2. Data governance frameworks for CAE
CAE simulation data is not a by-product of analysis; it is a governed engineering asset. A single vehicle crash model can contain thousands of parts, dozens of material cards, solver decks, and result files that feed certification decisions. Without a governance framework, organizations face duplicated models, untraceable result provenance, inconsistent material libraries, and audit failures when regulators or customers request evidence. A CAE data governance framework defines who owns data, what rules apply, how standards are enforced, and how compliance is demonstrated. This chapter explains how to structure that framework in an engineering organization and how to make it operational rather than aspirational.
Why CAE Needs a Dedicated Governance Layer
Generic enterprise data governance (often built around BI, master data, or document management) rarely fits CAE. CAE data has properties that demand specific treatment: files are large and binary, relationships between geometry, mesh, solver input, and results are version-sensitive, and the same physical part may be represented by different meshes for different load cases. Governance must therefore operate at the level of the simulation model and its dependency graph, not just at the level of folders and filenames.
A practical framework rests on four pillars: roles (accountability), policies (rules and decision rights), standards (technical conventions that make data comparable and reusable), and compliance (evidence that rules were followed). These pillars are connected: a policy without an owner is ignored, a standard without enforcement drifts, and compliance without standards produces meaningless audit trails.
Roles: Assigning Accountability Without Bottlenecks
Governance fails when it is owned by "the CAE team" as an undifferentiated group. Effective frameworks assign explicit, non-overlapping accountabilities. A workable role model for a mid-sized engineering organization includes:
- CAE Data Owner — typically the functional lead (e.g., crash, durability, NVH). Accountable for the correctness and completeness of simulation data in their domain, and for approving release of models and results.
- Simulation Method Owner — owns solver settings, element formulations, contact definitions, and correlation methodology. Approves changes to the method baseline that affect comparability of results.
- Data Steward — a working-level role responsible for metadata quality, library curation (materials, boundary conditions), and resolving naming or classification conflicts. This is the role that keeps the framework alive day to day.
- Analyst — creates and modifies models, must comply with standards, and is responsible for attaching correct metadata and evidence.
- Approver / Independent Reviewer — signs off results used for design decisions or certification; must be independent of the analyst for safety-relevant load cases.
The critical design principle is separation of data ownership from tool administration. The PLM/SDM administrator manages the system; the Data Owner manages the content. Conflating the two produces a system that is technically available but semantically untrustworthy.
Policies: Rules That Survive Contact With Deadlines
Policies should be few, explicit, and enforceable. A common failure mode is a 40-page governance document that no analyst reads. Instead, encode policies as short rules with clear triggers and consequences. Examples that work in practice:
- Versioning policy: every model released for a design decision must carry a semantic version (major.minor.patch) and a change note; major version increments require re-approval.
- Release gate policy: a result may not be referenced in a design review unless it has passed the domain quality checklist and been approved by the Data Owner.
- Retention policy: released models and results are retained for the vehicle program life plus a defined statutory period (e.g., 15 years for safety-relevant evidence); working files are retained for 2 years.
- Access policy: export of models containing restricted geometry or supplier-proprietary material data requires explicit approval and is logged.
Standards: Making Data Comparable and Reusable
Standards are the technical conventions that let one analyst's model be understood, compared, and reused by another. In CAE, the highest-value standards are:
| Standard area | Typical content | Why it matters |
|---|---|---|
| Solver baseline | Approved solver version, license feature, and solver settings profile per discipline | Prevents result drift caused by version changes; enables correlation |
| Units & coordinate frames | SI base units, global origin definition, local frame naming convention | Eliminates unit-conversion errors and misaligned load application |
| Material library | Controlled material cards with source, test date, and strain-rate coverage | Ensures traceability of material assumptions in certification |
| Metadata schema | Mandatory fields: program, variant, load case ID, mesh density level, solver, analyst, date | Makes models searchable and comparable; supports audit queries |
| Naming convention | Structured identifiers, e.g., PRG-VAR-LC-MESH-SOLVER-REV | Reduces ambiguity and enables automated validation |
Standards must be versioned themselves. When the material library moves from revision C to D, models built against C must remain identifiable, and the migration path must be documented. A practical mechanism is to store the standard revision in the model metadata and to validate it at check-in.
Compliance: Producing Evidence, Not Just Intentions
Compliance in CAE means being able to demonstrate, on request, that a result was produced under controlled conditions. This is not only a regulatory concern; it is increasingly a customer and supplier requirement. The evidence chain typically includes:
- Traceability: a link from the reported result back to the exact model revision, solver version, material library revision, and input deck.
- Approval record: who approved, when, and against which checklist version.
- Change history: what changed between revisions and why.
- Quality gate evidence: mesh quality metrics, convergence status, energy balance, and correlation results where applicable.
A useful operational test is the audit replay: pick a released result at random and attempt to reconstruct the exact simulation from the repository alone, without asking the original analyst. If the reconstruction requires undocumented knowledge, the compliance chain has a gap. Run this test quarterly; it exposes missing metadata far earlier than a formal audit.
Making the Framework Operational
A governance framework that lives only in a document will decay within two release cycles. Operationalize it through three mechanisms. First, embed validation in the toolchain: configure the SDM/PLM system to reject check-in when mandatory metadata is missing or when the solver version is outside the approved baseline. Second, review metrics monthly: track metadata completeness rate, percentage of released models with full traceability, and audit-replay success rate. Third, tie governance to engineering outcomes: show that governed models reduce rework and correlation effort, otherwise the framework will be perceived as overhead and bypassed under schedule pressure.
Finally, keep the framework proportionate. A small team may need only a Data Owner, a steward, and a one-page standard; a large OEM with certification obligations needs the full role model and formal compliance evidence. The framework should scale with the consequence of being wrong, not with organizational ambition. The next step in building maturity is to connect these governance elements to the concrete data model and metadata schema used in the SDM system, so that policies and standards become machine-checkable rather than manual.