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NASA NTRS2000

SeaWinds on QuikSCAT Mission and Early Science Results

SeaWinds on QuikSCAT (QSCAT) is a dedicated satellite remote sensing mission for measuring ocean surface wind speed and direction, using a spinning, pencil-beam Ku-band scatterometer. It is a replacement mission for NASA Scatterometer (NSCAT), which was launched on board of the Japan's Advanced Earth Observation System (ADEOS-1) in August 1996 and returned 10 months of high quality data before the mission was terminated in June, 1997 due to the failure of the ADEOS-1 spacecraft. Since the next NASA scatterometer mission, SeaWinds on ADEOS-2 (SeaWinds), will not be launched until November 2000, NASA decided to fill the data gap by launching the QSCAT mission. Furthermore, after year 2000. the potential exists for using both the QSCAT and SeaWinds to provide approximately 6 hours global coverage of the marine winds. QSCAT is currently scheduled for launch in April, 1999 from Vandenberg Air Force Base, using Titan-II launch vehicle. The purpose of this paper is to first present the mission objectives, the spacecraft and instrument design, ground receiving systems, the science data processing system, and the data products. We will then present the post-launch calibration and verification results of the QSCAT end-to-end sensor system. Finally, we present some of the key results obtained from the first two months of the mission, which include ocean surface wind measurements, ice detection and classification, global snow cover detection, and flood detection.

Tsai, Wu-Yang↗

NASA NTRS2008

Preliminary Design of a Ramjet for Integration with Ground-Based Launch Assist

This viewgraph presentation reviews the preliminary design of a ramjet for integration with a ground based launch assist. The reasons for the use of ground-based launch assist and the proposed mechanism for a system are reviewed. The use of a Optimal Trajectory by Implicit Simulation (OTIS), to model the flight and comparison with an actual rocket trajectory is given. The OTIS system is reviewed, The benefits of a launch assist system are analyzed concluding that a launch assist can provide supersonic speeds thus allowing ignition of ramjet without an onboard compressor. This means a further reduction in total launch weight. The Ramjet study is reviewed next. This included a review of the ONX simulations, the verification of the ONX results with the use of Holloman Sled experiment data as derived from the Feasibility of Ramjet Engine Test Capability on The Holloman AFB Sled Track. The conclusion was that the ONX system was not sufficient to meet the needs for the modeling required. The GECAT (Graphical Engine Cycle Analysis Tool) is examined. The results of the GECAT simulations was verified with data from Stataltex and D21 flights. The Next steps are: to create a GECAT Model of a launch assist ramjet, to adjust the geometry to produce the desired thrust, and to survey the ramjet's performance over a range of Mach numbers. The assumptions and requirements of a launch assist ramjet are given, and the acceptable flight regimes are reviewed.

Sayles, Emily L.↗

NASA NTRS2014

Rotating Balances Used for Fluid Pump Testing

Marshall Space Flight Center has developed and demonstrated two direct read force and moment balances for sensing and resolving the hydrodynamic loads on rotating fluid machinery. These rotating balances consist of a series of stainless steel flexures instrumented with semiconductor type, unidirectional strain gauges arranged into six bridges, then sealed and waterproofed, for use fully submerged in degassed water at rotational speeds up to six thousand revolutions per minute. The balances are used to measure the forces and moments due to the onset and presence of cavitation or other hydrodynamic phenomena on subscale replicas of rocket engine turbomachinery, principally axial pumps (inducers) designed specifically to operate in a cavitating environment. The balances are inserted into the drive assembly with power to and signal from the sensors routed through the drive shaft and out through an air-cooled twenty-channel slip ring. High frequency data - balance forces and moments as well as extensive, flush-mounted pressures around the rotating component periphery - are acquired via a high-speed analog to digital data acquisition system while the test rig conditions are varied continuously. The data acquisition and correction process is described, including the in-situ verifications that are performed to quantify and correct for known system effects such as mechanical imbalance, "added mass," buoyancy, mechanical resonance, and electrical bias. Examples of four types of cavitation oscillations for two typical inducers are described in the laboratory (pressure) and rotating (force) frames: 1) attached, symmetric cavitation, 2) rotating cavitation, 3) attached, asymmetric cavitation, and 4) cavitation surge. Rotating and asymmetric cavitation generate a corresponding unbalanced radial force on the rotating assembly while cavitation surge generates an axial force. Attached, symmetric cavitation induces no measurable force. The frequency of the forces can be determined a priori from the pressure environment while the magnitude of the hydrodynamic force is proportional to the pressure unsteadiness.

Skelley, Stephen↗

NASA NTRS2014

What Was Learned in Predicting Slender Airframe Aerodynamics with the F16-XL Aircraft

The CAWAPI-2 coordinated project has been underway to improve CFD predictions of slender airframe aerodynamics. The work is focused on two flow conditions and leverages a unique flight data set obtained with the F-16XL aircraft for comparison and verification. These conditions, a low-speed high angle-of-attack case and a transonic low angle-of-attack case, were selected from a prior prediction campaign wherein the CFD failed to provide acceptable results. In re-visiting these two cases, approaches for improved results include better, denser grids using more grid adaptation to local flow features as well as unsteady higher-fidelity physical modeling like hybrid RANS/URANS-LES methods. The work embodies predictions from multiple numerical formulations that are contributed from multiple organizations where some authors investigate other possible factors that could explain the discrepancies in agreement, e.g. effects due to deflected control surfaces during the flight tests, as well as static aeroelastic deflection of the outer wing. This paper presents the synthesis of all the results and findings and draws some conclusions that lead to an improved understanding of the underlying flow physics, and finally making the connections between the physics and aircraft features.

Rizzi, Arthur↗

NASA NTRSDate not supplied

I’ve Looked at Clouds from Both Sides, Now: Viewpoints from Surface and Spaceborne Lidar Systems

Clouds play a critical role in the Earth’s climate system because they are inextricably linked to the hydrological cycle and radiation budget. Information about cloud height, thickness, occurrence, and amount are critical inputs for a host of numerical applications involving climate research. Therefore, it is important to have highly accurate and quantitative data records of cloud properties that span several years and geographic regions. Verification of even the most basic modeling processes demands long term and continuous observations of global cloud occurrence, if there is to be any confidence in their fidelity. For a number of reasons, however, it is impossible to gauge the complexities of clouds from a single source. Fundamentally, an array of remote sensing methods is needed in order to provide a complete picture. Within the Micropulse Lidar Network (MPLNET), we have developed a new algorithm to improve the quality of our cloud products. The largest impact of the changes to the cloud detection algorithm is evident with high clouds (those with cloud base > 5 km). Furthermore, polarized measurements enable us to determine the cloud thermodynamic phase. Naturally, the next step is to investigate how these improvements compare with observations from spaceborne lidars (e.g. CALIOP) which have a better (unobstructed) view of high clouds. Recent advances in remote sensing have revealed that cirrus clouds are the most common cloud genus observed in the atmosphere. Furthermore, cirrus skew highly towards relatively low cloud optical depths, as observed from both surface and spaceborne viewpoints. The radiative impacts of these findings are quite significant, considering the cumulative effect cirrus exhibit when compared to low clouds.

Jasper R Lewis↗

NASA NTRS2020

A Brief Assessment of the Impact of Nearly 40 Years of Assimilated Observations Over the Amazon Basin

Adding a Forecast Sensitivity-based Observation Impact component to Version 2 of the Modern Era Retrospective-analysis for Research and Applications, the present study provides an assessment of the impact of nearly 40 years of observations on short-range (24-hr) forecasts over the Amazon basin. Under self-verification, forecast errors are found to slightly increase from the early data-sparse days to the more recent years, when data dramatically increase. Throughout the reanalysis, satellite radiances dominate in volume, but only before 1999 they dominate the impacts. Beyond 1999, over 50% of forecast error reduction is associated with conventional observations (radiosondes). Atmospheric Motion Vectors are also found to be large contributors to error reduction, but their contribution reduces in dry periods. In opposition to Atmospheric Motion Vectors, satellite radiances tend to contribute more in the dry season. Results provide motivation for additional conventional observations and the use of all-sky treatment of radiances.

F. L. R. Diniz↗

NASA NTRSDate not supplied

Advancements in Frequency Domain Analysis of Dual Centerline Propellant Tanks for Space Vehicle Stability and Design

This paper presents advancements in the frequency domain stability analysis for landing and ascent vehicle configurations with dual centerline propellant tanks, developed in support of Guidance, Navigation, and Control (GNC) insight activities for the Human Landing System project. The study focuses on the impact of axial thrust on the dual tank slosh danger zone solution. The frequency domain stability analysis tool is developed within the GeneraLized Aerospace Simulation in Simulink® (GLASS) framework at NASA Marshall Space Flight Center. The frequency domain stability analysis tool is used to determine slosh damping and control systems design requirements. The accuracy of the tool’s instability predictions is validated against time-domain outcomes within the GLASS simulation environment, showing consistent and anticipated results. Additionally, the method is validated by comparing its results with Frequency Domain Analysis and Comparison Tool Assuming Linearity (FRACTAL), a frequency domain analysis tool with extensive heritage and rigorous verification and validation using NASA Ares I-X and SLS flight data. The findings of this paper contribute to a better understanding of dual-tank slosh instability and support improved design and operation margins of space vehicles.

Han Woong (Brian) Bae↗

NASA NTRSDate not supplied

Advancements in Frequency Domain Analysis of Dual Centerline Propellant Tanks for Space Vehicle Stability and Design

Han Woong (Brian) Bae↗

NASA NTRS2024

Bumper Implementation of LMEEM

The Hypervelocity Impact Technology (HVIT) group at the NASA Johnson Space Center (JSC) maintains the Bumper 3 computer program (referred to as “Bumper”) to perform Micrometeoroid and Orbital Debris (MMOD) risk assessments for spacecraft. To perform its calculations, this program requires detailed mathematical models of the space environments and can include those models representing orbital debris, micrometeoroids, and lunar ejecta. The lunar ejecta environment is a projectile environment specific to the lunar surface. Due to the Moon’s lack of an atmosphere, meteoroids frequently strike the lunar surface. When this happens, the impact ejects material radially away from the impact point, which can pose a potential impact hazard to surface-based hardware. This lunar ejecta environment was previously described in 1969, in NASA Special Publication SP-8013, which was used as the basis for a model that will be referred to in this document as the “SP-8013 environment model,” or simply, “SP-8013.” Since around 2020, this model has been used in MMOD risk assessments for the Human Landing System (HLS) program and other lunar surface programs. A new lunar meteoroid ejecta environment was developed via a computer code named Lunar Meteoroid Ejecta Engineering Model (LMEEM), produced by the Natural Environments Branch (NEB) at Marshall Space Flight Center (MSFC). A global lunar surface environment definition using LMEEM is to be provided in the Cross-Program Design Specification for Natural Environments (DSNE) Revision J in 2024. Contact the NASA MSFC Natural Environments Branch for an in-depth discussion of the LMEEM environment model and its derivation. This environment model will supersede SP-8013 for MMOD risk assessments. To perform such assessments, Bumper must read data from text files output by LMEEM and use that data in risk calculations. The purpose of this document is to: •Demonstrate verification that the Bumper code has properly implemented the LMEEM environment model (Section 3) •Discuss the LMEEM environment as understood through an initial environment file (Section 4) •Compare the calculated risk due to the SP-8013 and LMEEM models (Section 5)

Bumper↗

DOE OSTI2022

Integrated Operations for Nuclear Business Operation Model Analysis and Industry Validation

The purpose of this report is to refine and analyze five work reduction opportunities first presented in INL/EXT-21-64134, Process for Significant Nuclear Work Function Innovation Based on Integrated Operations Concepts. This report seeks to further refine and analyze five work reduction opportunities first presented in the original report. Researchers selected five work reduction opportunities from the full Integrated Operations for Nuclear (ION) suite. A selected group of utilities then verified details and inputs from the original report. Categories for verification included capital cost, technology requirements, and savings. Researchers then modeled the data points and data ranges using probabilistic analysis which predicts the likelihood of positive or negative net present value. Research results show four out of the five work reduction opportunities have a greater than fifty percent chance of a positive net present value outcome when analyzed independently. When the five work reduction opportunities are grouped and analyzed together the model indicates a sixty percent chance that the outcome of all five taken together will be positive. The nuclear industry should interpret these results as encouraging. In line with the ION model, positive financial analysis supports the investment of capital dollars into existing nuclear power plants along the ION model. Implementation of the five work reduction opportunities in this report is likely to result in substantive long-term savings for the owners and operators of domestic nuclear power plants.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2012

James Webb Space Telescope Integrated Science Instrument Module Calibration and Verification of High-Accuracy Instrumentation to Measure Heat Flow in Cryogenic Testing

The James Webb Space Telescope (JWST) is an upcoming flagship observatory mission scheduled to be launched in 2018. Three of the four science instruments are passively cooled to their operational temperature range of 36K to 40K, and the fourth instrument is actively cooled to its operational temperature of approximately 6K. The requirement for multiple thermal zoned results in the instruments being thermally connected to five external radiators via individual high purity aluminum heat straps. Thermal-vacuum and thermal balance testing of the flight instruments at the Integrated Science Instrument Module (ISIM) element level will take place within a newly constructed shroud cooled by gaseous helium inside Goddard Space Flight Center's (GSFC) Space environment Simulator (SES). The flight external radiators are not available during ISIM-level thermal vacuum/thermal testing, so they will be replaced in test with stable and adjustable thermal boundaries with identical physical interfaces to the flight radiators. Those boundaries are provided by specially designed test hardware which also measures the heat flow within each of the five heat straps to an accuracy of less than 2 mW, which is less than 5% of the minimum predicted heat flow values. Measurement of the heat loads to this accuracy is essential to ISIM thermal model correlation, since thermal models are more accurately correlated when temperature data is supplemented by accurate knowledge of heat flows. It also provides direct verification by test of several high-level thermal requirements. Devices that measure heat flow in this manner have historically been referred to a "Q-meters". Perhaps the most important feature of the design of the JWST Q-meters is that it does not depend on the absolute accuracy of its temperature sensors, but rather on knowledge of precise heater power required to maintain a constant temperature difference between sensors on two stages, for which a table is empirically developed during a calibration campaign in a small chamber at GSFC. This paper provides a brief review of Q-meter design, and discusses the Q-meter calibration procedure including calibration chamber modifications and accommodations, handling of differing conditions between calibration and usage, the calibration process itself, and the results of the tests used to determine if the calibration is successful.

Comber, Brian↗

DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗

NASA NTRS1978

The GISS sounding temperature impact test

The impact of DST 5 and DST 6 satellite sounding data on mid-range forecasting was studied. The GISS temperature sounding technique, the GISS time-continuous four-dimensional assimilation procedure based on optimal statistical analysis, the GISS forecast model, and the verification techniques developed, including impact on local precipitation forecasts are described. It is found that the impact of sounding data was substantial and beneficial for the winter test period, Jan. 29 - Feb. 21. 1976. Forecasts started from initial state obtained with the aid of satellite data showed a mean improvement of about 4 points in the 48 and 772 hours Sub 1 scores as verified over North America and Europe. This corresponds to an 8 to 12 hour forecast improvement in the forecast range at 48 hours. An automated local precipitation forecast model applied to 128 cities in the United States showed on an average 15% improvement when satellite data was used for numerical forecasts. The improvement was 75% in the midwest.

Halem, M.↗

DOE OSTI2026

Quantifying Operational Drivers of Multimodal Biometric Verification in Aerial Surveillance

Multimodal biometric verification is increasingly applied across operational contexts ranging from close-range security cameras and building-mounted surveillance to long-range ground sensors and unmanned aerial system (UAS) imagery. Variations in acquisition conditions—such as image resolution, viewing geometry, and motion artifacts—pose significant challenges for cross-domain algorithmic generalization. This study evaluates two independent multimodal biometric verification systems developed under the Intelligence Advanced Research Projects Activity (IARPA) Biometric Recognition and Identification at Altitude and Range (BRIAR) program, comparing performance on close-range and aerial datasets. Close-range video served as a baseline to quantify the decline in verification performance on aerial footage. The dataset included six UAS platforms, spanning small quadcopters at 10m altitude to medium-sized fixed-wing aircraft at 360m. Mixed-effects logistic regression identified image resolution (head and body pixel counts), head height, sensor characteristics, and algorithm selection as primary determinants of verification success, whereas demographic attributes and mission gait were not significant predictors. Activity type and collection site influenced performance in close-range data but had negligible impact on UAS imagery. These results clarify modality-specific strengths and limitations and highlight opportunities to enhance cross-domain biometric verification.

Peluso, Alina [ORNL] (ORCID:0000000328950406)↗

NASA NTRS1989

Ice classification algorithm development and verification for the Alaska SAR Facility using aircraft imagery

The Alaska SAR Facility (ASF) at the University of Alaska, Fairbanks is a NASA program designed to receive, process, and archive SAR data from ERS-1 and to support investigations that will use this regional data. As part of ASF, specialized subsystems and algorithms to produce certain geophysical products from the SAR data are under development. Of particular interest are ice motion, ice classification, and ice concentration. This work focuses on the algorithm under development for ice classification, and the verification of the algorithm using C-band aircraft SAR imagery recently acquired over the Alaskan arctic.

Holt, Benjamin↗

DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗

DOE OSTI2021

Galaxy clustering in harmonic space from the dark energy survey year 1 data: compatibility with real-space results

ABSTRACT We perform an analysis in harmonic space of the Dark Energy Survey Year 1 Data (DES-Y1) galaxy clustering photometric data using products obtained for the real-space analysis. We test our pipeline with a suite of lognormal simulations, which are used to validate scale cuts in harmonic space as well as to provide a covariance matrix that takes into account the DES-Y1 mask. We then apply this pipeline to DES-Y1 data taking into account survey property maps derived for the real-space analysis. We compare with real-space DES-Y1 results obtained from a similar pipeline. We show that the harmonic space analysis we develop yields results that are compatible with the real-space analysis for the bias parameters. This verification paves the way to performing a harmonic space analysis for the upcoming DES-Y3 data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

VerifyIO: Ensuring Correctness of Consistency Semantics in Parallel I/O

Abstract—High-performance computing (HPC) applications generate and consume substantial amounts of data, typically managed by parallel file systems. These applications access file systems either through the POSIX interface or by using highlevel I/O libraries. While the POSIX consistency model remains dominant in HPC, emerging file systems and popular I/O libraries increasingly adopt alternative consistency models that relax semantics in various ways, creating significant challenges for correctness and portability. This paper addresses these challenges by proposing a trace-driven I/O consistency verification workflow, implemented in our open-source tool, VerifyIO, which collects execution traces, detects data conflicts, and verifies proper synchronization against specified consistency models. Our extensive evaluation of 91 test case executions across three widely used I/O libraries with four I/O consistency models reveals critical consistency issues at both application and implementation levels.

Consistency Semantics↗