Cost-Reducing Simulations and Models
APL uses simulations and models at every stage of the product development process and life cycle. This not only reduces development costs, but also opens up new possibilities for supporting individual subtasks or accompanying the entire development process. Physical, hybrid and empirical models are used during this process.
Would you like to learn more about our competencies in virtual drivetrain development? Then click here for an excerpt from our portfolio.
Disciplines of Virtual Drivetrain Engineering
Complex 3D models are created with the latest CAD design tools. From new design concepts on a white sheet of paper to modification and adaptation services and reverse engineering of existing hardware, we design your and our ideas.
Structural Mechanics of Individual Components and Assembly Units
APL covers all common fields of structural mechanics such as strength and stiffness analysis of single components as well as drive assemblies of all kinds. Intelligent topography optimisation for weight reduction as well as simulations of NVH behaviour complete our portfolio.
Thermomechanics of Components
Thermomechanical calculations provide valuable information on the expansion and distortion behaviour of drive components in operation. In particular, the focus lies on thermally highly stressed components such as crankcases and exhaust systems, but increasingly also on cooling systems (for example in traction batteries of electric vehicles).
Multibody simulations are used to investigate force, acceleration and vibration in complex, dynamic systems (such as the valve train of internal combustion engines). The tools are used both stand-alone and to generate boundary conditions for other simulation disciplines — for example, for loads involved in calculating friction in plain bearings.
Tribology — Friction and Abrasion
An important development goal for drivetrains of all kinds is the minimisation of friction and the associated increase in efficiency and service life. By combining in-house software with powerful commercial software, APL offers solutions up to and including lifetime prediction. This allows moving parts and bearings to be optimally designed with regard to material, lubricant and surface.
Wherever liquids flow, CFD calculation becomes part of the equation. Issues such as gas exchange, combustion and exhaust gas aftertreatment are dealt with, as well as the thermal management of traction batteries and cavitation-critical areas on components around which liquids flow. Depending on the operation purpose, we use 2D or 3D models as well as single-phase or multi-phase simulation approaches.
Electromagnetics and Electronics
In the field of e‑drive, simulations at component and system level also provide insight into electric motors, power electronics, batteries or converters. The development tasks include component design and function optimisation.
If a phenomenon cannot be represented by one physical discipline alone, various simulation tools from different subdisciplines are coupled. One example are CHT (Conjugate Heat Transfer) simulations, in which the heating and expansion of components can be calculated depending on the surrounding coolant flows.
APL uses 0D and 1D approaches to represent the various subcomponents on a system basis. Examples are oil, cooling, battery and injection systems on subsystem level or complete vehicle models for internal combustion, hybrid and electric drives.
APL uses simulation models in a reproducible, real-time Powertrain-in-the-Loop (XiL) test environment coupled with high-frequency online measurement methods to analyse the functional behaviour of drive components and resulting emissions.
Systematic Variation and Optimisation
Simulations enable developers to make design decisions in the early phase before the prototype is made available. This is why it’s so important to proceed intelligently when varying and optimising the parameters and thus to keep the number of variants and data volume controllable. Here, APL relies on tools such as statistical design of experiments (DoE) and multi-objective optimisation.