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At least 469 records · Page 26

An overview of wind-energy-production prediction bias, losses, and uncertainties

Abstract. The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the reported average energy prediction bias is declining. Herein, we present a literature review of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary of how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the article.

17 WIND ENERGY↗

Modeling the role of local crystallographic correlations in microstructures of Ti-6Al-4V using a correlated structure visco-plastic self-consistent polycrystal plasticity formulation

This paper presents a multi-level crystal plasticity-based simulation framework for modeling mechanical response and microstructure evolution of Ti-6Al-4V with α-lath/lamellar microstructures. Here, the model is a correlated structure visco-plastic self-consistent (CS-VPSC) formulation linking three scales: a single crystals micro-scale, a lath/lamellar colony meso-scale, and a lath/lamellar aggregate macro-scale. A selected hardening law for the evolution of critical resolved shear stress per slip system used in CS-VPSC is phenomenological. However, it adjusts the resistances of basal and prismatic slip systems based on the geometry of slip transfer between adjacent lamellae. Consistent with experimental evidences, the resolved shear stress on the pyramidal slip planes is dependent not only on the stress in the direction of slip but also on the two orthogonal shear stress components and the three normal stress components (non-Schmid effects). Electron backscatter diffraction (EBSD) data in conjunction with a procedure relying on α→β phase transformation is used to construct paired variants of α-laths/lamellae satisfying their local crystallographic correlations. The procedure fits volume fractions of individual laths/lamellae with the experimental EBSD data and selects a distribution of habit planes between adjacent variants with respect to the loading direction. The simulation framework is applied to interpret the deformation behavior in tension and compression along two sample directions of Ti-6Al-4V fabricated via laser powder bed fusion. Moreover, the model is used to simulate texture evolution during rolling of the material to large strains. It is demonstrated that the model is capable of predicting plastic anisotropy/asymmetry and the concomitant texture evolution. While the model reveals a significant effect of habit plane inclination with respect to the loading direction on yield stress, the comparison of the data and model predictions shows that a random distribution of habit planes fits the flow response. It is further inferred that the tension-compression asymmetry arises from the non-Schmid effects.

36 MATERIALS SCIENCE↗

Linking thermoset ink rheology to the stability of 3D-printed structures

Thermoset polymer composites show promise for additive manufacturing (AM) applications to address some of the limitations of the more widely used thermoplastic feedstock materials. Thermosets offer attractive mechanical properties while providing excellent interlayer bonding, high thermal and chemical stability, and reduced energy consumption as a result of deposition at room temperature. However, since thermoset resins rely on a crosslinking reaction to solidify, rather than quickly cooling like thermoplastics, viscoelastic properties must be relied upon to maintain deposited shape after deposition until crosslinking can occur. This fact has not impeded development and characterization of new thermoset feedstocks on the small scale, but recent efforts to increase scale of thermoset printing have highlighted issues with structural stability under self-weight. This study addresses issues of self-weight by investigating the mechanisms that cause collapse of tall, thin printed walls. Using nanoclay- and fumed silica-filled epoxy feedstocks, this work compares the collapse height for printed walls to stability models based on yielding and buckling mechanics. Inputs for these models – shear yield stress and storage modulus – were taken directly from parallel plate rheometry measurements. Model predictions were found to be in good agreement with experimental results, where both yielding and buckling behavior were observed, provided the rheological properties after a shear excursion were used as inputs. This work establishes a direct link between basic rheological properties of the feedstock, geometry of the printed object, and achievable height. Overall, the results presented highlight the importance of understanding recovery behavior in thermoset feedstocks and provide valuable guidance on the development of more effective direct-ink writing feedstock materials.

36 MATERIALS SCIENCE↗

Importance of bulk excitations and coherent electron-photon-phonon scattering in photoemission from PbTe(111): Ab initio theory with experimental comparisons

This paper presents a fully ab initio many-body photoemission framework that includes coherent three-body electron-photon-phonon scattering to predict the transverse momentum distributions and the mean transverse energies (MTEs) of bulk photoelectrons from single-crystal photocathodes. The need to develop such a theory stems from the lack of studies that provide complete understanding of the underlying fundamental processes governing the transverse momentum distribution of photoelectrons emitted from single crystals. For example, initial predictions based on density-functional theory calculations of effective electron masses suggested that the (111) surface of PbTe would produce very small MTEs (≤ 15 meV), whereas our experiments yielded MTEs ten to twenty times larger than these predictions and also exhibited a lower photoemission threshold than predicted. The ab initio framework presented in this paper correctly reproduces both the magnitude of the MTEs from our measurements in PbTe(111) and the observed photoemission below the predicted threshold. Our results show that both photoexcitations into states that propagate in the bulk of the material and coherent many-body electron-photon-phonon scattering processes, which initial predictions ignored, play surprisingly important roles in photoemission from PbTe(111). Lastly, from the lessons learned, we recommend a procedure for rapid computational screening of potential single-crystal photocathodes for applications in next-generation ultrafast electron diffraction and X-ray free-electron lasers, which will enable significant advances in condensed matter research.

36 MATERIALS SCIENCE↗

R –8 Dispersion Interaction: Derivation and Application to the Effective Fragment Potential Method

The anisotropic and isotropic R –8 dispersion contributions (disp8) are derived and implemented within the framework of the effective fragment potential (EFP) method formulated with imaginary frequency-dependent Cartesian polarizability tensors distributed at the centroids of the localized molecular orbitals (LMOs). Two forms of damping functions, intermolecular overlap-based and Tang–Toennies, are extended for disp8. To obtain LMO polarizability tensors centered at LMO centroids, an origin-shifting transformation is derived and implemented for the dipole–octopole polarizability tensor and the quadrupole–quadrupole polarizability tensor. The analytic gradient is derived and implemented for the isotropic disp8 contribution. Relative to the previously implemented empirical EFP disp8 energy, the isotropic disp8 component of the interaction energy improves the overall agreement of the EFP dispersion energies with the symmetry-adapted perturbation theory (SAPT) benchmarks, reducing the mean absolute errors (MAEs) and mean absolute percentage errors for most of the databases examined in this work. Further, while the anisotropic disp8 can further enhance the accuracy of the EFP dispersion energy and yield smaller MAEs, significantly overbound dispersion energies are predicted by the anisotropic disp8 when the maximum element in the intermolecular overlap matrix is greater than 0.1, possibly due to the breakdown of the approximations made in the EFP dispersion derivation at a short range. For potential energy scan databases, the newly developed EFP dispersion model with isotropic disp8 yields the overall correct curvature and good agreement with SAPT benchmarks around equilibrium and longer but overestimates the dispersion interactions at a short range. While the overlap-based dispersion-damping functions produce better MAEs than Tang–Toennies damping functions, further improvement is needed to better screen the large attractive dispersion energies at a short range (overlap >0.1).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Data from Unique Contributions of Chlorophyll and Nitrogen to Predict Crop Photosynthetic Capacity from Leaf Spectroscopy

The photosynthetic capacity or the CO2-saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that Vmax can be well predicted from spectra (r=0.81). The integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of Vmax (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to Vmax prediction.

Biomass Analytics↗

Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels

Abstract Irradiation increases the yield stress and embrittles light water reactor (LWR) pressure vessel steels. In this study, we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux, high fluence, extended life conditions. The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence, plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations, up to very high fluence. Notably, the machine learning model predictions for the high fluence, intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models. The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence. Similar approaches, applied to expanded databases, could be used to predict hardening in LWRs under life-extension conditions.

36 MATERIALS SCIENCE↗

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

The photosynthetic capacity or the CO 2 -saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C 4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that V max can be well predicted from spectra (r=0.81). Here, the integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of V max (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to V max prediction.

59 BASIC BIOLOGICAL SCIENCES↗

Multiplicity dependence of ${\Xi }_{\text{c}}^{+}$ and ${\Xi }_{\text{c}}^{0}$ production in pp collisions at $\sqrt{s}=13$ TeV

The first measurement at midrapidity (|y| < 0.5) of the production yield of the strange-charm baryons $Ξ$$^{+}_{c}$ and $Ξ$$^{0}_{c}$ as a function of transverse momentum (p T ) in different charged-particle multiplicity classes in proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ALICE experiment at the LHC is reported. The $Ξ$$^{+}_{c}$ baryon is reconstructed via the $Ξ$$^{+}_{c}$ → $Ξ$ – π + π + decay channel in the range 4 < p T < 12 GeV/c, while the $Ξ$$^{0}_{c}$ baryon is reconstructed via both the $Ξ$$^{0}_{c}$ → $Ξ$ – π + and $Ξ$$^{0}_{c}$ → $Ξ$ – e + ν e decay channels in the range 2 < p T < 12 GeV/c. The baryon-to-meson ($Ξ$$^{0}_{c}$ + /D 0 ) and the baryon-to-baryon ($Ξ$$^{0}_{c}$ + /Λ$^{+}_{c}$) production yield ratios show no significant dependence on multiplicity. In addition, the observed yield ratios are not described by theoretical predictions that model charm-quark fragmentation based on measurements at e + e − and e − p colliders, indicating differences in the charm-baryon production mechanism in pp collisions. A comparison with different event generators and tunings, including different modelling of the hadronisation process, is also discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗

DFT-guided flux synthesis of a family of layered titanates crystallizing in the lepidocrocite structure type

K 0.72 Fe 0.72 Ti 1.28 O 4 , a layered mixed-metal oxide belonging to the lepidocrocite structure type, was grown out of a molten KCl-KF mixture at 800 °C. The compound crystallizes in orthorhombic space group Cmcm and exhibits two-dimensional layers, composed of mixed Fe/Ti octahedra, separated by K ions that occupy the interlayer layer space and maintain charge balance. The targeted flux synthesis of the Ni, Cu, and Zn analogs was carried out after density functional theory (DFT) calculations predicted their formation to be energetically favorable, yielding K 0.77 Ni 0.38 Ti 1.62 O 4 , K 0.77 Cu 0.38 Ti 1.62 O 4 , and K 0.73 Zn 0.36 Ti 1.64 O 4 . Finally, all four compositions can be prepared via conventional solid-state technique by mixing and heating stoichiometric amounts of KNO 3 , appropriate transition metal oxide precursors, and TiO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A detailed genome-scale metabolic model of Clostridium thermocellum investigates sources of pyrophosphate for driving glycolysis

Lignocellulosic biomass is an abundant and renewable source of carbon for chemical manufacturing, yet it is cumbersome in conventional processes. A promising, and increasingly studied, candidate for lignocellulose bioprocessing is the thermophilic anaerobe Clostridium thermocellum given its potential to produce ethanol, organic acids, and hydrogen gas from lignocellulosic biomass under high substrate loading. Possessing an atypical glycolytic pathway which substitutes GTP or pyrophosphate (PP i ) for ATP in some steps, including in the energy-investment phase, identification, and manipulation of PP i sources are key to engineering its metabolism. Previous efforts to identify the primary pyrophosphate have been unsuccessful. Here, we explore pyrophosphate metabolism through reconstructing, updating, and analyzing a new genome-scale stoichiometric model for C. thermocellum, iCTH669. Hundreds of changes to the former GEM, iCBI655, including correcting cofactor usages, addressing charge and elemental balance, standardizing biomass composition, and incorporating the latest experimental evidence led to a MEMOTE score improvement to 94%. We found agreement of iCTH669 model predictions across all available fermentation and biomass yield datasets. The feasibility of hundreds of PP i synthesis routes, newly identified and previously proposed, were assessed through the lens of the iCTH669 model including biomass synthesis, tRNA synthesis, newly identified sources, and previously proposed PP i -generating cycles. In all cases, the metabolic cost of PP i synthesis is at best equivalent to investment of one ATP suggesting no direct energetic advantage for the cofactor substitution in C. thermocellum. Even though no unique source of PP i could be gleaned by the model, by combining with gene expression data two most likely scenarios emerge. First, previously investigated PP i sources likely account for most PP i production in wild-type strains. Second, alternate metabolic routes as encoded by iCTH669 can collectively maintain PP i levels even when previously investigated synthesis cycles are disrupted. Model iCTH669 is available at github.com/maranasgroup/iCTH669.

59 BASIC BIOLOGICAL SCIENCES↗

Lightning Interferometry Uncertainty, Beam Steering Interferometry, and Evidence of Lightning Being Ignited by a Cosmic Ray Shower

Abstract We present an uncertainty analysis on correlation‐based time delay estimate, the basis for broadband lightning interferometry. A normal interferometry would yield much higher uncertainties than the theoretically predicted lower bound due to finite correlation window lengths. However, if the signals are aligned in time, the uncertainties approach the lower bound and can be used to indicate the interferometric uncertainties. Based on this, we introduce a beam steering interferometry technique. It first estimates a direction centroid for the lightning sources with a normal interferometry and computes the time delays among the signals. It then shifts the raw data with the time delays to align the signals roughly in time and reprocess the aligned signals. It finally pushes the reprocessed results to their correct positions, based on the time delays estimated in the first step. We apply this technique on a fast positive breakdown (FPB) process that started a normal intracloud lightning. The FPB process is shown to have a much more complex structure and development than a normal interferometry would provide. More importantly, from both interferometric and polarization analyses, the FPB appears to be ignited by a cosmic ray shower (CRS). We estimate the radio frequency strength and frequency content related to a presumed CRS in a thunderstorm electric field and find that they are in agreement with the observations. We examine the electric field effect of the CRS front and find that it could raise the field above the threshold for positive breakdown and is capable of igniting the FPB (and the lightning).

Shao, Xuan‐Min↗

Half-Heusler alloys as emerging high power density thermoelectric cooling materials

Abstract To achieve optimal thermoelectric performance, it is crucial to manipulate the scattering processes within materials to decouple the transport of phonons and electrons. In half-Heusler (hH) compounds, selective defect reduction can significantly improve performance due to the weak electron-acoustic phonon interaction. This study utilized Sb-pressure controlled annealing process to modulate the microstructure and point defects of Nb 0.55 Ta 0.40 Ti 0.05 FeSb compound, resulting in a 100% increase in carrier mobility and a maximum power factor of 78 µW cm −1 K −2 , approaching the theoretical prediction for NbFeSb single crystal. This approach yielded the highest average zT of ~0.86 among hH in the temperature range of 300-873 K. The use of this material led to a 210% enhancement in cooling power density compared to Bi 2 Te 3 -based devices and a conversion efficiency of 12%. These results demonstrate a promising strategy for optimizing hH materials for near-room-temperature thermoelectric applications.

36 MATERIALS SCIENCE↗

Evidence for a spinon Kondo effect in cobalt atoms on single-layer 1T-TaSe 2

Quantum spin liquids are highly entangled, disordered magnetic states that are expected to arise in frustrated Mott insulators and to exhibit exotic fractional excitations such as spinons and chargons. Despite being electrical insulators, some quantum spin liquids are predicted to exhibit gapless itinerant spinons that yield metallic behaviour in the charge-neutral spin channel. For this study, we deposited isolated magnetic atoms onto single-layer 1T-TaSe 2 , a candidate gapless spin liquid, to probe how itinerant spinons couple to impurity spin centres. Using scanning tunnelling spectroscopy, we observe the emergence of new, impurity-induced resonance peaks at the 1T-TaSe 2 Hubbard band edges when cobalt adatoms are positioned to have maximal spatial overlap with the local charge distribution. These resonance peaks disappear when the spatial overlap is reduced or when the magnetic impurities are replaced with nonmagnetic impurities. Theoretical simulations of a modified Anderson impurity model show that the observed peaks are consistent with a Kondo resonance induced by spinons combined with spin-charge binding effects that arise due to fluctuations of an emergent gauge field.

36 MATERIALS SCIENCE↗

Quantitative decoding of coupled carbon and energy metabolism in Pseudomonas putida for lignin carbon utilization

Soil Pseudomonas species, which thrive on lignin derivatives, are widely explored for biotechnology applications in lignin valorization. However, how the native metabolism coordinates phenolic carbon processing with required cofactor generation remains poorly understood. Here, we achieve quantitative understanding of this metabolic balance through a detailed multi-omics investigation of Pseudomonas putida KT2440 grown on four common phenolic acid substrates: ferulate, p-coumarate, vanillate, and 4-hydroxybenzoate. Relative to succinate, proteomics reveals > 140-fold increase in transport and catabolic proteins for aromatics, but metabolomics identifies bottlenecks in initial catabolism to maintain favorable cellular energy charge, which is compromised in mutants with resolved bottlenecks. Up to 30-fold increase in pyruvate carboxylase and glyoxylate shunt proteins implies a metabolic remodeling confirmed by kinetic 13 C-metabolomics. Quantitative analysis by 13 C-fluxomics demonstrates coupling of this remodeling with cofactor production. Specifically, anaplerotic carbon recycling through pyruvate carboxylase promotes tricarboxylic acid cycle fluxes to generate 50-60% NADPH yield and 60-80% NADH yield, resulting in up to 6-fold greater ATP surplus than with succinate metabolism; the glyoxylate shunt sustains cataplerotic flux through malic enzyme for the remaining NADPH yield. This quantitative blueprint affords cofactor imbalance predictions in proposed engineering of key metabolic nodes in lignin valorization pathways.

09 BIOMASS FUELS↗

Correlation between the gas-phase metallicity and ionization parameter in extragalactic H II regions

The variations of the metallicity and ionization parameter in H II regions are usually thought to be the dominant factors that produce the variations we see in the observed emission line spectra. There is an increasing amount of evidence that these two quantities are physically correlated, although the exact form of this correlation is debatable in the literature. Simulated emission line spectra from photoionized clouds provide important clues about the physical conditions of H II regions and are frequently used for deriving metallicities and ionization parameters. Through a systematic investigation on the assumptions and methodology used in applying photoionization models, we find that the derived correlation has a strong dependence on the choice of model parameters. On the one hand, models that give consistent predictions over multiple emission-line ratios yield a positive correlation between the metallicity and ionization parameter for the general population of H II regions or star-forming galaxies. On the other hand, models that are inconsistent with the data locus in high-dimensional line ratio space yield discrepant correlations when different subsets of line ratios are used in the derivation. The correlation between the metallicity and ionization parameter has a secondary dependence on the surface density of the star formation rate (SFR), with the higher SFR regions showing a higher ionization parameter but weaker correlations. The existence of the positive correlation contradicts the analytical wind-driven bubble model for H II regions. We explore assumptions in both dynamical models and photoionization models, and conclude that there is a potential bias associated with the geometry. However, this is still insufficient to explain the correlation. Mechanisms that suppress the dynamical influence of stellar winds in realistic H II regions might be the key to solving this puzzle, though more sophisticated combinations of dynamical models and photoionization models to test are required.

79 ASTRONOMY AND ASTROPHYSICS↗

Characterizing magnetized plasmas with dynamic mode decomposition

Accurate and efficient plasma models are essential to understand and control experimental devices. Existing magnetohydrodynamic or kinetic models are nonlinear and computationally intensive and can be difficult to interpret, while often only approximating the true dynamics. In this work, data-driven techniques recently developed in the field of fluid dynamics are leveraged to develop interpretable reduced-order models of plasmas that strike a balance between accuracy and efficiency. In particular, dynamic mode decomposition (DMD) is used to extract spatio-temporal magnetic coherent structures from the experimental and simulation datasets of the helicity injected torus with steady inductive (HIT-SI) experiment. Three-dimensional magnetic surface probes from the HIT-SI experiment are analyzed, along with companion simulations with synthetic internal magnetic probes. A number of leading variants of the DMD algorithm are compared, including the sparsity-promoting and optimized DMD. Optimized DMD results in the highest overall prediction accuracy, while sparsity-promoting DMD yields physically interpretable models that avoid overfitting. These DMD algorithms uncover several coherent magnetic modes that provide new physical insights into the inner plasma structure. These modes were subsequently used to discover a previously unobserved three-dimensional structure in the simulation, rotating at the second injector harmonic. Finally, using data from probes at experimentally accessible locations, DMD identifies a resistive kink mode, a ubiquitous instability seen in magnetized plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗