LOAD ASSUMPTIONS FOR DURABILITY ...

In: ENGINEERING INTEGRITY, VOLUME 29, SEPTEMBER 2010. pp.8-19.

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LOAD ASSUMPTIONS FOR DURABILITY ASSESMENT OF AUTOMOTIVE STRUCTURE P. HEULER, M. FROST, H. ROCHLITZ AUDI AG D-85045 Ingolstadt, Germany ABSTRACT As an essential part of the product development and design, the derivation of load assumptions and design requirements has to reflect the concept of the product verification process. An overview is given on the key elements of the process of defining load assumptions for passenger cars where the full set of external loads acting on a vehicle has to be considered, but also the structural response, which leads to the final load requirements on a component level. Some information is presented on the process where load assumption and strength and fatigue design requirements are derived on the basis of a number of sources including systematic measurements on new vehicles, consolidation of measurements within a load data-base, numerical analyses and customer usage surveys. The topic of standardised loadtime histories is added by a comparison of load requirements given by CARLOS multi to those obtained from a test track. INTRODUCTION The derivation of load assumptions is one of the key and – in many cases – most challenging elements of any strength and durability design. There is a huge variety of approaches depending on the area of application. In some cases load assumptions or at least usage profiles are prescribed by standards and guidelines [1,2] or firmly agreed between manufacturer and customer. In other cases designers are more or less free to define load requirements for their products, of course, under consideration of general product liability rules. The automotive industry has to act predominantly according to the latter case. Because of the great variety of approaches, there is a lack of generic procedures for the process of defining load assumptions as compared to its counterpart in the fatigue design process, the numerically- and/or experimentally-based proof of strength and durability under given load conditions. With regard to general mechanical engineering, the German FKM guideline [3] may be considered as typical where safe strength and fatigue resistance values for a probability of survival of Pf,S = 97.5 % are used and a system of partial safety factors are recommended depending on the consequences of failures and possibility of inspection, but no recommendation has been made with regard to partial safety factors on load assumptions other than that to use “hard or severe” load assumptions. The definition of load requirements has to consider the whole process of strength and durability design verification with regard to aspects such as: deterministic or probabilistic approach, verification by testing or calculation, the level of sophistication of experimental or numerical verification procedures, knowledge on the scatter of important input variables, consequences of potential failures, former experience with similar problems, etc. If load requirements have to be defined individually for a given engineering task, basically we can differentiate between two levels (even if actual procedure(s) used or the load spectra obtained from different sources may not clearly exhibit this principle): first we have to decide on the overall usage and mission profiles of the structure under consideration. This may encompass the role, extent, and mixture of missions of aircraft, the overall requirements on

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driving distances and percentage of different road types (city, highway, rough road, trailer usage etc) of vehicles, the production programme of heavy steel machinery etc. In a second step the loads, forces and moments occurring under the different types of usage have to be determined by measurements, calculation or other means. For flexible structure very often the system response to external excitation provides a significant contribution to the load spectrum of a particular component. A difficult issue is the assessment of rare events (exceptional loading events and/or misuse) that may introduce very high loads. These high loads might heavily contribute to the general fatigue load requirements and, in most cases, dominate the overall static load requirements. Standardised load histories and load spectra available for various areas of application [4] add a specific aspect to the topic, since they generally cover the typical loading events and load spectra and give some guidance to the problem of derivation of load requirements. In most cases, however, these load histories and load spectra are not sufficient or have not been developed for the derivation of design load requirements of specific applications. One exception of this rule is the loading standard for trailer couplings [5] which has been accepted by UNECE as alternative to constant amplitude testing in Regulation No. 55 [22]. In the following, the general procedures and definitions are discussed which are in use at AUDI and – in a similar way - at others OEMs. Both durability (fatigue) and extreme load issues will be addressed. BASIC FATIGUE DESIGN CONSIDERATIONS Traditionally the safe life concept has been and is still being used for fatigue design of automotive structure which means (a) the anticipated loading environments for the entire usable life have to be estimated and (b) sufficient fatigue strength has to be demonstrated under these loading assumptions. Typically major cracks are not tolerated as a failure criterion which implicitly prevents the adoption of fracture mechanics based concepts i.e. a damage tolerance type of approach. This is due to two reasons: (a) inspections for fatigue cracks in primary structure are not an option for automotive vehicles and (b) fracture mechanics does not represent state of the art in automotive fatigue design which is relevant with regard to product liability considerations.

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Fig. 1: Probability density functions of load and strength (schematically) It has to be acknowledged that both the external loading and the fatigue strength are statistically distributed quantities. In Fig. 1, probability density functions (PDF’s) are schematically

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plotted for both quantities (load “L” and strength “S“). The scale on the abscissa has to be selected with regard to quantity under consideration; for example, it could represent the maximum single load vector induced by a certain manoeuvre in a given part of the structure. As another extreme, it could represent the weighted sum of load cycles of a spectrum representing a vehicle life condensed into a single quantity (such as the load intensity value – LIV, see below). Failure will occur if the load “L” is higher than the strength “S” i.e. if a new variable Z = S - L falls below zero. It is often assumed that both the load and strength distribution functions may be approximated by log-normal distributions. Then the distribution function of Z also follows a lognormal distribution and the probability of failure may be expressed by the well-known Gaussian integral. The difference mS - mL of the respective mean values may be interpreted as a safety factor, jSF, which depends on the levels of scatter of L and S and on the level of probability of failure, Pf log jSF = mS - mL = -uo ( sL2 + sS2)

(1)

where mL and mS = mean values of log load and log strength variables, sL and sS = standard deviations of log load and log strength variables, uo = standardised variable of the Gaussian distribution function depending on the probability of failure, Pf (for example uo(Pf = 10-2) = 2.326, uo(Pf = 10-3) = -3.091). Eq. (1) indicates that both the spread (scatter) of the loading L and of the strength S contribute to the magnitude of the necessary safety factor jSF, but depending on the ratio v = sL/sS, one or the other may dominate the size of jSF as shown in Fig. 2.

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Fig. 2: Relative contributions of scatter of loading and of strength to the safety factor jSF

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Fig. 3: Probability of failure, Pf, as achieved by combination of low strength components, Pf,S, with a given probability of exceedance of the load, Pe,L,

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Typically for chassis parts scatter of the load L is larger than that of the strength (v > 1). On the other hand, for engine parts the opposite is true (v < 1) due to limitations of rpm and/or due to the fact that critical loading events are dominated by resonance effects. Obviously the scatter values control the design requirements to a significant extent. Scatter of fatigue strength of mass production parts has been studied in collaborative studies covering a wide range of materials and manufacturing routes [6,7]. For design purposes, it is often sufficient to apply a scatter level of sS,N = 0.2 (chassis parts, life direction) and, for engine parts, sS,L = 0.02 to 0.05 along the load/stress axis. With regard to loading variability (scatter) under customer usage, it appears to be difficult to assign valid numbers due to obvious reasons. It is general practice to replace the approach of Eq. (1) by the following concept: For fatigue design, a “severe” load spectrum is introduced where Pe,L  1 % (for 99 out of 100 customers the design spectrum is not exceeded). This spectrum (or load level) is statistically combined with a low strength component, typically for Pf,S  0.1 % (999 out of 1000 parts have a strength that is higher than this part). Under this conditions that part has to survive the design goal which itself depends on the part’s criticality with regard to passenger safety. The true (calculated) level of probability of failure achieved depends on the ratio, v = sL/sS, and falls down to about Pf = 510-4 for v = 1, Fig. 3. Load Intensity Value - LIV Load and strength variables considered for design and discussed in the following sections are single and/or correlated load vectors (forces, moments) for extreme or fatigue-related single load cases and so-called load intensity values (LIV’s) for spectrum loading (LIV is also called pseudo-damage in some publications). LIV’s represent a condensation of load-time measurements or spectrum fatigue strength information compressed into a single number, and here the following approach is adopted: A rainflow count of the load-time signal under consideration and a Miner type “damage” calculation are conducted on the load ranges (no consideration of mean loads!) using a fictitious S-N (Wöhler) curve with a slope k = 5. The reference point of that S-N curve may be arbitrarily selected, but has to be kept constant. The result of this “damage” calculation may be interpreted as a condensed load intensity value for that particular measurement which may be compared to LIV’s of other measurements, for example, of different road profiles and/or suspension characteristics. The slope k = 5 has been chosen as an average slope for metallic components. It controls the relative weight of load ranges of different magnitude. For example, for k = 5 a 15 % increase of a load range would mean a factor of 2 (= 1.155) of relative weight. LIV’s can be treated and visualised as condensed load variables “L” or “S” on the abscissa of Fig. 1. Statistical treatment of the many load spectra obtained from test tracks and public roads is predominantly performed on the basis of LIV’s. DERIVATION OF STRENGTH AND DURABILITY DESIGN REQUIREMENTS OF AUTOMOTVE STRUCTURES - OVERVIEW The derivation of load assumptions for passenger cars is based on several elements that have to be utilised alone or in combination at different stages of the product development process. It is important to note that this is an ongoing task, i.e. load assumptions and requirements are not only placed at the beginning of a project without further change. Rather after initial statements on load requirements, progress of product definitions with regard to load-influencing parameters such as damper characteristics, spring travels, mass distribu4


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tions etc has to be monitored and load assumptions have to be updated if necessary. Furthermore, the availability (or lack) of models and/or hardware and the knowledge on features and properties influences or dictates the choice of methods adopted, be it numerical modelling or experimental load assessment. Measurements on prototype vehicles still represent an important source for the derivation of load assumptions. Frequently in the early stage mules are built up which consist of the new load-carrying chassis and body structure with the outer skin of a previous model in order to enable measurements on public roads. It is also possible to use a special vehicle for test track measurements where the features of a new car are resembled by mounting driveline and chassis parts into a basic steel frame [8]. It is obvious that the measured loads are representative for the development stage achieved at the time of the measurement. Since the number and extent of measurements and simulation runs is necessarily limited for a given project, it is essential to establish load statistics that allow to compare and rank individual measurements and to base decisions on load assumptions on a broader base. For that it is necessary to derive a procedure for normalisation of measurements in order to embrace results from vehicles with different axle and total weight, chassis types, wheel sizes etc. Further significant input information may stem from observations of actual customer usage. This is – in the end – the only relevant criterion, but it is obviously not so easy to collect these data with the required statistical significance in particular at the important “hard” tail of the distribution (1 % customer usage intensity). Nevertheless because of the importance of this kind of information, manufacturers have set up campaigns to measure real field load data at different levels of complexity and extent. Another source for load assumptions is given by the increasing capabilities of numerical simulation tools, in particular of the multi-body dynamics (MBD) simulation. Numerical simulation provides higher degrees of freedom with regard to assessment of varying designs and/or varying input parameters, but the reliability of the results has to be critically reviewed when absolute statements have to be made that may have a severe impact within a project. At AUDI, MBD load simulations are used for different purposes and at different stages as outlined below. In the following, some information is given on these elements of the load assumption and definition process.

LOAD MEASUREMENTS AND FATIGUE LOAD STATISTICS Baseline information on the set of load spectra for a new car project is obtained by measurements on public roads and test tracks under normal and extreme driving conditions backed up by numerical methods to read across relevant information from previous models. The test tracks include (a) sections of rough road with a high intensity of high vertical and longitudinal forces where a full life of a vehicle could be represented by distances between 2.000 to 20.000 km, Fig. 4, depending on the specifics of the individual test tracks, (b) courses with a combination of high speed sections, manoeuvre dominated (lateral - longitudinal forces) and rough road sections, (c) a number of single manoeuvres and events like extreme braking on even and rough road surfaces, snap start, acceleration on road surfaces with extremely different grip conditions etc.

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Fig. 4: Example of correlated load measurements (longitudinal, lateral, vertical)

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Fig. 5: Wheel force transducer

Measurements on public roads provide insight into load spectra more representative of customer usage. To support derivation of fatigue design requirements, AUDI uses a number of public road circuits which include different road types with selected percentages typically such as 56% country road (good and bad), 18 % highway, 20% city road, and 6 % rough road. These more general courses are backed up by special-purpose circuits, for example, for trailer towing operation. The percentages for the public roads have been selected based on statistics of customer usage distributions, but modified in order to consider the generally low load intensities prevailing on highways. The relatively high relative weight of rough road driving has been introduced to cover road surface qualities present in specific markets. The set-up of the measurement vehicle(s) is usually chosen in a way to produce high loads and load intensities: the axle weight is selected to cover the most severe configuration, the suspension characteristics correspond to sporty conditions, whereas for fatigue measurements the wheel size typically is one inch below the maximum series wheel. The setting of active and/or adjustable suspensions elements (damper, air spring etc) has to be chosen in a way to cover critical conditions. Measurement vehicles are equipped with wheel force transducers, Fig. 5, that provide all six translational and rotational load components acting at the wheel hub. These forces/moments establish the main body of data that describe the external loads acting on the vehicle. Additionally internal forces are measured by strain gages mounted on several parts of the chassis together with spring deflections and accelerations at different positions. Design requirements (max load conditions and LIV’s) are derived on the basis of actual as well as of previous measurements where some normalisation steps have to be introduced, see below. Fatigue design requirements are generated on the basis of the extrapolated nearservice measurements, and the corresponding (multi-channel) load sequences adopted for experimental and/or numerical simulation are collocated under addition of all relevant extreme manoeuvres and loading events that are within normal and limiting usage. Misuse events such as driving through deep potholes or hitting against road curbs are not included in the fatigue requirement, but have to be considered as single events using different failure criteria such as allowable deformation etc. Statistical Treatment and Normalisation of Load Measurements Load measurements on vehicles necessarily exhibit scatter, in particular those made on public roads. But also measurements on paved test tracks with more stringent driving instruc-

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tions show some scatter; for example, because obstacles may be hit at slightly different positions, speed etc. Generally scatter of vertical load components is smaller than that of horizontal load components that are more directly influenced by the mode of driving. In order to delimit random effects of single measurements and enable decisions on a broader database, it is advisable to establish a database where measured load data of many and, therefore, different vehicles are combined. Because rather different vehicles with regard to axle weight, suspension characteristics, wheel sizes etc, have to be compared, normalisation of the measured load data is necessary. This is performed using the extrapolated LIV’s of the individual measurements. Initially only axle weight has been considered as a normalisation parameter, but it became obvious quite early that further parameters have to be included. Theoretically these could be the unsprung mass (wheel, tire, wheel carrier, brake, parts of control arms) and the tire stiffness, but for the sake of simplicity, instead the wheel size and the tire sidewall dimension have been selected. 6

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Fig. 6: Load measurements (LIV’s) and predictions made from load data-base for 7 different vehicles extrapolated to individual full design requirements. Prediction1 = normalisation only by axle weight, prediction 2 = normalisation by axle weight, wheel size and tire sidewall height. All values normalised to design requirement of vehicle 1. Fig. 6 shows fatigue design requirements for the front axle vertical load and the rear axle longitudinal load, respectively, for several vehicle types derived from actual measurements compared to those derived from the load data-base. It is obvious that quite reasonable estimates can be made from the data-base with “prediction 2” which means that the chosen normalisation parameters and procedures capture the relevant chassis features in an appropriate manner. Nonetheless measurements are still run on new car projects in order to discover specifics of changed or new features and to adjust the load data-base. The load data-base can also be used to compare and rank individual load measurements and to identify systematic influences of a specific feature on the load level. An example is the effect of run-flat tires where the overall vertical, but also longitudinal intensity increases as compared to conventional tires by factors between 1.5 and 2.5 depending on the tire stiffness and the load direction considered. It is also possible to derive (preliminary) design load intensity levels solely based on the contents of the data-base by calculating de-normalised load intensities under consideration of the corresponding axle weights, wheels sizes, and tire dimensions, of a new car project.

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ASSESSMENT OF LONG-TERM CUSTOMER USAGE As mentioned above, traditionally measurements for generation of fatigue design requirements are being conducted on specific test tracks and selected circuits on public roads. There is positive experience with this procedure that has been proven to produce conservative designs fatigue-wise. As a disadvantage, however, the actual safety factors and - consequently - the extent of over-design with regard to fatigue are not known. Therefore in the automotive industry it is considered worthwhile to collect actual customer usage data in order to create a better insight into the characteristics (mean, scatter, distribution) of actual load environments acting on a fleet of vehicles, comp. Fig. 1. This means a considerable effort, because for being statistically significant a measurement with actual customers should encompass at least 10.000 km, more than 100 of such measurements would be needed and, of course, the number of individual “load“ channels per measurement cannot be reduced below certain limits. Different approaches are being applied for the collection of customer load data. One of these combines customer interviews on their usage (estimate percentages of road types - highway, city, country roads etc - and payloads) with in-house measurements for these usage types [9]. This approach has also been adopted for the derivation of the CARLOS Trailer Coupling Bike Carrier standardised load spectrum [5]. Another, but more extensive concept involves a fleet of real customer vehicles where load information is directly collected during normal customer usage [9-11]. Obviously the number of vehicles involved is an important cost driver, and the measurement time may vary between one month [9] and much longer intervals. It is also tried to identify representative (“placeholder”) customer road circuits [12] which encompass the typical elements of road surfaces of a certain market segment. In each case, the measured load data have to be processed and extrapolated with statistical concepts in order to arrive at loading envelopes that cover the required design goals. Over the last years the German automotive industry has conducted a long-running collaborative measurement campaign [11] on actual customer vehicles covering a wide range of vehicles types and engine power characteristics. A procedure had been developed where the driving mode and road surface conditions in actual customer vehicles could be monitored on a manoeuvre based data logging system using a small set of accelerometers and internal data of the electronic control units (speed, torque, gear position, braking action etc). From these data manoeuvre based load intensity values for the individual forces acting on the wheel hubs could be determined under use of a set of transfer functions that had been derived beforehand from a range of vehicles fully equipped with both conventional wheel force transducers and the customer vehicle measurement system. These transfer or correlation functions had to be determined for each vehicle type involved. For consolidation of customer usage data of different types of vehicles within a common database, a normalisation concept is applied that is very close to one mentioned above where axle weight, the inner un-sprung mass (excluding tire and outer part of wheel), and tire stiffness are used for normalisation. Thus a database has been created that covers more than 90 individual measurements each between 10.000 and 15.000 km per vehicle and, in total, more than 1.7 million km of real customer usage. Additionally these selected customers and, beyond that, even many more customers have been asked to report on their way of using cars with respect to mode and purpose of driving, payloads, etc. These customer measurements have been performed within developed markets as well as within emerging markets to cover the specifics of the vehicle usage worldwide.

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Using this full set of information statistically relevant distributions of load intensity values of the different forces (longitudinal, lateral, vertical) and moments acting on the wheel hub could be derived covering the anticipated life of the vehicle considered. From these distributions the mean values and – more relevant – the 1 % load intensity values could be estimated and compared with the load intensity values that have been derived based on the hitherto applied load requirements, Fig. 7. It had become clear that certain margins exist where present fatigue design requirements can be alleviated without jeopardising the desired safety levels. Kenngröße: Load channel: brakeBremskraft force links FS-Verteilung: Taxi Verteilung 15sp 40no 45zu 45zu 8AB 8AB 21 21 Relative Häufigkeit

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Fig. 7: Customer load intensity measurements and hitherto existing design load requirements for front axle components affected by braking force

USE OF MULTI-BODY DYNAMICS SIMULATION MBD simulation has become an important and established tool for the evaluation of ride and handling dynamics as well as comfort and acoustic (NVH) issues. It is also applied for the analysis of fatigue loads and forces induced by normal and severe usage up to extreme loading events that may occur at obstacles, pot holes or similar features. MDB simulation is applied at different stages of the development process and with different levels of complexity. Each of theses variants has its own merits and restrictions. In the following emphasis is placed on the use of MBD simulation to contribute to the immediate needs of product development where quite short response times are necessary. Besides that, of course, the more fundamental capabilities of the numerically based load prediction using MBD are also studied at AUDI. Load Assumptions for Initial Dimensioning of Chassis Components In the early stage only the basic properties of axles such as elasto-kinematics or some stiffness values are known. Frequently dynamic properties which are controlled by features such as mass distribution, inertia, and damping are not yet exactly defined. Therefore, at AUDI a quasi-static MBD simulation is applied for the initial definition of chassis component loads as follows: A set of (fictitious) quasi-static wheel loads is back-calculated from internal forces and moments measured on predecessor vehicles performing dynamic events such as rough road driving, cornering, braking on rough road, driving over obstacles and potholes etc. For design correlated load cases are extracted where one of the loads reaches an extreme level. Thus the dynamics of the real event is transferred to the fictitious wheel loads, though only a quasi-static MBD simulation is performed [13]. These loads are then used to predict the in-

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ternal forces of a new axle system. Since, however, the dynamic features of the measured chassis system influence these loads, it has to be checked whether this transfer is still valid. Fully dynamic MBD simulations of complete vehicles are used to perform this task, since only relative accuracies are required in this case. In any case, the transfer functions for consideration of axle load, un-sprung mass, and tire stiffness, as described above, are additionally introduced to read across from previous to the new chassis structures. The set of load cases encompass both fatigue-related cases and those that may be correlated with misuse. Accordingly for dimensioning the individual components by FE analyses, fatigue –related and plasticity-related stress or strain limits are adopted, respectively. Highly dynamic misuse events may be considered as crucial for validity of the abovementioned simplified approach. Fig. 8 shows predicted internal forces for rear axles of different types of vehicles compared to measurements performed later on these vehicles. The components considered are a tie rod, an upper wishbone and a shock absorber. 70

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Fig. 8: Application of quasi-static MDB simulation concept for prediction of design loads (under consideration of normalisation of axle weight, wheel size and tire wall height) The general accuracy of the prediction of design load levels based on the simplified MBD simulation procedure is quite reasonable, but two exceptions are observed: - For vehicle C the forces of the upper wishbone and of the tie rod are overestimated. This is due to the fact that a changed layout of spring and shock absorber has been introduced for this vehicle which changes the phase relationships of the internal forces and thus invalidates the set of load cases applied for the quasi-static MBD simulation to some extent. This kind of mismatch may be examined by use of fully dynamic MBD simulations of both the previous and the new set-up because here only relative accuracies are required. The vertical load direction is less sensitive to this kind of problem.

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- For vehicle D the loads predicted for components acting on external longitudinal (and lateral) loads are close to the actual ones, but the vertical load direction (shock absorber) is over-predicted. Including general experience this has to be attributed to differences of details of ride height between the vehicle used for the measurements and the MBD model. Even if a general trend to conservative estimates has to be stated, the latter example illustrates also some limitations of the concept which may be resolved by consideration of flexible components within the MBD model (instead of rigid ones) and an elaborate validation and verification of the dynamic model. The potential gain in accuracy, however, has to be contrasted with the necessary significantly increased effort and potential delay of results, as always in industrial application [14]. Virtual MBD Simulation of Complete Vehicles The limitations of the simplified approach may be overcome by transient MBD simulation. A hybrid approach uses as input information again measured load-time signals, for example for the wheel hub. In the early stage of product development, therefore, this kind of approach will be predominantly applied for comparative purposes. In a more complex approach load measurements are not required when complete vehicles including tires are virtually driven over selected road profiles. For this case additional key issues are the availability of realistic tire models [14], digitised road surfaces and driver models that allow for selection of varying road tracks and/or driving modes (speed, acceleration, braking etc). It is self-evident that complexity and effort for the establishment and verification of MBD models and running of the calculations increase significantly.

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Exceedances [log] Fig. 9: Measured and predicted loads and forces of a mid-size passenger vehicle (wheel size 18 inch) running over a rough road test track

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For illustration of present capabilities of the latter fully virtual concept, some results for rear axle loads and forces of selected components of a mid-size passenger vehicle driven over a rough road test track are given in Fig. 9 by level cross spectra. In an overall view the accuracy of the simulation appears to be reasonable; there are, however, some specific aspects that need a more detailed consideration because they might restrict direct use of this type of simulation results for derivation of design load requirements. It should be noted that an important prerequisite for the accuracies achieved in Fig. 9 is a MBD model of the vehicle fully validated by static and dynamic modes. This is not so often achievable over the full length of development due to limited cost and manpower resources which means that one has to expect larger deviations at some points in time. The data for the vertical load direction indicate that using appropriate tire model (F-Tire in this case) it is possible to model the direct contact of the rim flange with the tire wall which might occur on cars with low-section tires hitting against certain obstacles. This illustrates the capabilities of specific component models; the occurrence of this kind of event, however, depends on specific set-ups, and it can be speculated whether MDB models are close enough to the actual set-up under all circumstances. STANDARDISED LOAD HISTORIES – CARLOS multi Over the last decades, standardised load histories (SLH’s) have been developed for fatigue studies of a more generic nature [4,16,17]. These may include an evaluation of how different materials, detail geometries, surface treatments or manufacturing routes affect the fatigue behaviour of specimens and components; SLH’s are also applied for projects to develop or evaluate numerical life prediction models or in round robin programmes with several participating laboratories on fatigue-related experimental or analytical issues. It is common understanding that SLH’s do not refer - in the first instance - to a specific design problem, but comprise the typical features of the loading environment of a certain class of structures, vehicles etc. Nevertheless, test results obtained with SLH’s may be used to assess – on a relative basis – particular design problems. Recent developments such as the CARLOS group of SLH’s [5,18-21] are oriented towards the qualification of real components and structures, even up to the point where a specific SLH has become part of UNECE regulations [22] and may represent a core element of fatigue design requirements agreed between OEM’s and suppliers [5] . At this point the CARLOS multi [19], a standardised load history for the right hand front axle loads of passenger cars, will be shortly reviewed and compared to rough road fatigue spectra used for experimental fatigue qualification of front axle structures and full vehicles. CARLOS multi provides correlated load-time signals of longitudinal, lateral and vertical forces plus brake moment at a sample rate of 0.005 sec, each channel normalised by the static front axle load. The total length of one block corresponds to 5.4 hours which is equivalent to a driving distance of 40.000 km. For comparison to the contents of CARLOS multi, wheel hub loads (forces and braking moment) of a current AUDI vehicle are used that were observed on a rough road test track which is part of the design requirements. It should be noted that for the actual full load requirements of this vehicle additional cornering and braking manoeuvres are introduced which increase the requirements of lateral, longitudinal and braking load components. Fig. 10 shows the ratio of the CARLOS multi LIV’s normalised by the corresponding test track LIV’s both extrapolated to cover the full life of the vehicle under consideration. For the longitudinal and lateral wheel forces and the braking moment CARLOS multi provides total

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LIV Ratio

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load requirements quite close those of the test track. The vertical LIV of CARLOS multi, however, represents only about 40 % of corresponding value obtained from the test track. This may be due to the fact that at the time when CARLOS multi had been originated (1994) the average wheel sizes were certainly smaller than today. According to present experience, the LIV ratio of 40 % in vertical load corresponds to a wheel size that is about one inch smaller and correspondingly exhibits a larger tire wall size (at comparable tire diameters).

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Fig. 10: Wheel hub load spectra LIV ratios of CARLOS multi normalised by respective measurements on a rough road test track A global view of the phase relationships of the longitudinal, lateral, and vertical forces, is provided by the so-called “damage sphere” where the correlation between the three forces are visualised by use of the corresponding LIV’s of the forces themselves plus those of intermediate directions. Fig. 11 shows that both sequences are dominated by the vertical and - to a lesser extent – by the longitudinal load directions, but CARLOS multi exhibits additionally a higher amount of lateral forces.

Lateral Load

Test Track

CARLOS

Vertical

Vertical

Longitudinal Load

Longitudinal Load

Fig. 11: “Damage“ (=LIV) spheres of the rough road test track and CARLOS multi, respectively, indicating directions of high and low load intensities

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X Y

Z

Rough road test track

Relative damage [%]

X

Y

Z

CARLOS multi

Relative damage [%]

Fig. 12: Load intensities (LIVs) of wheel hub load spectra in the longitudinal (X), lateral (Y), vertical (Z) and intermediate directions, rough road test track vs. CARLOS multi

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Fig. 12 enables a more detailed look on the distribution of the longitudinal (X), lateral (Y), and vertical (Z) load intensities (LIVs) and their intermediate directions. Both distributions indicate the dominance of the vertical load intensities, but the test track exhibits lower levels in the X and Y directions which are balanced by additional manoeuvre load sections for the final design specification as already discussed above. With a view to these findings, it can be stated that on the level of external loads (wheel forces) CARLOS multi obviously provides a reasonable approximation of the design load requirements of front axle parts. Since on a component level the structural reaction may significantly contribute to the overall load requirements, a MBD study using CARLOS multi and the test track is shortly presented in the following. For this purpose, a representative segment of 300 sec has been selected from CARLOS multi where the load and LIV ratios are very close to those of the full sequence. Using forces from this segment of CARLOS multi and wheel hub forces measured on the test track, MBD simulations of the vehicle’s front axle have been performed. Similar to the LIV ratios of the external wheel hub forces, LIV ratios for various internal axle components are shown in Fig. 13, indicating higher load intensities predicted for the CARLOS multi segment, although the external load intensities of CARLOS multi tend be lower than those of the test track, Fig. 10.

shock absorber

upper rear wishbone

upper front wishbone

lower wishbone

control arm

suspension unit

1

spring travel

LIV Ratio

10

0.1

Fig 13: LIV ratios of chassis forces predicted by MDB simulation for CARLOS multi and wheel hub loads measured on a rough road test track This seemingly contradictory finding results from the fact that CARLOS multi does not provide a correct representation of forces in the frequency domain as shown in Fig.14. This feature had deliberately been decided by the originators of CARLOS multi, and it is clearly expressed in the corresponding documents [19] that it should be used only for fixed front axle parts where the frequency response does not play any role. Rather the time sequence of CARLOS multi had been optimised with regard to an optimum use of the capabilities of hydraulic actuators in the test field. Thus this example has been mentioned here not to discredit CARLOS multi, but only to emphasize again the relevance of the structural response for the definition of load requirements on a system and – even more so – on a component level.

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Test track CARLOS multi

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Test track


Test track

CARLOS multi

CARLOS multi

Frequency [Hz]

Fig. 14: Power spectral densities of wheel hub loads as measured on a rough road test track and incorporated into CAROS multi

CONCLUDING REMARKS The definition of load assumptions and design requirements is an essential part of any product definition and verification process. Many studies on in-service failures in general engineering conclude that errors with regard to load assumptions significantly contribute to field problems. Any systematic process of defining load assumptions has – in principle – to reflect the full set, type and amount of external load sources and the structural response of the system considered to the external excitations. In the automotive industry load assumptions and strength and fatigue design requirements are derived on the basis of a number of sources including systematic measurements on new vehicles, consolidation of measurements within a load data-base, customer usage surveys and numerical analyses. Thus a basis is provided that enables the engineers to derive adequate load requirements and – at the same time – to safely refine the process and the underlying assumptions to detect and reduce potential over-design margins. Standardised load-time histories and their corresponding background documentations may advantageously be used to enhance the knowledge on the specifics of loading environments of different engineering areas, but also on the way how to extract relevant data and compose appropriate new representative load-time histories and load spectra.

REFERENCES [1] [2] [3] [4]

EN 13104/13103: Railway applications - Wheelsets and bogies - Powered axles – Non-powered axles, Design method, 2007-02 Damage tolerance and fatigue evaluation of structure. Federal Aviation Regulation25.571, 1998. FKM Guideline: Analytical Strength Assessment, www.fkm-richtlinie.de, 2005. Heuler, P.; Klätschke, H.: Generation and use of standardised load spectra and load–time histories. Int Journal of Fatigue 27 (2005), No. 8, pp. 974-990.

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Bruder, T.; Weiland, S.: Standardized load assumptions for testing trailer coupling devices at passenger cars. Proc. Second Int Conf on Materials and Component Performance under Variable Amplitude Loading, Darmstadt, March 2009, DVM Berlin, pp. 135-142. Adenstedt, R.; Zenner, H.: Streuung der Schwingfestigkeit (Scatter of fatigue strength). In: Betriebsfestigkeit – Neue Entwicklungen bei der Lebensdauerberechnung von Bauteilen. DVM-Bericht 802, DVM Berlin, 2003, S. 129-138. Heuler, P.; Vogler, J.; Beste, A.: Ableitung von Festigkeitsforderungen für PKW unter Berücksichtigung statistischer Kriterien (Derivation of strength requirements for passenger cars under consideration of statistical criteria). Materialwissenschaft und Werkstofftechnik 34 (2003), No. 9, pp. 850-858. Oppermann, H.; Hackmair, C.; Wirth, C.: Numerical simulation methods for the evaluation of fatigue life in the virtual car development. (in German). VDI Reports No. 1967, VDI Verlag Düsseldorf, 2006, pp. 749-775. Thomas, J.-J.; Nguyen-Tajan, T.M.L.; Burry, P.: Structural durability in automotive design. In: Sonsino, C.M. (Ed.), Proc. First Symp on Structural Durability in Darmstadt, June 2005, pp. 159-173. Nagel, K.-D.: Langzeitfeldmessungen (Long-term in-service measurements). In: Moderne Entwicklungsprozesse sichern Wirtschaftlichkeit und Zuverlässigkeit. Report No. 125, DVM Berlin, 1998, pp. 107-109. Horst, M.; Schäfer, U.; Schmidt, R.: Ermittlung von statistisch abgesicherten Kunden-Kollektiven (Statistical determination of customer usage spectra). In: Fahrwerke und Betriebsfestigkeit, Report No.129, DVM Berlin, 2002, pp. 81-91. Braunroth, F.; Lieven, W.; Warnecke, U.: Basisdatengenerierung und Testprogramm – Kundenkorrelation für optimierte globale Dauerlaufprogramme (Basic test load data and correlation with customer data for generation of optimised vehicle endurance test programmes). In: Lastannahmen und Betriebsfestigkeit, Report No. 134, DVM Berlin, 2007, pp. 7988. Frost, M.; Rochlitz, H.; Runau, B.: Betriebsfestigkeits- und sonderereignisgerechte Auslegung von Fahrwerks- und Karosseriebauteilen durch Kopplung virtueller Lastkollektive und Belastungssystematik (Durability and strength design of vehicle components using virtual load spectra and a load data-base). In: Lastannahmen und Betriebsfestigkeit, Report No. 134, DVM Berlin, 2007, pp. 205216. Harty, D.: The myth of accuracy. Journal of the Engineering Integrity Society (EIS) 9 (2001), pp. 22-28. Frost, M.: Reifenmodelle zur MKS-Simulation (Tire models for MBD simulation). In: Räder, Reifen, Naben, Bremsen, Report No. 672, DVM Berlin, 2007, pp. 147-162. ten Have, A.A.: European approaches in standard spectrum development. In: Potter, J.M., Watanabe, R.T. (Eds.), Development of Fatigue Loading Spectra. ASTM-STP 1006, American Society for Testing and Materials, Philadelphia, 1989, pp. 35-75.

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Schütz, W.: Standardized stress-time histories - an overview. In: Potter, J.M., Watanabe, R.T. (Eds.), Development of Fatigue Loading Spectra. ASTM-STP 1006, American Society for Testing and Materials, Philadelphia, 1989, pp. 3-16. Schütz, D.; Klätschke, H.; Steinhilber, H.; Heuler, P.; Schütz, W.: Standardized load sequences for car wheel suspension components, Car Loading Standard – CARLOS. Fraunhofer-Institut für Betriebsfestigkeit (LBF), Darmstadt, IndustrieanlagenBetriebsgesellschaft mbH (IABG), Ottobrunn, LBF-Report No. FB -191, 1999. Schütz, D.; Klätschke; H.; Heuler, P.: Standardized multiaxial load sequences for car wheel suspension components - Car Loading Standard - CARLOS multi. Fraunhofer-Institut für Betriebsfestigkeit (LBF), Darmstadt, Report No. FB-201, 1994. Schütz, D.; Klätschke, H.: Standardized load sequences for car powertrains with manual gears - Car Loading Standard - CARLOS PTM. Fraunhofer-Institut für Betriebsfestigkeit (LBF), Darmstadt, Report No. 7558, 1997, (unpublished). Klätschke, H.; Standardized load sequences for car powertrains with automatic gears - Car Loading Standard - CARLOS PTA. Fraunhofer-Institut für Betriebsfestigkeit (LBF), Darmstadt, Report No. 110310/ 110370, 2002, (unpublished). UNECE: Uniform provisions concerning the approval of mechanical coupling components of combinations of vehicles. Regulation No. 55, Revision 1, Amendment 1, 12 May 2010.

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