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NASA NTRS2005

The contribution of CEOP data to the understanding and modeling of monsoon systems

CEOP has contributed and will continue to provide integrated data sets from diverse platforms for better understanding of the water and energy cycles, and for validating models. In this talk, I will show examples of how CEOP has contributed to the formulation of a strategy for the study of the monsoon as a system. The CEOP data concept has led to the development of the CEOP Inter-Monsoon Studies (CIMS), which focuses on the identification of model bias, and improvement of model physics such as the diurnal and annual cycles. A multi-model validation project focusing on diurnal variability of the East Asian monsoon, and using CEOP reference site data, as well as CEOP integrated satellite data is now ongoing. Similar validation projects in other monsoon regions are being started. Preliminary studies show that climate models have difficulties in simulating the diurnal signals of total rainfall, rainfall intensity and frequency of occurrence, which have different peak hours, depending on locations. Further more model diurnal cycle of rainfall in monsoon regions tend to lead the observed by about 2-3 hours. These model bias offer insight into lack of, or poor representation of key components of the convective,and stratiform rainfall. The CEOP data also stimulated studies to compare and contrasts monsoon variability in different parts of the world. It was found that seasonal wind reversal, orographic effects, monsoon depressions, meso-scale convective complexes, SST and land surface land influences are common features in all monsoon regions. Strong intraseasonal variability is present in all monsoon regions. While there is a clear demarcation of onset, breaks and withdrawal in the Asian and Australian monsoon region associated with climatological intraseasonal variability, it is less clear in the American and Africa monsoon regions. The examination of satellite and reference site data in monsoon has led to preliminary model experiments to study the impact of aerosol on monsoon variability. I will show examples of how the study of the dynamics of aerosol-water cycle interactions in the monsoon region, can be best achieved using the CEOP data and modeling strategy.

Lau, William K. M.↗

DOE OSTI2018

Ensemble cell-wide kinetic modeling of anaerobic organisms to support fuels and chemicals production

The goal of this project is to understand the metabolism and underlying regulatory networks of two anaerobic microorganisms, Moorella thermoacetica and Clostridium thermocellum by making use of Ensemble Modeling (EM) paradigm through integration of multiple omic information (transcriptomic, proteomic, metabolomic & fluxomic). Kinetic models have the potential to provide a mechanistic description of cellular metabolism by combining several layers of regulatory events into an integrated framework. The foundation of the kinetic model is a stoichiometric network description. To this end, we have developed and curated genome-scale metabolic (GSM) models of the organisms that will serve as scaffold for building the kinetic models. For M. thermoacetica, the GSM model was constructed and tested by the Stephanopoulos group with experimental growth rate. The GSM elucidated the energy conservation mechanism under autotrophic growth conditions. For C. thermocellum, the Maranas group has developed the second-generation GSM model through integration of experimental data. The Maranas group has also constructed a core metabolic kinetic model of the organism which includes the central metabolism and all relevant pathways for biofuels production. The stoichiometric and kinetic model predictions were tested extensively using experimental data for wild-type and 20 mutant strains. We have outlined below in detail the progress related to specific individual aims. Below are listed publications of research carried out based on this research grant support.

09 BIOMASS FUELS↗

DOE OSTI2015

Integration of microseismic monitoring data into coupled flow and geomechanical models with ensemble Kalman filter

Abstract Hydraulic stimulation of low‐permeability rocks in enhanced geothermal systems, shale resources, and CO 2 storage aquifers can trigger microseismic events, also known as microearthquakes (MEQs). The distribution of microseismic source locations in the reservoir may reveal important information about the distribution of hydraulic and geomechanical rock properties. In this paper, we present a framework for conditioning heterogeneous rock permeability and geomechanical property distributions on microseismic data. To simulate the multiphysics processes in these systems, we combine a fully coupled flow and geomechanical model with the Mohr‐Coulomb type rock failure criterion. The resulting multiphysics simulation constitutes the forecast model that relates microseismic source locations to reservoir rock properties. We adopt this forward model in an ensemble Kalman filter (EnKF) data assimilation framework to jointly estimate reservoir permeability and geomechanical property distributions from injection‐induced microseismic response measurements. We show that integration of a large number of spatially correlated microseismic data with practical ensemble sizes can lead to severe underestimation of ensemble spread, and eventually ensemble collapse. To mitigate the variance underestimation issue, two low‐rank data representation schemes are presented and discussed. In the first approach, microseismic data are projected onto a low‐dimensional subspace defined by the left singular vectors of the perturbed observation matrix. The second method uses a coarser grid for representing the microseismic data. A series of numerical experiments is presented to evaluate the performance of the proposed methods and to illustrate their applicability for assimilating microseismic data into coupled flow and geomechanical forward models to estimate multiphysics rock properties.

Tarrahi, Mohammadali↗

NASA NTRS2023

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers, and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Carl Malings↗

NASA NTRSDate not supplied

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Nathan R. Pavlovic↗

DOE OSTI2022

Analyzing the Multiscale Impacts of Implementing Energy-Efficient HVAC Improvements Through Energy Audits and Economic Input–Output Analysis

Abstract Heating, ventilation, and air-conditioning (HVAC) systems are usually an industry’s highest consumer of energy, most of which goes toward space cooling in buildings. Industrial energy-efficiency audits not only benefit manufacturers but also generate significant economic and environmental benefits to localities, states, and the nation. This article analyzes the micro- and macro scale impacts of implementing energy-efficient HVAC systems by integrating the industrial building energy data with the macroeconomic regional economic flow model. Micro-scale data include 10 years of historical energy, cost, and carbon dioxide savings achieved from energy-efficient HVAC implementation offered to manufacturers through industrial energy audits. The data were integrated into the macroeconomic modeling framework to illuminate the cascading regional economic impacts of implementing energy-efficient HVAC recommendations in manufacturing facilities. Results show that if recommendations had been implemented throughout all manufacturers in the region, $656 M energy costs would have been directly saved, 7.8 million metric tons of carbon dioxide emissions would have been avoided, and 4387 jobs could have been created, resulting in a total annual economic impact of $899 M stemming from direct, indirect, and induced impacts. The results offer insight into how industrial energy systems can be designed and provide models for how communities can accomplish a net-zero society.

Energy & Fuels↗

DOE OSTI2026

Microwave-Assisted Plastic Upcycling: Dynamic Data Reconciliation, Parameter Estimation, and Kinetic Modeling

Microwave (MW)-assisted catalytic pyrolysis offers a promising pathway for efficient plastic upcycling. This work develops an integrated modeling framework combining dynamic data reconciliation, a temperature-dependent rate model, and a yield model to represent the time-varying production rate of components in MW-assisted LDPE pyrolysis conducted in a batch reactor. An Arrhenius-type rate model with a temperature-dependent reaction order is developed. A biexponential correlation is proposed for the yield of gaseous products that enables to capture the evolving product formation behavior during conversion. In the yield correlation, one term is used to represent the initial increase in yield, reflecting the rapid formation of intermediate or primary products at the early stages of the reaction when a larger fraction of the reactant remains available. As conversion progresses, the influence of this term gradually diminishes. The other term accounts for the subsequent decrease in the predicted yield, representing secondary reactions such as further cracking or coke formation that reduce the concentration of certain products at higher conversion. The model is found to accurately represent reconciled experimental flow rate profiles from an in-house MW-assisted catalytic batch reactor for major products, including ethylene, ethane, 1-butene, and benzene, across 250−350 °C. Ethylene remains the dominant product but decreases from about 41.95% at 250 °C to 30.14% at 350 °C, while heavier products increase significantly, with 1-butene rising to nearly 8.37% and benzene reaching 2.17% at intermediate temperatures. The model shows that the ethylene production rate can be maximized at around 270 °C. The models developed in this work can be utilized for process optimization, reactor design and scale-up of microwave-assisted plastic conversion technologies, and economic analysis.

Damahe, Harish [West Virginia Univ., Morgantown, W↗

NASA NTRS2012

Integrating Engineering Data Systems for NASA Spaceflight Projects

NASA has a large range of custom-built and commercial data systems to support spaceflight programs. Some of the systems are re-used by many programs and projects over time. Management and systems engineering processes require integration of data across many of these systems, a difficult problem given the widely diverse nature of system interfaces and data models. This paper describes an ongoing project to use a central data model with a web services architecture to support the integration and access of linked data across engineering functions for multiple NASA programs. The work involves the implementation of a web service-based middleware system called Data Aggregator to bring together data from a variety of systems to support space exploration. Data Aggregator includes a central data model registry for storing and managing links between the data in disparate systems. Initially developed for NASA's Constellation Program needs, Data Aggregator is currently being repurposed to support the International Space Station Program and new NASA projects with processes that involve significant aggregating and linking of data. This change in user needs led to development of a more streamlined data model registry for Data Aggregator in order to simplify adding new project application data as well as standardization of the Data Aggregator query syntax to facilitate cross-application querying by client applications. This paper documents the approach from a set of stand-alone engineering systems from which data are manually retrieved and integrated, to a web of engineering data systems from which the latest data are automatically retrieved and more quickly and accurately integrated. This paper includes the lessons learned through these efforts, including the design and development of a service-oriented architecture and the evolution of the data model registry approaches as the effort continues to evolve and adapt to support multiple NASA programs and priorities.

Carvalho, Robert E.↗

NASA NTRS1999

An EVA Suit Fatigue, Strength, and Reach Model

The number of Extra-Vehicular Activities (EVAs) performed will increase dramatically with the upcoming Space Station assembly missions. It is estimated that up to 900 EVA hours may be required to assemble the Space Station with an additional 200 hours per year for maintenance requirements. Efficient modeling tools will be essential to assist in planning these EVAS. Important components include strength and fatigue parameters, multi-body dynamics and kinematics. This project is focused on building a model of the EVA crew member encompassing all these capabilities. Phase 1, which is currently underway, involves collecting EMU suited and unsuited fatigue, strength and range of motion data, for all major joints of the body. Phase 2 involves processing the data for model input, formulating comparisons between the EMU suits and deriving generalized relationships between suited and unsuited data. Phase 3 will be formulation of a multi-body dynamics model of the EMU capable of predicting mass handling properties and integration of empirical data into the model. Phase 4 will be validation of the model with collected EMU data from the Neutral Buoyancy Laboratory at NASA/JSC. Engineers and designers will use tie EVA suit database to better understand the capabilities of the suited individuals. This knowledge will lead to better design of tools and planned operations. Mission planners can use the modeling system and view the animations and the visualizations of the various parameters, such as overall fatigue, motion, timelines, reach, and strength to streamline the timing, duration, task arrangement, personnel and overall efficiency of the EVA tasks. Suit designers can use quantifiable data at common biomechanical structure points to better analyze and compare suit performance.

Maida, James C.↗

NASA NTRS2005

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes data management of NASA remote sensing data for Northern Eurasia Earth Science Partnership Initiative (NEESPI). Many types of ground and integrative (e.g., satellite, GIs) data will be needed and many models must be applied, adapted or developed for properly understanding the functioning of Northern Eurasia cold and diverse regional system. Mechanisms for obtaining the requisite data sets and models and sharing them among the participating scientists are essential. The proposed project targets integration of remote sensing data from AVHRR, MODIS, and other NASA instruments on board US- satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEPNCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗

NASA NTRS1987

Wind tunnel test IA300 analysis and results, volume 1

The analysis and interpretation of wind tunnel pressure data from the Space Shuttle wind tunnel test IA300 are presented. The primary objective of the test was to determine the effects of the Space Shuttle Main Engine (SSME) and the Solid Rocket Booster (SRB) plumes on the integrated vehicle forebody pressure distributions, the elevon hinge moments, and wing loads. The results of this test will be combined with flight test results to form a new data base to be employed in the IVBC-3 airloads analysis. A secondary objective was to obtain solid plume data for correlation with the results of gaseous plume tests. Data from the power level portion was used in conjunction with flight base pressures to evaluate nominal power levels to be used during the investigation of changes in model attitude, eleveon deflection, and nozzle gimbal angle. The plume induced aerodynamic loads were developed for the Space Shuttle bases and forebody areas. A computer code was developed to integrate the pressure data. Using simplified geometrical models of the Space Shuttle elements and components, the pressure data were integrated to develop plume induced force and moments coefficients that can be combined with a power-off data base to develop a power-on data base.

Kelley, P. B.↗

NASA NTRS1998

AXAF: Current Developments and Future Plans for the Scientific Data Analysis

AXAF, the Advanced X-ray Astrophysics Facility, is one of NASA's great observatories scheduled for launch in January of 1999 by a Space Shuttle. This facility will be available to scientists in the United States and to the international astronomical community over an anticipated mission lifetime of at least 5 years. The objective of AXAF is to make astrophysical observations in the 0.09 to 10.0 keV energy range with two imaging detectors, and two sets of transmission gratings giving high spatial (0.5 arcsec) and spectral (E/(Delta)E=100-2000) resolution. The AXAF Science Center (ASC) in Cambridge, MA, USA is responsible for obtaining and reviewing observation proposals submitted by users, providing long range science planning and performing all science data calibration, data analysis, science instrument trend analysis, and science data archiving. The ASC will distribute science data products to the users. We will review here the current developments and future plans for the AXAF scientific data analysis system, its infrastructure and new key features, including the global modelling and fitting environment, integrated data analysis GUIs and new analysis tools.

Fruscione, Antonella↗

NASA NTRS2004

Bias Reduction in Short Records of Satellite Soil Moisture

Although surface soil moisture data from different sources (satellite retrievals, ground measurements, and land model integrations of observed meteorological forcing data) have been shown to contain consistent and useful information in their seasonal cycle and anomaly signals, they typically exhibit very different mean values and variability. These biases pose a severe obstacle to exploiting the useful information contained in satellite retrievals through data assimilation. A simple method of bias removal is to match the cumulative distribution functions (cdf) of the satellite and model data. However, accurate cdf estimation typically requires a long record of satellite data. We demonstrate here that by wing spatial sampling with a 2 degree moving window we can obtain local statistics based on a one-year satellite record that are a good approximation to those that would be derived from a much longer time series. This result should increase the usefulness of relatively short satellite data records.

Reichle, Rolf H.↗

DOE OSTI2023

Transmission risk of Oropouche fever across the Americas

Abstract Background Vector-borne diseases (VBDs) are important contributors to the global burden of infectious diseases due to their epidemic potential, which can result in significant population and economic impacts. Oropouche fever, caused by Oropouche virus (OROV), is an understudied zoonotic VBD febrile illness reported in Central and South America. The epidemic potential and areas of likely OROV spread remain unexplored, limiting capacities to improve epidemiological surveillance. Methods To better understand the capacity for spread of OROV, we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data, coupled with high-resolution satellite-derived vegetation phenology. Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas. Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of different parameters such as different study areas and environmental predictors. Models estimate that up to 5 million people are at risk of exposure to OROV. Nevertheless, the limited epidemiological data available generates uncertainty in projections. For example, some outbreaks have occurred under climatic conditions outside those where most transmission events occur. The distribution models also revealed that landscape variation, expressed as vegetation loss, is linked to OROV outbreaks. Conclusions Hotspots of OROV transmission risk were detected along the tropics of South America. Vegetation loss might be a driver of Oropouche fever emergence. Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understanding exists on their sylvatic cycles. OROV transmission risk maps can be used to improve surveillance, investigate OROV ecology and epidemiology, and inform early detection.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

A scalable framework for quantifying field-level agricultural carbon outcomes

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4 ) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Xanthos

Xanthos is a Python package designed to quantify and analyze global water availability in history and in future at 0.5° × 0.5° spatial resolution and a monthly time step under a changing climate. Its performance was also tested through real applications. It is open-source, extendable and convenient to researchers who work on long-term climate data for studies of global water supply, and Global Change Assessment Model (GCAM). This package integrates inherent global gridded data maps, I/O modules, Water-Balance Model modules and diagnostics modules by user-defined configuration.

Source record↗

DOE OSTI2017

Xanthos

Source record↗

NASA NTRS2003

Predicting and Managing Lighting and Visibility for Human Operations in Space

Lighting is critical to human visual performance. On earth this problem is well understood and solutions are well defined and executed. Because the sun rises and sets on average every 45 minutes during Earth orbit, humans working in space must cope with ~ extremely dynamic lighting conditions varying from very low light conditions to severe glare and contrast conditions. For critical operations, it is essential that lighting conditions be predictable and manageable. Mission planners need to detelmine whether low-light video cameras are required or whether additional luminaires, or lamps, need to be flown . Crew and flight directors need to have up to date daylight orbit time lines showing the best and worst viewing conditions for sunlight and shadowing. Where applicable and possible, lighting conditions need to be part of crew training. In addition, it is desirable to optimize the quantity and quality of light because of the potential impacts on crew safety, delivery costs, electrical power and equipment maintainability for both exterior and interior conditions. Addressing these issues, an illumination modeling system has been developed in the Space Human Factors Laboratory at ASA Johnson Space Center. The system is the integration of a physically based ray-tracing package ("Radiance"), developed at the Lawrence Berkeley Laboratories, a human factors oriented geometric modeling system developed by NASA and an extensive database of humans and their work environments. Measured and published data has been collected for exterior and interior surface reflectivity; luminaire beam spread distribution, color and intensity and video camera light sensitivity and has been associated with their corresponding geometric models. Selecting an eye-point and one or more light sources, including sun and earthshine, a ~ snapshot of the light energy reaching the surfaces or reaching the eye point is computed. This energy map is then used to extract the required information needed for useful predictions. Using a validated, comprehensive illumination model integrated with empirically derived data, predictions of lighting and viewing conditions have been successfully used for Shuttle and Space Station planning and assembly operations. It has successfully balanced the needs for adequate human performance with the utili zation of resources. Keywords: Modeling, ray tracing, luminaires, refl ectivity, luminance, illuminance.

Maida, James C.↗