Latin Hypercube Research Articles

Failure Pressure Evaluation of Corroded Pipeline Using Semi-Empirical and Finite Element Analysis

Abstract Computational simulation using the finite element method (FEM) has proven to be one of the most efficient methods for the correct evaluation of the structural integrity of pipelines with corrosion-induced defects. In this paper, the results of the failure pressure of corroded pipelines obtained with a computer tool called PIPEFLAW based on nonlinear finite element analyses are presented and compared with the failure pressures of corroded pipes obtained with twelve (12) advanced semi-empirical models from the literature. A computational code was created in the Python language to automatically obtain the failure pressure of randomly generated cases of corroded pipes, which allows the analysis of multiple piping cases with isolated external corrosion defects in an idealized configuration. For this purpose, the LHS (Latin Hypercube Sampling) approach was adopted for the design of experiments to obtain a better distribution of cases in the sample space. Comparisons between the results using the FEM and semi-empirical models show, as expected, that the semi-empirical solutions are more conservative in most cases analyzed in this work. A statistical model is proposed that adheres to the relative error distribution for some of the models used. Moreover, the influence of some parameters (pipeline thickness, defect depth, defect length, defect width, yield stress and ultimate stress) on the failure pressure was evaluated. It was found that in most of the studied models the error increases as the defect depth increases.

Composition and sources of carbonaceous aerosol in the European Arctic at Zeppelin Observatory, Svalbard (2017 to 2020)

Abstract. We analyzed long-term measurements of organic carbon, elemental carbon, and source-specific organic tracers from 2017 to 2020 to constrain carbonaceous aerosol sources in the rapidly changing Arctic. Additionally, we used absorption photometer (Aethalometer) measurements to constrain equivalent black carbon (eBC) from biomass burning and fossil fuel combustion, using positive matrix factorization (PMF). Our analysis shows that organic tracers are essential for understanding Arctic carbonaceous aerosol sources. Throughout 2017 to 2020, levoglucosan exhibited bimodal seasonality, reflecting emissions from residential wood combustion (RWC) in the heating season (November to May) and from wildfires (WFs) in the non-heating season (June to October), demonstrating a pronounced interannual variability in the influence of WF. Biogenic secondary organic aerosol (BSOA) species (2-methyltetrols) from isoprene oxidation was only present in the non-heating season, peaking in July to August. Warm air masses from Siberia led to a substantial increase in 2-methyltetrols in 2019 and 2020 compared to 2017 to 2018. This highlights the need to investigate the contribution of local sources vs. long-range atmospheric transport (LRT), considering the temperature sensitivity of biogenic volatile organic compound emissions from Arctic vegetation. Tracers of primary biological aerosol particles (PBAPs), including various sugars and sugar alcohols, showed elevated levels in the non-heating season, although with different seasonal trends, whereas cellulose had no apparent seasonality. Most PBAP tracers and 2-methyltetrols peaked during influence of WF emissions, highlighting the importance of measuring a range of source-specific tracers to understand sources and dynamics of carbonaceous aerosol. The seasonality of carbonaceous aerosol was strongly influenced by LRT episodes, as background levels are extremely low. In the non-heating season, the organic aerosol peak was as influenced by LRT, as was elemental carbon during the Arctic haze period. Source apportionment of carbonaceous aerosol by Latin hypercube sampling showed mixed contributions from RWC (46 %), fossil fuel (FF) sources (27 %), and BSOA (25 %) in the heating season. In contrast, the non-heating season was dominated by BSOA (56 %), with lower contributions from WF (26 %) and FF sources (15 %). Source apportionment of eBC by PMF showed that FF combustion dominated eBC (70±2.7 %), whereas RWC (22±2.7 %) was more abundant than WF (8.0±2.9 %). Modeled BC concentrations from FLEXPART (FLEXible PARTicle dispersion model) attributed an almost equal share to FF sources (51±3.1 %) and to biomass burning. Both FLEXPART and the PMF analysis concluded that RWC is a more important source of (e)BC than WF. However, with a modeled RWC contribution of 30±4.1 % and WF of 19±2.8 %, FLEXPART suggests relatively higher contributions to eBC from these sources. Notably, the BB fraction of EC was twice as high as that of eBC, reflecting methodological differences between source apportionment by LHS and PMF. However, important conclusions drawn are unaffected, as both methods indicate the presence of RWC- and WF-sourced BC at Zeppelin, with a higher relative BB contribution during the non-heating season. In summary, organic aerosol (281±106 ng m−3) constitutes a significant fraction of Arctic PM10, although surpassed by sea salt aerosol (682±46.9 ng m−3), mineral dust (613±368 ng m−3), and typically non-sea-salt sulfate SO42- (314±62.6 ng m−3), originating mainly from anthropogenic sources in winter and from natural sources in summer.

Integration of Deep Learning with Vibration-Based Identification Method

The vibration-based identification method has long been used as an alternative to mechanical tests for material characterization. Its non-destructive nature, high efficiency in parameter identification of anisotropic materials and its ability to achieve structure-level homogenization make it very attractive for both academic community and industry. Due to the absence of explicit mapping from the result of vibration test to elastic constants, conventional vibration-based identification approaches generally rely on the minimization of a cost function built on modal properties or Frequency Response Function (FRF) curve. In this paper, a direct relation from FRFs to mechanical properties is established by deep neural network, which offers a new perspective to solve the inverse identification problem. Specifically, this mapping is approximately built with Multilayer Perceptrons (MLPs) and the network’s weights are learned from massive training data. The training data-i.e., the FRF curves, is generated by a finite element model whose input parameters are selected by Latin Hypercube Sampling (LHS). After training, the obtained model is validated by dataset unseen during training process and will be further used to predict mechanical properties of materials from FRFs obtained by vibration test. The effectiveness and efficiency of the developed method are demonstrated on the case study of an aluminum plate. Both the Young’s modulus and Poisson’s ratio are retrieved with high accuracy.

Optimal estimation of MC parameter in SAE J2601 hydrogen refuelling protocol based on modified formula and artificial neural networks

The MC method was officially released in SAE J2601 protocol in 2016 to refuel light-duty hydrogen fuel cell vehicles quickly and safely. For the non-communication MC method, the pressure target for controlling the end of filling is calculated based on the formula of the final hydrogen temperature, which contains a key parameter, MC. SAE has proposed two formulas to estimate the MC parameter. This article proposes a modified formula and artificial neural networks (ANN) model for MC parameter. To generate simulated data for fitting the modified formula and training the ANN model, a hydrogen filling model (0D1D) with a zero-dimensional gas and a one-dimensional tank wall is established and thoroughly verified. In establishing the ANN model, Latin hypercube sampling (LHS) is used to sample independent variables randomly, and genetic algorithms (GA) are used to optimize the initial weights and biases of the ANN. The Sobol sensitivity analysis is used to determine the importance of various initial conditions on the MC parameter. Research shows that the final fueling time and initial pressure have a greater impact on the MC parameter, while the ambient and precooling temperatures have little impact. The modified formula of the MC parameter improves R2 from the original 0.76073 to 0.98132 when fitting the MC simulated data. In situations of short filling time, the modified formula and ANN models of the MC parameter reduce the relative error between the 0D1D model by 13.8 % − 58.8 % and 16.6 % − 82.3 %, respectively, thus improving the calculation accuracy of the final hydrogen temperature and ending pressure target. Moreover, the ANN model can predict the MC parameter during the whole fueling process, while the MC formula utilizes the fixed value when the fueling time is less than 30 s. This work has positive significance for improving refuelling protocol.

Multi-Objective Optimization of Building Energy Saving Based on the Randomness of Energy-Related Occupant Behavior

Given the escalating global energy demand driven by building energy consumption, this study is dedicated to meticulously investigating efficient energy-saving strategies in buildings, with a keen focus on the impact of occupant behavior’s randomness on energy efficiency and multi-objective optimization. The methodology encompassed a thorough analysis of various energy consumption factors, including building envelope and architectural form. We employed Latin Hypercube Sampling for in-depth sampling studies across each factor’s reasonable range. Utilizing Sobol sensitivity analysis, we pinpointed variables of high sensitivity and embarked on multi-objective optimization targeting two primary indicators: energy consumption and thermal comfort. Leveraging the NSGA-II algorithm, we adeptly identified optimal solutions, culminating in the proposition of building energy-saving strategies anchored on the Pareto frontier. Through stochastic modeling simulations of occupant behavior in window opening and air conditioning usage, a comparison was made with models that do not consider occupant behavior. It was found that incorporating occupant behavior into energy-saving designs can reduce energy consumption by up to 20.20%, while ensuring thermal comfort. This approach can achieve improved energy efficiency and indoor comfort.

An Inversion Study of Reservoir Colluvial Landslide Permeability Coefficient by Combining Physical Model and Data-Driven Models

The saturated permeability coefficient (ks) is a key parameter for evaluating the seepage and stability of reservoir colluvial landslides. However, ks values obtained from traditional experimental methods are often characterized by large variations and low representativeness. As a result, there are significant deviations from actual observations when used in seepage field calculations for reservoir landslide analysis. This study proposes an intelligent inversion method that combines a physical model and a data-driven model for reservoir landslide ks based on actual groundwater level (GWL) monitoring data. This method combines Latin Hypercube Sampling (LHS), unsaturated flow finite element (FE) analysis, particle swarm optimization algorithm (PSO), and kernel extreme learning machine model (KELM). Taking the Hongyanzi landslide in Sichuan Province, China, as the research object, the GWL of the landslide under different ks was first obtained by LHS and transient seepage FE analysis. Then, a nonlinear functional relationship between ks and the landslide GWL was fitted based on the PSO-KELM model. Finally, the optimal landslide ks was obtained by minimizing the root-mean-squared error between the predicted and actual GWL using the PSO. A global sensitivity analysis was also conducted on the ks of different rock and soil layers to reveal their control rules on the calculation of landslide GWL. The research results demonstrate the feasibility of the proposed method and provide valuable information for similar landslides in practice.

Physically Informed Deep Learning Technique for Estimating Blood Flow Parameters in Four-Vessel Junction after the Fontan Procedure

This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh generation technique based on the parameterization of the junction’s geometry, coupled with an advanced physically regularized neural network architecture. Synthetic datasets are generated through stationary 3D Navier–Stokes simulations within immobile boundaries, offering a precise alternative to resource-intensive computations. A comparative analysis of standard grid sampling and Latin hypercube sampling data generation methods is conducted, resulting in datasets comprising 1.1×104 and 5×103 samples, respectively. The following two families of feed-forward neural networks (FFNNs) are then compared: the conventional “black-box” approach using mean squared error (MSE) and a physically informed FFNN employing a physically regularized loss function (PRLF), incorporating mass conservation law. The study demonstrates that combining PRLF with Latin hypercube sampling enables the rapid minimization of relative error (RE) when using a smaller dataset, achieving a relative error value of 6% on the test set. This approach offers a viable alternative to resource-intensive simulations, showcasing potential applications in patient-specific 1D network models of hemodynamics.

RBF-Based Integrated Optimization Method of Structural and Turning Parameters for Low-Floor Axle Bridge

The axle bridge plays a crucial role in the bogie of low-floor light rail vehicles, impacting operational efficiency and fuel economy. To minimize the total cost of the structure and turning of axle bridges, an optimization model of structural and turning parameters was built, with the fatigue life, maximum stress, maximum deformation, and maximum main cutting force as constraints. Through orthogonal experiments and multivariate variance analysis, the key design variables which have a significant impact on optimization objectives and constraints (performance responses) were identified. Then the optimal Latin hypercube design and finite element simulation was used to build a Radial Basis Function (RBF) model to approximate the implicit relationship between design variables and performance responses. Finally, a multi-island genetic algorithm was applied to solve the integrated optimization model, resulting in an 8.457% and 1.1% reduction in total cost compared with the original parameters and parameters of sequential optimization, proving the effectiveness of the proposed method.

Effect of training sample size, sampling design and prediction model on soil mapping with proximal sensing data for precision liming

Site-specific estimation of lime requirement requires high-resolution maps of soil organic carbon (SOC), clay and pH. These maps can be generated with digital soil mapping models fitted on covariates observed by proximal soil sensors. However, the quality of the derived maps depends on the applied methodology. We assessed the effects of (i) training sample size (5–100); (ii) sampling design (simple random sampling (SRS), conditioned Latin hypercube sampling (cLHS) and k-means sampling (KM)); and (iii) prediction model (multiple linear regression (MLR) and random forest (RF)) on the prediction performance for the above mentioned three soil properties. The case study is based on conditional geostatistical simulations using 250 soil samples from a 51 ha field in Eastern Germany. Lin’s concordance correlation coefficient (CCC) and root-mean-square error (RMSE) were used to evaluate model performances. Results show that with increasing training sample sizes, relative improvements of RMSE and CCC decreased exponentially. We found the lowest median RMSE values with 100 training observations i.e., 1.73%, 0.21% and 0.3 for clay, SOC and pH, respectively. However, already with a sample size of 10, models of moderate quality (CCC > 0.65) were obtained for all three soil properties. cLHS and KM performed significantly better than SRS. MLR showed lower median RMSE values than RF for SOC and pH for smaller sample sizes, but RF outperformed MLR if at least 25–30 or 75–100 soil samples were used for SOC or pH, respectively. For clay, the median RMSE was lower with RF, regardless of sample size.

Probabilistic Design Method for Aircraft Thermal Protective Layers Based on Surrogate Models

In this study, a probabilistic method was proposed for an aircraft’s thermal protective layers. The uncertainties of material properties, geometric dimensions, and incoming flow environments were considered for the design inputs. To accelerate the design efficiency, Latin hypercube sampling and surrogate models were built based on finite element method calculations to enhance the simulation efficiency. Thus, the Monte Carlo method can be implemented with such a fast simulation method and produce a massive number of samples for the uncertainty quantification and sensitivity analysis, exploring their impact on the back temperature of the thermal protection layer. Compared to the deterministic method with the extreme deviation design, the probabilistic design yields a weight reduction of 15.61%. This indicates that probabilistic design is an efficient approach to enhance the performance of aircraft and reduce the overall weight of the aircraft. The general goal of this study is to provide a new design method for the coating film of thermal protection systems by considering multiple sources of uncertainties.

A Story-Based Random Structure Modeling Method for Regional Buildings Earthquake Loss Estimation

To estimate earthquake losses for regional buildings, it is important to consider the varying seismic performances of different types of buildings. To achieve this, a dynamic elastic–plastic analysis based on detailed structural modeling is more accurate than the capacity spectrum method based on a single degree of freedom to obtain the response and damage of various structures under seismic activity and provide a more precise estimate of earthquake losses for buildings. Detailed information about the building facilities is necessary to create a fine structural model. However, obtaining precise actual structural details can be challenging with existing methods, especially when there are a large number of buildings to consider. This paper proposes a new method called story-based random structure (SBRS) modeling to address this issue, which is based on the common and reasonable layout of the structure. The process involves choosing design parameters that can represent the structural arrangement of a building as variables. These parameters include the materials’ type and strength, proportions, the components’ size, and other relevant factors. The values for these variables are determined based on engineering experience and design specifications. The range of values for these variables is also determined to ensure that all design requirements are met. Finally, the Latin Hypercubic Sampling (LHS) method is used to randomly sample and combine the variables, establishing a detailed structural model of the building with structural uncertainty. According to the results of the analysis, this method can simulate the structure and component information of a building by using a probabilistic approach, even without knowing the specific structural design. This method can be based on a small amount of readily available building information and solves the problem of the rapid and refined modeling of a large number of buildings at the regional scale. It also makes it possible to estimate the earthquake loss of regional buildings based on their seismic capacity.

GLOBAL SENSITIVITY ANALYSIS AND OPTIMAL CONTROL OF TYPHOID FEVER TRANSMISSION DYNAMICS

This paper presents a mathematical model aimed at studying the global behaviour and optimal control strategies for Typhoid fever. The primary objective of this study is to identify the most effective control strategy that minimizes the spread of the disease. To achieve this, we calculate the effective and basic reproduction numbers and utilize them to investigate the existence and stability of the equilibria. Furthermore, we investigate the global impact of each model parameter on the variables using Latin Hypercube Sampling and Partial Rank Correlation Coefficient. The necessary conditions of the optimal control problem are analyzed using Pontryagin’s maximum principle, and the numerical values of the model parameters are estimated using the maximum likelihood estimator. The results indicate that the optimal use of vaccination for susceptible individuals, as well as the screening and treatment of asymptomatic infected individuals, have a significant impact on reducing the spread of the disease in endemic regions.

Design optimization of the bamboo-inspired foam-filled tube for high-speed train collision energy absorption

This study first developed a novel bamboo-inspired foam-filled tube (BFFT) for potential application in high-speed trains, considering the practical use conditions. The BFFT was initially designed by mimicking the macro and micro characteristics of bamboo culms and understanding their axial crushing mechanical behavior. The key bionic parameters are drawn based on the crashworthiness indices of natural bamboos with various geometric sizes and growth ages. The numerical simulation model of the BFFT under axial impact load was established and verified using simplified super folding element (SSFE) theory and drop-weight experiments. A multi-objective optimization on the BFFT was conducted to find the optimal parameter alternative, integrating experimental design method of the optimal Latin hypercube, the response surface agent model and the non-dominated sorting genetic algorithm (NSGA-II). The crashworthiness performance of the optimized BFFT was compared with the initial design scheme, a conventional circular tube and other existing energy absorbers of rail vehicles. The results indicated that the EA, SEA and IPCF values of the optimized BFFT can be improved by 150.2 %, 63.3 %, and 48.5 %, respectively, when considering the single target requirement. In comparison to the initial design scheme and conventional circular tube, the SEA value of the final optimized BFFT increased by 44.7 % and 53.0 %, respectively. The proposed novel BFFT demonstrated a superior crashworthiness, providing inspiration for enhancing and optimizing passive safety performance of high-speed trains.

Data-driven-based distributionally robust optimization approach for a virtual power plant considering the responsiveness of electric vehicles and Ladder-type carbon trading

To encourage the utilization of decentralized renewable energy systems, a data-driven-based distributionally robust optimization (DRO) model is proposed for a virtual power plant (VPP) considering the responsiveness of electric vehicles (EVs) and a ladder-type carbon trading mechanism (LT-CTM). An EV virtual energy storage (EV-VES) calculation model for dispatchable power is formulated to facilitate the centralized scheduling of EVs, considering responsiveness and spatial–temporal distribution characteristics. Additionally, multiple kinds of energy storage systems and flexible loads are considered in the VPP. Furthermore, to handle multiple uncertainties for both the renewable energy and load, Latin hypercube sampling is applied to generate the renewable energy scenarios, and K-means clustering is used to obtain typical scenes. Then the DRO model is used to solve the dispatch problem. An LT-CTM is leveraged to reduce carbon emissions. The column and constraint generation algorithm is then employed to identify an optimal operational benefit solution. Finally, a multi-parameter coupling analysis of ladder-type carbon trading is used for the environmental protection and economy of the VPP. The results confirm the efficacy of the proposed day-ahead scheduling model, offering a theoretical reference for operators when making scheduling decisions.

A Methodology for Ship Cabin Equipment Layout Considering Human Factor Reliability Optimization

_ The paper proposes an innovative methodology of Human Cognitive Reliability- Cognitive Reliability and Error Analysis Method (HCR-CREAM) coupled with A* search and genetic algorithm (GA) to tackle ship cabin equipment layout considering human factor reliability optimization with the goal of minimizing human error probability (HEP) subjected to practical requirements. After establishing the mathematical model of cabin equipment inspection tasks in ship cabin equipment layout problem through HCR-CREAM and equipment geometric simplification, a method of the horizontal movement based on minimum distance is presented to avoid the equipment overlapping, then A* search is used for planning inspection paths and GA with selection, crossover, and mutation operators is applied to solve equipment layout results. A case of equipment layout in a certain ship engine room has been taken to carry out parameter sampling experiments by Latin Hypercube for GA. The results show the solution effect of GA is less affected by its parameter variation. And through the comparison with the initial equipment layout, the indicators influencing the HEP of the optimized result have been improved, thus significantly reducing HEP. Keywords ship cabin equipment layout; human factor reliability optimization; HCRCREAM; A* search; genetic algorithm

Two-stage low-carbon optimal dispatch of the power system considering demand response to defend large uncertainties and risks

Introduction: In order to promote the consumption of renewable energy, reduce carbon emissions, and take into account the uncertainty of renewable energy output and load fluctuations in the new power system that can affect the normal operation of market mechanism, a two-stage low-carbon optimization scheduling method for power system that considers demand response under multiple uncertainties is proposed in this paper.Methods: Uncertain scene sets are generated through Latin hypercube sampling and heuristic synchronous backpropagation method is used to reduce scenes to obtain typical scenes and their probabilities. Then, a one-stage optimization model is established with the goal of maximizing energy efficiency and corresponding demand response strategies are obtained. Green certificate carbon trading joint mechanism model consisting of tiered green certificate trading and time-sharing tiered carbon trading are established, and the output of two-stage units are optimized with the goal of minimizing comprehensive operating costs.Result: The simulation results show that the carbon emissions are decreased by 251.57 tons, the consumption rate of renewable energy is increased by 8.64%, and the total costs are decreased by 124.0612 million yuan.Discussion: From this, it can be seen that the dual layer low-carbon optimization scheduling strategy for power system considering demand response under multiple uncertainties can effectively reduce the operating costs and carbon emissions of the system, while balancing the economic and environmental aspects of power system operation.

Research on inversion of wheel-rail force based on neural network framework

Wheel-rail force is a critical parameter for vehicle-track-bridge (VTB) structure system, playing a pivotal role in ensuring the safe operation of trains and service performance of track and bridge structures. This paper proposes a methodological framework for efficient and accurate prediction of wheel-rail force, that bidirectional gated recurrent unit (Bi-GRU) neural network is developed and the hyperparameters of networks are optimized through grey wolf optimization (GWO). Based on the developed VTB system model, numerical experiments are carried out to produce datasets which are verified through field test. In order to make the data more universal and comprehensive, Latin hypercube sampling (LHS) is introduced to randomize the parameters of VTB model. The datasets generated from VTB system, with rail seat force as input and wheel-rail force as output, serve as the training data for framework. Additionally, the inversion effects under various working conditions are examined and discussed. The results indicate that the model can precisely inverse continuous wheel-rail forces from the moment the wheels enter the first test rail seat until leave the last test point of rail seat. When the neural network learns from the features of at least three or more rail seats on each track slab (with intervals not exceeding 2 rail seats), it can accurately inverse the time history curve of wheel-rail forces. The framework presents a viable alternative for the wheel-rail force analysis, and promising potential for the realization of online monitoring of wheel-rail force.

Design of battery shell stamping parameters for vehicles based on fusion of various artificial neural network models

The application of neural network model in engineering prediction is frequent. The BPE shell material was optimized, and the reliability of the new material was verified by modal simulation. The accuracy of finite element modeling was ensured by constrained mode experiments, and all variables were preprocessed by Latin hypercube sampling. The design parameters were determined by Monte Carlo simulation. Four different neural networks, including back propagation (BP), radial basis function (RBF), extreme learning machine (ELM) and wavelet neural network (WNN), are used to train and learn the dataset. The BPE weight reduction ratio was 14.3%, the stress was reduced by 18.6%, deformation displacement was reduced by 14.2%, and the first-order mode was increased by 29.1%.

Investigation of the effect of different structure parameters and operating factors on the integrated exhaust aftertreatment system for diesel engines and parameter importance analysis

With diesel emission regulations growing more stringent, it is important to study the effect of different structure parameters and operating factors on the integrated aftertreatment system consisting of diesel oxidation catalyst (DOC), catalytic diesel particulate filter (CDPF) and selective catalytic reduction (SCR) during the design and optimization of the aftertreatment system. In this paper, a simulation model capable of exploring the interaction between the aftertreatment components was developed based on some relevant reaction mechanism, and the model accuracy was verified well using the experiment data. Then the effect of different parameters on the overall performance of the aftertreatment system was analyzed qualitatively. Finally, the importance score of each parameter was determined quantitatively based on Latin Hypercubic Sampling method and Random Forests algorithm. The results showed that larger diameter or larger cell density in DOC could improve the aftertreatment performance effectively. The increase in the wall thickness of each component had a negative impact on the aftertreatment system, except for CDPF wall thickness, which improved the filtration efficiency for particular matter (PM). When total aftertreatment length was fixed, high conversion efficiencies for carbon monoxide (CO) and total hydrocarbons (THC) were distributed in the region with large DOC length ratio, and NOx conversion efficiency was affected simultaneously by the length ratio of DOC, CDPF and SCR. An increase in CDPF length ratio and a decrease in DOC and SCR length ratios could contribute to the reduction of total pressure drop. Finally, Random Forests algorithm provided a good identification of the importance score of each parameter. DOC length and diameter played a dominant role in CO and THC conversion. The aftertreatment diameter had higher effect on NOx conversion efficiency than SCR length and PM filtration efficiency was mainly affected by DOC and CDPF length. Based on these qualitative and quantitative results, a design strategy for the integrated aftertreatment system was proposed, which provided some ideas for improving aftertreatment performance.

Dynamic vehicle–track interaction and structural loading in a crossing panel – calibration and assessment of a model with a 3D representation of the crossing rail

A three-dimensional (3D) finite element model of a railway crossing panel for use in multibody simulation (MBS) of dynamic vehicle–track interaction is presented. It is a two-layer track model with stock rails and sleepers represented by beam elements and a crossing rail represented by 3D solid elements. The track model uses linear bushings for the rail fastenings and bi-linear bushings for the ballast to allow for potentially voided sleepers. Based on the output from the MBS, the structural loading of the crossing in terms of strains, stresses and sleeper-ballast contact pressures is extracted in a post-processing step. The model is calibrated and validated to measurement data from a comprehensively instrumented switch & crossing (S&C) demonstrator installed in the Austrian railway network as a part of the European research programme Shift2Rail. The applied procedure for the calibration and critical assessment of the crossing model is described in detail. It is based on a model parameterisation with eight parameters relating to the rail fastening and foundation stiffnesses and to a distribution of the ballast voids. The calibration method uses Latin hypercube samples to explore the parameter space in a sensitivity analysis before a parameter optimisation is performed using a gradient-based method on a response surface built from a polyharmonic spline. In a comparative study it is shown that the 3D model and a more conventional beam model of the crossing rail show similar calibration results and good agreement with the measured data. The 3D model allows for the extraction of stress concentrations in the crossing rail but has an increased computational time of about 30% compared to the beam model.