Automated Fiber Placement (AFP) is a manufacturing technique widely used for the serial production of aerospace parts. A deep understanding of the effect of lay-up defects is crucial for part and lay-up design. Currently, numerical models for structural simulation lack a precise representation of the internal structure of AFP laminates, which is crucial for understanding the impact of defects on mechanical properties. This paper presents a novel approach based on high-resolution micro-computed tomography (micro-CT) scans from specimens manufactured with AFP, which automatically creates a mesoscale numerical model incorporating as-fabricated defect morphologies. The hexahedral mesh, generated from the segmented plies of the micro-CT volume, accounts for ply thickness and out-of-plane fiber orientation. This approach is verified with mechanical testing and digital image correlation (DIC) under tensile loading. The simulation results align closely with experimental testing and accurately illustrate the influence of fiber waviness in various defect configurations, such as gaps and overlaps. The study shows that lay-up defects can lead to knockdown factors of up to 12% in tensile properties, with each defect creating a distinct pattern in the local strain. This model can serve as a benchmark for further numerical simulations and surrogate models of defect configurations under varying loading conditions.

Abstract Fiber‐metal laminates (FMLs) are generally regarded as excellent lightweight materials for advanced structure design. To enhance the mechanical properties, the common FMLs can be optimized using carbon fibers. However, the combination of carbon fibers with aluminum induces interfacial challenges. Preventing galvanic corrosion with elastomeric interlayers is an effective solution. The lay‐up configuration greatly effects the impact damage resistance of hybrid CFRP‐elastomer‐metal laminates (HyCEMLs). In this work, micro‐CT scans and optical micrographic inspections on HyCEMLs are conducted after impact tests to ascertain the microstructural origins behind the mechanical performance changes. In addition, finite element models of different HyCEML configurations are built to complement the limited experimental data. The damage mechanisms of HyCEML with different configurations under various impact conditions are further compared. The numerical results suggest that impact energy is a more informative measure in terms of damage mode and size than impact velocity and momentum. Results also indicate that when the thickness for each sub‐laminate of HyCEML is maintained the same, hybrid laminates with aluminum stacked outside is beneficial for delaying the occurrence of matrix cracking and delaminations, and enhances HyCEML's resistance to global deformation. These findings will contribute to engineering hybrid laminates with desired impact performance for lightweight load‐bearing structures. Highlights The hybrid laminate with elastomeric interlayers is a forward‐looking solution in impact applications. Impact energy is a more informative measure in terms of assessing the damage mode and extent in HyCEMLs. The influence of stacking sequence on damage mechanisms of HyCEMLs is evaluated. Microstructural origins behind variations of hybrid laminates in the impact resistance are revealed.

Random network models, constrained to reproduce specific statistical features, are often used to represent and analyze network data and their mathematical descriptions. Chief among them, the configuration model constrains random networks by their degree distribution and is foundational to many areas of network science. However, configuration models and their variants are often selected based on intuition or mathematical and computational simplicity rather than on statistical evidence. To evaluate the quality of a network representation, we need to consider both the amount of information required to specify a random network model and the probability of recovering the original data when using the model as a generative process. To this end, we calculate the approximate size of network ensembles generated by the popular configuration model and its generalizations, including versions accounting for degree correlations and centrality layers. We then apply the minimum description length principle as a model selection criterion over the resulting nested family of configuration models. Using a dataset of over 100 networks from various domains, we find that the classic configuration model is generally preferred on networks with an average degree above 10, while a layered configuration model constrained by a centrality metric offers the most compact representation of the majority of sparse networks.

Abstract In recent years, the synergy between artificial intelligence and turbulence big data has given rise to a new data-driven paradigm in turbulence research. Data-driven turbulence modeling has emerged as one of the forefront directions in fluid mechanics. Most existing studies focus on feature construction, selection, and the development of modeling frameworks, often overlooking the practical deployment and application of trained models. This paper examines the entire process from model construction to real-world deployment, using data-driven turbulence modeling for high Reynolds number flows over complex three-dimensional configurations as a case study. Key stages include data generation, input-output feature construction, model training, model compilation and optimization, deployment, and validation. We successfully implemented the entire workflow in a heterogeneous supercomputing environment and, through mixed programming techniques, integrated the resulting turbulence model into the Platform for Hybrid Engineering Simulation of Flows (PHengLEI) open-source software framework. This allowed for mixed-precision simulations, with the main equations solved in double precision and the turbulence model in half precision. The new computational framework was validated through large-scale parallel numerical simulations on grids with tens of millions of elements for three-dimensional complex configurations. The results highlight the efficiency of our model deployment, with overall computational efficiency improving by 13.35% and the turbulence model’s solution speed increasing by approximately 3.9 times. The accuracy of the computations was also confirmed, with the average relative error in the lift and drag coefficients calculated by the data-driven turbulence model within 3%. Across various computing nodes, the relative error in the computed aerodynamic coefficients remained within 1%, demonstrating the framework’s scalability. Notably, our contributions have been incorporated as a case study in the latest PHengLEI open-source project5 5 https://forge.osredm.com/PHengLEI/PHengLEI-TestCases/tree/master/Y02_ThreeD_M6_Unstruct_Branch_Ascend. .

У доповіді наведено результати перспективних досліджень у галузі математичного моделювання просторових конфігурацій, оптимізаційних методів геометричного покриття та приклади їх практичного застосування. Дослідження задач покриття складних областей об’єктами довільної форми має міждисциплінарний характер і ґрунтується на сучасних досягненнях математики, комп’ютерних наук, інформаційних технологій та штучного інтелекту. Такі задачі є складовою рішень широкого кола завдань, пов’язаних із різними системами моніторингу територій, логістики, зв’язку, розвитком регіональної та критичної інфраструктури тощо.

Modeling of charged self-gravitating compact configurations using conformal killing vector

CPOpt: A modular framework for genetic algorithm optimization and post-optimization analysis in complex charged particle optical design

Introduction. The article investigates the ways to optimise the design of a truss-to-column connection in a frame structure made of round tubes. A steel lattice frame with a span of 66 m has been studied. A spanning member of a frame in the form of a truss transmits loads to a frame column through a rigid truss-to-column connection. In such structures, the maximum bending moments are applied to a truss-to-column connection, thus the problem of regulating its geometry is one of the objectives to be solved within the optimal designing. The aim of this paper is to create the most rational design solution for such a connection.Materials and Methods. In the lattice frames, depending on the chosen design, 5 to 7 members subject to compression stress converge at its inner side. The conventional variants of executing this connection would require using the heavy wall tubes which would result in the excess of material consumption. Within the research, modeling and calculation of various configurations of the truss-to-column connection were executed. The analysis of the calculation results revealed the shortcomings of the studied truss-to-column connection in a frame structure.Results. As a result of the connection design optimisation, a new configuration was suggested, where the connection of a bottom chord was executed by a slotted gusset plate. Such a solution allows for the most complete use of the bearing capacity of the bottom chord cross section.Discussion and Conclusion. As a result of the optimisation, the proposed design of a truss-to-column connection makes it possible to reduce the metal consumption while maintaining the relative simplicity of manufacture. At the same time, by reducing the local stresses, it is possible to achieve the greater strength and reliability of a truss-to-column connection.

In the human phonation process, acoustic standing waves in the vocal tract can influence the fluid flow through the glottis. To investigate the amount of two-way coupling between the flow field and the acoustics, the supraglottal flow field has been recorded via high-speed particle image velocimetry (PIV) in a synthetic larynx model for several configurations with different vocal tract lengths. Based on the obtained velocity fields, aeroacoustic source terms were computed. Furthermore, the hydrodynamic pressure in the vocal tract was recorded via wall pressure measurements. The recordings revealed that near a vocal tract resonance frequency, the focal fold oscillation frequency jumps onto the resonance. This is accompanied by a substantial relative increase in aeroacoustic sound generation efficiency. At the same time, the glottal volume flow needed for stable vocal fold oscillation decreases strongly. The pressure measurements showed that acoustic anti-resonances of the vocal tract directly dampen the harmonic content of the hydrodynamic pressure field when matching frequencies. This result shows that back-coupling of the acoustics onto the flow field does not only occur in the case where the vocal fold oscillation frequency and a resonance frequency of the vocal tract match, but also under more other conditions.

IEEE 802.15.4-based industrial wireless sensor-actuator networks (WSANs) have been widely deployed to connect sensors, actuators, and controllers in industrial facilities. Configuring an industrial WSAN to meet the application-specified quality of service (QoS) requirements is a complex process, which involves theoretical computation, simulation, and field testing, among other tasks. Since industrial wireless networks become increasingly hierarchical, heterogeneous, and complex, many research efforts have been made to apply wireless simulations and advanced machine learning techniques for network configuration. Unfortunately, our study shows that the network configuration model generated by the state-of-the-art method decays quickly over time. To address this issue, we develop a ME ta-learning based R untime A daptation (MERA) method that efficiently adapts network configuration models for industrial WSANs at runtime. Under MERA, the parameters of the network configuration model are explicitly trained such that a small number of optimization steps with only a few new measurements will produce good generalization performance after the network condition changes. We also develop a data sampling method to reduce the measurements required by MERA at runtime without sacrificing its performance. Experimental results show that MERA achieves higher prediction accuracy with less physical measurements, less computation time, and longer adaptation intervals compared to a state-of-the-art baseline.

The residual stress field induced by interference-fit riveting in aircraft panel structures significantly affects the fatigue performance around the rivet holes. Common residual stress analytical models often overlook the non-uniformity of interference between the rivet and the hole, which impacts the applicability of these models. Addressing this issue, an analytical model of residual stress around the rivet hole is proposed for a typical single-riveted structure based on the thick-walled cylinder theory and Lame’s equations, considering the non-uniform interference along the axis of the rivet hole. This novel model is then extended to multi-riveted structures in fuselage panels. Using vector synthesis, analytical models for single-row double-rivets and double-row quadruple-rivets configurations were derived. The established analytical models provide a three-dimensional characterization of the residual stress field in typical riveted structures. Finally, the accuracy of the model is verified through X-ray diffraction experiments and FEM simulation results.

Scale-free configuration models are intimately connected to power law Galton–Watson trees. It is known that contact process epidemics can propagate on these trees and therefore these networks with arbitrarily small infection rate, and this continues to be true after uniformly immunizing a small positive proportion of vertices. So, we instead immunize those with largest degree: above a threshold for the maximum permitted degree, we discover the epidemic with immunization has survival probability similar to without, by duality corresponding to comparable metastable density. With maximal degree below a threshold on the same order, this survival probability is severely reduced or zero.

Background. Energy storage systems (ESS) provide uninterrupted power access to users and aim to minimize energy losses. These systems are extensively utilized in microgrids, facilitating smooth transitions between grid-connected and isolated microgrid operations. ESS devices with high power density must manage fluctuating loads, such as substations and distributed power sources like wind turbines. Despite existing standards for ESS management, these regulations are often limited to specific areas, leading to interoperability challenges due to the absence of unified modeling approaches and data models. Objective. The purpose of the paper is to explore methods for controlling energy storage systems and enhancing their interoperability. The study focuses on integrating ESS without necessitating changes in consumer control schemes and utilizing interoperable data models for rapid identification and configuration. Methods. The research involves analysing various methods used in electricity management systems, identifying interaction conflicts, and emphasizing the development of an interoperable network. The study also considers the potential of an ontology-based control system to achieve the desired level of interoperability. Results. The analysis indicates that most existing electricity management systems experience interaction conflicts, highlighting the need for an interoperable network. The proposed systems offer significant advantages in fault tolerance, reliability, and scalability. These systems ensure a smooth transition between networked and isolated operations in microgrids, allowing for instant reconnection to the grid when normal conditions are restored. Conclusions. The findings suggest that developing an interoperable ESS is crucial for improving system efficiency and reliability. Future research directions include forming an ontology-based control system to enhance interoperability. This approach is expected to address current interaction conflicts and pave the way for more resilient and scalable energy storage solutions.

Serverless computing has ushered in a transformative paradigm, with a promise to alleviate developers from the intricacies of infrastructure management. However, current serverless platforms predominantly offer only serverless compute capabilities. As a consequence, the application developers are once again tasked to explicitly provision and manage the backend services (BaaS), such as object stores or API gateways, the infrastructure, and the configuration models. This violates the main promise of serverless computing and erases much of the practical benefits of the serverless paradigm. It also introduces the challenges of managing the application execution environment, which includes maintaining provisioning and deployment scripts, configuring and managing access permissions, and scaling the services during runtime. To address these challenges, in this paper we introduce a novel paradigm for the next generation of serverless computing, called self-provisioning infrastructure. The self-provisioning infrastructure is an infrastructure that is capable to automatically and autonomously (with zero-configuration and zero-touch) provision serverless functions, their infrastructure, and their supporting BaaS services. To achieve this vision, we introduce novel design principles, models, and mechanisms that are formalized via novel programming, function, and system models. With this novel paradigm, we intend to fortify the core design principles of serverless computing but also extend them to the entire application execution environment. By doing so, we aim to enable the next-generation serverless computing in the Edge-Cloud continuum.

Coils are one of the basic elements employed in devices. They are versatile, in terms of both design and manufacturing, according to the desired inductive specifications. An important characteristic of coils is their bidirectional action; they can both produce and sense magnetic fields. Referring to sensing, coils have the unique property to inductively translate the temporal variation of magnetic flux into an AC voltage signal. Due to this property, they are massively used in many areas of science and engineering; among other disciplines, coils are employed in physics/materials science, geophysics, industry, aerospace and healthcare. Here, we present detailed and exact mathematical modeling of the sensing ability of the three most basic scalar assemblies of coaxial pick-up coils (PUCs): in the so-called zero derivative configuration (ZDC), having a single PUC; the first derivative configuration (FDC), having two PUCs; and second derivative configuration (SDC), having four PUCs. These three basic assemblies are mathematically modeled for a reference case of physics; we tackle the AC voltage signal, VAC (t), induced at the output of the PUCs by the temporal variation of the magnetic flux, Φ(t), originating from the time-varying moment, m(t), of an ideal magnetic dipole. Detailed and exact mathematical modeling, with only minor assumptions/approximations, enabled us to obtain the so-called sensing function, FSF, for all three cases: ZDC, FDC and SDC. By definition, the sensing function, FSF, quantifies the ability of an assembly of PUCs to translate the time-varying moment, m(t), into an AC signal, VAC (t). Importantly, the FSF is obtained in a closed-form expression for all three cases, ZDC, FDC and SDC, that depends on the realistic, macroscopic characteristics of each PUC (i.e., number of turns, length, inner and outer radius) and of the entire assembly in general (i.e., relative position of PUCs). The mathematical methodology presented here is complete and flexible so that it can be easily utilized in many disciplines of science and engineering.

Phase-grating moiré interferometers (PGMIs) have emerged as promising candidates for the next generation of neutron interferometry, enabling the use of a polychromatic beam and manifesting interference patterns that can be directly imaged by existing neutron cameras. However, the modeling of the various PGMI configurations is limited to cumbersome numerical calculations and backward propagation models which often do not enable one to explore the setup parameters. Here we generalize the Fresnel scaling theorem to introduce a k-space model for PGMI setups illuminated by a cone beam, thus enabling an intuitive forward propagation model for a wide range of parameters and experimental setups. The interference manifested by a PGMI is shown to be a special case of the Talbot effect, and the optimal fringe visibility is shown to occur at the moiré location of the Talbot distances. We derive analytical expressions for the contrast and the propagating intensity profiles in various conditions and provide the first analysis of the PGMI dark-field imaging signal when considering sample characterization. The model's predictions are compared to experimental measurements and good agreement is found between them. Last, we propose and experimentally verify a method to recover contrast at typically inaccessible PGMI autocorrelation lengths. The presented work provides a toolbox for analyzing and understanding existing PGMI setups and their future applications, for example extensions to two-dimensional PGMIs and characterization of samples with nontrivial structures. Published by the American Physical Society 2024

In our pursuit of understanding electron-phonon coupling (EPC) and their impact on material properties, we delved deep into the intricate role played by the Eliashberg function in governing electron self-energy. Through meticulous evaluation of tailored polynomial models approximating this function, we unearthed profound insights into how phonon interactions intricately modify electronic energy bands. Employing numerical computations, we meticulously unraveled both the real and imaginary aspects of electron self-energy, crucial in comprehending EPC effects in various materials. Investigating superconductivity within monolayer graphene and its interaction with diverse doping substances, our study led us to identify optimal polynomial models that accurately capture EPC behaviors, offering invaluable implications for predicting critical temperatures in superconducting materials. Expanding the parameters within our models allowed us to anticipate changes in self-energy models for higher-order configurations not explored in this study. Our selection of polynomial spanning degrees from n = 1 to 10 the efficacy of n = 2 (Debye) as the most realistic and accurate model, closely followed by n = 1, albeit occasional deviations observed in specific materials. These discrepancies often stemmed from noise model inaccuracies and parameter approximations. Our comprehensive approach outshone the traditional Kramer-Kronig transform in assessing electron-phonon interactions. Looking ahead, the application of multiple models to the Eliashberg function diagram holds immense promise for enhancing accuracy, despite the challenge of concurrently adjusting multiple input parameters. This integration of numerical modeling with experimental data forms a robust framework, empowering the prediction and fine-tuning of material properties vital for the future fabrication of devices. HIGHLIGHTS Exploring the use of polynomial models to approximate the Eliashberg function, enabling a detailed analysis of electron-phonon coupling (EPC) in materials. Confirm the Debye model's accuracy in predicting the electronic structure of doped graphene. The findings suggest enhanced EPC in materials like CaC6 and its potential to induce superconductivity in monolayer graphene. This work offers a framework for future research and applications in superconducting materials using ARPES data. GRAPHICAL ABSTRACT

DFT investigation of [formula omitted]-split interstitial paramagnetic centers in diamond

Fiber-reinforced polymers (FRP) have been widely used in shear strengthening applications of reinforced concrete (RC) beams. The accurate prediction of the FRP contribution to the shear strength of beams is essential for reliable design. Gene expression programming (GEP) has been widely utilized because it reliably expresses complex relationships between experimental variables. In this study, three new GEP models are proposed for three different strengthening configurations of FRP such as fully-wrapping, U wrapping, and side-bonding to predict the FRP contribution to shear strength. These models are developed using the most comprehensive database containing a total of 811 strengthened beams (350 fully-wrapped, 328 U-wrapped, and 133 side-bonded. Many variables have been considered in the proposed GEP models, including those that have been experimentally effective but are often neglected in existing literature equations, such as the shear span-to-effective depth ratio (a/d)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(a/d)$$\\end{document} and the stirrup ratio (ρw\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\rho }_{w}$$\\end{document}). Additionally, the reliability of existing equations in the literature and the proposed GEP models for predicting the FRP contribution to shear strength was statistically evaluated. As a result of this evaluation, the proposed GEP models for each strengthening configuration of FRP yielded the most accurate statistical results, with the lowest coefficient of variation (COV), and the highest coefficient of correlation (R).

We present measurements of the 2p-3d transition opacity of a hot molybdenum–scandium sample with nearly half-vacant molybdenum M-shell configurations. A plastic-tamped molybdenum–scandium foil sample is radiatively heated to high temperature in a compact D-shaped gold Hohlraum driven by ∼30 kJ laser energy at the SG-100 kJ laser facility. X rays transmitted through the molybdenum and scandium plasmas are diffracted by crystals and finally recorded by image plates. The electron temperatures in the sample in particular spatial and temporal zones are determined by the K-shell absorption of the scandium plasma. A combination of the IRAD3D view factor code and the MULTI hydrodynamic code is used to simulate the spatial distribution and temporal behavior of the sample temperature and density. The inferred temperature in the molybdenum plasma reaches a average of 138 ± 11 eV. A detailed configuration-accounting calculation of the n = 2–3 transition absorption of the molybdenum plasma is compared with experimental measurements and quite good agreement is found. The present measurements provide an opportunity to test opacity models for complicated M-shell configurations.