Reliable Simulation Research Articles

Molecular insights into the microstructure, dynamics, wettability and mechanical properties of sodium aluminum silicate hydrate with saturated nanopores

Sodium aluminosilicate hydrate (NASH) serves as the primary binding phase governing the physical, mechanical, and durability characteristics of geopolymer binders. However, the intricate interactions at the water-nanopore interface and the inherent heterogeneity of geopolymer binders present formidable challenges in exploring the intrinsic microstructure and micromechanical properties of this pivotal binding phase. In response to these challenges, we conducted reliable molecular dynamics simulations aimed at unraveling the intricate molecular-level features of NASH within saturated nanopores. Using the state-of-the-art simulations, results reveal that the structural properties of NASH remain unaffected by variations in nanopore size. Nevertheless, a critical nanopore size is identified, impacting the distribution of water molecules within the nanopore, as evidenced by the convergence of the water molecule number density. In terms of dynamics, diffusion of water molecules within the nanopore exhibits quasi-two-dimensionality, while in NASH demonstrates characteristics of supercooled liquids due to constraints imposed by the aluminosilicate network. Notably, the mechanical properties of NASH within saturated nanopores exhibit a significant decline as nanopore size increases, with uniaxial tension tests indicating the absence of strain softening. Additionally, the presence of hydroxyl groups on the NASH surface influences the adhesion of water molecules through hydrogen bonding, resulting in a pronounced hydrophilicity with a contact angle of 12.2°. The findings offer valuable insights into the behavior of NASH with saturated nanopores, shedding light on the molecular-level details of its structure, dynamics, mechanical properties, and wettability.

Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model

This study aims to investigate the impact of spatial rainfall distribution scenarios from ground observation stations on runoff simulation using hydrological modeling specific to the Rainfall-Runoff-Inundation (RRI) model. The RRI model was applied with six different spatial distribution scenarios of input rainfall, including Inverse Distance Weight (IDW), Thiessen polygon (TSP), Surface Polynomial (SPL), Simple kriging (SKG), and Ordinary kriging (OKG), to simulate the runoff of a 13,000 km2 watershed, namely the Nan River Basin in Thailand. This study utilized data from the 2014 storm event, incorporating temporal information from 28 rainfall stations to estimate rainfall in the spatial distribution scenarios. The six statistics, Volume Bias, Peak Bias, Root Mean Square Error, Correlation, and Mean Bias, were used to determine the accuracy of the estimated rainfall and runoff. Overall, the Simple kriging (SKG) method outperformed the other scenarios based on the statistical values to validate with measured rainfall data. Similarly, SKG demonstrated the closest match between simulated and observed runoff, achieving the highest correlation (0.803), the lowest Root Mean Square Error (164.48 cms), and high Nash-Sutcliffe Efficiency coefficient (0.499) values. This research underscores the practical significance of spatial interpolation methods, such as SKG, in combination with digital elevation models (DEMs) and landuse/soil type datasets, in delivering reliable runoff simulations considering the RRI model on the river basin scale.

Simulation framework and measurements of crystal collimation of proton beams at the Large Hadron Collider

Thanks to its versatility in deploying large kicks with high efficiency, crystal channeling is being integrated more and more into modern particle accelerators. For example, at the CERN Large Hadron Collider, crystals are used, or planned to be used for collimation and fixed-target opportunities for the Physics Beyond Colliders studies. In order to predict and optimize the performance in present and future applications, it is very important to have reliable simulation tools that integrate the crystal effects on the beam with multi-turn tracking. This work shows the setup of a simulation including magnetic tracking, detailed aperture models, particle-matter interactions as well as coherent physics processes in the crystals, using the coupling between SixTrack and FLUKA. A series of simulation tool benchmarks for collimation of multi-TeV protons has been carried out with LHC data.

An explicit updated Lagrangian fragile points method for dynamic fracture simulations

Dynamic fracture is a prevalent phenomenon in engineering structures subjected to dynamic loads, and reliable and simple numerical simulations of such phenomenon has been an ongoing research topic of computational mechanics. Although the finite element method (FEM) has been widely used for fracture simulations, there are still challenges such as the crack introduction and the mesh distortion. In recent years, meshless methods have emerged as potential alternatives to overcome these issues. In this paper, an explicit updated Lagrangian Fragile Points Method (FPM) is proposed for dynamic fracture simulations. The FPM is a point-based discontinuous meshless method, thus it on one hand circumvents the mesh distortion, and on the other hand allows for a simple, explicit introduction of cracks. In this paper, the formulations of the explicit updated Lagrangian FPM are introduced. Then the method is applied to various dynamic fracture problems including the spalling fracture and the crack branching, and for each cases the FPM provides convincing results. This paper shows that the explicit updated Lagrangian FPM is an effective yet simple numerical tool for predicting dynamic fracture. Note that though only small deformation dynamic fracture examples are used in this paper, the proposed method is able to handle finite strain problems, verified by some simple examples, since it is developed in the updated-Lagrangian form.

Impacts of precipitation uncertainty on hydrological ensemble simulations over the Ganjiang River basin

Study regionThe Ganjiang River basin (113°−117°E, 24°−30°N; 83,374 km2) is a large watershed with complex topography in the Poyang Lake basin in Jiangxi province, China. Study focusThis study evaluates three quantitative precipitation estimates (QPEs) over the Ganjiang River basin, namely the China Gauged-Based Daily Precipitation Analysis (CGDPA) data, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) data, and the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The study also investigates the impacts of precipitation uncertainty on hydrological ensemble streamflow simulations using the three QPEs as precipitation inputs of the Variable Infiltration Capacity (VIC) hydrological model. New hydrological insights for the regionAPHRODITE underestimates precipitation compared to CGDPA, while PERSIANN-CDR shows greater spatiotemporal variability. The ensemble mean streamflow demonstrates greater improvement compared to the results obtained from a single parameter set. Among the three QPEs, the simulations forced by CGDPA show the best deterministic and probabilistic verification scores, followed by APHRODITE. PERSIANN-CDR tends to underestimate evaporation and leads to the lowest score of ensemble streamflow simulations, but shows advantages in simulating extremely low streamflow. The study highlights that high-density gauge-based QPEs remain the most accurate source of precipitation inputs for reliable hydrological simulations, while satellite-gauge merged QPEs can provide valuable inputs for hydrological simulations over the basins where meteorological stations are scarce.

Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020

The pursuit of higher-resolution and more reliable spatial distribution simulation results for air pollutants is important to human health and environmental safety. However, the lack of high-resolution remote sensing retrieval parameters for gaseous pollutants (sulfur dioxide and ozone) limits the simulation effect to a 1 km resolution. To address this issue, we sequentially generated and optimized the spatial distributions of near-surface PM2.5, SO2, and ozone at a 1 km resolution in China through two approaches. First, we employed spatial sampling, random ID, and parameter convolution methods to jointly optimize a tree-based machine-learning gradient-boosting framework, LightGBM, and improve the performance of spatial air pollutant simulations. Second, we simulated PM2.5, used the simulated PM2.5 result to simulate SO2, and then used the simulated SO2 to simulate ozone. We improved the stability of 1 km-resolution SO2 and ozone products through the proposed sequence of multiple-pollutant simulations. The cross-validation (CV) of the random sample yielded an R2 of 0.90 and an RMSE of 9.62 µg∙m−3 for PM2.5, an R2 of 0.92 and an RMSE of 3.9 µg∙m−3 for SO2, and an R2 of 0.94 and an RMSE of 5.9 µg∙m−3 for ozone, which are values better than those in previous related studies. In addition, we tested the reliability of PM2.5, SO2, and ozone products in China through spatial distribution reliability analysis and parameter importance reliability analysis. The PM2.5, SO2, and ozone simulation models and multiple-air-pollutant (MuAP) products generated by the two optimization methods proposed in this study are of great value for long-term, large-scale, and regional-scale air pollution monitoring and predictions, as well as population health assessments.

Numerical simulation of interaction properties at wall-to-tie-column interface for confined masonry walls

The confined masonry (CM) structure consists of load bearing walls strengthened with nominally reinforced concrete tie-elements at the perimeter and other key locations. Various simulation studies using finite element analysis have been performed on CM walls for a better understanding of their response under in-plane and out-of-plane loads. However, for a reliable simulation of response, it is important to define the realistic interaction properties at the wall-to-tie-column interface for CM walls. The current method of defining the wall-to-tie column interaction for numerical simulation is either using monolithic interaction or frictional contact. This simplified assumption results in stiffer or flexible response of the CM walls. Thus, in the present study, the wall-to-tie-column interaction properties were evaluated using experimental and analytical investigation. Initially, flexure (tension) and shear bond tests were conducted on sub-assemblages consisting of masonry and concrete. The tensile and shear bond properties obtained from the experiments were used to calibrate the cohesive and friction interaction properties for numerical analysis. The proposed interaction properties such as bond stiffness, damage initiation, and damage evoluation provided better simulation of in-plane response of CM walls.

BEM modelling of a hydrodynamic lifting surface working in ground proximity

Present paper aims to assess the performance and limits of boundary element method for the reliable simulation of ground effect phenomena on hydrodynamic lifting surfaces. Benefitting from the ground effect, especially in extreme ground proximity, suggests a promising concept for lifting bodies. On the other hand, the more pronounced the ground effect, the more complicated flow physics it exhibits, making the problem harder to handle numerically. In this study, a potential-based boundary element method (BEM) is applied to the flow past a 3D rectangular wing within a wide range of ground clearances corresponding to out of ground effect (OGE), in ground effect (GE) and extreme ground effect (EGE) conditions. Both positive and negative angles of attack are tested to examine the lift and downforce producing cases. The potential flow around the airfoil is computed using the mixed constant-strength source and constant-strength dipole based panel method. The results show that BEM successfully predicts the lift force variation for a wing working in ground proximity (GE). However, approaching the bounds of extreme ground effect is seen to introduce a progressive decrease of the fidelity of the method. A similar tendency is observed in the downforce generating conditions, which is also an important scenario for ground vehicle aerodynamics. BEM remarkably overestimates the magnitude of the downforce when the ground clearance reaches up to the EGE conditions, still, it produces quite satisfactory results for the rest of the tested configurations. The pressure distributions are also provided to examine the flow field in the aforementioned cases. Overall, the results lead us to conclude that BEM stands as a robust and reliable prediction tool for ground effect on 3D wings but the extreme ground effect condition represents the limit of its accuracy range.

Determination of Citronellal-geraniol Phase Equilibrium Data and Distillation Simulation Study based on Aspen Plus Software

Aspen Plus is an efficient, reliable, and scalable large-scale process simulation software suitable for a variety of applications. It boasts excellent computational efficiency, high-quality physical property data, and comprehensive system integration, earning consistent acclaim within the industry. The separation of citronella oil components is increasingly expensive due to their thermal sensitivity and high separation difficulty. This study utilizes gas chromatography to analyze the volatile oil components and investigates the phase equilibrium relationship of citronellal -geraniol system in citronella oil. Experimental phase equilibrium data for the citronellal-geraniol system in citronella oil are obtained to provide the thermodynamic basis for the separation of the citronellal-geraniol system in citronella oil. Aspen Plus process simulation software is used to estimate the physical property parameters of the citronellal-geraniol system and optimize the distillation separation, providing support for the industrial production of high-purity separation of the citronellal-geraniol system.

Methods and means for researching microwave devices

This paper is considered to be a continuation of a previous publication devoted to applications of digital transformations and simulation modeling in aerospace engineering and security (IJITS, №3, 2023). The aim of the paper is to present methods and means for conducting microwave measurements and practical learning of real antennas, which are expressed in the design, modeling, and validation of the models and contribute to obtaining reliable simulation results. The theoretical framework of the study is supported by classifications of the main types of microwave devices and technologies used in innovative laboratories.

High-frequency Differential Mode Modeling of Universal Motor's Windings

The universal motor is a rotating electrical machine that can operate on either direct current or single-phase alternating current, similar to a DC motor. It has been widely used in various small and inexpensive drives for a long time, mostly in home appliances and hand tools. The noise generated by a universal motor is believed to be closely associated with the electromagnetic torque fluctuations of the machine, which are caused by variations in the current supplied to the motor. The power electronics utilized for controlling the motor's speed are responsible for these current changes. Accurate high-frequency motor models are crucial for reliable electromagnetic interference simulations in motor drive power electronic systems. Research efforts have expanded to explore different realistic configurations that can be used to investigate the electromagnetic compatibility behavior of electrical machines. This study describes a method for predicting the differential mode impedance of universal motor. The behavior of each motor winding has been individually studied through impedance measurements, starting with the armature, followed by the series winding, and finally the inductive compensating winding. The prediction results over a wide frequency range up to 1 MHz are in good agreement with the measurements and enabled us to propose a model circuit for each motor winding.

Parametric cohesive zone analysis of bi-adhesive single-step joints

The industry has advanced in replacing traditional joining methods, such as screwing/riveting and welding, especially the aeronautical and automotive industries, which have adopted the adhesive bonding method. This method has the advantages of simplifying the process, improving performance in terms of fatigue load, and improving the union between dissimilar materials. In addition, the development of computational numerical methods, with greater precision, has contributed to the dimensioning of joints, as well as the prediction of the resistance of these joints, which has contributed to more reliable simulations of different adhesive bonding solutions and their industrial implementation. Different modifications to the conventional designs are addressed in the literature to achieve the best results, including geometrical and material modifications. In the present research, the strength of single-step single-adhesive joints and bi-adhesive joints (BAJ) manufactured with AW 6082-T651 aluminium adherends is experimentally and numerically studied for different overlap lengths (LO). Three commercial adhesives were studied, from brittle to ductile, along with different adhesive combinations in the BAJ technique. The experimental work was mainly used for numerical model validation. The numerical analysis took advantage of triangular cohesive zone models (CZM) and included the study of peel and shear stresses, strength, and energy to failure. It was possible to validate the CZM accuracy by comparison with the experimental data. The analysis carried out showed that the BAJ technique did not reveal significant increases in strength compared to single-adhesive joints. However, when damage tolerance and dissipated energy are analysed, a noticeable increase in performance is observed, especially in joints with larger LO.

Real-time impulse response: a methodology based on Machine Learning approaches for a rapid impulse response generation for real-time Acoustic Virtual Reality systems

Simulation of high-definition binaural room impulse responses using conventional approaches involves a significant amount of computational resources, resulting in high computational time, making these approaches incapable of performing real-time high quality acoustic virtual reality. This research implemented a methodology for the rapid impulse response generation using the position of a moving listener inside a fixed sound field. The rapid generation of the impulse response is performed using its representative compressed dimension, with a smaller dimension than the original impulse response, learned by variational autoencoders and long short-term memory neural networks. First, the methodology selects a representative number of impulse responses covering the area of interest using a reliable room acoustic simulator. Second, it generates a dataset with sufficient impulse responses uniformly distributed through a data augmentation approach using a modified bilinear interpolation from the impulse responses previously simulated. Third, it applies an unsupervised model to positionally cluster the impulse responses to reduce the variability of the impulse responses in the given environment. Fourth, it splits the impulse response into time segments and generates a dataset per segment and cluster. Fifth, it trains a variational autocoder with a long short-term memory neural network model for each time segment cluster of impulse responses to infer the correspondent compressed impulse response part. In summary, the impulse response is generated by assigning the current listener position to the corresponding cluster and executing the decoders of the variational autoencoders with long short-term memory, trained previously. The findings are encouraging; the normalized mean absolute error of the impulse responses gathered by the interpolator and the impulse responses generated by the proposed model is less than 15% in the 88% of impulse responses reserved for testing. Moreover, the average of the absolute error of the interaural cross-correlation coefficient between the impulse responses obtained from the simulator and the proposed model is around 16%, implying that most acoustic characteristics of the real impulse are preserved. In addition, the computational time for generating the impulse response segment of 300 ms is approximately 65 ms, which is almost haft than the total system latency for a realistic auralization, 112 ms.

Effective diffusional limitation modeling of a heterogeneous reaction system for computational fluid dynamics application

Diffusional limitation of heterogeneous reaction catalysts is typically fitted or over-generalized without considering the catalyst’s physical parameters upon conducting computational fluid dynamics (CFD) analysis of an industrial-scale chemical reactor. Fitting and over-generalization are done to meet computational cost constraints. However, this reduces the reliability of local thermodynamic state analysis and application of calculated results. This study proposes an effective catalytic diffusion-limited model for reliable and cost-effective three-dimensional CFD simulations to overcome such difficulties. The proposed model utilizes simplified equations to compute the diffusional limitation by incorporating the physical parameters of the catalyst. The model is validated using Ni-based cylindrical catalyst pellets for steam–methane reforming and ammonia decomposition reactions, with in-house experiments supporting the fidelity of the model. For practical implementation, the model is applied to three-dimensional CFD simulations of a commercial-scale solid oxide fuel cell (SOFC) hotbox housing a large-scale reformer. Furthermore, a parametric study for inlet gas temperatures and catalyst size is carried out, which provides useful insights into how operating conditions and catalysts affect the transport phenomena and local thermodynamic state of the SOFC hotbox. Consequently, the practicality and versatility of the presented model are established through experiments and simulations.

Modeling Silicon-Dominant Anodes: Parametrization, Discussion, and Validation of a Newman-Type Model

Silicon is a promising anode material and can already be found in commercially available lithium-ion cells. Reliable modeling and simulations of new active materials for lithium-ion batteries are becoming more and more important, especially regarding cost-efficient cell design. Because literature lacks an electrochemical model for silicon-dominant electrodes, this work aims to close the gap. To this end, a Newman p2D model for a lithium-ion cell with a silicon-dominant anode and a nickel-cobalt-aluminum-oxide cathode is parametrized. The micrometer silicon particles are partially lithiated to 1200 mAh gSi−1. The parametrization is based on values from the electrode manufacturing process, measured values using lab cells, and literature data. Charge and discharge tests at six different C-rates up to 2C serve as validation data, showing a root-mean-squared error of about 21 mV and a deviation in discharge capacity of about 1.3%, both during a 1 C constant current discharge. Overall, a validated parametrization for a silicon-dominant anode is presented, which, to the best of our knowledge, is not yet available in literature. For future work, more in-depth studies should investigate the material parameters for silicon to expand the data available in the literature and facilitate further simulation work.

Fault analysis and reliability evaluation for motorized spindle of cycloidal gear grinding machine based on multi-source bayes

A Monte Carlo simulation method based on multisource bayes is proposed to improve the reliability of motorized spindles in cycloid gear grinding machines and reduce their failure rate. Based on field investigations and motorized spindle maintenance records, a fault tree model of a motorized spindle was established, and the fuzzy importance of each bottom event was evaluated. The fault tree of a motorized spindle was used as a Monte Carlo reliability simulation model, and its importance was used as the input parameter for the simulation. The reliability evaluation index of the motorized spindle was obtained at different simulation times. The feasibility and accuracy of the reliability simulation were verified by comparing the importance and simulation importance. A vibration test was designed for bearing faults with high importance, and fault extraction was performed by combining the wavelet packet transform and empirical mode decomposition. This method can also be used to simulate and analyze the reliability of other equipment or machine tools.

Generalisation of splitting methods based on modified potentials to nonlinear evolution equations of parabolic and Schrödinger type

The present work is concerned with the extension of modified potential operator splitting methods to specific classes of nonlinear evolution equations. The considered partial differential equations of Schrödinger and parabolic type comprise the Laplacian, a potential acting as multiplication operator, and a cubic nonlinearity. Moreover, an invariance principle is deduced that has a significant impact on the efficient realisation of the resulting modified operator splitting methods for the Schrödinger case.Numerical illustrations for the time-dependent Gross–Pitaevskii equation in the physically most relevant case of three space dimensions and for its parabolic counterpart related to ground state and excited state computations confirm the benefits of the proposed fourth-order modified operator splitting method in comparison with standard splitting methods.The presented results are novel and of particular interest from both, a theoretical perspective to inspire future investigations of modified operator splitting methods for other classes of nonlinear evolution equations and a practical perspective to advance the reliable and efficient simulation of Gross–Pitaevskii systems in real and imaginary time.

Sensor Systems for Extremely Harsh Environments

Sensors are key elements for capturing environmental properties and are today indispensable in the industry for monitoring and control of industrial processes. Many applications are demanding for highly integrated intelligent sensors to meet the requirements on safety, clean and energy efficient operation or to gain process information in the context of industry 4.0. While in many everyday objects highly integrated sensor systems are already state of the art, the situation in an industrial environment is clearly different. Frequently the use of sensor systems is impossible, due to the fact that the extreme ambient conditions of industrial processes like high operating temperatures or strong mechanical loads do not allow a reliable operation of sensitive electronic components. Eight Fraunhofer Institutes have bundled their competencies and have run the Fraunhofer Lighthouse Project ‘eHarsh’ to overcome this situation. The project goal was to realize sensor systems for extremely harsh environments, whereby sensor systems are not only pure sensor elements, rather containing one or multiple sensor elements and integrated readout electronics. Various technologies which are necessary for the realization of such sensor systems have been identified, developed and finally bundled in a technology platform. These technologies are e. g. MEMS and ceramic based sensors, SOI-CMOS based integrated electronics, board assembly and laser based joining technologies. All these developments have been accompanied by comprehensive tests, material characterization and reliability simulations. Based on the platform a pressure sensor for turbine applications has been realized to prove the performance of the eHarsh technology platform.

Numerical Modelling and Investigation of the Impact Behaviour of Single Guardrail Posts

Vehicle restraint systems are vital hardware elements in road safety engineering. The certification process of a vehicle restraint system includes full-scale crash tests, component testing and numerical simulation of these tests. To achieve reliable crash test simulation results, the soil–post interaction must be modelled to capture the behaviour realistically. There is no standardised approach for modelling the soil–post interaction in the praxis. In this study, the finite element method is utilised to investigate the soil–post response under quasi-static and dynamic impact loading. Two different modelling techniques are applied for this purpose. The first technique is the finite element continuum method, with the soil modelled using the advanced hypoplastic constitutive relation and calibrated using laboratory test data. The second technique is a lumped-parameter model, for which a systematic parameters calibration routine using basic soil properties is introduced. The numerical models are validated using a series of full-scale field tests performed by the authors on single posts in standard road shoulder materials. The performance comparison of the investigated modelling techniques shows that the hypoplastic constitutive relation can capture the post behaviour realistically under different loading conditions using the same parameter set. The introduced lumped-parameter model adequately simulates the post behaviour with high computational efficiency, which is very important when simulating several posts. The conducted parametric study elucidates that the soil’s relative density, the post’s embedment length, and the post-section modulus govern the single post’s lateral load-bearing behaviour and energy dissipation capacity.

A comprehensive study on the consequences of substituting energy crops by alternative substrates for biogas production in Germany

Using agricultural waste biomass and by-products (alternative substrates) for biogas production is a promising strategy to improve energy self-sufficiency in the European Union, and the sustainability of biogas production. A comprehensive study with three different types of alternative substrates was conducted on lab-scale. The methane potential of the untreated alternative substrates was 44.1–66.5% of maize silage. Promising substrate pre-treatment methods were identified, enhancing the methane potential by up to 46% and the degradation kinetics by up to 106%, respectively. Simulating the methane production in continuously operated digesters by parameters obtained in the batch tests was possible. High accuracy of the parameters was essential to obtain reliable simulation results. Applying the simulation approach to a case study revealed that the methane production is reduced by 36%–73% if the alternative substrates substitute maize silage. To analyze the economic feasibility, this was contrasted with prices for maize silage and electricity in Germany in 2022. Only maize straw showed the potential to replace maize silage during those hours that featured the 25% lowest electricity prices.