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NASA NTRS1990

Observations of CH in comets P/Brorsen-Metcalf and P/Halley

A common feature in the visual spectra of comets is the A squared delta - chi squared pi transition of CH at 4314 A (delta v = 0). This feature is very weak and is easily lost in the continuum or noise. However, the oscillator strength is very small, so even a weak feature indicates a large amount of CH. Understanding the parent of the CH in the comae of comets is very important for understanding the chemistry of comets and, thus, the early solar nebula. Potential parents for the CH are CH4 or more complex hydrocarbons, including polyoxy methanes (POMs). The ratio of CH4 to H2O is indicative of possible sites of formation of the comets. Past observations of comets have shown that the CH is present in the coma. However, because of the intrinsic weakness of the feature and the ease of photodissociating CH, the distribution of the CH in the coma has only been traced in the inner coma, where the fall-off is still 1/p, where p is the impact parameter. The first data sets tracing the distribution of CH to large distances from the nucleus in two comets is presented. Some preliminary models are presented pertaining to the parent of the CH.

Cochran, Anita L.↗

DOE OSTI2021

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone ( n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88–0.94]; 0.59[0.48–0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = −0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

Wu, Chao-Yi↗

NASA NTRS2015

Midlatitude Ice-Rich Ground on Mars: An Important Target for Science and In Situ Resource Utilization on Human Missions

The region of ROI is characterized by proven presence of near surface ground ice and numerous periglacial features. Midlatitude ground ice on Mars is of significant scientific interest for understanding the history and evolution of ice stability on Mars, the impact that changes in insolation produced by variations in Mars’ orbital parameters has on the regions climate, and could provide human exploration with a reliable and plentiful in situ resource. For both science and exploration, assessing the astrobiological potential of the ice is important in terms of (1) understanding the potential for life on Mars and (2) evaluating the presence of possible biohazards in advance of human exploration. Heldmann et al. (2014) studied locations on Mars in the Amazonis Planitia region where near surface ground ice was exposed by new impact craters (Byrne et al. 2009). The study examined whether sites in this region were suitable for human exploration including reviewing the evidence for midlatitude ground ice, discussing the possible explanations for its occurrence, assessing its potential habitability for modern life, and evaluating the resource potential. They systematically analyzed remote-sensing data sets to identify a viable landing site. Five sites where ground ice was exposed were examined with HiRise imaging and were classified according to (1) presence of polygons as a proxy for subsurface ice, (2) presence and abundance of rough topographic obstacles (e.g., large cracks, cliffs, uneven topography), (3) rock density, (4) presence and abundance of large boulders, and (5) presence of craters. A suitable landing site was found having ground ice at only 0.15m depth, and no landing site hazards within a 25 km landing ellipse. This paper presents results of that study and examines the relevance of this ROI to the workshop goals.

Human Missions↗

NASA NTRS2000

Sensing Clouds via Spacecraft Radio Occultation Observations

The presence of clouds dramatically alters the opacity and radiative transfer within Earth's atmosphere at both short and long wavelengths. Knowledge of cloud top and base is needed to estimate the Outgoing Long wave Radiation (OLR) to space and the net radiation at the surface from a given atmospheric profile. Satellite observations are required to provide the global perspective needed for climate. Cloud top pressure can be determined to some accuracy from spaceborne radiance measurements when cloud opacities are sufficiently large and cloud top temperatures are sufficiently low to readily them from the surface. Cloud base is particularly difficult to determine from space. The relatively small sensitivity of long wavelengths must be used to penetrate the clouds while at the same time providing sufficient sensitivity to detect the cloud base. We are studying the indirect sensitivity of radio occultation observations to clouds through their impact on the refractivity structure. The tradewind inversion is an excellent example where a very sharp refractivity structure coincides with the top of the tradewind cumulus and stratus clouds. In general, any cloud with sufficient IR opacity will have large transmissivity gradient at cloud top (base) which will result in large cooling (heating) which will create a thermal inversion at cloud top (base). Both liquid and ice clouds can reach this critical opacity. The thermal inversion and sharp change in specific humidity will cause a sharp change in the refractivity gradient which can be identified in the radio occultation results, particularly the signal amplitude. The occultations yield very accurate information on the height of such features. The heating at cloud base drives convection causing the air to follow a moist adiabat within the Cloud which further helps constrain the interpretation of the observations. The upward expansion of such opaque clouds due to convection may be an important mechanism supplying moisture into the upper troposphere. We will present simulations using high resolution radiosondes from field campaigns representing the expected signatures of such features. We will also present initial results of comparisons between GPS observations and GOES-derived cloud tops to assess the utility of this concept.

Kursinski, E. R.↗

DOE OSTI2026

Dual Context: Leveraging Structured Application Context for Code Generation and Runtime Feature Activation via Chat Interfaces

Integrating artificial intelligence (AI) capabilities into software applications typically involves two common paths. For developers, AI assists in generating and documenting source code and other related software engineering efforts. For users, AI assists them through question-and-answer exchanges via chatbots. Both approaches have their value, but neither effectively leverages the modularity of component-based architectures that modern web application frameworks offer. We implement a proof of concept within a centralized suite of applications used for the Atmospheric Radiation Measurement (ARM) Data Center Operational Tools, where we introduce a third integration path through the ARM Context Engine (ACE). ACE is a context driven system that uses structured contextual specifications to enable Large Language Models (LLMs) to render interactive and feature-rich user interface (UI) components directly within chat responses, alongside or in place of conventional text outputs. These specifications serve two important purposes across what we call code context and UI context. Code context provides AI-assisted development tools with structured application knowledge beyond raw code, including component relationships, architectural patterns and schematic information, enabling the generation of consistent, well-structured code. UI context defines the rules for enabling and rendering component features at runtime based on the user's natural language input, allowing end users to activate capabilities such as data export, filtering, and pagination within chat responses, without requiring code changes or redeployment. We demonstrate, through a comparative evaluation against general-purpose AI chatbots, that context-driven component rendering provides interactive capabilities that text-based responses cannot replicate, including deterministic component behavior, application-consistent design language, and on-demand feature activation. A development effort comparison further shows that features that traditionally require multi-step development cycles can be activated with a single naturallanguage request. In this ongoing work, we present ACE as an emerging approach to AI integration that positions modular, well-documented software architecture as the foundation for AI-ready applications. ACE treats context as a shared resource across both development and user-facing AI, bringing cohesion to conventionally disconnected efforts, bridging developer tooling and end-user capabilities within a single framework.

Tadimeti, Vijay [ORNL]↗

DOE OSTI2022

Main and Satellite Features in the Ni 2p XPS of NiO

The origin and assignment of the complex main and satellite XPS features of the cations in ionic compounds has been the subject of extensive theoretical studies using different methods. There is agreement that within a molecular orbital model one needs to take into account different types of configurations. Specifically, those where a core electron is removed but no other configuration changes are made and those where in addition to ionization there are also shake or charge transfer changes to the ionic configuration. However, there are strong disagreements about the assignment of XPS features to these configurations. The present work is directed toward resolving the origin of main and satellite features for the Ni 2p XPS of NiO based on ab initio molecular orbital wavefunctions for a cluster model of NiO. A major problem in earlier ab initio XPS studies of ionic compounds has been the use of a common set of orbitals that was not able to properly describe all the ionic configurations that contribute to the full XPS spectra. This is resolved in the present work by using orbitals that are optimized for averages of the occupations of the different configurations that contribute to the XPS. The approach of using State Averaged orbitals is validated through comparisons between different averages and through use of higher order excitations in the wavefunctions for the ionic states. It represents a major extension of our earlier work on the main and satellite features of the Fe 2p XPS of Fe 2 O 3 and proves the reliability and the generality of the assignments of the character and origin of the different features of the XPS obtained with orbitals optimized for State Averages. These molecular orbital methods permit the characterization of the ionic states in terms of the importance of shake excitations and of the coupling of ionization of 2p 1/2 and 2p 3/2 spin-orbit split sub shells. Further, the work lays the foundation for definitive assignments of the character of main and satellite XPS features and points to their origin in the electronic structure of the material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Feature Extraction for Pipeline Defects Inspection Based Upon Distributed Acoustic Fiber Optic Sensing Data

Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.

Pipeline Defects Inspection, Fiber-optic sensors, ↗

DOE OSTI2022

Solution-processable copper(I) iodide-based inorganic-organic hybrid semiconductors composed of both coordinate and ionic bonds

Three new one-dimensional (1D) All-In-One (AIO) type CuI-based hybrid semiconductors with both dative and ionic bonds between the inorganic and organic components have been successfully designed and synthesized by using the ligands containing both cationic center and coordination available sites. Structural analysis confirms that all compounds share the same formula of Cu 4 I 6 (L) 2 (L = ligand) and are composed of 1D-Cu 4 I 6 2- anionic chains that coordinate to cationic ligands. Additionally, these materials demonstrate high stability towards heat and moisture and excellent solution processability due to their unique bonding nature. They emit low energy light ranging from green to orange color (530-590 nm). The origin and mechanism of their emissions were studied by both experimental and theoretical methods. More importantly, all compounds can be easily fabricated into films by solution process, a desired but rare feature for most of CuI-based hybrids built of extended networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Combustion chemistry of alkenes and alkadienes

Alkenes formed, during the refining of crude oil, by cracking the heavier fractions are present in transportation fuels in significant amounts, up to as much as 15–20% in gasoline. Moreover, alkenes are also the major intermediate products of the oxidation of alkanes, which play a significant role in autoignition chemistry. This review has assessed the recent progress in gas-phase detailed kinetic model development for species with C=C double bond, mostly C2–C10 alkenes and 1,3-butadiene. The compiled knowledge on alkene combustion chemistry enabled a better understanding of the influence of the number and the position of the C=C double bond on the chemical kinetics and hence combustion behavior of alkenes in engines. At first, the article gives an extensive overview of fundamental combustion experiments by considering studies of C2–C10 alkenes and 1,3-butadiene in shock tubes, rapid compression machines, laminar flames, and jet-stirred and flow reactors. The value of the data from such experiments is critically discussed. Secondly, this article highlights the important reaction classes involved in alkene oxidation over low-, intermediate- and high-temperature ranges. Combustion chemistry covering C2 to C10 alkenes, with a special emphasis on C2 to C7 isomers is discussed by presenting a large body of experimental and modeling investigations. Detailed chemistry differences between alkene isomers and also between alkenes and alkanes are also addressed. Thirdly, the article presents important reaction pathways for PAH precursor formation in different alkenes. Finally, a summary of the distinguishing features of alkene combustion chemistry and an outlook towards future research in this area are presented. In conclusion, this review is focused on linear and branched chain alkenes, and the chemistry of cyclo-alkenes is not included.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A Machine Learning Approach for the Prediction of Formability and Thermodynamic Stability of Single and Double Perovskite Oxides

Perovskite oxides continue to attract huge interest due to their fascinating and wide-ranging properties for diverse applications. The tunability of these properties may be further enhanced by increasing their compositional complexity via double perovskite-ordered configurations containing multiple cations. In this work, we focus on an exhaustive chemical space of single and double oxide perovskites and optimally explore this space to identify novel compositions that are likely to form stable compounds. Critically, we examine the relationship between formability, the practical ability to synthesize a compound, and stability, the thermodynamic preference to form the structure. Our formability and stability training data sets were enumerated from the available experimental literature and in-house density functional theory computations and contained 1505 and 3469 examples, respectively, representing state-of-the-art in the current open literature in perovskite and double perovskite compounds. Subsequently, cross-validated and highly accurate machine learning classification models are built using these training data sets and employed to screen for novel stable oxide perovskites. The study identifies (1) atomic features relevant to prediction of formability and stability in perovskite and double perovskite compounds, (2) the importance of including energy contributions due to local structural relaxations going beyond the high symmetry perovskite phase, and (3) 437,828 double perovskite compounds that are likely to be stable and 891,188 compounds that are likely to be formable. From the intersection of this large chemical space of formable and stable oxide perovskites, 414 compositions are identified as the most promising candidates for future experimental synthesis of novel oxide perovskites. The developed models may be generalized and have implications beyond perovskite discovery if applied to other families of compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Fundamental Insights into Cathode Stability: Linking Compositional Tuning and Local Coordination in Complex Metal Oxides under Aqueous Transformations

Compositional tuning of complex metal oxides in Li-ion battery materials influences their performance as well as their end-of-life behavior, in particular, the tendency to release toxic metal cations in aqueous solution. We modeled ternary variants of a parent LiCoO 2 delafossite structure by varying the metal identity and relative amounts. This yielded ten model formulations of Li(A 4/6 B 1/6 C 1/6 )O 2 , where the material is enriched with the A metal and doped with B and C, with Ni, Mn, Co, Fe, Al, V, and Ti as constituent metals. To assess their stability in aqueous conditions, metal release energetics were calculated using a combination of Density Functional Theory calculations and thermodynamics. Metal release in ternary oxides is dictated by subtle variations in the coordination environment of the leaving group. To identify governing chemical features across diverse compositions with varying local coordination environments, we leverage random forest regression and descriptor importance analysis. A key result is that metal–oxygen orbital hybridization, quantified using a projected density-of-states-derived descriptor, H d/p , provides a physically grounded measure of interaction strength that governs metal release energetics. This refined perspective goes beyond conventional oxidation state considerations and offers more robust insights for materials science. Finally, we model defect surface-bound O 2 dimer formation as a proxy for reactive oxygen species (ROS) generation. The results show that Ni-rich compositions more readily stabilize spin-polarized O 2 dimers, corroborating experimental reports of an increased ROS-driven biological response. In conclusion, our results establish a compositional and electronic basis for metal release and surface oxygen reactivity that form a rationale for complex metal oxide design principles.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Predicting structural material degradation in advanced nuclear reactors with ion irradiation

Swelling associated with the formation and growth of cavities is among the most damaging of radiation-induced degradation modes for structural materials in advanced nuclear reactor concepts. Ion irradiation has emerged as the only practical option to rapidly assess swelling in candidate materials. For decades, researchers have tried to simulate the harsh environment in a nuclear reactor in the laboratory at an accelerated rate. Here we present the first case in which swelling in a candidate alloy irradiated ~ 2 years in a nuclear reactor was replicated using dual ion irradiation in ~ 1 day with precise control over damage rate, helium injection rate, and temperature and utilize physical models to predict the effects of radiation in reactors. The capability to predict and replicate the complex processes surrounding cavity nucleation and growth across many decades of radiation dose rate highlights the potential of accelerated radiation damage experiments. More importantly, it demonstrates the capability to predict the swelling evolution and the possibility to predict other features of the irradiated microstructure evolution that control material property degradation required to accelerate the development of new, radiation-tolerant materials.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

First detection of the BAO signal from early DESI data

We present the first detection of the baryon acoustic oscillations (BAOs) signal obtained using unblinded data collected during the initial 2 months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). From a selected sample of 261 291 luminous red galaxies spanning the redshift interval 0.4 < z < 1.1 and covering 1651 square degrees with a 57.9 per cent completeness level, we report a ∼5σ level BAO detection and the measurement of the BAO location at a precision of 1.7 per cent. Using a bright galaxy sample of 109 523 galaxies in the redshift range 0.1 < z < 0.5, over 3677 square degrees with a 50.0 per cent completeness, we also detect the BAO feature at ∼3σ significance with a 2.6 per cent precision. These first BAO measurements represent an important milestone, acting as a quality control on the optimal performance of the complex robotically actuated, fibre-fed DESI spectrograph, as well as an early validation of the DESI spectroscopic pipeline and data management system. Based on these first promising results, we forecast that DESI is on target to achieve a high-significance BAO detection at sub-per cent precision with the completed 5-yr survey data, meeting the top-level science requirements on BAO measurements. This exquisite level of precision will set new standards in cosmology and confirm DESI as the most competitive BAO experiment for the remainder of this decade.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Scalable Computation of Topological Abstractions for Scalar Data

Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial and on single machines. This paper presents the state of the art in the scalable computation of topological abstractions on scalar data, in shared memory parallel on single machines, and in distributed memory parallel on multiple machines. We highlight results for set‐based, graph‐based and complex‐based abstractions and organize the state of the art based on this taxonomy. The paper identifies parallelization and distribution techniques common in topological algorithms and highlights further areas of interest with underdeveloped efforts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Tomographic FLEET with a wedge array for multi-point three-component velocimetry

Femtosecond laser electronic excitation tagging (FLEET) velocimetry is an important diagnostic technique for seedless velocimetry measurements particularly in supersonic and hypersonic flows. Typical FLEET measurements feature a single laser line and camera system to achieve one-component velocimetry along a line, although some multiple-spot and multiple-component configurations have been demonstrated. In this work, tomographic imaging is used to track the three-dimensional location of many FLEET spots. A quadscope is used to combine four unique views onto a single high-speed image intensifier and camera. Tomographic reconstructions of the FLEET emission are analyzed for three-component velocimetry from multiple FLEET spots. Glass wedges are used to create many (nine) closely spaced FLEET spots with less than 10% transmission losses. These developments lead to a significant improvement in the dimensionality and spatial coverage of a FLEET instrument with some increases in experimental complexity and data processing. Multiple-point three-component FLEET velocimetry is demonstrated in an underexpanded jet.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology

With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the data and subject identities have also become an important issue and essential research topic. Without intentional safeguards, machine learning models may find patterns and features to improve task performance that are associated with private personal information. The privacy vulnerability of deep learning models for information extraction from medical textural contents needs to be quantified since the models are exposed to private health information and personally identifiable information. The objective of the study is to quantify the privacy vulnerability of the deep learning models for natural language processing and explore a proper way of securing patients’ information to mitigate confidentiality breaches. The target model is the multitask convolutional neural network for information extraction from cancer pathology reports, where the data for training the model are from multiple state population-based cancer registries. This study proposes the following schemes to collect vocabularies from the cancer pathology reports; (a) words appearing in multiple registries, and (b) words that have higher mutual information. We performed membership inference attacks on the models in high-performance computing environments. The comparison outcomes suggest that the proposed vocabulary selection methods resulted in lower privacy vulnerability while maintaining the same level of clinical task performance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Microtopographic and Depth Controls on Active Layer Chemistry in Arctic Polygonal Ground: Supporting Data

Polygonal ground is a signature characteristic of the Arctic, and permafrost thaw can potentially generate substantial feedbacks to Arctic ecosystems and climate. This study describes the first comprehensive spatial examination of active layer biogeochemistry that extends across high- and low-centered polygons and their features, including depth. Water chemistry measurements were made on active layer water samples collected near Barrow, Alaska during summer, 2012. Several significant differences in chemistry were observed between high- and low-centered polygons suggesting polygon types may be useful for landscape-scale geochemical classification. However, differences were found for polygon features (centers and troughs) for analytes that were not significant for type, suggesting that finer scale features control biogeochemistry in a different way than polygon type. Depth variations were also significant, demonstrating important multi-dimensional aspects of polygonal ground biogeochemistry. These results have major implications for understanding how polygonal ground ecosystems function, and how they may respond to future change. This data package includes one *.csv data file and one *.pdf user guide. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1972

The five-minute oscillations as nonradial pulsations of the entire sun.

Calculations of the stability coefficient show that the Sun should be pulsating as a unit in nonradial modes of high order. The pulsations are driven by the superadiabatic gradient of the low photosphere and by the same sensitive changes in opacity that are known to be important in variable stars. The existence of solar pulsations can explain many of the large scale features observed in the well known 5-minute oscillations of the solar atmosphere.

Wolff, C. L.↗