Search NASA⌕ Search

SEARCH · Search NASA

Results for “data challenges”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Mirador: A Simple, Fast Search Interface for Remote Sensing Data

A major challenge for remote sensing science researchers is searching and acquiring relevant data files for their research projects based on content, space and time constraints. Several structured query (SQ) and hierarchical navigation (HN) search interfaces have been develop ed to satisfy this requirement, yet the dominant search engines in th e general domain are based on free-text search. The Goddard Earth Sci ences Data and Information Services Center has developed a free-text search interface named Mirador that supports space-time queries, inc luding a gazetteer and geophysical event gazetteer. In order to compe nsate for a slightly reduced search precision relative to SQ and HN t echniques, Mirador uses several search optimizations to return result s quickly. The quick response enables a more iterative search strateg y than is available with many SQ and HN techniques.

Lynnes, Christopher↗
DOE OSTI2023

A Graphical Model for Fusing Diverse Microbiome Data

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high-dimensional features, in this case, transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each corresponds to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. Here, the inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Accelerating Advanced Light Source Science Through Multi-Facility HPC Workflows

Synchrotron light sources support a wide array of techniques to investigate materials, often producing complex, high-volume data that challenge traditional workflows. At the Advanced Light Source (ALS), we developed infrastructure to move microtomography data over ESnet to ALCF and NERSC, where CPU- and GPU-based algorithms generate 3D reconstructed volumes of experimental samples. We employ two data movement and reconstruction models: real-time processing as data streams directly to NERSC compute nodes, and automated file transfer to NERSC and ALCF file systems. The streaming pipeline provides users with feedback in under ten seconds, while the file-based workflow produces high-quality reconstructions suitable for deeper analysis in 20-30 minutes. This infrastructure enables users to utilize HPC resources without direct access to backend systems. We plan to extend this architecture to more endstations, supporting our beamline scientists and users.

Abramov, David↗
DOE OSTI2020

Sandia National Laboratories Ecosystem for Open Science: Metadata Schema v0.2 Description.

The Ecosystem for Open Science (eOS) initiative was established in 2019. Its objective is improving openness and sharing of data and information across Defense Nuclear Nonproliferation (DNN) Research and Development (R&D) activities. To support this initiative, the eOS team at Sandia National Laboratories (SNL) developed metadata and data standards and proposed a machine-readable metadata schema. The nuclear explosion monitoring field was selected as a focus area due to its the wide range of pertinent phenomenologies.We developed the DCAT-eOS-AP metadata schema extending the Data Catalog Vocabulary version 2 (DCATv2) standard using an application profile (AP), to fit the needs of multi-disciplinary NA-22 projects. The DCAT-eOS-AP metadata schema describes data at different levels of granularity ranging from general descriptions to more domain-specific granular metadata. Its implementation and serialization is flexible with the ability to include new file or data types. Thus, it will scale with the ever-increasing data management needs of government research. Due to the multitude of phenomenologies represented in the DCAT-eOS-AP schema, we anticipate that it will be easily extensible to various projects across many DOE mission areas. This document describes data management challenges faced within the DNN R&D portfolio and provides insight on how metadata and data standards/guidelines combined with a comprehensive metadata schema can add value to programs throughout the Department of Energy (DOE). It reviews the importance of metadata standards, FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles, and metadata schemas. Additionally, it summarizes input from subject matter experts (SME) at SNL and other National Laboratories that resulted in metadata and data standards/guidelines encompassing domains relevant to NA-22 projects. Finally, we discuss the DCAT-eOS-AP metadata development. Implementation recommendations and future development directions are included for those keen on adopting the DCAT-eOS-AP metadata schema.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2004

Turbomachine Sealing and Secondary Flows: Review of Rotordynamics Issues in Inherently Unsteady Flow Systems With Small Clearances - Part 2

Today's computational methods enable the determination of forces in complex systems, but without field validation data, or feedback, there is a high risk of failure when the design envelope is challenged. The data of Childs and Bently and field data reported in NASA Conference Proceedings serve as sources of design information for the development of these computational codes. Over time all turbomachines degrade and instabilities often develop, requiring responsible, accurate, turbomachine diagnostics with proper decisions to prevent failures. Tam et al. (numerical) and Bently and Muszynska (analytical) models corroborate and implicate that destabilizing factors are related through increases in the fluid-force average circumferential velocity. The stability threshold can be controlled by external swirl and swirl brakes and increases in radial fluid film stiffness (e.g., hydrostatic and ambient pressures) to enhance rotor stability. Also cited are drum rotor self-excited oscillations, where the classic fix is to add a split or severed damper ring or cylindrical damper drum, and the Benkert-Wachter work that engendered swirl brake concepts. For a smooth-operating, reliable, long-lived machine, designers must pay very close attention to sealing dynamics and diagnostic methods. Correcting the seals enabled the space shuttle main engine high-pressure fuel turbopump (SSME HPFTP) to operate successfully.

Hendricks, R. C.↗
NASA NTRS2013

Identification of Crew-Systems Interactions and Decision Related Trends

NASA Vehicle System Safety Technology (VSST) project management uses systems analysis to identify key issues and maintain a portfolio of research leading to potential solutions to its three identified technical challenges. Statistical data and published safety priority lists from academic, industry and other government agencies were reviewed and analyzed by NASA Aviation Safety Program (AvSP) systems analysis personnel to identify issues and future research needs related to one of VSST's technical challenges, Crew Decision Making (CDM). The data examined in the study were obtained from the National Transportation Safety Board (NTSB) Aviation Accident and Incident Data System, Federal Aviation Administration (FAA) Accident/Incident Data System and the NASA Aviation Safety Reporting System (ASRS). In addition, this report contains the results of a review of safety priority lists, information databases and other documented references pertaining to aviation crew systems issues and future research needs. The specific sources examined were: Commercial Aviation Safety Team (CAST) Safety Enhancements Reserved for Future Implementation (SERFIs), Flight Deck Automation Issues (FDAI) and NTSB Most Wanted List and Open Recommendations. Various automation issues taxonomies and priority lists pertaining to human factors, automation and flight design were combined to create a list of automation issues related to CDM.

Jones, Sharon Monica↗
NASA NTRS2026

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗
DOE OSTI2026

Modeling and Energy Analysis of Refrigeration Systems in Supermarkets: A Case Study

Refrigeration systems are the single largest consumer of electricity in supermarkets, accounting for approximately 40–60% of total store electricity use. As supermarkets face increasing pressures to improve efficiency and reduce operational costs, understanding the energy performance of refrigeration systems in supermarkets has become essential. In this paper, we investigate the energy performance and patterns of the refrigeration system using field measurements in one supermarket. The compressor coefficient of performance (COP) averaged 4.5 during nighttime steady-state operation, providing a practical efficiency benchmark. We develop simple physical-based load models for the display cases in the supermarket, where the refrigeration system uses R404A. The results show the display case models achieve a good agreement with measured data, validating its utility for energy estimation during unoccupied hours. Finally, the analysis reveals several challenges of data collection systems for refrigeration systems in the supermarket industry as well.

Chen, Yimin [ORNL] (ORCID:0000000338181655)↗
NASA NTRSDate not supplied

The NASA Earth Science Applied Sciences Disasters Program: Making EO Data and Expertise Available through the Disasters Mapping Portal to Inform Decision-Makers throughout the Disaster Cycle

The NASA Earth Science Applied Sciences Disasters Program promotes the use of Earth Observations (EO) to inform disaster risk reduction and resilience throughout the disaster cycle, from local to global scales, by harnessing NASA science and technology capabilities, through engagement with end users to demonstrate the value and impact of EO to support decision-making, and by supporting end users in their use of EO in decision-making while developing relationships to grow as a trusted source of relevant science and useful results. Through the NASA Earth Science Applied Sciences Disasters Program Mapping Portal, an Esri-backed hub of geospatially enabled disaster products, event-based and near-real-time products are hosted to provide end users with analysis-ready data and data services, keeping in mind their expressed EO needs. In this presentation, case studies of past events will be highlighted, showcasing how EO data and services were provided by the Program and utilized by end users during the disaster cycle. Additionally, this presentation will highlight some of the Program’s ongoing collaboration activities to assist end users in understanding and preparing for utilization of EO data from the Mapping Portal to inform their decision-making throughout the disaster cycle. Ongoing efforts to advance the science provided by the Program on the Mapping Portal through new developments and capabilities will be highlighted, and common challenges and data needs end users often encounter when using EO data and our Program’s innovative solutions to these challenges will be addressed. Furthermore, this presentation will touch on the challenges faced and the solutions created by the Mapping Portal team in managing and hosting a plethora of EO data for end users across various disciplines.

Ronan Lucey↗
DOE OSTI2020

Software Challenges For HL-LHC Data Analysis

The high energy physics community is discussing where investment is needed to prepare software for the HL-LHC and its unprecedented challenges. The ROOT project is one of the central software players in high energy physics since decades. From its experience and expectations, the ROOT team has distilled a comprehensive set of areas that should see research and development in the context of data analysis software, for making best use of HL-LHC's physics potential. This work shows what these areas could be, why the ROOT team believes investing in them is needed, which gains are expected, and where related work is ongoing. It can serve as an indication for future research proposals and cooperations.

Brann, Kim Albertsson↗
DOE OSTI2025

Homomorphic data compression for real time photon correlation analysis

The construction of highly coherent X-ray sources, combined with next-generation detectors that are larger and faster, has enabled new research opportunities across the scientific landscape. Among the techniques that benefit most from these advancements is X-ray photon correlation spectroscopy (XPCS), where faster acquisition unlocks the ability to study faster dynamics within samples. However, faster acquisition on larger detectors also introduces unprecedented challenges for online data processing and offline data storage. Such challenges are particularly prominent for XPCS, where real time analyses require simultaneous calculation of all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The offline compression scheme extends storage capacity by a factor of 40 while preserving key features in the lossy compressed data. Meanwhile, the online compression scheme reduces the computational time to below 1 ms, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗
DOE OSTI2026

Storing Affordability: Battery Storage as an Asset to Reduce Data Center Cost Shifts

This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.

25 ENERGY STORAGE↗
NASA NTRS1988

Influence of bulk turbulence and entrance boundary layer thickness on the curved duct flow field

The influence of bulk turbulence and boundary layer thickness on the secondary flow development in a square, 90 degree turning duct was investigated. A three-dimensional laser velocimetry system was utilized to measure the mean and fluctuating components of velocity at six cross-planes in the duct. The results from this investigation, with entrance boundary layer thickness of 20 percent, were compared with the thin boundary layer results documented in NASA CR-174811. The axial velocity profiles, cross-flow velocities, and turbulence intensities were compared and evaluated with regard to the influence of bulk turbulence intensity and boundary layer thickness, and the influence was significant. The results of this investigation expand the 90 degree curved duct experimental data base to higher turbulence levels and thicker entrance boundary layers. The experimental results provide a challenging benchmark data base for computational fluid dynamics code development and validation. The variation of inlet bulk turbulence intensity provides additional information to aid in turbulence model evaluation.

Crawford, R. A.↗
NASA NTRS2017

First Order Temporal Logic Monitoring with BDDs

Runtime verification is aimed at analyzing execution traces stemming from a running program or system. The traditional purpose is to detect the lack of conformance with respect to a formal specification. Numerous efforts in the field have focused on monitoring so-called parametric specifications, where events carry data, and formulas can refer to such. Since a monitor for such specifications has to store observed data, the challenge is to have an efficient representation and manipulation of Boolean operators, quantification, and lookup of data. The fundamental problem is that the actual values of the data are not necessarily bounded or provided in advance. In this work we explore the use of Binary Decision Diagrams (BDDs) for representing observed data. Our experiments show a substantial improvement in performance compared to related work.

Ulus, Dogan↗
DOE OSTI2025

Introducing a multiscale feature integration network for inpainting with applications to enhanced CMB map reconstruction

We introduce a novel neural network, SkyReconNet, which combines the expanded receptive fields of dilated convolutional layers along with standard convolutions, to capture both the global and local features for reconstructing the missing information in an image. We implement our network to inpaint the masked regions in a full-sky cosmic microwave background (CMB) map. Inpainting CMB maps is a particularly formidable challenge when dealing with extensive and irregular masks, such as galactic masks which can obscure substantial fractions of the sky. The hybrid design of SkyReconNet leverages the strengths of standard and dilated convolutions to accurately predict CMB fluctuations in the masked regions by effectively utilizing the information from surrounding unmasked areas. During training, the network optimizes its weights by minimizing a composite loss function that combines the structural similarity index measure (SSIM) and mean squared error (MSE). SSIM preserves the essential structural features of the CMB, ensuring an accurate and coherent reconstruction of the missing CMB fluctuations, while MSE minimizes the pixelwise deviations, thus enhancing the overall accuracy of the predictions. The predicted CMB maps and their corresponding angular power spectra align closely with the targets, achieving the performance limited only by the fundamental uncertainty of cosmic variance. The network’s generic architecture enables application to other physics-based challenges involving data with missing or defective pixels, systematic artifacts, etc. In conclusion, our results demonstrate its effectiveness in addressing the challenges posed by large irregular masks, offering a significant inpainting tool not only for CMB analyses but also for image-based experiments across disciplines where such data imperfections are prevalent.

Cosmic microwave background↗
DOE OSTI2021

A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study

High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. Here, we develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model's utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗