液冷与智算中心日报｜2026-08-29
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，电力并网与能源约束、AI 芯片供给与交付、智算中心 CapEx/扩建仍是主线，但基础设施约束已前置，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。
Quantifying AI data center nitrogen oxide (NO$_x$) emissions from space
AI data center power demand is spurring rapid deployment of on- and near-site natural gas turbines. Nitrogen oxide (NO$_x$) polluti…
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Quantifying AI data center nitrogen oxide (NO$_x$) emissions from space
- 发布时间
- 2026-08-23
- 作者
- Kevin D. Gauld、Daniel J. Varon、Nicholas Balasus、Daniel H. Cusworth
- 主题
- AI 运维优化
摘要
AI data center power demand is spurring rapid deployment of on- and near-site natural gas turbines. Nitrogen oxide (NO$_x$) pollution from this equipment is a growing concern but has not previously been quantified with atmospheric observations. Here we demonstrate space-based detection and quantification of NO$_x$ emissions from the SpaceXAI Colossus 2 power plant in Southaven, Mississippi. Using observations from the geostationary TEMPO satellite instrument, we detect a strong increase in local mean NO$_2$ column concentrations after the plant began operations in late 2025. We then use TEMPO to estimate two-week-average NO$_x$ source rates from August 2025 to mid-August 2026, calibrating against continuous emission monitoring system (CEMS) data from US power plants. TEMPO first detected NO$_x$ emissions in December 2025 at 460$\pm$180 kg h$^{-1}$. We find that emissions increased through August 2026, averaging 730$\pm$185 kg h$^{-1}$ after February 2026, roughly 16 times higher than expected from the facility's March 2026 permit for 41 turbines operating under best available control technology (BACT) requirements ($\sim$47 kg h$^{-1}$). Emissions at the expected level would be undetectable by our TEMPO analysis.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，AI 运维、负载预测和设施调优正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用仿真建模和情景分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断AI 工具是否能降低运维复杂度并提升可用性。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Kevin D. Gauld, Daniel J. Varon, Nicholas Balasus, 等. Quantifying AI data center nitrogen oxide (NO$_x$) emissions from space[J/OL]. (2026-08-23)[2026-08-29]. http://arxiv.org/abs/2608.22153v1.
Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes
Artificial-intelligence data centers running bulk-synchronous training can impose sub-second power swings. When several facilities …
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Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes
- 发布时间
- 2026-08-24
- 作者
- Chandan Chaudhary、Abanish Tiwari、Yansong Pei、Mohammed Ben-Idris、Joydeep Mitra
- 主题
- 算电协同
摘要
Artificial-intelligence data centers running bulk-synchronous training can impose sub-second power swings. When several facilities synchronize their training cycles, these load variations become spatially correlated and amplify the aggregate disturbance on the grid. A grid operator without access to data-center telemetry must infer this correlation from electrical measurements alone. However, the required observation time and the feasibility of detection on substation-deployable hardware remain uncharacterized. This paper develops a correlation-based detection method to classify the multi-facility operating regime from cross-facility power measurements. Analytical derivations and experimental validation show that the resulting detection confidence increases with the observation-window length at a rate governed by the load correlation time. The method is demonstrated in a real-time hardware-in-the-loop testbed, where load setpoints generated from a validated semi-Markov data-center load model are applied to an electromagnetic-transient grid simulation on a Real-Time Digital Simulator. A compact classifier built on pairwise power correlations runs on an edge device in this loop and determines whether the data-center load variations are independent or spatially correlated. The cross-facility correlation separates the independent and correlated cases across independent realizations. The held-out detection accuracy improves with the observation window, consistent with the predicted relation. A raw-waveform network fails to generalize, supporting pairwise correlation as the discriminative signal. The detector executes in real time on commodity edge hardware. A closed-loop demonstration against the running simulator tracks a regime change within one observation window.
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
Chandan Chaudhary, Abanish Tiwari, Yansong Pei, 等. Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes[J/OL]. (2026-08-24)[2026-08-29]. http://arxiv.org/abs/2608.22719v1.
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)[2026-08-29]. http://arxiv.org/abs/2608.16432v1.
A Configurable Thermal-Dynamic Model for AI Data Center Cooling Load Simu…
Cooling
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A Configurable Thermal-Dynamic Model for AI Data Center Cooling Load Simulation
- 发布时间
- 2026-07-30
- 作者
- Cletus Ngwerume、Lang Tong、Chee-Wooi Ten、Yi Hu
- 主题
- 热管理与液冷
摘要
Cooling
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：摘要显示作者采用仿真建模和情景分析，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
Cletus Ngwerume, Lang Tong, Chee-Wooi Ten, 等. A Configurable Thermal-Dynamic Model for AI Data Center Cooling Load Simulation[J/OL]. (2026-07-30)[2026-08-29]. https://arxiv.org/abs/2607.28962.
Beyond the Grid: Cost, Carbon, and Capital Requirements of On-Site Power …
Interconnection queues, not electricity prices, now govern where data centers can be built, and the standard levelized-cost compari…
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Beyond the Grid: Cost, Carbon, and Capital Requirements of On-Site Power Technologies for AI Data Centers
- 发布时间
- 2026-08-08
- 作者
- Eliseo Curcio
- 主题
- 算电协同
摘要
Interconnection queues, not electricity prices, now govern where data centers can be built, and the standard levelized-cost comparison answers a question no developer faces: it assumes a load profile, freezes the grid price while modeling the demand that moves it, and quotes busbar costs a facility cannot buy. This paper evaluates nine on-site supply technologies against a delivered grid whose price is endogenous to projected data-center demand, on a complete-site basis that retains standby charges, with measured GPU training load, delivered fuel prices, production-pathway carbon, and statutory 45V and 48E incentive mechanics. Nothing beats the wire: gas combined cycle produces at 47 USD/MWh but costs about 114 USD per megawatt-hour of complete site energy against a 92 USD grid; four-hour storage is physically capped near 18 percent of annual energy and, charged at the margin, dirtier than the grid; hydrogen from grid-priced power fails on cost and carbon together. An investment inversion converts these findings into capital terms: conversion-hardware learning buys nothing, because free hardware still exceeds the grid for every low-carbon arm, while global electrolyser deployment on sited sub-20 USD/MWh power brings PEM hydrogen power to about 2.2 times the grid at 300 billion USD and 1.9 times at 1 trillion USD (2.7 and 2.3 for the hydrogen engine), with a carbon reduction of roughly 85 percent (6.8-fold) against grid-power production. Grid parity is not purchasable at any budget. On-site supply is an access and depth product; most current investment targets the wrong term.
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：摘要显示作者采用仿真建模和情景分析，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
Eliseo Curcio. Beyond the Grid: Cost, Carbon, and Capital Requirements of On-Site Power Technologies for AI Data Centers[J/OL]. (2026-08-08)[2026-08-29]. http://arxiv.org/abs/2608.08170v1.
A Theory of Probabilistic Power Provisioning for Data Centers with Distri…
The growing power demands and variability of AI
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A Theory of Probabilistic Power Provisioning for Data Centers with Distributed Energy Storage
- 发布时间
- 2026-08-13
- 作者
- Can Emre Koksal、Richard A. Barry、Artun Sel
- 主题
- 热管理与液冷
摘要
The growing power demands and variability of AI
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
Can Emre Koksal, Richard A. Barry, Artun Sel. A Theory of Probabilistic Power Provisioning for Data Centers with Distributed Energy Storage[J/OL]. (2026-08-13)[2026-08-29]. https://arxiv.org/abs/2608.12993.
Techno-Economic Boundary Analysis of Small Modular Reactor Cogeneration f…
Hyperscale data centers are adding firm, high-utilization demand faster than grids can serve it, renewing interest in colocating th…
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Techno-Economic Boundary Analysis of Small Modular Reactor Cogeneration for Hyperscale Data Center IT and Cooling Loads
- 发布时间
- 2026-08-11
- 作者
- Honglin Li、Buxin She、Jie Zhang
- 主题
- 算电协同
摘要
Hyperscale data centers are adding firm, high-utilization demand faster than grids can serve it, renewing interest in colocating them with small modular reactors. Such a plant could earn revenue in two ways, selling low-carbon power and diverting steam to absorption chillers that serve a cooling load accounting for 20-40% of facility electricity use, but neither revenue stream has been priced across the conditions that must coincide. Here we co-optimize reactor dispatch, steam extraction, absorption cooling and grid exchange hourly for a 200 MW$_\mathrm{e}$ data center in the Electric Reliability Council of Texas (ERCOT) region, across 109 runs spanning capital, market, policy, financing and cooling efficiency. At 2023 mid-range reactor capital, the nuclear configurations cost 49-62% more than grid supply even with the Section 45Y production tax credit. The viable region opens near \$5,000 kW$_\mathrm{e}^{-1}$, and nth-of-a-kind capital makes them 77-89% cheaper in 2023, though between parity and 34% more expensive in the low-price 2024 market. A carbon price of \$53-64 tCO$_2^{-1}$ closes the mid-range gap under hourly export crediting. Absorption cooling is dispatched in response to hourly electricity prices and supplies 38% of annual cooling, at an added cost of \$9.2 million yr$^{-1}$ relative to the reactor-only plant; that gap closes at an installed absorption cost of \$60 kW$_\mathrm{c}^{-1}$ at baseline efficiency and \$570 kW$_\mathrm{c}^{-1}$ on a legacy-efficiency campus, against surveyed commercial prices of \$450-1,200 kW$_\mathrm{c}^{-1}$. Together these results delineate the capital, market and policy conditions under which colocated reactor cogeneration is competitive with grid procurement, and the range over which each condition moves the outcome.
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：摘要显示作者采用综述归纳和指标比较，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
Honglin Li, Buxin She, Jie Zhang. Techno-Economic Boundary Analysis of Small Modular Reactor Cogeneration for Hyperscale Data Center IT and Cooling Loads[J/OL]. (2026-08-11)[2026-08-29]. http://arxiv.org/abs/2608.10999v1.
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)[2026-08-29]. http://arxiv.org/abs/2608.19622v1.
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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Realizing Asymmetric Datarates via Energy Efficient Ethernet (EEE)
IEEE Standards Association · 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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SPACE: Semi-Partitioned CachE for Energy Efficient, Hard Real-Time Systems
IEEEComputerSociety · 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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Webinar - Data Center Thermal Management with Digital Twin
CYBERNET MALAYSIA · 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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The Cooling-Water Loop Saving 138 Million Gallons #engineering #powergrid…
Built Unseen · 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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Above the Cloud: Building Data Centers in Space - Richard Campbell - NDC …
NDC Conferences · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
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AI data centers are booming across America, raising concerns over energy …
Your Daily Note · 检索词：ACM SIGEnergy data center energy talk。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 7 条，热度分 21。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 1 条，热度分 3。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 1 条，热度分 3。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。
2024 ASHRAE Webinar: Adiabatic Solutions for Data Centers
Condair USA/CA · 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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产业热度指数 6/10
产业热度指数为 6/10：本期自动化检索记录到 8 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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NVIDIA Blackwell/GB200/GB300
昨日热度高，今日暂无新增高可信条目
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AI 芯片供给与交付
今日延续上榜
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智算中心 CapEx/扩建
今日延续上榜
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4. 最新视频观察
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Realizing Asymmetric Datarates via Energy Efficient Ethernet (EEE)
学术讲座 · IEEE Standards Association · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开SPACE: Semi-Partitioned CachE for Energy Efficient, Hard Real-Time Systems
学术讲座 · IEEEComputerSociety · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开Webinar - Data Center Thermal Management with Digital Twin
专家讲座 · CYBERNET MALAYSIA · 检索词：data center thermal management seminar
在 YouTube 打开The Cooling-Water Loop Saving 138 Million Gallons #engineering #powergrid #energydistribution
学术会议报告 · Built Unseen · 检索词：data center liquid cooling conference presentation
在 YouTube 打开2024 ASHRAE Webinar: Adiabatic Solutions for Data Centers
标准组织讲座 · Condair USA/CA · 检索词：ASHRAE data center cooling webinar
在 YouTube 打开Above the Cloud: Building Data Centers in Space - Richard Campbell - NDC Copenhagen 2026
学术会议报告 · NDC Conferences · 检索词：AI data center energy conference keynote
在 YouTube 打开AI data centers are booming across America, raising concerns over energy water and local communities
学术讲座 · Your Daily Note · 检索词：ACM SIGEnergy data center energy talk
在 YouTube 打开来源链接区
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- 论文池：已从本地论文池读取 20 条候选；池更新时间 2026-08-29 00:52。
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- 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。
- 视频推荐：当日未形成新候选，按上一日排序池顺延补位。
- 可选自动化增强服务不可用时，本页仅使用可追溯的公开来源与保守的规则化摘要，不补写无法核验的事实。