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NASA NTRS1987

Long term orbital storage of cryogenic propellants for advanced space transportation missions

A comprehensive study has developed the major features of a large capacity orbital propellant depot for the space-based, cryogenic OTV. The study has treated both the Dual-Keel Space Station and co-orbiting platforms as the accommodations base for the propellant storage facilities, and trades have examined both tethered and hard-docked options. Five tank set concepts were developed for storing the propellants, and along with layout options for the station and platform, were evaluated from the standpoints of servicing, propellant delivery, boiloff, micrometeoroid/debris shielding, development requirements, and cost. These trades led to the recommendation that an all-passive storage concept be considered for the platform and an actively refrigerated concept providing for reliquefaction of all boiloff be considered for the Space Station. The tank sets are modular, each storing up to 45,400 kg of LO2/LH2, and employ many advanced features to provide for microgravity fluid management and to limit boiloff. The features include such technologies as zero-gravity mass gauging, total communication capillary liquid acquisition devices, autogenous pressurization, thermodynamic vent systems, thick multilayer insulation, vapor-cooled shields, solar-selective coatings, advanced micrometeoroid/debris protection systems, and long-lived cryogenic refrigeration systems.

Schuster, John R.↗

NASA NTRS2020

Toward Coupled Data Assimilation in NASA’s GEOS: Developments in the Ocean Context

The Global Modeling & Assimilation Office (GMAO) at NASA GSFC produces analyses and predictions of the Earth system using various configurations of the Goddard Earth Observing System (GEOS) model and assimilation system. The current sub-seasonal-to-seasonal prediction system (GEOS-S2S) is based on a coupled atmosphere-ocean-land-ice configuration of GEOS which includes the Modular Ocean Model version 5 (MOM5) run at approximately 50-km resolution and a de-coupled OI-based ocean analysis that uses an initialization of MOM5 forced by the MERRA-2 reanalysis. GMAO will soon implement an updated GEOS-S2S system that will run at 25-km resolution and adopt aspects of the hybrid four-dimensional ensemble-variational (H4DEnVar) system already running in the production-version atmospheric analysis system, including a Local Ensemble Transform Kalman Filter (LETKF) to provide initial conditions for the oceanic state. This presentation will focus on developments to sustain the GMAO's systems on longer time horizons, where more radical transformations will be required to adapt to advanced computing environments, higher resolution and more diverse model components, and new observations for the Earth system. Results will describe progress toward a version of the GEOS coupled system that will be based around the Joint Effort for Data assimilation Integration (JEDI) framework being developed within Joint Center for Satellite Data Assimilation (JCSDA) and include an updated ocean model, MOM6. Discussion will focus specifically on the use of a Unified Forward Operator (UFO) for simulating observations and the Object Oriented Prediction System (OOPS) for providing the state estimate. These features are being developed as a multi-agency effort under the auspices of the JCSDA and are being adopted in the GMAO for all its applications of coupled data assimilation including S2S, numerical weather prediction, and reanalysis.

Mahajan, Rahul↗

DOE OSTI2019

Development of a Hybrid Lagrangian–Eulerian Model to Describe Spark-Ignition Processes at Engine-Like Turbulent Flow Conditions

With the engine technology moving toward more challenging (highly dilute and boosted) operation, spark-ignition processes play a key role in determining flame propagation and completeness of the combustion process. On the computational side, there is plenty of spark-ignition models available in literature and validated under conventional, stoichiometric spark ignition (SI) operation. Nevertheless, these models need to be expanded and developed on more physical grounds since at challenging operation they are not truly predictive. This paper reports on the development of a dedicated model for the spark-ignition event at nonquiescent, engine-like conditions, performed in the commercial CFD code converge. The developed methodology leverages previous findings that have expanded the use and improved the accuracy of Eulerian-type energy deposition models. In this work, the Eulerian energy deposition is coupled at every computational time-step with a Lagrangian-type evolution of the spark channel. Typical features such as spark channel elongation, stretch, and attachment to the electrodes are properly described to deliver realistic energy deposition along the channel during the entire ignition process. The numerical results are validated against schlieren images from an optical constant volume chamber and show the improvement in the simulation of the spark channel during the entire ignition event, with respect to the most commonly used energy deposition approach. Further developmental pathways are discussed to provide more physics-based features from the developed ignition model in the future.

42 ENGINEERING↗

DOE OSTI2017

A multi-machine scaling of halo current rotation

Halo currents generated during unmitigated tokamak disruptions are known to develop rotating asymmetric features that are of great concern to ITER because they can dynamically amplify the mechanical stresses on the machine. This paper presents a multi-machine analysis of these phenomena. More specifically, data from C-Mod, NSTX, ASDEX Upgrade, DIII-D, and JET are used to develop empirical scalings of three key quantities: the machine-specific minimum current quench time, $ \newcommand{\tauCQ}{\tau_{{\rm CQ}}} \tauCQ$; the halo current rotation duration, $ \newcommand{\trot}{t_{{\rm rot}}} \newcommand{\tr}{{\rm tr}} \trot$ ; and (3) the average halo current rotation frequency, $ \newcommand{\favg}{\langle f_{{\rm h}} \rangle} \favg$ . These data reveal that the normalized rotation duration, $ \newcommand{\tauCQ}{\tau_{{\rm CQ}}} \newcommand{\trot}{t_{{\rm rot}}} \newcommand{\tr}{{\rm tr}} \trot/\tauCQ$ , and the average rotation velocity, $ \newcommand{\vavg}{\langle {v}_{{\rm h}} \rangle} \vavg$ , are surprisingly consistent from machine to machine. Furthermore, comparisons between carbon and metal wall machines show that metal walls have minimal impact on the behavior of rotating halo currents. Finally, upon projecting to ITER, the empirical scalings indicate that substantial halo current rotation above $ \newcommand{\favg}{\langle \,f_{{\rm h}} \rangle} \favg=20$ Hz is to be expected. More importantly, depending on the projected value of $ \newcommand{\tauCQ}{\tau_{{\rm CQ}}} \tauCQ$ in ITER, substantial rotation could also occur in the resonant frequency range of 6–20 Hz. In conclusion, the possibility of damaging halo current rotation during unmitigated disruptions in ITER cannot be ruled out.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

A multi-machine scaling of halo current rotation

Halo currents generated during unmitigated tokamak disruptions are known to develop rotating asymmetric features that are of great concern to ITER because they can dynamically amplify the mechanical stresses on the machine. This paper presents a multi-machine analysis of these phenomena. More specifically, data from C-Mod, NSTX, ASDEX Upgrade, DIII-D, and JET are used to develop empirical scalings of three key quantities: (1) the machine-specific minimum current quench time, tauCQ; (2) the halo current rotation duration, trot; and (3) the average halo current rotation frequency, . These data reveal that the normalized rotation duration, trot/tauCQ, and the average rotation velocity, , are surprisingly consistent from machine to machine. Furthermore, comparisons between carbon and metal wall machines show that metal walls have minimal impact on the behavior of rotating halo currents. Finally, upon projecting to ITER, the empirical scalings indicate that substantial halo current rotation above = 20 Hz is to be expected. More importantly, depending on the projected value of tauCQ in ITER, substantial rotation could also occur in the resonant frequency range of 6-20 Hz. As such, the possibility of damaging halo current rotation during unmitigated disruptions in ITER cannot be ruled out.

Halo currents↗

NASA NTRSDate not supplied

On A Higher Order Method for Anonymous Feature Processing

Some feature-driven navigation sources, such as cameras or lidars, often require measurement-to-feature associations between the collected data and an onboard feature catalog to be performed upstream of the filter. Standard navigation practice suggests the use of Kalman updates with measurements that have first passed residual editing tests, but this is often insufficient to prevent updates based upon incorrectly associated data, leading to filter degradation and divergence. Recent work has developed the anonymous feature processing (AFP) technique that eliminates reliance upon explicit feature associations outside of the filter entirely while maintaining desirable estimation performance. This paper continues by exploring the approximation employed by AFP, and a higher order approximation is presented to further improve the estimation performance of the AFP update.

James S Mccabe↗

DOE OSTI2018

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.

Biochemistry & Molecular Biology↗

NASA NTRS1991

Scale interactions and diabatic processes during the development of an Atlantic blocking event

Results of a case study of the development of a major blocking event over the North Atlantic Ocean are reported with emphasis on the potential roles of interactions between synoptic-scale systems and diabatic processes in the large-scale development. The approach used here involves the analysis of the evolution of the potential temperature on a constant potential vorticity (PV) surface, where the value of PV is chosen to be representative of that of the tropopause. The approach is shown to provide a simple way to qualitatively identify significant features in the development of the block, including scale interactions and diabatic processes.

Morgan, Michael C.↗

NASA NTRS2003

Integrating Oil Debris and Vibration Measurements for Intelligent Machine Health Monitoring

A diagnostic tool for detecting damage to gears was developed. Two different measurement technologies, oil debris analysis and vibration were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig. An oil debris sensor and the two vibration algorithms were adapted as the diagnostic tools. An inductance type oil debris sensor was selected for the oil analysis measurement technology. Gear damage data for this type of sensor was limited to data collected in the NASA Glenn test rigs. For this reason, this analysis included development of a parameter for detecting gear pitting damage using this type of sensor. The vibration data was used to calculate two previously available gear vibration diagnostic algorithms. The two vibration algorithms were selected based on their maturity and published success in detecting damage to gears. Oil debris and vibration features were then developed using fuzzy logic analysis techniques, then input into a multi sensor data fusion process. Results show combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spur gears. As a result of this research, this new diagnostic tool has significantly improved detection of gear damage in the NASA Glenn Spur Gear Fatigue Rigs. This research also resulted in several other findings that will improve the development of future health monitoring systems. Oil debris analysis was found to be more reliable than vibration analysis for detecting pitting fatigue failure of gears and is capable of indicating damage progression. Also, some vibration algorithms are as sensitive to operational effects as they are to damage. Another finding was that clear threshold limits must be established for diagnostic tools. Based on additional experimental data obtained from the NASA Glenn Spiral Bevel Gear Fatigue Rig, the methodology developed in this study can be successfully implemented on other geared systems.

Dempsey, Paula J.↗

NASA NTRSDate not supplied

Generic Programming in Fortran 202Y

Back in 2018, the Fortran committee began planning new features for what is now expected to be Fortran 2023, "generic programming" was at the top of the list of requests from the user community. Here, generic programming refers to the ability to specify algorithms that are parameterized in terms of deferred _types_ (as opposed to values). Concrete algorithms are subsequently generated when the generic algorithms are "instantiated" with specific types. After significant consideration the language committee decided that the necessary changes to Fortran to support generic programming would be too large and complex to complete within a single release cycle of the standard. Instead, a new "generics" subgroup was formed with myself as the lead, and we were charged with developing generic programming features for the subsequent release _after_ the F2023, internally referred to as Fortran 202Y. I.e, the new subgroup was provided the luxury of developing the new capabilities across two full release cycles of the standard. This talk will describe the current approach to generics being pursued by the subgroup as well as the current status halfway through the development cycle. I will conclude with some motivating examples implemented with notional syntax.

Fortran↗

DOE OSTI2018

Nanoscale organization in the fluorinated room temperature ionic liquid: Tetraethyl ammonium (trifluoromethanesulfonyl)(nonafluorobutylsulfonyl)imide

Fluorinated Room Temperature Ionic Liquids (FRTILs) are a branch of ionic liquids that is the object of growing interest for a wide range of potential applications, due to the synergic combination of specifically ionic features and those properties that stem from fluorous tails. So far limited experimental work exists on the micro- and mesoscopic structural organization in this class of compounds. Such a work is however necessary to fully understand morphological details at atomistic level that would have strong implications in terms of bulk properties. In this work, we use the synergy between X-ray and neutron scattering together with molecular dynamics simulations to access structural details of a technologically relevant FRTIL that is characterised by an anion bearing a long enough fluorinated tail to develop specific morphological features. In particular, we find the first experimental evidence that in FRTILs bearing an asymmetric bis(perfluoroalkyl)sulfonyl-imide anion, fluorous side chains tend to be spatially segregated into nm-scale spatial heterogeneities. This feature together with the well-established micro-segregation of side alkyl chains in conventional RTILs leads to the concept of triphilic ILs, whose technological applications are yet to be fully developed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Mild hybridisation of turboprop engine with high-power-density integrated electric drives

Here this paper shares with the aerospace community a case study of turboprop mild hybridisation using a recently developed integrated drive system in the University of Nottingham, UK, within the ACHIEVE project under EU H2020 CleanSky 2 program (project No. 737814). The developed drive system enables green taxiing of a turboprop aircraft while on the ground with its engine off, and as an electrical generator when the turboprop is in the air. The entire system is designed to be able to integrate within the power auxiliary gear box (PAGB) of a turboprop aircraft. Some of the key features of the developed system include a high-speed permanent magnet machine (up to 14,200rpm) with dual three-phase design, SiC-based high power density (11.8kW/L for the power converter, 35.3kW/L and 7.2kW/kg for the machine active parts), integrated cooling design for high-temperature operation (>130°C ambient temperature), fault tolerance consideration with dual channel operation capabilities and sensorless control for entire operational conditions. This paper is giving an overview of the design process of the electrical machine, power converters, and its cooling of the entire drive. Numerical analysis (FEM and CFD) and some experimental results are presented to demonstrate the effectiveness and the desired performance of the developed integrated drive system.

42 ENGINEERING↗

DOE OSTI2022

Ambassador Project (Final Report)

SEL, in collaboration with its partners, performed research, development and demonstration of a trust, data, and resource management software layer enabling multiple software applications from different suppliers to operate in a Software Defined Infrastructure. The Energy sector has successfully deployed Software Defined Networking (SDN) technology and is rapidly scaling deployments. One major benefit of SDN is the abstracted centralized nature of the orchestration and management of the network. This architecture enables end users to deploy new technology and capabilities as quickly as they perform firmware upgrades to field devices. When an end user needs to roll trucks to install new equipment, they simply deploy new software to run on top of this SD-Infrastructure. This has attracted many software companies to develop value added features and introduce new capabilities, which is all good for the industry but has also introduced new challenges, including how to manage trust between these software applications, how to manage the data integrity and access, and how to manage access to the network resources each of these software applications are demanding. Completion of this project developed and demonstrated a safe and reliable resource scheduler and cybersecurity trust management communication platform allowing multiple software applications to operate in a SD-Infrastructure resulting in the commercialization of a software eco-system and architecture that maintains interoperability, cybersecurity, and reliability where the end user can safely scale out their software to leverage the powerful benefits of SDN.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

SIMULATeQCD: A simple multi-GPU lattice code for QCD calculations

The rise of exascale supercomputers has fueled competition among GPU vendors, driving lattice QCD developers to write code that supports multiple APIs. Moreover, new developments in algorithms and physics research require frequent updates to existing software. These challenges have to be balanced against constantly changing personnel. At the same time, there is a wide range of applications for HISQ fermions in QCD studies. This situation encourages the development of software featuring a HISQ action that is flexible, high-performing, open source, easy to use, and easy to adapt. In this technical paper, we explain the design strategy, provide implementation details, list available algorithms and modules, and show key performance indicators for SIMULATeQCD, a simple multi-GPU lattice code for large-scale QCD calculations, mainly developed and used by the HotQCD collaboration. The code is publicly available on GitHub.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

EMRALD Technology Advancements for Commercial Grade Dedication Readiness

Event Modeling Risk Assessment using Linked Diagrams (EMRALD) is a software tool developed at Idaho National Laboratory for researching the capabilities of dynamic probabilistic risk assessment. It provides a simple interface to represent complex interactions often seen when developing dynamic models. EMRALD can also interface with other applications by modifying inputs as well as running and using their results within EMRALD for dynamic and integrated assessment. To enable wider industry use cases, collaborative work between Idaho National Laboratory and FPoliSolutions was performed through the technology commercialization fund TCF-20-21448. This report covers the additional features and capabilities developed under this work.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

A concentrated solar cavity absorber with direct heat transfer through recirculating metallic particles

A recirculating flow solar particle cavity absorber (receiver) is modeled to investigate the flow behavior and heat transfer characteristics of a novel developing concept. It features a continuous recirculating flow of non-reacting metallic particles (black silicon carbide) with air which are used as a thermal enhancement medium. The aim of the present study is to numerically investigate the thermal behavior and flow characteristics of the proposed concept. The proposed solar particle receiver is modeled using two phase discrete particle model (DPM), RNG k-flow model and discrete ordinate (DO) radiation model. Numerical analysis is carried out considering a solar receiver with only air and the mixture of non-reacting particles and air as a heat transfer as well as heat carrying medium. The parametric investigation is conducted considering the incident solar flux on the receiver aperture and changing air flow rate and recirculation rate inside the receiver. A stand-alone feature of the recirculating flow solar particle receiver concept is that the particles are directly exposed to concentrated solar radiation monotonously through recirculating flow inside the receiver and results in efficient irradiation absorption and convective heat transfer to air that help to achieve high temperature air and consequently increase in thermal efficiency. This paper presents, results from the developed concept and highlights its flow behavior and potential to enhance the heat transfer from metallic particles to air by maximizing heat carrying capacity of the heat transfer medium. The imposed milestones for the present system will be helpful to understand the radiation absorption mechanism of the particles in a recirculating flow based receiver, the thermal transport between the particles, the air and the cavity, and the fluid dynamics of the air and particle in the cavity.

14 SOLAR ENERGY↗

DOE OSTI2020

GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models

Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multilayered case. In this article, we consider the problem of semisupervised learning with multilayered graphs. Though deep network embeddings, e.g., DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the interlayer dependences for building multilayered graph embeddings. Using empirical studies on several benchmark data sets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison with the state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Improving deep learning performance for predicting large-scale geological ${{CO}_{2}}$ sequestration modeling through feature coarsening

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

58 GEOSCIENCES↗