液冷与智算中心日报｜2026-08-09
追踪液冷技术、AI 智算中心、数据中心能效、学术论文、产品发布、政策标准、投融资与供应链动态的每日中文报告。
1. 今日一句话总结
24小时内，资本继续加码智算中心，但电力、审批与能效约束已前置，液冷和算电协同正转为项目准入项。
从公开信号看，资本并未因为约束而降温，资本开支仍向AI数据中心与液冷环节集中，说明头部厂商和基础设施资本仍在前置锁定园区、容量和交付窗口；但与此同时，扩建继续推进，但电力、选址审批与能源获取仍是主约束，意味着行业竞争的关键变量已不再只是“拿到多少 GPU”，而是“能否把 GPU 放进一个可并网、可散热、可控成本、可持续运行的系统”。技术侧技术侧继续围绕高带宽互连与服务器能效优化，论文侧论文侧继续指向算电协同、液冷优化与能效度量重构，共同指向同一个趋势：单点器件优化的边际价值在下降，网络、供电、储能、液冷和调度软件的系统级协同正在上升为真正的产能约束。对产业链而言，未来更稀缺的不是单一硬件，而是把算力、热管理和能源调度耦合起来的工程交付能力。
学术与产业速览
将论文、视频、产业动态和政策项压缩为可快速扫描的标签；每个标签只保留题目、摘要和来源入口。
学术
论文、研究趋势、学术视频与方法论线索。
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
- 发布时间
- 2026-08-04
- 作者
- Arkadev Roy、Daniel Klawson、Jianheng Luo、Yiyang Zhi、Sirui Tang、Ming Wu
- 主题
- 热管理与液冷
摘要
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.
中文解读
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。
参考文献
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
- 发布时间
- 2026-08-05
- 作者
- Rouzbeh Haghighi、Ali Hassan、Sina Mohammadi、Marcus Chen I Wada、Wencong Su
- 主题
- 算电协同
摘要
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.
中文解读
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。
参考文献
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
- 发布时间
- 2026-07-29
- 作者
- 暂无可靠最新数据
- 主题
- 热管理与液冷
摘要
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.
中文解读
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。
参考文献
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
- 发布时间
- 2026-07-30
- 作者
- Ahmed Alfatlawi、Masoud H. Nazari
- 主题
- 算电协同
摘要
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.
中文解读
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。
参考文献
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
- 发布时间
- 2026-07-31
- 作者
- Cletus Ngwerume、Lang Tong、Chee-Wooi Ten、Yi Hu
- 主题
- 算电协同
摘要
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.
中文解读
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。
参考文献
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
- 发布时间
- 2026-07-29
- 作者
- Feiyu Cai、Jing Qiu、Yi Yang、Chenxi Zhang、Xinlei Wang、Baichuan Liu、Junhua Zhao
- 主题
- 算电协同
摘要
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.
中文解读
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。
参考文献
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
- 发布时间
- 2026-07-29
- 作者
- Qi He、Chunyu Qu、Wenjie Zuo
- 主题
- 算电协同
摘要
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.
中文解读
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。
参考文献
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
- 发布时间
- 2026-07-20
- 作者
- Siqi Yan、Jiebao Zhang、Xi Yao、Juan Huang、Ye Shi
- 主题
- 算电协同
摘要
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.
中文解读
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。
参考文献
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 · 检索词：data center liquid cooling conference presentation。适合作为技术背景或研究趋势补充。
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Why Amazon Built aPrivate Power Grid
Iberius Polonius · 检索词：AI datacenter power grid university lecture。适合作为技术背景或研究趋势补充。
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Green and Sustainable Data Centers
NPTEL-NOC IITM · 检索词：IEEE data center energy efficiency lecture。适合作为技术背景或研究趋势补充。
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IEEE 2017:Optimizing Green Energy, Cost, and Availability in Distributed …
Java First IEEE Final Year Projects · 检索词：IEEE data center energy efficiency lecture。适合作为技术背景或研究趋势补充。
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电力并网与能源约束
本期命中 13 条，热度分 32。可作为论文检索、技术路线和后续研究跟踪关键词。
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智算中心 CapEx/扩建
本期命中 11 条，热度分 25。可作为论文检索、技术路线和后续研究跟踪关键词。
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AI 芯片供给与交付
本期命中 2 条，热度分 5。可作为论文检索、技术路线和后续研究跟踪关键词。
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产业
产业新闻、技术产品、政策标准、投融资、项目和产业视频。
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Sponsored: How high-performance…
发布时间：2026-08-08；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-08；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-08；近 7 天补充观察，非 24 小时窗口内；可核验指标：$733 million；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-08；近 7 天补充观察，非 24 小时窗口内；可核验指标：$400；细节以来源原文为准，本页不复述未核验扩展信息
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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：Sponsored: Efficient data cente…
发布时间：2026-08-07；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-07；近 7 天补充观察，非 24 小时窗口内；可核验指标：60MW；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-07；近 7 天补充观察，非 24 小时窗口内；可核验指标：1GW；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-07；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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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 …
发布时间：2026-08-08；检索窗口内；可核验指标：150kW；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-04；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-07；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-04；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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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…
发布时间：2026-08-04；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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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 …
发布时间：2026-08-04；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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ASHRAE ITALY - LIQUID COOLING AND CHALLANGES IN IMPLEMENTATION
ASHRAE Italy · 检索词：ASHRAE data center cooling webinar。用于补充产业、产品或工程部署观察。
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Cooling Strategies for Data Center Design and Energy Efficiency with CFD …
SimScale · 检索词：ASHRAE data center cooling webinar。用于补充产业、产品或工程部署观察。
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KEPUNI Automatic Welding Machine for Stainless Steel Data Center Liquid C…
KEPUNI Merrick · 检索词：OCP data center cooling workshop。用于补充产业、产品或工程部署观察。
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Kv vs Cv: Convert Valve Flow Coefficients in Seconds | AI Data Center Liq…
immersekool · 检索词：OCP data center cooling workshop。用于补充产业、产品或工程部署观察。
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产业热度指数 10/10
产业热度指数为 10/10：本期自动化检索记录到 22 条候选条目，指数按候选条目数量、来源可信度和栏目覆盖度保守计算。
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4. 最新视频观察
technotrans – Cooling Solutions for Datacenters & E-Mobility | Investor Presentation 2026
学术会议报告 · mwb research AG · 检索词：data center liquid cooling conference presentation
在 YouTube 打开Why Amazon Built aPrivate Power Grid
专家讲座 · Iberius Polonius · 检索词：AI datacenter power grid university lecture
在 YouTube 打开ASHRAE ITALY - LIQUID COOLING AND CHALLANGES IN IMPLEMENTATION
标准组织讲座 · ASHRAE Italy · 检索词：ASHRAE data center cooling webinar
在 YouTube 打开Cooling Strategies for Data Center Design and Energy Efficiency with CFD (ASHRAE 90.4)
标准组织讲座 · SimScale · 检索词：ASHRAE data center cooling webinar
在 YouTube 打开Green and Sustainable Data Centers
学术讲座 · NPTEL-NOC IITM · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开IEEE 2017:Optimizing Green Energy, Cost, and Availability in Distributed Data Centers
学术讲座 · Java First IEEE Final Year Projects · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开KEPUNI Automatic Welding Machine for Stainless Steel Data Center Liquid Cooling Pipelines
行业论坛 · KEPUNI Merrick · 检索词：OCP data center cooling workshop
在 YouTube 打开Kv vs Cv: Convert Valve Flow Coefficients in Seconds | AI Data Center Liquid Cooling #aidc #dlc #d2c
行业论坛 · immersekool · 检索词：OCP data center cooling workshop
在 YouTube 打开来源链接区
本次检索说明
- 当前自动化环境未配置 Tavily、Bing News 或 SerpAPI 检索密钥；脚本将使用公开 RSS/Atom、公共 arXiv 接口与固定监测源，不会编造产业新闻。
- 公开 RSS/Atom：The Register：检索失败，原因：HTTP 403
- 公开 RSS/Atom：NVIDIA Blog：未检索到符合条件的高相关条目。
- 论文池：已从本地论文池读取 13 条候选；池更新时间 2026-08-09 10:33。
- 论文推荐：已启用 latest 模式，优先输出本期候选池中发布时间最新的论文。
- x.ai 论文解读：文本生成失败，已回退到规则化论文摘要；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 1 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 2 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 3 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 4 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 5 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 6 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 7 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 8 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- AI 分析：x.ai 调用失败，已回退到规则化模板；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit…