Search NASA⌕ Search

SEARCH · Search NASA

Results for “Conditional”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

The Cr Redox Record of fO2 Variation in Angrites. Evidence for Redox Conditions of Angrite Petrogenesis and Parent Body

Angrites represent some of the earliest stages of planetesimal differentiation. Not surprisingly, there is no simple petrogenetic model for their origin. Petrogenesis has been linked to both magmatic and impact processes. Studies demonstrated that melting of chondritic material (e.g. CM, CV) at redox conditions where pure iron metal is unstable (e.g., IW+1 to IW+2) produced angrite-like melts. Alternatively, angrites were produced at more reducing conditions (<IW) with their exotic melt compositions resulting from carbonates in the source or from nebular condensation. Clearly, understanding what role fO2 plays in producing angrite magmas is critical for deciphering their petrogenesis and extending our understanding of primordial melting of asteroids. Calculations for the fO2 conditions of angrite crystallization are limited, and only preliminary attempts been made to understand the changes in fO2 that occurred during petrogenesis. Many of the angrites have phase assemblages which provide conflicting signals about redox conditions during crystallization (e.g., Fe metal and a Fe-Ti oxide with potential Fe3+. There have been several estimates of fO2 for angrites. Most notably, experiments examined the variation of DEu/DGd with fO2, between plagioclase and fassaitic pyroxene in equilibrium with an angrite melt composition. They used their observations to estimate the fO2 of crystallization to be approximately IW+0.6 for angrite LEW 86010. This estimate is only a "snapshot" of fO2 conditions during co-crystallization of plagioclase and pyroxene. Preliminary XANES analyses of V redox state in pyroxenes from D'Orbigny reported changes in fO2 from IW-0.7 during early pyroxene crystallization to IW+0.5 during latter episodes of pyroxene crystallization [15]. As this was a preliminary report, it presented limited information concerning the effects of pyroxene orientation and composition on the V valence measurements, and the effect of melt composition on valence and partitioning behavior of V. A closer examination of fO2 as recorded by Cr valence state in olivine will allow us to test models for primordial melting of chondritic material to produce the angrite parent melts. Here, we report the our initial stages of examining the origin and conditions of primordial melting on the angrite parent body and test some of the above models by integrating an experimental study of Cr and V valence partitioning between olivine [OL] and an angrite melt, with micro-scale determinations of Cr and V oxidation state in OL in selected "volcanic" angrites.

Shearer, Charles K.↗
NASA NTRS2016

The Effects of Ambient Conditions on Helicopter Harmonic Noise Radiation: Theory and Experiment

The effects of ambient atmospheric conditions, air temperature and density, on rotor harmonic noise radiation are characterized using theoretical models and experimental measurements of helicopter noise collected at three different test sites at elevations ranging from sea level to 7000 ft above sea level. Significant changes in the thickness, loading, and blade-vortex interaction noise levels and radiation directions are observed across the different test sites for an AS350 helicopter flying at the same indicated airspeed and gross weight. However, the radiated noise is shown to scale with ambient pressure when the flight condition of the helicopter is defined in nondimensional terms. Although the effective tip Mach number is identified as the primary governing parameter for thickness noise, the nondimensional weight coefficient also impacts lower harmonic loading noise levels, which contribute strongly to low frequency harmonic noise radiation both in and out of the plane of the horizon. Strategies for maintaining the same nondimensional rotor operating condition under different ambient conditions are developed using an analytical model of single main rotor helicopter trim and confirmed using a CAMRAD II model of the AS350 helicopter. The ability of the Fundamental Rotorcraft Acoustics Modeling from Experiments (FRAME) technique to generalize noise measurements made under one set of ambient conditions to make accurate noise predictions under other ambient conditions is also validated.

Greenwood, Eric↗
NASA NTRS2016

Impact of Flight Enthalpy, Fuel Simulant, and Chemical Reactions on the Mixing Characteristics of Several Injectors at Hypervelocity Flow Conditions

The high total temperatures or total enthalpies required to duplicate the high-speed flight conditions in ground experiments often place stringent requirements on the material selection and cooling needs for the test articles and intrusive flow diagnostic equipment. Furthermore, for internal flows, these conditions often complicate the use of nonintrusive diagnostics that need optical access to the test section and interior portions of the flowpath. Because of the technical challenges and increased costs associated with experimentation at high values of total enthalpy, an attempt is often made to reduce it. This is the case for the Enhanced Injection and Mixing Project (EIMP) currently underway in the Arc-Heated Scramjet Test Facility at the NASA Langley Research Center. The EIMP aims to investigate supersonic combustion ramjet (scramjet) fuel injection and mixing physics, improve the understanding of underlying physical processes, and develop enhancement strategies and functional relationships between mixing performance and losses relevant to flight Mach numbers greater than 8. The experiments will consider a "direct-connect" approach and utilize a Mach 6 nozzle to simulate the combustor entrance flow of a scramjet engine. However, while the value of the Mach number is matched to that expected at the combustor entrance in flight, the maximum value of the total enthalpy for these experiments is limited by the thermal-structural limits of the uncooled experimental hardware. Furthermore, the fuel simulant is helium, not hydrogen. The use of "cold" flows and non-reacting mixtures of fuel simulants for mixing experiments is not new and has been extensively utilized as a screening technique for scramjet fuel injectors. In this study, Reynolds-averaged simulations are utilized (RAS) to systematically verify the implicit assumptions used by the EIMP. This is accomplished by first performing RAS of mixing for two injector configurations at planned nominal experimental conditions. The mixing parameters of interest, such as mixing efficiency and total pressure recovery, are then computed and compared to the values obtained from RAS under the true enthalpy conditions and using helium and hydrogen. Finally, the impact of combustion on mixing, often deemed small enough to neglect at hypervelocity conditions, is assessed by comparing the results obtained from the hydrogen-fueled reacting and non-reacting RAS. For reacting flows, in addition to mixing efficiency and total pressure recovery, the combustion efficiency and thrust potential are also considered. In all of the simulations, the incoming air Mach number and the fuel-to-air ratio are the same, while the total pressure, total enthalpy, and the fuel simulant vary depending on the case considered. It is found that under some conditions the "cold" flow experiments are a good approximation of the flight.

Drozda, Tomasz G.↗
NASA NTRS2017

Characterization of a Pressure-Fed LOX/LCH4 Reaction Control System Under Simulated Altitude and Thermal Vacuum Conditions

A liquid oxygen, liquid methane (LOX/LCH4) reaction control system (RCS) was tested at NASA Glenn Research Center's Plum Brook Station in the Spacecraft Propulsion Research Facility (B-2) under simulated altitude and thermal vacuum conditions. The RCS is a subsystem of the Integrated Cryogenic Propulsion Test Article (ICPTA) and was initially developed under Project Morpheus. Composed of two 28 lbf-thrust and two 7 lbf-thrust engines, the RCS is fed in parallel with the ICPTA main engine from four propellant tanks. 40 tests consisting of 1,010 individual thruster pulses were performed across 6 different test days. Major test objectives were focused on system dynamics, and included characterization of fluid transients, manifold priming, manifold thermal conditioning, thermodynamic vent system (TVS) performance, and main engine/RCS interaction. Peak surge pressures from valve opening and closing events were examined. It was determined that these events were impacted significantly by vapor cavity formation and collapse. In most cases the valve opening transient was more severe than the valve closing. Under thermal vacuum conditions it was shown that TVS operation is unnecessary to maintain liquid conditions at the thruster inlets. However, under higher heat leak environments the RCS can still be operated in a self-conditioning mode without overboard TVS venting, contingent upon the engines managing a range of potentially severe thermal transients. Lastly, during testing under cold thermal conditions the engines experienced significant ignition problems. Only after warming the thruster bodies with a gaseous nitrogen purge to an intermediate temperature was successful ignition demonstrated.

Atwell, Matthew J.↗
NASA NTRS2019

Validation of Proposed Go-Around Criteria Under Various Environmental Conditions

This paper evaluates the effects of environmental conditions on touchdown performance under varying approach states and validates proposed go-around criteria developed using data from a previously conducted study under these various environmental conditions. An experiment was conducted using Boeing 737-800 and Airbus A330-200 Level D full-flight simulators in which 24 pilots flew multiple approaches under different approach conditions and environmental variables. Pilots were instructed to always land the aircraft, even from conditions considered to be an unstable approach. Various touchdown performance metrics were analyzed. In addition, pilots’ perceptions of risk under the various unstable approach conditions and resulting landings were assessed. The results of the study revealed that wind speed/direction and visibility had a stronger effect on touchdown performance than the approach parameters. Specifically, wind had a highly significant effect on longitudinal and lateral touchdown point, as well as a significant effect on sink-rate at touchdown. Wind and visibility, along with localizer deviation, also had a strong effect on pilots’ perception of risk and workload ratings. Furthermore, the study confirmed that touchdown performance was similar among the runs with a 300-foot and 500-foot starting gate, as was found in the previously conducted experiment. These results support the previous finding that lowering the go-around decision gate to 300-foot might be acceptable, but suggest that certain environmental conditions might warrant altered thresholds of the proposed go-around criteria at this gate. Finally, the findings of this experiment highlight the importance of environmental factors in the assessment of risk of unwanted outcomes on approach and landing.

Transport Aircraft↗
NASA NTRSDate not supplied

Risk Assessment of Obstacle Collision for UAVs under off-nominal conditions

Enabling operations of unmanned aerial vehicles (UAVs) in low-altitude airspace, demands the need of robust risk monitoring framework for assessing the safety of airspace, ground-structures and people. As widespread applications emerge, the need of risk assessment becomes increasingly important for UAV flights beyond visual line-of-sight, especially subjected to off-nominal conditions introduced by component failures, degraded controllability or environmental disturbances such as wind gusts in an urban canyon. From a safety perspective, collision with obstacles can be detrimental not only to the vehicle and payload, but also to the structure and people on ground. Although it is safe to assume that approved UAVs would be equipped with collision avoidance systems, risk of collision which can be predicted for a flight trajectory even before the UAV encounters any obstacle is beneficial to resolve contingencies in its decision-making under off-nominal conditions. In this paper, a framework is presented for computing the risk of collision with obstacle based on a UAV's predicted trajectory, proximity to static and dynamic obstacles, sub-system state-of-health and external wind conditions. The conditional probability of trajectory deviation is generated using a Bayesian Belief Network (BBN) based on on-board sensor measurements. Further, a kinematic 3-DOF model is implemented to compute deviation in UAV's trajectory subjected to one case study of off-nominal condition i.e. wind gusts. Finally, the integrated risk factor is demonstrated on real data from experimental flights of an octocopter at NASA Langley Research Center, in presence of simulated obstacles and wind conditions. The proposed approach would enable risk-informed decision making process for timely mitigation of current and future unsafe events.

Portia Banerjee↗
NASA NTRSDate not supplied

Risk Assessment of Obstacle Collision for UAVs under Off-nominal Conditions

Enabling operations of unmanned aerial vehicles (UAVs) in low-altitude airspace demands the need of robust risk monitoring framework for assessing the safety of airspace, ground structures and people. As widespread applications emerge, the need of risk assessment becomes increasingly important for UAV flights beyond visual line-of-sight, especially subjected to off-nominal conditions introduced by component failures, degraded controllability or environmental disturbances such as wind gusts in an urban canyon. From a safety perspective, collision with obstacles can be detrimental not only to the vehicle and payload, but also to the structure and people on ground. Although it is safe to assume that approved UAVs would be equipped with collision avoidance systems, risk of collision which can be predicted for a flight trajectory even before the UAV encounters any obstacle is beneficial to resolve contingencies in its decision-making under off-nominal conditions. In this paper, a framework is presented for computing the risk of collision with obstacle based on a UAV’s predicted trajectory, proximity to static and dynamic obstacles, sub-system state-of-health and external wind conditions. The conditional probability of trajectory deviation is generated using a Bayesian Belief Network (BBN) based on on-board sensor measurements. Further, a kinematic 3-DOF model is implemented to compute deviation in UAV’s trajectory subjected to one case study of off-nominal condition i.e. wind gusts. Finally, the integrated risk factor is demonstrated on real data from experimental flights of an octocopter at NASA Langley Research Center, in presence of simulated obstacles and wind conditions. The proposed approach would enable risk-informed decision making process for timely mitigation of current and future unsafe events.

Bayesian network↗
NASA NTRS2023

Effects of Ambient Conditions on Helicopter Harmonic Noise Radiation: Theory and Experiment

The effects of ambient atmospheric conditions, air temperature, and density on rotor harmonic noise radiation are characterized using theoretical models and experimental measurements of helicopter noise collected at three different test sites at elevations ranging from sea level to 7000 ft above sea level. Significant changes in the thickness, loading, and blade–vortex interaction noise levels and radiation directions are observed across the different test sites for an AS350 helicopter flying at the same indicated airspeed and gross weight. However, the radiated noise is shown to scale with ambient pressure when the flight condition of the helicopter is defined in nondimensional terms. Although the effective tip Mach number is identified as the primary governing parameter for thickness noise, the nondimensional weight coefficient also impacts lower harmonic loading noise levels, which contribute strongly to low-frequency harmonic noise radiation both in and out of the plane of the horizon. Strategies for maintaining the same nondimensional rotor operating condition under different ambient conditions are developed using an analytical model of single main rotor helicopter trim and confirmed using a CAMRAD II model of the AS350 helicopter. The ability of the Fundamental Rotorcraft Acoustics Modeling from Experiments (FRAME) technique to generalize noise measurements made under one set of ambient conditions to make accurate noise predictions under other ambient conditions is also validated.

rotorcraft↗
DOE OSTI2026

Multi-Objective design of interlocking metasurfaces using conditional diffusion models

Unit cell design remains a major challenge for interlocking metasurfaces, a promising joining technology for dissimilar materials, due to the complex, competing, multivariate design space and the need for rapid adaptation to varying performance requirements. This study explores Conditional Diffusion Models as a design optimization tool for interlocking metasurfaces. Given the complex, competing, multivariate design space for interlocking metasurfaces, unit cell design remains a major challenge for this joining technology. We trained a conditional diffusion model on 25,000 finite element analysis-simulated interlocking metasurface unit cells to generate designs with tailored thermo-mechanical properties (tensile strength, shear strength, and thermal conductivity) based on specified performance criteria. The model demonstrated a success rate of approximately 72 % in producing designs that met specified property bounds. The conditional diffusion model generated both thermally resistive and conductive designs, revealing clear trends in design characteristics: taller, dendritic structures were advantageous for tensile loads, while shorter, robust designs excelled in shear applications. Our findings indicate that the model's performance is more influenced by the breadth of the design space than by the quantity of training data, highlighting the importance of expansive design domains for generating innovative solutions. This work establishes conditional diffusion models as a highly efficient and adaptable tool for rapid interlocking metasurface unit cell design, paving the way for advancements in multi-material joining technologies, as well as highlighting the justification to leverage conditional diffusion models as design tools across complex design domains.

Conditional diffusion models↗
DOE OSTI2024

Precision Structure Engineering of High-Entropy Oxides under Ambient Conditions

High-entropy oxides (HEOs) have unveiled a unique frontier in the realm of heterogeneous catalysis, taking advantage of the entropic effect and increased complexities to deliver ultrahigh stability and large tuning capability. However, current HEO synthesis mainly relies on high-temperature annealing approaches affording HEOs possessing no or low surface area, inferior active site exposure efficiency, and low controllability over the structure tuning. The grand challenge lies in producing high-quality HEO catalysts with high active site utilization efficiency, which relies on precision structure engineering, preferably under mild conditions. In this work, an in situ lattice engineering approach was developed to afford a supported HEO catalyst under ambient conditions. The HEO compositions (CuCoFeNiMnO x ) were uniformly integrated into the lattice of CeO 2 driven by cavitation-induced nucleation being generated via ultrasonication. The as-afforded catalysts were featured by high surface area, atomically dispersed HEO compositions, active redox properties, abundant oxygen vacancies (O V ), antiagglomeration, and high phase stability under harsh conditions. Compared with the ex situ introduction of HEO on the surface, the in situ method provides dual benefits to maintain the dispersity of HEO via entropic and lattice confinement effects. Engineering the complex HEO within the lattice of fluorite-structured CeO 2 also yields abundant defects (e.g., O V ) and active metal sites with strong reducing properties (e.g., Ce 3+ and Cu + ), which greatly improves the activity of the lattice oxygen and tunability of the adsorption behavior of the guest molecules, especially in the presence of impurities (e.g., water and propane). The catalytic performance of the supported HEO catalyst in oxidative procedures surpasses the pure dense phase HEO as well as the ex situ-generated catalysts. Further, the synthesis approach being developed in this work, together with the fundamental understanding in structure evolution and reaction mechanism, showcases a facile pathway under ambient conditions to generate stable catalysts capable of maintaining structural robustness in high-temperature conditions while delivering enhanced catalytic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Digital Twin Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derate while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Digital-Twin-Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derates while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Development of physics-consistent conditional diffusion model to overcome data scarcity in critical heat flux

Deep generative modeling provides a powerful pathway to overcome data scarcity in energy-related applications where experimental data are often limited. By learning the underlying probability distribution of the training dataset, deep generative models, such as the diffusion model, can generate high-fidelity synthetic samples that statistically resemble the training data. Such synthetic data generation can significantly enrich the size and diversity of the available training data, and more importantly, improve the robustness of downstream machine learning models in predictive tasks. The objective of this paper is to investigate the effectiveness of diffusion models for overcoming data scarcity in nuclear energy applications. By leveraging a public dataset on critical heat flux which covers a wide range of commercial nuclear reactor operational conditions, we developed a diffusion model that can generate an arbitrary amount of synthetic samples. Since a vanilla diffusion model can only generate samples randomly, we also developed a conditional diffusion model capable of generating targeted critical heat flux data under user-specified thermal-hydraulic conditions. The performance of the diffusion model was evaluated based on its ability to capture empirical feature distributions and pair-wise correlations, as well as to maintain physical consistency. The results showed that both the diffusion model and conditional diffusion model can successfully generate realistic and physics-consistent critical heat flux data. Furthermore, uncertainty quantification results demonstrate that the conditional diffusion model is highly effective in augmenting critical heat flux data while maintaining acceptable levels of uncertainty.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

NOx mediated formation of ε – UO 3 during thermal decomposition of uranyl nitrate hexahydrate under static air conditions

The thermal decomposition of uranyl nitrate hexahydrate in air at temperatures of 650 – 800 °C is expected to yield α – U 3 O 8 through intermediate UO 3 phases. Here, n this study, Raman spectroscopy complimented by powder X-ray diffraction revealed the unexpected formation of ε – UO 3 as an accompanying phase during thermal decomposition of UNH at 700 °C under static air conditions. Experiments demonstrated that ε – UO 3 does not form during the initial ramp up stage nor during the high temperature heating period; but instead forms during the cooling stage between 250 – 400 °C. Additional experiments revealed that UNH initially decomposes through an amorphous UO 3 intermediate prior to α – U 3 O 8 formation. Attempts to bypass the U 3 O 8 precursor in the formation of ε – UO 3 were unsuccessful, supporting the necessity of U 3 O 8 (α – U 3 O 8 in this study) in the formation of ε – UO 3 . The observed phase evolution is proposed to result from the NOx species generated during UNH thermal decomposition, which create localized oxidizing conditions within the furnace under static air conditions. Furthermore, a predominantly phase pure ε – UO 3 was synthesized under static air conditions through the addition of an extra plateau at 250 °C during the cooling step. These findings demonstrate the importance of gas-phase chemistry and cooling conditions in uranium oxide phase evolution and provide additional insight into ε – UO 3 formation pathways.

Epsilon uranium trioxide↗
DOE OSTI2025

Structural Evolution and Stability of Rh/TiO 2 Catalysts under CO 2 Hydrogenation Conditions: Influence of the Initial Rh Structure

Characterizing catalyst stability by identifying the predominant mechanisms, timescales and driving forces of catalyst reconstruction under relevant reaction conditions is necessary for the design and commercialization of new catalysts. Here, in this paper, we study Rh/TiO 2 catalysts under CO 2 hydrogenation conditions (773 K, 75% H 2 , 25% CO 2 ) at high conversion and utilize reactivity studies along with ex-situ and in-situ spectroscopy and microscopy to characterize changes in catalyst activity and structure as a function of time on stream and the initial catalyst structure. This is a prototypical catalyst for CO 2 hydrogenation where Rh structure and Rh-TiO 2 interactions have been proposed to explain reactivity, selectivity (between CO and CH 4 formation) and catalyst stability. The influence of the initial Rh structure (varying from Rh single atoms to Rh nanoparticles), support stability, regeneration and pretreatment(s), and the chemical potential(s) of the reaction environment on reaction selectivity and catalyst stability were explored. The product selectivity between CO and CH 4 was determined to be dependent on the relative fraction of Rh single atoms and Rh nanoparticle-TiO 2 interfacial sites under reaction conditions, each exhibiting distinct stability under prolonged time on stream. Surprisingly, Rh single atoms exhibited stability for the duration of 90 h reactivity measurements, even at high Rh density (≥ 1.8 Rh atoms/nm 2 ) on the support, while Rh nanoparticles sintered under reaction conditions. As a result, all catalysts exhibited increasing selectivity to CO with increasing time on stream (> 10 h). We conclude the distribution of Rh structures evolved over time under reaction conditions through three distinct reconstruction mechanisms (Rh particle fragmentation, Ostwald ripening, and particle migration and coalescence) that occurred on varying timescales. Catalyst stability on the ~90 h time scale was ultimately controlled by the initial Rh structure.

25 ENERGY STORAGE↗
DOE OSTI2025

The Influence of Land‐Surface Conditions on the 2020–2021 Western US Drought

Abstract In summer 2021, 90% of the western United States (WUS) experienced drought, with over half of the region facing extreme or exceptional conditions, leading to water scarcity, crop loss, ecological degradation, and significant socio‐economic consequences. Beyond the established influence of oceanic forcing and internal atmospheric variability, this study highlights the importance of land‐surface conditions in the development of the 2020–2021 WUS drought, using observational data analysis and novel numerical simulations. Our results demonstrate that the soil moisture state preceding a meteorological drought, due to its intrinsic memory, is a critical factor in the development of soil droughts. Specifically, wet soil conditions can delay the transition from meteorological to soil droughts by several months or even nullify the effects of La Niña‐driven meteorological droughts, while drier conditions can exacerbate these impacts, leading to more severe soil droughts. For the same reason, soil droughts can persist well beyond the end of meteorological droughts. Our numerical experiments suggest a relatively weak soil moisture‐precipitation coupling during this drought period, corroborating the primary contributions of the ocean and atmosphere to this meteorological drought. Additionally, drought‐induced vegetation losses can mitigate soil droughts by reducing evapotranspiration and slowing the depletion of soil moisture. This study highlights the importance of soil moisture and vegetation conditions in seasonal‐to‐interannual drought predictions. Findings from this study have implications for regions like the WUS, which are experiencing anthropogenically‐driven soil aridification and vegetation greening, suggesting that future soil droughts in these areas may develop more rapidly, become more severe, and persist longer.

Jiang, Yelin [Lamont‐Doherty Earth Observatory Col↗
DOE OSTI2026

Dynamic strength of iron under pressure-temperature conditions of Earth’s inner core

Iron (Fe) is a primary constituent of terrestrial planetary cores, yet its rheological properties under extreme conditions remain uncertain. Here we present direct measurements of Fe strength at 310-430 GPa pressures and 3700-5800 K temperatures, obtained using Rayleigh-Taylor (RT) instability experiments at the National Ignition Facility. Single-crystal α-Fe samples with [001] and [111] orientations are shock-ramp compressed past the α-ε transition along paths approaching Earth’s inner core conditions. We find that ε-Fe derived from [001] α-Fe is consistently stronger (11-20 GPa) than that from [111] α-Fe (8-18 GPa), contrary to the trend at ambient conditions. Large-scale molecular dynamics simulations reproduce this atypical strength anisotropy and attribute it to microstructural evolution during the phase transition and subsequent ε-phase plasticity. Ripple growth analysis further constrains viscosities of 100-170 Pa·s under the driven conditions. Our results provide experimental benchmarks for Fe rheology at inner-core conditions, with implications for seismic anisotropy and the geodynamo.

Condensed-matter physics↗
DOE OSTI2024

Physical discovery in representation learning via conditioning on prior knowledge

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally, the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we present an empirical study of an approach based on conditioning the data on the known (continuous) physical parameters and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoder (cVAE) approach does not rely on the existence of the invariant transforms and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing us to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic data set and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via piezoresponse force microscopy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗