry, metals, corrosivity, organics and alpha spectrometry (note: alpha spectrometry was cancelled during the performance of this work with concurrence from CHPRC). Results may help elucidate the components that led to the unexpected reaction in the canyon as well as inform the radiological and hazardous characteristics. The specific anions, gamma emitters, organics and metals requested by CHPRC are provided in the analytical reports sections. The individual analyses were conducted under the Plutonium Finishing Plant (PFP) Floor Pan Evaluation Project Quality Assurance Project Plan (PFP Floor Pan Evaluation QAPP, Revision 0.) developed by PNNL specifically for this project. The final reports for each analysis set are included in this compilation of the results. Each package was reviewed under the PFP Floor Pan Evaluation Project Quality Assurance Project Plan so no additional reviews were conducted in this compilation task. The Gas Generation Rates in Appendix G were conducted under the PNNL “How Do I…” quality assurance program and were NOT conducted under the Plutonium Finishing Plant (PFP) Floor Pan Evaluation Project Quality Assurance Project Plan (PFP Floor Pan Evaluation QAPP, Revision 0.).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multi-Gas Sensors for Enhanced Reliability of SOFC Operation

GE Research, in partnership with SUNY Polytechnic Institute (SUNY Poly), designed, built, and tested gas sensors for in situ monitoring of H 2 and CO anode tail gases produced with on-site steam reforming in solid oxide fuel cell (SOFC) systems. The knowledge of the H 2 /CO ratio of these anode tail gases should allow accurate determination and control of the efficiency of the reforming process in the SOFC system and should deliver a lower operating cost for SOFC customers. The project objectives were to achieve multi-gas monitoring capability with a single multivariable sensor, and to sustain this performance in the presence of gaseous interferences and a potential poison for the sensor. The duration of the project was 24 months with the project structure that included three technical tasks such as (1) development of design rules of photonic nanostructures for H 2 and CO gas detection, (2) laboratory validation of photonic nanostructures for selective H 2 and CO gas detection, and (3) validation of photonic nanostructures for initial stability and poison-resistance against H 2 S. To build multivariable sensors for selective H 2 and CO gas detection in the presence of interferences, we expanded our earlier knowledge of multi-gas sensors into new fabrication and functionalization methodologies as well as into new methodologies for the spectral data analysis of multi-gas responses. We have advanced our design rules of the three-dimensional (3D) photonic nanostructures that allowed detection of H 2 and CO at high temperatures as individual gases and as their mixtures and rejection of interferences such as CO 2 , H 2 O, CH 4 , and other hydrocarbons for SOFC applications. Our advanced design rules should be attractive for building the new generation of cost-effective industrial sensors. Stability and poison resistance of our 3D photonic nanostructures was tested in the laboratory conditions. While initially we utilized conventional machine learning data analysis tools, we have found that they were unable to correct for the sensor drift. Thus, we have implemented new methods of machine learning for the analysis of our spectral data. These learnings pave the way to move the future studies into advanced testing of effects of interferences, aging and field tests. In future, our work will continue to advance our sensing designs to operate in conditions with known and unknown interferences by implementing nanostructures with enhanced spectral diversity of responses to gaseous species of interest and interferences. Our systematic reduction of technical risks in this completed project and in future studies will ensure transition of this sensing technology to commercialization.

03 NATURAL GAS↗
DOE OSTI2019

WBS 4.1.2.31 Bioproduct Transition System Dynamics

Bioproducts are chemicals derived from a biomass feedstock; they can be produced on their own or as co-products of a biorefinery that also produces biofuels. Bioproducts have historically been difficult to scale up and bring to market, despite a broad understanding developed within BETO of the various conversion processes that can be used to produce bioproducts. Expanding the bioproducts industry by bringing more bioproducts to market would have a variety of positive impacts. Growth in the bioproducts industry would support the biofuels industry by enabling biorefinery co-products that yield additional revenue streams, allowing biofuel selling prices to be reduced and expanding the biofuels market. A larger bioproducts industry also has the potential to reduce carbon and other emissions associated with the U.S. chemical sector and contribute to increased price stability in some areas of the chemical market by shifting portions of the chemical sector away from reliance on fossil feedstocks. The key barrier to realizing these positive impacts is a lack of knowledge around the factors that prevent or enable bioproducts to reach the commercial market. To date, there has been essentially no research done on how successful bioproducts (commercially produced bioproducts that have captured market share) become successful, why failed bioproducts fail, and what the similarities and differences are between successful and failed bioproducts. The current project addresses this knowledge gap with a decision support tool that can be used by investors, technology developers and government agencies to bring more bioproducts to market, thereby simultaneously growing the bioproducts and biofuels industries. Development of the decision support tool was based on research on the technology development process and success rates in analogous industries, data gathered during previous BETO-funded projects, and a series of interviews with subject-matter experts within BETO, the bioproducts industry, and the investment community. This information formed a knowledge base around how bioproducts are developed from applied research to commercial scale was assembled, which was used to build the Bioproduct Transition Dynamics (BTD) model, a system dynamics (SD) model that tracks bioproduct development projects from applied research through the 'valley of death' of piloting and demoing to commercial scale production. The end-of-project goal is to release the BTD, along with supporting documentation, as a transparent open-source model that will inform decisions around bioproduct development and investments and enable more bioproducts to reach the market. The BTD takes as inputs techno-economic data on specific bioproducts, behavioral parameters for developers and investors, expectations around government support, and exogenous market factors. Model outputs include bioproduct technological progress over time, a cash flow statement for the development project, and market share estimates. Following initial model development, a sensitivity analysis was performed on a subset of model inputs to determine, first, if the BTD could be used to answer analysis questions of interest to BETO, and second, which of the inputs considered were most influential on bioproduct success. Results indicate that the BTD can be used to identify scenarios that enable bioproduct development, and that 'soft' or semi-quantitative factors play a large role in determining if a bioproduct will reach the market. A workshop was also held for subject-matter experts and prospective BTD users in July 2018. The workshop covered BTD model logic, input data and assumptions as well as the sensitivity analysis results and a discussion of potential use cases. Workshop feedback was used to identify additional model development tasks and was incorporated into a draft technical report completed in September 2018. BTD model development, validation and application will continue through 2019.

09 BIOMASS FUELS↗
DOE OSTI2023

Organizational and psychological measures for data center energy efficiency: barriers and mitigation strategies

Abstract It was last estimated that in 2020, data centers comprised approximately 2% of total US electricity consumption, with an estimated annual growth rate of 4%. As our country increasingly relies on information technology (IT), our data centers (DCs) will need to increase their energy efficiency (EE) to stabilize their energy consumption. The task of studying EE in DCs is complicated by the interconnected nature of humans and mission-critical technical systems. Moreover, the literature tends to focus on technology solutions such as improvements to IT equipment, cooling infrastructure, and software, without addressing organizational and psychological drivers. Our research demystifies the complex interactions between humans and DCs, by asking What non-technical barriers impede EE investment decision-making and/or implementing energy management strategies? To begin to answer this question, we perform a literature review of 86 resources, ranging from peer-reviewed journal publications to handbooks. We also consider related fields such as organizational behavioral management and energy intensive buildings. We develop a public Zotero library, perform content coding, and complete a rudimentary network analysis. Our findings from the literature review suggest that (1) technological solutions are abundant in the literature but fall short of providing practical guidance on the pitfalls of implementation, (2) making energy efficiency a priority at the executive level of organizations will be largely ineffective if the IT and facilities staff are not directly incentivized to increase EE, and (3) there is minimal current understanding of how the individual psychologies of IT and facilities staff affect EE implementation in DCs. In the next phase of our research, we plan to interview data center operators/experts to ground-truth our literature findings and collaboratively design decarbonization policy solutions that target organizational structure, empower individual staff, and foster a supportive external market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

The globus compute dataset: An open function-as-a-service dataset from the edge to the cloud

Here we present a unique function-as-a-service (FaaS) dataset capturing the use of the Globus Compute (previously funcX) platform. Globus Compute implements a federated model via which users may deploy endpoints on arbitrary remote computers, from the edge to high performance computing (HPC) cluster, and they may then invoke Python functions on those endpoints via a reliable cloud -hosted service. The dataset covers 31 weeks and includes 2121472 task submissions from 252 users executed on 580 remote computing endpoints. It includes 277386 registered functions. We describe the dataset and various observations, some that are similar to other FaaS datasets, for example, that 74% of tasks run for less than 1 s, and some that are unique to Globus Compute, for example, that endpoints are used in different ways and that the majority of functions are related to scientific computing and machine learning. To the best of our knowledge, this dataset represents the first federated FaaS dataset that includes user workloads, distributed computing endpoints, and analysis of registered function bodies. We expect the dataset to be useful for researching FaaS architectures, workload modeling, container warming, and other distributed computing architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

SECARB-USA: A Sub-Basin Framework for Future CO 2 Infrastructure Development

Task 4 of the SECARB-USA Project is designed to evaluate the regional needs and challenges for the development of a safe and environmentally sound CO 2 transport infrastructure. This infrastructure would transport CO 2 capture from power generating plants and/or industrial sources and distribute it to oil fields for associated storage in EOR operations, saline storage, and/or other suitable storage sites. The objective of this report on Sub-Basin Prospective Areas (Deliverable 4.1.a), is to provide the framework for understanding how regional infrastructure development could occur. The sub-basin prospective areas, in conjunction with the locations of CO 2 sources and pipeline infrastructure (for source-sink matching networks), form the basis for the analysis that will be performed in the Techno-Economic Analysis of Infrastructure Buildout Scenarios (Deliverable 4.1.b).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantum annealing for jet clustering with thrust

Quantum computing holds the promise of substantially speeding up computationally expensive tasks, such as solving optimization problems over a large number of elements. In high-energy collider physics, quantum-assisted algorithms might accelerate the clustering of particles into jets. In this study, we benchmark quantum annealing strategies for jet clustering based on optimizing a quantity called “thrust” in electron-positron collision events. Here, we find that quantum annealing yields similar performance to exact classical approaches and classical heuristics, after tuning the annealing parameters. Without tuning, comparable performance can be obtained through a hybrid quantum/classical approach.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

On the Variation of Hardness Due to Uniaxial and Equi-Biaxial Residual Surface Stresses at Elastic–Plastic Indentation

It is established long since that the material hardness is independent of residual stresses at predominantly plastic deformation close to the contact region at indentation. Recently though, it has been shown that when elastic and plastic deformations are of equal magnitude this invariance is lost. For materials such as ceramics and polymers, this will complicate residual stress determination but can also, if properly understood, provide additional important information for performing such a task. Indeed, when the residual stresses are equi-biaxial, the situation is quite well understood, but additional efforts have to be made to understand the mechanical behavior in other loading states. Presently therefore, the variation of hardness, due to residual stresses, is examined at a uniaxial stress state. Correlation with global indentation quantities is analyzed, discussed and compared to corresponding equi-biaxial results. Cone indentation of elastic–perfectly plastic materials is considered.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Scaling Up Data-Parallel Analytics Platforms: Linear Algebraic Operation Cases

Linear algebraic operations such as matrix manipulations form the kernel of many machine learning and other crucial algorithms. Scaling up as well as scaling out such algorithms are key to supporting large scale data analysis that require efficient processing over millions of data samples. To this end, we present, ARION, a hardware acceleration based approach for scaling-up individual tasks of Spark, a popular data-parallel analytics platform. We support both linear algebraic operations of between two dense matrices, and between sparse and dense matrices in distributed environments. ARION provides a flexible control of acceleration according to matrix density, along with efficient scheduling based on runtime resource utilization. We demonstrate the benefit of our approach for general matrix multiplication operations over large matrices with up to four billion elements by using Gramian matrix computation that is commonly used in machine learning. Experiments show that our approach achieves more than 2× and 1.5× end-to-end performance speedups for dense and sparse matrices, respectively, and up to 57.04× faster computation compared to MLlib, a state of the art Spark-based implementation. This work is sponsored in part by the NSF under the grants: CNS-1565314, CNS-1405697, and CNS-1615411. The manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Xu, Luna↗
DOE OSTI2014

Secure Power Systems Professional Phase III Final Report: Recruiting, Selecting and Developing Secure Power Systems Professionals

With significant numbers of power system experts retiring and a continuing trend of increasing complexity in modern power systems, there is a need throughout the power industry for cybersecurity awareness and competence in tasks that previously had no cyber components. To address these challenges, the U.S. Department of Energy established the Secure Power Systems Professional (SPSP) project to develop resources for job definition and competency analysis along with tools to guide assessment and curriculum design and to help identify and develop required knowledge, skills, and abilities in technical and operational power systems cybersecurity. The major accomplishments in Phase III of the project, which were built upon foundations laid in the first two phases, were (a) development and fielding of a Recruitment Guide for Human Resources and Hiring Managers and a Guide to Developing SPSPs; (b) development of psychometrically validated job profiles for identified SPSP job roles; (c) development of behavioral interview guidelines that list competencies necessary to perform each job role; and (d) development of guidelines that aid in evaluating individual and team performance and that inform strategic planning for organizations in selection, development and retention of SPSP professionals. The outcomes of the project can be immediately applied by human resources professionals, recruiters and hiring managers to assist in the recruitment, selection, and training of SPSPs, as well as to identify needed skills to grow existing employees into SPSPs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A Mechanistic Reliability Assessment of RVACS and Metal Fuel Inherent Reactivity Feedbacks

GE Hitachi Nuclear Energy (GEH) and Argonne National Laboratory (Argonne) participated in a two year collaboration to modernize and update the probabilistic risk assessment (PRA) for the PRISM sodium fast reactor. At a high level, the primary outcome of the project was the development of a next-generation PRA that is intended to enable risk-informed prioritization of safety- and reliability-focused research and development. A central Argonne task during this project was a reliability assessment of passive safety systems, which included the Reactor Vessel Auxiliary Cooling System (RVACS) and the inherent reactivity feedbacks of the metal fuel core. Both systems were examined utilizing a methodology derived from the Reliability Method for Passive Safety Functions (RMPS), with an emphasis on developing success criteria based on mechanistic system modeling while also maintaining consistency with the Fuel Damage Categories (FDCs) of the mechanistic source term assessment. This paper provides an overview of the reliability analyses of both systems, including highlights of the FMEAs, the construction of best-estimate models, uncertain parameter screening and propagation, and the quantification of system failure probability. In particular, special focus is given to the methodologies to perform the analysis of uncertainty propagation and the determination of the likelihood of violating FDC limits. Additionally, important lessons learned are also reviewed, such as optimal sampling methodologies for the discovery of low likelihood failure events and strategies for the combined treatment of aleatory and epistemic uncertainties.

Grabaskas, David↗
DOE OSTI2021

Enabling Scalable and Extensible Memory-mapped Datastores in Userspace

Exascale workloads are expected to incorporate data-intensive processing in close coordination with traditional physics simulations. These emerging scientific, data-analytics and machine learning applications need to access a wide variety of datastores in flat files and structured databases. Programmer productivity is greatly enhanced by mapping datastores into the application process's virtual memory space to provide a unified “in-memory” interface. Currently, memory mapping is provided by system software primarily designed for generality and reliability. However, scalability at high concurrency is a formidable challenge on exascale systems. Also, there is a need for extensibility to support new datastores potentially requiring HPC data transfer services. In this article, we present UMap , a scalable and extensible userspace service for memory-mapping datastores. Furthermore, through decoupled queue management, concurrency aware adaptation, and dynamic load balancing, UMap enables application performance to scale even at high concurrency. We evaluate UMap in data-intensive applications, including sorting, graph traversal, database operations, and metagenomic analytics. Our results show that UMap as a userspace service outperforms an optimized kernel-based service across a wide range of intra-node concurrency by 1.22-1.9 × . We performed two case studies to demonstrate UMap 's extensibility. First, a new datastore residing in remote memory is incorporated into UMap as an application-specific plugin. Second, we present a persistent memory allocator Metall built atop UMap for unified storage/memory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Overall protein structure quality assessment using hydrogen-bonding parameters

Atomic model refinement at low resolution is often a challenging task. This is mostly because the experimental data are not sufficiently detailed to be described by atomic models. To make refinement practical and ensure that a refined atomic model is geometrically meaningful, additional information needs to be used such as restraints on Ramachandran plot distributions or residue side-chain rotameric states. However, using Ramachandran plots or rotameric states as refinement targets diminishes the validating power of these tools. Therefore, finding additional model-validation criteria that are not used or are difficult to use as refinement goals is desirable. Hydrogen bonds are one of the important noncovalent interactions that shape and maintain protein structure. These interactions can be characterized by a specific geometry of hydrogen donor and acceptor atoms. Systematic analysis of these geometries performed for quality-filtered high-resolution models of proteins from the Protein Data Bank shows that they have a distinct and a conserved distribution. Here, it is demonstrated how this information can be used for atomic model validation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

From Escherichia coli mutant 13 C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13 C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13 C-Metabolic Flux Analysis ( 13 C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13 C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

DECOVALEX-2023, Task F Specification, Revision 8

This report is the revised (Revision 8) Task F specification for DECOVALEX-2023. Task F is a comparison of the models and methods used in deep geologic repository performance assessment. The task proposes to develop a reference case for a mined repository in a fractured crystalline host rock and a reference case for a mined repository in a salt formation. Teams may choose to participate in the comparison for either or both of the reference cases. For each reference case, a common set of conceptual models and parameters describing features, events, and processes that impact performance will be given, and teams will be responsible for determining how best to implement and couple the models. The comparison will be conducted in stages, beginning with a comparison of key outputs of individual process models, followed by a comparison of a single deterministic simulation of the full reference case, and moving on to uncertainty propagation and uncertainty and sensitivity analysis. This report provides background information, a summary of the proposed reference cases, and a staged plan for the analysis.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

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↗