Search NASA⌕ Search

SEARCH · Search NASA

Results for “understanding”

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 181 records · Page 10

Understanding The Top-Of-Atmosphere Fluxes Difference Between Aerocom Phase III Models And The CERES Product: Clear-Sky Perspective

The Clouds and the Earth’s Radiant Energy System (CERES) project produces a long-term global climate data record (CDR) that can be used to detect decadal changes in the Earth’s radiation budget (ERB) from the surface to the top-of-atmosphere (TOA). The CERES Energy Balanced and Filled (EBAF) product includes monthly mean shortwave (SW), longwave (LW), and net TOA all-sky and clear-sky radiative fluxes over 1-degree latitude by 1-degree longitude regions. The EBAF SW and LW fluxes are adjusted within their uncertainties to be consistent with the heat storage in the Earth-atmosphere system (Johnson et al. 2016). EBAF also provides a gap-free monthly mean clear-sky flux map by inferring clear-sky fluxes from both CERES and MODIS measurements (Loeb et al. 2018). In this study, we compare the TOA clear-sky fluxes from Aerocom phase III output with those from the CERES EBAF products. Flux differences over the ocean are generally smaller than over the land, and the magnitude of the differences shows seasonal and regional dependency. To understand the flux differences, aerosol optical depths (AOD) from the Aerocom models are compared with the satellite retrievals from MODIS and MISR. Over the ocean, the AOD differences and the flux differences show consistent regional features, indicating that the differences between models and observations are robust as the CERES EBAF clear-sky SW fluxes and MODIS/MISR AODs are determined independently. However, very little resemblance is found between the AOD and flux differences over the land. To further understand the cause of the flux differences over the land, we compare the land surface albedo from MODIS with the albedo from the models and find consistency in regional albedo differences and flux differences. Monthly regional radiative kernels of AOD and surface albedo derived using the MERRA-2 reanalysis data (Thorsen et al. 2020) are applied to AOD and surface albedo differences between models and observations. For most of the models, the AOD and surface albedo differences can explain most of the flux differences between models and CERES EBAF. The EOS era satellites have provided 20 years of carefully calibrated and validated observations that are suitable for trend analysis. Both the MODIS AOD and CERES EBAF clear-sky SW flux show decreasing trends over the coastal regions of eastern China and the eastern United States due to the emission control policies enforced in both countries, and an increasing trend off the coast of India. The AOD and clear-sky SW flux trends are less consistent over land, as the surface albedo changes complicate the clear-sky SW flux trend. The AOD and clear-sky SW flux trends from the Oslo model HIST run are also examined. However, none of the aforementioned regional trends are found in the model results.

Wenying Su↗

Understanding the Space Weathering of Mercury Through Laboratory Experiments

Introduction: Airless surfaces across the solar system are continually modified by energetic particles from solar wind and micrometeoroid bombardment [1,2]. This process is known as space weathering, and it alters the chemical, microstructural, and optical properties of surface regoliths on airless bodies, including Mercury. On the Moon and S-type asteroids, the reflectance spectral signatures of space weathering include reddening (increasing reflectance with increasing wavelength), darkening (lowering of reflectance), and the attenuation of characteristic absorption bands [2]. Such spectral changes are driven by the production of Fe-bearing nanoparticles(npFe) through both solar wind irradiation and micrometeoroid bombardment. While our understanding of space weathering for the Moon and near-Earth S-types asteroids is advanced, insight into how these processes operate on other planetary bodies is limited. In particular, Mercury experiences a uniquely intense space weathering environment than planetary counterparts at 1 AU, including a moreintense solar wind flux and higher velocity micrometeoroid impacts [4]. Additionally, Mercury has a surface composition unique in the inner solar system, including regions of the surface with very low albedo known as the low reflectance material (LRM), which is enriched in carbon, likely graphite, up to 4wt.% [5].In addition, the concentration of Fe across Mercury’s surface islow (<2 wt.%) compared to the Moonor S-type asteroids asteroids[6]. Our understanding of the effects of space weathering on C-rich and Fe-poor phases is limited. Since Fe plays a critical role inthe development of space weathering characteristicson other airless surfaces(e.g., npFe), its limited availability may significantly affect the development of space weathering features in Mercury surface materials. We can simulate space weathering processes in the laboratory to explore their effects on the microstructural, chemical, and spectral characteristics of Mercury surface materials[7]. Here we used pulsed laser irradiation to simulate the short duration, high-temperature events associated with micrometeoroid impacts. We performed coordinated analyses including reflectance spectroscopy and electron microscopy to investigate the spectral, chemical, and microstructural changes in these mercurian analog samples. Methods: For these experiments, we usedforsteritic olivine with varying FeOcontents, a mineral phase proposed to be abundant on the surface of Mercury. We mixed each sample with graphite to simulate LRM regions of the surface. Wesynthesized the olivinesamplesat 1-bar at NASA’s Johnson Space Centerand prepared pressed powder pellets for laser irradiation [8].We prepared three samples, each with a base layer of olivineto maintain structural integrity and topped witha surface layer containing the graphite-olivine mixture: 1) Sample SC-001 San Carlos olivine(Fo90.91),2)Sample F-S-002 with0.05 wt.% FeO olivine, and 3) F-T-004 with 0.53 wt.% FeO olivine. Each sample was mixed with 5 wt.% powdered graphite and had grain sizes ranging from 45to 125μm. We irradiated each sample usinga pulsed Nd-YAG laser, (l=1064 nm, ~6 ns pulse duration, energy of 48 mJ/pulse) while undervacuumat Northern Arizona University. The laser was rastered1x and then 5x over the surfaceof each sample to simulate progressive space weathering. We collected in situreflectance spectra from the samples after each laser pulse witha Nicolet IS50 Fourier-Transform Infrared spectrometer (lfrom 0.65-2.5 μm). We used an FEI Nova NanoSEM200scanning electron microscope (SEM) and a Hitachi TM4000 Plus benchtop SEM at Purdue University to image the surface morphology and topography of the samples. We extracted thin sections for analysis in the transmission electron microscope(TEM)using the FEI Helios NanoLab 660 focused ion beam (FIB) SEM at the University of Arizona.We performed analysis of the microstructural and chemical characteristics of the samples using the 200 keV JEOL 2500 scanning TEM at Johnson Space Center. Reflectance Spectroscopy Results:Reflectance spectra for each sample are shown in Fig. 1.SC-001:The spectrum of the unirradiated sample exhibits a weak 1.0 μm absorption feature, associated with Fe2+in the olivine,and low overall reflectance (Fig. 1a). Thereflectance and the depth of the absorption band increases after 1x laser raster but are at their lowest after 5x laser rasters.F-T-004:The unirradiated sample has a blue-sloped spectrum with low reflectance without identifiable absorption features (Fig. 1b). With progressive laser irradiation, the sample reflectance increases and becomes strongly red-sloped.F-S-002:The unirradiated sample exhibits a dark, blue-slopedspectrum. The brightness of the sample increases significantly from <0.2average reflectance over >0.8 reflectance in the most irradiated sampleand thespectral slope also becomesslightly reddened (Fig. 1c). Microstructural and Chemical Analysis: Two primary alteration textures were observed in the samples exposed to simulated space weathering: 1) fluffy C-rich,and 2) vesiculated melt. The fluffy C-rich texture is composed oflow-densitydeposits distributed across the surface of the sample(Fig. 2A). Analysis of a FIB section extracted from a low-density C-rich region in sample SC-001 reveals multiple globule-type deposits, discrete from stacked graphite, likely produced via melting from the laser irradiation [9].The vesiculated melt textureis smooth and uniformly distributed across isolated regions of the sample surface. The vesicles measure up to 100s of nmin diameter. Analysis of a FIB section from this texture was extracted from sample F-T-004 reveals a layer of amorphous melt material, close to 100 nm thick and uniform across the FIB section (Fig. 2B). Isolated regions of this melt layer contain small nanoparticles, <5 nm in diameter. Chemical analysis through energy dispersive X-ray spectroscopy reveals the composition of this layer is enriched in Si and depleted in Mg and O compared to the underlying sample. Implications for Space Weathering on Mercury: Previous experiments simulating space weathering of Mercury have showndarkening and reddening of spectra[7,10].However, our use of low-Fe materials and graphite to create a sample set more analogous to the mercurian surface. Our results indicate that sample composition plays a significant and important role in the space weathering of Mercury. In particular, our spectral data demonstrates a strong correlation between spectral slope, Fe content, and simulated space weathering. While the variation in FeO content between samples F-S-002 and F-T-004 is <0.6 wt.%, the spectra deviate from flat to strongly red-sloped(F-T-004). This reddeningmay be linked to the presence of very small nanoparticles observed in the melt textures extracted from sample F-T-004. For the SC-001 sample, the fluffy C-rich textures may be developed by the amalgamation of small graphite particles into these unique morphologies. Such observations indicate that space weathering on Mercury may result in both familiar and new microstructural and chemical characteristics. References: [1]Hapke B. (2001) J. Geophys. Res.-Planet.,106,10039–10073. [2]Pieters C.M. and Noble S.K. (2016) J. Geophys. Res-Planet., 121, 1865–1884. [3] Lucey P.G., and Riner, M.A. (2011) Icarus,212, 451-462.[4]CintalaM.J.(1992)J. Geophys. Res.-Planet.,97,947–973.[5]Klima R.L.et al.(2018)Geophys.Res.Letters, 45, 2945–2953. [6]Nittler L.R., et al. (2011) Science 333, 1847-1850.[7]Sasaki S. and Kurahashi E. (2004) Space weathering on Mercury, Adv.Space Res., 33, 2152-2155.[8] Vander KaadenK.E., et al. (2018) LPSCXLIX, Abstract 1230. [9] McGlaun M.L. et al. (2019) LPSCL, Abstract 2019. [10] TrangD.et al. (2018)LPSCXLIX,Abstract2083

M S Thompson↗

Understanding Resilience Optimization Architectures With an Optimization Problem Repository

Optimizing a system’s resilience can be challenging, especially when it involves considering both the inherent resilience of a robust design and the active resilience of a health management system to a set of computationally-expensive hazard simulations. While prior work has developed specialized architectures to effectively and efficiently solve combined design and resilience optimization problems, the comparison of these architectures has been limited to a single case study. To further study resilience optimization formulations, this work develops a problem repository which includes previously-developed resilience optimization problems and additional problems presented in this work: a notional system resilience model, a pandemic response model, and a cooling tank hazard prevention model. This work then uses models in the repository at large to understand the characteristics of resilience optimization problems and study the applicability of optimization architectures and decomposition strategies. Based on the comparisons in the repository, applying an optimization architecture effectively requires understanding the alignment and coupling relationships between the design and resilience models, as well as the efficiency characteristics of the algorithms. While alignment determines the necessity of a surrogate of resilience cost in the upper-level design problem, coupling determines the overall applicability of a sequential, alternating, or bilevel structure. Additionally, the application of decomposition strategies is dependent on there being limited interactions between variable sets, which often does not hold when a resilience policy is parameterized in terms of actions to take in hazardous model states rather than specific given scenarios.

Resilience↗

Colorado Front Range Disasters: Understanding the Impact of Forest Management on the Cameron Peak and CalWood Fire

Along the Colorado Front Range, forest management has gained significant attention due to uncharacteristically large fires that burned late in 2020. The Cameron Peak Fire (largest in Colorado recorded history) and the CalWood Fire collectively burned an estimated 219,019 acres from August through December of 2020. Project partners at the Coalition for the Poudre River Watershed, Colorado State Forest Service, Ben Delatour Scout Ranch, The Nature Conservancy, Colorado Forest Restoration Institute, and Colorado State University were interested in understanding the effectiveness of previous forest treatments in reducing burn severity within the Cameron Peak Fire and the CalWood Fire. We first collated a forest treatment dataset from pre-existing datasets by reclassifying over 29,000 treatments, which occurred across the Northern Colorado Front Range between 1970-2020. Secondly, we mapped three burn severity indices using Landsat 8 OLI and Sentinel-2 MSI Earth observations and compared them to soil burn severity field data. Thirdly, a total of 35 topographic, disturbance, forest structure, and treatment predictor variables were generated across the fires. Finally, we assessed relationships between these predictor variables and burn severity using the random forest algorithm. Model results indicate that the primary drivers of burn severity were elevation and distance to treatment edge for the Cameron Peak Fire and fire area and forest canopy cover for the CalWood Fire. Further analysis of these variables paired with field data is necessary to understand the relationship between burn severity and treatments to guide future restoration efforts, improve forest resiliency, and mitigate fire risks.

Neal Swayze↗

Jobos Bay Water Resources: Using Earth Observations to Analyze Shoreline Changes and Understand the Effects of Sea Level Rise in Southern Puerto Rico

Jobos Bay is located on the southern coast of Puerto Rico, which is known for intense hurricane seasons and increased seasonal storm surge. Scientists at Jobos Bay National Estuarine Research Reserve (JBNERR) are concerned that sea level rise will exacerbate coastal damage from these weather events. Using NASA Earth observations, our team analyzed coastal change, land use land cover change (LULC), mangrove forest extent, and water quality of Jobos Bay. Using Google Earth Engine, we evaluated coastal change and mangrove forest habitat within the study region by classifying NASA Earth observation imagery. We created historic LULC composite images to observe how land use changes over time and improve understanding of urbanization in the watershed. Leveraging previous water quality studies, our team compared water quality datasets generated by the Optical Reef and Coastal Area Assessment (ORCAA) tool to in situ sensors provided by JBNERR partners to understand the overall quality of water in the study area with respect to turbidity, chlorophyll-a, sea surface temperature, and colored dissolved organic matter (CDOM) concentrations. We discovered that 17% of the reserve has shifted from land to water since 1997 and lost 4.85 square kilometers of mangrove habitat over the past decade. Results from this study will inform the scientists of JBNERR and community members of the regional impacts of sea level rise. Being the first comprehensive study done in the estuary in nearly a decade, this serves as a baseline for future conservation efforts and research in the estuary.

Olivia Spencer↗

Introduction to Aircraft Icing and NASA’s Approach to Understanding It

This presentation provides a brief introduction to aircraft icing and NASA’s approach to understanding it. Specific topics that will be discussed include ice formation on an aircraft, the different types of ice, parameters that influence icing, engine and rotorcraft icing, and NASA’s approach to understanding icing through flight tests, wind tunnels tests, and computational tools. The presentation concludes with a look into the future of aviation and how it will be affected by icing.

Icing↗

Filling the Gaps in Understanding Solar Flares

Solar flares provide a laboratory for plasma physics and magnetohydrodynamic processes. While the mechanism of transferring magnetic flux between topological domains via reconnection is accepted in a general sense, some of the finer details remain to be worked out: What elusive thresholds, pertaining to which physical quantities, determine the initiation of flux transfer that cascades into the energy release observed in the impulsive phase of a flare? What role does turbulence play in accelerating or prolonging magnetic reconnection? To what degree do magnetic waves contribute to the transport of energy, or to the acceleration of particles in the impulsive phase? While our innate human curiosity and our instinctive impulse to explore compel us to investigate these intricacies of million-degree magnetized plasmas, the impacts of space weather on our technology-dependent society and economy obligate us to pursue a quantitative understanding, with the ultimate goal being trustworthy predictive capabilities. In this presentation, I will touch upon a few aspects of solar flares where improvements in observational and analytical capabilities can be expected to help us fill gaps in our understanding, as well as some of the recent, current, and near-term developments that are carrying the field forward.

David E. McKenzie↗

Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition

Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.

Chow, Edward↗

Understanding Plant Nitrogen Form Preference Responses to Elevated CO2 Earth and Space Environments

Future long-duration missions to the International Space Station (ISS) and beyond will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO2, and it is therefore crucial to understand plant responses to elevated CO2 (eCO2) environments. Much focus has been given to plant photosynthetic and performance parameters in response to eCO2, but studies on the effects of eCO2 on nitrogen uptake are poorly understood. Our previous work at The University of Sheffield, UK using novel stable isotope approaches has shown enhanced ammonium uptake and preference compared to nitrate at eCO2 in varieties of spring barley, hypothesized to be an indirect consequence of changes in photosynthesis and photorespiration. However, several varieties did not display altered preference under eCO2 and the universality of this response remains to be understood. In this NASA NPP project, several candidate crop species such as lettuce, radish and tomato will be screened using stable isotopes to assess whether N preference changes in favor of ammonium in response to eCO2 and whether this remains true at super-elevated CO2 (seCO2), which is often experienced on the ISS. Photosynthetic and related measurements will help to disentangle the relationship between these responses and photosynthesis. This work will enable the development of optimized nutrient regimes for candidate crops in space and in future Lunar and Martian habitats and will pave the way for selection of crop varieties adapted to an eCO2 and/or seCO2 environment. Moreover, this research will further our understanding of plant responses to the eCO2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security. This NPP Fellowship is funded by NASA Space Biology.

Luke Leslie Fountain↗

Challenges in Understanding Radiation Belt Dynamics: Insights from Two Storm Periods

The periods of May 27 - June 5, 2017 and Oct 24 — 29, 2016 are 'unusual' in terms of radiation belt dynamics and their solar wind driving conditions. The first period was under the influence of a slow CME-led major geomagnetic storm with Dstmin = -125 nT and the second period was under high speed solar wind streams. Observations from Van Allen Probes show great variabilities in different electron energy channels for both periods. During the second period of Oct 24 - 29, 2016, electron fluxes are found to be near the highest upper limit among various storms during 2013–2018 (Hua, Bortnik and Ma, 2022). In this paper, we provide solar wind sources and geomagnetic conditions for these two storm periods and point out challenges in understanding, modeling, and forecasting radiation belt dynamics. In-depth analysis of modeling results utilizing radiation belt models available at the Community Coordinated Modeling Center such as VERB and CIMI will be performed. Initial modeling results indicate rather large discrepancies with the observations. Model validation using different metrics introduced in Zheng et al. (2019) will be carried out to gain a deeper understanding of the physical processes involved and to identity potential causes of modeling inadequacies.

Yihua Zheng↗

Understanding Plant Nitrogen Form Preference Responses to Elevated CO2 Earth and Spaceflight Cabin Environments

Future long-duration missions to the International Space Station (ISS) and beyond will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO2, and it is therefore crucial to understand plant responses to elevated CO2 (eCO2) environments. Much focus has been given to plant photosynthetic and performance parameters in response to eCO2, but studies on the effects of eCO2 on nitrogen uptake are poorly understood. Our previous work at The University of Sheffield, UK using novel stable isotope approaches has shown enhanced ammonium uptake and preference compared to nitrate at eCO2 in varieties of spring barley, hypothesised to be an indirect consequence of changes in photosynthesis and photorespiration. However, several varieties did not display altered preference under eCO2 and the universality of this response remains to be understood. In this NASA NPP project, several candidate crop species such as lettuce, radish and tomato will be screened using stable isotopes to assess whether N preference changes in favor of ammonium in response to eCO2 and whether this remains true at super-elevated CO2 (seCO2), which is often experienced on the ISS. Photosynthetic and related measurements will help to disentangle the relationship between these responses and photosynthesis. This work will enable the development of optimized nutrient regimes for candidate crops in space and in future Lunar and Martian habitats and will pave the way for selection of crop varieties adapted to an eCO2 and/or seCO2 environment. Moreover, this research will further our understanding of plant responses to the eCO2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security. This NPP Fellowship is funded by NASA Space Biology.

Luke Fountain↗

Understanding Plant Nitrogen Form Preference Responses to Elevated CO2 Earth and Spaceflight Cabin Environments.

Future long-duration missions to the International Space Station (ISS) and beyond will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO2, and it is therefore crucial to understand plant responses to elevated CO2 (eCO2) environments. Much focus has been given to plant photosynthetic and performance parameters in response to eCO2, but studies on the effects of eCO2 on nitrogen uptake are poorly understood. Our previous work at The University of Sheffield, UK using novel stable isotope approaches has shown enhanced ammonium uptake and preference compared to nitrate at eCO2 in varieties of spring barley, hypothesised to be an indirect consequence of changes in photosynthesis and photorespiration. However, several varieties did not display altered preference under eCO2 and the universality of this response remains to be understood. In this NASA NPP project, several candidate crop species such as lettuce, radish and tomato will be screened using stable isotopes to assess whether N preference changes in favor of ammonium in response to eCO2 and whether this remains true at super-elevated CO2 (seCO2), which is often experienced on the ISS. Photosynthetic and related measurements will help to disentangle the relationship between these responses and photosynthesis. This work will enable the development of optimized nutrient regimes for candidate crops in space and in future Lunar and Martian habitats and will pave the way for selection of crop varieties adapted to an eCO2 and/or seCO2 environment. Moreover, this research will further our understanding of plant responses to the eCO2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security. This NPP Fellowship is funded by NASA Space Biology.

space plant biology↗

Towards Understanding Data Requirements for Developing Automatic Speech Recognition Systems for Air Traffic Control

In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. Given the many applications focusing on transcribing air traffic control and management communication, this paper explores the training of OpenAI's Whisper model across multiple existing public and private air traffic control voice datasets in an effort to improve robustness. Combining roughly 60+ hours of various air traffic datasets, our goal is to train a unified Whisper model and expect an average word error rate reduction across testing datasets. Furthermore, this work aims to understand the data quantity requirements for achieving state-of-the art results by comprehensively training Whisper on varying dataset sizes. This work has the potential to improve automatic speech recognition performance across the domain, improve understanding of the quantity of data required by an aviation speech recognition system, and lastly provide metrics to compare and improve upon in future research.

ATC↗

Towards an improved understanding of the Antarctic coastal zone and its contribution to future global sea level

Understanding the coastal zone of the Antarctic Ice Sheet, where it interacts with the Southern Ocean and warmer air masses, is crucial for predicting Antarctica's influence on the global climate. This region has multiple tipping mechanisms that could trigger large, rapid, and potentially irreversible changes in the coming centuries. The Antarctic Ice Sheet remains the largest source of uncertainty in future sea-level projections. Insufficient knowledge of bed topography beneath the ice shelves and the coastal ice sheet is not yet well documented, but is a major source of this uncertainty. This review assesses current knowledge of the coastal zone and highlights methods to investigate it, including aerogeophysical surveys, ground- and ship-based measurements, satellite observations, and computer modeling. An ensemble analysis of published bed topography datasets identifies significant data gaps and their regional distribution, framed in the context of current ice-sheet behavior and potential instability. We propose scientific priorities and guidelines for future aerogeophysical surveys, advocating for a comprehensive, coordinated international effort to build a next-generation dataset of Antarctic bed properties. Such an initiative would significantly advance understanding of the role of coastal processes in ice-sheet dynamics, reducing uncertainties in sea-level rise projections and enhancing predictions of future ocean and climate changes.

Kenichi Matsuoka↗

Towards an Improved Understanding of the Antarctic Coastal Zone and Its Contribution to Future Global Sea Level

Understanding the coastal zone of the Antarctic Ice Sheet (AIS), where it interacts with the Southern Ocean and warmer air masses, is crucial for predicting Antarctica's influence on the global climate and sea level. This region has multiple tipping mechanisms that could trigger large, rapid, and potentially irreversible changes in the AIS, the Southern Ocean and their global connections in the coming centuries. The AIS remains the largest source of uncertainty in future sea-level projections. Bed topography beneath the ice shelves and the coastal ice sheet is not yet well documented, and is a major source of this uncertainty. This review assesses current knowledge of the coastal zone and highlights methods to investigate it, including aerogeophysical surveys, ground- and ship-based measurements, satellite observations, and computer modeling. An ensemble analysis of published bed topography data sets identifies significant data gaps and their regional distribution, framed in the context of current ice-sheet behavior and potential instability. We propose scientific priorities and guidelines for future aerogeophysical surveys, advocating for a comprehensive, coordinated international effort to build a next-generation data set of Antarctic bed properties. Such an initiative would significantly advance understanding of the role of coastal processes in ice-sheet dynamics, reducing uncertainties in sea-level rise projections and improving predictions of future ocean and climate changes.

Kenichi Matsuoka↗

Advancing Understanding of Geothermal Representation in the Power Sector to Accelerate Deployment

Driven mostly by decarbonization goals, geothermal interest in the US power sector has grown considerably, gradually evolving from being considered a niche technology, to being recognized as a viable source of clean, baseload, grid-balancing power, and renewable electric power generation. Furthermore, recent technical advances that could greatly accelerate deployment in the near future, and US federal incentives for low-carbon generation technologies, including geothermal, can enable opportunities for integrating geothermal into utilities resource planning portfolios. However, in general, utilities do not have in-house expertise to evaluate geothermal technologies and its potential role in helping decarbonize the grid as well as help them achieve their individual decarbonization goals. To date, geothermal is rarely included in Capacity Expansion Models (CEM) which utilities use in their planning activities and resource/technologies prioritization. To this end, EPRI and NREL are working together in a DOE-GTO funded research project to improve geothermal understanding (opportunities, value, risks) among the power industry to help accelerate geothermal deployment. In the present paper we describe the approach and preliminary findings of this work, focused on two topics: 1) Expand the degree of understanding on the value, opportunity, and risk of geothermal technologies among utilities and related companies/groups, specifically around geothermal for power generation. 2) Improve representation of geothermal power technologies in capacity expansion models (CEM).

capacity expansion models↗

Across the Scales of the Nucleus: Understanding Short Range Correlations from Medium Modification to Probe Independence

The atomic nucleus presents an intricate system due to the non-linear forces described by Quantum Chromodynamics (QCD) that govern its structure. The range of scales involved is remarkable; the most massive nuclei weigh approximately five orders of magnitude more than the quarks that compose them. The nucleus can be analyzed at various levels, from quarks to hadrons to the nucleus as a whole. Short-Range Correlations (SRCs) within the nucleus play a significant role that spans these diverse scales. At the most fundamental level, SRCs influence the interaction between nucleons. The nucleon-nucleon (NN) interaction, arising from QCD, is crucial in determining nuclear properties. SRCs serve as valuable probes for measuring this NN interaction, as the nucleons within SRCs become effectively decoupled from the rest of the nucleus. Multiple experimental techniques, including electron scattering, have been employed to investigate the NN interaction through SRCs. However, our first project demonstrates that inclusive measurements alone are inadequate to constrain this interaction fully. Moving to the scale of the nucleus, SRCs contribute to the high-momentum tail of the nuclear spectral function. While the low-momentum region is characterized by nucleons exhibiting bulk properties, nucleons begin to pair into SRCs at higher momenta. Our research aims to bridge the understanding between the mean-field portion of the nucleus and its high-momentum SRC components. Additionally, SRCs affect the quark structure of protons, as evidenced by the EMC effect, which indicates that quarks behave differently when protons are embedded within a nucleus—an effect referred to as medium modification. This thesis explores the correlation between SRCs and medium modification across various experimental setups. Finally, we seek to establish an interpretation of the nuclear ground state. Accomplishing this requires demonstrating that our SRC observables are independent of the probe’s scale and scheme. The concluding project of this thesis illustrates how we utilize triple coincidence quasi-elastic scattering across a range of (Q2) values to develop a model-dependent framework for understanding SRC distributions within the nucleus’s ground-state wavefunction.

Denniston, A. W. Denniston [University of Tel Aviv↗

A Data-Driven Approach to Recognizing and Understanding Human Contributions to Aviation Safety

Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence, and are influenced by, how decision makers characterize problems and identify solutions. In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on safety-producing behaviors, safety management and system design decisions are based on a small sample of non-representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to aviation safety represents a significant and largely unrecognized risk when making policy decisions about safety management and system design. Opportunities exist to leverage the vast amount of data that have already been collected, or could be easily obtained, to increase our understanding of human contributions to safety in commercial aviation. This presentation will focus on those opportunities as well as the challenges associated with collecting and analyzing data on operators’ safety-producing behaviors.

Safety↗