ETAS ASCMO-STATIC

Data-based modeling for stationary behavior

ETAS ASCMO-STATIC enables data-based modeling of the stationary behavior of complex systems. It provides a wealth of functions and options for visualizing, analyzing, and optimizing the system behavior. You can also use ETAS ASCMO-STATIC for creating experimental designs based on the DoE (design of experiments) methodology.

ETAS ASCMO-STATIC online documentation

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Your benefits

Easy to use

Intuitive graphical interface, easy-to-use for engineers.

Accurate

Accurate modeling, analysis, and optimization of large, complex systems with less manual effort.

Time saving

Reduced testbench time with test plan design (DoE) and efficient modeling.

ETAS ASCMO-STATIC: key functions at a glance

ETAS ASCMO-STATIC can be used in particular for modeling relevant variables for electric or classic drives. Depending on the drive concept, precise predictions can be made for efficiency, temperatures, current, and voltage of electric drive components, as well as variables such as fuel consumption and emissions of combustion engines. Based on these models, you can visualize dependencies and interactions and carry out optimizations.

Design of experiments (DoE)

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The design of experiments (DoE) element enables you to achieve maximum model accuracy with minimum measurement effort. The design of experiments (DoE) module (ExpeDes) makes it easy to plan the underlying measurements and to propose the position of the measuring points in a methodical and space-filling way. It allows you to adjust the range of parameter variation, the time and effort required for measurement, the order and compression of measuring points, and to incorporate test bench automation requirements. Graphical representations of the points in all dimensions facilitate straightforward validation of your DoE plan.

Modeling

ETAS ASCMO-STATIC makes it easy for less experienced users to apply the advanced modelling methods required for accurate, robust prediction of system behavior. At the same time, it gives experts the freedom to adjust model parameters with a high degree of flexibility. In ETAS ASCMO-STATIC, modelling is based on Gaussian process models. In this way, the tool automatically determines the specific mathematical function that best represents the real system behavior.

Creating such a model is simple. After selecting the relevant inputs (influencing variables) and outputs (target values) of the system, you can immediately start training the model without further parameterization. The tool also provides other algorithms for special tasks such as classification and complexity reduction. All models can be exported in a variety of formats, including Simulink, C code, and FMU/FMI.

Visualization and system analysis

ETAS ASCMO-STATIC offers you a wide range of options for analyzing and visualizing the behavior of the modelled system. One of the standard views is the so-called intersection plot, which allows multidimensional dependencies to be displayed in a straightforward manner. It shows how variations in the inputs affect the output, while also indicating the reliability of the model prediction. In addition, ETAS ASCMO-STATIC offers you several other ways to visualize dependencies and influences, such as calibration maps or scatter plots.

Optimization in multiple dimensions

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ETAS ASCMO-STATIC allows you to optimize the input variables for controlling for example an engine by defining various criteria such as minimization/maximization, upper/lower limit, and target values for the output variables. These criteria can either be weighted or the full trade-off curve can be calculated from which you can then select the appropriate compromise. For engine applications, you can perform a global optimization over the entire operating range and obtain an optimum calibration of all input variables in a very short time. Results can be exported in a variety of formats, including DCM, CDFX, Excel, and CSV.

Virtual measuring instrument

Image of a laptop with an ETAS ASCMO-STATIC user interface on the screen showing bar chart.

ETAS ASCMO-STATIC allows you to use the models as a virtual measuring instrument to artificially generate large amounts of measured data. You can define any grid size for the input variables in terms of step size, number of steps and free break points directly in the tool or by importing Excel lists. The model then calculates the corresponding output for all desired parameter combinations of input variables. Just like real measurements (for example from an engine test bench), this artificial data can be used, analyzed graphically, and exported as a CSV file for further processing in Excel.

Compatibility

ETAS ASCMO-STATIC supports all relevant data formats, exports models in various formats, and integrates with MATLAB®, for example for custom functions and scripting.

ETAS ASCMO-STATIC add-ons

The functionalities of ETAS ASCMO-STATIC can be expanded for specific tasks using the add-ons listed below.

ETAS ASCMO-GO (global optimization)

ETAS ASCMO-GO extends the optimization method of ETAS ASCMO-STATIC by considering the entire (global) operating range of an engine. This allows, for example, the direct optimization of engine parameters while maintaining the smoothness of the characteristic map and adhering to typical driving cycles.

ETAS ASCMO-MCI (model compression)

ETAS ASCMO-MCI allows you to create models with significantly reduced memory requirements and computational time without compromising the desired model accuracy. It also provides a special way of identifying functional correlations between inputs and outputs of a given system.

ETAS ASCMO-ME (model export)

ETAS ASCMO-ME enables models generated with ETAS ASCMO to be exported in the following formats: C code, MATLAB®/Simulink®, Python, ETAS INCA / MDA, Excel VBA, GT‑SUITE, or FMI/FMU. These models can be used outside the ASCMO environment without the need for license verification.

ETAS ASCMO-SDK (software development kit)

The ETAS ASCMO-SDK provides a MATLAB® interface to ETAS ASCMO. It allows you to remotely control ASCMO via the command line and via scripts. It also allows user-defined functionality to be integrated, for example any kind of visualization. Moreover, you can use it to connect to test bench automations.

ETAS ASCMO-SIG (signal trace modeling)

ETAS ASCMO-SIG enables you to create signal curve models, such as cylinder pressure curves based on static inputs. The dependencies of the signal curves on the input parameters can be visualized and optimized with respect to a target curve.

ETAS ASCMO-ODCM (online DoE with constraint modeling)

ETAS ASCMO-ODCM can be coupled with test bench automation systems such as INCA-FLOW and reduce the risk of issues of the system under test. Depending on the results of the already measured points, it calculates which candidates should be skipped and which measured, reducing unstable system states.

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