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At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Numerical modeling of electromagnetic field spatiotemporal evolution to evaluate the effects on calcium carbonate crystallization

Calcium carbonate (CaCO 3 ) scaling is a significant impediment to water systems. Electromagnetic field (EMF) treatment is a promising approach to control scaling owing to its simplicity and low or no energy requirements. However, the underlying mechanisms by which EMF impacts CaCO 3 crystallization remain unclear due to the challenges in measuring the EMFs in feed solutions and the lack of a fundamental understanding of the applied EMFs and the observed physicochemical phenomena. To fill this knowledge gap, a high-fidelity COMSOL model was first developed to simulate EMFs in bulk solutions for three alternating current-induced EMF devices with different configurations and properties. These were then integrated with experimental data to unveil the underlying mechanism by which applied EMFs alter the physicochemical processes. The study revealed that even low-strength EMFs (e.g., electric fields <0.15 V/m and magnetic fields <0.03 mT) promoted CaCO 3 precipitation in bulk solutions. The electric fields created by these EMF devices resulted in higher Lorentz force compared to their induced magnetic fields. In conclusion, the methodology of this study offers the capability to predict the effectiveness of different EMF devices in facilitating crystallization processes, and these mechanistic insights lay the foundation for the smart design of EMF devices for diverse water treatment applications.

COMSOL simulation↗

DOE OSTI2025

Encoding hierarchical 3D architecture through inverse design of programmable bonds

The ability to fabricate materials and devices at small scales by design has resulted in tremendous technological progress. However, the need for engineered three-dimensional (3D) nanoscale materials requires new strategies for organizing nanocomponents. Here we demonstrate an inverse design approach for the assembly of nanoparticles into hierarchically ordered 3D organizations using DNA voxels with directional, addressable bonds. By identifying intrinsic symmetries in repeating mesoscale structural motifs, we prescribe a set of voxels, termed a mesovoxel, that are assembled into target 3D crystals. The relationship between different degrees of encoded information used for voxel bonds and the fidelity of assembly is investigated using experimental and computational methods. We apply this assembly strategy to create periodic 3D nanoparticle ordered organizations, including structures with low-dimensional elements, helical motifs, a nanoscale analogue of a face-centred perovskite crystal and a distributed Bragg reflector based on a crystal with plasmonic and photonic length-scale regimes.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Analytical comparisons of handheld LIBS and XRF devices for rapid quantification of gallium in a plutonium surrogate matrix

This work compares a portable laser-induced breakdown spectroscopy (LIBS) analyzer to a portable X-ray fluorescence (XRF) device for quantification of gallium (Ga) in a plutonium surrogate matrix of cerium (Ce) for the first time. Calibration methods are developed with spectra of Ce–Ga samples from both devices. Here, metrics such as limit of detection (LoD) and mean average percent error (MAPE) are examined to evaluate calibration performance. While the portable LIBS device can yield a nearly instantaneous analytical measurement, its accuracy is hampered by self-absorption. By employing a self-absorption correction and increasing gating delay, LIBS calibrations with errors in the low single percents and LoDs of 0.1% Ga were constructed. The XRF device produces calibrations with superlative sensitivity, yielding LoDs for gallium in the low tens of parts-per-million (ppm), two orders of magnitude lower than the corrected LIBS models. However, a clear trade-off of measurement fidelity is established between the instantaneous analysis of the LIBS device and the minutes-long XRF measurement yielding superior detection limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Bayesian inference of anisotropic 2D small-angle scattering from sparse measurement

Here, we present a Bayesian inference framework for reconstructing anisotropic two-dimensional small-angle scattering (2D SAS) patterns from sparse, noisy, or partially missing data. The method combines a symmetry-aware angular basis with radial Gaussian process priors to enable accurate, training-free interpolation and denoising. Computational benchmarks demonstrate reliable recovery of both isotropic and high-order anisotropic features under severe data reduction. Experimental validations on stretched polymers, sheared wormlike micelles, and carbon fibers show improved fidelity and resolution compared to raw measurements, achieving comparable accuracy with up to 50-fold fewer detected neutrons. This approach enables quantitative structural analysis under low-flux, time-limited, or single-shot conditions, extending the applicability of 2D SAS techniques to compact neutron sources and mechanically driven soft matter systems undergoing transient structural changes.

Tung, Chi-Huan [Oak Ridge National Laboratory (ORN↗

DOE OSTI2026

In situ quantum verification of polarization-stabilized optical channels

The active stabilization of polarization channels is a task of growing importance as quantum networks move to deployed demonstrations over existing fiber infrastructure. However, the uniquely strict requirements for high-fidelity qubit transmission complicate the extent to which classical solutions may apply to future quantum networks, particularly in terms of recognizing noise sources present in low-flux, nonunitary channels. Here we introduce an in situ benchmarking approach that augments a classical polarization tracking system, limited to unitary correction, with simultaneously transmitted quantum light for ancilla-assisted process tomography of the full quantum map. Implemented in a quantum local-area network, our method uses the reconstructed map both to validate the classical compensation and to expose noise sources it fails to capture. A sliding measurement window that continuously updates the estimated quantum process further increases sensitivity to rapid channel fluctuations. Our results should unlock new opportunities for in situ channel characterization in quantum-classical coexistence networks.

Stevens, Matthew L [Arizona State University]↗

DOE OSTI2022

Primitive quantum gates for dihedral gauge theories

We describe the simulation of dihedral gauge theories on digital quantum computers. The nonabelian discrete gauge group $D_N$-- the dihedral group -- serves as an approximation to $U(1)\times\mathbb{Z}_2$ lattice gauge theory. In order to carry out such a lattice simulation, we detail the construction of efficient quantum circuits to realize basic primitives including the nonabelian Fourier transform over $D_N$, the trace operation, and the group multiplication and inversion operations. For each case the required quantum resources scale linearly or as low-degree polynomials in $n=\log N$. We experimentally benchmark our gates on the Rigetti Aspen-9 quantum processor for the case of $D_4$. The fidelity of all $D_4$ gates was found to exceed $80\%$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Breakup dynamics in a pressure-swirl injector for urea-water solution applications: A computational study

The co-optimization of in-cylinder combustion and after-treatment technology has become a major aspect in engine design and development, with the goal of meeting the increasingly restrictive emission regulations in the transportation industry. Selective Catalytic Reduction is a robust technology to control the emission of NO x , and the injection of urea in water solution is the exhaust tailpipe is a key aspect of its operation. The proposed work uses high-fidelity Computational Fluid Dynamics to characterize the atomization dynamics of the liquid jet in relevant cross-flow conditions. The study focuses on a commercial low-pressure (9 bar) pressure-swirl injector which is characterized in its internal geometry through high-resolution X-ray micro-computational tomography. The internal two-phase flow has been modeled according to the volume-of-fluid approach in a large eddy simulation framework and validated against near-nozzle X-ray radiography measurement. Moreover, characterizing the breakup dynamics for the swirling hollow cone formation, and assessing the influence of the cross-flow in the breakup dynamics was completed. The results have been reported proposing Re-Oh maps and probability density functions of the spray kinematics. Higher cross-flow momentum generates an increase in the jet intact length and a reduction of the liquid droplet diameters. The axial momentum of the jet is affected by the cross-flow already in the near-nozzle region, determining a relevant deviation of the spray velocities. In conclusion, this work aims to inform the initialization of Eulerian-Lagrangian spray models through the assignment of droplet kinematics and static one-way coupling between volume-of-fluid results and Lagrangian spray parcels, to be used for system-size domain simulations.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Investigation of modified uni-traveling carrier photodiode for cryogenic microwave photonic links

Quantum devices present the potential for unparalleled computing and communications capabilities; however, the cryogenic temperatures required to successfully control and read out many qubit platforms can prove to be very challenging to scale. Recently, there has emerged an interest in using microwave photonics to deliver control signals down to ultracold stages via optical fiber, thereby reducing thermal load and facilitating dense wavelength multiplexing. Photodetectors can then convert this optical energy to electrical signals for qubit control. The fidelity of the quantum operations of interest therefore depend heavily upon the characteristics of the photodiode, yet experimental demonstrations of fiber-coupled photodetection systems at low temperatures are relatively few in number, leaving important open questions regarding how specific detectors may perform in real-world cryogenic settings. In this work, we examine a highly linear modified uni-traveling carrier photodiode (MUTC-PD) under C-band illumination (1530–1565 nm) at three temperature regimes (300 K, 80 K, and ∼4 K) and multiple bias conditions. Our findings of reduced responsivity but preserved bandwidth are consistent with previous studies, while our saturation tests suggest a variety of potential applications for MUTC-PDs in cryogenic microwave photonics with and without electrical bias. Overall, our results should provide a valuable foundation for the continued and expanding use of this detector technology in quantum information processing.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Hydrogen Storage for Flexible Fossil Fuel Power Generation: Integration of Underground Hydrogen Storage with Gas Turbine (Final Report)

As the nation continues to encourage, through market structures and financial incentives, the proliferation of intermittent renewable electricity, how to optimize the ever-changing electric grid and identify means to retain and improve resilience, while ensuring continued reductions in GHG emissions, will be critical. According to Bloomberg, wind & solar generated 10.5% of US electricity in 2020 and that percentage continues to grow. In support of expanding renewable energy use, and to address its intermittent nature, this project will develop the Hydrogen Storage for Flexible Fossil Fuel Power Generation platform that is dispatchable, reliable, repeatable and have the ability to produce zero or negative carbon power while interfacing with geology capable of CO2 and hydrogen storage. GTI Energy (GTIE) and team members Illinois State Geological Survey (ISGS), Mitsubishi Heavy Industries America (MHIA), Ameren Illinois, Hexagon Purus, and the Low Carbon Resources Initiative (LCRI) completed a Phase I Conceptual Study under contract DE-FE0032012 for Hydrogen Storage for Flexible Fossil Fuel Power Generation: Integration of Underground Hydrogen Storage with Gas Turbine. The Hydrogen Storage for Flexible Fossil Fuel Power Generation platform addresses the intermittent nature of the expanding use of Variable Renewable Energy (VRE) generation. The low cost of the electricity (COE) generated results in greater dispatch and more operation at higher power levels (higher efficiency), fewer short intervals, and fewer start/stop cycles. The reliable, resilient system can produce zero carbon power and store hydrogen. It will demonstrate hydrogen storage in geologic formations like those used in natural gas underground storage thus enabling large scale storage of hydrogen in sedimentary strata across the United States rather than in geographically restricted salt caverns. The Phase I study confirmed the system is feasible and generates power at lower cost than other low carbon approaches. The demonstration defines the pathway for broad commercial application and will accelerate the development of larger systems suitable for centralized utility scale electricity production. The study advanced the maturity of the H 2 storage-based system with flexible power generation by completing a Pre-FEED study (Phase II). The Pre-FEED focused on the selected Energy Farm on the University of Illinois Urbana-Champaign (UIUC) site that includes above ground and underground hydrogen storage, low-carbon hydrogen production (GTI’s Compact Hydrogen Generator, CHG) with underground CO2 sequestration, and a 40-MW class gas turbine. The Pre-FEED addressed the entire system and its interconnection to the natural gas and electric grid and mitigation of key risks, such as storage behavior, load-following, and system operation. During Phase 1 of the project, the team completed key tasks, which moved the entire demonstration project, specific components and approaches closer to commercialization. These Phase I Accomplishments include: Completing System Requirements Review; Completing System Layout and Modeling - Heat & Mass Balance and Process Flow Diagram; Completing modelling of 9 turbine performance cases; Evaluating rock strata for underground storage of hydrogen and sequestration of carbon dioxide; Completing initial modelling of underground storage of hydrogen and withdrawal with evaluation of loss and water production; Identifying roadable storage for above ground hydrogen storage; Identifying existing electrical infrastructure for receiving/delivering electricity; Identifying existing gas supply infrastructure for receiving natural gas; Document concept design/development plans in required reports. Conclusions: The 12-month Feasibility study in Phase I study was completed and confirmed the system is feasible and generates power at lower cost than other low carbon approaches and even lower cost than the reference NGCC plant without carbon capture when taking advantage of 45Q carbon credits. The study enabled the fidelity of the concept to be improved and allowed identification of the requirements for the system. Defining the individual system and component requirements was performed via the system requirements review with the whole team. These requirements were then incorporated into and iterated with our Heat & Mass Balance process model and process flow diagrams were generated to reflect the overall system. This information was then used to complete the TEA and show economic feasibility. Large scale non-salt geologic storage of hydrogen is an enabling technology for a hydrogen-fired turbine that can be retrofitted into large-scale electric generating units (EGU). Our demonstration will include 428 MWh or ~4 hours full load of hydrogen storage (above and underground). Carbon capture inherent to the CHG process can capture 90% CO 2 (with upgrades to >98%). This system provides a COE of 23% savings relative to an NGCC with a post combustion amine system. Our proposed storage system decouples carbon capture and hydrogen production from power production; therefore, we expect our proposed system’s efficiency and variable COE to be superior resulting in overall higher dispatch and reduced deep cycling. Our demonstration will be full to multi-day hydrogen storage and has the potential for longer (seasonal) duration commercially. The demonstration defines the pathway for broad commercial application and will accelerate the development of larger systems suitable for centralized utility scale electricity production.

03 NATURAL GAS↗

DOE OSTI2023

EMT and Phasor-Domain Co-simulation of a Low Frequency AC Transmission System for Offshore Wind Integration

This work investigates system level interactions of multi-terminal low frequency high-voltage alternating current (LF-HVac) system using back-to back converters and grid-level 60 Hz system. A co-simulation framework by integrating high- fidelity dynamic model of multi-terminal LF-HVac system in electromagnetic transient (EMT) simulation platform and phasor model of IEEE 39 bus system in transient-stability (phasor) program. The co-simulation framework for multi-terminal LF- HVac system with offshore wind integrated in a bulk power system is developed successfully and its steady state and dynamic operation is demonstrated and compared with EMT simulation. The co-simulation results show accurate representation of the dynamic responses with reduced computation burden and time compared to EMT platform.

low-frequency transmission, co-simulation, electro↗

DOE OSTI2025

Radiation damage effects in beryllium for next generation neutrino beam targetry (Final Technical Report)

Current and future high-power accelerators put severe requirements on materials used for target and beam windows and target facilities have been recognized as a critical challenge in development of future particle accelerators. In accelerators, window and target materials are exposed to extreme conditions, which include bombardment with very high energy protons (1- 100 GeV) and thermomechanical shock waves. Radiation can cause direct damage in the material, and it leads to production of transmutation products (especially helium), both phenomena having a potential adverse effect on the stability and durability of the target/window material. At high enough temperatures, He can aggregate to form gas bubbles, which in turn cause significant dimensional changes (swelling), enable easy crack propagation, and eventually cause failure by fracture. On the other hand, if the temperature is too low, radiation damage accumulates in the form of internal defects (e.g., dislocations), leading to hardening and a decreased ductility of the material. In this project, we will focus on beryllium since it is considered to be one of the candidate materials for beam windows and targets in the next-generation proton accelerators, e.g., the Long Baseline Neutrino Facility (LBNF). Radiation effects in Be have been studied in the context of nuclear fusion reactor applications. However, key differences exist between reactor and accelerator conditions, including neutron vs. proton irradiation, continuous vs. pulsed beam flux, much higher energies of bombarding particles in accelerators, and higher operating temperatures for typical reactors. For example, the impact of beam pulsing on the radiation damage and the He bubble kinetics is largely unknown. While results obtained on Be from fusion research might not be directly transferrable to understanding target materials, there is an opportunity to bring state-of-the-art tools from materials research in nuclear reactors to aid design of target and beam window materials in high-power accelerators. To this end, the overarching goal of this project are to develop an experimentally-validated computational framework capable of predicting radiation damage evolution in beryllium relevant to beam window and target conditions, focusing on He bubble formation and growth as a function of irradiation temperature. Our model will be based on the cluster dynamics formalism, where size distribution of defects and He bubbles is simulated as a function of time, temperature, and radiation dose. Parameters for the model will be taken from published experiments and from high-fidelity atomistic simulations proposed in this project. In addition, we will carry out a series of targeted ex-situ and in-situ dual-beam experiments using low-energy protons to provide critical data for validation of the model on the effects of radiation on He clustering, He bubble distribution, and dislocation loop density/size in proton irradiated Be.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Innovative Symmetrical Electrolyte Architecture Enables Ultra-Flat and Thermal Resilient Protonic Ceramic Electrochemical Cells

Protonic ceramic electrochemical cells (PCECs) represent a promising class of solid‐state energy conversion devices capable of high‐efficiency hydrogen production and power generation. However, the practical deployment of planar PCECs is fundamentally constrained by severe structural deformation and mechanical failure during fabrication, stemming from asymmetric shrinkage between the thin electrolyte and the thick NiO‐based support layer. Here, in this work, a functionally integrated, symmetry‐engineered double‐sided electrolyte (DE) design is unveiled, which not only suppresses thermally induced curvature but also unlocks significant gains in electrochemical performance and stability. This architecture intrinsically balances shrinkage dynamics across the cell bilaterally, enabling the fabrication of ultra‐flat 5 × 5 cm 2 cells with sub‐100 µm thickness variation. A numerical solid mechanics simulation is introduced to investigate and interpret this achievement. Beyond structural advantages, the DE configuration enhances the cell operational stability, delivering a low open‐circuit voltage degradation of 9.5 mV/100 h across 80 thermal cycles. This work establishes a compelling paradigm wherein architectural symmetry directly translates to both mechanical fidelity and functional enhancement, offering a promising route toward PCECs scale‐up.

08 - HYDROGEN↗

DOE OSTI2026

Hourly PM 2.5 Estimates across California from 2018 to 2023

This study presents a new data set of hourly PM 2.5 concentrations across California from 2018 to 2023 at a three-kilometer resolution. This data set was developed by assimilating observations from PurpleAir and the U.S. EPA Air Quality System monitors into wildfire smoke forecasts from the High-Resolution Rapid Refresh Smoke (HRRR-Smoke) model using the Gridpoint Statistical Interpolation (GSI) three-dimensional variational data assimilation framework. Archived forecasts of modeled wildfire smoke PM 2.5 from HRRR-Smoke create the background field for assimilation, which is then corrected using surface observations of total PM 2.5 . The resulting reanalysis from GSI provides an estimate of total PM 2.5 that minimizes error from both the observational and the model data. Validation results indicate strong performance, with monthly R 2 values ranging from 0.73 to 0.91 across the six-year data set, comparable to other PM 2.5 data sets. Case studies are presented for three major fire events, the 2018 Camp Fire, 2019 Kincade Fire, and 2020 Lightning Complex Fires to demonstrate the data set’s fidelity in resolving plume dynamics and local exposure patterns. Root-mean-squared error averaged over each month scales with average PM 2.5 concentrations, resulting in a low error under typical conditions but higher absolute errors during extreme smoke events. This is the first long-term, hourly PM 2.5 data set of its kind for California and enables the generation of subdaily exposure metrics, such as peak hourly concentrations, exceedance durations, and time-of-day exposure peaks. The novelty and strong validation of this data set make it a compelling resource for future studies on the impact and significance of subdaily PM 2.5 exposure.

PM2.5↗

DOE OSTI2021

Report on High-Fidelity Dynamic Modeling of a Coal-Fired Steam Power Plant

As a result of the growth of renewables including solar and wind energy with fluctuating production, fossil fuel power plants are being required to cycle between high and low power production. This cycling is both at a greater frequency and over a wider range than in the past. In many cases, power plants are not designed for this type of cycling operation but can nonetheless endure these challenging operation requirements under the right conditions. In this project, optimal solution for enhanced flexible operations are being investigated using model based estimation and control techniques. To support the development of model-based estimator and model-based controls at GE Global Research, GE Steam Power configured a dynamic model using a reference steam plant design including the boiler, turbine, and water/steam conditioning systems as well as the controls needed for plant cycling with stability and reliability. The dynamic model was built using the APROS® software from VTT, and then calibrated to multiple load conditions from full load (100%TMCR) to partial loads (75%TMCR, 50%TMCR and 25%TMCR) based on internally developed steady state heat balance models at the unit level. These internal heat balance models are based on first principles and extensive engineering experiences from GE Steam Power as an OEM and a services provider. This topical report presents the structure of the unit level dynamic model, the tuning process, and representative simulation results from typical load cycling simulations using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

FAST.Farm Development and Validation of Structural Load Prediction Against Large Eddy Simulations

FAST.Farm is a mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on several principles of the dynamic wake meandering (DWM) model, but also addresses limitations of previous DWM implementations. Previous FAST.Farm studies have shown the similarities and differences between FAST.Farm and large eddy simulations for rigid turbine cases. The objective of this work is to quantify the ability of FAST.Farm to accurately predict turbine structural response in a small wind farm. This is done by comparing FAST.Farm structural response to SOWFA-OpenFAST results for three laterally-aligned turbines. The purpose of this study is to characterize the similarities and differences between FAST.Farm and a higher fidelity model for predicting turbine structural response in a wind farm for differing atmospheric inflows. Strong statistical agreement was found between FAST.Farm and SOWFA-OpenFAST structural response for the non-waked upstream turbine, and good agreement was found for the downstream turbines for most structural quantities. Higher differences were seen for downstream turbines with low ambient turbulence intensity or yawed turbines, suggesting areas for FAST.Farm wake dynamics modeling improvements. For all cases and turbines, small statistical differences were seen between blade deflections and bending moments, with larger differences for tower-top and tower-base bending moments. Overall, the results establish confidence for applying FAST.Farm to wind farm power and loads analyses and identify areas where further model validations and model improvements should be targeted.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2024

Parametric reduced-order modeling for component-oriented treatment and localized nonlinear feature inclusion

Abstract We propose coupling a physics-based reduction framework with a suited response decomposition technique to derive a component-oriented reduction (COR) approach, which is suitable for assembly systems featuring localized nonlinearities. Dependencies on influencing parameters are injected into the reduced-order model (ROM), thus ensuring robustness and validity over a domain of parametric inputs, while capturing nonlinear effects. The implemented approach employs individual component modes to capture localized features while additionally relying on reduced modes of a global nature to approximate the system’s dynamics accurately. The global modes are derived from a linear monolithic system, defined as a result of a coordinate separation scheme, which permits the proposed COR-ROM to naturally couple the response between linear and nonlinear subdomains. The derived low-order representation utilizes a proper orthogonal decomposition projection and is additionally reinforced with the inclusion of a hyper-reduction technique to capture the underlying high-fidelity model response while providing accelerated computations. The resulting approach is exemplified in the synthetic case studies of a four-story shear frame with multiple nonlinear regions driven by hysteresis and a large-scale kingpin connection featuring plasticity.

Vlachas, Konstantinos (ORCID:000000029124219X)↗

DOE OSTI2020

Online adaptive basis refinement and compression for reduced-order models via vector-space sieving

In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions to high-fidelity full-order models (FOMs). However, there is no a priori assurance that these approximate solutions are accurate; their accuracy depends on the ability of the low-dimensional trial basis to represent the FOM solution. As a result, ROMs can generate inaccurate approximate solutions, e.g., when the FOM solution at the online prediction point is not well represented by training data used to construct the trial basis. To address this fundamental deficiency of standard model-reduction approaches, this work proposes a novel online-adaptive mechanism for efficiently enriching the trial basis in a manner that ensures convergence of the ROM to the FOM, yet does not incur any FOM solves. The mechanism is based on the previously proposed adaptive $h$-refinement method for ROMs, but improves upon this work in two crucial ways. First, the proposed method enables basis refinement with respect to any orthogonal basis (not just the Kronecker basis), thereby generalizing the refinement mechanism and enabling it to be tailored to the physics characterizing the problem at hand. Second, the proposed method provides a fast online algorithm for periodically compressing the enriched basis via an efficient proper orthogonal decomposition (POD) method, which does not incur any operations that scale with the FOM dimension. We report these two features allow the proposed method to serve as (1) a failsafe mechanism for ROMs, as the method enables the ROM to satisfy any prescribed error tolerance online (even in the case of inadequate training), and (2) an efficient online basis-adaptation mechanism, as the combination of basis enrichment and compression enables the basis to adapt online while controlling its dimension.

42 ENGINEERING↗

DOE OSTI2021

Physics-constrained, low-dimensional models for magnetohydrodynamics: First-principles and data-driven approaches

Plasmas are highly nonlinear and multiscale, motivating a hierarchy of models to understand and describe their behavior. However, there is a scarcity of plasma models of lower fidelity than magnetohydrodynamics (MHD), although these reduced models hold promise for understanding key physical mechanisms, efficient computation, and real-time optimization and control. Galerkin models, obtained by projection of the MHD equations onto a truncated modal basis, and data-driven models, obtained by modern machine learning and system identification, can furnish this gap in the lower levels of the model hierarchy. This work develops a reduced-order modeling framework for compressible plasmas, leveraging decades of progress in projection-based and data-driven modeling of fluids. We begin by formalizing projection-based model reduction for nonlinear MHD systems. To avoid separate modal decompositions for the magnetic, velocity, and pressure fields, we introduce an energy inner product to synthesize all of the fields into a dimensionally consistent, reduced-order basis. Next, we obtain an analytic model by Galerkin projection of the Hall-MHD equations onto these modes. We illustrate how global conservation laws constrain the model parameters, revealing symmetries that can be enforced in data-driven models, directly connecting these models to the underlying physics. We demonstrate the effectiveness of this approach on data from high-fidelity numerical simulations of a three-dimensional spheromak experiment. Finally, this manuscript builds a bridge to the extensive Galerkin literature in fluid mechanics and facilitates future principled development of projection-based and data-driven models for plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗