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Power Query Automation for Multi-Run CAE Data Processing

30 Chapters

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Multi-run CAE data challenges

overview of simulation output formats, file structures, and the need for automated consolidation in Power Query.

Power Query fundamentals for CAE engineers

M language basics, query folding concepts, and the Power Query Editor interface for data transformation.

Connecting to CAE result files

importing CSV, Excel, and text-based solver outputs from multiple run directories using folder connectors and file path parameters.

Dynamic file path handling

using parameters and custom functions to process runs stored in nested folders with variable naming conventions.

handlingparameters

Extracting metadata from file names and folder structures

parsing run IDs, load cases, and timestamps using column from examples and text functions.

Extractingmetadata

Combining multiple files with inconsistent schemas

handling missing columns, header variations, and delimiter differences across solver versions.

Combiningmultiple

Data cleaning for simulation outputs

removing unit rows, handling scientific notation, and converting text to numeric types with locale considerations.

cleaningsimulation

Pivoting and unpivoting CAE result tables

restructuring output data for comparison across runs and load steps.

Pivotingunpivoting

Grouping and aggregating multi-run results

computing min, max, average, and statistical summaries per run using Group By operations.

Groupingaggregating

Merging run-level metadata with result data

joining tables on run identifiers and load case keys to enrich simulation outputs.

run-levelmetadata

Appending runs with different time steps

aligning transient results and handling interpolation or resampling needs in Power Query.

Appendingdifferent

Error handling and data quality checks

identifying failed runs, missing files, and corrupt records using conditional columns and error trapping.

handlingidentifying

Parameterizing solver executables and input decks

using Power Query to generate run configuration tables for batch execution.

Parameterizingexecutables

Automating report-ready tables

creating summary tables for CAE reports with custom formatting and calculated columns.

Automatingreport-ready

Incremental refresh strategies for large CAE datasets

setting up query folding and partitioning to handle growing run archives.

Incrementalstrategies

Performance optimization for multi-file queries

reducing data load, using Table.Buffer strategically, and minimizing expensive operations.

Performanceoptimization

Building a reusable query template

designing a Power Query solution that adapts to new run folders with minimal changes.

Buildingreusable

Integrating Power Query with Power BI for CAE dashboards

loading transformed data into data models and creating interactive visualizations.

Scheduling and refreshing automated queries

setting up gateway connections and refresh schedules for CAE data pipelines.

Schedulingrefreshing

Version control and documentation for Power Query solutions

managing query scripts and documenting transformation steps for team use.

documentationsolutions

consolidating modal analysis results across multiple runs: extracting frequencies and mode shapes from text files.

consolidatinganalysis

processing transient thermal simulation outputs: combining time-history data from multiple load cases.

processingtransient

comparing crash simulation results: aggregating acceleration and intrusion data from multiple impact scenarios.

comparingsimulation

automating CFD post-processing: extracting pressure and velocity fields from multiple solver runs.

fatigue analysis data aggregation: merging stress results from multiple load steps and cycles.

analysisaggregation

Advanced M functions for CAE data

using List.Accumulate, Table.TransformColumns, and custom functions for complex transformations.

Advancedfunctions

Handling large text files with fixed-width formats

parsing solver outputs with consistent column positions using Text.Range and Text.Middle.

Handlingfixed-width

Dynamic column selection and renaming

using Table.ColumnNames and List functions to adapt to changing output schemas.

selectionrenaming

Data validation and reconciliation

cross-checking Power Query results against solver summary files and manual calculations.

validationreconciliation

Deploying Power Query solutions in engineering workflows

sharing queries, integrating with existing CAE processes, and maintaining automation over time.

Deployingsolutions