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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Turbulent Flow Simulation at the Exascale: Opportunities and Challenges Workshop: August 4-5, 2015, Washington, D.C.

This report details the impact exascale will bring to turbulent-flow simulations in applied science and technology. The need for accurate simulation of turbulent flows is evident across the DOE applied-science and engineering portfolios, including combustion, plasma physics, nuclear-reactor physics, wind energy, and atmospheric science. The workshop brought together experts in turbulent-flow simulation, computational mathematics, and high-performance computing. Building upon previous ASCR workshops on exascale computing, participants defined a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

End-to-end Provenance Traceability and Reproducibility Through "Palletized'' Simulation Data

Trusting simulation output is crucial for Sandia's mission objectives. We rely on these simulations to perform our high-consequence mission tasks given our treaty obligations. Other science and modelling needs, while they may not be high-consequence, still require the strongest levels of trust to enable using the result as the foundation for both practical applications and future research. To this end, the computing community has developed work- flow and provenance systems to aid in both automating simulation and modelling execution, but to also aid in determining exactly how was some output created so that conclusions can be drawn from the data. Current approaches for workflows and provenance systems are all at the user level and have little to no system level support making them fragile, difficult to use, and incomplete solutions. The introduction of container technology is a first step towards encapsulating and tracking artifacts used in creating data and resulting insights, but their current implementation is focused solely on making it easy to deploy an application in an isolated "sandbox" and maintaining a strictly read-only mode to avoid any potential changes to the application. All storage activities are still using the system-level shared storage. This project was an initial exploration into extending the container concept to also include storage and to use writeable containers, auto generated by the system, as a way to link the contained data back to the simulation and input deck used to create it.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

End-to-end Provenance Traceability and Reproducibility Through "Palletized'' Simulation Data

Trusting simulation output is crucial for Sandia's mission objectives. We rely on these simulations to perform our high-consequence mission tasks given our treaty obligations. Other science and modelling needs, while they may not be high-consequence, still require the strongest levels of trust to enable using the result as the foundation for both practical applications and future research. To this end, the computing community has developed work- flow and provenance systems to aid in both automating simulation and modelling execution, but to also aid in determining exactly how was some output created so that conclusions can be drawn from the data. Current approaches for workflows and provenance systems are all at the user level and have little to no system level support making them fragile, difficult to use, and incomplete solutions. The introduction of container technology is a first step towards encapsulating and tracking artifacts used in creating data and resulting insights, but their current implementation is focused solely on making it easy to deploy an application in an isolated "sandbox" and maintaining a strictly read-only mode to avoid any potential changes to the application. All storage activities are still using the system-level shared storage. This project was an initial exploration into extending the container concept to also include storage and to use writable containers, auto generated by the system, as a way to link the contained data back to the simulation and input deck used to create it.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FY24 LLNL Laboratory Directed Research and Development (LDRD) Project Accomplishments

Large duration energy storage is the key to couple renewable energy generation with power supply. This project aimed to advance the durability of a low-cost and eco-friendly flow battery technology based on iron chemistry to speed up the technology readiness level rapidly and radically. A novel device which is called “an artificial kidney” was innovated through disruptive research to integrate with the flow battery for rebalancing capacity. Key results at 50 cm2 scale demonstrates that artificial kidney enabled retaining the storage capacity of the flow battery over 100 cycles. Comparison of outcome of this work showing 0% capacity degradation to the state-of-the-art technology corroborates that this novel system is a game changing innovation.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Outcomes from the DOE Workshop on Turbulent Flow Simulation at the Exascale

This paper summarizes the outcomes from the Turbulent Flow Simulation at the Exascale: Opportunities and Challenges Workshop, which was held 4-5 August 2015, and was sponsored by the U.S. Department of Energy Office of Advanced Scientific Computing Research. The workshop objective was to define and describe the challenges and opportunities that computing at the exascale will bring to turbulent-flow simulations in applied science and technology. The need for accurate simulation of turbulent flows is evident across the U.S. Department of Energy applied-science and engineering portfolios, including combustion, plasma physics, nuclear-reactor physics, wind energy, and atmospheric science. The workshop brought together experts in turbulent-flow simulation, computational mathematics, and high-performance computing. Building upon previous ASCR workshops on exascale computing, participants defined a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements ( R 2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower ( R 2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. In conclusion, an extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Radiobiology, Omics and Microdosimetry of Systemic and Targeted Radiotherapeutics Workshop

The Radiobiology, Omics and Microdosimetry of Systemic and Targeted Radiotherapeutics Workshop fostered engagement across disciplines on critical topics that impact the development of targeted radionuclide therapies, bringing together scientific and medical professionals actively involved in radiotherapeutic design, optimization testing, patient treatment, and clinical trials along with basic science researchers, whose novel techniques have the potential to further our understanding of targeted radionuclide therapy. Discussion of the applications of radiotherapeutics to pediatric and adult, brain, prostate and blood cancers, and metastatic disease allowed scientific research to be presented in the context of real-world medical applications and provided an important backdrop to the basic research performed in computers, laboratories, and animal facilities. The workshop format, presentations, and panel discussions were designed to consider a holistic approach to the development of systemic and targeted radiotherapies, integrating radiobiology and microdosimetry, biological assessments on the microscale, and physiologically relevant models of tumor and tissue microenvironments. Important needs highlighted in the workshop include better models to assess the biological effects of radionuclide therapy, novel strategies to target radiotherapeutics to disease sites with high specificity, and new dosimetry techniques that address the importance of tumor microenvironments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Electrochemical Electron Transfer and Proton-Coupled Electron Transfer: Effects of Double Layer and Ionic Environment on Solvent Reorganization Energies

Electron transfer and proton coupled electron transfer (PCET) reactions at electrochemical interfaces play an essential role in a broad range of energy conversion processes. The reorganization energy, which is a measure of the free energy change associated with solute and solvent rearrangements, is a key quantity for calculating rate constants for these reactions. We present a computational method for including the effects of the double layer and ionic environment of the diffuse layer in calculations of electrochemical solvent reorganization energies. This approach incorporates an accurate electronic charge distribution of the solute within a molecular-shaped cavity in conjunction with a dielectric continuum treatment of the solvent, ions, and electrode using the integral equations formalism polarizable continuum model. The molecule-solvent boundary is treated explicitly, but the effects of the electrode-double layer and double layer-diffuse layer boundaries, as well as the effects of the ionic strength of the solvent, are included through an external Green’s function. The calculated total reorganization energies agree well with experimentally measured values for a series of electrochemical systems, and the effects of including both the double layer and ionic environment are found to be very small. This general approach was also extended to electrochemical PCET and produced total reorganization energies in close agreement with experimental values for two experimentally studied PCET systems. This research was supported as part of the Center for Molecular Electrocatalysis, an Energy Frontier Research Center, funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences.

Ghosh, Soumya↗
DOE OSTI2021

How AI Predicts the Untrained and Unseen

Focus Area: Model predictability improvements (Primary); Data optimization (secondary); Data complexity insights (secondary). The Scientific Challenge: If we believe that a future under extreme conditions will look very differently from today, we can likely agree that ML/AI models trained on past and present datasets will not be adequate to make reliable predictions into the future. This is true for water cycling, as well as biogeochemistry and other Earth system components and behaviors. Additionally, ML/AI models are inherently non-physical. Despite the flourishing success of ML/AI in many applications, such as computer vision, natural language process, and gaming, even the most sophisticated AI models don’t understand the very basic physical laws. Therefore, a natural question is: Can we trust ML/AI based predictions of Earth system behaviors that are fundamentally driven by physical laws? So, are physics models with meticulous process representation a better choice? Not exactly. Physical models, when firmly rooted in first principles, work great at predicting behaviors of systems with a well-defined set of boundary conditions and variables. However, as a complex system, the number of parameters and the degree of complexity and dynamics in processes, coupling, and scale dependent emergent behaviors make the Earth system behaviors very challenging to predict with physical models composed of deterministic laws. In addition, due to the lack of fundamental understandings, physical models often implement empirical correlations derived from observations with biases from locality of data generation. Because correlation is not necessarily causation or comply with first principles, scaling of model predictions beyond locality is often invalid. Beyond the limitation of models, physics or AI, our knowledge of the Earth system is limited by the lack of observational technologies and resources. Insufficient data density, dimensionality and diversity only offer a sliced (or projected) view of the Earth system, e.g. Plato’s Cave analogy, limiting our capability to better understand and represent fundamental processes in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

The electronic structure changes and the origin of the enhanced optical properties in N-doped anatase TiO{sub 2}—A theoretical revisit

We have investigated the electronic structure changes and the origin of the enhanced optical properties of N-doped anatase TiO{sub 2} using first-principles density-functional theory calculations. To determine the band gap variations induced by N-doping, we developed a new approach to locate the effective valence band maximum (VBM) by characterizing the degree of localization of the N-induced states in the band structures of various N-doped TiO{sub 2} systems. Our calculations show that the band gap variations are largely affected by the local bonding structures and doping concentration of the substitutional N atoms. As the N content is up to certain level, some local bonding structures can indeed cause band gap reduction due to the formation of band-like delocalized states above the VBM of TiO{sub 2}, while other local bonding configurations may simply form localized impurity states in the band gap. Accordingly, the N-induced localized and delocalized electronic states can exist simultaneously to contribute to the enhanced optical properties of anatase TiO{sub 2}. Our computational approach also provides a new way to investigate the band gap engineering of other wide band gap semiconductor material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Pressurized subsampling system for pressured gas-hydrate-bearing sediment: Microscale imaging using X-ray computed tomography

A pressurized subsampling system was developed for pressured gas hydrate (GH)-bearing sediments, which have been stored under pressure. The system subsamples small amounts of GH sediments from cores (approximately 50 mm in diameter and 300 mm in height) without pressure release to atmospheric conditions. The maximum size of the subsamples is 12.5 mm in diameter and 20 mm in height. Moreover, our system transfers the subsample into a pressure vessel, and seals the pressure vessel by screwing in a plug under hydraulic pressure conditions. In this study, we demonstrated pressurized subsampling from artificial xenon-hydrate sediments and nondestructive microscale imaging of the subsample, using a microfocus X-ray computed tomography (CT) system. In addition, we estimated porosity and hydrate saturation from two-dimensional X-ray CT images of the subsamples.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Botnet behaviour analysis: How would a data analytics-based system with minimum a priori information perform?

Botnets, as one of the most aggressive threats, has used different techniques, topologies, and communication protocols in different stages of their lifecycle since 2003. Hence, identifying botnets has become very challenging specifically given that they can upgrade their methodology at any time. Various detection approaches have been proposed by the cyber-security researchers, focusing on different aspects of these threats. In this work, 5 different botnet detection approaches are investigated. These systems are selected based on the technique used and type of data used where 2 are public rule–based systems (BotHunter and Snort) and the other 3 use machine learning algorithm with different feature extraction methods (packet payload based and traffic flow based). On the other hand, 4 of these systems are based on a priori knowledge while one is using minimum a priori information. The objective here is to evaluate the effectiveness of these approaches under different scenarios (eg, multi-botnet and single-botnet classifications) as well as exploring how a system with minimum a priori information would perform. The goal is to investigate if a system with minimum a priori information could result in a competitive performance compared to systems using a priori knowledge. The evaluation is shown on 24 publicly available botnet data sets. Results indicate that a machine learning–based system with minimum a priori information not only achieves a very high performance but also generalizes much better than the other systems evaluated on a wide range of botnet structures (from centralized to decentralized botnets).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A simple and practical process modeling methodology for pressure swing adsorption

Although many dynamic models exist for the design and simulation of pressure swing adsorption (PSA) processes, these models involve the solution of a complex system of coupled partial differential equations. Process engineers need a simple, practical, and yet robust short-cut model that helps decide whether to implement a PSA system in a process flowsheet. This work presents a “virtual” moving bed modeling methodology that considers only mass and energy balances and adsorption isotherms to describe the cyclic steady state behavior of PSA systems. Similar to tray efficiencies in distillation calculations, adsorption efficiencies are further introduced to account for system “non-ideality.” Finally, a lab-scale air separation system is used to illustrate the application of this modeling methodology.

42 ENGINEERING↗
DOE OSTI2021

FleCSI Intro 2.0 [Slides]

FleCSI is a programming system or framework for developing mulitphysics application codes. FleCSI provides two primary capabilities--runtime abstraction layer and topology data structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.

42 ENGINEERING↗