1. CAE simulation data landscape

Computer-aided engineering (CAE) is not a single artifact but a dense ecosystem of interdependent files, models, and metadata. Before any discussion of data management or quality control can be meaningful, we must first map what actually exists in a CAE environment: which data types are produced, where they originate, how they relate to one another, and why they become difficult to govern across a product's development lifecycle. This chapter builds that map and establishes the vocabulary used throughout the rest of the course.

What Counts as CAE Simulation Data

Practitioners often reduce "CAE data" to solver result files, but a simulation deliverable is really a chain of dependent artifacts. A useful classification separates them by role rather than by file extension:

• Geometry and CAD references. Native CAD parts and assemblies, neutral formats (STEP, IGES, JT), and the simplified or defeatured geometry prepared specifically for meshing. These carry revision identity and are the most common source of downstream inconsistency.
• Meshes. Volume and surface meshes in solver-native or neutral formats (e.g., Nastran .bdf, Abaqus .inp, ANSYS .msh, CGNS), together with mesh quality metrics and meshing scripts.
• Model setup and solver input decks. Material definitions, boundary conditions, loads, contact definitions, solver control parameters, and the assembled input file that the solver actually consumes.
• Results and derived data. Raw result databases (e.g., .odb, .rst, .op2), field outputs, time-history curves, and post-processed quantities such as safety factor, fatigue life, or frequency response.
• Metadata and provenance. Solver version, unit system, analyst identity, run date, input file hashes, correlation status, and approval records. This layer is what makes the other four interpretable and auditable.

The critical insight is that these categories are coupled. A result is only valid for a specific geometry revision, mesh, material set, and solver configuration. Break that chain and the result becomes an orphan — numerically intact but semantically meaningless.

Where CAE Data Comes From

Data sources are distributed across organizational and tool boundaries, which is precisely why lifecycle management is hard. Typical origins include:

• CAD/PDM systems supplying geometry at specific revisions, often through automated export jobs.
• Pre-processors and meshing tools generating meshes and setup files, sometimes with embedded scripts that encode tribal knowledge.
• HPC clusters and job schedulers producing large result sets and solver logs, frequently on scratch storage with retention policies that differ from enterprise backup.
• Post-processing and reporting tools creating derived plots, spreadsheets, and presentation-ready summaries.
• Manual analyst input — material cards, correlation notes, assumptions — which is the least structured and most fragile source.

Because these sources evolve on different cadences (CAD daily, meshes weekly, results per study), the CAE data landscape is inherently asynchronous. A single "simulation" is really a snapshot across several independently versioned streams.

The Lifecycle and Its Failure Modes

CAE data moves through recognizable phases: creation, review, release, reuse, revision, and retirement. Each phase introduces characteristic challenges.

These are not abstract risks. In practice, the most expensive failures are silent: a result file that opens fine but was computed against a superseded mesh, or a material card edited after the run without re-solving. Quality control in CAE is largely about making such silent failures visible.

A Worked Example: Tracing One Study

Consider a bracket stiffness study. The analyst exports bracket_rev_C.step from CAD, meshes it to roughly 480k tetrahedral elements, assigns an aluminum material card, applies a 500 N load at a mounting boss, and solves with a linear static solver. The result shows a maximum displacement of 0.42 mm. That number is only trustworthy if we can answer: which geometry revision, which mesh, which material properties, which solver version, and which unit system?

A minimal provenance record — even a structured text sidecar — makes this answerable:

study_id: BRK-STIFF-014
geometry: bracket_rev_C.step  sha256: 9f2c...a71
mesh: bracket_C_480k.msh     elem: C3D10  count: 481,204
material: AL6061-T6  E=68.9 GPa  nu=0.33  rho=2700 kg/m3
load: 500 N  direction: -Z (global)  location: boss_face_3
solver: linear-static v2023.2  units: mm-N-MPa
result: max_disp = 0.42 mm  (node 118342)
status: reviewed  reviewer: J.D.  date: 2024-03-11

With this record, a reviewer can detect that the geometry hash no longer matches the current CAD revision, or that the material card was later modified. Without it, the 0.42 mm figure is an unverifiable claim. The example values above are illustrative and intended to show the shape of a provenance record, not measured results.

Visualizing the Data Landscape

The diagram below summarizes how the five data categories connect across sources and lifecycle phases, and where traceability links are most likely to break.

Why This Landscape Is Hard to Manage

Three structural properties make CAE data fundamentally harder to govern than, say, source code or office documents. First, volume and heterogeneity: result databases routinely reach tens of gigabytes, while metadata is kilobytes — they cannot live in the same store with the same policies. Second, derivation depth: a single result may depend on a dozen upstream files, each with its own version, so lineage is a graph rather than a linear history. Third, tool fragmentation: CAD, meshing, solving, and post-processing are usually separate vendor ecosystems with incompatible identifiers, making automated linkage the exception rather than the rule.

Recognizing these properties early shapes every downstream decision. A team that treats CAE data as "just files on a share" will inevitably rebuild provenance by hand; a team that models it as a versioned, linked graph can automate review, reuse, and audit. The remainder of this course builds the practices — naming conventions, provenance capture, quality gates, and lifecycle policies — that turn this landscape into a controlled system.