Space-saving high-density modular data systems and energy-efficient cooling systems
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Solution Overview
Problem
Traditional data center cooling systems are inefficient due to oversized infrastructure, high energy consumption, and limited geographical applicability, particularly in high-density data centers with fluctuating IT loads and extreme wet-bulb conditions.
Innovation Solution
The modular data pod system with a close-coupled cooling system that uses polygonal shapes for efficient air circulation and incorporates a chiller-less design with subcooling capabilities, allowing for flexible deployment and operation in various environments, including high wet-bulb conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional chiller plants are used to cool entire data centers, then cooling capacity is sufficient, but energy consumption increases significantly and cost efficiency decreases
Solution Approach 1:
The data center is divided into multiple modular data pods, each with its own dedicated cooling system. This segmentation allows each module to be cooled independently based on its specific IT load, preventing the entire facility from being cooled at full capacity when only partial cooling is needed, thereby reducing overall energy consumption.
Solution Approach 2:
Each data pod receives cooling capacity matched to its specific thermal load rather than receiving uniform cooling from a centralized system. The cooling capacity is locally adjusted based on the actual IT equipment density and heat generation in each pod, optimizing energy efficiency while maintaining reliable cooling where needed.
2Reliability
If traditional chiller plants are designed for peak power consumption capacity, then sufficient cooling is provided, but cost efficiency drops when operating below capacity
Solution Approach 1:
The centralized chiller plant is replaced with multiple smaller, modular cooling systems distributed across data pods. Each module can be independently activated or deactivated based on actual cooling demand, allowing the system to operate at optimal efficiency levels regardless of whether total data center load is at peak or below capacity.
Solution Approach 2:
The cooling system configuration is dynamically adjusted by activating or deactivating specific data pod modules based on real-time cooling demand. This dynamic reconfiguration allows the system to maintain cost efficiency across varying load conditions while ensuring adequate cooling capacity is always available where needed.
3Use of energy by moving object
If air-cooled free cooling systems are used, then operational costs are reduced, but geographical applicability is limited to cool, dry climates
Solution Approach 1:
The data pod cooling system is designed to universally accommodate multiple operating conditions and geographical environments. Each pod can operate in free-cooling mode when ambient conditions permit, switch to adiabatic-assisted cooling in moderate conditions, or utilize mechanical cooling when high heat loads or extreme wet-bulb temperatures require additional cooling capacity, making the system adaptable to any geographical location.
Solution Approach 2:
The system dynamically changes its cooling parameters and operational mode based on ambient conditions and IT load requirements. By adjusting between different cooling strategies (free-cooling, adiabatic-assisted, mechanical), the system maintains cost efficiency while adapting to varying geographical and environmental conditions.
4Adaptability or versatility
If adiabatic-assisted cooling systems are used, then geographical reach is expanded, but cooling tolerance is limited and insufficient for high density data centers
Solution Approach 1:
Different cooling strategies are applied to different data pods based on their specific IT load density and local environmental conditions. High-density pods with greater heat generation can utilize mechanical cooling systems, while lower-density pods operate with adiabatic-assisted or free-cooling systems, optimizing both geographical adaptability and cooling tolerance across the entire facility.
Solution Approach 2:
The overall cooling infrastructure is designed as a universal system capable of handling diverse cooling requirements across multiple data pods. Each pod can independently select its appropriate cooling mode based on local conditions, allowing the system to achieve both broad geographical reach and sufficient cooling tolerance for high-density applications.
5Area of stationary object
If modular data pod systems are implemented, then space utilization and scalability are improved, but system complexity increases
Solution Approach 1:
The data center is segmented into standardized modular data pods that can be independently designed, deployed, and managed. This segmentation simplifies the overall system by breaking down complexity into manageable, repeatable units while maximizing space utilization through efficient modular packaging and configuration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution reduces energy costs and capital expenditures, enhances scalability, and maintains high efficiency across different geographical and environmental conditions, supporting high-density data center operations with improved space utilization and adaptability.
Implementation Method 1
an air circulator configured to continuously circulate air through the first, second, and third volumes
Implementation Method 2
incorporates a chiller-less design with subcooling capabilities
Implementation Method 3
The adiabatic-assisted system is a cooling system assisted by adiabatic water
Data Source
AI summary
A space-saving, high-density modular data pod and a method of cooling a plurality of computer racks are disclosed. The modular data pod includes an enclosure including wall members contiguously joined to one another along at least one edge of each wall member in the shape of a polygon and a data pod covering member. Computer racks arranged within the enclosure form a first volume between the inner surface of the wall members and first sides of the computer racks. A second volume is formed of second sides of the computer racks. A computer rack covering member encloses the second volume and the data pod covering member form a third volume coupling the first volume to the second volume. An air circulator continuously circulates air through the first, second, and third volumes. The method includes circulating air between the first and second volumes via the third volume and the computer racks.