Precise Numerical Models Research Articles

Dynamic model adjustment of a honeycomb sandwich panel using response surface methods and parametric optimization algorithms

The aeronautical and aerospace industries have been evolving in technologies that improve the efficiency of their structures, where, in this context, sandwich panels with honeycomb cores are one of the frequently employed technologies in aircraft and nano satellites. This type of structure presents superior mechanical properties compared to traditional panels, as well as a low structural weight due to its porous core. However, they exhibit complex characteristics both geometrically and behaviorally, requiring the use of more sophisticated computational tools such as finite element analysis packages. Precise numerical models are required to allow for reliable analysis during the design process of aerospace structures. The most commonly employed approach is to create a numerical model and refine it through adjustments using experimental data. This study presents the modal numerical analysis of a sandwich panel with a honeycomb core using finite elements and proposes an adjustment of the obtained numerical model. The panel is composed of Aluminum 2024 skins and an Aluminum 5056 honeycomb core. This panel had its vibration modes and frequencies previously identified through experimental tests. Once the numerical and experimental values are compared, a numerical-experimental adjustment is proposed using the optimization algorithms NLPQL, MISQP, and Genetic, which are applied through two methodologies, with and without the use of a metamodel, where the metamodel is constructed using the kriging algorithm. The optimization problem was formulated considering the minimization of a function defined as the sum of squared differences between experimental and numerical frequencies. The ANSYS Workbench software package was utilized for the numerical modeling, which also provided the necessary optimization algorithms. The results demonstrate good agreement with the experimental data, and the numerical adjustments made using the optimization algorithms proved to be effective.

Machine Learning‐Guided Design of 10 nm Junctionless Gate‐All‐Around Metal Oxide Semiconductor Field Effect Transistors for Nanoscaled Digital Circuits

In this paper, we introduce an innovative design approach based on combined numerical simulations and machine learning (ML) analysis to investigate the design key parameters of ultra‐low scale junctionless gate‐all‐around (JLGAA) field‐effect transistor (FET) devices. To this end, precise 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the current–voltage (I–V) characteristics of 10 nm‐scale JLGAA FET devices. The influence of design parameter variations and high‐k dielectric material on the subthreshold characteristics is thoroughly examined. Various ML algorithms were employed to analyze and classify the key design parameters influencing the subthreshold figures‐of‐merit (FoMs), the subthreshold swing (SS) factor and ION/IOFF ratio. The obtained results highlight that channel radius and channel doping design parameters are particularly important for affecting swing factor behavior. Similarly, these features also play a significant role in predicting and affecting ION/IOFF current ratio values. Additionally, machine learning is used to determine the optimal design parameters for each figure of merit (FoM) output value. In this context, the models effectively predicted both ION/IOFF current ratios and SS classification, with Naive Bayes achieving an accuracy of 90.8% for ION/IOFF and 92.6% for SS, showcasing the model's robustness in these classification tasks.

Implementation of an experimental set-up for insertion loss measurements of sonic crystal acoustic screens

The use of metamaterials for the implementation of noise control devices in the context of environmental acoustics is a topic that has gained more and more attention in recent years. The complex design of such devices and the difficulty of their construction in real-world applications complicates the acquisition of experimental data related to their acoustical performance. Numerical simulation tools are used to simulate the behaviour of these novel noise control devices in their design phase and help to validate the designs by comparing them with experimental data. This data, usually acquired under controlled conditions, holds significant importance in confirming the increasingly precise numerical models that have been developed over the years to simulate the behaviour of these physical systems. In this paper, an experimental set-up that enables acquisition of acoustical performance data for a particular metamaterial-based noise control device, consisting of networks of isolated scatterers, is presented. This prototype possesses several distinctive characteristics that render it particularly interesting, including its adaptability, ease of use, speed of obtaining results, and, particularly, its economic viability in relation to the tests that can be conducted in an anechoic chamber.

High-precision U-Pb geochronology for the Miocene Climate Optimum and a novel approach for calibrating age models in deep-sea sediment cores

Abstract Scientific ocean drilling cores recovered years ago (legacy cores), especially as recovered by rotary drilling, commonly show incomplete recovery and core disturbance. We present a novel method to date such cores by presenting the first high-precision U-Pb zircon ages targeting the duration of the Miocene Climate Optimum (MCO; ca. 17–14 Ma) from volcanic ashes at Ocean Drilling Program Site 1000 (on the Nicaragua Rise in the Caribbean Sea). We place these ages within a newly developed framework to address incomplete core recovery and use them to calibrate a high-resolution bulk carbonate δ13C and δ18O record. Our Site 1000 ages show that volcanism of the Columbia River Basalt Group (CRBG) large igneous province was coincident with the interval of greatest sustained MCO warmth at this site. However, if the CRBG were the primary driver of the MCO, our chronology may allow for outgassing preceding volcanism as a major source of CO2. We thus document a promising new way to obtain highly resolved, accurate, and precise numerical age models for legacy deep-sea sediment cores that does not depend on correlation to other records.

Quantitative analysis of the numerical simulation uncertainties from geological models in CO2 geological storage: A case study of Shenhua CCS project

The intensifying global climate change has prompted the imperative implementation of CO2 capture and storage (CCS) projects as a mitigation strategy. Ensuring the safety and reliability of these projects requires meticulous validation, including the establishment of geological models and conducting numerical simulations. In CO2 geological storage initiatives, the limitation of well data during the initial stages leads to data deficiency. This scarcity compromises the precision of geological and numerical models, hindering their ability to accurately depict actual subsurface conditions. Meanwhile, parameters related to heterogeneity significantly also impact storage effectiveness and safety. This study addresses these challenges by utilizing the Shenhua CCS demonstration project as a case study. Various heterogeneous parameters are selected, and local and global sensitivity analysis methods are subsequently introduced to determine the ranges and sequences of these parameters in numerical simulations. The simulation results can aid in assessing the influence of various heterogeneous parameters on the CO2 plume and bottom hole pressure. The study establishes the importance ranking of various heterogeneous parameters under different temporal and spatial conditions through sensitivity analysis. The findings reveal the following key points:1. During the small-scale injection period, the CO2 plume is particularly sensitive to variations in net-to-gross and vertical permeable properties.2. During and after larger-scale injections, the net-to-gross significantly impacts plume evolution, while bottom hole pressure is predominantly influenced by variations in vertical permeable properties.3. Both the CO2 plume and well bottom pressure are primarily affected by changes in sand body morphologies, especially at low net-to-gross scenarios.These conclusions assist in prioritizing the collection of critical parameter data in CCS projects, facilitating the establishment of more precise and reliable geological and numerical simulation models. The heightened accuracy and reliability of these models contribute to improving their predictive capabilities, ultimately guiding engineering practices.

Validation of the added mass effect in the narrow gaps of nuclear reactor pressure vessels

Nuclear reactor pressure vessels (RPVs) must be robust to external environmental excitations such as seismic loads. Precise numerical models that predict the dynamic responses to external loads are crucial for evaluating the safety of RPVs. Notably, in RPVs submerged in reactor coolant, it is necessary to examine the added mass effect of the fluid via fluid-structure interaction (FSI) analysis, employing either an analytical approach or a numerical analysis. The present study focuses on modeling and analyzing the added mass effect in the narrow gaps associated with the dense structural layout in the reactor. In this context, this paper validates analytical and numerical models that accurately describe the added mass effect for two cases: (i) simple concentric cylinders with a narrow gap filled with fluid and (ii) a complex small-scale RPV with narrow gaps also filled with fluid. Initially, the analytical model, employing Lagrange's equation of motion and velocity potential, and the numerical model, utilizing FE modeling in acoustic FSI through ANSYS, were validated through experimental modal testing for the added mass effect in the narrow gap between simple concentric cylinders. The analytical model for the concentric cylinders indicates that the added mass diverges to infinity as the gap approaches zero, which should be examined carefully. Through experiments for the concentric cylinders, the analytical and FEM-based added mass effects were validated with a natural frequency error of 3 %. Subsequently, based on the results for the concentric cylinders, a complex numerical model of the small-scale reactor with complex boundary conditions was developed and cross-validated through corresponding experimental modal testing of the small-scale RPV. The added mass model for the narrow gaps in the RPV was validated by substantial agreement of eight representative modes, as demonstrated by a 5 % natural frequency error.

Enhancing the fiber-based modeling approach for flexure-shear behavior in seismic evaluation of long-span railway masonry arch bridges

Masonry arch bridges are considered a significant part of the rail and road transportation infrastructures in many countries, and seismic assessment of these structures is of great importance for increasing their resilience. Despite some shortcomings in comparison to more complicated modeling techniques, i.e. continuous and discrete techniques, one-dimensional continuous modeling approaches such as the fiber beam approach are more practical for engineering purposes. Although the fiber models can acceptably predict the axial and flexural behavior of structural members, shear or flexure-shear failure behavior, which could be one of the dominant failure modes in masonry structures, cannot be predicted appropriately in this approach. The purpose of this article is to enhance fiber-based modeling approach to consider these brittle failure modes for the seismic assessments of long-span masonry arch bridges. Using the existing analytical-empirical prediction equations reported on the results of stone masonry walls, an iterative approach is proposed to enhance fiber-based model to include flexure-shear interaction. the proposed method is verified with a series of arch samples with different geometric properties with results from discontinuous models of the sample arches. Following that, different models of a span of a case-study masonry arch bridge is created, verified with health monitoring field observation, and analyzed to compare from different aspects and validate the application of the fiber beam method along with its enhancements. The results of the proposed method for fiber models show promising conformity with the results of more precise numerical models, and it can be used for the seismic evaluation of long-span stone masonry bridges.

A data-driven approach for linking models of large-scale bridges and monitoring data

Predictive maintenance of large-scale structures such as bridges requires precise numerical models to describe their current condition. Typically, solving an inverse problem is necessary to determine model parameters from Structural Health Monitoring (SHM) data. However, conventional methods such as Finite Element Updating through optimization algorithms demand substantial computational resources, as numerous parameter combinations need to be assessed to identify the optimal model state in each calibration step. Consequently, these methods are only partially suitable for creating digital twins of bridges.This paper introduces an alternative approach by treating the inverse problem as a model parameter classification problem. This involves establishing a model database that covers a wide range of damage states. Notably, this method eliminates the need for multiple simulations during the application phase, as simulations are performed only once in an offline context. Subsequently, a classification algorithm is trained based on this database, enabling real-time selection of the best-fit model for practical applications using SHM data, without the necessity for additional simulations.Transparency in algorithm decisions is crucial for infrastructure maintenance, therefore, optimal classification trees from the field of interpretable machine learning are employed. Decision trees offer a balance between high accuracy and interpretability while providing additional advantages, such as sensor placement evaluation. In summary, this approach demonstrates the potential for linking numerical models with SHM data through the application of interpretable machine learning techniques, facilitating real-time decision-making for the preservation and management of critical infrastructure.

Experimental and numerical investigation of flexural properties of larch beams reinforced with different layer numbers

Recent applications demonstrated how fiber-reinforced polymer (FRP) composites can improve the structural capabilities of glulam beams, particularly regarding their flexural and shear strength. With the development of precise numerical models, such systems can be optimized. There is currently a dearth of information in the literature on numerical models that can accurately anticipate the nonlinear behavior of low-grade glued laminated timber beams reinforced with FRP. In this study, larch beams were reinforced with carbon fiber reinforced polymer fabric 1, 2 and 3 layers. The effect of the number of floors on the flexural properties of the beams in reinforcement was investigated experimentally and numerically. As a result of the study, the best flexural properties were achieved with 3-layer reinforcement. It was observed that 1- and 2-layer reinforcement compared to the reference beam were also significantly effective. Numerical analyzes gave close values with experimental test results. As a result of comparing the results obtained from the numerical model with the experimental findings, it was concluded that the FRP fabric managed to significantly increase the performance of larch timber. The model is a useful tool for examining the effect of reinforcement coefficient and will be used for optimization of the larch beam.Recent applications demonstrated how fiber-reinforced polymer (FRP) composites can improve the structural capabilities of glulam beams, particularly regarding their flexural and shear strength. With the development of precise numerical models, such systems can be optimized. There is currently a dearth of information in the literature on numerical models that can accurately anticipate the nonlinear behavior of low-grade glued laminated timber beams reinforced with FRP. In this study, larch beams were reinforced with carbon fiber reinforced polymer fabric 1, 2 and 3 layers. The effect of the number of floors on the flexural properties of the beams in reinforcement was investigated experimentally and numerically. As a result of the study, the best flexural properties were achieved with 3-layer reinforcement. It was observed that 1- and 2-layer reinforcement compared to the reference beam were also significantly effective. Numerical analyzes gave close values with experimental test results. As a result of comparing the results obtained from the numerical model with the experimental findings, it was concluded that the FRP fabric managed to significantly increase the performance of larch timber. The model is a useful tool for examining the effect of reinforcement coefficient and will be used for optimization of the larch beam.

Corrosion effect analysis on thermal-hydraulics of LBE/water spiral tube steam generator under oxygen-controlled environment

The advancement of utilizing liquid Lead-Bismuth Eutectic (LBE) as a cooling medium is hindered by its pronounced corrosive properties. The impact of LBE corrosion on the performance of heat exchangers is significant over the long term, however, this aspect has been largely overlooked in previous studies. Historically, there has been a dearth of LBE corrosion models that account for flow accelerated corrosion. Concurrently, precise numerical models and coupling methods for comprehending the interplay between LBE corrosion and thermal-hydraulics have not been developed. Nevertheless, these contents are pivotal for the advancement of liquid LBE cooling mediums. To delve into the flow and heat transfer characteristics of heat exchangers subjected to LBE corrosion, enhancements were made to capture the accelerated removal effect of LBE corrosion flow. Subsequently, a bidirectional coupling model encompassing LBE corrosion and flow heat transfer was formulated. The Computational Fluid Dynamics (CFD) method was employed to simulate the corrosion and flow heat transfer in a spiral tube steam generator under conditions of saturated oxygen concentration and an oxygen-controlled environment. The results indicate that the corrosion of liquid LBE forms an oxidation layer, which hinders the heat transfer in the spiral tube steam generator. This leads to a significant increase in the wall friction coefficient and a decrease in the surface Nusselt number. However, it does not have a significant effect on the water side evaporation process of the spiral tube steam generator. The implementation of an oxygen-controlled environment can effectively reduce the influence of LBE corrosion on the flow heat transfer of the spiral tube steam generator. Especially under high temperature conditions, it can reduce the near wall temperature of LBE change by up to 57.5% and the outlet temperature change by up to 61.5%. The oxygen-controlled environment results in prolonged local hypoxia time, with a more pronounced effect at higher temperatures.

Numerical modeling of FS-trench IGBTs by TCAD and its parameter extraction method

The electrical behavior of the IGBTs is the key factors to the application of IGBTs. Technology Computer Aided Design (TCAD) is one of the most effective methods for characterizing of electrical behavior of IGBT. However, the great difficulties in extracting the model's parameters hinder its widely applications. In the present work, a high-precision TCAD model for FS-Trench IGBT was established and verified by experiments. Firstly, the parameters necessary for TCAD models were analyzed in theoretical and experimental. The key process parameters need to be determined in a value range table. A Convolutional Neural Network (CNN) was proposed and trained to form a relationship from characteristic curves to key process parameters. Subsequently, the numerical model's parameters were established by combining the trained CNN model with experimental data. Finally, the TCAD model was established and the simulations of IGBTs were carried out and verified by experiments. Results demonstrate that the proposed TCAD model is well consistent with experiments with a Root Mean Square Error (RMSE) of about 3.7 %. This work may provide a feasible method to obtain highly precise numerical models of power semiconductor devices. It may also promote the application of Artificial Intelligence in the device modeling and optimization design.

Study on Numerical Models in Predicting Surface Deformation Caused by Underground Coal Mining

Accurate prediction of surface deformation due to the extraction of underground coal seams is a significant challenge in geotechnical engineering. This task is further compounded by the growing trend for coal to be extracted under buildings by leaving coal pillars in place. To rationalize the design of mining schemes to ensure effective control of surface deformation, precise 3D numerical models of different mining schemes were developed in this paper. Two schemes were compared and analyzed based on the results of numerical simulations. Also, to verify the reliability of the prediction results from numerical simulations, the probability integral method was used for this prediction. The results show that the prediction results of the numerical simulations are close to those calculated by the probabilistic integral method, which proves the reliability of the numerical simulation prediction. The optimal mining scheme was determined by comparing the predicted results from the analysis of theoretical calculations and numerical simulations.

Structural Identification of Masonry Residential Buildings Considering Wall Openings

Although one-story residential masonry structures are thought not to be vulnerable to seismic actions, many heavily damaged and/or collapsed instances of these types of structures have been observed in the past strong earthquake events. Hence, the evaluation of their safety requires much attention in terms of more precise numerical models. In-situ vibration tests together with laboratory tests on masonry specimens provide valuable information for structural parameter identification that can be used to develop accurate numerical models. These numerical models then can be used for evaluation of the response and seismic safety. While many specific methods and parameters can be adopted in numerical modeling, linear material properties of a structure are expedient in response analysis. Hence, an equation to be used to determine the homogenized linear model parameters for masonry walls with openings is proposed in this study. The equation has been developed based on the percentage of the openings on the wall. The effect of wall openings on the stiffness and the total strength of one-story masonry structures have been evaluated by using the experimental data and the calibrated finite element models. In-situ ambient vibration and material tests have been conducted on three masonry buildings with identical materials and the results from these experiments were used to verify the accuracy of the formulation.

EXPERIMENTAL AND NUMERICAL STUDY OF SEWING SEAMS OF AUTOMOBILE SEAT COVERS UNDER UNIDIRECTIONAL AND MULTIAXIAL LOADING

In industrial textiles, knowing the exact characteristics and behaviour of materials is important. A several precise studies and numerical models of material structures of fabrics, foams, and threads have been developed, but the behaviours of entire functional parts have been researched to a lesser extent. For car seats, industrial textiles not just cover the underlying foam but also increase rigidity of the seat cushion and influence viscoelastic behaviours of foams. Moreover, strength of sewn seams is one of the main quality parameters. Herein, four polyester and polyamide threads were sewn on a material used for car seat covers through lockstitch sewing. Combinations of these materials were studied using static tests in the unidirectional and multiaxial variants. The experimental measurements recorded using a high-speed camera and computer tomography were used to create CAD models. Numerical simulations were conducted using these models and the obtained material models. These model studies help predict and describe the stresses emerging within various types of textile and the threads in their connections. The simulation results agree well with the experimental results

Influence of bone microstructure distribution on developed mechanical energy for bone remodeling using a statistical reconstruction method

The development of a predictive model for bone remodeling is becoming increasingly important for medical applications such as bone surgery or bone substitutes like prostheses. However, as bone remodeling is a complex multiphysics phenomenon and difficult to quantify experimentally, predictive numerical models remain, at best, phenomenologically driven. Patient dependency is often ignored as its influence is usually considered secondary, although it is known to play an important role over long periods of time. Another difficulty to study this patient dependency is the availability of experimental samples to carry out extensive analyses. Using our recently developed statistical reconstruction framework, a set of “bone like” microstructures with variety of distributions has been created to study pseudo “patient variabilities.” The method provides similar effective stiffness tensor, equivalent stresses, and strain energy distributions for the original and the statistically reconstructed samples. The main outcome of this study is the correlation of similar effective mechanical properties between samples when bone remodeling will depend on the local strain energy distribution as a function of each bone microstructure. It is expected that two different microstructures with equivalent bone volume fraction will lead to identical bone remodeling in a short period of time, whereas this needs to be proven for long term evolution. This work could be used to develop precise predictive numerical models while developing parametric studies on an infinite number of virtual samples and correlating patient dependency with more precise mechanobiological numerical models.

Combined methodology for three-dimensional slope stability analysis coupled with time effect: a case study in Germany

High-steep slopes in open pit mines are much more likely to collapse due to mining operations. Challenges such as data acquisition, precise numerical models and adaptable methodologies have impeded more reliable results of slope stability analysis based on the current methods. Within this context, this paper proposes a combined methodology using light detection and ranging technology to capture high-resolution slope geometry, three-dimensional geological and geotechnical modeling technologies for creating high-quality numerical simulation models and finite-element slope stability analyses combined with a new automatic strength reduction technique to analyze complex geotechnical problems. At the end, the methodology introduces a time series analysis to improve the reliability of the calculated factor of safety. A case study in the deepest open pit mine in Hambach, Germany, was conducted to test and demonstrate the effectiveness and applicability of the proposed methodology.

An Integrated Approach for optimization of Pulsed ND: YAG Laser Beam Welding process

Laser beam welding is a non-traditional, advanced technique used for similar & dissimilar materials and is widely used in various industries like automobile, aerospace, nuclear reactors, etc. at a faster pace. As it is a complex process, it is very difficult to find the optimal process parameters. The primary point of welding is to acquire a high quality joints and requiring little to no effort. However, without optimization, it is impractical to accomplish minimal effort welding. The principle work of this exploration is to display, break down and improve weld bead geometry in the powerful ND: YAG laser butt welding of Inconel 625. However, in view of the literature review, the responses are continuous and have impact on the welding geometry. In this way the feasible varieties of the judgment factors are started by coordinating trial tests. The Design of Experiment (DOE) system is used to generating the experimental plan and then conducting the experiments according to the plan. After recording the responses Reaction Surface Methodology (RSM) is received for precise expectation numerical models to evaluate the response variables and are created from the experimental information. Artificial Neural Networks (ANNs) have adopted from the MATLAB software for analysing the output response regression and provide the best curve fitting among the input and output variables.

FROM “MODELS” TO “REALITY”, AND RETURN. SOME REFLECTIONS ON THE INTERACTION BETWEEN SURVEY AND INTERPRETATIVE METHODS FOR BUILT HERITAGE CONSERVATION

Abstract. It's well known that more and more accurate methodologies and automatic tools are now available in the field of geometric survey and image processing and they constitute a fundamental instrument for cultural heritage knowledge and preservation; on the other side, very smart and precise numerical models are continuously improved and used in order to simulate the mechanical behaviour of masonry structures: both instruments and technologies are important part of a global process of knowledge which is at the base of any conservation project of cultural heritage. Despite the high accuracy and automation level reached by both technologies and programs, the transfer of data between them is not an easy task and defining the most reliable way to translate and exchange information without data loosing is still an open issue. The goal of the present paper is to analyse the complex process of translation from the very precise (and sometimes redundant) information obtainable by the modern survey methodologies for historic buildings (as laser scanner), into the very simplified (may be too much) schemes used to understand their real structural behaviour, with the final aim to contribute to the discussion on reliable methods for cultural heritage knowledge improvement, through empiricism.

On a Possible Unified Scaling Law for Volcanic Eruption Durations.

Volcanoes constitute dissipative systems with many degrees of freedom. Their eruptions are the result of complex processes that involve interacting chemical-physical systems. At present, due to the complexity of involved phenomena and to the lack of precise measurements, both analytical and numerical models are unable to simultaneously include the main processes involved in eruptions thus making forecasts of volcanic dynamics rather unreliable. On the other hand, accurate forecasts of some eruption parameters, such as the duration, could be a key factor in natural hazard estimation and mitigation. Analyzing a large database with most of all the known volcanic eruptions, we have determined that the duration of eruptions seems to be described by a universal distribution which characterizes eruption duration dynamics. In particular, this paper presents a plausible global power-law distribution of durations of volcanic eruptions that holds worldwide for different volcanic environments. We also introduce a new, simple and realistic pipe model that can follow the same found empirical distribution. Since the proposed model belongs to the family of the self-organized systems it may support the hypothesis that simple mechanisms can lead naturally to the emergent complexity in volcanic behaviour.

Predictability of the dispersion of Fukushima-derived radionuclides and their homogenization in the atmosphere

Long-range simulation of the dispersion of air pollutants in the atmosphere is one of the most challenging tasks in geosciences. Application of precise and fast numerical models in risk management and decision support can save human lives and can diminish consequences of an accidental release. Disaster at Fukushima Daiichi nuclear power plant has been the most serious event in the nuclear technology and industry in the recent years. We present and discuss the results of the numerical simulations on dispersion of Fukushima-derived particulate 131I and 137Cs using a global scale Lagrangian particle model. We compare concentrations and arrival times, using two emission scenarios, with the measured data obtained from 182 monitoring stations located all over the Northern Hemisphere. We also investigate the homogenization of isotopes in the atmosphere. Peak concentrations were predicted with typical accuracy of one order of magnitude showing a general underestimation in the case of 131I but not for 137Cs. Tropical and Arctic plumes, as well as the early detections in American and European midlatitudes were generally well predicted, however, the later regional-scale mixing could not be captured by the model. Our investigation highlights the importance of the parameterization of free atmospheric turbulence.