Liquid Cooling and AI Data Center Daily | 2026-08-09
A daily English mirror of liquid cooling, AI data center efficiency, research papers, products, policy, financing, and supply-chain signals.
1. Executive brief
This English edition mirrors the same public-source dataset used by the Chinese daily report for 2026-08-09.
- Collection window: 2026-08-08 08:00 北京时间 - 2026-08-09 08:00 北京时间.
- Coverage snapshot: 8 industry items; 4 technology items; 8 paper or white-paper items; 8 video signals.
- Current hot topics: 电力并网与能源约束, 智算中心 CapEx/扩建, AI 芯片供给与交付, NVIDIA Blackwell/GB200/GB300.
- The heat score is 10/10 and should be read as a source-density signal, not as an investment indicator.
All claims should be verified against the original source links listed at the end of this report.
Academic and Industry Briefs
Papers, videos, industry updates, policy, financing, and projects are compressed into scannable tags with a title, summary, and source link.
Academic
Research papers, methods, research-oriented videos, and academic signals.
Zero-change foundry compatible silicon photonics MEMS optical switch
Large-scale photonic switches are emerging as essential devices for energy-efficient optical interconnect in data centers and AI/ML…
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Zero-change foundry compatible silicon photonics MEMS optical switch
- Published
- 2026-08-04
- Authors
- Arkadev Roy, Daniel Klawson, Jianheng Luo, Yiyang Zhi, Sirui Tang, Ming Wu
- Theme
- 热管理与液冷
Abstract
Large-scale photonic switches are emerging as essential devices for energy-efficient optical interconnect in data centers and AI/ML clusters as a key enabler for high-bandwidth and low-latency connectivity. Combining micro-electro-mechanical (MEMS) based mechanical reconfigurability with silicon photonic integrated circuits can enable a large-scale, low-loss, programmable platform required for large-scale optical circuit switches. We demonstrate a broadband silicon photonics MEMS switch with more than 30 dB extinction ratio operating in C-band using a zero-change foundry-compatible process and Back-end-of-Line (BEOL) post-processing. The optical switch element exhibits an insertion loss of less than 1.5 dB with a low static power consumption of approx 20 nW at maximum actuation voltage. Our results illustrate that MEMS-based silicon photonics modulators and phase shifters can be used alongside standard silicon photonics components seamlessly in scenarios where performance in terms of footprint, extinction ratio, broad bandwidth, and low-loss operation is of paramount importance.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Arkadev Roy, Daniel Klawson, Jianheng Luo, 等. Zero-change foundry compatible silicon photonics MEMS optical switch[J/OL]. (2026-08-04)[2026-08-09]. http://arxiv.org/abs/2608.03146v1.
A Stackelberg-Bayesian Capacity-Market Game of Carbon Regulation and Seco…
Artificial intelligence (AI) data centers are driving rapid electricity load growth across all U.S. ISO/RTO regions, raising both s…
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A Stackelberg-Bayesian Capacity-Market Game of Carbon Regulation and Second-Life Battery Investment under AI Data-Center Load Growth
- Published
- 2026-08-05
- Authors
- Rouzbeh Haghighi, Ali Hassan, Sina Mohammadi, Marcus Chen I Wada, Wencong Su
- Theme
- 算电协同
Abstract
Artificial intelligence (AI) data centers are driving rapid electricity load growth across all U.S. ISO/RTO regions, raising both system costs and carbon exposure. This study develops a three-level Stackelberg--Bayesian game in which a regulator (leader) sets carbon penalties and subsidies, a single ISO capacity market clears against an energy balance modeled as a classical generation-expansion problem, and technology-specific investors (followers) decide capacity and operation under incomplete information, yielding a Bayesian Nash equilibrium. The AI impact is captured parsimoniously as an additional load-growth factor on a greenfield-incremental expansion, isolating how much new capacity the growth pulls in and which technology fills it. Within this framework, we consider second-life battery (SLB) storage competing against new/first-life storage for capacity-market revenue. We quantify how a carbon tax, a renewable subsidy, and an SLB subsidy reshape the equilibrium investment mix, carbon emissions, and profit. Different scenarios are compared at the end based on cost-effectiveness and reduced carbon emissions.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Rouzbeh Haghighi, Ali Hassan, Sina Mohammadi, 等. A Stackelberg-Bayesian Capacity-Market Game of Carbon Regulation and Second-Life Battery Investment under AI Data-Center Load Growth[J/OL]. (2026-08-05)[2026-08-09]. http://arxiv.org/abs/2608.03989v1.
From Individual to Shared Ownership: A Coalitional Game Approach to Susta…
This paper proposes a cooperative game-theoretic framework for sustainable co-investment in shared infrastructure under regulatory …
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From Individual to Shared Ownership: A Coalitional Game Approach to Sustainable Co-investment
- Published
- 2026-07-29
- Authors
- No reliable data
- Theme
- 热管理与液冷
Abstract
This paper proposes a cooperative game-theoretic framework for sustainable co-investment in shared infrastructure under regulatory incentives. Multiple heterogeneous operators co-invest in a common infrastructure whose production capability evolves over time and is subject to operational variability. A regulator supports the deployment through incentive mechanisms designed to align individual economic investment objectives with the coalitional one. We formulate the co-investment problem as a transferable-utility (TU) coalitional game in which the value generated by cooperation depends on heterogeneous operational profiles, dynamic resource availability, investment costs, and regulatory incentive level. We show that the proposed coalitional game can be reformulated as a linear production game (LPG), whose dual prices yield a constructive and stable allocation of the cooperative surplus. Finally, we illustrate the proposed framework through a case study on co-investment among data center operators in shared renewable energy infrastructure, supported by government subsidies promoting renewable energy consumption.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
佚名. From Individual to Shared Ownership: A Coalitional Game Approach to Sustainable Co-investment[J/OL]. (2026-07-29)[2026-08-09]. http://arxiv.org/abs/2607.26725v1.
Inter-Area Oscillation Damping in Data-Center-Integrated Power Systems
This paper develops explicit dynamic models of a hyperscale data center, including its heating, ventilation, and air conditioning (…
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Inter-Area Oscillation Damping in Data-Center-Integrated Power Systems
- Published
- 2026-07-30
- Authors
- Ahmed Alfatlawi, Masoud H. Nazari
- Theme
- 算电协同
Abstract
This paper develops explicit dynamic models of a hyperscale data center, including its heating, ventilation, and air conditioning (HVAC) and uninterruptible power supply (UPS) subsystems, and integrates them into a small-signal stability framework to investigate the impact of data center demand response on power system inter-area oscillations. Through eigenvalue analysis and time-domain simulations, the results demonstrate that UPS-based demand response can enhance inter-area oscillation damping. In contrast, the HVAC subsystem is shown to be inherently incapable of providing effective oscillation damping due to its limited thermal response bandwidth. A gradient-based optimization algorithm is used to tune the UPS controller gain to maximize the damping ratio of the critical inter-area mode. The effectiveness of the proposed approach is validated using the IEEE 39-bus test system.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Ahmed Alfatlawi, Masoud H. Nazari. Inter-Area Oscillation Damping in Data-Center-Integrated Power Systems[J/OL]. (2026-07-30)[2026-08-09]. http://arxiv.org/abs/2607.27575v1.
A Configurable Thermal-Dynamic Model for AI Data Center Cooling Load Simu…
Cooling demand constitutes a significant and flexible component of AI data center electricity consumption, but time-synchronized me…
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A Configurable Thermal-Dynamic Model for AI Data Center Cooling Load Simulation
- Published
- 2026-07-31
- Authors
- Cletus Ngwerume, Lang Tong, Chee-Wooi Ten, Yi Hu
- Theme
- 算电协同
Abstract
Cooling demand constitutes a significant and flexible component of AI data center electricity consumption, but time-synchronized measurements are scarce and constant coefficient-of-performance models cannot represent thermal dynamics. This letter proposes a configurable thermal dynamic simulation model for hybrid air- and liquid-cooled data centers. Unlike existing models centered on temperature prediction or equipment-level cooling analysis, the proposed model is designed to generate dynamic cooling electricity profiles for long-duration power system studies. The model is validated using operational telemetry from the Marconi100 supercomputer. Compared with the baseline, the proposed model reduces the mean absolute error from 95.80 to 20.88~kW and the root-mean-square error from 109.79 to 27.27~kW. Evaluation over approximately 520 daily profiles further shows improved reproduction of daily peak demand and intraday variability. The proposed model provides a computationally tractable means of generating physically interpretable cooling load profiles for power system studies.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用实验验证、原型测试或测量对比，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Cletus Ngwerume, Lang Tong, Chee-Wooi Ten, 等. A Configurable Thermal-Dynamic Model for AI Data Center Cooling Load Simulation[J/OL]. (2026-07-31)[2026-08-09]. http://arxiv.org/abs/2607.28962v1.
Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agen…
As a major contributor to carbon emissions, the decarbonization of power systems has garnered significant societal attention. Nodal…
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Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework
- Published
- 2026-07-29
- Authors
- Feiyu Cai, Jing Qiu, Yi Yang, Chenxi Zhang, Xinlei Wang, Baichuan Liu, Junhua Zhao
- Theme
- 算电协同
Abstract
As a major contributor to carbon emissions, the decarbonization of power systems has garnered significant societal attention. Nodal carbon intensity (NCI), a critical factor in carbon-oriented demand response, has traditionally been determined through ex-post calculations. However, this ex-post approach introduces latency in low-carbon dispatch. To address this, this paper presents a proactive ex-ante spatial-temporal carbon response framework. At its core, we develop a novel deep learning-based hierarchical design, enhanced by a dual-stage attention mechanism and a large language model (LLM)-based multi-agent cooperation system, to accurately forecast day-ahead NCI. This design effectively mitigates the impact of renewable energy uncertainty and enhances predictive resilience. On the demand side, the framework proposes a spatial-temporal carbon scheduling model that integrates geographically dispatchable loads (GDLs), including mobile energy storage systems (MESSs) and distributed data centers (DDCs). Leveraging high-accuracy day-ahead NCI predictions, the framework can effectively reduce system emissions by quickly responding to carbon intensity fluctuations. The proposed framework is tested on the modified IEEE 33-bus system. According to the simulation results, the impacts of proposed framework on dispatching latency and emission outcomes are analyzed. The results demonstrate that under a one-hour reduction in carbon scheduling latency, the proposed model and methodology can achieve over 30% emission reduction. This research breaks through the limitations of passive carbon accounting, advancing toward proactive carbon management. It offers an intelligent solution that accelerates the transition to cleaner power systems while directly supporting sustainable production goals.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向跨地域数据中心负载与电力资源之间的调度关系。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Feiyu Cai, Jing Qiu, Yi Yang, 等. Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework[J/OL]. (2026-07-29)[2026-08-09]. http://arxiv.org/abs/2607.26560v1.
Planning Waste-to-Energy-Coupled AI Data Centers Through Grade-Matched Co…
AI data-center growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand.…
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Planning Waste-to-Energy-Coupled AI Data Centers Through Grade-Matched Cooling and Corridor Screening
- Published
- 2026-07-29
- Authors
- Qi He, Chunyu Qu, Wenjie Zuo
- Theme
- 算电协同
Abstract
AI data-center growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand. This study develops a boundary-consistent screening framework for waste-to-energy (WtE)-coupled AI data-center cooling. It treats cooling as an energy service that can be supplied through grade matching rather than only through electricity-driven mechanical chilling. The framework translates plant-side exportable heat into corridor-level planning metrics by accounting for thermal attenuation, absorption conversion, and parasitic electricity for delivery and auxiliaries. In a reference case, a regulated WtE plant processing 1500 t/day of municipal solid waste at 10 MJ/kg provides about 78.1 MWth of exportable heat. At a 20 km corridor, this yields about 53.0 MW of delivered cooling and 8.0 MWe of net avoided cooling electricity after parasitic loads. The coupled system is governed by operating regimes rather than a single efficiency score. Under baseline assumptions, full thermal coverage extends to about 20.9 km, the quality-adjusted criterion remains positive to about 22.9 km, and net electricity relief remains positive to about 44.7 km. For a 1 GW IT campus at 70 percent utilization and a 5 km corridor, net grid relief ranges from about 116.9 to 264.4 MW across scenarios. The required WtE footprint ranges from about 3 to 148 representative plants, or 0.6 to 40 full-load-equivalent plants at a 25 percent displacement target. The framework identifies when WtE-coupled cooling is corridor-feasible, when hybrid operation is required, and when infrastructure scale becomes the binding constraint. It is intended for screening and comparison, not project-specific hydraulic or plant-cycle design.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Qi He, Chunyu Qu, Wenjie Zuo. Planning Waste-to-Energy-Coupled AI Data Centers Through Grade-Matched Cooling and Corridor Screening[J/OL]. (2026-07-29)[2026-08-09]. http://arxiv.org/abs/2607.26324v1.
A Predict-then-Schedule framework for Power Distribution Networks with AI…
The surge of GPU-intensive workloads in artificial intelligence (AI) data centers drives massive energy demands, leading to soaring…
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A Predict-then-Schedule framework for Power Distribution Networks with AI Data Centers
- Published
- 2026-07-20
- Authors
- Siqi Yan, Jiebao Zhang, Xi Yao, Juan Huang, Ye Shi
- Theme
- 算电协同
Abstract
The surge of GPU-intensive workloads in artificial intelligence (AI) data centers drives massive energy demands, leading to soaring costs and significant stress on local power distribution networks. Coordinating delay-tolerant workload scheduling with power grid conditions via precise workload prediction can mitigate these issues. However, a critical gap remains in conventional approaches, i.e., minimizing prediction error does not necessarily lead to minimized downstream operational loss. Hence, this paper proposes an end-to-end Predict-Then-Schedule (PTS) framework that integrates upstream workload prediction with downstream scheduling optimization. By leveraging differentiable convex optimization, the PTS framework maps input features directly to optimal scheduling and enables gradient-based training. Furthermore, to respect the data center's capacity, a workload over-shifted loss combining electricity cost with a penalty for load-shedding is introduced to evaluate scheduling quality. Experiments demonstrate that the proposed framework significantly reduces operational cost and enhances system security compared to the conventional two-stage baseline.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Siqi Yan, Jiebao Zhang, Xi Yao, 等. A Predict-then-Schedule framework for Power Distribution Networks with AI Data Centers[J/OL]. (2026-07-20)[2026-08-09]. http://arxiv.org/abs/2607.17514v1.
technotrans – Cooling Solutions for Datacenters & E-Mobility | Investor P…
mwb research AG · Query: data center liquid cooling conference presentation。Useful as technical or research context.
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Why Amazon Built aPrivate Power Grid
Iberius Polonius · Query: AI datacenter power grid university lecture。Useful as technical or research context.
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Green and Sustainable Data Centers
NPTEL-NOC IITM · Query: IEEE data center energy efficiency lecture。Useful as technical or research context.
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IEEE 2017:Optimizing Green Energy, Cost, and Availability in Distributed …
Java First IEEE Final Year Projects · Query: IEEE data center energy efficiency lecture。Useful as technical or research context.
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电力并网与能源约束
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智算中心 CapEx/扩建
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心 CapEx/扩建
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AI 芯片供给与交付
Same-source item from the Chinese report. Verify details against the original linked source: AI 芯片供给与交付
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Industry
Industry news, products, policy, financing, projects, and market-oriented videos.
电力与能源约束观察：Data Center Dynamics 发布相关报道（原文标题：Bridge DC launches prefabricat…
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智算中心/数据中心建设进展：Data Center Dynamics 发布相关报道，涉及 $733 million（原文标题：NTT Data r…
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心/数据中心建设进展：Data Center Dynamics 发布相…
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 $400（原文标题：Former employees sue Pr…
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 60MW（原文标题：TCC Concept Limited's d…
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：Data Center Dynamics 发布相关报道，涉…
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AI 算力基础设施动态：Data Center Dynamics 发布相关报道，涉及 1GW（原文标题：Indosat reinforces AI…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：Data Center Dynamics 发布相关报…
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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：SMR firm Nuclea inks MoU with N…
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液冷与热管理进展：ServeTheHome 发布相关报道，涉及 150kW（原文标题：Delta’s GoCool-150 Goes …
Same-source item from the Chinese report. Verify details against the original linked source: 液冷与热管理进展：ServeTheHome 发布相关报道，涉及 150kW（…
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电力与能源约束观察：ServeTheHome 发布相关报道（原文标题：AMD Helios Architecture Deep Dive: The…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：ServeTheHome 发布相关报道（原文标题：AMD…
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技术与产品进展：Data Center Knowledge 发布相关报道（原文标题：Do Data Centers Really Drive Up…
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技术与产品进展：Data Center Knowledge 发布相关报道（原文标题：Samsung Takes Floating Data Cen…
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政策、标准或能效观察：Data Center Knowledge 发布相关报道（原文标题：Building Data Center Infrast…
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电力与能源约束观察：Data Center Knowledge 发布相关报道（原文标题：Texas Orders Statewide Audit …
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ASHRAE ITALY - LIQUID COOLING AND CHALLANGES IN IMPLEMENTATION
ASHRAE Italy · Query: ASHRAE data center cooling webinar。Useful for product, market, or deployment context.
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Cooling Strategies for Data Center Design and Energy Efficiency with CFD …
SimScale · Query: ASHRAE data center cooling webinar。Useful for product, market, or deployment context.
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KEPUNI Automatic Welding Machine for Stainless Steel Data Center Liquid C…
KEPUNI Merrick · Query: OCP data center cooling workshop。Useful for product, market, or deployment context.
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Kv vs Cv: Convert Valve Flow Coefficients in Seconds | AI Data Center Liq…
immersekool · Query: OCP data center cooling workshop。Useful for product, market, or deployment context.
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产业热度指数 10/10
Same-source item from the Chinese report. Verify details against the original linked source: 产业热度指数 10/10
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4. Video signals
technotrans – Cooling Solutions for Datacenters & E-Mobility | Investor Presentation 2026
学术会议报告 · mwb research AG · Query: data center liquid cooling conference presentation
Open on YouTubeWhy Amazon Built aPrivate Power Grid
专家讲座 · Iberius Polonius · Query: AI datacenter power grid university lecture
Open on YouTubeASHRAE ITALY - LIQUID COOLING AND CHALLANGES IN IMPLEMENTATION
标准组织讲座 · ASHRAE Italy · Query: ASHRAE data center cooling webinar
Open on YouTubeCooling Strategies for Data Center Design and Energy Efficiency with CFD (ASHRAE 90.4)
标准组织讲座 · SimScale · Query: ASHRAE data center cooling webinar
Open on YouTubeGreen and Sustainable Data Centers
学术讲座 · NPTEL-NOC IITM · Query: IEEE data center energy efficiency lecture
Open on YouTubeIEEE 2017:Optimizing Green Energy, Cost, and Availability in Distributed Data Centers
学术讲座 · Java First IEEE Final Year Projects · Query: IEEE data center energy efficiency lecture
Open on YouTubeKEPUNI Automatic Welding Machine for Stainless Steel Data Center Liquid Cooling Pipelines
行业论坛 · KEPUNI Merrick · Query: OCP data center cooling workshop
Open on YouTubeKv vs Cv: Convert Valve Flow Coefficients in Seconds | AI Data Center Liquid Cooling #aidc #dlc #d2c
行业论坛 · immersekool · Query: OCP data center cooling workshop
Open on YouTubeSources
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