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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

OctoFAS: A Two-Level Fair Scheduler That Increases Fairness in Network-Based Key-Value Storage

We identified a fairness problem in a network-based key-value storage system using Intel Storage Performance Development Kit (SPDK) in a multitenant environment. In such an environment, each tenant’s I/O service rate is not fairly guaranteed compared to that of other tenants. To address the fairness problem, we propose OctoFAS, a two-level fair scheduler designed to improve overall throughput and fairness among tenants. The two-level scheduler of OctoFAS consists of (i) inter-core scheduling and (ii) intra-core scheduling. Through inter-core scheduling, OctoFAS addresses the load imbalance problem that is inherent in SPDK on the storage server by dynamically migrating I/O requests from overloaded cores to underloaded cores, thereby increasing overall throughput. Intra-core scheduling prioritizes handling requests from starving tenants over well-fed tenants within core-specific event queues to ensure fair I/O services among multiple tenants. OctoFAS is deployed on a Linux cluster with SPDK. Through extensive evaluations, we found that OctoFAS ensures that the total system throughput remains high and balanced, while enhancing fairness by approximately 10% compared to the baseline, when both scheduling levels operate in a hybrid fashion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

FAIR Data Meets FAIR Software

Modern scientific research is increasingly defined by the interplay between data, software, and the workflows that connect them. Yet while the FAIR (Findable, Accessible, Interoperable, Reusable) principles have become foundational for scientific data stewardship, the same level of structure and expectation has only recently begun to extend to research software. This talk covers why and how FAIR principles are being applied to data and software to support data reuse. It outlines the gaps in current sharing norms, the growing federal emphasis on persistent identifiers and public access, and the opportunities created when datasets, computational workflows, code, and models are linked through rich, standardized metadata. Practical implementation pathways for the EIC and JLab communities are described, including datacards for structured dataset documentation and provenance-aware workflows. By aligning data lifecycle management with FAIR-aligned software practices, the scientific community can advance toward autonomous knowledge graphs, generative workflows, and high-quality, AI-ready scientific datasets.

McSpadden, Diana [Thomas Jefferson National Accele↗
DOE OSTI2024

Towards FAIR Workflows for Federated Experimental Sciences

A de-centralized, peer-to-peer AI metadata framework is demonstrated which can enable end-to-end metadata & lineage tracking for distributed Machine Learning pipelines spanning edge, High Performance Computing, and cloud environments. With a specific example of end-to-end microscopy algorithm and datasets, the proposed method shows how to enable reproducibility, audit trail, provenance of metadata artifacts. The emerging needs of automation in experimental sciences, ML-centric workflows, and FAIR metadata management across federated compute environments is addressed.

machine learning↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
NASA NTRSDate not supplied

Governing Data Findability, Accessibility, Interoperability and Reusability (FAIR) Compliance

The most recent data strategy documents at both the federal and NASA levels stipulate that systems should strive for the data they manage to be Findable, Accessible, Interoperable, and Reusable (FAIR). The NASA Life Sciences Portal (NLSP) has already begun leading efforts in this area for HRP, initiating efforts to comply with the FAIR principles. The broad interpretation of the FAIR principles has led to a plethora of tools that use a splay of metrics specifically but variably developed to judge how compliant data and systems are with the principles. A recent review [3] identified and studied 1,180 metrics across 20 publicly available tools for checking FAIR compliance of data and systems. Because of their very recent development, many organizations and data systems managers and developers have not yet had adequate time or resources to understand these FAIR compliance tools and metrics, their variations in design, accuracy or ease of application to their specific data sets and systems. Thus, it would be best for larger organizations like NASA to approach formulating a strategy for governance of FAIR compliance that can be flexibly applied and is adaptable to an evolving awareness knowledge of FAIR compliance methods and tools. In September 2024, the NASA Science Mission Directorate(SMD) organized a workshop on NASA science data repositories, including the topics of implementing FAIR and governing FAIR compliance across SMD. The initial part of these FAIR discussions focused on developing consensus around required science metadata fields. This is challenging given the diverse nature of NASA’s scientific data portfolio, the variety of metadata models and vocabularies used, and variable level of resources available to curate these data. Later discussion focused on three possible approaches to governing FAIR compliance: distributed, in which various programs, projects or systems define their own methods for assessing FAIR compliance, reporting results up appropriate management lines; centralized, in which higher-level organization(s) specify compliance tools or methods for the various data systems; and multi-level, in which a group comprised of individuals with expertise from multiple levels with organizations is formed to provide guidance and/or specifications for governing FAIR compliance. We report on the recommendations this session yielded, and how these might be shaped specifically to help implement and govern the compliance with FAIR of Human Research Program data and systems.

governance↗
DOE OSTI2023

Fairness-Aware Distributed Energy Coordination for Voltage Regulation in Power Distribution Systems

The accelerating deployment of solar photovoltaics into low-voltage distribution networks can cause reverse power flow and overvoltage problems. However, if coordinated properly, the real and reactive power flexibility of these resources enables distribution operators to manage their networks more efficiently. Existing literature is rich in droop-based control (Volt-Watt and Volt-VAr) and optimization-based distributed energy coordination for four-quadrant control of photovoltaics to prevent overvoltage issues. While optimal coordination can effectively mitigate overvoltage, it tends to treat resources at sensitive parts of the grid unfairly. Here, to address this concern, we propose a distributed optimal power flow formulation that incorporates fairness in curtailing photovoltaic generation and utilizes the reactive power capability of smart inverters. The proposed distributed formulation allows for scalable resource aggregation that can be leveraged to achieve fairness within a certain segment of the grid and/or fairness across the entire network. Fair curtailment of photovoltaic systems is demonstrated with aggregation at each of two layers in a distribution network: 1) area-level fairness and 2) feeder-level fairness. To explore the trade-off between fairness and optimal utilization, the fairness-aware control actions are compared against the performance of a centralized controller that aims to maximize the aggregate PV generation without incorporating fairness. Simulation results show that introducing area-level fairness increased curtailment by 0.0101 percentage points and feeder-level fairness increased curtailment by 0.0458 percentage points compared to a fairness-agnostic control.

Poudel, Shiva↗
NASA NTRS1987

Reduction of hub- and pylon-fairing drag

A wind tunnel test was conducted to obtain data on several drag reduction methods for rotorcraft hubs. The objective of the test was to use small-scale models to develop the technology to substantially reduce hub drag. A helicopter test model, which did not incorporate a rotor, was used to study single-rotor fairing configurations. The rotor shaft assembly was modeled with nonrotating hardware. Drag trend data was obtained for the following fairing configuration changes: hub-fairing camber, hub-fairing thickness ratio, hub-fairing surface curvature, hub-fairing height with respect to the fuselage, inclusion of blade shanks in the hub fairings, hub- and pylon-fairing gaps, pylon-fairing cross-sectional geometry, pylon-fairing thickness ratio and camber. Substantial drag reductions could potentially result from application of the test results to helicopter hub- and pylon-fairing design.

Young, Larry A.↗
NASA NTRS1993

Experimental investigation of advanced hub and pylon fairing configurations to reduce helicopter drag

New hub and pylon fairing designs were tested on a one-fifth scale Bell Helicopter Textron Model 222 helicopter with a bearingless main rotor hub. The blades were not installed for this test. The fairings were designed by NASA and Bell Helicopter Textron under a joint program and tested in the Ames Research Center 7-by 10-Foot Wind Tunnel. All six aircraft forces and moments were measured using the tunnel scales system. Previous research has identified the integrated hub and pylon fairing approach as the most efficient in reducing helicopter drag. Three hub fairings and three pylon fairings were tested (in various combinations) resulting in a total of 16 different configurations, including the baseline helicopter model without fairings. The geometry of the new fairings is described in detail. Test results are presented in the form of plots of the six model forces and moments. The data show that model drag can be reduced by as much as 20 percent by combining a small hub fairing (that has a circular arc upper surface and a flat lower surface) integrated with a nontapered pylon fairing. To minimize drag, the gap between the lower surface of the hub and upper surface of the pylon fairing must be kept to a minimum. Results show that the aerodynamic effects of the fairings on static longitudinal and directional stability can also be important.

Martin, D. M.↗
NASA NTRS1991

Hub and pylon fairing integration for helicopter drag reduction

The results of testing hub and pylon fairings mounted on a one-fifth scale helicopter with the goal of reducing parasite drag are presented. Lift, drag, and pitching moment, as well as side force and yawing moment, were measured. The primary objective of the test was to validate the drag reduction capability of integrated hub and pylon configurations in the aerodynamic environment produced by a rotating hub in forward flight. In addition to the baseline helicopter without fairings, three hub fairings and three pylon fairings were tested in various combinations. The three hub fairings tested reflect two different conceptual design approaches to implementing an integrated fairing configuration on an actual aircraft. The design philosophy is discussed in detail and comparisons are made between the wind tunnel models and potential full-scale prototypes. The data show that model drag can be reduced by as much as 20.8 percent by combining a small hub fairing with circular arc upper and flat lower surfaces and a nontapered 34-percent thick pylon fairing. Aerodynamic effects caused by the fairings, which may have a significant impact on static longitudinal and directional stability, were observed. The results support previous research which showed that the greatest reduction in model drag is achieved if the hub and pylon fairings are integrated with minimum gap between the two.

Martin, D. M.↗
NASA NTRS2001

The NASA Goddard Space Flight Center Virtual Science Fair

This report describes the development of the NASA Goddard Space Flight Center Virtual Science Fair, including its history and outgrowth from the traditional regional science fairs supported by NASA. The results of the 1999 Virtual Science Fair pilot program, the mechanics of running the 2000 Virtual Science Fair and its results, and comments and suggestions for future Virtual Science Fairs are provided. The appendices to the report contain supporting documentation, including the original proposal for this project, the judging criteria, the user's guide and the judge's guide to the Virtual Science Fair Web site, the Fair publicity brochure and the Fair award designs, judges' and students' responses to survey questions about the Virtual Science Fair, and lists of student entries to both the 1999 and 2000 Fairs.

Bolognese, Jeff↗
NASA NTRS2002

The NASA Goddard Space Flight Center Virtual Science Fair

This report describes the development of the NASA Goddard Space Flight Center Virtual Science Fair, including its history and outgrowth from the traditional regional science fairs supported by NASA. The results of the 1999 Virtual Science Fair pilot program, the mechanics of running the 2000 Virtual Science Fair and its results, and comments and suggestions for future Virtual Science Fairs are provided. The appendices to the report include the original proposal for this project, the judging criteria, the user's guide and the judge's guide to the Virtual Science Fair Web site, the Fair publicity brochure and the Fair award designs, judges' and students' responses to survey questions about the Virtual Science Fair, and lists of student entries to both the 1999 and 2000 Fairs.

Bolognese, Jeff↗
NASA NTRS1986

Experimental investigation of rotorcraft hub and shaft fairing drag reduction

A wind-tunnel test was conducted to obtain data on several rotorcraft hub and shaft fairing drag reduction configurations. Aerodynamic loads and moments were acquired for each test configuration. Limited wake pressure measurements and flow visualization (tuft) photographs were obtained for some configurations. All hub and shaft fairing configurations were tested on a 1/5-scale XH-59A model fuselage. Both coaxial and single rotor configurations were tested. All rotor assemblies were modeled with nonrotating hardware. The drag reduction methods tested included cambered elliptical hub fairings, several different shaft fairings, and strakes. Test data show that significant drag reductions can be attained with certain fairing configurations. The lowest drag values for the single rotor configurations were obtained for a cambered elliptical hub fairing with a large thickness airfoil shaft fairing. The lowest coaxial configuration drag values were obtained with cambered elliptical hub fairings and a long chord intermediate shaft fairing.

Young, L. A.↗
DOE OSTI2026

Aligning NASA Earth Science Data Stewardship with FAIR Principles: Outcomes, Recommendations, and Future Directions

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR is being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA's existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data more findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.

Data Product↗
NASA NTRS1989

Helicopter hub fairing and pylon interference drag

A wind tunnel test was conducted to study the aerodynamics of helicopter hub and pylon fairings. The test was conducted in the 7-by 10 Foot Subsonic Wind Tunnel (Number 2) at Ames Research Center using a 1/5-scale XH-59A fuselage model. The primary focus of the test was on the rotor hub fairing and pylon mutual interference drag. Parametric studies of pylon and hub fairing geometry were also conducted. This report presents the major findings of the test as well as tabulated force and moment data, flow visualization photographs, and graphical presentations of the drag data. The test results indicate that substantial drag reduction can be attained through the use of a cambered hub fairing with circular arc upper surface and flat lower surface. Furthermore, a considerable portion of the overall drag reduction is attributed to the reduction in the hub-on-pylon interference drag. It is also observed that the lower surface curvature of the fairing has a strong influence on the hub fairing and on pylon interference drag. However, the drag reduction benefit that was obtained by using the cambered hub fairing with a flat lower surface was adversely affected by the clearance between the hub fairing and the pylon.

Graham, D. R.↗
DOE OSTI2026

Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis

Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments. This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t-tests to compare per-sample IoU performance against baseline configurations. Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Ariane 5 Fairing Preparations for James JWST Space Telescope

The James Webb Space Telescope (JWST) is a large, infrared space telescope operating at Lagrange point 2. JWST is a joint effort between NASA, ESA, and CSA and was launched from the Centre Spatial Guyanais (CSG) on an Ariane 5 rocket in December 2021. The Ariane 5 payload faring (PLF), as shown in Figure 1, interior and Vehicle Equipment Bay membranes received multiple cleanings, detailed inspections, and verification sampling to achieve necessary cleanliness levels. Investigation of PLF cleanliness began early in the JWST program to test the Fairing Acoustic Protection (FAP) panels. Initial PLF cleaning was performed at RUAG in Switzerland prior to shipment to the launch site in collaboration with NASA and ESA. The cleaning procedure used at RUAG premises was based on heritage of the PLF cleaning procedure for ESA/Herschel-Planck and NASA/Mars2020 missions on an Atlas V launcher. The Ariane 5 fairing was packaged in a clean manner and inspected and cleaned upon arrival to CSG. Silicon wafers and particle fall out (PFO) plates were used to monitor the internal fairing cleanliness prior to encapsulation. Just after encapsulation, PFO plates and calcium fluoride (CaF2) crystals were placed inside the fairing on a specific tool door for monitoring the cleanliness inside the fairing during the ventilation phase. The fairing itself was specially sealed to protect the inner environment with just a small, doored porthole accessible via diving board for final closeout of the purge interface. Fairing cleanliness was achieved and mitigated contamination distribution upon launch.

James Webb Space Telescope↗