Next Article in Journal
Medical Range Radiation Dosimeter Based on Polymer-Embedded Fiber Bragg Gratings
Next Article in Special Issue
Experimental Validation of LiDAR Sensors Used in Vehicular Applications by Using a Mobile Platform for Distance and Speed Measurements
Previous Article in Journal
A Highly Sensitive and Miniature Optical Fiber Sensor for Electromagnetic Pulse Fields
Previous Article in Special Issue
Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 21
Issue 23
10.3390/s21238138
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of Ride Comfort in a Railway Passenger Car Depending on a Change of Suspension Parameters

by
Ján Dižo
1,*,
Miroslav Blatnický
1,
Juraj Gerlici
1,
Bohuš Leitner
2,
Rafał Melnik
3,
Stanislav Semenov
4,
Evgeny Mikhailov
4 and
Mariusz Kostrzewski
5
1
Department of Transport and Handling Machines, Faculty of Mechanical Engineering, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia
2
Department of Fire Engineering, Faculty of Security Engineering, University of Žilina, St. 1. Mája, 010 26 Žilina, Slovakia
3
Faculty of Computer Science and Food Science, Lomza State University of Applied Sciences, Akademicka 1, 18-400 Łomża, Poland
4
Department of Logistics and Traffic Safety, Educational and Scientific Institute of Transport and Building, Volodymyr Dahl East Ukrainian National University, Central Avenue 59A/403, 93400 Severodonetsk, Ukraine
5
Division of Construction Fundamentals of Transport Equipment, Faculty of Transport, Warsaw University of Technology, St. Koszykowa 75, 00-662 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(23), 8138; https://doi.org/10.3390/s21238138
Submission received: 29 October 2021 / Revised: 25 November 2021 / Accepted: 1 December 2021 / Published: 6 December 2021
(This article belongs to the Special Issue Feature Papers in Vehicular Sensing)
Browse Figures
Versions Notes

Abstract

:
Ride comfort for passengers remains a pressing topic. The level of comfort in a vehicle can influences passengers’ preferences for a particular means of transport. The article aims to evaluate the influence of changes in suspension parameters on the ride comfort for passengers. The theoretical background includes a description of the applied method for a creating the virtual model of an investigated vehicle as well as the method of evaluating the ride comfort. The ride comfort of the vehicle is assessed based on the standard method, which involves calculating the mean comfort method, i.e., ride comfort index NMV in chosen points on a body floor. The NMV ride comfort index (Mean Comfort Standard Method) requires the input of acceleration signals in three directions. The rest of the article offers the results of simulation computations. The stiffness–damping parameters of the primary and secondary suspension systems were changed at three levels and the vehicle was run on the real track section. The ride index NMV was calculated for all three modifications of the suspension system in the chosen fifteen points of the body floor. It was found that lower values in the stiffness of the secondary suspension system lead to lower levels of ride comfort in the investigated railway passenger car; however, lower values in the stiffness–damping parameters of the primary suspension system did not decrease the levels of ride comfort as significantly.

1. Introduction

A vehicle is a complex mechanical system with specific properties. In term of mechanics, it is a multibody system, the behavior of which can be analyzed based on valid criteria regarding the track and the surrounding environment [1,2,3,4,5].
The process of designing a vehicle has to meet specific requirements and operational conditions. These requirements mainly include running safety and ride comfort for passengers and they are described, verified and tested according to standards [6,7,8,9].
The main purpose of this work is to analyze the properties of a railway passenger car by means of simulation computations performed in a commercial simulation software. For these purposes, models of a railway passenger car and a track are created based on the available data. The research examines how changes in the railway passenger car parameters and operational conditions influence the ride comfort for passengers. The evaluation of ride comfort for passengers requires knowledge of the acceleration of given locations of a railway passenger car body [10,11,12,13,14]. The vehicle model is created in the Simpack software package (Dassault Systèmes, Vélizy-Villacoublay, France). The software library contains a measuring element, an accelerometer, which serves the purpose of measuring acceleration. After performing simulation computations, the acceleration signal is processed in the software PostProcessor. As a result, ride comfort indices are quantified.
The current state of simulation computations allows precise estimations and predictions of the dynamic properties of a vehicle in real operational conditions [15,16,17,18,19,20,21]. It is important to set up a virtual model with characteristics representing real states as accurately as possible. In the simulation software, the multibody system of the railway passenger car consists of rigid bodies interconnected by spring-damping elements [22,23]. The analyzed model of a vehicle includes the set of acceleration sensors, i.e., accelerometers, which measure the acceleration of the vehicle body in all three directions [24]. These acceleration signals are essential when calculating the ride comfort indices, based on which the ride comfort for passengers is assessed [25,26,27].

2. Motivation and the Research Goal

The authors of the presented research have for a long time devoted themselves to investigating the running properties and behaviors of wheeled means of transport. Their research is focused mainly on ride safety, passenger ride comfort and ways to improve them [28,29,30]. For these purposes, they have created many simulation models, performed a large number of computer simulations and undertaken investigations by means of test stands [31,32]. Their research deals mainly with rail vehicles [28,33,34,35] and road vehicles [36,37,38,39,40]. During these activities, various phenomena have been observed. Among them, it has been observed that it is possible to signify decreased running safety or running comfort by investigating changes in the parameters of vehicles, such as: mass and inertia parameters, parameters of suspension systems, the design of contact elements (the wheel/rail contact), the influence of input parameters, including the excitation of a vehicle’s mechanical system, stochastic phenomena during the running of a vehicle on a track and other effects [29,30,41].
This work presents and concludes selected findings about the influence of changes in the stiffness–damping parameters of a suspension system of a railway passenger car on passenger ride comfort. The method for evaluating ride comfort is based on the EN 12299:2009 standard [42].

3. Ride Comfort for Passengers

The analysis of ride comfort for passengers plays an essential role in the process of assessing vehicles. Ride comfort is a complex term, which includes a set of factors specifying thermal comfort, acoustic comfort [43,44], air quality and, in particular, the vibration of passengers [45,46,47]. Sensations related to ride comfort also depend on factors which characterize the environment in which a passenger is placed. Among other factors, the position of a passenger, the operational conditions and the time of exposure are all included. Despite the fact that the ride comfort level is closely related to these described factors, a quantitative evaluation of ride comfort is relatively difficult to achieve because of the subjective nature of the perception of various effects. The influence of various factors which relate to ride comfort is investigated by means of approximate methods describing their effects on a human body. Ride comfort for passengers is assessed by means of indices of ride comfort, which depend on one or more parameters characterizing the environment in which a passenger is located [41,47,48,49].
The above factors all have an effect on ride comfort, but by far the most important are vibrations and oscillations acting on the human body. These can influence in various ways and even worsen the organic functions of a person, can increase exhaustion and can even lead to health damage. When it comes to evaluating vehicle vibrations, total vibrations are important, i.e., vibrations which are transmitted from a seat or a floor to the passenger’s entire body [48,49,50].
The quantification of ride comfort for passengers is evaluated either by a direct method, i.e., by means of evaluating the comfort of a real vehicle, or by an indirect method, i.e., by means of evaluating specific measured data. The indirect method is based on the knowledge of acceleration signals relating to specific locations on a vehicle. Acceleration signals are filtered and weighted by functions which take into account the sensitivity of the human body in response to vibrations in various directions [48,51,52,53].
When vibrations act on a human body, forced oscillations of spcific parts of the body or the entire body are excited. In particular, this depends on both how the vibrations are transmitted to the human body and on the physical properties of the oscillations (i.e., intensity and frequency). When the excitation frequency is close to the eigenfrequencies of important human organs, severe consequences can occur. In the case of vertical oscillations, the eigenfrequencies of a human body range from 4 to 6 Hz. In this frequency range, resonance oscillations of the upper part of a body as well as the vertebrae and stomach are reached. In a horizontal direction, the resonance oscillation occurs in the range of 1 to 3 Hz. Generally, the human body can accept higher levels of horizontal oscillation than vertical oscillation [48,49]. A dynamic model of a human body is shown in Figure 1.
Signals of accelerations acting to a human body are the main parameters, based on which the ride comfort for passengers regarding vibrations is determined and evaluated.

3.1. Evaluation of Ride Comfort for Passengers

The sensations which a passenger experiences during the running of a vehicle due to vibrations are measured and assessed by means of the EN 12299 standard [42] and are recognized as the following [42,54,55,56,57]:
  • Average comfort: average lasting feeling evaluated by means of the indices NMV (Mean Comfort Standard Method), NVA and NVD (Mean Comfort Complete Method);
  • Continuous comfort: sensations evaluated during a certain time period by means of the indices CCx, CCy and CCz (Continuous Comfort);
  • Comfort during rail vehicle running in a curve: discomfort experienced during entering a curve or experienced during running in opposite curves by means of the index PCT (Comfort on Curve Transitions);
  • Comfort during discrete events: discomfort experienced based on transient vibrations in a straight track and in curves for the index PDE (Comfort on Discrete Events).
This research is focused on the investigation and evaluation of average ride comfort for passengers measured on a rail vehicle floor by means of the index NMV. Details of the application of this index for evaluating the ride comfort for passengers are listed in Table 1.
Signals of measured accelerations in individual directions x, y and z (i.e., accelerations ax, ay and az) are entered into the ride comfort calculation.
A local coordinate system is placed on a vehicle floor and it is defined by:
  • An origin: in the middle of bogies pivots, the x axis for a longitudinal direction, the y axis for a lateral direction and the z axis for a vertical direction;
  • Angle φ: the definition of rotary motion around the x axis.

Calculation of the Index NMV

The index NMV, calculated by a standard method, quantifies the ride comfort level on a rail vehicle floor for a sitting passenger. It is necessary to know the values of acceleration in the longitudinal (x axis), lateral (y axis) and vertical (z axis) directions. Values of acceleration are subsequently weighted by the weight functions Wb and Wd a frequency range of 0.4 to 100 Hz [42,47,55].
The mean square value of these accelerations are calculated as follows [42,58,59]:
a j η W i ( t ) = 1 T ⋅ ∫ t − T T [ x ¨ W i * ( τ ) ] 2 ⋅ d τ ,    j = x , y , z ,
where T = 5 s and t is a multiple of 5 s. Modified values of accelerations are statistically evaluated in the corresponding directions and a summation function is determined. Finally, 95%-percentiles of the distribution function in 5 s intervals are estimated from histograms. The calculation process of the ride comfort index NMV is depicted in Figure 2.
The resulting values of the ride comfort index NMV is given by the formulation [42]:
N M V = 6 ⋅ ( a x P 95 W d ) 2 + ( a y P 95 W d ) 2 + ( a z P 95 W b ) 2 .
A specific calculated value of the ride comfort index NMV is compared with the scale introduced in Table 2 for the final determination of the ride comfort in the given operational conditions.

3.2. The Method of Evaluation of Output Signals from Simulation Computations

The results of dynamic analyses of the investigated railway passenger car with the defined parameters and under the defined operational conditions are described below.
As the main criterion, the level of ride comfort for passengers of a railway passenger car is evaluated based on the values of the ride comfort indices. Acceleration signals in three directions, x, y and z, are the main inputs for their calculation. Fifteen locations are chosen on the car body floor from which acceleration signals are measured. These points are shown in Figure 3.
Acceleration signals have to be processed according to the valid standard [42,58]. Details are described in the following section. The software used includes a database with filters, which are applied to the obtained acceleration signals. The procedure of signal processing consists of several steps, which are shown in Figure 4.
Figure 4 has three individual parts for every direction of acceleration, i.e., the x direction, the y direction and the z direction.

4. Analysis of Ride Comfort of a Railway Passenger Car by Means of the Standard Method

When one uses commercial computational software for creating a virtual model of a vehicle, a mathematical model of a multibody system is generated automatically by the software. The number of equations depends on the complexity of the multibody system. The analyzed railway passenger car consists of rigid bodies and each of them has six degrees of freedom in the space. Couples between bodies restrict some of degrees of freedom. A scheme of the railway passenger car is shown in Figure 5.
The considered computational model of the railway passenger car includes mechanical as well as kinematic couples. Its dynamics are described by means of differential-algebraic equations [60,61,62], which are generally formed as follows:
[ M D T D 0 ] ⋅ [ q Λ ] = [ F γ ]
where M is the mass matrix, D is the Jacobian matrix, q is the vector of generalized coordinates, Λ is the vector of Lagrange multipliers, F is the load vectors (including kinematic excitation, external forces, etc.), and γ = D ⋅ q ¨ .
The dynamic analysis of a vehicle is required to define initial conditions, e.g., a position vector and/or a velocity vector. Based on this information, Equation (1) is set up and solved for the desired accelerations, which are subsequently integrated in time together with velocities.

4.1. A Description of a Multibody Wagon Model

A multibody model of the referenced rail vehicle was chosen for performing simulation computations. It is a two bogies four-axle railway passenger car. Wheelsets are guided in a bogie by means of two swinging arms and they are suspended by coil springs and hydraulic dampers on a frame. Yaw dampers eliminate the rotary motions of a bogie against the body of the wagon. A secondary suspension between bogie frames and the wagon body consists of coil springs and hydraulic dampers. Additional dampers are mounted in a lateral direction in order to reduce tilting of the body [63].
The wagon model was set up in the Simpack multibody software package. This software is a commercial multibody simulation software used for simulating non-linear motion of large and complex multibody systems.
In terms of the computational model, the created wagon model is composed of rigid bodies which are connected by massless spring-damper elements. An illustration of the multibody wagon model is shown in Figure 6.
The assembly of the wagon in the Simpack package includes three subsystems (in Simpack called substructures):
  • A front bogie;
  • A rear bogie;
  • A wagon body.
The bogie of the wagon with marked individual components of the primary and secondary suspension system is depicted in Figure 7.
The designation of springs and dampers is as follows:
  • kP—stiffness of the primary spring;
  • kS—stiffness of the secondary spring;
  • bPV—damping coefficient of the primary vertical damper;
  • bPY—damping coefficient of the primary yaw damper;
  • bPL—damping coefficient of the primary lateral damper;
  • bSV—damping coefficient of the secondary vertical damper.

4.2. A Description of a Track Model

The multibody model also contains a track model, on which the wagon runs during simulations. The track model includes the definition of a track geometry, rail profiles input files, track irregularities and other important parameters.
In our research, we have used a model of a real track section. The chosen track model is suitable for performing simulation computations and subsequent evaluations of ride comfort because it includes straight sections as well as sections with curves of various radii, i.e., sections with superelevation ramps and transient sections. The track gauge was 1435 mm, the rail cant 1:40 and the rail head profile UIC60. There parameters are prescribed in the source input file. An illustration of the track model is shown in Figure 8.
The track model also includes track irregularities. The definition of irregularities in the track model is important in terms of the excitation of the railway passenger car in order to simulate as real conditions as possible [64,65,66]. The track irregularities can be defined in the Simpack software as predefined stochastic irregularities according to the standard [67], either by means of the input file containing real measured irregularities or by means of a harmonic function, which is the simplest way. Our model includes track irregularities measured on the track. Thus, such a track model generates excitation of the vehicle and acceleration signals can be detected [68,69,70].

4.3. Simulation Analysis and Results

As described above, the main objective of this research is to investigate how the change of suspension parameters in the railway passenger car effects the ride comfort for passengers. Stiffness–damping parameters of springs and dampers, namely values of the parameters kP, kS, bPV and bSV (Figure 7), in the primary and secondary suspension systems were changed in three levels, at which both the ratio of stiffness of the secondary and primary springs as well as the ratio of damping of the vertical secondary and the vertical primary dampers were preserved. The damping coefficients of the rest of the dampers were unchanged. The characteristics of the springs were linear, while the characteristics of the dampers were non-linear.
The definition of the spring-damping parameters was based on the original parameters named as “Original”. The ratio of the stiffness kS/kP is 0.587. In the case of the dampers, an inclination of the tangent was changed, at which the ratio of the extreme values was preserved. Then, numerous combinations of kP and kS, as well as the characteristics of bPV and bVS, were defined and tested by means of simulations. This work presents only the finally chosen combinations of kP and kS and bPV and bSV in order to illustrate their effects on the ride comfort index NMV. These parameters are summarized in Table 3. The percentage of kP, kS, bPV and bSV expresses percentage by which the original value was changed. It was changed both to lower (sign “−”) and higher (sign “+”) values.
Simulations of running the railway passenger car were performed for various speeds. This section includes the resulting values of the ride comfort NMV distribution on the floor only for two selected speeds, namely for the speed of 60 km∙h−1 and 110 km∙h−1. These speeds were chosen as representative samples for evaluation of the acceleration signals, because a speed of 60 km∙h−1 is the average speed of trains on the track and the speed 110 km∙h−1 is the maximum speed at which trains can safely run through curves on a track.
Figure 9, Figure 10, Figure 11, Figure 12, Figure 13 and Figure 14 show the distribution of the ride comfort index NMV on the wagon body floor for various running conditions. These histograms are arranged according to the scheme shown in Figure 3.
The first two figures (Figure 9 and Figure 10) show the distribution of the ride comfort index NMV for the “Original” parameters of the suspension system (Table 3). Another two figures (Figure 11 and Figure 12) include the calculated results for the “Modification I” parameters (Table 3) when both the stiffness–damping coefficients of the primary suspension systems are softer and the stiffness–damping parameters of the secondary suspension system are stiffer. Finally, last two figures (Figure 13 and Figure 14) provide an overview of the distribution of the ride comfort index NMV for “Modification II” parameters, i.e., when the stiffness–damping parameters of the primary suspension are stiffer and the stiffness–damping parameters of the secondary suspension are softer. A description of the findings of the presented research are described in detail in the following section.

5. Discussion

The evaluation of the achieved results show that in the case of the railway passenger car running on the modelled real track, the ride comfort depends on the suspension parameters together with the running speed. A higher running speed leads to lower ride comfort. This is obvious for all three versions of the suspension parameters, i.e., for the “Original” (Figure 9 and Figure 10), for the “Modification I” (Figure 11 and Figure 12) as well as for the “Modification II” (Figure 13 and Figure 14) versions. It can be understood that higher running speeds causes greater excitations of the wagon body. Acceleration sensors detect greater values of acceleration, which are entered into the calculation of the ride comfort indices.
Our other findings include the values of the ride comfort indices. When we look at the results for the “Original” parameters, we can see that for both running speeds the values are under 1.5. This means that the ride is evaluated as very comfortable (Table 3). However, comparing these results for the chosen speed we can recognize that a higher running speed means a lower ride comfort.
The configuration “Modification I”, i.e., the softer suspension elements of the primary suspension system (both springs and dampers) and the stiffer suspension elements of the secondary suspension system (Figure 11 and Figure 12), lead to slightly higher values in the ride comfort indices NMV on the body floor at certain points and also indices of slightly lower values. This is recognized at both a speed of 60 km∙h−1 and a speed of 110 km∙h−1. These differences are not significant, and the ride can still be evaluated as very comfortable (Table 3).
The differences are more pronounced for the suspension parameters configuration “Modification II”, i.e., stiffer springs and dampers in the primary suspension and softer springs and dampers in the secondary suspension (Figure 13 and Figure 14). In case of the railway passenger car running at a speed of 60 km∙h−1, the resulting ride comfort indices are similar to the “Original” and “Modification I” configurations. However, the values of the ride comfort indices for the running speed of 110 km∙h−1 are higher and close to the limit of 1.5, which is a boundary between very comfortable and comfortable. It can be seen that higher values of the index NMV are calculated for all measured points on the body floor. This is interesting because the secondary suspension is intended mainly to ensure the comfort of passengers. This is because the softer springs and dampers in the secondary suspension leads to a greater tilt of the wagon body and thus accelerations in the y direction influence the resulting value of the NMV index. Despite the fact that this phenomenon could also be expected for the configuration “Modification I”, the achieved results do not confirm this. Another fact should be noted. The calculated ride comfort index NMV is primarily determined for evaluating the ride comfort on straight tracks. Also, our results suggest that the application of the ride comfort index NMV for evaluating ride comfort in curves has to be justified.
The presented results are so far only a part of more extensive and complex research focused on investigating the influence of the parameters of vehicles on ride comfort and running safety. It is important to assess the influence of the weight of the wagon body on the ride comfort. If the weight of the vehicle is higher, it can be expected that the values of the ride comfort index would be even higher due to greater centrifugal acceleration (y direction) as well as vertical (z direction) and longitudinal (x direction) acceleration.
Furthermore, springs with the suitable non-linear characteristics can be mounted. Such a suspension system is able to preserve similar (or even the same) waveforms of responses regardless of the weight of the vehicle.
Future research will be focused on evaluating the influence of changes in the parameters described above on ride comfort and safety. The principle applied in this research, which was used for a particular rail vehicle, can be also used in compliance with certain specifications in order to examine the characteristics of road vehicles, work machines, rescue vehicles and other similar vehicles.

6. Conclusions

Computer simulations of vehicle movement on a track represent a state of art way to investigate and evaluate their behavior in term of their dynamics.
The analysis of ride comfort for passengers remains a problem. The subjective experience of the comfort level which a passenger experiences in a vehicle can significantly affects their preference for a given means of a transport. In order to evaluate of ride comfort by means of simulation computations, the indirect method was used. This method is based on identifying the acceleration of the required points in different directions, their statistical processing and the calculation of a specific number (the comfort index).
In the presented research:
  • The commercial simulation software Simpack was used;
  • A virtual model of a railway passenger car was created;
  • Passenger ride comfort was evaluated for the chosen parameters of the vehicle;
  • The ride comfort index NMV on the body floor was calculated and graphs illustrating the NMV indices distributed on the body floor were depicted;
  • It was concluded that the stiffness of the springs and damping coefficients of dampers of the secondary suspension system affected ride comfort more significantly than the stiffness–damping parameters of the primary suspension system.

Author Contributions

Conceptualization, J.D., M.B. and S.S.; methodology, J.G., B.L. and M.K.; software, J.D. and J.G.; validation, J.D., B.L., R.M. and M.K.; formal analysis, J.D., M.B., E.M. and M.K.; investigation, J.D., R.M., E.M. and S.S.; resources, J.D. and M.B.; writing—original draft preparation, J.D.; writing—review and editing, J.G., R.M. and M.K.; supervision, J.G. and B.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Cultural and Educational Grant Agency of the Ministry of Education of the Slovak Republic in the project No. KEGA 023ŽU-4/2020: Development of advanced virtual models for studying and investigation of transport means operation characteristics. This research was supported by the Cultural and Educational Grant Agency of the Ministry of Education of the Slovak Republic in the project No. KEGA 036ŽU-4/2021: Implementation of modern methods of computer and experimental analysis of the properties of vehicle components in the education of future vehicle designers. This research was also supported by the Slovak Research and Development Agency of the Minis-try of Education. Science. Research and Sport of the Slovak Republic in Educational Grant Agency of the Ministry of Education of the Slovak Republic in the project No. VEGA 1/0558/18: Research of the interaction of a braked railway wheelset and track in simulated operational conditions of a vehicle running in a track on the test bench.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Maňurová, M.; Suchánek, A. The Analysis of a Rail Vehicle with a Tilting Bogie. Manuf. Technol. 2016, 16, 917–923. [Google Scholar] [CrossRef]
  2. Šťastniak, P.; Smetanka, L.; Drozdziel, P. Computer aided simulation analysis for wear investigation of railway wheel running surface. Diagnostyka 2019, 20, 63–68. [Google Scholar]
  3. Yu, Y.W.; Zhao, L.L.; Zhou, C.C. A new vertical dynamic model for railway vehicle with passenger-train-track coupling vibration. Proc. Inst. Mech. Eng. Part K-J. Multi-Body Dyn. 2020, 234, 134–146. [Google Scholar] [CrossRef]
  4. Wu, Y.; Zeng, J.; Qu, S.; Shi, H.L.; Wang, Q.S.; Wei, L. Low-Frequency Carbody Sway Modelling Based on Low Wheel-Rail Contact Conicity Analysis. Shock Vib. 2020, 2020, 71049. [Google Scholar] [CrossRef]
  5. Rodríguez, A.; Sañudo, R.; Miranda, M.; Gómez, A.; Benavente, J. Smartphones and tablets applications in railways, ride comfort and track quality. Transition zones analysis. Measurement 2021, 182, 109644. [Google Scholar] [CrossRef]
  6. Uhl, T.; Chudzikiewicz, A. Noise, vibration and durability problems in rail vehicle design. In Proceedings of the 1st International ISMA Workshop on Noise and Vibration in Agricultural and Biological Engineering, Leuven, Belgium, 13–14 September 2000; Sas, P., Moens, D., Eds.; Department Agro Enen & Econ, Catholic University Leuven: Leuven, Belgium, 2000. [Google Scholar]
  7. Koziak, S.; Chudzikiewicz, A.; Opala, M.; Melnik, R. Virtual software testing and certification of railway vehicle from the point of view of their dynamics. In Proceedings of the 13th International Scientific Conference on Sustainable, Modern and Safe Transport (TRANSCOM 2019), Novy Smokovec, Slovakia, 29–31 May 2019; Bujnak, J., Guagliano, M., Eds.; Elsevier: Amsterdam, The Netherlands, 2019. [Google Scholar]
  8. Lovska, A.; Fomin, O. A new fastener to ensure the reliability of a passenger car body on a train ferry. Acta Polytech. 2020, 60, 478–485. [Google Scholar] [CrossRef]
  9. Lovska, A.; Fomin, O.; Horban, A.; Radkevych, V.; Skok, P.; Skliarenko, I. Investigation of the dynamic loading of a body of passenger cars during transportation by rail ferry. EUREKA Phys. Eng. 2019, 4, 91–100. [Google Scholar] [CrossRef] [Green Version]
  10. Lack, T.; Gerlici, J.; Hlavňa, V. The vehicle comfort. Case study. Eksploat. Niezawodn. Maint. Reliab. 2006, 32, 89–91. [Google Scholar]
  11. Loulová, M.; Suchánek, A.; Harušinec, J. Evaluation of the Parameters Affecting Passenger Riding Comfort of a Rail Vehicle. Manuf. Technol. 2017, 17, 224–231. [Google Scholar] [CrossRef]
  12. Chudzikiewicz, A.; Sowinski, B.; Szulczyk, A. Statistical Parameters of Vibrations as Measures of Rail Vehicle Condition. In Proceedings of the 17th International Congress on Sound and Vibration, Cairo, Egypt, 18–22 July 2010; The International Institute of Acoustics & Vibration: Auburn, AL, USA, 2010. [Google Scholar]
  13. Figlus, T.; Szafraniec, P.; Skrúcaný, T. Methods of measuring and processing signals during tests of the exposure of a motorcycle driver to vibration and noise. Int. J. Environ. Res. Public Health 2019, 16, 3145. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Skrúcaný, T.; Šarkan, B.; Figlus, T.; Synák, F.; Vrábel, J. Measuring of noise emitted by moving vehicles. In Proceedings of the Dynamics of Civil Engineering and Transport Structures and Wind Engineering (DYN-WIND), Trstena, Slovakia, 21–25 May 2017; Melcer, J., Kortasova, K., Eds.; EDS Sciences-Web of Conferences: Les Ulis, France, 2017. [Google Scholar]
  15. Saga, M.; Vasko, M.; Handrik, M.; Kopas, P. Contribution to Random Vibration Numerical Simulation and Optimisation of Nonlinear Mechanical Systems. Sci. J. Sil. Univ. Technol. Ser. Transp. 2019, 103, 143–154. [Google Scholar] [CrossRef]
  16. Klimenda, F.; Soukup, J.; Skošilasová, B.; Skošilas, J. Vertical vibration of the vehicle when crossing over transverse speed bumps. Manuf. Technol. 2020, 20, 55–59. [Google Scholar] [CrossRef]
  17. Nangolo, N.F.; Klimenda, F. System identification for underdamped mechanical systems. In Proceedings of the 52nd International Conference on Experimental Stress Analysis (EAN 2014), Marianske Lazne, Czech Republic, 2–5 June 2014. [Google Scholar]
  18. Smetanka, L.; Šťastniak, P.; Harušinec, J. Wear research of railway wheelset profile by using computer simulation. In Proceedings of the Machine Modelling and Simulations 2017, Sklene Teplice, Slovakia, 5–8 September 2017; Vasko, M., Handrik, M., Jakubovicova, L., Kopas, P., Blatnicka, M., Baniari, V., Stalmach, O., Sapietova, A., Saga, M., Eds.; EDP Sciences: London, UK, 2017. [Google Scholar]
  19. Jedliński, Ł.; Caban, J.; Krzywonos, L.; Wierzbicki, S.; Brumerčík, F. Application of vibration signal in the diagnosis of IC engine valve clearance. J. Vibroeng. 2015, 17, 175–187. [Google Scholar]
  20. Konieczny, L.; Burdzik, R.; Figlus, T. Use of mbs (adams/car) software in simulations of vehicle suspension systems. Transp. Probl. 2014, 9, 111–118. [Google Scholar]
  21. Jedlinsky, L.; Caban, J.; Krzywonos, L.; Wierzbicki, S.; Brumercík, F. Application of the vibration signal in the diagnosis of the valve clearance of an internal combustion engine. Vibroeng. Procedia 2014, 3, 14–19. [Google Scholar]
  22. Nozhenko, O.; Hauser, V.; Kravchenko, K.; Loulová, M. Reducing tram car’s curve-pasing resistance by double treaded wheel profile. Manuf. Technol. 2018, 18, 79–83. [Google Scholar] [CrossRef]
  23. Nozhenko, O.; Kravchenko, K.; Loulová, M.; Hauser, V. Double treaded wheelset riding regime change in strongly curved track from the derailment-safety point of view. Manuf. Technol. 2018, 18, 303–308. [Google Scholar] [CrossRef]
  24. Lewis, T.D.; Jiang, J.Z.; Neild, S.A.; Gong, C.; Iwnicki, S.D. Using an inerter-based suspension to improve both passenger comfort and track wear in railway vehicles. Veh. Syst. Dyn. 2020, 58, 472–493. [Google Scholar] [CrossRef]
  25. Mucka, P.; Stein, G.J.; Tobolka, P. Whole-body vibration and vertical road profile displacement power spectral density. Veh. Syst. Dyn. 2020, 58, 630–656. [Google Scholar] [CrossRef]
  26. Lack, T.; Gerlici, J. Vehicles dynamical properties analysis from the point of view of comfort for passengers. Arch. Transp. 2007, 19, 91–110. [Google Scholar]
  27. Chudzikiewicz, A.; Stelmach, A.; Wawrzynski, W.; Firlik, B.; Czechyra, B. Vibro-acoustic evaluation of a light rail vehicle. In Proceedings of the 23rd International Congress on Sound and Vibration: From Ancient to Modern Acoustics, Athens, Greece, 10–14 July 2016; Vogiatzis, K., Kouroussis, G., Crocker, M., Pawelczyk, M., Eds.; University of Thessaly: Athens, Greece, 2016. [Google Scholar]
  28. Dižo, J.; Gerlici, J.; Lack, T. Using of computer simulation for evaluation of ride comfort of rail vehicles. Railw. Transp. Logist. 2013, 9, 48–56. (In Slovak) [Google Scholar]
  29. Dižo, J. Evaluation of ride comfort for passengers by means of computer simulation. Manuf. Technol. 2015, 15, 8–14. [Google Scholar] [CrossRef]
  30. Dižo, J.; Blatnický, M.; Melnik, R. Assessment of the passenger ride comfort for a coach by means of simulation computations. LOGI Sci. J. Transp. Logist. 2017, 8, 24–32. [Google Scholar] [CrossRef] [Green Version]
  31. Mikhailov, E.; Sapronova, S.; Tkachenko, V.; Semenov, S.; Smyrnova, I.; Kholostenko, Y. Improved solution of guiding of railway vehicle in curves. In Proceedings of the 23rd International Scientific Conference on Transport Means 2019 (TRANSPORT MEANS 2019), Palanga, Lithuania, 2–4 October 2019; Kaunas University of Technology: Kaunas, Lithuania, 2019. [Google Scholar]
  32. Mikhailov, E.; Semenov, S.; Dizo, J.; Kravchenko, K.; Blatnicky, M. Possibilities of Reducing of a Rail Vehicle Running Resistance by Means of a New Wheel Design. In Proceedings of the 24th International Conference on Current Problems in Rail Vehicles (ProRAIL 2019), Žilina, Slovakia, 17–19 September 2019; Kalincak, D., Ed.; University of Žilina: Žilina, Slovakia, 2019. [Google Scholar]
  33. Dižo, J.; Blatnický, M.; Steišūnas, S.; Skočilasová, B. Assessment of a rail vehicle running with the damaged wheel on a ride comfort for passengers. In Proceedings of the Machine Modelling and Simulations 2017, Sklene Teplice, Slovakia, 5–8 September 2017; Vasko, M., Handrik, M., Jakubovicova, L., Kopas, P., Blatnicka, M., Baniari, V., Stalmach, O., Sapietova, A., Saga, M., Eds.; EDP Sciences: London, UK, 2017. [Google Scholar]
  34. Chudzikiewicz, A.; Bogacz, R.; Kostrzewski, M.; Konowrocki, R. Condition monitoring of railway track systems by using acceleration signals on wheelset axle-boxes. Transport 2018, 33, 555–566. [Google Scholar] [CrossRef] [Green Version]
  35. Chudzikiewicz, A.; Bogacz, R.; Kostrzewski, M. Using acceleration signals recorded on a railway vehicle wheelsets for rail track condition monitoring. In Proceedings of the 7th European Workshop on Structural Health Monitoring (EWSHM 2014), Nantes, France, 8–11 July 2014; Université de Nantes: Nantes, France, 2014. [Google Scholar]
  36. Leitner, B. The software tool for mechanical structures dynamic systems identification. In Proceedings of the 15th International Conference on Transport Means (TRANSPORT MEANS 2011), Kaunas, Lithuania, 20–21 October 2011; Ostasevicius, V., Ed.; Kaunas University of Technology: Kaunas, Lithuania, 2011. [Google Scholar]
  37. Dižo, J.; Blatnický, M. Investigation of ride properties of a three-wheeled electric vehicle in terms of driving safety. In Proceedings of the 13th International Scientific Conference on Sustainable, Modern and Safe Transport (TRANSCOM 2019), Novy Smokovec, Slovakia, 29–31 May 2019; Bujnak, J., Guagliano, M., Eds.; Elsevier: Amsterdam, The Netherlands, 2019. [Google Scholar]
  38. Dižo, J.; Blatnický, M. Evaluation of vibrational properties of a three-wheeled vehicle in terms of comfort. Manuf. Technol. 2019, 19, 197–203. [Google Scholar]
  39. Dižo, J.; Blatnický, M.; Barta, D.; Melnik, R. Application of simulation computations in investigation of vibration properties of a tricycle. Diagnostyka 2019, 20, 97–104. [Google Scholar] [CrossRef]
  40. Leitner, B. Fatigue damage analysis and fatigue life prediction of lorry frame under random excitation. In Proceedings of the 18th International Conference on Transport Means (TRANSPORT MEANS 2014), Kaunas, Lithuania, 23–24 October 2014; Kaunas University Technol Press, Kaunas University of Technology: Kaunas, Lithuania, 2014. [Google Scholar]
  41. Lack, T.; Gerlici, J. Analysis of vehicles dynamic properties from the point of view of passenger comfort. Commun. Sci. Lett. Univ. Žilina 2008, 10, 10–18. [Google Scholar]
  42. EN 12299:2009. In Railway Applications. Ride Comfort for Passengers—Measurement and Evaluation; European Committee of the Regions: Brussels, Belgium, 2009.
  43. Caban, J.; Maksym, P.; Zarajczyk, J. Acoustic comfort tests in the tractor cabin. In Proceedings of the 23rd International Scientific Conference on Transport Means 2019 (TRANSPORT MEANS 2019), Palanga, Lithuania, 2–4 October 2019; Kaunas University of Technology: Kaunas, Lithuania, 2019. [Google Scholar]
  44. Zvolenský, P.; Kašiar, L.; Volna, P.; Barta, D. Simulated Computation of the Acoustic Energy Transfer through the Structure of Porous Media in Application of Passenger Carriage Body. In Proceedings of the 10th International Scientific Conference (TRANSBALTICA 2017), Vilnius, Lithuania, 4–5 May 2017; Bureika, G., Yatskiv, I., Prentkovskis, O., Maruschak, P., Eds.; Vilnius Gediminas Technical University: Vilnius, Lithuania, 2017. [Google Scholar]
  45. Isteník, R.; Barta, D.; Mucha, W. Influence of the wheels on the automobile dynamics. Komunikacie 2004, 6, 26–28. [Google Scholar]
  46. Ni, Y.Q.; Ye, S.Q.; Song, S.D. An experimental study on constructing MR secondary suspension for high-speed trains to improve lateral ride comfort. Smart Struct. Syst. 2016, 18, 53–74. [Google Scholar] [CrossRef]
  47. Kardas-Cinal, E. Statistical Analysis of Dynamical Quantities Related to Running Safety and Ride Comfort of a Railway Vehicle. Sci. J. Sil. Univ. Technol. Ser. Transp. 2020, 106, 63–72. [Google Scholar] [CrossRef]
  48. Vlk, F. Dynamics of Engine Vehicles, 1st ed.; Nakladatelství a vydavatelství František Vlk: Brno, Czech Republic, 2003; pp. 193–197. (In Czech) [Google Scholar]
  49. Jiang, Y.; Chen, B.K.; Thompson, C. A comparison study of ride comfort indices between Sperling’s method and EN 12299. Int. J. Rail Transp. 2019, 7, 279–296. [Google Scholar] [CrossRef]
  50. Kardas-Cinal, E. Statistical Method for Investigating Transient Enhancements of Dynamical Responses due to Random Disturbances: Application to Railway Vehicle Motion. J. Vib. Acoust. Trans. ASME 2020, 142, 061008. [Google Scholar] [CrossRef]
  51. Deng, C.X.; Zhou, J.S.; Thompson, D.; Gong, D.; Sun, W.J.; Sun, Y. Analysis of the consistency of the Sperling index for rail vehicles based on different algorithms. Veh. Syst. Dyn. 2021, 59, 313–330. [Google Scholar] [CrossRef]
  52. Mizrak, C.; Esen, I. Determining Effects of Wagon Mass and Vehicle Velocity on Vertical Vibrations of a Rail Vehicle Moving with a Constant Acceleration on a Bridge Using Experimental and Numerical Methods. Shock Vib. 2015, 2015, 83450. [Google Scholar] [CrossRef]
  53. Vaičiūnas, G.; Steišūnas, S. Sperling’s comfort index study in a passenger car with independently rotating wheels. Transp. Probl. 2021, 16, 121–130. [Google Scholar] [CrossRef]
  54. Herrero, A. Towards Optimization of a High Speed Train Bogie Primary Suspension. Master’s Thesis, Chalmers University of Technology, Göteborg, Sweden, 2013. [Google Scholar]
  55. Dumitriu, M.; Stănică, D.I. Study on the Evaluation Methods of the Vertical Ride Comfort of Railway Vehicle—Mean Comfort Method and Sperling’s Method. Appl. Sci. 2021, 11, 3953. [Google Scholar] [CrossRef]
  56. Dumitriu, M.; Cruceanu, C. Influence of carbody vertical flexibility on ride comfort of railway vehicles. Arch. Mech. Eng. 2017, 64, 221–238. [Google Scholar] [CrossRef]
  57. Vaičiūnas, G.; Steišūnas, S.; Bureika, G. Specification of estimation of a passenger car ride smoothness under various exploitation conditions. Eksploat. Niezawodn. Maint. Reliab. 2021, 23, 719–725. [Google Scholar] [CrossRef]
  58. SISO 2631–1: 1985 and 1997. In Mechanical Vibration and Shock. Evaluation of Human Exposure to Whole-Body Vibration. Part 1: General Requirements; International Organization for Standardization: Geneve, Switzerland, 1997.
  59. Graa, M.; Nejlaoui, M.; Houidi, A.; Affi, Z.; Romdhane, L. Development of a reduced dynamic model for comfort evaluation of rail vehicle systems. Proc. Inst. Mech. Eng. Part K-J. Multi-Body Dyn. 2016, 230, 489–504. [Google Scholar] [CrossRef]
  60. Shevtsov, Y.I. Wheel/Rail Interface Optimisation; Delft University of Technology: Delft, The Netherlands, 2008. [Google Scholar]
  61. Muñoz, S.; Aceituno, J.F.; Urda, P.; Escalona, J.L. Multibody model of railway vehicles with weakly coupled vertical and lateral dynamics. Mech. Syst. Signal Process. 2019, 115, 570–592. [Google Scholar] [CrossRef]
  62. Bao, Y.L.; Li, Y.L.; Ding, J.J. A Case Study of Dynamic Response Analysis and Safety Assessment for a Suspended Monorail System. Int. J. Environ. Res. Public Health 2016, 13, 1121. [Google Scholar] [CrossRef] [PubMed]
  63. Opala, M.; Korzeb, J.; Koziak, S.; Melnik, R. Evaluation of Stress and Fatigue of a Rail Vehicle Suspension Component. Energies 2021, 14, 3410. [Google Scholar] [CrossRef]
  64. Leitner, B.; Figuli, L. Fatigue life prediction of mechanical structures under stochastic loading. In Proceedings of the Machine Modelling and Simulations, Sklene Teplice, Slovakia, 5–8 September 2017; Vasko, M., Handrik, M., Jakubovicova, L., Kopas, P., Blatnicka, M., Baniari, V., Stalmach, O., Sapietova, A., Saga, M., Eds.; EDP Sciences: London, UK, 2017. [Google Scholar]
  65. Loulová, M.; Suchánek, A.; Harušinec, J.; Strážovec, P. Analysis of a railway vehicle with unevenness on wheel. Manuf. Technol. 2018, 18, 266–272. [Google Scholar] [CrossRef]
  66. Saga, M.; Jakubovicova, J. Simulation of Vertical Vehicle Non-Stationary Random Vibrations Considering Various Speeds. Sci. J. Sil. Univ. Technol. Ser. Transp. 2014, 84, 113–118. [Google Scholar]
  67. ISO/DIS 23054-1. Railway Applications—Track Geometry Quality—Part 1: Characterisation of Track Geometry and Track Geometry Quality; International Organization for Standardization: Geneva, Switzerland, 2021. [Google Scholar]
  68. Pombo, J.; Ambrósio, J. An alternative method to include track irregularities in railway vehicle dynamic analyses. Nonlinear Dyn. 2012, 68, 161–176. [Google Scholar] [CrossRef]
  69. Panunzio, A.M.; Puel, G.; Cottereau, R.; Simon, S.; Quost, X. Construction of a stochastic model of track geometry irregularities and validation through experimental measurements of dynamic loading. Veh. Syst. Dyn. 2017, 55, 399–426. [Google Scholar] [CrossRef]
  70. Javaid, O.; Choi, D.H. Effect of Track Irregularities on the Response of Two-Way Railway Tracks. Appl. Sci. 2020, 10, 11. [Google Scholar] [CrossRef] [Green Version]
Sensors 21 08138 g001 550
Figure 1. A dynamic model of a human body [48,49].
Figure 1. A dynamic model of a human body [48,49].
Sensors 21 08138 g001
Sensors 21 08138 g002 550
Figure 2. The calculation process for the ride comfort index NMV.
Figure 2. The calculation process for the ride comfort index NMV.
Sensors 21 08138 g002
Sensors 21 08138 g003 550
Figure 3. Locations of points for evaluation of ride comfort for passengers on the body floor.
Figure 3. Locations of points for evaluation of ride comfort for passengers on the body floor.
Sensors 21 08138 g003
Sensors 21 08138 g004 550
Figure 4. The procedure of processing acceleration signals in the Simpack software postprocessor: (a) for the x direction; (b) for the y direction; (c) for the z direction.
Figure 4. The procedure of processing acceleration signals in the Simpack software postprocessor: (a) for the x direction; (b) for the y direction; (c) for the z direction.
Sensors 21 08138 g004
Sensors 21 08138 g005 550
Figure 5. A scheme of the investigated railway passenger car.
Figure 5. A scheme of the investigated railway passenger car.
Sensors 21 08138 g005
Sensors 21 08138 g006 550
Figure 6. An illustration of the multibody wagon model created in the Simpack package.
Figure 6. An illustration of the multibody wagon model created in the Simpack package.
Sensors 21 08138 g006
Sensors 21 08138 g007 550
Figure 7. An illustration of the created multibody model of the bogie with designated individual suspensions components.
Figure 7. An illustration of the created multibody model of the bogie with designated individual suspensions components.
Sensors 21 08138 g007
Sensors 21 08138 g008 550
Figure 8. An illustration of the created railway track.
Figure 8. An illustration of the created railway track.
Sensors 21 08138 g008
Sensors 21 08138 g009 550
Figure 9. The distribution of the index NMV for a speed of 60 km∙h−1: the “Original” parameters.
Figure 9. The distribution of the index NMV for a speed of 60 km∙h−1: the “Original” parameters.
Sensors 21 08138 g009
Sensors 21 08138 g010 550
Figure 10. The distribution of the index NMV for a speed of 110 km∙h−1: the “Original” parameters.
Figure 10. The distribution of the index NMV for a speed of 110 km∙h−1: the “Original” parameters.
Sensors 21 08138 g010
Sensors 21 08138 g011 550
Figure 11. The distribution of the index NMV for a speed of 60 km∙h−1: the “Modification I” parameters.
Figure 11. The distribution of the index NMV for a speed of 60 km∙h−1: the “Modification I” parameters.
Sensors 21 08138 g011
Sensors 21 08138 g012 550
Figure 12. The distribution of the index NMV for a speed of 110 km∙h−1: the “Modification I” parameters.
Figure 12. The distribution of the index NMV for a speed of 110 km∙h−1: the “Modification I” parameters.
Sensors 21 08138 g012
Sensors 21 08138 g013 550
Figure 13. The distribution of the index NMV for a speed of 60 km∙h−1: the “Modification II” parameters.
Figure 13. The distribution of the index NMV for a speed of 60 km∙h−1: the “Modification II” parameters.
Sensors 21 08138 g013
Sensors 21 08138 g014 550
Figure 14. The distribution of the index NMV for a speed of 110 km∙h−1: the “Modification II” parameters.
Figure 14. The distribution of the index NMV for a speed of 110 km∙h−1: the “Modification II” parameters.
Sensors 21 08138 g014
Table
Table 1. Measured quantities and locations for evaluating the ride comfort of passengers by means of the index NMV [42].
Table 1. Measured quantities and locations for evaluating the ride comfort of passengers by means of the index NMV [42].
Average Comfort, the Standard Method
Ride Comfort IndexMeasured QuantityMeasured Location
NMVAccelerations in three directions (x, y, z)Floor
Table
Table 2. A scale for evaluating the ride comfort index NMV [42].
Table 2. A scale for evaluating the ride comfort index NMV [42].
Value of the NMV IndexLevel of the Ride Comfort
NMV < 1.5Very comfortable
1.5 ≤ NMV < 2.5Comfortable
2.5 ≤ NMV < 3.5Medium comfortable
3.5 ≤ NMV < 4.5Uncomfortable
NMV ≥ 4.5Very uncomfortable
Table
Table 3. A list of the parameters of the spring-damper elements.
Table 3. A list of the parameters of the spring-damper elements.
StiffnessDamping
DesignationNamekPkSbPVbSV
OOriginal0%0%0%0%
IModification I−45%+45%−45%+45%
IIModification II+45%−45%+45%−45%
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Dižo, J.; Blatnický, M.; Gerlici, J.; Leitner, B.; Melnik, R.; Semenov, S.; Mikhailov, E.; Kostrzewski, M. Evaluation of Ride Comfort in a Railway Passenger Car Depending on a Change of Suspension Parameters. Sensors 2021, 21, 8138. https://doi.org/10.3390/s21238138

AMA Style

Dižo J, Blatnický M, Gerlici J, Leitner B, Melnik R, Semenov S, Mikhailov E, Kostrzewski M. Evaluation of Ride Comfort in a Railway Passenger Car Depending on a Change of Suspension Parameters. Sensors. 2021; 21(23):8138. https://doi.org/10.3390/s21238138

Chicago/Turabian Style

Dižo, Ján, Miroslav Blatnický, Juraj Gerlici, Bohuš Leitner, Rafał Melnik, Stanislav Semenov, Evgeny Mikhailov, and Mariusz Kostrzewski. 2021. "Evaluation of Ride Comfort in a Railway Passenger Car Depending on a Change of Suspension Parameters" Sensors 21, no. 23: 8138. https://doi.org/10.3390/s21238138

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop