SEARCH · Engineering Papers

Results for “decadal prediction”

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.

SourceResearch connectionFrom yearThrough year

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

At least 649 records · Page 36

All sources NASA NTRS DOE OSTI

NASA NTRS2017

Developing a Robust, Interoperable GNSS Space Service Volume (SSV) for the Global Space User Community

For over two decades, researchers, space users, Global Navigation Satellite System (GNSS) service providers, and international policy makers have been working diligently to expand the space-borne use of the Global Positioning System (GPS) and, most recently, to employ the full complement of GNSS constellations to increase spacecraft navigation performance. Space-borne Positioning, Navigation, and Timing (PNT) applications employing GNSS are now ubiquitous in Low Earth Orbit (LEO). GNSS use in space is quickly expanding into the Space Service Volume (SSV), the signal environment in the volume surrounding the Earth that enables real-time PNT measurements from GNSS systems at altitudes of 3000 km and above. To support the current missions and planned future missions within the SSV, initiatives are being conducted in the United States and internationally to ensure that GNSS signals are available, robust, and yield precise navigation performance. These initiatives include the Interagency Forum for Operational Requirements (IFOR) effort in the United States, to support GPS SSV signal robustness through future design changes, and the United Nations-sponsored International Committee on GNSS (ICG), to coordinate SSV development across all international GNSS constellations and regional augmentations. The results of these efforts have already proven fruitful, enabling new missions through radically improved navigation and timing performance, ensuring quick recovery from trajectory maneuvers, improving space vehicle autonomy and making GNSS signals more resilient from potential disruptions. Missions in the SSV are operational now and have demonstrated outstanding PNT performance characteristics; much better than what was envisioned less than a decade ago. The recent launch of the first in a series of US weather satellites will employ the use of GNSS in the SSV to substantially improve weather prediction and public-safety situational awareness of fast moving events, including hurricanes, flash floods, severe storms, tornados and wildfires. Thus, the benefits of the GNSS expansion and use into the SSV are tremendous, resulting in orders of magnitude return in investment to national governments and extraordinary societal benefits, including lives saved and critical infrastructure and property protected. However, this outstanding success is tempered by dual challenges: that for GPS, the current SSV specifications do not adequately protect SSV future use; and that for GNSS, the capabilities that are currently available are not protected in the future by specifications.

Bauer, Frank H.↗

NASA NTRS2017

Developing a Robust, Interoperable GNSS Space Service Volume (SSV) for the Global Space User Community

Bauer, Frank H.↗

NASA NTRS2016

MLI Blanket Effective Emittance Variance and Its Effect on Spacecraft Propellant Line Thermal Control

Multi-Layer Insulation (MLI) blankets commonly employed for temperature control of spacecraft propellant lines consistently show large variances in their effective emittances. These variances are primarily due to workmanship, hence are hard to control or predict. Unfortunately, the variances are also the primary drivers of propellant line temperatures, thus leading to large predicted temperature uncertainties and increased risks to the success of space missions. JPL has gained significant knowledge of these variances from Mars spacecraft designs over more than two decades. This knowledge has led to improved analytical approaches to bound existing designs, emphasis on increased temperature visibility for design verification and model correlation, as well as methods for improving the robustness of propellant line thermal designs.

Miller, Jennifer R.↗

NASA NTRSDate not supplied

Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) Aeroshell Performance

On November 10, 2022, NASA launched the Low-earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) reentry vehicle (RV)as a secondary payload mounted inside the launch vehicle (LV) adaptor on an Atlas V 401 out of the Vandenburg Space Force Base (VSFB). The primary payload, Joint Polar Satellite System-2 (JPSS-2), was delivered successfully to a sun-synchronous trajectory shortly after launch, at which point the Centaur upper stage performed a burn to de-orbit the system. Once on the desired trajectory to enter the atmosphere, the top of the payload adaptor was ejected to expose the LOFTID RV. Next, the LOFTID aeroshell, a Hypersonic Inflatable Aerodynamic Decelerator (HIAD), was deployed and inflated without issue. The Centaur pointed the LOFTID RV to the desired attitude to enter the atmosphere, spun the assembly to three rpm, separated the LOFTID RV, and finally performed a divert maneuver to prevent the Centaur from re-contacting the LOFTID RV after atmospheric entry. The LOFTID RV maintained the design attitude and spin rate from separation to atmospheric entry, a duration of roughly 30 minutes, demonstrating the aeroshell did not experience any energy damping from inflatable structure flexibility. The LOFTID RV entered the atmosphere over Alaska at >8km/sec and decelerated as designed, demonstrating aerodynamically stable flight from hypersonic entry through subsonic parachute deployment. On-board visible-spectrum cameras captured the physical response of the heatshield through all phases of flight. Co-located infrared cameras captured the temperature distribution of the aftside of the heat shield anchored to a distribution of thermocouples on the inflatable structure (IS) in the field of view. Thermocouples were also embedded in the forward side of the aeroshell both in the Flexible Thermal Protection System (FTPS) as well as the IS. This paper will document aeroshell’s response to the atmospheric entry. The visible-spectrum cameras captured mechanical response of the deployable aeroshell to the loads encountered in the different phases of entry. Events seen on the visible-spectrum cameras correlate with the measurements of the load cell pins on webbing elements that attach the aeroshell to the centerbody structure. Thermocouple data captured verifies the aerothermal response of the aeroshell was in-kind with pre-flight analysis predictions although somewhat lower in most locations. This data will enable enhancement of HIAD predictive capability. Post-flight inspection of the aeroshell after splashdown and ocean recovery shows that all the aeroshell materials and manufacturing techniques developed over the past two decades of HIAD technology development performed as expected at large scale, a scale which cannot be replicated in ground test facilities. The LOFTID RV remained high in the water after touching down under parachute, and maintained buoyancy and integrity for the hour it took to position the recovery vessel near the vehicle after splashdown. There was no indication the HIAD would have experienced an issue maintaining buoyancy for many more hours after water impact as the component maintained adequate inflation pressure more than 24 hours after water extraction with no additional inflation gas in tanks, as the inflation gas remaining in the tank was purposely vented prior to splashdown.

HIAD↗

DOE OSTI2023

Measuring the Neutron Spin Asymmetry A1n in the Valence Quark Region in Hall C at Jefferson Lab

The quest to understand how the nucleon spin is decomposed into its constituent quark and gluon spin and orbital angular momentum (OAM) components has been at the forefront of nuclear physics for decades. Due to the non-perturbative nature of Quantum Chromodynamics (QCD) - the theory describing how quarks and gluons bind together to form protons and neutrons - making absolute predictions of nucleon spin structure is generally difficult, especially as a function of its quark and gluon longitudinal momentum fraction x. Measurements involving nucleon spin structure serve as a sensitive test for QCD, including ab-initio lattice QCD calculations due to the advent of the quasi-PDF formalism, and various predictions that diverge at large-x. The neutron spin asymmetry An 1 at high?x is a key observable for probing nucleon spin structure. In the valence domain (x > 0.5), sea effects are expected to be negligible, and so the total nucleon spin is considered to be carried by the valence quarks. The valence region can therefore enable us to study the role of quark OAM and other non-perturbative effects of the strong force. An 1 was measured in the deep inelastic scattering region of 0.40 < x < 0.75 and 6 < Q2 < 10 GeV2 in Hall C at Jefferson Lab using a 10.4 GeV longitudinally polarized electron beam, upgraded polarized 3He target, and the High Momentum Spectrometer (HMS) and Super High Momentum Spectrometer (SHMS). E12- 06-110 provides the first precision data in the valence quark region above x = 0.60, and its preliminary results proved consistent with earlier data disqualifying a pQCD model that excluded quark OAM. Combined with previous world proton data, the ratio of the polarized-to-unpolarized up quark momentum distribution (?u + ?u)/(u + u) remained positive at large-x, and the down quark (?d + ?d)/(d + d) remained negative.

Rehfuss, Melanie↗

DOE OSTI2022

Measured proton electromagnetic structure deviates from theoretical predictions

The visible world is founded on the proton, the only composite building block of matter that is stable in nature. Consequently, understanding the formation of matter relies on explaining the dynamics and the properties of the proton’s bound state. A fundamental property of the proton involves the response of the system to an external electromagnetic field. It is characterized by the electromagnetic polarizabilities that describe how easily the charge and magnetization distributions inside the system are distorted by the electromagnetic field. Moreover, the generalized polarizabilities map out the resulting deformation of the densities in a proton subject to an electromagnetic field. They disclose essential information about the underlying system dynamics and provide a key for decoding the proton structure in terms of the theory of the strong interaction that binds its elementary quark and gluon constituents. Of particular interest is a puzzle in the electric generalized polarizability of the proton that remains unresolved for two decades. Here we report measurements of the proton’s electromagnetic generalized polarizabilities at low four-momentum transfer squared. We show evidence of an anomaly to the behaviour of the proton’s electric generalized polarizability that contradicts the predictions of nuclear theory and derive its signature in the spatial distribution of the induced polarization in the proton. Here, the reported measurements suggest the presence of a new, not-yet-understood dynamical mechanism in the proton and present notable challenges to the nuclear theory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2020

Toward More Realistic Simulation and Prediction of Dust Storms on Mars

Major (regional and global)dust storms dominate weather and climate variability on present-day Mars. Absorption and scattering of visible and IR radiation by dust strongly affect the thermal state of the thin Mars atmosphere, while dust provides condensation nuclei for water and CO2 cloud particles, which also affect radiative fluxes. Global dust storms (GDS) occur ~three times per Mars decade and to date have been observed in only northern fall and winter,when the global circulation is strongest. Tenfold increases in column dust opacity are typical during GDS, with intense vertical motions transporting dust to far higher altitudes than usual. The key processes and feedbacks that produce GDS remain poorly understood, hence current atmospheric models fail to self-consistently simulate observed dust storm activity. This means we have no predictive capability for when GDS may occur, either now or in Mars’s past. And crucially, due to the huge impact of GDS on the atmosphere and surface, our lack of ability to simulate realistic dust storms has major implications for Mars science and exploration: from understanding the present climate (§2.1),to modeling past climates and water cycles (§2.2), to exploring how wind, water, and dust cycles varied over time to interpret geology and assess potential habitability(§2.3), to quantifying and mitigating risks posed to Mars missions (§2.4)

Claire E Newman↗

NASA NTRS2004

Regional Assimilation of NASA Atmospheric Infrared Sounder (AIRS) Data

The NASA Short-term Prediction Research and Transition (SPORT) Center seeks to accelerate the infusion of NASA Earth Science Enterprise (ESE) observations, data assimilation and modeling research into NW S forecast operations and decision-making. The Atmospheric Infrared Sounder (AIRS), is expected to advance climate research and weather prediction into the 21 st century. It is one of six instruments onboard Aqua, a satellite that is part of NASA s Earth Observing System. AIRS, along with two partner microwave sounding instruments, represents the most advanced atmospheric sounding system ever deployed in space. The system is capable of measuring the atmospheric temperature in the troposphere with radiosonde accuracies of 1 K over 1 km-thick layers under both clear and cloudy conditions, while the accuracy of the derived moisture profiles will exceed that obtained by radiosondes. It is imperative that the scientific community is prepared to take full advantage of next-generation satellite data that will become available within the next decade. The purpose of this paper is to describe a procedure designed to optimally assimilate AIRS data at high spatial resolution over both land and ocean. The assimilation system used in this study is the Local Analysis and Prediction System (LAPS) developed at the Forecast System Laboratory used extensively around the globe. Results will focus on quality control issues associated with AIRS, optimal assimilation strategies, and the impact of the AIRS data on subsequent numerical forecasts at 12 km produced by the next generation Weather Research and Forecast (WRF) model.

Chou, Shih-Hung↗

DOE OSTI2023

Mechanisms of Pacific decadal variability in ESMs: the roles of stochastic forcing, feedbacks & external forcing

This project explored how and why climate patterns in the Pacific Ocean—specifically Pacific Decadal Variability (PDV)—change over time, and how well leading Earth System Models (ESMs) capture these changes under different climate scenarios. By analyzing large climate model datasets and running new simulations, the team investigated how ocean and atmosphere processes interact to shape PDV. The study tested a leading scientific hypothesis using both simplified and complex models, helping to uncover the key mechanisms that drive long-term climate swings in the Pacific. This work improves our ability to detect, predict, and understand how human-driven climate change may influence major ocean-climate patterns in the future.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Facilitating better and faster simulations of aerosol-cloud interactions in Earth system models

Focal Area(s): 1. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising a hierarchy of models. 2. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: One major challenge that Earth system models (ESMs) face in providing credible prediction of the Earth system and its water cycle characteristics (e.g., mean state, variability, and extreme events) is to accurately simulate aerosol-cloud interactions (ACI). The physical, chemical, and dynamical processes affecting ACI are extremely complex and they range from nanoscale to planetary scale. In each model development cycle, scientists spend significant efforts investigating model deficiencies and uncertainties associated with aerosols (e.g., emissions, chemical processes, aerosol microphysics, and transport) and clouds (e.g., macrophysics, microphysics, turbulence, and large-scale circulation) in order to develop improved treatments. However, despite decades of active research, ACI is still a major source of uncertainty in climate projections, even though great progress has been made. Specific scientific challenges include: (i) Parameterizations are developed based on limited data; (ii) The complexity of a parameterization required for accurate predictions is not understood; (iii) Incomplete and unknown physics leads to errors in the fully coupled Earth system; and (iv) Complex physics is computationally too expensive to employ in ESMs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Using Atom Dynamics to Map the Defect Structure Around an Impurity in Nano-Hematite

The geochemical cycling of actinides such as U is strongly controlled by Fe (oxyhydr)oxides. Indeed, the strong affinity of these minerals for U has factored into the design of geologic repositories for the long-term storage of nuclear waste. Many decades of work have focused on detailing the local coordination environment U associated with hematite (a-Fe2O3), a common and stable Fe(III) oxide, in order to better predict the fate and transport of U in the sub-surface. Here, for the first time, molecular dynamic simulations and atomically resolved electron microscopy of uranium atom mobility under the beam were used to map the topology of defect clusters surrounding structurally incorporated U in hematite. The ability to observe vacancies by means of metal atom hopping can be used to directly probe defects in other materials, with promising applications in geochemistry and materials science.

Ilton, Eugene S.↗

DOE OSTI2023

Explainable deep learning for insights in El Niño and river flows

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A review on machine learning-guided design of energy materials

Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Ternary Nitride Materials: Fundamentals and Emerging Device Applications

Interest in inorganic ternary nitride materials has grown rapidly over the past few decades, as their diverse chemistries and structures make them appealing for a variety of applications. Due to synthetic challenges posed by the stability of N 2 , the number of predicted nitride compounds dwarfs the number that have been synthesized, offering a breadth of opportunity for exploration. This review summarizes the fundamental properties and structural chemistry of ternary nitrides, leveraging metastability and the impact of nitrogen chemical potential. A discussion of prevalent defects, both detrimental and beneficial, is followed by a survey of synthesis techniques and their interplay with metastability. Throughout the review, we highlight applications (such as solid-state lighting, electrochemical energy storage, and electronic devices) in which ternary nitrides show particular promise.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Verification and Validation of High Explosive Reactive Burn Models Implemented in LANL's EAP and LAP Code Base

Reactive burn models represent a significant leap in high explosive (HE) modeling capability. The first generation of engineering models of HE detonation are called programmed burn models and they are largely based on the distance between a prescribed detonation point and each zone in a simulation. There have been many advancements to programmed burn models over the years and when the assumptions upon which they are based are met, a properly tuned programmed burn model can be highly accurate but if any of their assumptions is not met, as is the case for corner turning or weakly initiated HE burn, they will give the wrong answer. Reactive burn models represent an entirely new way of modeling HE burn. They use the local conditions of a zone – e.g. temperature, pressure or density – as calculated by a hydrocode to determine if and when the zone is going to detonate and if so, how rapidly. This difference opens up an entirely new set of capabilities for HE modeling. It makes it possible to accurately and predictively model phenomena like the effect of confinement and the formation of dead zones. Reactive burn models have seen sustained development effort at LANL for at least the last decade but several recent developments make it timely to transition reactive burn models from a research topic to a production tool. The main goal of this milestone is to facilitate and accelerate the adoption of reactive burn as a commonly available modeling option, with recommendations on the resolution that will be required and uncertainties associated with their modeling choices. To achieve this, we have performed verification, validation, and uncertainty quantification (UQ) assessments of AWSD and SURF/SURFplus in xRage and FLAG on a variety of different problems.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

Evolving Multi-hazard Machine Learning Modeling for Advanced Risk-Informed Infrastructure Resilience Assessment

The socioeconomic impacts of pipeline incidents have escalated over the past three decades, revealing the limitation of traditional risk modeling methods when applied to extensive pipeline networks. This research aims to develop machine learning (ML) models that effectively identify, rank, and predict the diverse hazards and socioeconomic consequences associated with pipeline incidents. Utilizing historical data on pipeline incidents alongside weather and oceanographic data from the 1980s onward, the Houston metropolitan area serves as a testbed for the proposed methodologies. The research segments the combined datasets into three consecutive periods, demonstrating the efficacy of the updated model in predicting future events, particularly concerning precipitation rate data. Despite the challenges posed by a relatively limited dataset, local-level ML modeling offers valuable insights into the spatial and temporal dynamics of multiple hazards that contribute to pipeline incidents. These findings hold significant implications for future research, particularly in understanding and mitigating risks in various locations across the Gulf Coast and other coastal regions.

42 ENGINEERING↗

DOE OSTI2025

Design Report on the MiniFuel Instrumented Test Apparatus for Understanding Radiation Effects

Most nuclear fuels irradiations at Oak Ridge National Laboratory (ORNL) over the past decade have been conducted using MiniFuel—a static capsule design employing subscale fuel specimens to collect separate-effects irradiated fuel performance data. Irradiation conditions for MiniFuel experiments are predicted pre-test using reactor physics, and thermal models are verified post-irradiation via SiC dilatometry and various spectrometry methods. Relevant fuel performance parameters are also observed post-irradiation in a hot cell, thereby providing a single data point for each parameter representing the cumulative effects of the irradiation conditions. Substantially more data can be harvested from a single test and within a shorter duration by instrumenting irradiation vehicles and measuring desired quantities in situ. This report presents the design and analysis of the MiniFuel INstrumented Irradiation Test Apparatus for Understanding Radiation Effects (MINITAURE)—an instrumented test rig based on the separate-effects MiniFuel concept that aims to capture fission gas release (FGR) and thermal conductivity degradation of fuel specimens during irradiation in the High Flux Isotope Reactor (HFIR). MINITAURE will be integrated with the Materials Irradiation Facility (MIF) located in the HFIR building outside the reactor containment. The MIF will act as the instrumentation and control center for the experiment, enabling real-time feedback from in situ sensors and control of irradiation temperatures via a gas delivery system. Two unique capsule designs were developed to capture each phenomenon: the thermal conductivity capsule, which uses a thermopile method to estimate fuel specimen thermal conductivity, and the fission gas release capsule, which will have continuous flowing gas communication to high-purity germanium detectors that are housed in the MIF for monitoring FGR. This report details the reactor physics and heat transfer modeling activities that were used to inform the experiment design and predict capsule performance. It also describes out-of-pile activities conducted to stand up this new capability and verify the measurement techniques. Modeling efforts to date have demonstrated the feasibility of the in situ measurement techniques and supported the development of the MINITAURE assembly configuration. Out-of-pile testing of the thermal conductivity measurement shows promise in capturing relative changes in thermal conductivity. However, significant errors exist in the measured absolute value, posing a need for further refinement.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1990

Analyses of composite fuselage structure under various loading conditions

With the characteristics of high strength/weight and stiffness/weight ratios in advanced composite materials, composite plate structures have been successfully applied to secondary load-carrying structural components in the aerospace industry for the past two decades. Recently, filament wound composite shells are being considered for design of primary fuselage structures. To implement the design the structural response under various loading conditions needs to be predicted accurately. Therefore, it is essential to establish a rigorous analytical solution in the area of composite laminated shells. A closed form solution is presented that predicts the response of a composite shell subjected to internal pressure, axial tension, bending, and torsion. The material of the shell is assumed to be general cylindrically anisotropic. Based on the theory of cylindrical anisotropic elasticity coupled partial differential governing equations are developed using Lehknitskii's stress function approach. The general expressions for the stresses and displacements in the composite cylinders under these loading conditions is discussed. Three examples (45) off-axis unidirectional, (45/45) unsymmetric, and (45/45)(sub s) symmetric angle-ply fiber-reinforced laminated shells are shown to illustrate the effect of radius-to-thickness ratios, coupling, and stacking sequence.

Yuan, Fuh-Gwo↗