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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Impact of Extreme Weather Events on Physical Protection System Effectiveness for a Hypothetical Small Modular Reactor Facility: Systems Analysis and Considerations

This report will summarize the group's work to provide recommendations to secure nuclear facilities before, during and after an extreme weather event. Extreme weather events can have drastic impacts to nuclear facilities as seen by the earthquake and subsequent tsunami at the Fukushima Daiichi Nuclear Power Plant in 2011. Recent hurricanes in the United States including Hurricane Harvey demonstrate the devastating effects these storms can have on infrastructure and the surrounding communities. The group is attempting to identify the gaps that potential small modular reactor (SMR) facilities will need to address in order to provide adequate site security before, during and after extreme weather events. This effort proceeded in three parts to provide insights and recommendations to secure Small Modular Reactor facilities for extreme weather events:(1) a literature review of academic articles as well as relevant documents including the existing regulatory framework and recommendations from the IAEA, NRC, and DOE, (2) subject matter expert interviews from a wide variety of security backgrounds, and (3) modeling and simulation on a hypothetical SMR facility. Special attention was paid to the interactions between stakeholders and nuclear facility design considerations, particularly the topics of safety and security. Engineering design issues from safety and security perspectives were discussed and included in simulation. Each step informed the proceeding, with the result including full tabletop scenarios of EWE impacts to security system effectiveness on the hypothetical model. This systems-level analysis provides results to inform recommendations to secure SMR facilities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Drought and Extreme Heat Impacts to Data Centers in Northern California

Commonly, data centers are designed and operated on the assumption of stationarity of the climate. Historical weather data informs the basis of their design and operations. However, as a result of climate change, this assumption is not valid and poses a great risk for data centers. In the coming decades, Northern California’s drought and extreme heat risk is projected to increase significantly. This report provides a drought and extreme heat impacts assessment of data centers in this region. Expert solicitation of data center experts enabled a tailored approach to this report. We identify the climate variables and analytics relevant for assessing data center-specific impacts from extreme heat and drought and demonstrate how to access and understand future climate projections from climate models. We emphasize the importance of including future climate projections into data center design and planning. This report is an important first step towards building an effective extreme heat and drought adaptation strategy for data centers in Northern California. While our geographical focus in this report is Northern California, many findings in this report are broadly applicable.

58 GEOSCIENCES↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments [Slides]

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. The ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

14 SOLAR ENERGY↗
DOE OSTI2022

North China Plain as a hot spot of ozone pollution exacerbated by extreme high temperatures

Abstract. A large population in China has been exposed to both severe ozone (O3) pollution and extreme heat under global warming. Here, the spatiotemporal characteristics of coupled extremes in surface O3 and heat (OPCs) over China are investigated using surface observations, a process-based chemical transport model (GEOS-Chem), and multi-model simulations from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). North China Plain (NCP; 37–41∘ N; 114–120∘ E) is identified as a hot spot of OPCs, where more than half of the O3 pollution days are accompanied by high temperature extremes. OPCs over NCP exceeded 40 d during 2014–2019, exhibiting an increasing trend. Both O3 concentrations and temperatures are elevated during OPCs compared with O3 pollution days occurring individually (OPIs). Therefore, OPCs impose more severe health impacts to humans than OPIs, but the stronger health effects are mainly driven by the higher temperatures. GEOS-Chem simulations further reveal that enhanced chemical production resulting from hot and stable atmospheric conditions under anomalous weather patterns primarily contributes to the exacerbated O3 levels during OPCs. In the future, CMIP6 projections suggest increased occurrences of OPCs over NCP in the middle of this century, but by the end of this century, OPCs may decrease or increase depending on the pollutant emission scenarios. However, for all future scenarios, extreme high temperatures will play an increasingly important role in modulating O3 pollution in a warming climate.

Wang, Pinya (ORCID:0000000170347868)↗
DOE OSTI2024

Wintertime extreme warming events in the high Arctic: characteristics, drivers, trends, and the role of atmospheric rivers

Abstract. An extreme warming event near the North Pole, with 2 m temperature rising above 0 °C, was observed in late December 2015. This specific event has been attributed to cyclones and their associated moisture intrusions. However, little is known about the characteristics and drivers of similar events in the historical record. Here, using data from European Centre for Medium-Range Weather Forecasts Reanalysis, version 5 (ERA5), we study these winter extreme warming events with 2 m temperature over a grid point above 0 °C over the high Arctic (poleward of 80° N) that occurred during 1980–2021. In ERA5, such wintertime extreme warming events can only be found over the Atlantic sector. They occur rarely over many grid points, with a total absence during some winters. Furthermore, even when occurring, they tend to be short-lived, with the majority of the events lasting for less than a day. By examining their surface energy budget, we found that these events transition with increasing latitude from a regime dominated by turbulent heat flux into the one dominated by downward longwave radiation. Positive sea level pressure anomalies which resemble blocking over northern Eurasia are identified as a key ingredient in driving these events, as they can effectively deflect the eastward propagating cyclones poleward, leading to intense moisture and heat intrusions into the high Arctic. Using an atmospheric river (AR) detection algorithm, the roles of ARs in contributing to the occurrence of these extreme warming events defined at the grid-point scale are explicitly quantified. The importance of ARs in inducing these events increases with latitude. Poleward of about 83° N, 100 % of these events occurred under AR conditions, corroborating that ARs were essential in contributing to the occurrence of these events. Over the past 4 decades, both the frequency, duration, and magnitude of these events have been increasing significantly. As the Arctic continues to warm, these events are likely to increase in both frequency, duration, and magnitude, with great implications for the local sea ice, hydrological cycle, and ecosystem.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

artificial intelligence↗
DOE OSTI2025

Performance of Convection-Permitting and Convection-Parameterized Models in Reproducing the Extreme Precipitation Intensity Relationship with Surface Conditions

Here, this study investigates the warm-season extreme precipitation–temperature scaling relationship in CONUS404, a convection-permitting (4 km) Weather Research and Forecasting (WRF) Model simulation over the conterminous United States for the past four decades, and compares it with the WRF-Thermodynamic Global Warming (WRF-TGW) historical simulation at a coarser resolution (12 km) using parameterized convection. We also analyze the NCEP stage IV and NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) datasets as observational benchmarks. We examine how extreme precipitation intensity (EPI) varies with temperature and saturation deficit over representative regions based on hourly data. The stage IV and IMERG data show a similar pattern of EPI variation with temperature and saturation deficit, except that the EPI peak is lower in IMERG than in stage IV. Under dry and hot conditions, EPI decreases too rapidly with elevated saturation deficit in both CONUS404 and WRF-TGW compared to observations, but the performance of CONUS404 is superior to WRF-TGW. When the near-surface atmosphere is saturated or close to saturated, both CONUS404 and WRF-TGW produce higher peak values of EPI relative to the observational references; IMERG exhibits scaling rates close to the Clausius–Clapeyron (C–C) relationship, while CONUS404, WRF-TGW, and stage IV all demonstrate super-C–C scaling behaviors. Despite marked warming over the past four decades, in both CONUS404 and WRF-TGW, the scaling relationship between EPI and temperature in a saturated atmosphere remains stable and robust. This indicates a strong potential for the EPI–temperature scaling rate under saturation to be used as an emergent constraint in reducing uncertainties of future extreme precipitation projection.

Atmosphere↗
DOE OSTI2023

Projected changes in extreme rainfall and temperature events and possible implications for Cameroon's socio-economic sectors

Extreme events like flooding, droughts and heatwave are among the factors causing huge socio-economic losses to Cameroonians. Investigating the potential response of rainfall and temperature extremes to global warming is therefore critically needed for tailoring and adjusting the country's policies. Recent datasets have been developed for this purpose within the Coordinated Output for Regional Evaluations (CORDEX-CORE) initiative, at ~25 km grid spacing. These regional climate models were used to dynamically downscaled four global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), under the optimistic and pessimistic representative concentration pathways (RCPs) 2.6 and 8.5, respectively. These models were employed in this study for characterizing the response of Cameroon's extreme precipitation and temperature events to global warming, using seven indices defined by the Expert Team on Cli mate Change Detection and Indices. Under global warming, the maximum number of consecutive dry (wet) days' is expected to increase (decrease). However, the annual total rainfall amount is expected to increase, mainly due to the intensification of very wet days and daily rainfall intensity. Furthermore, the temperature-based indices reveal an increase (decrease) in the total annual hot (cold) days, and overall, changes intensify with increased radiative forcing. The high-mitigated low-emission pathway RCP2.6 features attenuated changes, and even sometimes adapts to reverse the sign of changes. Designing reliable policies to limit the risks associated with the above changes is required, as their socio-economic consequences are likely to include food insecurity, heat-related illness, population impoverishment, price rises and market instability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modeling the impact of extreme summer drought on conventional and renewable generation capacity: Methods and a case study on the Eastern U.S. power system

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation’s energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. Here, this paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Levelized cost of charging of extreme fast charging with stationary LMO/LTO batteries

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

25 ENERGY STORAGE↗
DOE OSTI2020

Correlating the influence of porosity, tortuosity, and mass loading on the energy density of LiNi 0.6 Mn 0.2 Co 0.2 O 2 cathodes under extreme fast charging (XFC) conditions

Extreme fast charging capabilities along with high energy density of Li-ion batteries are the key factors to increase the adoption of electric vehicles while eliminating the problem of range anxiety. The U.S Department of Energy has a goal of <12 min charging time with energy density of > 200 Wh kg -1 . A combined improvement in the electrode architecture, electrolyte properties, and separator membrane is necessary to achieve this goal. Cells with thin electrodes are capable of extreme fast charging at the expense of low energy density and high cost. Electrode engineering can maximize energy density. In this work, the influence of porosity, mass loading and charging protocols on capacity and energy density and electrode kinetics are investigated under extreme fast charging conditions. Increasing the mass loading from 11.5 mg cm -2 to 25 mg cm -2 compromises the rate performance due to the mass transport limitation and underutilization of thick electrodes. Furthermore, reducing the electrode porosity from 50% to 35% improves the rate performance ascribed to shorter Li ion diffusion length. Symmetric cells are cycled to verify the performance of the half cells, suggesting that Li metal plating is the rate limiting step under high current density.

25 ENERGY STORAGE↗
DOE OSTI2021

Classification and computation of extreme events in turbulent combustion

In the design of practical combustion systems, ensuring safety and reliability is an important requirement. For instance, reliably avoiding lean blowout, flame flashback or inlet unstart is critical for ensuring safe operation. Currently, the science of predicting such events is based on prior experience, limited modeling or diagnostic tools and purely statistical approaches. Even though computational and experimental tools for studying combustion devices have vastly advanced in the last three decades, the analysis of such failure events has not been pursued widely. While the use of data for model development and calibration is being widely accepted, the extension to failure events introduces numerous challenges. In particular, the focus here is on so-called data-poor problems, where the cost of generating data is extremely high and is not easily amenable to existing computational and experimental approaches. Data-poor problems are particularly relevant when related to extreme events (also called anomalous events) that can lead to catastrophic failure of the system. It is argued that transient events that describe such failure can have different causal mechanisms. To develop the scientific inference process, a classification of such problems is used to determine specific modeling paths as well as computational tools needed. Research opportunities in the emerging field of extreme event prediction are highlighted in order to identify critical and immediate needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Vulnerability and resilience of urban energy ecosystems to extreme climate events: A systematic review and perspectives

We reviewed the present studies on the vulnerability and resilience of the energy ecosystem (most parts of the energy ecosystem), considering extreme climate events. This study revealed that the increased interactions formed during the transformation of the energy landscape into an ecosystem could notably increase the vulnerability of the energy infrastructure. Such complex ecosystem cannot be assessed using the present state of the art models used by the energy system modelers. Therefore, this study introduces a novel analogy known as the COVID analogy to understand the propagation of disruption within and beyond the energy ecosystem and organized the present state of the art based on the COVID analogy. The analogy helps to categorize the vulnerability of the energy infrastructure into three stages. The study revealed that although there are many publications covering the vulnerability and resilience of the energy infrastructure, considering extreme climate events, the majority are focused on the direct impact of extreme climate on the energy ecosystem. In addition, most of the studies do not consider the impact of future climate variations during this assessment. The propagation of disruptions was assessed mainly for wildfires and hurricanes. Further, there is a clear research gap in considering vulnerability assessment for interconnected energy infrastructure. Here, the transformation of energy systems into a complex ecosystem notably increases the complexity, making it difficult to assess vulnerability and resilience. A shift from a centralized to decentralized modeling architecture could be beneficial when considering the complexities brought by that transformation. Hybrid models consisting of both physical and data-driven machine learning techniques could also be beneficial in this context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Implication of stratospheric aerosol geoengineering on compound precipitation and temperature extremes in Africa

Three Coupled Model Intercomparison Project 5 (CMIP5) models that simulated the G4 experiment of the Geoengineering Model Intercomparison Project (GeoMIP) were used to investigate the impact of stratospheric aerosol injection (SAI) on combined temperature and precipitation extremes in Africa that can have greater negative impacts on human and the environment than individual rainfall or temperature extremes. The examined compound extremes included the dry (R warm|dry and R cold|dry ) and wet (R warm|wet and R cold|wet ) modes assessed during the injection (SAI, 2050-2069) and post-injection (postSAI, 2070-2089) periods compared with the historical period (1986-2005). We found a significant projected change in the occurrence of both wet and dry modes during SAI and postSAI related to the historical period. The magnitude and sign of this change depend on the season and the geographical location. During the SAI and postSAI, the wet (R warm|wet and R cold|wet ) modes are projected to be significantly lower while the dry modes are noted to increase in a large part of African continent depending on the season and the geographical location and may consequently leads to an increase of the droughts prone areas. Furthermore, the termination effect is noted to re duce the occurrence of dry modes, which may reduce the potential negative effects of the injection after halting. As the effect may vary from one region to another and according to the season, it suggested assessing the key sector impacts of SAI. Thus, this change in dry modes due to SAI could affect all activities which depend on water resources such as water supply, agriculture and food production, energy demand, and production with adverse effects on health, security, and sustainable development, but this needs to be assessed and quantified at regional scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Thiohalorhabdus methylotropha sp. nov., an extremely halophilic autotrophic methylotiotroph from hypersaline lakes

So far, there have been no reports of trimethylamine (TMA)-utilizing extremely halophilic microorganisms in hypersaline habitats. Our aerobic enrichments at 4 M total Na + with 5 mM TMA inoculated with surface sediments from hypersaline soda (at pH 9.5) or chloride-sulfate (at pH 7) lakes in southwestern Siberia were successful only for the latter. The initial enrichment included both bacteria and haloarchaea but only the bacterial component was able to grow as a pure culture with TMA. Strain Cl-TMA forms a new-species lineage within the genus Thiohalorhabdus which includes extremely halophilic and obligate lithoautotrophic sulfur-oxidizing gammaproteobacteria. Cl-TMA can grow methyloautotrophically utilizing TMA, dimethylamine (DMA) and methanol (MeOH) as the electron donors or chemolithoautotrophically with thiosulfate. Mixotrophic growth was also observed with the three methyl compounds and thiosulfate. Carbon is assimilated autotrophically via the Calvin-Benson-Basham pathway. Unlike the type species of Thiohalorhabdus, T. denitrificans , Cl-TMA was incapable of anaerobic growth via denitrification. The isolate belongs to extreme halophiles growing between 2.5 and 5 M NaCl with an optimum at 3–3.5 M. Genome analysis identified two gene clusters coding for PQQ-dependent methanol dehydrogenases (MxaFI and XoxF), four homologues of the formaldehyde activating enzymes (Faes), a TMA/DMA oxidation locus, and two cluster of genes encoding an N-methylglutamate dehydrogenase pathway (NMGP) for methylamine oxidation. The first steps of C 1 -subtrate conversions are followed by the tetrahydrofolate (THF)-linked and tetrahydromethanopterin (H4MPT)-linked formaldehyde oxidation pathways and two formate dehydrogenases. All of those signatures of methylotrophy were absent in T. denitrificans . In contrast, genes for two key sulfur oxidation enzymes, thiosulfate dehydrogenase TsdAB and sulfide dehydrogenase FccAB, that are present in the type species are missing in Cl-TMA. Thiosulfate is oxidized to sulfate by a combination of an incomplete Sox cycle and an sHdr system. Strain Cl-TMA T (JCM 35977 = UQM 41915) is proposed to be classified as Thiohalorhabdus methylotrophus sp. nov.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Extreme Temperature Cryptography Based On Nitrogen-Incorporated Ultrananocrystalline Diamond

Physical entropy sources that remain stable under extreme temperatures are essential for cryptography in emerging technological frontiers in deep space exploration, geothermal energy harvesting, and nuclear energy. However, conventional semiconductor platforms fail to generate stable and reliable cryptographic keys above 200 degrees C due to performance degradation. Here, we report a diamond-based cryptographic primitive that exploits the defect-rich sp 2 -bonded grain boundary network in nitrogen-incorporated ultrananocrystalline diamond (n-UNCD) film as a robust entropy source to generate cryptographic keys that remain operationally stable even after enduring extreme temperatures of 700 degrees C for 54 h while also surviving thermal cycling between room temperature and 700 degrees C for 48 h. The strength of the generated keys is assessed through several cryptographic metrics such as bit uniformity, entropy, hamming distances, and correlation coefficients, all of which are found to be near their respective ideal values. Moreover, the generated keys pass the NIST SP 800 and SP 800-90B tests and are also resilient to supply bias variations and a regression-based machine learning attack model based on the Fourier series. The robustness of the keys is attributed to the better thermal stability and chemical inertness of the n-UNCD film. This is supported by high-resolution energy-dispersive X-ray spectroscopy (EDS), which shows no significant lateral diffusion of metal atoms into the n-UNCD layer, and by Raman spectroscopy, which reveals no significant changes in the bonding configuration of the n-UNCD structure. Our findings highlight the remarkable potential of n-UNCD film for extreme environment cryptography by expanding the operational limits of conventional hardware security platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Dynamic Amplification of Subtropical Extreme Precipitation in a Warming Climate

Projected precipitation changes in a warming climate vary considerably, spatially, and between intensities. The changes can be greater or less than the ~7% K -1 Clausius-Clapeyron (CC) prediction, owing to dynamic effects. Using two global-climate-model large ensembles, we quantify the dynamically induced changes to precipitation extremes from the present (1996–2005) to late-21st-century (2071–2080) climates, as a function of recurrence interval, focusing on the subtropics. We separate non-CC changes into a term proportional to the present-day vertical-velocity spatial pattern (i.e., an amplification or damping thereof by a constant factor) and a residual. The amplitude term varies with recurrence interval, approximately canceling (doubling) CC for moderate (large) extremes, increasing precipitation variability. Contrastingly, the residual is quasi-uniform across recurrence intervals but spatially heterogeneous, weakening extremes over dry zones. This residual may be related to Hadley cell expansion, although this explanation is insufficient to explain many features, and other possible mechanisms are discussed.

54 ENVIRONMENTAL SCIENCES↗