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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Resistive wall mode stability and resonant field amplification in MAST high beta plasma

The n=1 (n is the toroidal mode number) resistive wall mode (RWM) instability and the resonant field amplification (RFA) due to a stable RWM response are numerically investigated for a MAST high-pressure plasma scenario, utilizing the MARS-F (Liu Y.Q. et al 2000 Phys. Plasmas 7 3681) and MARS-K (Liu Y.Q. et al 2008 Phys. Plasmas 15 112503) codes, for the purpose of understanding the potential RWM behavior in MAST-U plasmas. Ideal kink stability analysis identifies a target plasma, with parameters similar to that of the reference equilibrium reconstructed from a MAST high-pressure discharge, that accesses the RWM regime. The unstable n=1 RWM for the target equilibrium is subject to strong damping by the plasma toroidal flow and/or the drift kinetic effects from thermal particles. As a result, the mode is found to be stable under the experimental flow conditions, or even without flow stabilization if drift kinetic stabilization is included. The stability prediction is robust against variation of the assumed resistive wall minor radius. Active magneto-hydrodynamic (MHD) spectroscopy modeling, using the magnetic coils designed for controlling the edge localized modes (ELMs) in MAST-U as the antenna, shows strong resonant field amplification due to a stable RWM response in the target plasma. Maximal amplification, of 6-7 times larger than the applied vacuum field within the plasma, is obtained assuming a coil phasing of 120-180 degrees between the upper and lower rows of the ELM control coils. The MHD-kinetic hybrid model predicts generally higher RFA than the fluid model, in particular near the low-field and high-field sides of the plasma boundary surface. Furthermore, the MARS-F/K modeling thus shows that the RWM in MAST-U is subject to strong flow and kinetic stabilization, but can nevertheless be detected by active MHD spectroscopy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Simulated Active Region Emission and Dynamics: A STEREO Perspective

We present detailed three-dimensional simulations of active regions resulting from numerical models of sunspots coupled to coronal excitation and emission. The models incorporate a fully three-dimensional magnetoconvection calculation, described in a poster by Hurlburt and Rucklidge, potential field extrapolations from the sunspot model boundary conditions, steady-state coronal loops powered by the convective motions at the surface, EUV and X-ray instrument response functions, and a full voxel rendering. The result is a simulated dynamical active region in three dimensions which enables us to explore coronal heating and its relationship to the dynamics of the photosphere and convection zone. The 3D rendering of the resulting EUV emission allows us to investigate the expected coronal signatures of the EUVI instruments on board the twin STEREO spacecraft to be launched in 2004. The hybrid model developed here also provides a simulation testbed for the development of future STEREO image reconstruction tools: an integral component in the access to STEREO data by the solar physics community.

Alexander, David↗
NASA NTRS2010

The Interaction of the Solar Wind with Solar Probe Plus - 3D Hybrid Simulation: The Study for the Distance 4.5Rs - Report 1

Our report devotes a 3D numerical hybrid model of the interaction of the solar wind with the Solar Probe spacecraft. The SPP model includes 3 main parts, namely, a non-conducting heat shield, a support system, and cylindrical section or spacecraft bus that contains the particle analysis devices and antenna. One observes an excitation of the low frequency Alfven and whistler type wave directed by the magnetic field with an amplitude of about (0.06-0.6) V/m. The compression waves and the jumps in an electric field with an amplitude of about (0.15-0.7) V/m were also observed. The wave amplitudes are comparable to or greater than previously estimated max wave amplitudes that SPP is expected to measure. The results of our hybrid simulation will be useful for understanding the plasma environment near the SPP spacecraft at the distance 4.5 Rs. Future simulation will take into account the charging of the spacecraft, the charge separation effects, an outgassing from heat shield, a photoionization and an electron impact ionization effects near the spacecraft.

Lipatov, Alexander S.↗
NASA NTRS2010

The Interaction of the Solar Wind with Solar Probe Plus - 3D Hybrid Simulation: The Study for the Distance 4.5Rs - Report 1

Our report devotes a 3D numerical hybrid model of the interaction of the solar wind with the Solar Probe spacecraft. The Solar Probe Plus (SPP) model includes 3 main parts, namely, a non-conducting heat shield, a support system, and cylindrical section or spacecraft bus that contains the particle analysis devices and antenna. One observes an excitation of the low frequency Alfven and whistler type wave directed by the magnetic field with an amplitude of about (0.06-0.6) V/m. The compression waves and the jumps in an electric field with an amplitude of about (0.15-0.7) V/m were also observed. The wave amplitudes are comparable to or greater than previously estimated max wave amplitudes that SPP is expected to measure. The results of our hybrid simulation will be useful for understanding the plasma environment near the SPP spacecraft at the distance 4.5 Rs. Future simulation will take into account the charging of the spacecraft, the charge separation effects, an outgassing from heat shield, a photoionization and an electron impact ionization effects near the spacecraft.

Lipatov, Alexander S.↗
DOE OSTI2021

Excitation of fishbone-like mode in tokamaks due to bounce resonances of trapped thermal ions

When the drift kinetic effect of thermal ions is taken into account, a high-frequency fishbone-like mode (FLM) is found to be driven unstable by trapped thermal ions (TTIs) in tokamak plasmas, according to self-consistent magneto-hydrodynamic (MHD)-kinetic hybrid modeling utilizing the MARS-K code [Liu et al., Phys. Plasmas 15, 112503 (2008)] as well as an analytic theory. It is found that, similar to energetic particles, TTIs can also stabilize the internal kink mode, whereas the FLM is excited when the effective beta of TTIs exceeds a threshold value. The real frequency of the FLM is comparable to the bounce frequency of TTIs. The mode structure of the FLM can be significantly different from the conventional step-like function for the associated plasma radial displacement. This drift kinetic induced modification of the mode structure near the q = 1 surface is captured by non-perturbative MHD-kinetic hybrid computations with MARS-K. Furthermore, the FLM can only be triggered by TTIs at sufficiently high thermal temperatures. Both the FLM and the internal kink can be stabilized by sufficiently fast plasma toroidal rotation and parallel sound wave damping. These two conditions of high thermal temperature and (fast) flow stabilization, though making it challenging to observe the TTI-driven FLM in present day experiments, are favorable for the mode excitation in future reactor scale devices.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Modeling, Analyzing, and Mitigating Dissonance Between Alerting Systems

Alerting systems are becoming pervasive in process operations, which may result in the potential for dissonance or conflict in information from different alerting systems that suggests different threat levels and/or actions to resolve hazards. Little is currently available to help in predicting or solving the dissonance problem. This thesis presents a methodology to model and analyze dissonance between alerting systems, providing both a theoretical foundation for understanding dissonance and a practical basis from which specific problems can be addressed. A state-space representation of multiple alerting system operation is generalized that can be tailored across a variety of applications. Based on the representation, two major causes of dissonance are identified: logic differences and sensor error. Additionally, several possible types of dissonance are identified. A mathematical analysis method is developed to identify the conditions for dissonance originating from logic differences. A probabilistic analysis methodology is developed to estimate the probability of dissonance originating from sensor error, and to compare the relative contribution to dissonance of sensor error against the contribution from logic differences. A hybrid model, which describes the dynamic behavior of the process with multiple alerting systems, is developed to identify dangerous dissonance space, from which the process can lead to disaster. Methodologies to avoid or mitigate dissonance are outlined. Two examples are used to demonstrate the application of the methodology. First, a conceptual In-Trail Spacing example is presented. The methodology is applied to identify the conditions for possible dissonance, to identify relative contribution of logic difference and sensor error, and to identify dangerous dissonance space. Several proposed mitigation methods are demonstrated in this example. In the second example, the methodology is applied to address the dissonance problem between two air traffic alert and avoidance systems: the existing Traffic Alert and Collision Avoidance System (TCAS) vs. the proposed Airborne Conflict Management system (ACM). Conditions on ACM resolution maneuvers are identified to avoid dynamic dissonance between TCAS and ACM. Also included in this report is an Appendix written by Lee Winder about recent and continuing work on alerting systems design. The application of Markov Decision Process (MDP) theory to complex alerting problems is discussed and illustrated with an abstract example system.

Song, Lixia↗
DOE OSTI2022

Toroidal modeling of energetic passing particle drift kinetic effects on tearing mode stability

Abstract Drift kinetic effects of the neutral beam injection induced passing energetic particles (EPs) on the linear stability of the n = 1 tearing mode (TM) (with the dominant poloidal harmonic of m = 2) are numerically investigated utilizing the MARS-K code (Liu et al 2008 Phys. Plasmas 15 112503), in a tokamak plasma with finite equilibrium pressure and anisotropic thermal transport. In the low plasma pressure regime, it is found that co- (counter-) passing EPs stabilize (destabilize) the TM, agreeing with previous studies. However, as the plasma pressure increases beyond a critical value, it is found that co-passing EPs also destabilize the mode. An in-depth analysis reveals that the net effect of co-passing EPs is a result of competition between the stabilizing contribution from the non-adiabatic drift kinetic terms and the destabilizing contribution associated with adiabatic terms, with the latter becoming more dominant at higher equilibrium pressure. Non-perturbative magnetohydrodynamic-kinetic hybrid modeling also finds that co- and counter-passing EPs modify the TM eigenfunction differently, with the counter-passing EPs enhancing the sideband harmonics. Furthermore, effects of the plasma resistivity and toroidal rotation, as well as that of the equilibrium distribution of EPs in the particle pitch angle space, are also investigated, showing asymmetric results on the TM stability between the co- and counter-passing EPs. The first order finite orbit width correction is found to be stabilizing with co-passing EPs and destabilizing with counter-passing particles. Finally, drift resonances between passing EPs and the TM induce finite frequency to the mode and generate finite net torques inside the plasma, due to the neoclassical toroidal viscosity and the Reynolds stress associated with 3D perturbations.

Physics↗
DOE OSTI2024

Hybrid particle-spectral method for kinetic plasma simulations

A hybrid model for numerical solutions of the Vlasov–Poisson equations is presented, which blends spectral and particle approaches. The model splits the distribution function for plasma species into both spectral and particle representations in the velocity space to combine the advantages of each approach. The spectral representation leverages asymmetrically weighted Hermite basis, whereas the particle representation leverages the particle-in-cell method. Configuration phase space is decomposed with the Fourier method, which is well suited for periodic problems. We derive conservation equations for mass, momentum, and energy for the proposed combined method. It is shown that the coupling error between the two methods is absent in the semi-discrete setting (not taking into account time discretization). Finally, numerical test cases are presented simulating a weak electron beam interaction with plasma, leading to beam–plasma instability. The initially localized electron beam evolved into a highly non-equilibrium distribution function in the velocity space. A small growth rate and the resonance nature of instability make it difficult to obtain accurate solutions for purely particle methods due to noise, which falls as ∼1/Np with a number of particles. At the same time, purely spectral methods may require a large number of modes to capture the highly non-equilibrium state of the evolved beam. We show that the hybrid method is well suited for such problems: it reproduces the linear stage as well as nonlinear dynamics with sufficient accuracy using a highly non-equilibrium distribution function.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Hybrid particle-spectral method for kinetic plasma simulations

A hybrid model for numerical solutions of the Vlasov–Poisson equations is presented, which blends spectral and particle approaches. The model splits the distribution function for plasma species into both spectral and particle representations in the velocity space to combine the advantages of each approach. The spectral representation leverages asymmetrically weighted Hermite basis, whereas the particle representation leverages the particle-in-cell method. Configuration phase space is decomposed with the Fourier method, which is well suited for periodic problems. We derive conservation equations for mass, momentum, and energy for the proposed combined method. It is shown that the coupling error between the two methods is absent in the semi-discrete setting (not taking into account time discretization). Finally, numerical test cases are presented simulating a weak electron beam interaction with plasma, leading to beam–plasma instability. The initially localized electron beam evolved into a highly non-equilibrium distribution function in the velocity space. A small growth rate and the resonance nature of instability make it difficult to obtain accurate solutions for purely particle methods due to noise, which falls as ~1/$\sqrt{N_p}$ with a number of particles. At the same time, purely spectral methods may require a large number of modes to capture the highly nonequilibrium state of the evolved beam. We show that the hybrid method is well suited for such problems: it reproduces the linear stage as well as nonlinear dynamics with sufficient accuracy using a highly non-equilibrium distribution function.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2021

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

Dense time-series remote sensing data with detailed spatial information are highly desired for the monitoring of dynamic earth systems. Due to the sensor tradeoff, most remote sensing systems cannot provide images with both high spatial and temporal resolutions. Spatiotemporal image fusion models provide a feasible solution to generate such a type of satellite imagery, yet existing fusion methods are limited in predicting rapid and/or transient phenological changes. Additionally, a systematic approach to assessing and understanding how varying levels of temporal phenological changes affect fusion results is lacking in spatiotemporal fusion research. The objective of this study is to develop an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images. To systematically assess the effects of varying levels of phenological changes, we identify image phenological transition dates and design three temporal phenological change scenarios representing rapid, moderate, and minimal phenological changes. The hybrid deep learning model, alongside three benchmark fusion models, is assessed in different scenarios of phenological changes. Results indicate the hybrid deep learning model yields significantly better results when rapid or moderate phenological changes are present. It holds great potential in generating high-quality time-series datasets of both high spatial and temporal resolutions, which can further benefit terrestrial system dynamic studies. The innovative approach to understanding phenological changes’ effect will help us better comprehend the strengths and weaknesses of current and future fusion models.

spatiotemporal fusion↗
DOE OSTI2024

Imaging the wakes of jets with energy-energy-energy correlators

As the partons in a high energy jet propagate through the droplet of quark-gluon plasma (QGP) produced in a heavy-ion collision they lose energy to, kick, and are kicked by the medium. The resulting modifications to the parton shower encode information about the microscopic nature of QGP. A direct consequence, however, is that the momentum and energy lost by the parton shower are gained by the medium and, since QGP is a strongly coupled liquid, this means that the jet excites a wake in the droplet of QGP. After freezeout, this wake becomes soft hadrons with net momentum in the jet direction meaning that what an experimentalist later reconstructs as a jet includes hadrons originating from both the modified parton shower and its wake. This has made it challenging to find experimental observables that provide an unambiguous view of the dynamical response of a droplet of QGP to a jet shooting through it. Recent years have seen significant substantial advances in the theoretical and experimental understanding of the substructure of jets, in particular, using correlation functions, $\langle$ ($\vec{n}$ 1 )•••$\mathcal{E}$($\vec{n}$ k ) $\rangle$, of the energy flux operator in proton-proton collisions and, recently, in heavy-ion collisions. So far, such studies have focused primarily on the two-point correlator, which allows for the identification of the angular scale of the underlying dynamics. Higher-point correlators hold the promise of mapping out the dynamics themselves. In this paper we perform the first study of the shape-dependent three-point energy-energy-energy correlator in heavy-ion collisions. Using the Hybrid Model to simulate the interactions of high energy jets with the QGP medium, we show that the three-point correlator presents us with a striking new opportunity. We find that hadrons originating from wakes are the dominant contribution to the three-point correlator in the kinematic regime in which the three points are well-separated in angle, forming a roughly equilateral triangle. This equilateral region of the correlator is far from the region populated by collinear vacuum emissions, making it a canvas on which jet wakes are laid out, where experimentalists can map their shapes. Our work provides a key step towards the systematic use of energy correlators to image and unravel the dynamical response of a droplet of QGP that has been probed by a passing jet, and motivates numerous experimental and theoretical studies.

Jets and Jet Substructure↗
DOE OSTI2022

Glycerol Binding at the Narrow Channel of Photosystem II Stabilizes the Low-Spin S 2 State of the Oxygen-Evolving Complex

The oxygen-evolving complex (OEC) of photosystem II (PSII) cycles through redox intermediate states S i (i = 0–4) during the photochemical oxidation of water. The S 2 state involves an equilibrium of two isomers including the low-spin S 2 (LS-S 2 ) state with its characteristic electron paramagnetic resonance (EPR) multiline signal centered at g = 2.0, and a high-spin S 2 (HS-S 2 ) state with its g = 4.1 EPR signal. The relative intensities of the two EPR signals change under experimental conditions that shift the HS-S 2 /LS-S 2 state equilibrium. Here, we analyze the effect of glycerol on the relative stability of the LS-S 2 and HS-S 2 states when bound at the narrow channel of PSII, as reported in an X-ray crystal structure of cyanobacterial PSII. Our quantum mechanics/molecular mechanics (QM/MM) hybrid models of cyanobacterial PSII show that the glycerol molecule perturbs the hydrogen-bond network in the narrow channel, increasing the pK a of D1-Asp61 and stabilizing the LS-S 2 state relative to the HS-S 2 state. Here, the reported results are consistent with the absence of the HS-S 2 state EPR signal in native cyanobacterial PSII EPR spectra and suggest that the narrow water channel hydrogen-bond network regulates the relative stability of OEC catalytic intermediates during water oxidation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

NeRVI: Compressive neural representation of visualization images for communicating volume visualization results

We present NeRVI, a new deep-learning approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results. Based on an image-based implicit neural representation, our approach represents tens of thousands of high-resolution rendering images parametrized by different parameters via a hybrid model of multilayer perceptrons and convolutional neural networks. Here, our model predicts images and corresponding masks, and the masks are utilized for loss computation and network training to capture fine structural details and small components. In conjunction with model quantization and weight encoding, NeRVI yields highly compact compressive neural representations while preserving the image fidelity well. We demonstrate the effectiveness of NeRVI with isosurface rendering and direct volume rendering images generated from multiple data sets and compare NeRVI with other state-of-the-art deep learning-based (InSituNet, SIREN, NeRF, and NeRV) methods. Quantitative and qualitative results show that NeRVI provides an alternative solution that augments domain scientists' ability to manage, represent, and communicate scientific visualization output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A BOUT++ extension for full annular tokamak edge MHD and turbulence simulations

For tokamak edge plasma simulation, a plasma simulation framework BOUT++ employs a dual coordinate system to simulate moderate-n and high-n plasma instability with reasonable computational cost, where n is the toroidal mode number. This coordinate system however limits the computational domain to the toroidal wedge (full torus divided into N parts in the toroidal direction) for computational efficiency and the use of flute-ordering approximation in the field solver calculating the flow potential from the vorticity which may not be valid for low-n modes. Improving numerical treatment of low-n modes is however indispensable to address simulations of low-n current-driven edge localized mode (ELM), ELM control by resonant magnetic perturbations (RMPs), edge turbulence with RMPs and so on. In this work, BOUT++ is extended to simulate the interplay between $n=0$, low-n and high-n plasma components in a full annular tokamak edge domain through hybrid modeling of the flow potential and the vorticity. Low-n modes of flow potential are calculated in an orthogonal flux surface coordinate and high-n modes in the dual coordinate system separately in Fourier space. Finally, the proposed scheme can capture an interplay between $n=1$ global modes and high-n turbulence during pedestal collapse in a full annular torus domain with a circular cross section.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A systematic review of machine learning in groundwater monitoring

With increasing concerns about water scarcity, groundwater has become crucial since this resource provides most of the freshwater needs. However, various human and natural activities often contaminate the groundwater, making it unsuitable for use. Over the years, scientists and engineers have used many methods to predict and track groundwater contamination as part of environmental monitoring. Consequently, there is an urgent need for improved methods, particularly in the face of increasing contamination. Machine learning has sometimes been used to monitor groundwater, air quality, and climate. Traditional methods must be improved due to the complexity and large amount of environmental data. This includes using hybrid models that combine traditional and new techniques. Despite the use of machine learning in many scientific areas, there is a lack of comprehensive reviews focusing on its use in environmental monitoring, especially groundwater monitoring. We aim to fill this gap by exploring machine-learning applications in groundwater monitoring. We discuss relevant methods, their limitations, and future potential. We summarize research on automating data processing and model training using groundwater sensor data. Our research underscores the transformative potential of machine learning to revolutionize long-term groundwater monitoring and contamination detection, providing valuable insights for future research and practical applications.

AI/ML↗
DOE OSTI2021

Probing Early-Time Dynamics and Quark-Gluon Plasma Transport Properties with Photons and Hadrons

In this work, we use a hybrid model which relies on QCD effective kinetic theory - KøMPøST - to dynamically bridge the gap between IP-Glasma initial states and viscous hydrodynamics, for the theoretical interpretation of heavy-ion collision results. The hydrodynamic phase is then followed by dynamical freeze-out handled by UrQMD. It is observed that the new pre-equilibrium/pre-hydro phase does influence the extraction of transport coefficients. We also show how the early dynamics is reflected in photon observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Exploring nuclear structure with multiparticle azimuthal correlations at the LHC

Details of the nuclear structure of Xe, such as the quadrupole deformation and the nuclear diffuseness, are studied by extensive measurements of anisotropic-flow-related observables in Xe–Xe collisions at a centre-of-mass energy per nucleon pair $\sqrt{s_{NN}}$ = 5.44 TeV with the ALICE detector at the LHC. The results are compared with those from Pb–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV for a baseline, given that the 208 Pb nucleus exhibits a very weak deformation. Furthermore, comprehensive comparisons are performed with a state-of-the-art hybrid model using IP-Glasma+MUSIC+UrQMD. It is found that among various IP-Glasma+MUSIC+UrQMD calculations with different values of nuclear parameters, the one using a nuclear diffuseness parameter of α 0 = 0.492 and a nuclear quadrupole deformation parameter of β 2 = 0.207 provides a better description of the presented flow measurements. These studies represent the first systematic exploration of nuclear structure at TeV energies, utilizing a comprehensive set of anisotropic flow observables. The measurements serve as a critical experimental benchmark for rigorously testing the interplay between nuclear structure inputs and heavy-ion theoretical models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗