AKCP Announces sensorCFD AI for Quicklime
AKCP, the world’s oldest and largest supplier of networked wired and wireless sensor solutions, announces sensorCFD AI, an advanced airflow simulation and AI-driven analytics tool integrated into the Quicklime DCIM platform.
sensorCFD AI combines Computational Fluid Dynamics (CFD) simulation with real-world environmental sensor data and embedded AI analytics. This allows data center operators not only to visualize airflow behavior, but also to automatically interpret simulation results and receive clear guidance on how to improve cooling efficiency and operational performance.
By analyzing how air moves through racks, containment systems, and cooling infrastructure, sensorCFD AI identifies airflow inefficiencies such as hot spots, cold air bypass, recirculation, and uneven rack cooling. The AI engine inside Quicklime then evaluates these results and produces an automated report that highlights problems, explains their root causes, and recommends corrective actions.
AI-Powered Analysis of CFD Simulations
Traditional CFD tools produce complex simulation outputs that require expert interpretation. Engineers must manually review airflow patterns, temperature gradients, and pressure maps to determine what problems exist and how to correct them.
Quicklime changes this process entirely.
The embedded AI tools within Quicklime automatically analyze the CFD simulation output and generate a clear, actionable report that includes:
Identification of thermal risks and airflow inefficiencies
Root-cause analysis of cooling problems
Recommendations for corrective actions
Suggestions for optimizing airflow management
Potential infrastructure improvements that reduce cooling energy consumption
Instead of requiring a dedicated CFD specialist, Quicklime effectively provides an intelligent expert system that continuously reviews your data center’s thermal performance.
It’s like having a CFD engineer on-site 24/7, 365 days a year, helping you maintain optimal cooling efficiency.
Example of analysis done on rack airflow using sensorCFD
Designed for Data Center Operators and Service Providers
sensorCFD AI is designed for data center managers, colocation providers, and consulting engineers who want the benefits of CFD modeling without the complexity traditionally associated with these tools.
AKCP also offers a low-cost data center modeling service, allowing organizations to quickly build a digital model of their facility and begin running airflow simulations.
Traditional CFD software packages used in data center design can cost $25,000 per year per seat, often requiring specialized expertise to operate. sensorCFD™ delivers similar analytical capabilities directly within the Quicklime platform, making CFD analysis far more accessible to operations teams.
Build Your Data Center Digital Twin
sensorCFD includes an integrated CAD-based modeling tool that allows users to draw and define the physical layout of their data center. A typical workflow begins by modeling a containment pod or row of racks.
Once the physical layout is defined, operational parameters are added, including:
Rack power load (kW per rack)
Rack inlet and outlet temperatures
CRAH/CRAC supply and return air temperatures
Cooling airflow rates
Room layout and containment design
With these values defined, the simulation engine calculates airflow patterns and temperature distribution across the facility.
The Quicklime AI engine then reviews the CFD output, identifies anomalies, and produces an intelligent operational report that helps operators quickly understand what is happening inside the data center and how to improve it.
Example Insights Generated by Quicklime AI
After analyzing sensor data and CFD simulation results, the Quicklime AI engine can generate reports highlighting actionable improvements within the facility. Examples include:
Cooling Inefficiency Detection
AI identifies racks receiving excess cooling airflow while nearby racks experience insufficient cooling. The report recommends airflow balancing adjustments or containment improvements.
Hot Spot Root Cause Analysis
Instead of simply detecting high temperatures, the system determines the underlying cause, such as recirculation due to missing blanking panels or airflow obstruction.
Airflow Bypass Identification
The AI identifies cold air bypassing the racks and returning directly to the cooling system, reducing cooling efficiency and increasing operating costs.
CRAH / CRAC Optimization
The system evaluates airflow and temperature patterns to determine whether cooling units are over-provisioned, improperly balanced, or operating at inefficient setpoints.
Energy Efficiency Improvements
Based on airflow modeling and sensor data, Quicklime recommends operational adjustments that can deliver energy efficiency improvements of up to 10%.
- About the Author
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For over two decades at AKCP, I have been focused on a single mission: bringing complete visibility, security, and efficiency to the world’s critical infrastructure.
I believe that in the modern data center, AI is only as good as the data it receives. My goal is to ensure facilities have the precise sensor facts needed to control AI opinions, ultimately reducing PUE, releasing stranded capacity, and ensuring maximum uptime.
