3 E Network Deploys CFD Simulation and Digital Twin Technologies for Mikkeli AI Data Center
3 E Network is building a CFD- and digital twin-based management layer for its Mikkeli AI data center to improve thermal control and long-term reliability.
Rhea-AI Summary
3 E Network Technology Group (MASK) has integrated Computational Fluid Dynamics (CFD) simulation and Digital Twin technologies into its planned AI computing center in Mikkeli, Finland. The company applies a “software-defined engineering” approach to manage the facility across design, deployment and operations, aiming to handle future ultra-high-density thermal loads.
During design and deployment, 3 E Network uses “chip-to-facility” multi-physics simulation for chip-level thermal testing, liquid cooling micro-fluidic optimization, airflow containment, power infrastructure thermal safety and interaction with local Nordic free-cooling conditions. Once operational, a continuously synchronized digital twin is intended to support virtual validation of heavy liquid-cooled racks, workload-aware thermal orchestration linked with sensors, and ongoing scenario modeling for racks exceeding 100kW, to support lifecycle reliability and energy-efficient AI compute operations.
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Details
Market reaction after AI infrastructure deployment: MASK -15.45%
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Key Figures
- Future thermal load
- 100kW+ per rack
- Virtual scenario modeling for future compute clusters
- Centralized power shelves
- 48V DC
- Thermal boundary analysis for power infrastructure
- Virtual facility replica
- 1:1
- Rack placement, pipeline clash detection, and load-bearing simulations
Previous AI Reports
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Earlier Mikkeli blueprint supported future Vera Rubin architecture, liquid cooling, and high-density infrastructure.
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Key Terms
computational fluid dynamics technical
digital twin technical
coolant distribution units technical
power usage effectiveness technical
AI-generated analysis. How Rhea-AI works. Not financial advice.
HONG KONG, Sept. 14, 2026 (GLOBE NEWSWIRE) -- 3 E Network Technology Group Limited (Nasdaq: MASK) (the “Company” or “3 E Network”), a business-to-business (“B2B”) information technology (“IT”) business solutions provider, committed to becoming a next-generation artificial intelligence (“AI”) infrastructure solutions provider, today announced the integration of Computational Fluid Dynamics (“CFD”) simulation and Digital Twin technologies into its key AI computing center in Mikkeli, Finland. Following the earlier announcement of the facility’s physical blueprint designed to be compatible with the NVIDIA Vera Rubin architecture, the Company is now applying “software-defined engineering” to integrate these technologies into the management of the data center across its design, physical deployment and daily operations, to support the infrastructure’s ability to accommodate future ultra-high-density thermal loads.
Design and Deployment Phase: Multi-Physics Simulation
Unlike the relatively stable workloads of traditional cloud data centers, AI computing centers generate significant “Transient Power Spikes” during large-scale model training as cluster nodes operate in synchronization. During the design and deployment phases, traditional macro room-level airflow assessments are increasingly challenged. To address this, 3 E Network has combined high-precision CFD computational power with 3D spatial modeling to conduct “Chip-to-Facility” multi-physics analysis across the following engineering areas:
- Chip-Level Thermal Coupling and Architecture Testing: In the virtual space, anticipated thermal load metrics for next-generation architectures, such as Vera Rubin, are incorporated into the model to conduct redundancy testing under extreme conditions. The simulation extends to the compute-node level to identify potential thermal shadowing effects caused by high-density stacking, guiding the facility’s initial architectural design and cross-generational compatibility planning.
- Micro-Fluidic Optimization for Liquid Cooling Deployment: Using precise facility spatial topology data, CFD is applied to high-density blind-mate piping and manifold networks to calculate the pressure drop distribution of Coolant Distribution Units. This aims to guide the placement of the physical piping network to support balanced coolant flow to compute cores and reduce the risks of pump cavitation and localized boiling.
- Airflow Containment and Aisle Management: The system dynamically simulates the supply air pathways of Computer Room Air Handlers within the digital model to optimize the placement of physical containment components. This is intended to reduce hot- and cold-air short-circuiting and support more energy-efficient cooling.
- Thermal Boundary Analysis for Power Infrastructure: To support the high power requirements of 48V DC centralized power shelves and rack-level Battery Backup Units, CFD thermal field simulation technology is extended to electrical facilities. This assists in confirming that the physical installation locations of components such as high-voltage busbars and smart PDUs remain within safe thermal boundaries.
- External Polar Free-Cooling and Aerodynamic Interaction: The system incorporates local meteorological data from Mikkeli, Finland, to simulate the interactions between chiller plants and natural wind fields within the facility’s exterior 3D model. This guides the design of external exhaust systems to reduce heat recirculation, facilitating the effective utilization of the Nordic region’s natural free-cooling.
Operations Phase: Closed-Loop Management of Facilities and Data
Upon the delivery of the data center, compute clusters require significant capital investment and operational management. The digital management foundation built by 3 E Network is an interactive system that continuously synchronizes data with the physical facility to support daily operations:
- Virtual Pre-Placement Validation for High-Density Racks: Recognizing that next-generation liquid-cooled compute racks have significantly increased weights and require strict blind-mate alignment precision, the operations team can conduct pipeline clash detection and load-bearing simulations within a 1:1 virtual replica prior to any new hardware expansion. This effectively reduces rework rates in physical construction and supports the safe installation of hardware equipment.
- Workload-Aware Thermal Orchestration: The digital management platform is planned to integrate with out-of-band environmental sensors. By comparing real-time operational telemetry against foundational CFD prediction baselines, the system aims to assist operations teams in adjusting the operating parameters of chillers and liquid cooling pumps in anticipation of power spikes from model training. This seeks to optimize Power Usage Effectiveness continually while maintaining compute stability.
- Routine Scenario Modeling for Cross-Generational Evolution: Operations teams can import anticipated future thermal load parameters of 100kW+ per rack into an isolated virtual testing sandbox at any time for simulation drills. This enables the facility to continually evaluate power and cooling capacity margins without disrupting existing active compute tasks, providing data support for the future generational evolution of the cluster.
Engineering Vision: Enhancing Lifecycle Reliability through Software-Defined Engineering
As the AI compute industry transitions to high-density chip architectures, engineering standards for data center thermal management and power distribution are becoming increasingly rigorous. The integration of CFD simulation and Digital Twin technology reflects a broader shift in intelligent computing center construction towards a “data-driven” and “highly predictable” direction.
Dr. Tingjun Yang, Chief Executive Officer of 3 E Network, stated: “In the Mikkeli project, we are not only building robust physical infrastructure but also establishing a predictive digital management system designed to support the facility throughout its lifespan. By integrating multi-physics computation and spatial virtualization technologies across design, deployment, and operations, we use software-defined engineering to continuously evaluate and mitigate physical risks. This reflects 3 E Network’s prudent approach to capital-intensive investments and is intended to support the long-term reliability of high-value AI compute clusters hosted at the facility. We will continue to apply high engineering standards in developing intelligent computing center solutions designed to balance operational stability with energy efficiency.”
About 3 E Network Technology Group Limited
3 E Network Technology Group Limited is a business-to-business (“B2B”) information technology (“IT”) business solutions provider committed to becoming a next-generation artificial intelligence (“AI”) infrastructure solutions provider. It upholds the industry consensus of “AI and energy symbiosis” and has a strong vision in the field of energy investment. The Company’s business comprises two main portfolios: the data center operation services portfolio and the software development portfolio. For more information, please visit the Company’s website at https://3emask.com/.
Forward-Looking Statements
Certain statements in this announcement are forward-looking statements. These forward-looking statements involve known and unknown risks and uncertainties and are based on the Company’s current expectations and projections about future events that the Company believes may affect its financial condition, results of operations, business strategy, and financial needs. Investors can identify these forward-looking statements by words or phrases such as “approximates,” “assesses,” “believes,” “hopes,” “expects,” “anticipates,” “estimates,” “projects,” “intends,” “plans,” “will,” “would,” “should,” “could,” “may” or similar expressions. The Company undertakes no obligation to update or revise publicly any forward-looking statements to reflect subsequent events or circumstances, or changes in its expectations, except as may be required by law. Although the Company believes that the expectations expressed in these forward-looking statements are reasonable, it cannot assure you that such expectations will turn out to be correct, and the Company cautions investors that actual results may differ materially from the anticipated results and encourages investors to review other factors that may affect the Company’s future results in the Company’s registration statement and other filings with the U.S. Securities and Exchange Commission.
For more information, please contact:
3 E Network Technology Group Limited
Investor Relations Department
Email: ird@3emask.com
Website: https://3emask.com/
FAQ
AI-generated questions and answers. How Rhea-AI works. Not financial advice.
How will CFD and Digital Twin technologies be used during the Mikkeli data center’s design and deployment?
3 E Network combines CFD with 3D spatial modeling to run chip-to-facility multi-physics analyses. The simulations cover chip-level thermal coupling and redundancy testing for next-generation architectures such as Vera Rubin, micro-fluidic optimization of liquid cooling piping and manifolds, airflow containment and aisle management, thermal boundary analysis for 48V DC power shelves and rack-level battery backup units, and interaction of external exhaust systems with local wind fields to better leverage Nordic free-cooling potential.
What operational benefits does the digital twin platform aim to provide once the AI data center is running?
Upon delivery of the data center, the digital twin is intended to mirror the physical facility in real time to support daily management. It allows virtual pre-placement validation for high-density, liquid-cooled racks, including clash detection and load-bearing simulation. It is planned to integrate with out-of-band environmental sensors so operations teams can compare real-time telemetry with CFD baselines, adjust chiller and pump settings in anticipation of power spikes, and continually model future scenarios such as 100kW+ per rack without interrupting active compute tasks.
What long-term engineering vision does 3 E Network describe for the Mikkeli AI computing center?
The company describes a shift toward data-driven and highly predictable intelligent computing center construction, using software-defined engineering to link multi-physics computation and spatial virtualization across the facility’s lifecycle. Dr. Tingjun Yang states that the goal is to build not only robust physical infrastructure but also a predictive digital management system that continuously evaluates and mitigates physical risks, supports prudent handling of capital-intensive investments and aims to balance operational stability with energy efficiency for high-value AI compute clusters.