Siemens Industry Software NV

A computer-implemented method for modelling an object subjected to boundary conditions from an object model that defines a component by a three-dimensional boundary representing format is provided. The method includes providing boundary conditions including loads and/or constraints to the object, and providing the object model. The boundary representation of the object model is tessellated, obtaining an object tessellation. An approximate convex decomposition is applied to the object tessellation, obtaining three-dimensional cells respectively defined from each other by splitting planes. A numerical model is generated by applying a discontinuous Galerkin method to the three-dimensional cells. Determination of a load capacity, an improvement of the design, or the generation, in each case, of the component, are provided.

A method for generating a virtual model of a component using a computer-aided design (CAD) environment including a design database is provided. The design database is used to store designs. The CAD environment provides predefined design elements. At least some of the designs include predefined design elements. In order to increase an efficiency with which models are created, a design databank contains information from the design database relating to designs. In a design phase, the method further includes selecting at least one design element, determining designs that contain the selected design element from the design databank, and displaying information relating to at least one of the determined designs.

A computing platform for simulating an industrial system and a method of managing the simulation are disclosed. The method relates to generating at least one instance of a simulation model of the industrial system. The method includes: generating the instance using a contiguous memory allocator for a dynamic memory region associated with a first processor of the heterogenous processors; enabling a second processor to use the instance in the dynamic memory region based on a memory pointer associated with the address of a copy of the instance; and simulating the industrial system by the second processor by accessing the instance-copy.

A method and system for performing seamless analysis of geometric components in multi-domain collaborative simulation environments. A method includes generating a simulation interface object corresponding to a geometric component in a first simulation environment. The simulation interface object includes load and boundary conditions associated with the geometric component. The method includes dynamically accessing the load and boundary conditions associated with the geometric component in a second simulation environment via the simulation interface object. The first simulation environment and the second simulation environment correspond to different domains. The method includes performing analysis of the geometric component in the second simulation environment based on the load and boundary conditions. Moreover, the method includes generating results of the analysis of the geometric component on a graphical user interface associated with the second simulation environment.

A computer-implemented method for modelling variations of repeating parts of a component is disclosed herein. The method includes providing parameter sets for part configurations of a plurality of different of parts, wherein the parameters of each parameter set respectively define the part configuration of a part; selecting a selection of the part configurations; providing a part model for each part configuration of the selection, wherein the part model relates forces and displacements to locations of the respective part; and building an approximator, wherein the approximator is provided for interpolating the part models to approximate a part model of a part configuration that does not belong to the selection of the part configurations.

A computer-implemented method is provided for predicting a fatigue response of a material. The method includes receiving a user input specifying one or more surface roughness parameters that characterize a surface of a material for which fatigue life is to be predicted. The method further includes generating at least one realistic virtual surface profile from the specified one or more surface roughness parameters. The method further includes predicting fatigue life of the material in dependence of a stress field applied to the generated virtual surface profile. In accordance with specific embodiments, the prediction of the fatigue life may be carried out using finite element analysis based simulations, machine learning methods, or combinations thereof.

A machine learning module is provided which is trained to generate from a design data record specifying a design variant of a product, a first performance signal quantifying a predictive performance of the design variant and a predictive uncertainty of the predictive performance. A variety of design data records each specifying a design variant of the product is generated. For a respective design data record, the following steps are performed: a first performance signal and a corresponding predictive uncertainty are generated, depending on the predictive uncertainty, a simulation yielding a second performance signal quantifying a simulated performance of the corresponding design variant is either run or skipped, and a performance value is derived from the second performance signal if the simulation is run or, otherwise, from the first performance signal. Depending on the derived performance values, a performance-optimizing design data record is determined and output to control the production plant.

A system and method for accelerated simulation setup includes receiving a description of a new problem for simulation, extracting input data and output data of previous simulation results, generating a representation of data based on the extracted input data and output data, and quantifying similarities between the new problem and the extracted input data and output data to identify a candidate simulation for the new problem. A machine learning component infers a solution output for the new problem based on extrapolation or interpolation of outputs of the candidate simulation, thereby conserving resources by eliminating a simulation generation and execution. Alternatively, an efficient simulation setup can be generated using the queried knowledge, input variables, and input parameters corresponding to the candidate simulation.