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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.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗
DOE OSTI2025

DESI 2024 VI: cosmological constraints from the measurements of baryon acoustic oscillations

We present cosmological results from the measurement of baryon acoustic oscillations (BAO) in galaxy, quasar and Lyman-α forest tracers from the first year of observations from the Dark Energy Spectroscopic Instrument (DESI), to be released in the DESI Data Release 1. DESI BAO provide robust measurements of the transverse comoving distance and Hubble rate, or their combination, relative to the sound horizon, in seven redshift bins from over 6 million extragalactic objects in the redshift range 0.1 < z < 4.2. To mitigate confirmation bias, a blind analysis was implemented to measure the BAO scales. DESI BAO data alone are consistent with the standard flat ΛCDM cosmological model with a matter density Ω m =0.295±0.015. Paired with a baryon density prior from Big Bang Nucleosynthesis and the robustly measured acoustic angular scale from the cosmic microwave background (CMB), DESI requires H 0 =(68.52±0.62) km s -1 Mpc -1 . In conjunction with CMB anisotropies from Planck and CMB lensing data from Planck and ACT, we find Ω m =0.307± 0.005 and H 0 =(67.97±0.38) km s -1 Mpc -1 . Extending the baseline model with a constant dark energy equation of state parameter w, DESI BAO alone requirew=-0.99 +0.15 -0.13 . In models with a time-varying dark energy equation of state parametrised by w 0 and w a , combinations of DESI with CMB or with type Ia supernovae (SN Ia) individually prefer w 0 > -1 and w a < 0. This preference is 2.6σ for the DESI+CMB combination, and persists or grows when SN Ia are added in, giving results discrepant with the ΛCDM model at the 2.5σ, 3.5σ or 3.9σ levels for the addition of the Pantheon+, Union3, or DES-SN5YR supernova datasets respectively. For the flat ΛCDM model with the sum of neutrino mass ∑ m ν free, combining the DESI and CMB data yields an upper limit ∑ m ν < 0.072 (0.113) eV at 95% confidence for a ∑ m ν > 0 (∑ m ν > 0.059) eV prior. These neutrino-mass constraints are substantially relaxed if the background dynamics are allowed to deviate from flat ΛCDM.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Modeling Deformable Linear Objects for Autonomous Robotic Outfitting of Lunar Surface Systems

This paper presents structural models of deformable linear objects (DLOs). DLOs are a subclass of deformable objects that encompasses common outfitting elements such as cables and ropes. Models are validated through hardware experiments, and integration in a robotic autonomy architecture for space environments is discussed. A persistent human presence on the lunar surface is one of the next major milestones in space exploration. This requires the development of robust extraplanetary construction technologies including structures and materials modeling and robotic systems. Previous robotic construction technology development has primarily focused on structural assembly, with significantly less focus on robotically performed outfitting tasks to instantiate subsystems providing power, data, life support, etc. These tasks involve manipulation of highly flexible elements, which are difficult to model, such as cable harnesses, ropes, and hoses. Robotic manipulation of DLOs, especially cable harnesses, is an active area of research as cable harnesses are essential for providing power and data to space assets. DLO models that can be used for robot manipulator trajectory generation are necessary for autonomous operation of lunar infrastructure. There are many proposed methods for modeling DLOs, and they primarily fall into three types: 1) discrete model-based, 2) continuum model-based, and 3) Neural Network-based. These types each have pros and cons, and the tradeoff between model accuracy and computational speed informs which type should be used. An understanding of this trade-off is imperative for real-time control of autonomous systems. High computational requirements reduce the speed of the model, making real-time control difficult, while accuracy is critical to preventing collisions. Discrete models, such as a mass-spring multibody representation, require relatively few calculations, and accuracy is directly tied to the step size of the discretization. Continuum models, such as a B-spline representation or a Cosserat rod model (a mix of continuous and discrete), are more informed of the structural properties of the cable and are much more accurate than a rigid body mass-spring model, but at significant computational cost. A Neural Network approach can provide an online solution with very few computational steps, but properly generating training data can be difficult and validation for an in-space application is not trivial. This paper explores the trade-off between different modeling approaches and compares accuracy and computational speed/complexity of the three types mentioned above. Model accuracy is evaluated using a cable in a static configuration. True cable shape is obtained using a depth camera for RGB images and point-cloud segmentation. The purpose of this experiment is to evaluate the trade-offs of different approaches to the DLO modeling problem. Understanding the tradeoffs between different cable modeling techniques paves the way for developing robotic control and planning architectures necessary for real-time manipulation of DLOs for lunar infrastructure outfitting. Real-time control is required for robotic systems to be able to actively manipulate a cable in a harsh environment where model and sensor errors compound, and environmental conditions can cause significant disturbances. Cable routing must be performed in areas with high density of objects/obstacles: through truss structures, near solar panels or mirror arrays, next to bundles of electrical equipment. Understanding the best way to plan and manipulate a cable without disrupting the environment or damaging the cable is imperative to robotic outfitting operations on the lunar surface.

Amy M Quartaro↗
NASA NTRS1999

Meteoric Magnesium Ions in the Martian Atmosphere

From a thorough modeling of the altitude profile of meteoritic ionization in the Martian atmosphere we deduce that a persistent layer of magnesium ions should exist around an altitude of 70 km. Based on current estimates of the meteoroid mass flux density, a peak ion density of about 10(exp 4) ions/cm is predicted. Allowing for the uncertainties in all of the model parameters, this value is probably within an order of magnitude of the correct density. Of these parameters, the peak density is most sensitive to the meteoroid mass flux density which directly determines the ablated line density into a source function for Mg. Unlike the terrestrial case, where the metallic ion production is dominated by charge-exchange of the deposited neutral Mg with the ambient ions, Mg+ in the Martian atmosphere is produced predominantly by photoionization. The low ultraviolet absorption of the Martian atmosphere makes Mars an excellent laboratory in which to study meteoric ablation. Resonance lines not seen in the spectra of terrestrial meteors may be visible to a surface observatory in the Martian highlands.

Pesnell, William Dean↗
DOE OSTI2025

Using ARM Observations to Evaluate Process-Interactions in MCS Simulations Across Scales (Final Progress Report)

This project, funded by DOE Atmospheric System Research (DE-SC0020050), focused on improving the representation of mesoscale convective systems (MCSs) in numerical weather and climate models by leveraging high-resolution observations from the DOE Atmospheric Radiation Measurement (ARM) program. The research aimed to evaluate model sensitivities to grid spacing, microphysics, and planetary boundary layer (PBL) schemes, with a particular emphasis on improving convection parameterization for high-resolution modeling. Findings from this work highlight several key advancements. Model validation against ARM radar wind profiler data from the Southern Great Plains (SGP) and Manaus (MAO) sites revealed systematic biases in simulated convective mass flux profiles, leading to the development of an observationally constrained evaluation framework for diagnosing and improving model performance. Sensitivity analyses demonstrated that the representation of Amazonian MCSs was highly dependent on PBL scheme selection, while mid-latitude MCSs were more strongly influenced by microphysics parameterizations. A series of high-resolution WRF simulations, ranging from 4 km to 125 m grid spacing, provided insight into the behavior of convective drafts across scales. While updraft properties converged at sub-kilometer resolutions, biases in downdraft intensity persisted even at the finest resolution tested, emphasizing the need for further refinements in model physics. Additionally, comparisons of MCS vertical structures between mid-latitude and tropical environments revealed stronger updrafts and larger mass flux in mid-latitude MCSs, providing critical insights for improving climate model representations of storm-scale dynamics. The project’s findings have already contributed to advancing numerical modeling capabilities, particularly in WRF, MPAS, ICON, and DOE’s SCREAM model, by refining how convective processes are represented in high-resolution climate simulations. Results were disseminated through peer-reviewed publications, conference presentations, and ARM/ASR Research Highlights, engaging the broader scientific community. The project also provided valuable training opportunities for two postdoctoral researchers, who played central roles in model development, analysis, and dissemination of results. Their work contributed to several publications and conference presentations, helping prepare them for careers in atmospheric modeling. By improving the simulation of MCSs, this research directly supports the development of next-generation climate models capable of more accurately representing extreme precipitation and convective processes. The insights gained will inform future improvements in convective parameterization and guide the design of high-resolution weather and climate simulations, ultimately enhancing the reliability of climate projections and weather forecasts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Discovery of a New Soft Gamma Repeater, SGR 1627-41

We report the discovery of a new soft gamma repeater (SGR), SGR 1627-41, and present BATSE observations of the burst emission and BeppoSAX Narrow-Field Instrument observations of the probable persistent X-ray counterpart to this SGR. All but one burst spectrum are well fit by an optically thin thermal bremsstrahlung model with kT values between 25 and 35 keV. The spectrum of the X-ray counterpart, SAX J1635.8-4736, is similar to that of other persistent SGR X-ray counterparts. We find weak evidence for a periodic signal at 6.41 s in the light curve for this source. Like other SGRs, this source appears to be associated with a young supernova remnant, G337.0-0.1. Based upon the peak luminosities of bursts observed from this SGR, we find a lower limit on the dipole magnetic field of the neutron star of B(sub dipole) approximately > 5 x 10(exp 14) G.

Woods, Peter M.↗
DOE OSTI2015

Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: The bioconcentration factor (BCF)

The bioconcentration factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R{sup 2}) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100 L/kg for Chemical Safety Assessment; 500 L/kg for Classification and Labeling; 2000 and 5000 L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R{sup 2}>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot–Gobas models. As classifiers, ACD and log P-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R{sup 2}{sub ext}=0.59), followed by the EPISuite Meylan model (R{sup 2}{sub ext}=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R{sup 2}=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R{sup 2}=0.85) and sensitivity (average>0.70) for new compounds in the AD but not present in the training set. However, no single optimal model exists and, thus, it would be wise a case-by-case assessment. Yet, integrating the wealth of information from multiple models remains the winner approach. - Highlights: • REACH encourages the use of in silico methods in the assessment of chemicals safety. • The performances of nine BCF models were evaluated on a benchmark database of 851 chemicals. • We compared the models on the basis of both regression and classification performance. • Statistics on chemicals out of the training set and/or within the applicability domain were compiled. • The results show that QSAR models are useful as weight-of-evidence in support to other methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Evolution of a multi-step phosphorelay signal transduction system in Ensifer : recruitment of the sigma factor RpoN and a novel enhancer-binding protein triggers acid-activated gene expression: A model for acid-activated lpiA-acvB expression

Most Ensifer strains are comparatively acid sensitive, compromising their persistence in low pH soils. In the acid-tolerant strain Ensifer medicae WSM419, the acid-activated expression of lpiA is essential for enhancing survival in lethal acidic conditions. In this work, we characterise a multi-step phosphorelay signal transduction pathway consisting of TcsA, TcrA, FsrR, RpoN and its cognate enhancer-binding protein EbpA , which is required for the induction of lpiA and the downstream acvB gene. The fsrR, tcrA, tcsA and rpoN genes were constitutively expressed, whereas lpiA and acvB were strongly acid-induced. RACE mapping revealed that lpiA/acvB were co-transcribed as an operon from an RpoN promoter. In most Ensifer species, lpiA/acvB is located on the chromosome and the sequence upstream of lpiA lacks an RpoN-binding site. Almost all Ensifer meliloti strains completely lack ebpA, tcrA, tcsA and fsrR regulatory loci. In contrast, E. medicae strains have lpiA/acvB and ebpA/tcrA/tcsA/fsrR co-located on the pSymA megaplasmid, with lpiA/acvB expression coupled to an RpoN promoter. Here we provide a model for the expression of lpiA/acvB in E. medicae . This unique acid-activated regulatory system provides insights into an evolutionary process which may assist the adaptation of E. medicae to acidic environmental niches.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

Microdynamic modelling of joint-dominated truss-based structures

A methodology for the mathematical modeling of preloaded joint-dominated truss-based space structures applicable in the microdynamic regime is presented. First, various factors which could affect the microdynamic behavior of joint-dominated truss structures are examined. Then, mathematical models for various types of joints involving contact deformations are derived from Hertzian contact theory. Their dynamic behavior is studied analytically and numerically by means of computer simulation. It was found that both synchronous and asynchronous oscillations having the same orders of magnitude are excited by sinusoidal load perturbations in the microdynamic regime. These oscillations persist even in the presence of light linear damping. The integration of the derived joint models with those for the elastic links of preloaded planar truss structures is discussed.

Wang, P. K. C.↗
DOE OSTI2022

Enabling machine learning-ready HPC ensembles with Merlin

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

The Effect of Ice Nuclei Efficiency on Arctic Mixed-Phase Clouds from Large-Eddy Simulations

The effects of ice nuclei (IN) efficiency on the persistent ice formation in Arctic mixed-phase clouds (AMCs) are investigated using a large-eddy simulation model, coupled to a bin microphysics scheme with a prognostic IN formulation. In the three cases where the IN efficiency is high, ice formation and IN depletion are fast. When the IN concentration is 1 and 10 g -1 , IN are completely depleted and the cloud becomes purely liquid phase before the end of the 24-h simulation. When the IN concentration is 100 g -1 , the IN supply is sufficient but the liquid water is completely consumed so that the cloud dissipates quickly. In the three cases when the IN efficiency is low, ice formation is negligible in the first several hours but becomes significant as the temperature is decreased through longwave cooling. Before the end of the simulation, the cloud is in mixed phase when the IN concentration is 1 and 10 g -1 but dissipates when the IN concentration is 100 g -1 . In the case where two types of IN are considered, ice formation persists throughout the simulation. Analysis shows that as the more efficient IN are continuously removed through ice formation, the less efficient IN gradually nucleate more ice crystals because the longwave cooling decreases the cloud temperature. This mechanism is further illustrated with a simple model. These results indicate that a spectrum of IN efficiency is necessary to maintain the persistent ice formation in AMCs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N 2 O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N 2 O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N 2 O fluxes from US cropland. Trained and validated on ~12,000 N 2 O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N 2 O at both training (R 2 = 0.84, RMSE = 16.4 g N ha −1 d −1 ) and held-out testing sites (R 2 = 0.84, RMSE = 6.2 g N ha −1 d −1 ). Analyses identified six dominant N 2 O drivers: soil organic carbon (SOC), NH 4 + , NO 3 - , water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N 2 O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N 2 O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

AI↗
DOE OSTI2022

Understanding Solution State Conformation and Aggregate Structure of Conjugated Polymers via Small Angle X-ray Scattering

Donor-acceptor (D-A) conjugated polymers are high-performance organic electronic materials that exhibit complex aggregation behavior. Understanding the solution state conformation and aggregation of conjugated polymers is crucial for controlling morphology during thin-film deposition and the subsequent electronic performance. However, a precise multiscale structure of solution state aggregates is lacking. Here, we present an in-depth small-angle X-ray scattering (SAXS) analysis of the solution state structure of an isoindigo-bithiophene-based D-A polymer (PII-2T) in chlorobenzene and decane as our primary system. Modeling the system as a combination of hierarchical fibrillar aggregates mixed with dispersed polymers, we extract information about conformation and multiscale aggregation and also clarify the physical origin of features often observed but unaddressed or misinterpreted in small-angle scattering patterns of conjugated polymers. The persistence length of the D-A polymer extracted from SAXS agrees well with a theoretical model based on the dihedral potentials. Additionally, we show that the broad high q structure factor peak seen in scattering profiles can be attributed to lamellar stacking occurring within the fibril aggregates and that the low q aggregate scattering is strongly influenced by the polymer molecular weight. Overall, the SAXS profiles of D-A polymers in general exhibit a sensitive dependence on the co-existence of fibrillar aggregate and dispersed polymer chain populations. We corroborate our findings from SAXS with electron microscopy of freeze-dried samples for direct imaging of fibrillar aggregates. Finally, we demonstrate the generality of our approach by fitting the scattering profiles of a variety of D-A polymers based on thieno-isoindigo (PTII-2T), diketopyrrolopyrrole (DPP2T-TT, DPP-BTZ, PDPP2FT-C- 16 ), naphthalenediimide (P(NDI2OD-T2)), and a conjugated block copolymer P3HT-b-DPPT-T. The results presented here establish a picture of the D-A polymer solution state structure and provide a general method of interpreting and analyzing their scattering profiles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

RECOVERY FROM GIANT ERUPTIONS IN VERY MASSIVE STARS

We use a hydro-and-radiative-transfer code to explore the behavior of a very massive star (VMS) after a giant eruption—i.e., following a supernova impostor event. Beginning with reasonable models for evolved VMSs with masses of 80 M{sub ⊙} and 120 M{sub ⊙}, we simulate the change of state caused by a giant eruption via two methods that explicitly conserve total energy. (1) Synthetically removing outer layers of mass of a few M{sub ⊙} while reducing the energy of the inner layers. (2) Synthetically transferring energy from the core to the outer layers, an operation that automatically causes mass ejection. Our focus is on the aftermath, not the poorly understood eruption itself. Then, using a radiation-hydrodynamic code in 1D with realistic opacities and convection, the interior disequilibrium state is followed for about 200 years. Typically the star develops a ∼400 km s{sup −1} wind with a mass loss rate that begins around 0.1 M{sub ⊙} yr{sup −1} and gradually decreases. This outflow is driven by κ-mechanism radial pulsations. The 1D models have regular pulsations but 3D models will probably be more chaotic. In some cases a plateau in the mass-loss rate may persist about 200 years, while other cases are more like η Car which lost >10 M{sub ⊙} and then had an abnormal mass loss rate for more than a century after its eruption. In our model, the post-eruption outflow carried more mass than the initial eruption. These simulations constitute a useful preliminary reconnaissance for 3D models which will be far more difficult.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Verification and Validation of Simulation Framework for Analysis of Traumatic Brain Injury

Many U.S. troops deployed in Iraq and Afghanistan have suffered blast-related, closed-head injuries as a result of being within non-lethal distance of a detonated improvised explosive device (IED). Consequently, we are conducting a modeling & simulation investigation, coupled with clinical diagnostics of brain injury, into the mechanisms associated with blast to the human head that give rise to traumatic brain injury (TBI). In particular, we are attempting to identify the specific conditions of focused stress wave energy within the brain, resulting from blast loading to the head, which define a threshold for persistent brain injury. As these threshold injury conditions are identified, one can then employ a modeling & simulation toolset to assess the efficacy of various helmet designs to mitigate blast exposure that would otherwise lead to traumatic brain injury. In this report, we describe our efforts to establish the role of stress wave interactions in the genesis of traumatic brain injury (TBI) from exposure to explosive blast. We discuss our effort to correlate simulation predictions with clinical assessments of TBI subjects whose brain injuries have been diagnosed by advanced magnetic resonance imaging techniques. This work has been funded through the U.S. Naval Health Research Center, Office of Naval Research, Mr. James Mackiewicz, project funding manager.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Quantifying the role of land-atmosphere feedbacks in mediating near-surface temperature persistence

Weather persistence from month to month is an important source of predictability on the intraseasonal time-scale. Persistence in the lower troposphere is thought to be mainly due to dynamical feedbacks between the atmosphere and physical variables at or below the surface. Many recent improvements of dynamical seasonal forecasting models have been due to better initialisations and representations of the lower boundary. While many physical mechanisms that mediate persistence are well-understood, they have not previously been considered simultaneously in a causal framework. Here we use linear methods that are widely used in the field of causal inference in statistics to identify mediators between persistent near-surface temperature anomalies from one month to the next. The variables considered are soil temperature and soil moisture at four levels, and snow depth. In Northern Europe, the high persistence of temperature in summer is shown to be mediated mainly by soil temperature anomalies. The high persistence in winter is mediated by snow depth and soil moisture in addition to soil temperature. The annual cycles of persistence are also investigated for the mid- and high latitudes of the Northern Hemisphere, confirming the important role of soil temperature in summer. Snow depth is an important mediator when, and where, its interannual variability is large: in winter at midlatitudes and in spring and autumn at high latitudes. The novel contribution of this study is the introduction of a linear causal methodology and a holistic approach that allows quantification and comparison of the different roles of soil variables and snow depth in mediating near-surface temperature persistence.

Meteorology & Atmospheric Sciences↗
NASA NTRS1988

Mentat: An object-oriented macro data flow system

Mentat, an object-oriented macro data flow system designed to facilitate parallelism in distributed systems, is presented. The macro data flow model is a model of computation similar to the data flow model with two principal differences: the computational complexity of the actors is much greater than in traditional data flow systems, and there are persistent actors that maintain state information between executions. Mentat is a system that combines the object-oriented programming paradigm and the macro data flow model of computation. Mentat programs use a dynamic structure called a future list to represent the future of computations.

Grimshaw, Andrew S.↗
NASA NTRS1999

Discovery of a New Soft Gamma Repeater, SGR 1627-41

We report the discovery of a new soft gamma repeater (SGR), SGR 1627-41, and present BATSE observations of the burst emission and BeppoSAX NFI observations of the probable persistent X-ray counterpart to this SGR. All but one burst spectrum are well fit by an optically thin thermal bremsstrahlung (OTTB) model with kT values between 25 and 35 keV. The spectrum of the X-ray counterpart, SAX J1635.84736, is similar to that of other persistent SGR X-ray counterparts. We find weak evidence for a periodic signal at 6.41 s in the light curve for this source. Like other SGRs, this source appears to be associated with a young supernova remnant G337.0-0.1. Based upon the peak luminosities of bursts observed from this SGR, we find a lower limit on the dipole magnetic field of the neutron star B_dipole greater than 5e10(exp 14) Gauss.

Woods, Peter↗