LMS Virtual.Lab Correlation

Transcription

1 LMS Virtual.Lab Correlation

2 LMS Virtual.Lab Correlation Systematic validation from the bottom up 2 LMS Virtual.Lab Correlation

3 LMS Virtual.Lab Correlation 3

4 LMS Virtual.Lab Correlation Systematic validation from the bottom up To guarantee realistic high fidelity simulations, it is essential that simulation models meet stringent accuracy standards. Ensuring reliable simulation results requires component, subsystem and full-system models to be compared with experimental data, or alternatively validated models of similar structures. Building and validating system models from the bottom up is the only way to prevent accumulating inaccuracies. Besides more reliable what-if analyses, validated models provide a better understanding of assumptions made regarding material properties, connections, joints and boundary conditions. Validation-driven model updating Deducing model improvements using validation output is not always obvious. To facilitate model updating driven by validations, LMS Virtual.Lab Correlation offers specialized features to identify specific locations that need improvement. For example, it runs sensitivity analyses that efficiently retain the most influential specified parameters. Users can also automatically update models using internal and external algorithms, such as Nastran Solution 200, which focuses on tuning modal frequencies and response functions. Correlating structural characteristics Although static physical tests serve many design purposes, models used for vibro-acoustic simulations usually require systematic test-based validation of dynamic properties. LMS Virtual.Lab Correlation helps correlate physical test results and prepare structural tests. A comprehensive tool set significantly facilitates simulated and measured mode shape comparison and operational deflection shapes and response functions. Using the original FE model as a basis to provide optimal comparison positions, and the required number of excitation and response points helps avoid testing errors and redundancy. Compare and validate FE models based on test data Identify the cause of modeling errors Define system targets and improve simulation models Improve measurements by defining optimal sensor and excitation device location 4 LMS Virtual.Lab Correlation LMS International info@lmsintl.com

5 Pre-Test When preparing measurements for physical structures, one can use modal information of preliminary Finite Element models to define the optimal measurement set-up. For a modal test set-up, this means defining a set of measuring points and excitation points. LMS Virtual.Lab Correlation provides tools to quickly carry out this pre-test analysis in a user-friendly way. The objective is to obtain a measurement set-up that guarantees high quality measurement data. Correlation Once good test data for the physical model is available, LMS Virtual.Lab Correlation allows its users to quantify the geometrical and dynamic (FRF and Modal) resemblance between the test model and its FE equivalent model. Several correlation metrics, like MAC and FRAC, are available to study (mode) shape or frequency response function correlation interactively. Specialized algorithms and post-processing tools allow to localize the problem locations of bad shape correlation and give insight in stiffness differences between the two models. Sensitivity and Updating After the dynamic correlation between two models has been quantified, LMS Virtual.Lab allows users to easily setup and drive Nastran Sol200 to obtain the sensitivity of FE dynamic properties towards a set of design parameters to decide which parameters to change to obtain better correlation results. Using Sol200 sensitivities, MAC and frequency difference sensitivities are derived to use for modal updating. A broad range of sensitivities can also be computed using LMS Virtual.Lab Optimization: the user can define a variety of dynamic properties to optimize for a very broad range of design parameters. Once the set-up of design parameters (inputs) and correlation metrics (outputs) is in place, LMS Virtual.Lab offers the possibilities to carry out Design of Experiments, Response Surface Modeling and Updating with several local and global optimization algorithms. LMS Virtual.Lab Correlation Pre-Test Correlation Sensitivity Model Update Define an optimal set of measurement and excitation points. Validate the correlation between dynamic test and FE properties. Study the sensitivity of dynamic properties towards structural properties. Improve correlation by updating the FE model. LMS International info@lmsintl.com LMS Virtual.Lab Correlation 5

6 LMS Virtual.Lab Correlation VL-COR.03.2 LMS Virtual.Lab Correlation offers tools to ensure that high-quality FE models are used in a CAE environment and that correct sensor and excitation locations are employed in a dynamic physical structure test environment. For pre-test analysis, users can create an optimal test geometry from an existing FE model. LMS Virtual.Lab Correlation interactively creates a test wireframe on top of the FE mesh and directly quantifi es its quality according to relevant mode capturing and modal excitation. In case of poor sensor location set quality, LMS Virtual.Lab Correlation provides an easy way to analyze why the model was off-target. Users can easily change the test geometry and directly assess new quality levels using the MAC (Modal Assurance Criterion). The DPR (Driving Point Residue) criterion is used for the excitation point set. LMS Virtual.Lab Correlation also lets users easily and quickly compare the dynamic behavior of two models and deal with incompatible meshes (test and/or FE). It helps users to quantitatively articulate the degree of shape correlation using a MAC matrix. If the MAC values are too low to subjectively correlate the modes, the MAC Contribution (MACCo) criterion points out the differences to be examined. In this way, users can verify different modeling assumptions by comparing reference or measurement data. This improves model and simulation reliability. An orthogonality check between two models adds a degree of correlation accuracy by using the mass matrix to compare system dynamics. For this, LMS Virtual.Lab Correlation sets up the Nastran DMIG Solution to obtain reduced system mass matrices required for orthogonality checks between test and FE modes. The FRAC (Frequency Response Assurance Criterion) compares transfer functions between two models and provides information about global stiffness and mass modeling errors. Features Universal access to test and FE data for models, modes and frequency spectra Modal Assurance Criterion (MAC) and MAC Contribution (MACco) support error localization Visual Shape correlation for side-byside model animation (FE or Test) Frequency Response Assurance Criterion (FRAC) Orthogonality check for better dynamic correlation Driving Point Residue (DPR) for shaker location identification Export data to LMS Test.Lab or a universal file format Benefits Maximum test information with minimized excitation and measurement locations Increase measurement productivity with direct LMS Test.Lab integration Confirm FE simulation model validity using measurements Identify modeling errors or evaluate modeling strategies Improve simulation model reliability Animation of preliminary FE models helps understand which locations to include in a physical test. Interactive creation of the wireframe of measurement points. Check the quality of the FE model with respect to the test model set using MAC. Localize problem areas using MAC Contribution. 6 LMS Virtual.Lab Correlation LMS International info@lmsintl.com

7 LMS Virtual.Lab Model Updating LMS Virtual.Lab Model Updating is a model correlation and updating tool that improves simulation model quality based on reference data. With LMS Virtual.Lab Model Updating, analysts can make models that match reality more closely. FE models are first correlated with reference models, which are typically test models, but can also be FE models. The next step is to compute dynamic property sensitivity with respect to design parameter uncertainties. This can be done by inserting a Nastran Sol200 case from LMS Virtual.Lab Desktop. In this way, users can easily define element group properties for sensitivity analysis, including material and property data. Dynamic targets can be the total system mass, a specific eigenfrequency that is poorly correlated, vibration levels for unit load conditions or mode shapes. Sensitivity information is then used to update or optimize the Nastran model to match real-life condition better. For non-nastran users, the FE model can still be updated or optimized, using LMS Virtual.Lab Optimization. LMS Virtual.Lab Model Updating easily handles incompatible geometries that typically occur when comparing test and FE models. Models can be correlated geometrically through alignment, sizing and mapping procedures. LMS Virtual. Lab Model Updating provides numerical tools, such as MAC (Modal Assurance Criterion), FRAC (Frequency Response Assurance Criterion) as well as tools to check orthogonality between two models, directly driving the Nastran Guyan reduction. The MAC combined with Nastran Sol200 sensitivities for mode shapes and eigenfrequencies helps users to compute and study MAC and frequency difference sensitivity for mode pair sets. These sensitivities help obtain the best dynamic match between two models. LMS Virtual.Lab Model Updating can deal adequately with mode switching during the updating process. This ensures that the correct FE shape is used in correlation with the reference model during the automated updating process. VL-COR.04.2 Features Input design parameters for material and element properties Targets for mass, modal frequencies and vibration levels Modal Assurance Criterion (MAC), Mode Pair Table and MAC Contribution (MACco) Frequency Response Assurance Criterion (FRAC) Frequency difference sensitivity and MAC sensitivity DOE, Response Surface Modeling and several optimization algorithms Benefits Confirm FE simulation model validity using measurements Identify modeling errors or evaluate modeling strategies Improve Nastran model reliability with integrated optimization capabilities Study dynamic correlation between test and FE model sinteractively. Detect areas of bad correlation using MAC contribution. Investigate the sensitivity of dynamic properties towards design parameters. Scan the design space and update the dynamic properties using LMS Virtual.Lab Optimization. LMS International info@lmsintl.com LMS Virtual.Lab Correlation 7

8 LMS is an engineering innovation partner for companies in the automotive, aerospace and other advanced manufacturing industries. With approximately 30 years of experience, LMS helps customers get better products to market faster and turn superior process efficiency into key competitive advantages. With a unique combination of 1D and 3D simulation software, testing systems and engineering services, LMS tunes into mission critical engineering attributes, ranging from system dynamics, structural integrity and sound quality to durability, safety and power consumption. With multi-domain solutions for thermal, fluid dynamics, electrical and mechanical system behavior, LMS can address the complex engineering challenges associated with intelligent system design. LMS INTERNATIONAL Researchpark Z1, Interleuvenlaan 68 B-3001 Leuven [Belgium] T F info@lmsintl.com Thanks to our technology and dedicated people, LMS has become the partner of choice of more than 5,000 leading manufacturing companies worldwide. LMS is certified to ISO9001:2000 quality standards and operates through a network of subsidiaries and representatives in key locations around the world. For more information on LMS, visit Worldwide For the address of your local representative, please visit