Automation Workflow for Simulating Erosion in Turbomachinery Blades

The operational bottleneck is the manual, sequential process of setting up, running, and analyzing coupled Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) simulations to model sand, ash, or droplet impact. Each simulation cycle requires expert configuration of ANSYS Fluent or STAR-CCM+, manual job submission to HPC clusters like Slurm, and tedious post-processing to extract erosion rates. This creates weeks of delay in evaluating a single coating or blade geometry, leading to conservative material choices, efficiency loss, and reactive maintenance. A custom automation workflow compresses this cycle by 80%, directly linking predictive insights to procurement and maintenance planning systems like SAP or Maximo.

Implementation requires an orchestrator, built with LangGraph or Prefect, to manage the stateful workflow. It triggers on new CAD models from Siemens Teamcenter or a design change request. Agents automate geometry cleanup, mesh generation with ANSYS Meshing, and parameter sweeps for impact angles and particle sizes. Simulations are dispatched to an HPC cluster; monitoring agents watch for convergence issues and adjust solver settings. Results are parsed automatically, with erosion maps and predicted service life written to a centralized material database. Critical findings exceeding set thresholds automatically generate alerts and draft work orders in the CMMS, creating a closed-loop from simulation to operational action.

This workflow automates the high-fidelity, multi-physics simulation of particulate impact on compressor and turbine blades, a traditionally manual and compute-intensive bottleneck in power generation and aviation R&D. By orchestrating ANSYS Fluent or OpenFOAM for CFD with DEM solvers like LIGGGHTS, it systematically evaluates coating and base material performance under harsh environmental conditions. The operational upside comes from compressing weeks of manual setup, job submission, and result synthesis into hours, directly reducing lab iteration costs and accelerating the qualification of more durable materials.

Implementation integrates with PLM systems like Teamcenter or Windchill for geometry triggers and SAP for material data. The orchestrator, built on LangGraph, manages specialized agents for mesh generation, solver parameterization, and convergence monitoring. Critical controls include automated validation against historical test data, confidence scoring for each simulation run, and mandatory human-in-the-loop review gates for low-confidence or high-risk predictions before reports are published to engineering and maintenance systems.

Phase 1 establishes the core data and orchestration foundation. We integrate with your PLM (e.g., Teamcenter, Windchill) and CAD systems to automate geometry ingestion and preprocessing. A central orchestrator, built on LangGraph, manages job submission to your HPC cluster (using Slurm or Kubernetes) and initializes baseline ANSYS Fluent or OpenFOAM simulations. This phase delivers automated setup and execution, reducing manual preparation from days to hours and providing a controlled environment for validating the simulation chain before introducing complex automation.

Phase 2 introduces intelligent multi-agent logic using CrewAI for autonomous parameter sweeping, DEM coupling for particle impact modeling, and convergence monitoring. A critical human-in-the-loop gate is implemented for approving high-risk material or coating recommendations before they proceed. Phase 3 focuses on production hardening: integrating results with ERP (SAP, Oracle) or MRO systems for maintenance scheduling, deploying comprehensive observability dashboards for simulation drift and cost tracking, and final handoff. This phased approach de-risks the build, ensures each stage delivers operational savings, and creates a scalable architecture for continuous simulation-driven decision-making.

Governance begins with a formalized change management process for the simulation logic and material models, typically managed in a system like Git or a PLM tool like Siemens Teamcenter. Each automated simulation run must be triggered by an auditable event, such as a new material specification in the ERP or a flagged inspection report from the CMMS. Approval gates are embedded before any simulation result can influence a procurement or maintenance work order, ensuring a human engineer validates the AI's proposed coating or replacement recommendation against operational context and safety margins.

Phased rollout is critical for risk mitigation. Phase 1 executes simulations in a shadow mode, comparing AI-predicted erosion rates against historical lab data without triggering actions. Phase 2 introduces the workflow for non-critical components in a single power plant or engine model, with all outputs requiring co-signoff by a materials engineer. Phase 3 expands to full fleet coverage and autonomous work order generation for standard coating replacements, but retains mandatory review for any finding that suggests accelerated wear or potential unplanned outage. Observability is built on logging each agent's decision, simulation parameters, and result confidence to tools like Grafana or Datadog for continuous performance monitoring.

This workflow automates the complex, multi-physics simulation of particle impact on turbomachinery blades, a traditionally manual and compute-intensive bottleneck. By orchestrating ANSYS Fluent or OpenFOAM for fluid dynamics with LIGGGHTS or EDEM for discrete element modeling, it systematically evaluates material loss across thousands of operational hours in days. The business value is direct: reducing costly physical test rigs, accelerating coating qualification by 70-80%, and preventing efficiency loss and forced outages in power generation and aviation through predictive maintenance.

Implementation requires a central Simulation Knowledge Graph, built on Neo4j or AWS Neptune, that links material properties, historical erosion patterns, and simulation results. This graph enables agents to retrieve relevant boundary conditions and validate new runs against past data. Critical controls include automated convergence checks to halt wasteful HPC spend, human-in-the-loop approval for high-risk predictions, and integration with CMMS like SAP PM or Maximo to trigger inspection work orders. The result is a closed-loop, audit-ready system that turns simulation from a research activity into a core operational asset.