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At least 307 records · Page 17

Electronic structure, magnetic correlations, and superconducting pairing in the reduced Ruddlesden-Popper bilayer La 3 Ni 2 O 6 under pressure: Different role of d 3 z 2 - r 2 orbital compared with La 3 Ni 2 O 7

The recent discovery of superconductivity in bilayer La 3 Ni 2 O 7 (327-LNO) under pressure stimulated much interest in layered nickelates. However, superconductivity was not found in another bilayer nickelate system, La 3 Ni 2 O 6 (326-LNO), even under pressure. To understand the similarities and differences between 326-LNO and 327-LNO, using density functional theory and the random phase approximation (RPA), we systematically investigate 326-LNO under pressure. The large crystal-field splitting between the e g orbitals caused by the missing apical oxygen moves the d 3z 2 -r 2 orbital farther away from the Fermi level, implying that the d 3z 2 -r 2 orbital plays a less important role in 326-LNO than in 327-LNO. This also results in a smaller bandwidth for the d x 2 -y 2 orbital and a reduced energy gap for the bonding-antibonding splitting of the d 3z 2 -r 2 orbital in 326-LNO, as compared to 327-LNO. Moreover, the in-plane hybridization between the d x 2 -y 2 and d 3z 2 -r 2 orbitals is found to be small in 326-LNO, while it is much stronger in 327-LNO. Furthermore, the low-spin ferromagnetic state is found to be the likely ground state in 326-LNO under high pressure. The weak interlayer coupling suggests that s ± -wave pairing is unlikely in 326-LNO. The robust in-plane ferromagnetic coupling also suggests that d-wave superconductivity, which is usually caused by antiferromagnetic fluctuations of the d x 2 -y 2 orbital, is also unlikely in 326-LNO. These conclusions are supported by our many-body RPA calculations of the pairing behavior. Additionally, contrasting with the cuprates, for the bilayer cuprate HgBa 2 CaCu 2 O 6 , we find a strong self-doping effect of the d x 2 -y 2 orbital under pressure, with the charge of Cu being reduced by approximately 0.13 electrons from 0 GPa to 25 GPa. In contrast, we do not observe such a change in the electronic density in 326-LNO under pressure, establishing another important difference between the nickelates and the cuprates.

36 MATERIALS SCIENCE↗

Anisotropic Characterizations of Electrospun PAN Nanofiber Mats Using Design of Experiments

This paper deals with the dielectric and mechanical characterizations of polyacrylonitrile (PAN)-aligned electrospun nanofiber mats. A two factor three level full factorial experiment is conducted to understand the effect of various parameters on dielectric and mechanical responses. These responses are recorded against randomly oriented and aligned nanofiber mats. Improved properties of electrospun mats have applications in the field of energy storage and nanocomposite reinforcement. Dielectric and mechanical characterizations of PAN mats are vital, as the aligned electrospun mats were found to be useful in advanced energy and mechanical reinforcement applications. Therefore, it is paramount to understand the effects of system parameters to these properties. The design of experiment (DoE) includes two factors and three level full factorial experiments with concentrations of PAN solutions at 8 wt.%, 9 wt.%, and 10 wt.%, and speed of the rotating mandrel (collector) at 3 volt (V), 4 V, and 5 V inputs. The electric field intensity used in the experiment is 1 kV/cm. DoE is conducted to understand the nonlinear interactions of parameters to these responses. The dielectric and mechanical characterizations of 8 wt.%, 9 wt.%, and 10 wt.% with different speeds for the original and improved systems are discussed. It was observed that at 9 wt.% and at all mandrel speeds, the dielectric and tensile properties are optimum.

42 ENGINEERING↗

Protein-Mimetic Self-Assembly with Synthetic Macromolecules

Protein-mimetic amphiphiles have significant promise as a platform to access the complex functions of natural biological materials and incorporate the tunability and environmental resilience of synthetic materials. The fields of polymer chemistry and chemical biology have concurrently approached the development of biomimetic amphiphiles with materials ranging from random amphiphilic copolymers to peptide–lipid conjugates. In this Perspective, we incorporate strategies from diverse chemical arenas for controlling assembled morphologies and dynamics of protein-mimetic synthetic macromolecules. An overview of significant advances in peptide amphiphiles and single-chain polymer nanoparticles provides the foundation for comparing recent advances in the implementation of multiple intermolecular interactions and computational strategies to fine-tune the assembled structures. We aim to bridge these fields, combining insights from multiple disciplines to inspire new approaches for the development of protein-mimetic materials, as these assemblies have far-reaching applications including in the development of new sensors, catalysts, and therapeutics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evolution of the electrothermal instability from thick rod z pinches subject to dynamically and statically applied axial magnetic field

LDRD Project 229427 aimed to determine how electrothermal instability (ETI) driven heating on a z-pinch rod pulsed with intense current evolves under mixed magnetic field (azimuthal + axial) conditions, which is pertinent to pulsed-power-driven magnetically-insulated transmission lines and physics targets. Experiments focused on diagnosing ETI-driven heating from deliberately-machined and well-characterized micron-scale surface defects (referred to as engineered defects or ED). Prior to the start of this project, understanding of how unmagnetized (B z =0) ED evolve had been obtained—simulations largely reproduce the experimentally observed high temperature spots which develop at the poles of bare/uncoated ED. Project 229427 extended the Mykonos Facility ED experimental platform to include axial field. In the first class of experiments, axial field was provided “dynamically” via a helical return can (HRC). In this case, B z and B θ rise at the same rate. Generally, the HRC generated magnetic field at a fixed polarization angle Φ B =arctan(B z /B θ )=15° on the rod's surface. In the second class of experiments, axial field was provided “statically” via a slow-rising (millisecond) external Helmholtz coil pair. In this case, B z was effectively constant/static throughout the 100 ns rise of the Mykonos current. For either case, a primary goal was to determine whether ETI provides a helical seed perturbation for the subsequent growth of the helical magneto Rayleigh-Taylor modes observed in MagLIF (static B z ) and dynamic screw pinch (DSP, dynamic B z ) experiments. When dynamic field was applied using an HRC, emissions from individual ED aligned toward Φ B , while emissions from ED within pairs elongated and preferentially merged along Φ B . These data strongly support that for a randomized defect distribution, heating from nearby current-density perturbations will favorably merge about Φ B to generate an extended seed perturbation that aligns toward the surface-field polarization, and this may impact the orientation of subsequent MRT growth on imploding liners. The results from the static field experiments were largely inconclusive, as any ETI heating rotation, if present, was obscured/overwhelmed by local/random heating from ED rim imperfections.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Interplay of Bright Triplet and Dark Excitons Revealed by Magneto‐Photoluminescence of Individual PbS/CdS Quantum Dots

Abstract A low‐temperature polarization‐resolved magneto‐photoluminescence experiment is performed on individual PbS/CdS core/shell quantum dots (QDs). The experiment enables a direct measurement of the exciton Landé g factor and the anisotropic zero‐field splitting of the lowest emissive bright exciton triplet in PbS/CdS QDs. While anisotropic splittings of individual QDs distribute randomly in 104—325 μeV range, the exciton Landé g factors increase from 0.95 to 2.70 as the emission energy of the QD increases from 1.0 to 1.2 eV. The tight‐binding calculations allow to rationalize these trends as a direct consequence of reducing a cubic symmetry of QD via addition/removal of a few (<70) atoms from the surfaces of the PbS core. Furthermore, it is observed that while right ( σ + ) and left ( σ − ) circularly polarized photoluminescence (PL) peaks split linearly with magnetic field as expected for Zeeman effect, the energy splitting between X and Y linearly polarized PL peaks remains nearly unchanged. The theoretical study reveals rich and complex magnetic field‐induced interplay of bright triplet and dark exciton states explaining this puzzling behavior. These findings fill the missing gaps in the understanding of lead salt QDs and provide foundation for development of classical and quantum light sources operating at telecommunication wavelengths.

Kim, Younghee↗

Spectral fluctuations in the Sachdev-Ye-Kitaev model

We present a detailed quantitative analysis of spectral correlations in the Sachdev-Ye-Kitaev (SYK) model. We find that the deviations from universal Random Matrix Theory (RMT) behavior are due to a small number of long-wavelength fluctuations (of the order of the number of Majorana fermions N ) from one realization of the ensemble to the next one. These modes can be parameterized effectively in terms of Q-Hermite orthogonal polynomials, the main contribution being due to scale fluctuations for which we give a simple analytical estimate. Our numerical results for N = 32 show that only the lowest eight polynomials are needed to eliminate the nonuniversal part of the spectral fluctuations. The covariance matrix of the coefficients of this expansion can be obtained analytically from low-order double-trace moments. We evaluate the covariance matrix of the first six moments and find that it agrees with the numerics. We also analyze the spectral correlations in terms of a nonlinear σ-model, which is derived through a Fierz transformation, and evaluate the one and two-point spectral correlation functions to two-loop order. The wide correlator is given by the sum of the universal RMT result and corrections whose lowest-order term corresponds to scale fluctuations. However, the loop expansion of the σ-model results in an ill-behaved expansion of the resolvent, and it gives universal RMT fluctuations not only for q= 4 or higher even q-body interactions, but also for the q= 2 SYK model albeit with a much smaller Thouless energy while the correct result in this case should have been Poisson statistics. In our numerical studies we analyze the number variance and spectral form factor for N = 32 and q = 4. We show that the quadratic deviation of the number variance for large energies appears as a peak for small times in the spectral form factor. After eliminating the long-wavelength fluctuations, we find quantitative agreement with RMT up to an exponentially large number of level spacings for the number variance or exponentially short times in the case of the spectral form factor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Biased degenerate ground-state sampling of small Ising models with converged quantum approximate optimization algorithm

The quantum alternating operator ansatz, a generalization of the quantum approximate optimization algorithm (QAOA), is a quantum algorithm used for approximately solving combinatorial optimization problems. QAOA typically uses the transverse field mixer as the driving Hamiltonian. One of the interesting properties of the transverse field driving Hamiltonian is that it results in nonuniform sampling of degenerate ground states of optimization problems. In this study, we numerically examine the fair sampling properties of the transverse field mixer QAOA, and Grover mixer QAOA (GM-QAOA), which provides theoretical guarantees of fair sampling of degenerate optimal solutions, up to a large enough p such that the mean expectation value converges to an optimal approximation ratio of 1. This comparison is performed with high-quality heuristically computed, but not necessarily optimal, QAOA angles, which give strictly monotonically improving solution quality as p increases. These angles are computed using the Julia based numerical simulation software JuliQAOA. Fair sampling of degenerate ground states is quantified using the Shannon entropy of the ground-state amplitudes distribution. The fair sampling properties are reported on several quantum signature Hamiltonians from previous quantum annealing fair sampling studies. Small random fully connected spin glasses are shown, which exhibit exponential suppression of some degenerate ground states with transverse field mixer QAOA. The transverse field mixer QAOA simulations show that some problem instances clearly saturate the Shannon entropy of 0 with a maximally biased distribution that occurs when the learning converges to an approximation ratio of 1 while other problem instances never deviate from a maximum Shannon entropy (uniform distribution) at any p step. Published by the American Physical Society 2025

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Random quasi-phase-matching for pulse characterization from the near to the long wavelength infrared

Experiments requiring ultrafast laser pulses require a full characterization of the electric field to glean meaning from the experimental data. Such characterization typically requires a separate parametric optical process. As the central wavelength range of new sources continues to increase so too does the need for nonlinear crystals suited for characterizing these wavelengths. Here we report on the use of poly-crystalline zinc selenide as a universal nonlinear crystal in the frequency resolved optical gating characterization technique from the near to long-wavelength infrared. Due to its property of random quasi-phase-matching it’s capable of phase matching second-harmonic and sum-frequency generation of ultra-broadband pulses in the near and long wavelength infrared, while being crystal orientation independent. With the majority of ultra-fast laser sources being in this span of wavelengths, this work demonstrates a greatly simplified approach towards ultra-fast pulse characterization spanning from the near to the long-wavelength infrared. To our knowledge there is no single optical technique capable of such flexible capabilities.

Davis, Brandin (ORCID:0000000166964548)↗

Further adoption of conservation tillage can increase maize yields in the western US Corn Belt

Conservation tillage can reduce soil erosion, increase soil health, and decrease labor and fuel input costs. Despite these benefits, potential yield impacts remain an important concern for farmers considering adoption. Previous research suggests that conservation tillage is likely to have the largest yield benefits in more arid conditions, but a lack of field-level analyses across climatic, management and soil conditions limits confidence in such predictions. Satellite imagery provides the opportunity to monitor agricultural lands at sub-field resolution across large spatial scales and wide environmental gradients. Here we investigate the maize yield impacts of conservation tillage in the semi-arid western US Corn Belt, using sub-field resolution datasets on tillage practices and crop yields derived from satellite data spanning four states (Nebraska, Kansas, South Dakota, and North Dakota) between 2008 and 2020. On these datasets, we estimate heterogenous yield outcomes for several thousand maize fields across gradients in climate, soil quality and irrigation status by using a causal forests analysis, an adaptation of the random forests machine-learning algorithm for causal inference on observational data. We find that long-term adoption of conservation tillage increased rainfed maize yields by an average of 9.9% in the region. Impacts on irrigated yields were small and not statistically significant. These results, along with an analysis of variables related to greater than average yield benefits, indicate that improved water infiltration and retention are the primary reasons for conservation tillage benefits. Despite yield benefits, many fields estimated to see increased yields under long term low till have not adopted the practice. Therefore, we identify specific counties likely to benefit most from increased levels of adoption. Our results strengthen the understanding of the impacts of conservation agriculture on crop yields and help define environments and counties most likely to benefit from conservation tillage.

54 ENVIRONMENTAL SCIENCES↗

Quantification and prediction of solidification textures under additive manufacturing conditions

Crystallographic textures are a major determinant of the macroscale anisotropic properties of polycrystalline metallic alloys produced in a wide range of additive manufacturing (AM) processes. Here, we introduce a statistical method that can accurately quantify the degree of orientational order of textures despite the large random fluctuations in the orientation of individual grains inherent in AM processes. The method, demonstrated for laser and resolidification of AlSi thin films, extends Z-scoring to a dynamical regime to assess the statistical significance of observed textures compared to randomly generated ones at different stages of solidification. We further show that, combined with phase-field modeling, this method can be used to infer fundamental anisotropic properties of the solid-liquid interface that are essential for texture prediction, and are compared here to the results of atomistic simulations. In addition, phase-field modeling reveals that, even at rapid AM solidification rates, the observed 〈110〉-dominated textures in the AlSi thin films are controlled predominantly by the anisotropy of the interface free-energy and sheds light on the physical mechanism of grain competition. These results significantly enhance both the existing tools for the quantification and prediction of AM crystallographic textures and our basic understanding of their formation.

36 MATERIALS SCIENCE↗

Surrogate Hessian accelerated structural optimization for stochastic electronic structure theories

In this work, we present an efficient energy-based method for structural optimization with stochastic electronic structure theories, such as diffusion quantum Monte Carlo (DMC). This method is based on robust line-search energy minimization in reduced parameter space, exploiting approximate but accurate Hessian information from a surrogate theory, such as density functional theory. The surrogate theory is also used to characterize the potential energy surface, allowing for simple but reliable ways to maximize statistical efficiency while retaining controllable accuracy. We demonstrate the method by finding the minimum DMC energy structures of the selected flake-like aromatic molecules, such as benzene, coronene, and ovalene, represented by 2, 6, and 19 structural parameters, respectively. In each case, the energy minimum is found within two parallel line-search iterations. The method is near-optimal for a line-search technique and suitable for a broad range of applications. It is easily generalized to any electronic structure method where forces and stresses are still under active development and implementation, such as diffusion Monte Carlo, auxiliary-field Monte Carlo, and stochastic configuration interaction, as well as deterministic approaches such as the random-phase approximation. Accurate and efficient means of geometry optimization could shed light on a broad class of materials and molecules, showing high sensitivity of induced properties to structural variables.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Enhancing high-fidelity neural network potentials through low-fidelity sampling

The efficacy of neural network potentials (NNPs) critically depends on the quality of the configurational datasets used for training. Prior research using empirical potentials has shown that well-selected liquid–solid transitional configurations of a metallic system can be translated to other metallic systems. This study demonstrates that such validated configurations can be relabeled using density functional theory (DFT) calculations, thereby enhancing the development of high-fidelity NNPs. Training strategies and sampling approaches are efficiently assessed using empirical potentials and subsequently relabeled via DFT in a highly parallelized fashion for high-fidelity NNP training. Our results reveal that relying solely on energy and force for NNP training is inadequate to prevent overfitting, highlighting the necessity of incorporating stress terms into the loss functions. To optimize training involving force and stress terms, we propose employing transfer learning to fine-tune the weights, ensuring that the potential surface is smooth for these quantities composed of energy derivatives. This approach markedly improves the accuracy of elastic constants derived from simulations in both empirical potential-based NNPs and relabeled DFT-based NNPs. Overall, this study offers significant insights into leveraging empirical potentials to expedite the development of reliable and robust NNPs at the DFT level.

97 MATHEMATICS AND COMPUTING↗

Gamma Spectrometry Code Rodeo for Uranium Enrichment—FY 2022 Report

In the first two quarters of FY22, data acquisition continued at ORNL and LLNL using uranium sources of known enrichments. This was an FY21 task which could not be completed in FY21 because of problems encountered with the ORNL M400 CZT in Q4 of FY21, and the subsequent repairs. The detector was received back from H3D in the first of September 2021 , and the measurements resumed . Measurements using the repaired detector were completed in Q1 of FY22. The spectra were distributed by ORNL to the analyzing labs. Analysis results were received in Q2 of FY2022. The results from the various codes were intercompared and an ANOVA analysis was performed. Random and systematic uncertainties were established for each code. The ANOVA results and discussions were included in a revised version of FY21Annual Report issued in March 2022. A paper was presented at the INMM 2022Annual Conference, with the analysis results from the various isotopic codes, and the ANOVA table with random and systematic uncertainties. The Project Work Plan (PWP) for FY22 included a task to perform field testing of the M400 CZT and the analysis codes using UF 6 cylinder measurements at the Framatome Fuel Fabrication Facility in Richland, WA. PNNL was the lead for the field testing task. PNNL drafted a Field Test Plan, and refined it based on comments received from the team. PNNL coordinated with Framatome facility, the logistics of carrying out the field testing . A collimator and shield made out of TFlex (tungsten impregnated polymer) was designed and professionally manufactured. The collimators were used in the field test measurements. The measurements at Framatome were completed on April 21, 2022. A total of 34 Type 30B cylinders were measured using three M400 detectors (PNNL, LLNL, and ORNL detectors). Measurements using M400 were taken at two locations on the side of each cylinder and from the end-on bottom location. Additionally, HPGe measurements were taken at the end-on location to establish ground truth. Cylinder wall thickness measurements were also made at all three locations. To gain a better understanding of the effect of background from surrounding cylinders, the same five cylinders measured individually in low background locations were measured again in the cylinder storage yards. Due to inclement weather, manufacturer delays, shipping delays, and equipment failure, the measurement campaign spanned twice as long compared to the original timeline. Gamma-ray spectra from M400 and HPGe detectors, along with the cylinder data and photographs were organized and shared with the collaborators for further analysis. Spectra were analyzed by the participating laboratories. FY22 PWP also consists of tasks related to plutonium source measurements, adapting the codes to analyze plutonium spectra, and inter-comparison of the results from various codes. Plutonium spectra are being acquired at LANL, ORNL, and LLNL. LANL, SNL and LLNL are in the process of modifying FRAM, GADRAS, and CZTU, respectively. The plutonium related tasks will be completed in Q2 of FY23.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Line Faults Classification Using Machine Learning on Three Phase Voltages Extracted from Large Dataset of PMU Measurements

An end-to-end supervised learning method is developed to classify transmission line faults in a twoyear field-recorded dataset that includes synchronized measurements of three-phase voltages recorded by 38 Phasor Measurement Units (PMU) sparsely located in in the US Western Grid interconnection. Statistical analysis is performed to extract features from this large dataset to train Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) classifiers initially. The training further leverages a simulated dataset from a synthetic grid with 12 PMUs to increase the number of faults of types infrequently seen in the field-recorded dataset. Training the classification models with the combined dataset resulted in a classification accuracy of 97.7%. This is a significant improvement over 89.7% to 92.5% accuracy obtained by relying on the field-recorded dataset alone.

47 OTHER INSTRUMENTATION↗

Fast-RF-Shimming: Accelerate RF shimming in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) offers an elevated signal-to-noise ratio (SNR), enabling exceptionally high spatial resolution that benefits both clinical diagnostics and advanced research. However, the jump to higher fields introduces complications, particularly transmit radiofrequency (RF) field ($B^{+}_{1}$) inhomogeneities, manifesting as uneven flip angles and image intensity irregularities. These artifacts can degrade image quality and impede broader clinical adoption. Traditional RF shimming methods, such as Magnitude Least Squares (MLS) optimization, effectively mitigate $B^{+}_{1}$ inhomogeneity, but remain time-consuming. Recent machine learning approaches, including RF Shim Prediction by Iteratively Projected Ridge Regression and other deep learning architectures, suggest alternative pathways. Although these approaches show promise, challenges such as extensive training periods, limited network complexity, and practical data requirements persist. In this paper, we introduce a holistic learning-based framework called Fast-RF-Shimming, which achieves a 5000 ​× ​speed-up compared to the traditional MLS method. In the initial phase, we employ random-initialized Adaptive Moment Estimation (Adam) to derive the desired reference shimming weights from multi-channel $B^{+}_{1}$ fields. Next, we train a Residual Network (ResNet) to map $B^{+}_{1}$ fields directly to the ultimate RF shimming outputs, incorporating the confidence parameter into its loss function. Finally, we design Non-uniformity Field Detector (NFD), an optional post-processing step, to ensure the extreme non-uniform outcomes are identified. Comparative evaluations with standard MLS optimization underscore notable gains in both processing speed and predictive accuracy, which indicates that our technique shows a promising solution for addressing persistent inhomogeneity challenges.

Deep learning↗

Binary pseudorandom array test standard optimized for characterization of large field-of-view optical interferometers

Recently, a technique for calibrating the modulation transfer function (MTF) of a broad variety of metrology instrumentation has been demonstrated. This technique is based on test samples structured as one-dimensional binary pseudo-random (BPR) sequences and two-dimensional BPR arrays (BPRAs). The inherent power spectral density of BPR gratings (sequences) and arrays has a deterministic white-noise-like character that allows direct determination of the MTF with uniform sensitivity over the entire spatial frequency range and field-of-view of an instrument. As such, the BPR samples satisfy the characteristics of a test standard: functionality, ease of specification and fabrication, reproducibility, and low sensitivity to manufacturing error. Here we discuss our recent developments directed to the optimization of the sample design, fabrication, application, and data processing procedures, suitable for thorough characterization of large aperture optical interferometers. Compared with the previous coded-aperture based design, the improved, 'highly randomized' BPRA pattern of the new test standard provides better accuracy and reliability of instrument MTF and aberration characterization, and enables operation optimization of large aperture optical interferometers. We describe the pattern generation algorithm and tests to verify the compliance to desired BPRA topography. The data acquisition and analysis procedures for different applications of the technique are also discussed.

Yashchuk, Valeriy V.↗

Specific features of fluorescence transfer in multiply scattering randomly inhomogeneous layers under intense laser pumping

Based on the analysis of experimental data on the effect of the pulsed laser pump intensity on the spectral properties and the size of the fluorescent response zone in randomly inhomogeneous fluorescent layers, we found that the amplification of spontaneous and stimulated emission significantly affects the statistical properties of the propagation lengths of the fluorescent field partial components in the layers. The experiments are performed with layers of SiO{sub 2} and TiO{sub 2} nanoparticles saturated with rhodamine 6G, pumped by 532-nm laser radiation in the intensity range corresponding to the transient regime from excitation of spontaneous fluorescence to random lasing in the layer. The experimental data are compared with the results of statistical modelling of fluorescence transfer. It is shown that, even at a pump intensity below the random lasing threshold, the spontaneous emission amplification in a layer leads to a significant increase in the contributions to the fluorescence response from partial components with propagation lengths much larger than the layer thickness. This can be interpreted as a manifestation of the quasi-waveguide effect, in which the probability of propagation of diffuse fluorescence components along the layer over distances many times greater than its thickness and the size of the pumped region increases significantly with a decrease in the characteristic radiation amplification length in the layer. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Fragmentation analysis of a bar with the Lip-field approach

The Lip-field approach was introduced in Moës and Chevaugeon (2021) as a new way to regularize softening material models. It was tested in 1D quasistatic in Moës and Chevaugeon (2021) and 2D quasistatic in Chevaugeon and Moës (2021): this paper extends it to 1D dynamics, on the challenging problem of dynamic fragmentation. The Lip-field approach formulates the mechanical problem to be solved as an optimization problem, where the incremental potential to be minimized is the non-regularized one. Spurious localization is prevented by imposing a Lipschitz constraint on the damage field. Here, the displacement and damage field at each time step are obtained by a staggered algorithm, that is the displacement field is computed for a fixed damage field, then the damage field is computed for a fixed displacement field. Indeed, these two problems are convex, which is not the case of the global problem where the displacement and damage fields are sought at the same time. The incremental potential is obtained by equivalence with a cohesive zone model, which makes material parameters calibration simple. A non-regularized local damage equivalent to a cohesive zone model is also proposed. It is used as a reference for the Lip-field approach, without the need to implement displacement jumps. These approaches are applied to the brittle fragmentation of a 1D bar with randomly perturbed material properties to accelerate spatial convergence. Both explicit and implicit dynamic implementations are compared. Favorable comparison to several analytical, numerical and experimental references serves to validate the modeling approach.

36 MATERIALS SCIENCE↗