液冷与智算中心日报｜2026-08-24
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。
Real-Time Control of Sustainable Data Centers: A Two-Layer Model Predicti…
This paper proposes a two-layer model predictive control (MPC) framework for the real-time operation of data centers integrated wit…
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Real-Time Control of Sustainable Data Centers: A Two-Layer Model Predictive Control Framework with Workload Flexibility and Heat Recovery
- 发布时间
- 2026-08-17
- 作者
- Wenyu Liu、Enea Figini、Mario Paolone
- 主题
- 余热回收
摘要
This paper proposes a two-layer model predictive control (MPC) framework for the real-time operation of data centers integrated with on-site photovoltaic generation, battery energy storage, waste heat recovery, and district heating. The upper layer employs scenario-based stochastic optimization to jointly optimize intraday market participation, workload scheduling, and energy management under uncertainty. The lower layer adopts an adaptive tube-based MPC strategy that compensates short-term disturbances while tracking the dispatch references given by the upper layer. The framework further integrates multi-horizon forecasting to support real-time decision making. Microservice-based simulation studies under representative clear-sky and overcast operating conditions demonstrate that the proposed framework accurately tracks dispatch plans despite fast photovoltaic and workload fluctuations. Compared with single-layer control strategies, the adaptive lower-layer controller substantially reduces real-time dispatch deviations and the associated imbalance costs. In addition, the proposed framework naturally adapts to seasonal operating conditions and responds to carbon-aware operating signals, offering a practical approach for economically efficient, sustainable, and grid-supportive operation of future data centers.
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，余热回收、热泵耦合和二次能源利用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，它可用于判断数据中心余热能否从成本项转化为能源资产。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
Wenyu Liu, Enea Figini, Mario Paolone. Real-Time Control of Sustainable Data Centers: A Two-Layer Model Predictive Control Framework with Workload Flexibility and Heat Recovery[J/OL]. (2026-08-17)[2026-08-24]. http://arxiv.org/abs/2608.16432v1.
Shift or curtail? How much data-center flexibility is worth depends on th…
Data-center growth risks overbuilding power grid infrastructure and stranding capital. Flexible data-center operation can defer inf…
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Shift or curtail? How much data-center flexibility is worth depends on the host power grid
- 发布时间
- 2026-08-20
- 作者
- Saroj Khanal、Geon Roh、Boyu Yao、Abraham Silverman、Dennice Gayme、Charalambos Konstantinou、Jip Kim、Yury Dvorkin
- 主题
- 算电协同
摘要
Data-center growth risks overbuilding power grid infrastructure and stranding capital. Flexible data-center operation can defer infrastructure investments, but its value depends on the flexibility mechanism and the host power grid characteristics. We classify data-center load as firm, flexible or interruptible, and embed them in capacity expansion applied to market-organized, fossil-heavy PJM and carbon-capped, centrally coordinated Korea. In PJM, the flexibility value is spatial: shifting workloads between zones reduces system cost by 6% in 2028 and 19% in 2038, avoiding 4.4 GW and 8.9 GW of gas and nuclear generation. In Korea, it is temporal: shifting load into midday solar hours makes 0.5 GW of additional solar worth building in 2028 and avoids 1.2 GW of gas and 0.3 GW of batteries in 2038. In both, realistic event-shape limits diminish the value of curtailment. The results show that flexibility procurement and its value are driven by grid characteristics and policy objectives.
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
Saroj Khanal, Geon Roh, Boyu Yao, 等. Shift or curtail? How much data-center flexibility is worth depends on the host power grid[J/OL]. (2026-08-20)[2026-08-24]. http://arxiv.org/abs/2608.19622v1.
LLM-Powered Predictive Decision-Making for Sustainable Data Center Operat…
The growing demand for AI-driven workloads, particularly from Large Language Models (LLMs), has raised concerns about the significa…
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LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations
- 发布时间
- 2026-08-19
- 作者
- Hanzhao Wang、Jingxuan Wu、Yumeng Li、Yu Pan、Guanting Chen
- 主题
- 芯片与算力
摘要
The growing demand for AI-driven workloads, particularly from Large Language Models (LLMs), has raised concerns about the significant energy and resource consumption in data centers. This work introduces a novel LLM-based predictive scheduling system designed to enhance operational efficiency while reducing the environmental impact of data centers. Our system utilizes an LLM to predict key metrics such as execution time and energy consumption from source code, and it has the potential to extend to other sustainability-focused metrics like water usage for cooling and carbon emissions, provided the data center can track such data. The predictive model is followed by a real-time scheduling algorithm that allocates GPU resources, aiming to improve sustainability by optimizing both energy consumption and queuing delays. With fast inference times, the ability to generalize across diverse task types, and minimal data requirements for training, our approach offers a practical solution for data center scheduling. This framework demonstrates strong potential for advancing sustainability objectives in AI-driven infrastructure. Through our collaboration with a data center, we achieved a 32% reduction in energy consumption and a 30% decrease in waiting time.
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，芯片、服务器和高密度算力部署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：研究重点指向能效评价口径、运营指标和优化目标的系统化梳理。意义：对日报读者而言，它可用于判断芯片路线和服务器密度变化如何传导到机房设计。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
Hanzhao Wang, Jingxuan Wu, Yumeng Li, 等. LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations[J/OL]. (2026-08-19)[2026-08-24]. http://arxiv.org/abs/2608.18503v1.
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)[2026-08-24]. http://arxiv.org/abs/2607.26725v1.
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)[2026-08-24]. http://arxiv.org/abs/2607.28962v1.
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)[2026-08-24]. http://arxiv.org/abs/2607.27575v1.
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)[2026-08-24]. http://arxiv.org/abs/2608.03989v1.
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)[2026-08-24]. http://arxiv.org/abs/2607.26560v1.
How Data Centers Manage Intense Heat: Cooling Systems Explained
Equinix · 检索词：data center thermal management seminar。适合作为技术背景或研究趋势补充。
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Vertiv Investor Conference 2026 | Data Center Liquid Cooling Production S…
i101 · 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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Energy Efficiency of Data Centers
Institute for Systems Research · 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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Data Centric Evolving Power Grid recording
Engineering Institute of Technology · 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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"High Capacity, Energy Efficient Interconnects for Data Centers" - John B…
The Institute for Energy Efficiency · 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 13 条，热度分 28。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 3 条，热度分 7。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：Lithium-ion batteries are resha…
发布时间：2026-08-23；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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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Ignis and Acciona look to build…
发布时间：2026-08-22；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（原文标题：Building data centers is gettin…
发布时间：2026-08-22；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 300MW、1GW（原文标题：Terronova sta…
发布时间：2026-08-22；近 7 天补充观察，非 24 小时窗口内；可核验指标：300MW、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，涉及 160MW（原文标题：Vapat Enters the data …
发布时间：2026-08-22；近 7 天补充观察，非 24 小时窗口内；可核验指标：160MW；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（原文标题：Nvidia backs data center pow…
发布时间：2026-08-22；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（原文标题：Edge data center for AI workloa…
发布时间：2026-08-21；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（原文标题：Rocket Lab has no plans to ente…
发布时间：2026-08-21；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：The Register 发布相关报道（原文标题：Cloverleaf deal is latest example of N…
发布时间：2026-08-22；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：Data Center Hardware Highligh…
发布时间：2026-08-18；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：Data Center Dynamics 发布相关报道（原文标题：Sponsored: What coolant should …
发布时间：2026-08-22；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 Dynamics 发布相关报道（原文标题：Sponsored: Powering data cente…
发布时间：2026-08-22；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：PJM Strategy Targets Data Cen…
发布时间：2026-08-21；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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财报与资本开支观察：Data Center Knowledge 发布相关报道，涉及 $3（原文标题：AI Infrastructure Pushe…
发布时间：2026-08-21；近 7 天补充观察，非 24 小时窗口内；可核验指标：$3；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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Enabling 1MW Data Center Racks through Innovations in Power and Liquid Co…
Open Compute Project · 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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OCP Datacenter Engineering Workshop @ DCD Colo & Cloud, September 25th 20…
Open Compute Project · 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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OCPSummit19 - EW: Advanced Cooling - Eco-system Enabling of Liquid Coolin…
Open Compute Project · 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
How Data Centers Manage Intense Heat: Cooling Systems Explained
专家讲座 · Equinix · 检索词：data center thermal management seminar
在 YouTube 打开Vertiv Investor Conference 2026 | Data Center Liquid Cooling Production Scales For AI Systems
学术会议报告 · i101 · 检索词：data center liquid cooling conference presentation
在 YouTube 打开Energy Efficiency of Data Centers
学术讲座 · Institute for Systems Research · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开Data Centric Evolving Power Grid recording
专家讲座 · Engineering Institute of Technology · 检索词：AI datacenter power grid university lecture
在 YouTube 打开Enabling 1MW Data Center Racks through Innovations in Power and Liquid Cooling
行业论坛 · Open Compute Project · 检索词：OCP data center cooling workshop
在 YouTube 打开"High Capacity, Energy Efficient Interconnects for Data Centers" - John Bowers
学术讲座 · The Institute for Energy Efficiency · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开OCP Datacenter Engineering Workshop @ DCD Colo & Cloud, September 25th 2017, Dallas TX
行业论坛 · Open Compute Project · 检索词：OCP data center cooling workshop
在 YouTube 打开OCPSummit19 - EW: Advanced Cooling - Eco-system Enabling of Liquid Cooling Ingredients
行业论坛 · Open Compute Project · 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：ServeTheHome：未检索到符合条件的高相关条目。
- 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 20 条候选；池更新时间 2026-08-24 12:04。
- 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…