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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Computational model for autophagic vesicle dynamics in single cells

Macroautophagy (autophagy) is a cellular recycling program essential for homeostasis and survival during cytotoxic stress. This process, which has an emerging role in disease etiology and treatment, is executed in four stages through the coordinated action of more than 30 proteins. An effective strategy for studying complicated cellular processes, such as autophagy, involves the construction and analysis of mathematical or computational models. When developed and refined from experimental knowledge, these models can be used to interrogate signaling pathways, formulate novel hypotheses about systems, and make predictions about cell signaling changes induced by specific interventions. Here, we present the development of a computational model describing autophagic vesicle dynamics in a mammalian system. We used time-resolved, live-cell microscopy to measure the synthesis and turnover of autophagic vesicles in single cells. The stochastically simulated model was consistent with data acquired during conditions of both basal and chemically induced autophagy. The model was tested by genetic modulation of autophagic machinery and found to accurately predict vesicle dynamics observed experimentally. Furthermore, the model generated an unforeseen prediction about vesicle size that is consistent with both published findings and our experimental observations. Taken together, this model is accurate and useful and can serve as the foundation for future efforts aimed at quantitative characterization of autophagy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Computational and Experimental Investigation of Interfacial Area in Near-Field Diesel Spray Simulation

The dense spray region in the near-field of diesel fuel injection remains an enigma. This region is difficult to interrogate with light in the visible range and difficult to model due to the rapid interaction between liquid and gas. In particular, modeling strategies that rely on Lagrangian particle tracking of droplets have struggled in this area. To better represent the strong interaction between phases, Eulerian modeling has proven particularly useful. Models built on the concept of surface area density are advantageous where primary and secondary atomization have not yet produced droplets, but rather form more complicated liquid structures. Surface area density, a more general concept than Lagrangian droplets, naturally represents liquid structures, no matter how complex. These surface area density models, however, have not been directly experimentally validated in the past due to the inability of optical methods to elucidate such a quantity. Optical diagnostics traditionally measure near-spherical droplet size far downstream, where the spray is optically thin. Using ultra-small-angle x-ray scattering (USAXS) measurements to measure the surface area and x-ray radiography to measure the density, we have been able to test one of the more speculative parts of Eulerian spray modeling. In conclusion, the modeling and experimental results have been combined to provide insight into near-field spray dynamics.

42 ENGINEERING↗
DOE OSTI2018

Continuous Particulate Filter State of Health Monitoring Using Radio Frequency Sensing

Reliable means for on-board detection of particulate filter failures or malfunctions are needed to meet diagnostics (OBD) requirements. Detecting these failures, which result in tailpipe particulate matter (PM) emissions exceeding the OBD limit, over all operating conditions is challenging. Current approaches employ differential pressure sensors and downstream PM sensors, in combination with particulate filter and engine-out soot models. These conventional monitors typically operate over narrowly-defined time windows and do not provide a direct measure of the filter’s state of health. In contrast, radio frequency (RF) sensors, which transmit a wireless signal through the filter substrate provide a direct means for interrogating the condition of the filter itself. Here, this study investigated the use of RF sensors for the continuous measurement of filter trapping efficiency, which was compared to downstream measurements with an AVL Microsoot Sensor, and a PM sampling probe simulating the geometry and installation configuration of a conventional PM sensor. The study included several particulate filter failure modes, both above and below the OBD threshold. Finally, the results confirmed the use of RF sensors to provide a direct and continuous measure of the particulate filter’s state of health over a range of typical in-use operating conditions, thereby significantly increasing the time window over which filter failures may be detected.

42 ENGINEERING↗
DOE OSTI2018

On the predictability of land surface fluxes from meteorological variables

Previous research has demonstrated that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear toprovide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

58 GEOSCIENCES↗
DOE OSTI2020

The CMIP6 Data Request (DREQ, version 01.00.31)

The data request of the Coupled Model Intercomparison Project Phase 6 (CMIP6) defines all the quantities from CMIP6 simulations that should be archived. This includes both quantities of general interest needed from most of the CMIP6-endorsed model intercomparison projects (MIPs) and quantities that are more specialized and only of interest to a single endorsed MIP.The complexity of the data request has increased from the early days of model intercomparisons, as has the data volume.In contrast with CMIP5, CMIP6 requires distinct sets of highly tailored variables to be saved from each of the more than 200 experiments.This places new demands on the data request information base and leads to a new requirement for development of software that facilitates automated interrogation of the request and retrieval of its technical specifications.The building blocks and structure of the CMIP6 Data Request (DREQ), which have been constructed to meet these challenges, are described in this paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens.Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn,stimulate advances in machine learning as well.

58 GEOSCIENCES↗
DOE OSTI2023

Autonomous Inversion of In Situ Deformation Measurement Data for Injection-Induced Stress Change

Geologic carbon storage (GCS) is likely to play a key part of the global effort to dramatically reduce CO2 emissions and perhaps even reduce atmospheric CO2 concentrations through carbon negative operations. A critical part of effort to commercialize and widely deploy this technology is developing the capability to rapidly assimilate real-time monitoring data into a form that will enable site operators to make decisions to manage the safe and efficient operations. Two of the risks associate with GCS are the risk of inducing fractures in the sealing formations that can create leakage pathways and the risk of inducing earthquakes of sufficient magnitude to cause public concern, property damage, or safety risks. To properly manage these risks the site operator needs to know the initial state of stress, the change in stress induced by injection, and the relationship between operational parameters such as injection rate and pressure and the change in stress. Current methods of estimating the change in stress require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. These characterization methods interrogate the geologic formations using length scales, loading rates or magnitudes that are quite different from those encountered by the actual storage system. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method that uses automatic differentiation and a finite-element based geomechanical model perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. Furthermore, only isotropic linear elasticity is considered in this paper, the approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.

Burghardt, Jeffrey A.↗
DOE OSTI2019

Live-cell imaging reveals enhancer-dependent Sox2 transcription in the absence of enhancer proximity

Enhancers are important regulatory elements that can control gene activity across vast genetic distances. However, the underlying nature of this regulation remains obscured because it has been difficult to observe in living cells. Here, we visualize the spatial organization and transcriptional output of the key pluripotency regulator Sox2 and its essential enhancer Sox2 Control Region (SCR) in living embryonic stem cells (ESCs). We find that Sox2 and SCR show no evidence of enhanced spatial proximity and that spatial dynamics of this pair is limited over tens of minutes. Sox2 transcription occurs in short, intermittent bursts in ESCs and, intriguingly, we find this activity demonstrates no association with enhancer proximity, suggesting that direct enhancer-promoter contacts do not drive contemporaneous Sox2 transcription. Our study establishes a framework for interrogation of enhancer function in living cells and supports an unexpected mechanism for enhancer control of Sox2 expression that uncouples transcription from enhancer proximity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

The BackMAP Python module: how a simpler Ramachandran number can simplify the life of a protein simulator

Protein backbones occupy diverse conformations, but compact metrics to describe such conformations and transitions between them have been missing. This report re-introduces the Ramachandran number ($\mathscr{R}$) as a residue-level structural metric that could simply the life of anyone contending with large numbers of protein backbone conformations (e.g., ensembles from NMR and trajectories from simulations). Previously, the Ramachandran number ($\mathscr{R}$) was introduced using a complicated closed form, which made the Ramachandran number difficult to implement. This report discusses a much simpler closed form of $\mathscr{R}$ that makes it much easier to calculate, thereby making it easy to implement. Additionally, this report discusses how $\mathscr{R}$ dramatically reduces the dimensionality of the protein backbone, thereby making it ideal for simultaneously interrogating large numbers of protein structures. For example, 200 distinct conformations can easily be described in one graphic using $\mathscr{R}$ (rather than 200 distinct Ramachandran plots). Finally, a new Python-based backbone analysis tool—BackMAP—is introduced, which reiterates how $\mathscr{R}$ can be used as a simple and succinct descriptor of protein backbones and their dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1968

Selective video blanking technique

Adverse viewing effects caused by faulty photosensitive elements are eliminated. A linear maximal /or nonmaximal/ sequence generator gives a pseudorandom pulse train to selectively blank the display monitor during specified mosaic interrogation times. The outputs minimize the length of the required shift register generator.

Saboe, M. M.↗
NASA NTRS1968

Developmental solid-state imaging system.

Developmental imaging system for NASA solid state TV camera, describing sensor matrix fabrication and interrogation system mechanization for image readout

Anders, R. A.↗