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Results for “Fairness”

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 55 records · Page 3

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NASA NTRS1989

Evaluation of VSAERO in prediction of aerodynamic characteristics of helicopter hub fairings

A low-order panel code, VSAERO, was used to predict the aerodynamic characteristics of helicopter hub fairings. Since the simulation of this kind of bluff body by VSAERO was not documented before, the VSAERO solutions were correlated with experimental data to establish their validity. The validation process revealed that simulation of the aerodynamic environment around a hub fairing was sensitive to several modeling parameters. Some of these parameters are body and wake panels arrangement, streamwise and spanwise separation location, and the most prominent one-the wake modeling. Three wake models were used: regular wake, separated wake, and jet model. The regular wake is a wake with negligible thickness (thin wake). It is represented by a single vortex sheet. The separated wake and the jet model in the present application are wakes with finite thickness (thick wake). They consist of a vortex sheet enclosing a region of low-energy flow. The results obtained with the reqular wake were marginally acceptable for sharp-edged hub fairings. For all other cases under consideration, the jet model results correlated slightly better. The separated wake, which seemed to be the most appropriate model, caused the solution to diverge. While the regular wake was straight-forward to apply in simulations, the jet model was not. It requires the user to provide information about the doublet strength gradient on wake panels by guessing the efflux velocities at the wake shedding location. In summary, VSAERO neither predicts accurately the aerodynamic characteristics of helicopter hub fairings nor was cost effective.

Louie, Alexander↗

NASA NTRS1990

Ground test program for new Atlas payload fairings

To establish the competitiveness of the revitalized family of Atlas launch vehicles (I, II, IIA, and IIAS) a new series of payload fairings, an 11-foot and a 14-foot diameter version, were designed to accommodate the widest possible variety of satellites. Because these aluminum fairings are new designs, the plant at which they are produced is new, and launch customers are very anxious to fly their payloads, an ambitious and efficient test program is essential. Five major tests have been planned for completion within the span of one calendar year. One of these has been completed, with every indication that it was a success, one is currently under way, and two more are scheduled to start in the month of April. Through effective use of test assets, facilities, and personnel, all testing will be completed, allowing the fairing design to be completely characterized and then qualified through analysis prior to first launch of each of the fairings.

Robbins, Michael J.↗

NASA NTRS1960

Effect of Afterbody Terminal Fairings on the Performance of a Pylon-Mounted Turbojet-Nacelle Model

An investigation of the effect of afterbody terminal fairings on the performance of a pylon-mounted turbojet-nacelle model has been conducted in the Langley 16-foot transonic tunnel. A basic afterbody having a boattail angle of 16 deg was investigated with and without terminal fairings. The equivalent boattail angle, based on the cross-sectional area of the afterbody and terminal fairings, was 8 deg. Therefore, a simple body of revolution with a boattail angle of 8 deg was included for comparison. The tests were made at an angle of attack of 0 deg, Mach numbers of 0.80 to 1.05, jet total-pressure ratio of 1 to approximately 5, and an average Reynolds number per foot of 4.1 x 10(exp 6). A hydrogen peroxide jet simulator was used to supply the hot-jet exhaust. The results indicate that addition of terminal fairings to a 16 deg boattail afterbody increased the thrust-minus-drag coefficients and provided the lowest effective drag of the three configurations tested.

Willis, Conrad M.↗

NASA NTRS1995

Delta XTE Fairing Installation at Complex 17-B CCAS

Live footage of the Delta XTE (X-Ray Timing Explorer) fairing installation is presented. The fairing is installed to provide a smooth surface for the airflow. The primary purpose of the fairing is to reduce drag. The installation of the fairing occurred at complex 17-B CCAS (Cape Canaveral Air Station).

Source record↗

NASA NTRS2009

Electromagnetic Cavity Effects from Transmitters Inside a Launch Vehicle Fairing

This paper provides insight into the difficult analytical issue for launch vehicles and spacecraft that has applicability outside of the launch industry. Radiation from spacecraft or launch vehicle antennas located within enclosures in the launch vehicle generates an electromagnetic environment that is difficult to accurately predict. This paper discusses the test results of power levels produced by a transmitter within a representative scaled vehicle fairing model and provides preliminary modeling results at the low end of the frequency test range using a commercial tool. Initially, the walls of the fairing are aluminum and later, layered with materials to simulate acoustic blanketing structures that are typical in payload fairings. The effects of these blanketing materials on the power levels within the fairing are examined.

Trout, Dawn↗

NASA NTRS2011

Evaluation of Lightning Induced Effects in a Graphite Composite Fairing Structure

Defining the electromagnetic environment inside a graphite composite fairing due to near-by lightning strikes is of interest to spacecraft developers. This effort develops a transmission-line-matrix (TLM) model with a CST Microstripes to examine induced voltages. on interior wire loops in a composite fairing due to a simulated near-by lightning strike. A physical vehicle-like composite fairing test fixture is constructed to anchor a TLM model in the time domain and a FEKO method of moments model in the frequency domain. Results show that a typical graphite composite fairing provides adequate shielding resulting in a significant reduction in induced voltages on high impedance circuits despite minimal attenuation of peak magnetic fields propagating through space in near-by lightning strike conditions.

Trout, Dawn H.↗

NASA NTRS2011

Multi-functional annular fairing for coupling launch abort motor to space vehicle

An annular fairing having aerodynamic, thermal, structural and acoustic attributes couples a launch abort motor to a space vehicle having a payload of concern mounted on top of a rocket propulsion system. A first end of the annular fairing is fixedly attached to the launch abort motor while a second end of the annular fairing is attached in a releasable fashion to an aft region of the payload. The annular fairing increases in diameter between its first and second ends.

Camarda, Charles J.↗

NASA NTRS2011

Ascent Heating Thermal Analysis on Spacecraft Adaptor Fairings

When the Crew Exploration Vehicle (CEV) is launched, the spacecraft adaptor (SA) fairings that cover the CEV service module (SM) are exposed to aero heating. Thermal analysis is performed to compute the fairing temperatures and to investigate whether the temperatures are within the material limits for nominal ascent aeroheating case. The ascent heating is analyzed by using computational fluid dynamics (CFD) and engineering codes at Marshall Space Flight Center. The aeroheating environment data used for this work is known as Thermal Environment 3 (TE3) heating data. One of the major concerns is with the SA fairings covering the CEV SM and the SM/crew launch vehicle (CLV) flange interface. The TE3 heating rate is a function of time, wall temperature, and the spatial locations. The implementation of the TE3 heating rate as boundary conditions in the thermal analysis becomes challenging. The ascent heating thermal analysis on SA fairings and SM/CLV flange interface are performed using two commercial software packages: Cullimore & Ring (C&R) Thermal Desktop (TD) 5.1 and MSC Patran 2007r1 b. TD is the pre-and post-processor for SINDA, which is a finite-difference-based solver. In TD, the geometry is built and meshed, the boundary conditions are defined, and then SINDA is used to compute temperatures. MSC Pthermal is a finite-element- based thermal solver. MSC Patran is the pre- and post-processor for Pthermal. Regarding the boundary conditions, the convection, contact resistance, and heat load can be imposed in different ways in both programs. These two software packages are used to build the thermal model for the same analysis to validate each other and show the differences in the modeling details.

Wang, Xiao Yen↗

NASA NTRS2009

Electromagnetic Cavity Effects from Transmitters Inside a Launch Vehicle Fairing

Trout, Dawn H.↗

NASA NTRS2014

Testing Orions Fairing Separation System

Traditional fairing systems are designed to fully encapsulate and protect their payload from the harsh ascent environment including acoustic vibrations, aerodynamic forces and heating. The Orion fairing separation system performs this function and more by also sharing approximately half of the vehicle structural load during ascent. This load-share condition through launch and during jettison allows for a substantial increase in mass to orbit. A series of component-level development tests were completed to evaluate and characterize each component within Orion's unique fairing separation system. Two full-scale separation tests were performed to verify system-level functionality and provide verification data. This paper summarizes the fairing spring, Pyramidal Separation Mechanism and forward seal system component-level development tests, system-level separation tests, and lessons learned.

Martinez, Henry↗

NASA NTRS2021

Thunderstorm and Fair-weather Quasi-Static Electric Fields Over Land and Ocean

Natural lightning and the associated clouds are known to behave differently over land and ocean, but many questions remain. We expand the related observational datasets by obtaining simultaneous quasi-static electric field observations over coastal land, near-shore water, and deep ocean regions during both fair-weather and thunderstorm periods. Oceanic observations were obtained using two 3-m NOAA buoys that were instrumented with Campbell Scientific electric field mills to measure the quasi-static electric fields. These data were compared to selected electric field records from the existing on-shore electric field mill suite of 31 sensors at Kennedy Space Center (KSC). Lightning occurrence times, locations and peak current estimates for both onshore and ocean were provided by the U.S. National Lightning Detection Network. The buoy instruments were first evaluated on-shore at the Florida coast, and the first system was calibrated for field enhancements and to confirm proper behavior of the system in elevated-field environments. The buoys were then moored 20 mi and 120 mi off the coast of KSC in February (20 mi) and August (120 mi) 2014. Diurnal fair-weather fields at both ocean sites matched will with each other and with those found during the Carnegie cruise, but mean values were 33% smaller, due at least in-part to constraints on the calibration procedure. Diurnal fair-weather fields variations at coastal and inland sites were a poorer match than offshore, likely because the offshore environment is “cleaner” with limited variations in local space charge, lower surface aerosol densities, little surface heating to disturb the surface charge layer during fair weather, and fewer local radioactive sources to modulate the near-surface electrical conductivity. Storm-related static fields were 4-5× larger at both oceanic sites than over land, likely due to decreased screening by near-surface space charge produced by corona current. The time-evolution of the electric field and field changes during storm approach are sufficiently different over land and ocean to warrant further study. This work shows the quality, accuracy, and reliability of these data, and has demonstrated the practicality of off-shore electric field measurements for safety- and launch-related decision making at KSC.

Lightning↗

DOE OSTI2025

Fair Concurrent Training of Multiple Models in Federated Learning

Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL applications may increasingly require multiple FL tasks to be trained simultaneously, sharing clients’ computing resources, which we call Multiple-Model Federated Learning (MMFL). Current MMFL algorithms use naïve average-based client-task allocation schemes that often lead to unfair performance when FL tasks have heterogeneous difficulty levels, as the more difficult tasks may need more client participation to train effectively. Furthermore, in the MMFL setting, we face a further challenge that some clients may prefer training specific tasks to others, and may not even be willing to train other tasks, e.g., due to high computational costs, which may exacerbate unfairness in training outcomes across tasks. We address both challenges by firstly designing FedFairMMFL, a difficulty-aware algorithm that dynamically allocates clients to tasks in each training round, based on the tasks’ current performance levels. We provide guarantees on the resulting task fairness and FedFairMMFL’s convergence rate. We then propose novel auction designs that incentivizes clients to train multiple tasks, so as to fairly distribute clients’ training efforts across the tasks, and extend our convergence guarantees to this setting. Here, we finally evaluate our algorithm with multiple sets of learning tasks on real world datasets, showing that our algorithm improves fairness by improving the final model accuracy and convergence speed of the worst performing tasks, while maintaining the average accuracy across tasks.

Federated learning↗

NASA NTRSDate not supplied

Laying The Foundations for FAIR-ER Science: ISA And LSDA Data Submission Process in NASA’s Evolving Data Management Environment

The Life Sciences Data Archive (LSDA) archives data resulting from research on the effects of spaceflight on humans and the development of countermeasures to mitigate spaceflight hazards. Archivists work with researchers to ensure that unique and high value data products and their metadata are preserved and managed to support current and future research. Currently, LSDA is updating its procedures and data submission requirements in response to the evolving data preservation environment at NASA. LSDA is implementing best practices for research data management through the establishment of clear data submission guidelines, integration of the FAIR (Findability, Accessibility, Interoperability, Reusability) principles, and use of the ISA (Investigation, Study, Assay) research metadata framework for data discoverability and transparency into the data management processes. These changes directly impact LSDA’s requirements for research data submissions. The newly revised Research Data Submission Agreement (RDSA), formerly the Data Submission Agreement (DSA), introduces ISA-compatible metadata collection standards to LSDA’s process. Adherence to LSDA’s data submission guidelines enhances the FAIR-ness of the repository’s collections for future users. This presentation will discuss (1) how submission of research data and associated metadata are impacted by current data management policies, (2) benefits of the adoption of FAIR principles and the ISA metadata framework for retrospective studies utilizing existing LSDA datasets and historic data collections, and (3) the support LSDA will provide to researchers during this transition.

Data submission↗

NASA NTRSDate not supplied

Laying the Foundations for FAIR-er Science: ISA and the LSDA Data Submission Process in NASA's Evolving Data Management Environment

LSDA↗

DOE OSTI2025

Open-Source and FAIR Research Software for Proteomics

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Optimization of Fairing Geometry for ORPC Modular RivGen Power System: Cooperative Research and Development (Final Report)

The work will optimize the hydrodynamic performance and flow augmentation of Ocean Renewable Power Company's (ORPC) modular fairing for their cross-flow marine hydrokinetic (MHK) turbine, using a computational fluid dynamics study. The influence of the fairing cross-sectional shape and rotor-fairing spacing will be assessed, to maximize power production and minimize structural loads.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

HPC-FAIR: A Framework Managing Data and AI Models for Analyzing and Optimizing Scientific Applications

The increasing reliance on machine learning (ML) to analyze and optimize large-scale scientific applications on supercomputers faces a significant bottleneck: the lack of readily available, high-quality training datasets and the difficulty in reusing existing AI models. This project was motivated by the urgent need to address the “FAIR” principles (Findability, Accessibility, Interoperability, Reusability) for both training datasets and AI models in the high-performance computing (HPC) domain. The project developed HPC-FAIR, a high-performance computing data management framework designed to centralize HPC-related datasets and AI models within a unified hub. To ensure interoperability, the framework established a standardized representation and vocabulary (ontology) for both data and models. HPC-FAIR also implemented automated workflows to streamline data processing, model access, and benchmarking. Additionally, the project focused on optimizing data harnessing efficiency through advanced techniques like deep reuse and compression-based analytics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗