Simulation processes occur in three distinct phases during vehicle development:
1. Early concept selection using simplifi ed (concept) models – these analy- ses are normally done individually for each attribute, and common concepts need to be agreed before detailed component design starts.
2. Virtual verifi cation and x-functional optimization of x-attribute agreed
‘frozen’ design – virtual series.
3. Support of actual attribute issues on ‘verifi cation prototypes’ and cor- relation of CAE models.
8.4.1 Virtual series
Once design has achieved a maturity level that allows creation of simulation models for all individual attributes, a so-called ‘CAD freeze’ will kick-off a ‘virtual series’ process. The main concept of virtual series is that early assessments for all attributes and teams need to be based on one common design well before the fi rst hardware prototypes are created for the fi nal engineering ‘sign-off’. Virtual series are by no means restricted just to typical CAE attributes like safety, durability, noise, cooling, etc., but are also applied to analyses on cost, weight, manufacturing, etc.
Once the design is frozen, all component areas, including their suppliers, will start ‘meshing’ components they are responsible for. These meshes can still be attribute-independent, i.e. can be used for both safety (high defor- mation requiring non-linear material laws) as well as noise calculations (small deformations within linearity range).
Each attribute CAE team will then incorporate these FE meshes as well as supplier models into attribute-specifi c simulation models. Model cre- ation typically consists of 70% chasing for data (e.g. actual dynamic mount stiffness under operational load; mass, centre of gravity and moment of inertia for all parts for which no FE mesh is required or available) and 30%
modelling and analysis.
All simulation results need to be jointly reviewed to agree on corrective actions (these may be based on dedicated model modifi cations) and to incorporate these changes into CAD design for the next ‘virtual series freeze’. The timespan between CAD freeze and reporting on the vehicle noise and vibration refi nement results is typically in the order of 6–10
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weeks; this often causes a signifi cant delay of CAE analyses with respect to design status and the need to kick off the prototype build process before the fi nal virtual series could be completed.
8.4.2 Noise and vibration refi nement deliverables for virtual series
Each component CAE model that is required for a full vehicle noise and vibration simulation model will fi rst be checked and analysed by the respon- sible component area to ensure that the component will meet its targets.
Typical noise-relevant component attributes are dynamic stiffness, compo- nent eigenfrequencies and mode shapes, noise and vibration transfer func- tions as well as source levels, e.g. shaking forces and noise radiation of powertrains. If any component signifi cantly fails to meet its noise-related target, full vehicle noise performance will also probably not meet its targets.
Typical virtual series deliverables for full vehicle noise and vibration refi nement are:
• Vehicle modal alignment: all component modes should properly be separated to avoid any ‘resonance catastrophe’ under operating conditions.
• Vehicle sensitivity to ‘unit excitation’: this can be a vibration transfer function (vehicle response at the customer perception point due to, e.g., unit mount force, calculated by FE tools), a noise transfer function (p/F, also calculated by FE tools) or noise reduction (sound pressure difference between engine bay and passenger cabin, calculated by SEA tools).
• Response to typical vehicle load cases, e.g. structure-borne response to idle excitation (loads of the powertrain applied to a full vehicle FE model), response to wheel imbalance (nibble, solved by FE or system dynamics tools depending on vehicle content for electronic tools to reduce steering wheel rotation – EPAS), response to road excitation or road noise (road surface irregularities applied to tyre patch points using an FE tool); vehicle acceleration noise (including all load paths in the TPA tool); and high-frequency noises like wind noise (derive the shape- related pressure fl uctuation on the outer vehicle skin from CFD simula- tion and apply this as load for SEA analyses).
The ‘status assessments’ that need to be prepared for the virtual series report will be followed by further activities to identify the root causes for missing attribute targets. Transfer path analyses as well as modal contribu- tion analyses are typical methods applied for these noise and vibration refi nement investigations (see Plates VII and VIII between pages 114 and 115).
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Based on such fi ndings, high-level corrective actions like increasing local stiffness by adding reinforcements to the CAE model or shifting component modes in the respective CAE model will be investigated. If these changes solve the issues, these concepts will be communicated to the respective component areas to develop feasible solutions that need to be incorpor- ated into the design for the next virtual series or for prototypes, as appropriate.
8.4.3 Simulation of design status versus x-functional optimization
Virtual series as described above would just deliver vehicle noise and vibra- tion refi nement status for nominal design. This would not make use of one main advantage of virtual analyses: CAE enables analysis of several differ- ent designs within a fraction of the time, efforts and resources that would be needed for creating and testing real hardware variants. Consequently, simulations should
• take into account variability of design parameters such as mount stiff- ness on attribute performance,
• investigate parameter settings which enable more robust designs,
• include x-functional optimization to fi nd a best compromise between attribute performances. The attributes offering most potential confl icts for noise and vibration refi nement are vehicle dynamics, durability, weight and cost.
Each of these tasks requires a series of simulations using different input parameter sets. There is no longer a need to submit each simulation run manually; several optimization packages are available on the market that automatically submit a series of analyses on individual tools as listed above, tailored to variability of input parameters, and that carry out post- processing and optimization.
In real-case applications, every simulation can take hours or even days.
In these cases, the time to run a single analysis makes running more than a few simulations prohibitive. The design of experiment (DOE) tech- nique is a smart approach to achieving a maximum amount of knowledge from a reduced number of calculations or tests [11]. Further analyses can be performed on a response surface that is a multi-dimensional hyper- surface created by interpolation of the well-distributed results on the full vehicle CAE model. This response surface represents a meta-model of the original problem and can be used to perform the optimization. Just the outcome of this optimization needs to be verifi ed on the original CAE model.
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8.4.4 Confi dence in noise and vibration refi nement simulations
Quality and confi dence in CAE simulation results depend on the quality of each single involved sub-model. Some vehicle noise and vibration refi ne- ment phenomena can be predicted with high confi dence by CAE (e.g.
powertrain rigid body modes in a vehicle, bending frequencies of a power- train), whereas prediction of some other noise and vibration refi nement characteristics (e.g. trimmed body noise transfer functions) cannot be pre- dicted with an accuracy of less than say 3dB. Consequently, road-induced low-frequency noise – which involves trimmed body noise transfer func- tions for suspension attachment points – cannot be predicted with suffi cient accuracy by CAE.
Consequently, a ‘full analytical sign-off’ for vehicle noise and vibration refi nement is still not achievable. Therefore all attribute assessments should be done jointly between attribute experts and simulation experts. In the case of road noise, any analytical upfront assessment might be based on a surrogate metric by comparing suspension forces of different suspension designs.
In order to enable early CAE analyses for the next vehicle program, CAE teams need to spend reasonable resources on correlating the latest CAE models with current production hardware.