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At least 307 records · Page 17

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NASA NTRS2012

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery

Space Propulsion↗

NASA NTRS2020

The case for a multi-channel polarization sensitive LIDAR for investigation of insolation-driven ices and atmospheres

All LIDAR instruments are not the same, and advancement of LIDAR technology requires an ongoing interest and demand from the community to foster further development of the required components. The purpose of this white paper is to make the decadal survey panel aware of the need for further technical development, and the potential payoff of investing experimental time, money and thought into the next generation of LIDARs. Technologies for development: We advocate for future development of LIDAR technologies to measure the polarization state of the reflected light at selected multiple wavelengths, chosen according to the species of interest (e.g., H2O and CO2 in the Martian setting).Key scientific questions: In the coming decade, dollars spent on these LIDAR technologies will go towards addressing key climate questions on Mars and other rocky bodies, particularly those with seasonally changing (i.e. insolation driven) plumes of multiple icy volatiles such as Mars, Enceladus, Triton, or Pluto, and insolation-driven dust lifting, such as cometary bodies and the Moon. We will show from examining past Martian and terrestrial lidars that orbital and landed LIDARs can be effective for producing new insights into insolation-driven processes in current planetary climate on several bodies, beyond that available to our current fleet of largely passive instruments on planetary missions

Adrian Jon Brown↗

NASA NTRS2021

Design and Development of Nano-electro Fuel Batteries and Rim-driven Motors for Electrified Aircraft Applications

The National Aeronautics and Space Administration Convergent Aeronautics Solutions project seeks to determine feasibility of emerging technologies in aeronautics. The Aqueous, QUick-Charging Battery Integration For Electric flight Research study pursued the feasibility of nano-electro fuel and rim-driven motor technologies integrated together for aircraft implementation. Development of the NEF technology was in partnership with Influit Energy, LLC (Chicago, Illinois). A rim-driven motor, designed by the project team, sized to represent one of 24 motors in the propulsion system of the tandem electric super-short takeoff and landing aircraft is discussed. The development of this electric super-short takeoff and landing concept was in partnership with The Boeing Company (Chicago, Illinois). The integrated system design of the nano-electro fuel and rim-driven motor technologies within the wing section was in partnership with Empirical Systems Aerospace, Inc. (ESAero) (San Luis Obispo, California). The feasibility benchmark for the nano-electro fuel battery was to attain 100 mA/sq. cm within two years, a 50-fold increase over the demonstrated cell performance before the Convergent Aeronautics Solutions Aqueous, QUick-Charging Battery Integration For Electric flight Research activity. The team achieved 85 mA/sq. cm in the nano-electro fuel flow cell at the end of the two years. The Aqueous, QUick-Charging Battery Integration For Electric flight Research project team determined the technology to be both inflammable and nonexplosive (unless multiple, simultaneous system failures occur) which would provide a safer alternative to conventional lithium-ion based battery systems.

Kurt V. Papathakis↗

NASA NTRS2023

Historical Changes in Wind-Driven Ocean Circulation Can Accelerate Global Warming

Mitigation and adaptation strategies for climate change depend on accurate climate projections for the coming decades. While changes in radiative heat fluxes are known to contribute to surface warming, changes to ocean circulation can also impact the rate of surface warming. Previous studies suggest that projected changes to ocean circulation reduce the rate of global warming. However, these studies consider large greenhouse gas forcing scenarios, which induce a significant buoyancy-driven decline of the Atlantic Meridional Overturning Circulation. Here, we use a climate model to quantify the previously unknown impact of changes to wind-driven ocean circulation on global surface warming. Wind-driven ocean circulation changes amplify the externally forced warming rate by 17% from 1979 to 2014. Accurately simulating changes to the atmospheric circulation is key to improving near-term climate projections.

wind stress↗

NASA NTRS2022

Hybrid simulations of a tangential discontinuity-driven foreshock bubble formation in comparison with a hot flow anomaly formation

Hot flow anomalies (HFAs) and foreshock bubbles (FBs) are significant foreshock transients that can accelerate particles and disturb the magnetosphere-ionosphere system. Yet, their early formation mechanisms are still not fully understood. To investigate the formation of tangential discontinuity (TD)-driven FBs and HFAs, we use 2-D local hybrid simulations where a reflected or an injected warm foreshock ion beam can interact with a TD whose half-thickness is comparable to the ion inertial scale. We show that the foreshock ions perform a partial gyration within, or across, the TD. Bulk motion differences between partially-gyrating foreshock ions and fluid-electrons lead to the generation of currents. As the trigger, these foreshock-driven currents change the magnetic field topology around the TD and force the frozen-in solar wind plasma to redistribute along with the field lines, shaping the foreshock transient. This confirms a recently proposed kinetic formation model. The extent of the magnetic field direction change across the TD within the foreshock ion gyromotion determines the current profile and thus the type of foreshock transient that forms. For a thin TD, the foreshock ions generate a current that is much stronger on the upstream side than the downstream side, forming an FB with one upstream compressional boundary. For the same foreshock ion gyroradius and magnetic shear, a thick TD yields comparable foreshock-driven currents on the upstream and downstream sides, forming an HFA with two compressional boundaries. Our study suggests that the TD thickness is one of the factors that determine the formation of FBs and HFAs

Andrew Vu↗

NASA NTRS2023

A Wind-Driven Snow Redistribution Module for Alpine3d V3.3.0: Adaptations Designed for Downscaling Ice Sheet Surface Mass Balance

Ice sheets gain mass via snow accumulation at the ice sheet surface, which is the primary component of surface mass balance. On the Antarctic ice sheet, winds redistribute snow resulting in surface mass balance that is variable in both space and time. Representing wind-driven snow redistribution processes in models is critical for local assessments of surface mass balance, repeat altimetry studies, and interpretation of ice core accumulation records. To this end, we have adapted Alpine3D, an existing distributed snow modeling framework, to downscale Antarctic surface mass balance to horizontal resolutions up to 1 km. In particular, we have introduced a new two-dimensional advection-based wind-driven snow redistribution module that is driven by an offline coupling between WindNinja, a wind downscaling model, and Alpine3D. We then show that large accumulation variability can be at least partially explained by terrain-induced wind speed variations which subsequently redistribute snow around rolling topography. By comparing Alpine3D to airborne-derived snow accumulation measurements within a testing domain over Pine Island Glacier in West Antarctica, we demonstrate that our Alpine3D downscaling approach improves surface mass balance estimates when compared to MERRA-2, a global atmospheric reanalysis which we use as atmospheric forcing. In particular, when compared to MERRA-2, Alpine3D reduces simulated surface mass balance root mean squared error by 23.4 mm w.e.yr−1 (13%) and increases variance explained by 24%. Despite these improvements, Alpine3D still underestimates observed accumulation variability, thus providing an opportunity for future model improvement.

Eric Keenan↗

NASA NTRSDate not supplied

Temporal and Spatial Dynamics of Nitric Oxide Production at High Latitudes During an ICME-driven Storm on Dec. 14, 2006

During geomagnetic storms driven by interplanetary coronal mass ejections (ICMEs), Earth’s upper atmosphere neutral mass density is directly driven by the geomagnetic storm cycle from intense driving and subsequent recovery. The neutral density heating and cooling times are shorter for stronger storms and longer for weaker storms. The influx of energy (Joule Heating and precipitation) into Earth’s upper atmosphere during the storm main phase allows for the enhanced production of nitric oxide (NO) at high latitudes, which in turn cools the thermosphere by radiating away excess energy. As a result, greater NO production results in quicker thermospheric cooling. Yet while the production of NO on a global scale has been examined in detail, the space and time evolution of NO is not well-known. In this study, we investigate the effects of a particular ICME-driven storm, which occurred on Dec. 14th, 2006, on the production of NO at high latitudes. We compare NO measurements from the Thermosphere, Ionosphere, Mesosphere Dynamics (TIMED) spacecraft to neutral mass density measurements from the Challenging Minisatellite Payload (CHAMP) spacecraft and find that the impact of the shock prior to the storm, not just the onset of the storm itself, is responsible for an increase in NO production. We also observe a dawn-dusk asymmetry in high-latitude NO production and discuss how the geometry of the solar wind driver may play a role in creating this asymmetry. Finally, we discuss how the improved hemispheric and local time coverage provided by the GDC and DYNAMIC missions will enable more robust studies of the spatiotemporal evolution of NO during storms. Since NO is an important factor in regulating the neutral density of the thermosphere, such studies will be relevant for more accurate orbital drag calculations.

Kevin Delano↗

NASA NTRS2023

Natural sunlight-driven oxidation of Mn 2+ (aq) and heterogeneous formation of Mn oxides on hematite

The oxidation of dissolved Mn 2+ (aq) plays a critical role in driving manganese cycles and regulating the fate of essential elements and contaminants in environmental systems. Based on sluggish oxidation rate, abiotic processes have been considered less effective oxidation pathway for manganese oxidation in environmental systems. Interestingly, a recent study (Jung et al., 2021) has shown that the rapid photochemical oxidation of Mn 2+ (aq) could be a feasible scenario to uncover the potential significance of abiotic Mn 2+ (aq) oxidation. Nevertheless, the significance of photochemical oxidation of Mn 2+ (aq) under natural sunlight exposure remains unclear. Here, we demonstrate the rapid photocatalytic oxidation of Mn 2+ (aq) and the heterogeneous growth of tunnel-structured Mn oxides under simulated freshwater and seawater conditions in the presence of natural sunlight and hematite. The natural sunlight-driven photocatalytic oxidation of Mn 2+ (aq) by hematite showed kinetic constants of 1.02 h -−1 and 0.342 h −1 under freshwater and seawater conditions, respectively. The natural sunlight-driven photocatalytic oxidation rates are quite comparable to the results obtained from the previous laboratory test using artificial sunlight, which has ∼4.5 times stronger light intensity. It is likely because of ∼5.5 times larger light exposure area in the natural sunlight-driven photocatalytic oxidation than that of the laboratory test using artificial sunlight. We also elucidate the roles of cation species in controlling the oxidation rate of Mn 2+ (aq) and the crystalline structure of Mn oxide products. Specifically, in the presence of large amounts of cations, the oxidation rate of Mn 2+ (aq) was slower likely because of competitive adsorption. Furthermore, our findings highlight that Mn 2+ contributes significantly to the formation of large-tunneled Mn oxides. These results illuminate the importance of abiotic photocatalytic processes in controlling the redox chemistry of Mn in real environmental aqueous systems on the oxidation of Mn 2+ (aq), and provide an environmentally sustainable approach to effectively remediate water contaminated with Mn 2+ (aq) using natural sunlight.

Junyeong Choi↗

NASA NTRS2023

Effects of Optimisation Parameters on Data-Driven Magnetofrictional Modelling of Active Regions

Context . The solar magnetic field plays an essential role in the formation, evolution, and dynamics of large-scale eruptive structures in the corona. The estimation of the coronal magnetic field, the ultimate driver of space weather, particularly in the ‘low’ and ‘middle’ corona, is presently limited due to practical difficulties. Data-driven time-dependent magnetofrictional modelling (TMFM) of active region magnetic fields has been proven to be a useful tool to study the corona. The input to the model is the photospheric electric field that is inverted from a time series of the photospheric magnetic field. Constraining the complete electric field, that is, including the non-inductive component, is critical for capturing the eruption dynamics. We present a detailed study of the effects of optimisation of the non-inductive electric field on the TMFM of AR 12473. Aims . We aim to study the effects of varying the non-inductive electric field on the data-driven coronal simulations, for two alternative parametrisations. By varying parameters controlling the strength of the non-inductive electric field, we wish to explore the changes in flux rope formation and their early evolution and other parameters, for instance, axial flux and magnetic field magnitude. Methods . We used the high temporal and spatial resolution cadence vector magnetograms from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). The non-inductive electric field component in the photosphere is critical for energising and introducing twist to the coronal magnetic field, thereby allowing unstable configurations to be formed. We estimated this component using an approach based on optimising the injection of magnetic energy. Results . Our data show that flux ropes are formed in all of the simulations except for those with the lower values of these optimised parameters. However, the flux rope formation, evolution and eruption time varies depending on the values of the optimisation parameters. The flux rope is formed and has overall similar evolution and properties with a large range of non-inductive electric fields needed to determine the non-inductive electric field component that is critical for energising and introducing twist to the coronal magnetic field. Conclusions . This study shows that irrespective of non-inductive electric field values, flux ropes are formed and erupted, which indicates that data-driven TMFM can be used to estimate flux rope properties early in their evolution without needing to employ a lengthy optimisation process.

A. Kumari↗

DOE OSTI2020

Data-driven modeling and control of dynamical systems using Koopman and Perron-Frobenius operators

This dissertation studies the data-driven modeling and control problem of nonlinear systems by exploiting the linear operator theoretic framework involving Koopman and Perro-Frobenius operator. A systematic linear-operator based controller design procedure has been established, which can be used to solve a variety of nonlinear control problems, including feedback stabilization using control Lyapunov functions, optimal quadratic regulation using Koopman eigenfunctions and convex optimization formulation of optimal control problem using P-F and Koopman operator approximation. As the core of data-driven modeling, we first propose a new algorithm for the finite-dimensional approximation of the linear transfer Koopman and Perron-Frobenius operator from time-series data. We argue that the existing approach for the finite-dimensional approximation of these transfer operators such as Dynamic Mode Decomposition (DMD) and Extended Dynamic Mode Decomposition (EDMD) do not capture two important properties of these operators, namely positivity and Markov property. The algorithm we propose preserves these two properties. We call the proposed algorithm as naturally structured DMD (NSDMD) since it retains the inherent properties of these operators. Naturally structured DMD algorithm leads to a better approximation of the steady-state dynamics of the system regarding computing Koopman and Perron- Frobenius operator eigenfunctions and eigenvalues. However, preserving positivity property is critical for capturing the real transient dynamics of the system. This positivity property of the transfer operators and it's finite-dimensional approximation play an important role for controller and estimator design of nonlinear systems. To solve the feedback stabilization problem for nonlinear control systems, we tried to take advantage of the Koopman operator framework. The Koopman operator approach provides a linear representation for a nonlinear dynamical system and a bilinear representation for a nonlinear control system. The problem of feedback stabilization of a nonlinear control system is then transformed to the stabilization of a bilinear control system. We propose a control Lyapunov function (CLF)-based approach for the design of stabilizing feedback controllers for the bilinear system. The search for finding a CLF for the bilinear control system is formulated as a convex optimization problem. This leads to a schematic procedure for designing CLF-based stabilizing feedback controllers for the bilinear system and hence the original nonlinear system. Another advantage of the proposed controller design approach outlined in this dissertation is that it does not require explicit knowledge of system dynamics. In particular, the bilinear representation of a nonlinear control system in the Koopman eigenfunction space can be obtained from time-series data. Next, we study the optimal quadratic regulation problem for nonlinear systems. The linear operator theoretic framework involving the Koopman operator is used to lift the dynamics of nonlinear control system to an infinite-dimensional bilinear system. The optimal quadratic regulation problem for nonlinear system is formulated in terms of the finite-dimensional approximation of the bilinear system. A convex optimization-based approach is proposed for solving the quadratic regulator problem for bilinear system. We applied a variety of examples and compared the simulation results between our framework and conventional LQR control using linearized model. For more general optimal control problems, we provide a density-function based convex formulation for the optimal control problem of the nonlinear system. The convex formulation relies on the duality result in the stability theory of a dynamical system involving density function and Perron-Frobenius operator. The optimal control problem is formulated as an infinite-dimensional convex optimization program. The finite-dimensional approximation of the optimization problem relies on the recent advances made in the data-driven computation of the Koopman operator, which is dual to the Perron-Frobenius operator. Simulation results are presented to demonstrate the application of the developed framework.

Huang, Bowen↗

DOE OSTI2023

Data-Driven Model Predictive Control for Temperature Management of Heat Pipe Microreactor

To enable the self-regulating capability of heat pipe (HP) microreactors, an anticipatory control strategy through model predictive control (MPC) could proactively respond to potential disturbances and deviations in operating setpoints. However, a key factor prohibiting the widespread adoption of MPCs in nuclear applications is the effort and computational costs associated with learning and calibrating first-principles-based process models when the target system is complex and when there are gaps between modeled and target reactor systems. In this paper, we demonstrate data-driven MPC using three approaches for modeling the system dynamics, including a linear state-space model, feedforward neural network, and recurrent neural networks long short-term memory. We present the development and validation process of each model and compare the performance of data-driven MPCs in controlling the temperatures of selected HPs at the evaporator and condenser regions in a 37-HP-monolith system. Our results show that, qualitatively, all data-driven MPCs are producing similar control actions, while quantitatively, with artificial neural nets (especially feedforward neural nets), MPC can better follow drastic changes in setpoints with smallest errors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Hierarchical Data-Driven Protection for Microgrid with 100% Renewable Penetration: Preprint

The accurate detection and isolation of faults is critical for the reliable operation of microgrids (MGs). Traditional protection approaches are even more challenged for 100% renewable MGs because inverter-based resources (IBRs) are the only sources for fault current which are usually low and unpredictable/non-uniform. This calls for new protection scheme that can identify IBR fault responses and detect faults in MGs. Data-driven based protection can learn the pattern of IBR fault responses and make the correct decision to identify faults. Therefore, this paper presents a data-driven approach for fault localization in island MGs. The approach builds a training dataset of comprehensive fault scenarios that can be used to learn fault characteristics from processed measurements. The localization task is modeled as a binary classification problem at each relay, which simplifies the learning process. Then, a hierarchical decision mechanism is used to identify the fault location. The proposed approach is assessed using an exemplary MG with several grid-forming (GFM) and grid-following (GFL) inverters, where accurate estimation of fault location is achieved. The data-driven based protection approach developed in this paper provides a generic framework and useful guidance for power system protection engineers to achieve reliable protection for MGs with 100% renewables.

artificial intelligence↗

DOE OSTI2023

Aerodynamic Sensitivity of a Novel Data-Driven Airfoil Shape Representation Framework

We explore the aerodynamic implications of a novel data-driven separable shape tensor framework used to represent discrete airfoil shapes. In this study, we construct a data-driven parameter space defined by separable shape tensors and informed by tens of thousands of distinct airfoils. We use this design space to generate new airfoil designs to study parametric sensitivities with respect to various aerodynamic responses. We use a HAM2D RANS solver to approximate the lift, drag, and moment coefficients for the generated airfoils at two different angles-of-attack. We analyze the robustness and sensitivities of using the separable shape tensor design space by examining the coverage of the aerodynamic response space, uncovering low-dimensional polynomial ridge approximations, and computing various sensitivity metrics. The results show that the data-driven design space produce significant variation in target aerodynamic quantities and facilitate highly accurate approximations (R^2 > 0.96) of one- and two-dimensional structures in each aerodynamic response. This further reduces the effective dimension to enable simplified design and optimization tasks.

aerodynamics↗

DOE OSTI2023

Membranes in Solar-Driven Evaporation: Design Principles and Applications

In this study, solar-driven evaporation process brings exciting opportunities to recover clean water and resources in a sustainable way from diverse sources like seawater and wastewater. Separation membranes, as a vital material in many environmental and energy applications, can contribute significantly to this process owing to their structural features. However, the unique roles of membranes in solar evaporator construction and process design are seldom recognized and not summarized yet from scientific principles and application demands, which forms the motivation of this review. Herein, the roles of membranes in different processes based on solar-driven evaporation are focused and the design principles of membrane materials and devices to meet the requirements of these applications are discussed. Fabrication strategies for photothermal membranes are introduced primarily, followed by a discussion on how to design membrane materials, devices, and processes to pursue optimal performance and realize advanced functions accompanied by evaporation. Furthermore, the future of this field is forecast with both challenges and opportunities.

14 SOLAR ENERGY↗

DOE OSTI2024

Autonomous control for Heat-Pipe microreactor using Data-Driven model predictive control

To enable a self-regulating capability for heat pipe (HP) microreactors, an anticipatory control strategy achieved via model predictive control (MPC) could proactively respond to potential disturbances and deviations in operating setpoints. This paper demonstrates data-driven methods for predicting the distribution and transient of temperatures and heat fluxes at selected components and regions in a 37-HP system, based on which the optimal control actions in response to changes in user-defined setpoints can be found. We present the development and validation of linear state-space model, feedfoward, and recurrent neural networks. Here, we compare the performance of MPCs with different modeling approaches in terms of following setpoints for temperatures and averaged output heat fluxes. The accuracies of the three data-driven models are similar, but the control actions initiated by neural-network-based MPC can better adapt to drastic changes in setpoints yet generate the smallest errors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Electrochemical Characterization of Photo-Driven Hole-Scavenging by Cadmium Sulfide Quantum Dot-Nitrogenase Biohybrid Complexes

Light-driven biohybrid systems that couple semiconductor nanocrystals with enzymes offer a promising strategy for solar-to-chemical energy-conserving reduction reactions, yet are often limited by inefficient hole scavenging. Hole scavenging is critical for maintaining charge separation in the light-absorbing electron donor molecule and, thus, for sustaining catalytic turnover, making quantification essential to improving system efficiency. Here, we introduce a photoelectrochemical approach for real-time monitoring of hole scavenging in cadmium sulfide (CdS) quantum dot (QD)-nitrogenase MoFe protein biohybrids that catalyze the reduction of H+ and N2. Using hydroquinone (HQ) as the sacrificial electron donor (SED), oxidation of HQ to benzoquinone (BQ) by photogenerated valence-band holes is coupled to the electrochemical reduction of BQ at an electrode, enabling quantification of the hole-scavenging reaction via chronoamperometry. This approach provides a continuous, real-time readout of charge-transfer dynamics under illumination. Systematic variation of light intensity and donor concentration reveals that hole scavenging scales with photon flux and exhibits a non-linear dependence on SED concentration, with an optimum at intermediate HQ concentrations. These results establish chronoamperometry as a powerful tool for probing photoinduced charge transfer, advancing the quantitative understanding of hole-scavenging in biohybrid systems, and offering a generalizable framework for optimizing solar-driven biocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Data-driven learning of nonlocal physics from high-fidelity synthetic data

A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. Here, we extract nonlocal models from data, circumventing these challenges and providing data-driven justification for the resulting model form. Extracting data-driven surrogates is a major challenge for machine learning (ML) approaches, due to nonlinearities and lack of convexity — it is particularly challenging to extract surrogates which are provably well-posed and numerically stable. Our scheme not only yields a convex optimization problem, but also allows extraction of nonlocal models whose kernels may be partially negative while maintaining well-posedness even in small-data regimes. To achieve this, based on established nonlocal theory, we embed in our algorithm sufficient conditions on the non-positive part of the kernel that guarantee well-posedness of the learnt operator. These conditions are imposed as inequality constraints to meet the requisite conditions of the nonlocal theory. We demonstrate this workflow for a range of applications, including reproduction of manufactured nonlocal kernels; numerical homogenization of Darcy flow associated with a heterogeneous periodic microstructure; nonlocal approximation to high-order local transport phenomena; and approximation of globally supported fractional diffusion operators by truncated kernels.

42 ENGINEERING↗

DOE OSTI2022

Data-driven, multi-moment fluid modeling of Landau damping

Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in the systems. In this work, we apply a deep learning architecture to learn fluid partial differential equations (PDEs) of a plasma system based on the data acquired from a fully kinetic model. Here, the learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effect such as Landau damping. Based on the learned fluid closure, the data-driven, multi-moment fluid modeling can well reproduce all the physical quantities derived from the fully kinetic model. The calculated damping rate of Landau damping is consistent with both the fully kinetic simulation and the linear theory. The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve the fluid closure and reduce the computational cost of multi-scale modeling of global systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗