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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

R3IT, Rapid Rural Resilient Interconnection Toolkit, Final Technical Report

Utilities nationwide are beginning to experience multiple solar interconnection requests per day. This request volume will soon overwhelm utility engineers, especially at smaller utilities which serve the majority of the landmass of the United States. In this project, we developed open-source software tool that automates the interconnection approval process and removes 80% of the time required to approve an interconnection and hence allow more solar to be quickly and safely integrated with the grid. This software is accompanied by a guidebook detailing interconnection best practices and utility lessons learned. Competing solar interconnection software is extremely expensive at ten times the cost of typical distribution engineering tools, it’s closed source which makes it hard to integrate into utility workflows, and it doesn’t address the hardest power flow modeling challenges of interconnection screening. Our solution was built built with our utility partners who are already experiencing multiple interconnection requests per day and integrates best practices from our past work with DOE and the cooperative community on solar integration. The resulting free and open-source solution has been disseminated through our media channels and conferences that engage over 1,900 utilities nationwide.

14 SOLAR ENERGY↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.

Tibbetts, Jake↗
DOE OSTI2023

AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses m{1,2}∈[3M⊙,50M⊙], and individual spins sz{1,2}∈[−0.9,0.9]; and which include the (ℓ,|m|)={(2,2),(2,1),(3,3),(3,2),(4,4)} modes, and mode mixing effects in the ℓ=3,|m|=2 harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals.

Tian, Minyang↗
NASA NTRS2008

NASA's Agency-Wide Strategy for Environmental Regulatory Risk Analysis and Communication

NASA's mission is to pioneer the future in space exploration, scientific discovery, and aeronautics research. To help enable existing and future programs to pursue this mission, NASA has established the Principal Center for Regulatory Risk Analysis and Communication (RRAC PC) to proactively identify, analyze, and communicate environmental regulatory risks to the NASA community. The RRAC PC is chartered to evaluate the risks posed to NASA Programs and facilities by environmentally related drivers. The RRAC PC focuses on emerging environmental regulations, as well as risks related to operational changes that can trigger existing environmental requirements. Changing regulations have the potential to directly affect program activities. For example, regulatory changes can restrict certain activities or operations by mandating changes in how operations may be done or limiting where or how certain operations can take place. Regulatory changes also can directly affect the ability to use certain materials by mandating a production phase-out or restricting usage applications of certain materials. Such changes can result in NASA undertaking material replacement efforts. Even if a regulation does not directly affect NASA operations, U.S. and international regulations can pose program risks indirectly through requirements levied on manufacturers and vendors of components and materials. For example, manufacturers can change their formulations to comply with new regulatory requirements. Such changes can require time-consuming and costly requalification certification for use in human spaceflight programs. The RRAC PC has implemented several strategies for proactively managing regulatory change to minimize potential adverse impacts to NASA Programs and facilities. This presentation highlights the lessons learned through establishing the RRAC PC, the process by which the RRAC PC monitors and distributes information about emerging regulatory requirements, and the cross-Agency cooperation that is vital to supporting NASA's mission.

Duda, Kristen↗
NASA NTRS2008

NASA's Agency-wide Strategy for Environmental Regulatory Risk Analysis and Communication

NASA's mission is to pioneer the future in space exploration, scientific discovery, and aeronautics research. To help enable existing and future programs to pursue this mission, NASA has established the Principal Center for Regulatory Risk Analysis and Communication (RRAC PC) to proactively identify, analyze, and communicate environmental regulatory risks to the NASA community. The RRAC PC is chartered to evaluate the risks posed to NASA Programs and facilities by environmentally related drivers. The RRAC PC focuses on emerging environmental regulations, as well as risks related to operational changes that can trigger existing environmental requirements. Changing regulations have the potential to directly affect program activities. For example, regulatory changes can restrict certain activities or operations by mandating changes in how operations may be done or limiting where or how certain operations can take place. Regulatory changes also can directly affect the ability to use certain materials by mandating a production phase-out or restricting usage aPi'iications of certain materials. Such changes can result in NASA undertaking material replacement efforts. Even if a regulation does not directly affect NASA operations, U.S. and international regulations can pose program risks indirectly through requirements levied on manufacturers and vendors of components and materials. For example, manufacturers can change their formulations to comply with new regulatory requirements. Such changes can require time-consuming and costly requalification certification for use in human spaceflight programs. The RRAC PC has implemented several strategies for proactively managing regulatory change to minimize potential adverse impacts to NASA Programs and facilities. This presentation highlights the lessons learned through establishing the RRAC PC, the process by which the RRAC PC monitors and distributes information about emerging regulatory requirements, and the cross-Agency cooperation that is vital to supporting NASA's mission.

Duda, Kristen↗
NASA NTRSDate not supplied

Towards Quantifying and Correlating RTV Intumescence to Entry-Relevant Conditions

Room Temperature Vulcanizing silicone (RTV) is a high-temperature adhesive used as a gap-filler between Thermal Protection System (TPS) tiles for heatshields on numerous missions. It is also adopted to bond instrumentation plugs of temperature and pressure sensors into heatshield’s tiles. While RTV has been traditionally assumed to be a non-porous and non-ablating material, numerous experiments have shown that RTV pyrolyzes and becomes highly porous as it is heated. Experimental data has also shown heating rate-dependent swelling (or intumescence) and shrinking of RTV, these volume changes may cause roughness-induced boundary layer transition. Outgassing and surface oxide formation of RTV upon decomposition also occur, which can be sources of contamination of heat shield sensors. This combination of detrimental effects motivates a critical need to develop a high-fidelity model for RTV ablation. As a first critical step in RTV modeling, a comprehensive material properties database for RTV was collected that account for the lack of data in properties such as pyrolysis char yield, shape change, virgin and char porosity. The next step is to quantify the volume change of RTV as a function of temperature. While this was done in previous campaigns, limited data was collected which showed a possible heating rate dependent intumescent behavior. To further understand the intumescence phenomenon of RTV, numerous dedicated experiments were performed at Beamline 8.3.2 of the Advanced Light Source, where RTV samples were heated while X-ray scans were taken in situ. Micro-Computed Tomography scans were taken in situ for low heating rates (≤ 60 °C/min), whereas continuous radiography scans were taken for higher heating rates (≤ 1500 ºC/min). Unconstrained samples and samples constrained in different holders (quartz, graphite, FiberForm and PICA) were tested to determine the change in intumescence due to confinement. Using deep learning techniques, the tomographies and radiographies are segmented to obtain volume change, porosity, pore size distributions and connectivity. From the results obtained, modifications to the previous RTV intumescence model are proposed, along with preliminary comparisons to testing RTV at high-enthalpy facilities.

RTV↗
DOE OSTI2022

Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference

Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulation-based inference (SBI) approach to estimate cosmological constraints from a simplified galaxy cluster observation analysis. Using data generated from the Quijote simulation suite and analytical models, we train a machine learning algorithm to learn the probability function between cosmological parameters and the possible galaxy cluster observables. The posterior distribution of the cosmological parameters at a given observation is then obtained by sampling the predictions from the trained algorithm. Our results show that the SBI method can successfully recover the truth values of the cosmological parameters within the 2σ limit for this simplified galaxy cluster analysis, and acquires similar posterior constraints obtained with a likelihood-based Markov Chain Monte Carlo method, the current state-of the-art method used in similar cosmological studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

X-ray and molecular dynamics study of the temperature-dependent structure of molten NaF-Zr⁢F 4

The local atomic structure of NaF-Zr⁢F 4 (53–47 mol%) molten system and its evolution with temperature are examined with x-ray scattering measurements which are then used to validate the quality of ab initio and neural network-based molecular dynamics (NNMD) calculations in the temperature range 515–700°⁢C. The machine-learning enhanced NNMD calculations offer improved efficiency while maintaining accuracy at higher distances compared to ab initio calculations. Looking at the evolution of the pair distribution function with increasing temperature, a fundamental change in the liquid structure within the selected temperature range, accompanied by a slight decrease in overall correlation is revealed. NNMD calculations indicate the coexistence of three different fluorozirconate complexes: [Zr⁢F 6 ] 2– , [Zr⁢F 7 ] 3– , and [Zr⁢F 8 ] 4– , with a shift in the dominant coordination state from the 7-coordinated Zr cation toward a 6-coordinated cation with increasing temperature. The study also highlights the metastability of different local coordination structures, with frequent interconversions between the 6- and 7-coordinate states. Analysis of the Zr-F-Zr angular distribution function reveals the presence of both “edge-sharing” and “corner-sharing” fluorozirconate complexes with specific bond angles and distances in accord with previous studies, while the next-nearest-neighbor cation-cation correlations demonstrate a clear preference for unlike cations as nearest-neighbor pairs, emphasizing nonrandom arrangement. Finally, these findings contribute to a comprehensive understanding of the complex local structure of the molten salt, providing insights into temperature-dependent preferences and correlations within the molten system.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Polarized and unpolarized gluon PDFs: Generative machine learning applications for lattice QCD matrix elements at short distance and large momentum

Lattice quantum chromodynamics (QCD) calculations share a defining challenge by requiring a small finite range of spatial separation z between quark/gluon bilinears for controllable power corrections in the perturbative QCD factorization, and a large hadron boost p z for a successful determination of collinear parton distribution functions (PDFs). However, these two requirements make the determination of PDFs from lattice data very challenging. We present the application of generative machine learning algorithms to estimate the polarized and unpolarized gluon correlation functions utilizing short-distance data and extending the correlation up to z p z ≲ 14 , surpassing the current capabilities of lattice QCD calculations. We train physics-informed machine learning algorithms to learn from the short-distance correlation at z ≲ 0.36 fm and take the limit, p z → ∞ , thereby minimizing possible contamination from the higher-twist effects for a successful reconstruction of the polarized gluon PDF. We also expose the bias and problems with underestimating uncertainties associated with the use of model-dependent and overly constrained functional forms, such as x α ( 1 − x ) β and its variants to extract PDFs from the lattice data. We propose the use of generative machine learning algorithms to mitigate these issues and present our determination of the polarized and unpolarized gluon PDFs in the nucleon. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. Here, the deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Pixel Anomaly Detection Tool : a user-friendly GUI for classifying detector frames using machine-learning approaches

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Coupling Big Data Analytics and Reactive Transport Modeling for Cost-effective Groundwater Monitoring - 17163

This study presents an innovative approach for sustainable and cost-effective groundwater monitoring. This approach takes advantage of recent advances in various technologies: (1) in situ autonomous sensors, (2) big data analytics, and (3) parallel high-performance computing for flow and reactive transport modeling. In situ sensors are used to periodically measure the key variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control contaminant mobility and the plume spatial and temporal distribution. Based on a limited number of groundwater sampling, the data analytics methods-data mining and machine learning-allow us to identify and quantify the correlations between the in situ-measured variables and contaminant concentrations, and also to detect significant changes associated with the plume mobility. In addition, a state-of-the-art parallel numerical flow and reactive transport simulator Amanzi, and uncertainty quantification software Agni are used to provide an improved physical and mechanistic understanding of the contaminated groundwater system behavior, and to predict the long-term plume distribution for optimizing and adapting the monitoring strategy. Amanzi and Agni were developed as part of the Advanced Simulation Capability for Environmental Management (ASCEM) program of the DOE Office of Environmental Management. Such modeling is critical, particularly, for assessing the impact of climate change and associated hydrological shifts. The developed approach is expected to significantly reduce the groundwater sampling frequency and associated cost. In addition, the real-time information on plume mobility serves as an early warning system, improving the resiliency of contaminated or potentially contaminated sites. We demonstrate this approach using as an example the Savannah River Site (SRS) F-Area, where groundwater is contaminated by various radionuclides, including uranium, tritium and technetium. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Establishing nationwide power system vulnerability index across US counties using interpretable machine learning

Power outages have become increasingly frequent, intense, and prolonged in the US due to climate change, aging electrical grids, and rising energy demand. However, largely due to the absence of granular spatiotemporal outage data, we lack data-driven evidence and analytics-based metrics to quantify power system vulnerability. This limitation has hindered the ability to effectively evaluate and address vulnerability to power outages in US communities. Here, in this work, we collected ∼179 million power outage records at 15-min intervals across 3022 US contiguous counties (96.15 % of the area) from 2014 to 2023. We developed a power system vulnerability assessment framework based on three dimensions (intensity, frequency, and duration) and applied interpretable machine learning models (XGBoost and SHAP) to compute Power System Vulnerability Index (PSVI) at the county level. Our analysis reveals a consistent increase in power system vulnerability across the US counties over the past decade. We identified 318 counties across 45 states as hotspots for high power system vulnerability, particularly in the West Coast (California and Washington), the East Coast (Florida and the Northeast area), the Great Lakes megalopolis (Chicago-Detroit metropolitan areas), and the Gulf of Mexico (Texas). Our heterogeneity analysis indicates that urban counties and those located along regional transmission boundaries tend to exhibit significantly higher vulnerability. Our results highlight the significance of the proposed PSVI for evaluating the vulnerability of communities to power outages. The findings underscore the widespread and pervasive impact of power outages across the country and offer crucial insights to support infrastructure operators, policymakers, and emergency managers in formulating policies and programs aimed at enhancing the resilience of the US power infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Towards a data-driven model of hadronization using normalizing flows

We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2009

A New Monte Carlo Filtering Method for the Diagnosis of Mission-Critical Failures

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the settings most likely to cause a mission-critical failure. This research benchmarks two treatment learning methods against standard optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. It is shown that these treatment learners are both faster than traditional methods and show demonstrably better results.

Gay, Gregory↗
NASA NTRS2004

The Development of Two Science Investigator-led Processing Systems (SIPS) for NASA's Earth Observation System (EOS)

In 2001, NASA Goddard Space Flight Center's Laboratory for Terrestrial Physics started the construction of a science Investigator-led Processing System (SIPS) for processing data from the Ozone Monitoring Instrument (OMI) which will launch on the Aura platform in mid 2004. The Ozone Monitoring Instrument (OMI) is a contribution of the Netherlands Agency for Aerospace Programs (NIVR) in collaboration with the Finnish Meteorological Institute (FMI) to the Earth Observing System (EOS) Aura mission. It will continue the Total Ozone Monitoring System (TOMS) record for total ozone and other atmospheric parameters related to ozone chemistry and climate. OMI measurements will be highly synergistic with the other instruments on the EOS Aura platform. The LTP previously developed the Moderate Resolution Imaging Spectrometer (MODIS) Data Processing System (MODAPS), which has been in full operations since the launches of the Terra and Aqua spacecrafts in December, 1999 and May, 2002 respectively. During that time, it has continually evolved to better support the needs of the MODIS team. We now run multiple instances of the system managing faster than real time reprocessings of the data as well as continuing forward processing. The new OMI Data Processing System (OMIDAPS) was adapted from the MODAPS. It will ingest raw data from the satellite ground station and process it to produce calibrated, geolocated higher level data products. These data products will be transmitted to the Goddard Distributed Active Archive Center (GDAAC) instance of the Earth Observing System (EOS) Data and Information System (EOSDIS) for long term archive and distribution to the public. The OMIDAPS will also provide data distribution to the OMI Science Team for quality assessment, algorithm improvement, calibration, etc. We have taken advantage of lessons learned from the MODIS experience and software already developed for MODIS. We made some changes in the hardware system organization, database and software to adapt the system for OMI. We replaced the fundamental database system, Sybase, with an Open Source RDBMS called PostgreSQL, and based the entire OMIDAPS on a cluster of Linux based commodity computers rather than the large SGI servers that MODAPS uses. Rather than relying on a central I/O server host, the new system distributes its data archive among multiple server hosts in the cluster. OMI is also customizing the graphical user interfaces and reporting structure to more closely meet the needs of the OMI Science Team. Prior to 2003, simulated OMI data and the science algorithms were not ready for production testing. We initially constructed a prototype system and tested using a 25 year dataset of Total Ozone Mapping Spectrometer (TOMS) and Solar Backscatter Ultraviolet Instrument (SBUV) data. This prototype system provided a platform to support the adaptation of the algorithms for OMI, and provided reprocessing of the historical data aiding in its analysis. In a recent reanalysis of the TOMS data, the OMIDAPS processed 108,000 full orbits of data through 4 processing steps per orbit, producing about 800,000 files (400 GiB) of level 2 and greater data files. More recently we have installed two instances of the OMIDAPS for integration and testing of OM1 science processes as they get delivered from the Science Team. A Test instance of the OMIDAPS has also supported a series of "Interface Confidence Tests" (ICTs) and End-to-End Ground System tests to ensure the launch readiness of the system. This paper will discuss the high-level hardware, software, and database organization of the OMIDAPS and how it builds on the MODAPS heritage system. It will also provide an overview of the testing and implementation of the production OMIDAPS.

Tilmes, Curt↗