1. Course overview

Computer-aided engineering (CAE) produces far more data than most teams can consume manually. A single linear static run may emit hundreds of result sets across load cases, and a design-of-experiments (DOE) study can multiply that by dozens of configurations. Excel remains the most widely available tool for turning that raw output into decisions: it is where engineers compare variants, compute safety margins, build summary tables, and assemble the reports that go to design reviews. This course teaches you to do that work systematically — and, more importantly, to automate it so that repetitive post-processing becomes a reliable, repeatable pipeline rather than a manual ritual.

What This Course Covers

The scope spans the full path from solver output to delivered report. You will work with exported result files (CSV, tab-delimited text, and structured tables), learn to import and validate them, build calculation sheets for derived quantities such as safety factors and mass estimates, and then automate the whole chain using Power Query, structured formulas, PivotTables, and VBA or Office Scripts. The final third of the course focuses on report generation: templated workbooks, chart automation, and export to PDF for distribution.

Three principles shape every chapter:

  • Traceability. Every number in a report must be traceable to a source file, a load case, and a calculation rule. Hard-coded values that "someone typed once" are treated as defects.
  • Repeatability. Running the same analysis twice must produce the same workbook, byte-for-byte where possible. Manual copy-paste steps are eliminated or isolated into a single documented action.
  • Scalability. A workflow that handles three load cases should handle thirty without redesign. This drives the choice of tables, named ranges, and query-based imports over cell-by-cell formulas.

The CAE Data Workflow

Before automating anything, you need a clear picture of where data is born and where it must end up. A typical structural analysis workflow has five stages, and Excel touches four of them:

  1. Pre-processing. Geometry cleanup, meshing, material assignment, and boundary conditions. Excel's role here is limited but real: managing material libraries, load case matrices, and mesh convergence study logs.
  2. Solving. The solver runs and writes result files. Excel does not participate, but the output format it produces determines everything downstream.
  3. Post-processing. Extracting peak stresses, displacements, reaction forces, and frequencies. This is where most manual Excel work currently happens — and where automation delivers the largest time savings.
  4. Analysis and comparison. Computing margins, comparing design variants, and building trade-off tables. Excel's calculation engine and PivotTables excel here.
  5. Reporting. Assembling charts, tables, and conclusions into a review-ready document. Templating and scripted export make this a one-click operation.

The diagram below maps these stages and shows which Excel capabilities attach to each. Note that the solver stage is deliberately left outside Excel's boundary — a common mistake is trying to drive the solver from Excel macros, which adds fragility without real benefit.

1. Pre-processing Materials, load cases, mesh study logs 2. Solving Solver runs (outside Excel) 3. Post-processing Peak stress, displacement, reaction forces 4. Analysis Margins, variant comparison 5. Report Charts, PDF export Excel capability layer Power Query import · Structured tables · Named ranges · PivotTables · Formulas · VBA / Office Scripts · Chart templates Data handoff format Solver → CSV / tab-delimited text → Power Query import → structured table → calculation sheet → report template Each handoff is a validation checkpoint: row counts, unit consistency, and load-case completeness are verified before the next stage consumes the data.

Why Excel, and Where Its Limits Lie

Excel is not a database and not a solver. It is a calculation and presentation layer, and it is exceptionally good at that. The practical implication is that you should push data validation and heavy computation upstream where possible — into the solver's own scripting or a Python pre-processor — and reserve Excel for what it does best: transparent, auditable calculations that a reviewer can inspect cell by cell.

A useful rule of thumb for CAE workbooks: if a sheet contains more than roughly 50,000 rows of raw solver output, move the filtering and aggregation into Power Query and load only the summarized result into the worksheet. Excel handles a million rows, but interactive performance degrades quickly once volatile formulas and conditional formatting are layered on top.

The Automation Mindset

Automation is not primarily about writing macros. It is a way of structuring work so that repetition is eliminated by design. Engineers who adopt this mindset consistently do four things differently:

  • They separate data from logic. Raw imported data lives in its own table; calculations reference that table through structured references rather than fixed cell addresses. When a new load case arrives, the table grows and the formulas follow automatically.
  • They name things. Named ranges, named tables, and named queries replace Sheet1!$B$2:$B$400 with PeakStress. A reviewer reading =MAX(PeakStress)/YieldStrength understands the intent immediately.
  • They record the recipe, not the result. A Power Query step list or a documented VBA routine is a recipe that can be re-run. A manually formatted summary table is a result that must be rebuilt by hand next time.
  • They build for the next engineer. Comments, a README sheet, and consistent naming mean the workflow survives staff turnover — a real risk in project-based CAE teams.
A practical starting test: hand your workbook to a colleague who has never seen it and ask them to update it with a new result file. If they need to ask you which cells to overwrite, the workflow is not yet automated — it is merely documented.

A Worked Example: From Solver Output to Margin Table

Consider a bracket subjected to three load cases. The solver exports a CSV with columns LoadCase, NodeID, VonMises_MPa, and Disp_mm. The material yield strength is 275 MPa. A manual workflow would open the CSV, sort by stress, copy the peak values into a summary sheet, and type the margin formula three times. An automated workflow does this instead:

1. Data > Get Data > From Text/CSV → select bracket_results.csv
2. In Power Query: set LoadCase to Text, VonMises_MPa and Disp_mm to Decimal Number
3. Filter out rows where NodeID is null (solver header artifacts)
4. Group By LoadCase → Max of VonMises_MPa, Max of Disp_mm
5. Load to worksheet as table "LoadSummary"
6. In the calculation sheet:
   =MAX(LoadSummary[MaxVonMises])/275   → governing margin
   =INDEX(LoadSummary[LoadCase],MATCH(MAX(LoadSummary[MaxVonMises]),LoadSummary[MaxVonMises],0))  → governing case

The expected output is a three-row summary table plus two derived cells: the governing load case and its margin. Verification is straightforward — the row count in LoadSummary must equal the number of distinct load cases in the CSV, and the maximum stress must match a manual sort of the raw file. A common failure symptom is a margin of exactly 1.0 or a blank result, which almost always indicates that the stress column was imported as text; the fix is to set the column type explicitly in Power Query rather than relying on locale-based auto-detection.

What You Will Be Able to Do

By the end of this course you will be able to take a folder of solver exports and produce a validated, charted, review-ready report through a repeatable process — one that a colleague can re-run without your involvement. The chapters that follow build this capability incrementally: data import and cleaning first, then calculation structure, then automation, then reporting. Each chapter assumes the previous one's workbook as its starting point, so the course produces a single evolving artifact rather than disconnected exercises.

Do not skip the data validation chapters even if your solver exports look clean. In practice, unit mismatches (mm vs. m), missing load cases after a failed solve, and duplicate node rows are the three most common causes of incorrect margins in CAE reports — and all three are caught by the validation checks taught in the next section.
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