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A Conceptual Architecture for National Biosurveillance: Moving Beyond Situational Awareness to Enable Digital Detection of Emerging Threats

Despite numerous calls for improvement, the U.S. biosurveillance enterprise remains a patchwork of uncoordinated systems that fail to take advantage of the rapid progress in information processing, communication, and analytics made in the past decade. By synthesizing components from the extensive biosurveillance literature, we propose a conceptual framework for a national biosurveillance architecture and provide suggestions for implementation. The framework differs from the current federal biosurveillance development pathway in that it is not focused on systems useful for “situational awareness,” but is instead focused on the long-term goal of having true warning capabilities. Therefore, a guiding design objective is the ability to digitally detect emerging threats that span jurisdictional boundaries, because attempting to solve the most challenging biosurveillance problem first provides the strongest foundation to meet simpler surveillance objectives. Core components of the vision are: (1) a whole-of-government approach to support currently disparate federal surveillance efforts that have a common data need, including those for food safety, vaccine and medical product safety, and infectious disease surveillance; (2) an information architecture that enables secure, national access to electronic health records, yet does not require that data be sent to a centralized location for surveillance analysis; (3) an inference architecture that leverages advances in ‘big data’ analytics and learning inference engines—a significant departure from the statistical process control paradigm that underpins nearly all current syndromic surveillance systems; and, (4) an organizational architecture with a governance model aimed at establishing national biosurveillance as a critical part of the U.S. national infrastructure. Although it will take many years to implement, and a national campaign of education and debate to acquire public buy-in for such a comprehensive system, the potential benefits warrant increased consideration within the U.S. government.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Biocuration: Distilling data into knowledge

Data, including information generated from them by processing and analysis, are an asset with measurable value. The assets that biological research funding produces are the data generated, the information derived from these data, and, ultimately, the discoveries and knowledge these lead to. From the time when Henry Oldenburg published the first scientific journal in 1665 (Proceedings of the Royal Society) to the founding of the United States National Library of Medicine in 1879 to the present, there has been a sustained drive to improve how researchers can record and discover what is known. Researchers' experimental work builds upon years and (collectively) billions of dollars' worth of earlier work. Today, researchers are generating data at ever-faster rates because of advances in instrumentation and technology, coupled with decreases in production costs. Unfortunately, the ability of researchers to manage and disseminate their results has not kept pace, so their work cannot achieve its maximal impact. Strides have recently been made, but more awareness is needed of the essential role that biological data resources, including biocuration, play in maintaining and linking this ever-growing flood of data and information. The aim of this paper is to describe the nature of data as an asset, the role biocurators play in increasing its value, and consistent, practical means to measure effectiveness that can guide planning and justify costs in biological research information resources' development and management.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Imaging Bragg Edge Analysis TooLs for Engineering Structures (iBeatles)

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) provides pulsed neutrons with energies varying from epithermal to cold. In preparation for VENUS, the neutron imaging beamline to be located at beam port 10, we have performed a series of experiments focused on wavelength-dependent radiography and computed tomography for a broad range of applications, from materials science to biological tissues.One of the time-of-flight (TOF) techniques that is of interest to the scientific community is the 2-dimensional mapping of phases and average crystalline plane orientation in samples both ex-situ and during applied stresses such as tensile loading and heating. This technique is known as Bragg edgeimaging and relies on the identification of changes of transmission values, fitting of the edge to measure its displacement, and thus identify the shift in lattice parameter due to stresses. One of the challenges of TOF imaging measurements is the amount of data and the inability to observe Bragg edge shifts in real time during an experiment. Thus, we have been focusing on creating a Python-based interface that allows fast data processing and instantaneous mapping and fitting of the Bragg edges, and their evolution through time. Python libraries and Jupyter notebooks have been implemented to facilitate decision making during an experiment. The advantage of the notebooks is the possibility to guide an experiment as they can quickly process and display Bragg edge data. These notebooks can be used independently, or can be combined in a Python Graphical User Interface (GUI) tool called iBeatles. This interface permits visualization and fitting of the Bragg edges, and ultimately back-projects the fitting results onto the radiographs to display a strain map. Assuming data collection has sufficient statistics, the strain mapping analysis can be performed on a pixel-by-pixel basis. This development is a step forward toward a better user experience at the future VENUS beamline in terms of live feedback and productivity. Analysis that used to take days of switching between different applications can now be done in minutes within the

Bilheux, JeanChristophe [Oak Ridge National Labora↗

ARM Cloud Radar Simulator Package for Global Climate Models Value-Added Product

It has been challenging to directly compare U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility ground-based cloud radar measurements with climate model output because of limitations or features of the observing processes and the spatial gap between model and the single-point measurements. To facilitate the use of ARM radar data in numerical models, an ARM cloud radar simulator was developed to converts model data into pseudo-ARM cloud radar observations that mimic the instrument view of a narrow atmospheric column (as compared to a large global climate model [GCM] grid-cell), thus allowing meaningful comparison between model output and ARM cloud observations. The ARM cloud radar simulator value-added product (VAP) was developed based on the CloudSat simulator contained in the community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) (Bodas-Salcedo et al., 2011), which has been widely used in climate model evaluation with satellite data (Klein et al., 2013, Zhang et al., 2010). The essential part of the CloudSat simulator is the QuickBeam radar simulator that is used to produce CloudSat-like radar reflectivity, but is capable of simulating reflectivity for other radars (Marchand et al., 2009; Haynes et al., 2007). Adapting QuickBeam to the ARM cloud radar simulator within COSP required two primary changes: one was to set the frequency to 35 GHz for the ARM Ka-band cloud radar, as opposed to 94 GHz used for the CloudSat W-band radar, and the second was to invert the view from the ground to space so as to attenuate the beam correctly. In addition, the ARM cloud radar simulator uses a finer vertical resolution (100 m compared to 500 m for CloudSat) to resolve the more detailed structure of clouds captured by the ARM radars. The ARM simulator has been developed following the COSP workflow (Figure 1) and using the capabilities available in COSP wherever possible. The ARM simulator is written in Fortran 90, just as is the COSP. It is incorporated into COSP to facilitate use by the climate modeling community. In order to evaluate simulator output, the observational counterpart of the simulator output, radar reflectivity-height histograms (CFAD) is also generated from the ARM observations. This report includes an overview of the ARM cloud radar simulator VAP and the required simulator-oriented ARM radar data product (radarCFAD) for validating simulator output, as well as a user guide for operating the ARM radar simulator VAP.

54 ENVIRONMENTAL SCIENCES↗

Argonne Leadership Computing Facility: 2021 Operational Assessment Report

This Operational Assessment Report describes how the Argonne Leadership Computing Facility (ALCF) met or exceeded every one of its goals for calendar year (CY) 2021 as an advanced scientific computing center. In CY 2021, the ALCF operated its production resource, Theta, an Intel-based Cray XC40 system (11.7-petaflops) augmented with 24 NVIDIA DGX A100-based nodes (3.9-petaflops) that supports diverse workloads, integrating data analytics with artificial intelligence (AI) training and learning in a single platform. In 2021, we began deploying Polaris, our newest 40- petaflops system, and augmented this powerful testbed system with an additional 28 nodes to support the integration of real-time experiments and HPC resources. We also deployed our two largest storage systems yet, named Grand and Eagle, that will bring new services to our users and will power data-driven research for years to come. Last year, Theta delivered a total of 20.8 million node-hours to 16 Innovative and Novel Computational Impact on Theory and Experiment (INCITE) projects and 7.2 million node-hours to ASCR Leadership Computing Challenge (ALCC) projects (32 awarded during the 2020–2021 ALCC year and 17 awarded during the 2021–2022 ALCC year), as well as substantial support to Director’s Discretionary (DD) projects (5.5 million node-hours). As Table ES.1 shows, Theta performed exceptionally well in terms of overall availability (95.1 percent), scheduled availability (99.4 percent), and utilization (98.1 percent; Table 2.1). As of the submission date of this document, ALCF’s user community has published 249 papers in high-quality, peer-reviewed journals and technical proceedings. At the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’21), Argonne researchers won two HPCwire Readers’ Choice Awards and were part of a Gordon Bell Prize finalist team recognized for developing an AI-enabled, multi-resolution simulation framework for studying complex biomolecular machines. Their framework was used to observe the SARS-CoV-2 replication-transcription machinery in action, by directly integrating experimental data. ALCF also provided a comprehensive program of high-performance computing (HPC) support services to help our community make productive use of the facility’s diverse and growing collection of resources. We are now entering the exascale era, with exascale machines being planned for national laboratories across the country, including Aurora at Argonne National Laboratory (Argonne) in 2023. ALCF researchers have been leading and guiding numerous strategic activities that will push the boundaries of what’s possible in computational science and engineering and allow us to deliver science on day one.

97 MATHEMATICS AND COMPUTING↗

Strategic Energy Management Plan for the Santa Ynez Band of Chumash Indians

This plan outlines the Santa Ynez Band of Chumash Indians’ comprehensive energy management strategy including an assessment of current practices, a commitment to improving energy performance and reducing overall energy use, and recommended actions to achieve these goals. Vision Statement: The primary objective of the Strategic Energy Management Plan is to implement energy efficiency, energy security, conservation, education, and renewable energy projects that align with the economic goals and cultural values of the community to improve the health and welfare of the tribe. The intended outcomes of implementing the energy plan include job creation, capacity building, and reduced energy costs for tribal community members, and tribal operations. By encouraging energy independence and local power production the plan will promote self-sufficiency. Mission & Objectives: The Strategic Energy Plan will provide information and suggestions to guide tribal decision-making and provide a foundation for effective management of energy resources within the Santa Ynez Band of Chumash Indians (SYBCI) community. The objectives of developing this plan include; Assess current energy demand and costs of all tribal enterprises, offices, and facilities; Provide a baseline assessment of the SYBCI’s energy resources so that future progress can be clearly and consistently measured, and current usage better understood; Project future energy demand; Establish a system for centralized, ongoing tracking and analysis of tribal energy data that is applicable across sectors, facilities, and activities; Develop a unifying vision that is consistent with the tribe’s long-term cultural, social, environmental, and economic goals; Identify and evaluate the potential of opportunities for development of long-term, cost effective energy sources, such as renewable energy, energy efficiency and conservation, and other feasible supply- and demand-side options; and Build the SYBCI’s capacity for understanding, managing, and developing energy resources by identifying training, distribution of information materials, and community meeting needs and opportunities

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Hysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activity: Model Archive

This Modeling Archive is in support of an NGEE Arctic publication "Hysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activity" in the Journal Biogeosciences (https://doi.org/10.5194/bg-17-5849-2020), which includes the model data used in the publication. CH4 emissions from terrestrial systems are posited to increase, which can offset mitigation efforts and accelerate climate change. Yet, the accuracy of modeled CH4 emissions is sensitive to the prescribed CH4 production (or emission) temperature dependencies that are currently uncertain. Here, we use a comprehensive biogeochemistry model (ecosys) to investigate factors modulating CH4 production and emission rates across a permafrost thaw gradient encompassing a partly thawed bog and a fully thawed fen. We find that seasonally varying substrate availability drives lower and higher modeled methanogen biomass and activity, and thereby CH4 production, during the earlier and later periods of the thawed season, respectively. Package follows the Model data archiving guidelines with data in a *.zip file with three subfolders containing *.csv files and raw model output files; a data dictionary table (data_dictionary.csv) and two model output description tables (ecosys_plantspecies_output_notes.csv and ecosys_soil_ouput_notes.csv) to explain the format and meaning of individual output variables; and a user guide as a *.pdf. A detailed model description can be found in the supplement of (Grant, 2013). The ecosys source code is available at https://doi:10.5281/zenodo.3906642. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Development of an Efficient Approach to Perform Neutronics Simulations for Plutonium-238 Production

Conversion of 238Pu decay heat into usable electricity is imperative to power National Aeronautics and Space Administration (NASA) deep space exploration missions; however, the current stockpile of 238Pu is diminishing and the quality is less than ideal. In response, the US Department of Energy and NASA have undertaken a program to reestablish a domestic 238Pu production program and a technology demonstration sub-project has been initiated. Neutronics simulations for 238Pu production play a vital role in this project because the results guide reactor safety-basis, target design and optimization, and post-irradiation examination activities. A new, efficient neutronics simulation tool written in Python was developed to evaluate, with the highest fidelity possible with approved tools, the time-dependent nuclide evolution and heat deposition rates in 238Pu production targets irradiated in the High Flux Isotope Reactor (HFIR). The Python Activation and Heat Deposition Script (PAHDS) was developed specifically for experiment analysis in HFIR and couples the MCNP5 and SCALE 6.1.3 software quality assured tools to take advantage of an existing high-fidelity MCNP HFIR model, the most up-to-date ORIGEN code, and the most up-to-date nuclear data. Three cycle simulations were performed with PAHDS implementing ENDF/B-VII.0, ENDF/B-VII.1, and the Hybrid Library GPD-Rev0 cross-section libraries. The 238Pu production results were benchmarked against VESTA-obtained results and the impact of various cross-section libraries on the calculated metrics were assessed.

depletion↗

A Guide for Selecting Appropriate Human Factors Methods and Measures in Control Room Modernization Efforts in Nuclear Power Plants

Abstract. Many of the U.S. nuclear power plants are approaching the end of their 60-year licensing period. The U.S. Department of Energy Light Water Reactor Sustainability Program is conducting targeted research to extend the lives and ensure long-term reliability, productivity, safety, and security of these plants through targeted research, such as with integrating advanced digital instrumentation and control technologies in the main control room. There are many challenges to this, one being the integration of human factors engineering in the design and evaluation of these upgrades. This paper builds upon recent efforts in developing utility-specific guidance for integrating human factors engineering in the control room modernization process by providing commonly used data collection methods that are applicable at various phases of the upgrade process. Advantages and disadvantages of each method is provided for consideration of an optimal human factors evaluation plan to be used throughout the lifespan of the upgrade process.

99 GENERAL AND MISCELLANEOUS↗

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. Lastly, this survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.

3D crop modeling↗

NARAC Dispersion Model Product Integration With RadResponder

Work on enhanced cooperation and interoperability of Nuclear Incident Response Teams (NIRT) is a joint effort between DHS/FEMA, DOE/NNSA and EPA. One such effort was the integration between the RadResponder Network, a resource sponsored by FEMA for the management of radiological data during an emergency, and the National Atmospheric Advisory Center (NARAC), a DOE/NNSA modeling resource whose predictions are used to aid radiological emergency preparedness and response. Working together under a FEMA-sponsored project these two radiological response assets developed a capability to read and display plume model prediction results from the NARAC computer system in the RadResponder software tool. As a result of this effort, RadResponder users have been provided with NARAC modeling predictions of contamination areas, radiological dose levels, and protective action areas (e.g., areas warranting worker protection or sheltering/evacuation) to help guide protective action decisions and field monitoring surveys, and gain key situation awareness following a radiological/nuclear accident or incident (e.g., nuclear power plant accident, radiological dispersal device incident, or improvised nuclear detonation incident). This document describes the details of this integration effort.

54 ENVIRONMENTAL SCIENCES↗

Fertilizer management for global ammonia emission reduction

Crop production is a large source of atmospheric ammonia (NH 3 ), which poses risks to air quality, human health and ecosystems. However, estimating global NH 3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy. In this report we develop a machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH 3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr -1 , lower than previous estimates that did not fully consider fertilizer management practices. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr -1 ) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44–56%) for rice, 27% (24–28%) for maize and 26% (20–28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1–2.6 and 5.5 ± 5.7% under SSP5–8.5 by 2030–2060. However, targeted fertilizer management has the potential to mitigate these increases.

54 ENVIRONMENTAL SCIENCES↗

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗

Genome resources for three modern cotton lines guide future breeding efforts

Cotton ( Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars (‘UGA230’, ‘UA48’ and ‘CSX8308’) and updating the ‘TM-1’ cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from ‘Pima’ cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

59 BASIC BIOLOGICAL SCIENCES↗

Investigating high-energy proton-induced reactions on spherical nuclei: Implications for the preequilibrium exciton model

A number of accelerator-based isotope production facilities utilize $100-200$ MeV proton beams due to the high production rates enabled by high-intensity beam capabilities and the greater diversity of isotope production brought on by the long range of high-energy protons. However, nuclear reaction modeling at these energies can be challenging because of the interplay between different reaction modes and a lack of existing guiding cross section data. A Tri-lab collaboration has been formed between the Lawrence Berkeley, Los Alamos, and Brookhaven National Laboratories to address these complexities by characterizing charged-particle nuclear reactions relevant to the production of established and novel radioisotopes. In the inaugural collaboration experiments, stacked-targets of niobium foils were irradiated at the Brookhaven Linac Isotope Producer ($E_p=200$ MeV) and the Los Alamos Isotope Production Facility ($E_p=100$ MeV) to measure $^{93}$Nb(p,x) cross sections between $50-200$ MeV. The results were compared with literature data as well as the default calculations of the nuclear model codes TALYS, CoH, EMPIRE, and ALICE. The default code predictions largely failed to reproduce the measurements. Therefore, we developed a standardized procedure, which determines the reaction model parameters that best reproduce the most prominent reaction channels in a physically justifiable manner. Overall, the primary focus of the procedure was to determine the best parameterization for the pre-equilibrium two-component exciton model. This modeling study revealed a trend towards a relative decrease for internal transition rates at intermediate proton energies ($E_p=20-60$ MeV) in the current exciton model as compared to the default values. The results of this work are instrumental for the planning, execution, and analysis essential to isotope production.

43 PARTICLE ACCELERATORS↗

Multilevel Techniques for Compression and Reduction of Scientific Data---The Multivariate Case

We develop a technique for multigrid adaptive reduction of data (MGARD). Special attention is given to the case of tensor product grids, where our approach permits the use of nonuniformly spaced grids in each direction, which can prove problematic for many types of data reduction methods. An important feature of our approach is the provision of guaranteed, computable bounds on the loss incurred by the reduction of the data. Many users are leery of lossy algorithms and will only consider using them provided that numerical bounds on the pointwise difference between the original and the reduced datasets are given. Accordingly, we develop techniques for bounding the loss measured in the $L^{\infty}(\Omega)$ norm, and we show that these bounds are realistic in the sense that they do not significantly overestimate the actual loss. Here, the resulting loss indicators are used to guide the adaptive reduction of the data so that the reduced dataset meets a user-prescribed tolerance or memory constraint. Illustrative numerical examples, including the reduction of data arising from the simulation of a nonlinear reaction-diffusion problem, a turbulent channel flow, and a climate simulation, are provided.

97 MATHEMATICS AND COMPUTING↗

Air Leakage and Air Transfer Between Garage and Living Space

This research project focused on evaluation of air transfer between the garage and living space in a single-family detached home constructed by a production homebuilder in compliance with the 2009 International Residential Code and the 2009 International Energy Conservation Code. The project gathered important information about the performance of whole-building ventilation systems and garage ventilation systems as they relate to minimizing flow of contaminated air from garage to living space. Important gaps and barriers addressed by this research were: (1) Developing recommendations for effective methods for precluding the flow of contaminated air from garages to living space. (2) Creating a resource for guiding home builders and retrofitters to achieve garage isolation by use of mechanical ventilation equipment. (3) Providing field measurement support for revision of the EPA Indoor airPLUS program requirements, which are part of the DOE Challenge Home program. Field test data provided in the form of technical report showing blower door test results for air leakage measurements and tracer gas testing results. Waldorf, MD 20602

Array↗