This image illustrates the purpose and outcomes of temperature prediction approaches in data centers, showing how each method serves different operational needs.
Purpose and Results Framework
CFD Approach – Validation and Design Purpose
Input:
- Setup Data: Physical infrastructure definitions (100% RULES-based)
- Pre-defined spatial, material, and boundary conditions
Process: Physics-based simulation through computational fluid dynamics
Results:
- What-if (One Case) Simulation: Theoretical scenario testing
- Checking a Limitation: Validates whether proposed configurations are “OK or not”
- Used for design validation and capacity planning
ML Approach – Operational Monitoring Purpose
Input:
- Relation (Extended) Data: Real-time operational data starting from workload metrics
- Continuous data streams: Power, CPU, Temperature, LPM/RPM
Process: Data-driven pattern learning and prediction
Results:
- Operating Data: Real-time operational insights
- Anomaly Detection: Identifies unusual patterns or potential issues
- Used for real-time monitoring and predictive maintenance
Key Distinction in Purpose
CFD: “Can we do this?” – Validates design feasibility and limits before implementation
- Answers hypothetical scenarios
- Provides go/no-go decisions for infrastructure changes
- Design-time tool
ML: “What’s happening now?” – Monitors current operations and predicts immediate future
- Provides real-time operational intelligence
- Enables proactive issue detection
- Runtime operational tool
The diagram shows these are complementary approaches: CFD for design validation and ML for operational excellence, each serving distinct phases of data center lifecycle management.
With Claude
Lechuck Park 