Simulation Procedure Research Articles

Site-specific traffic modelling and simulation for a major Italian highway based on weigh-in-motion systems accounting for gross vehicle weight limitations

The present-day road traffic with the persistent change in the type and volume of vehicles needs to be specifically investigated for effective safety management of aging highway infrastructures. Actual traffic data can be implemented in refined procedures for stochastic simulation of road infrastructure performance, structural health monitoring (SHM), definition of weight limits on highways, and traffic-informed structural safety checks. While weigh-in-motion (WIM) systems had been widely used in many countries, their installation on Italian highways was mostly discussed and carried out only after the catastrophic collapse of the Polcevera bridge in 2018. This study presents a statistical data analysis, probabilistic models, and a simulation procedure for highway traffic, based on measurements of two WIM systems located along European route E45 close to Naples, Italy. Different limitations to maximum gross vehicle weight (GVW) were enforced at the locations of the two WIM systems, according to the Italian road code and the Italian guidelines for risk classification, safety assessment and monitoring of existing bridges, respectively. WIM data sets were filtered to exclude erroneous traffic data and vehicle classes defined according to the number of axles and axle distance were statistically characterised, allowing the derivation of probabilistic models for all traffic parameters of interest. A simulation methodology to generate random traffic load from the WIM data is also presented for its possible use in probabilistic performance assessment and traffic informed SHM of road infrastructures such as bridges.

Target temperature-based simulation method for predicting performance and designing aircraft thermal anti-icing systems

The prevention of ice build-up on aircraft surfaces is crucial for safe operations. Thermal ice protection systems, particularly electro-thermal systems, are commonly employed to mitigate ice formation. However, the weight of electric batteries used in these systems significantly impacts aircraft performance, necessitating the minimization of power load during design. Despite this requirement, effective anti-icing operation mandates a sufficient heat flux to maintain surface temperature above the freezing point and evaporate any runback water film. Previous research has utilized numerical simulation analysis to predict the performance of electro-thermal anti-icing systems. The conjugate heat transfer method has been commonly employed to predict temperature distribution on the protected surface. However, non-isothermal surfaces exhibit distinct local convective heat transfer coefficients that influence local thermal balance, unlike isothermal surfaces that adopt the loosely-coupled method for computational efficiency. In this study, we present a novel target temperature-based anti-icing simulation method that predicts the required heat flux for thermal ice protection systems without employing the conjugate heat transfer method. Our objective was to calculate the necessary heat flux to achieve the desired non-isothermal surface temperature for effective anti-icing. By eliminating the excessive iteration process associated with the coupling method for conjugate heat transfer, we have streamlined the simulation procedure. Through validation, target temperature-based anti-icing simulation method, when compared to the tight coupling method, demonstrated the ability to predict the same surface water flow rates while reducing computation time by up to 90 %. Furthermore, when applied to design problems, our method achieved approximately a 10 % reduction in the required heat capacity for anti-icing compared to experimental values. As a result, target temperature-based anti-icing simulation method offers a more efficient alternative to traditional conjugate heat transfer methods, providing valuable insights for both predicting anti-icing system performance and optimizing aircraft design.

Fuzzy Multi-Item Newsvendor Problem: An Application to Inventory Management

This paper proposes a novel approach to the fuzzy newsvendor problem for inventory management applications. The main contributions of the paper are the following: a new credibility estimation is proposed, to explore the neighborhood around the most impactful demand scenarios; a simulation procedure was designed for the different demand scenarios, which allows comparison of the proposed approach with classical and fuzzy multi-item newsvendor problems; a modified genetic algorithm (GA) is introduced to ameliorate previous genetic algorithms in both the generation and evaluation of solutions. The new formulation of the fuzzy newsvendor problem, together with the modified GA, were shown to improve the average profit by up to 55% in problems with low-budget scenarios.

Efficient reliability analysis of stochastic dynamic first-passage problems by probability density evolution analysis with subset supported point selection

This study introduces a novel point selection procedure for the Probability Density Evolution Method (PDEM) to estimate time-dependent reliability and failure probabilities in dynamic systems under first-passage failure conditions. The method integrates and modifies features of the Subset simulation procedure to adaptively generate dependent sample sets suitable for a reliability analysis by a direct probability integration approach levering PDEM. Performance function assessments are used as weighting factors in the Subset supported Point Selection (S-PS), enhancing the ultimate failure probability estimation accuracy. The presented approach effectively identifies samples in the failure region, particularly benefiting for dynamic systems under stochastic excitation, tested with random dimensions up to 60. It also offers a computationally efficient structural reliability estimation procedure by analyzing full time–history responses. The proposed method provides deeper insights into rare failure events and mechanisms through the visualization of intermediate results. This research presents an advanced framework for estimating structural reliability and understanding critical events in dynamic systems.

Financial dynamics, green transition and hydrogen economy in Europe

The introduction of the Next Generation funding scheme provides an opportunity for the EU-25 to make significant financial progress towards a radical and fully sustainable Green economy. This study examines the financial implications of a complete transition to hydrogen energy. In this paper, we propose the Hydrogen Production Safe Supply and Storage energy model by analyzing the conversion of electricity from offshore wind farms to hydrogen, which serves as both an energy storage and fuel for a variety of sectors, including transportation, households, industries, and governments. We analyze the financial impact of the Green transition across the EU-25 economies, based on a panel auto-regressive distributed lag model and the simulation procedure proposed by Jordan and Philips (2018). In addition to electricity production and consumption, the variables used include greenhouse gas emissions, real output per capita, and total environmental tax revenue. According to the study's financial insights, increased renewable electricity production results in a significant reduction in household energy consumption, as well as an increase in consumption in the transportation, industrial, and public sectors. These findings underscore the economic implications of transitioning to renewable energy and highlight the financial imperatives, such as targeted state aid, the immediate expansion of the hydrogen supply network, further research on safe mobile hydrogen containment, and an extensive upgrade of the regional hydrogen supply network within the EU-25.

Computational Fluid Dynamics Investigation of Hydrodynamic Forces and Moments Acting on Stern Rudder Plane Configurations of a Submarine

This study presents the predicted hydrodynamic characteristics of different rudder plane configurations on the stern of a full-scale submarine in deep water, which are obtained using the Reynolds-Averaged Navier–Stokes method in Ansys Fluent Solver. First, the results obtained for the X-rudder plane configuration are verified according to previous numerical and experimental results in order to assess the accuracy of the simulation procedure. The X-rudder plane, Y-rudder plane, and Cross-rudder plane configurations in deep water with deflection angles ranging from −21 degrees to +21 degrees are then simulated. Next, the hydrodynamic forces and moments of the Cross-plane, X-plane, and Y-plane rudder configurations obtained through simulation are analyzed using Taylor’s expansion to estimate the hydrodynamic coefficients. The obtained results demonstrate that the X-force of the X-plane rudder configuration is larger than the corresponding forces acting on the Cross-plane rudder and Y-plane rudder configurations. Meanwhile, the Y-force and Z-force of the X-plane rudder configuration are significantly greater than the corresponding forces of the left configurations. The same tendency can be seen in the moment of the X-plane rudder about the y- and z-axes. However, the roll moment induced by the Y-plane and Cross-plane rudder configurations is significantly larger than that under the X-plane rudder configuration.

Statistical Issues on the Neutrino Mass Hierarchy with Δχ2

The neutrino mass hierarchy determination (ν MHD) is one of the main goals of the major current and future neutrino experiments. The statistical analysis usually proceeds from a standard method, a single-dimensional estimator 1D−Δχ2 that shows some drawbacks and concerns, together with a debatable strategy. The drawbacks and considerations of the standard method will be explained through the following three main issues. The first issue corresponds to the limited power of the standard method. The Δχ2 estimator provides us with different results when different simulation procedures were used. Regarding the second issue, when χminNH2 and χminIH2 are drawn in a 2D map, their strong positive correlation manifests χ2 as a bidimensional variable, instead of a single-dimensional estimator. The overlapping between the χ2 distributions of the two hypotheses leads to an experiment sensitivity reduction. The third issue corresponds to the robustness of the standard method. When the JUNO sensitivity is obtained using different procedures, either with Δχ2 as one-dimensional or χ2 as two-dimensional estimator, the experimental sensitivity varies with the different values of the atmospheric mass, the input parameter. We computed the oscillation of Δχ2¯ with the input parameter values, Δm2input. The MH significance using the standard method, Δχ2, strongly depends on the values of the parameter Δm2input. Consequently, the experiment sensitivity depends on the precision of the atmospheric mass. This evaluation of the standard method confirms the drawbacks.

Innovative approach to estimate structural damage using linear regression and K-nearest neighbors machine learning algorithms

Conventional structural design methodologies often utilize elastic analysis techniques, such as the equivalent static force method and the response spectrum method. While these methods are known for their simplicity and computational efficiency, they prove inadequate in capturing the extent of structural damage caused by seismic forces. Additionally, employing nonlinear dynamic analysis to estimate structural damage represents a challenging and intricate task, posing difficulties for many structural designers. Consequently, the objective of this paper is to present an innovative methodology for evaluating seismic structural damage of moment-resisting frame structures. This involves the utilization of machine learning algorithms, which have been trained and tested on a large data set generated using a newly developed and numerically efficient simulation procedure. The machine learning algorithms employ both linear regression and K-nearest neighbors approaches to accurately replicate the Park-Ang structural damage index.

CO2 inside sI clathrate-like cages: Automated construction of neural network/machine learned guest-host potential and quantum spectra computations.

We present new results on the underlying guest-host interactions and spectral characterization of a CO2 molecule confined in the cages of the sI clathrate hydrate. Such types of porous solids raise computational challenges, as they are of practical interest as gas storage/capture materials. Accordingly, we have directed our efforts toward addressing their modeling in a proper manner, ensuring the quality of the input data and the efficiency of the computational approaches. The computational procedure for spectral simulations, within the multi-configurational time-dependent Hartree framework, involves the development of a fully coupled Hamiltonian, including an exact kinetic energy operator and a many-body representation of the potential, along with dipole moment surfaces, both obtained through neural network machine learning techniques. The resulting models were automatically trained and tested on extensive datasets generated by PW86PBE-XDM calculations, following the outcome of previous benchmark studies. Our simulations enable us to explore various aspects of the quantized dynamics upon confinement of CO2@D/T, such as constrained rotational-translational quantum motions and the averaged position/orientation of the CO2 guest in comparison to the experimental data available. Particularly notable are the distinct energy patterns observed in the computed spectra for the confined CO2 in the D and T cages, with a considerably high rotational-translational coupling in the CO2@T case. Leveraging reliable computations has proved instrumental, highlighting the sensitivity of the spectral features to the shape and strength of the potential interactions, with the explicit description of many-body contributions being significant.

Computational prediction of impact sound insulation of a full-scale timber floor applying a FEM simulation procedure

During the recent decades, simulation procedures involving the finite element method (FEM) have been developed to enable prediction of impact sound insulation of timber slabs and floors. FEM simulations have previously been applied for timber floors mainly in low frequencies despite their evident ability to operate over a wide frequency range. Additionally, the validation processes have involved calibrations and experimental modal analyses which can seldom be performed in product development tasks concerning full structures. The purpose of this research was to study the applicability of a FEM simulation procedure to predict normalised impact sound pressure levels Ln of a full-scale timber floor if material data provided by the product manufacturers is used. The floor was studied at three construction stages where the FEM simulations were performed for the bare floor and the rib slab, and the floor covering was considered in the post-processing stage in case of the full floor. The impact force excitation generated by the ISO standard tapping machine was described with the recently published procedure involving explicit dynamics analysis. To serve the purposes of this study, the FEM models were fully constructed before the measurements were carried out. According to the results, the 1/3-octave Ln of the full floor and the floor without covering was predicted with a 0 to 9 dB accuracy depending on the frequency band and the single-number quantities with a 0 to 4 dB accuracy. The single-number quantities of the bare rib slab were underestimated with the simulations by 4 to 5 dB. Probable causes for the discrepancies between the simulation and measurement results pointed mainly to the uncertain material properties but possibilities for modelling inaccuracies could not be excluded.

Latarjet procedure: biomechanical evaluation of 2-screw coracoid fixation

Coracoid nonunion is a relevant complication following the Latarjet procedure and is influenced by multiple factors, including the method of graft fixation. The purpose of this study was to evaluate and characterize the biomechanical properties of various two-screw fixation constructs used for coracoid graft fixation in the Latarjet procedure. Forty model scapulae (Sawbones Inc., Vashon, WA, USA) were used for this study. A 15% anterior inferior glenoid bone defect was created. The coracoid was osteotomized at the juncture of the vertical and horizontal aspects, transferred to the anterior-inferior edge of the glenoid, and fixed with either two 3.5mm fully threaded cannulated cortical screws, two 3.5mm fully threaded solid cortical screws, two 3.5mm partially threaded cannulated screws, or two 4.5mm partially threaded malleolar screws (MS). Biomechanical testing was performed with an Instron material testing machine (Instron Corp., Norwood, MA, USA) by applying loads to the lateral aspect of the transferred coracoid graft. The constructs were preconditioned with nondestructive cyclical loading (0N-20N) to determine construct stiffness. After 100 cycles of dynamic loading, the construct was loaded to failure to determine ultimate failure load, yield displacement, and mode of failure. All failures were associated with plastic deformation of the screws and coracoid graft fracture. There was a significantly lower initial stiffness for partially threaded cannulated screws compared to MS (186±49.3N/mm vs. 280±65.5N/mm, P=.01) but no significant differences among the other constructs. There was no difference in ultimate failure load (P=.18) or yield displacement (P=.05) among constructs. Two screw coracoid fixation of the coracoid in a simulated classic Latarjet procedure with 3.5mm fully threaded cortical and cannulated screws is comparable to 4.5mm MS in strength, stiffness, and displacement at failure. On the other hand, partially threaded 3.5mm cannulated screws provide inferior fixation stiffness and could potentially affect clinical outcomes.

Detection of Outliers in Circular Regression Model Via DFBETAcIS Statistic

The outlier issues in circular regression models have recently received much attention. The presence of outliers may cause the sign and magnitude of regression coefficients to vary, resulting in inaccurate model development and incorrect prediction. Many methods for detecting outliers in a circular regression model have been proposed in previous studies such as COVRATIO, D, M, A, and Chord statistics, but it is suspected that they are not very successful in the presence of multiple outliers in a data set since the masking and swamping is not considered in their studies. This study aimed to develop an outlier detection procedure using DFBETAc statistic for circular cases, where this new statistic will investigate and identify multiple outliers in the Jammalamadaka and Sarma circular regression model (JSCRM) by considering masking and swamping effect. Monte Carlo simulations are used to determine the corresponding cut-off point and the power of performance is investigated. The performance of the proposed statistic is evaluated by the proportion of detected outliers and the rate of masking and swamping. The simulation procedure is applied at 10% and 20% contamination levels for varying sample sizes. The results show that the proposed DFBETAcIS statistic for JSCRM successfully detect the outliers. For illustration purposes, this process is applied to wind direction data.

Finite element simulation of the complete sheet metal blanking cycle: Effect of blanking clearance on force curve and cut edge quality

Sheet metal blanking process is a manufacturing process that is widely used in sheet metal forming and particularly in the electronics and automotive industries. The outcome and quality of the blanked parts are affected by various parameters such as the blanking clearance, the sheet metal mechanical properties and the wear of tools. However, experimental campaigns to assess the effects of these parameters on the process outcome can be prohibitively expensive for the industry. As a result, numerical simulation of the blanking process is becoming increasingly important to gain insight into the process and study these effects.The present paper provides a novel finite element (FE) simulation procedure to predict the complete blanking cycle including sheet metal cutting, sheet metal springback, punch penetration in die, and stripping phases. A blanking force curve showing the punch force at all phases can thus be obtained. The commercial finite element code ABAQUS® has been chosen to develop this procedure using ABAQUS/Explicit® code for the cutting phase and ABAQUS/Standard® code for the remaining phases. Based on this procedure, the influence of the blanking clearance on the punch force-displacement curve and on the cut edge quality has been studied. The obtained results have been compared to previous literature results and experimental findings.

A Geant4 procedure for precise simulation of PGNAA prompt $$\gamma$$-ray spectrum in a wide energy range up to 8 MeV

A Geant4 procedure for precise simulation of PGNAA prompt $$\gamma$$-ray spectrum in a wide energy range up to 8 MeV

Design of a gradient elastic modulus transition layer based on film-substrate adhesion

The codeformation process of Ti6Al4V (TC4) substrate and chromium nitride (CrN) films with dense/loose CrN and gradient elastic modulus CrN/CrNx/Cr(N) (G-CrN) structures under normal impact load were simulated by changing the thicknesses of the films or the loose CrN and G layers. The dispersed distribution of interfacial tensile stresses and strains of G-CrN films improved the layers’ codeformation, compared with the firm large layer of the dense/loose CrN films. The stress reduced as the loose/G layer thicknesses increased. The 4.7 μm CrN films with various thicknesses of the G-layers, 0.2 µm, 0.6 µm, 0.75 µm and 0.9 µm, were prepared on TC4 to evaluate the film-substrate adhesions and verify the simulated deformation procedure. A G-layer between a ceramic film and its alloy substrate is needed, and the 0.83–1.08 µm thicknesses of the G-layers were suggested for the CrN/TC4 system varied with the films’ thicknesses. The dispersed distribution of the tensile stress and strain within the G-CrN film was the mechanism of the film-substrate adhesion improvement.

Sitting versus standing work postures during simulated laparoscopic surgery: in terms of user preferences, comfort, performance and biomechanics

Prolonged standing in surgery has been linked to an increased risk of musculoskeletal disorders. The aim of this study was to determine whether sitting could serve as an alternative work posture in laparoscopic procedures. Twenty medical students in their third and fourth years were recruited. Sitting and standing were compared at two task complexity levels on a laparoscopic surgery simulator. Measured variables included user posture preferences, perceived discomfort, performance and biomechanics. Electromyography data from the upper trapezius and erector spinae muscles were analysed. Results showed that posture did not affect surgical performance and erector spinae muscle activation. Sitting showed higher muscle activation at the trapezius muscles; however, perceived discomfort was unaffected. Most participants preferred sitting for the difficult task and standing for the easy task. Findings showed that sitting, with appropriate seat design considerations, could serve as an alternative or even as a preferred work posture for simulated laparoscopic procedures.

Poisson image deblurring with frame-based nonconvex regularization

Poisson image deblurring, which aims to restore the latent image from its blurred and noisy observation, has drawn significant attention in image processing. Due to its ill-posed nature, enhancing image quality often involves incorporating a well-defined prior to effectively regularize the ill-posed inverse problem. Building upon the framelet system, we propose a frame-based nonconvex regularization method for Poisson image deblurring. The method is formulated by combining a data-fitting term with the difference of two norms, namely ℓ1 and ℓ2, on the latent image. We solve the optimization problem by combining the difference of convex functions algorithm (DCA) with the alternating direction method of multipliers (ADMM), establishing its convergence. We further employ a simulated annealing procedure and show that proposed algorithm almost certainly converges to a global minimum. Two different approaches are employed to handle the frame-based ℓ1 norm within the ADMM framework. In particular, the frame-based nonconvex regularization method is also considered for the blind Poisson problem. An effective recovery model and its algorithm are presented. Experimental results demonstrate the effectiveness of our proposed models compared with other models in terms of quantitative metrics and visual quality.

Analysis of multi-modal public transportation system performance under metro disruptions: A dynamic resilience assessment framework

The well-functioning multi-modal public transportation systems play crucial roles in reducing traffic congestion, alleviating environmental pollution, and improving mobility. There are variants of disruptions that may impede the smooth functioning of public transportation systems and challenge their normal operations. It is essential to develop resilient multi-modal public transportation systems, and a reasonable resilience assessment framework serves as the foundation for developing such systems. In this paper, we propose a resilience assessment framework based on performance-based measurement metrics. We develop a dynamic simulation procedure that considers the impacts of disruptions on passenger choices of path and departure time. By incorporating these features into the simulation procedure, we can effectively model the variations of passenger flow under disruptions. Finally, to demonstrate the proposed resilience assessment framework and dynamic simulation procedure, the multi-modal public transportation system in Beijing is considered as a case study. The results reveal that the system exhibits good resistance and recovery capabilities, but it lacks robustness. Additionally, we discuss the effects of downtime, bus frequency, bus bridging services, and proportions of commuters on resilience, which lead to several valuable policy implications.

The Influence of Eugenol in Lubrication Properties of Crude Jatropha Curcas Lin

In machining processes, vegetable oil based lubricant has very promising future to substitute petroleum based lubricant. Due to sustainability issue, lubricants from natural resources is sought after as complement or even substitute for non renewable lubricants. Jatropha curcas Lin oil (JCO) is one of such natural oil. When tested as lubricant, JCO demonstrates good performance and decomposition properties. The research on JCO usually study the performance the oil by characterizing of friction reduced parameters and rarely to analyze deeper in terms of the role of molecular interaction between components of vegetable oil and metal surfaces. To analyses the moleculer interaction, a commercially available software for molecular interaction simulation is employed. In order to simulate the interactions, the procedure of simulation are firstly construct molecular model of JCO components. The model then is employed as basis for the software to calculate interactive forces of each atom or molecule with the neighboring one thru their positions. From the simulation results, it was concluded that value of dipole moment, polarisability, and bond energy of Jatropha oil compound decreased when the concentration of eugenol is increasing. The conclusion is hold true up to 20% of eugenol additon.

In silico study of alkaloids with quercetin nucleus for inhibition of SARS-CoV-2 protease and receptor cell protease.

Covid-19 disease caused by the deadly SARS-CoV-2 virus is a serious and threatening global health issue declared by the WHO as an epidemic. Researchers are studying the design and discovery of drugs to inhibit the SARS-CoV-2 virus due to its high mortality rate. The main Covid-19 virus protease (Mpro) and human transmembrane protease, serine 2 (TMPRSS2) are attractive targets for the study of antiviral drugs against SARS-2 coronavirus. Increasing consumption of herbal medicines in the community and a serious approach to these drugs have increased the demand for effective herbal substances. Alkaloids are one of the most important active ingredients in medicinal plants that have wide applications in the pharmaceutical industry. In this study, seven alkaloid ligands with Quercetin nucleus for the inhibition of Mpro and TMPRSS2 were studied using computational drug design including molecular docking and molecular dynamics simulation (MD). Auto Dock software was used to evaluate molecular binding energy. Three ligands with the most negative docking score were selected to be entered into the MD simulation procedure. To evaluate the protein conformational changes induced by tested ligands and calculate the binding energy between the ligands and target proteins, GROMACS software based on AMBER03 force field was used. The MD results showed that Phyllospadine and Dracocephin-A form stable complexes with Mpro and TMPRSS2. Prolinalin-A indicated an acceptable inhibitory effect on Mpro, whereas it resulted in some structural instability of TMPRSS2. The total binding energies between three ligands, Prolinalin-A, Phyllospadine and Dracocephin-A and two proteins MPro and TMRPSS2 are (-111.235 ± 15.877, - 75.422 ± 11.140), (-107.033 ± 9.072, -84.939 ± 10.155) and (-102.941 ± 9.477, - 92.451 ± 10.539), respectively. Since the binding energies are at a minimum, this indicates confirmation of the proper binding of the ligands to the proteins. Regardless of some Prolinalin-A-induced TMPRSS2 conformational changes, it may properly bind to TMPRSS2 binding site due to its acceptable binding energy. Therefore, these three ligands can be promising candidates for the development of drugs to treat infections caused by the SARS-CoV-2 virus.