Liquid Cooling and AI Data Center Daily | 2026-08-28
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 2026-08-28.
- Collection window: 2026-08-27 08:00 北京时间 - 2026-08-28 08:00 北京时间.
- Coverage snapshot: 8 industry items; 3 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, PUE/WUE 与能效优化.
- 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.
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
- Published
- 2026-08-24
- Authors
- Chandan Chaudhary, Abanish Tiwari, Yansong Pei, Mohammed Ben-Idris, Joydeep Mitra
- Theme
- 算电协同
Abstract
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.
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
Chandan Chaudhary, Abanish Tiwari, Yansong Pei, 等. Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes[J/OL]. (2026-08-24)[2026-08-28]. http://arxiv.org/abs/2608.22719v1.
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
- Published
- 2026-08-23
- Authors
- Kevin D. Gauld, Daniel J. Varon, Nicholas Balasus, Daniel H. Cusworth
- Theme
- AI 运维优化
Abstract
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}$). Emissions at the expected level would be undetectable by our TEMPO analysis.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，AI 运维、负载预测和设施调优正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用仿真建模和情景分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断AI 工具是否能降低运维复杂度并提升可用性。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Kevin D. Gauld, Daniel J. Varon, Nicholas Balasus, 等. Quantifying AI data center nitrogen oxide (NO$_x$) emissions from space[J/OL]. (2026-08-23)[2026-08-28]. http://arxiv.org/abs/2608.22153v1.
Generalizing Thermal Transport in High-Contrast Metamaterials through Int…
The rapid growth of generative AI has intensified the need for efficient heat dissipation in large-scale data centers. To control h…
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Generalizing Thermal Transport in High-Contrast Metamaterials through Interfacial Fresnel Reflection
- Published
- 2026-08-26
- Authors
- Seung Hyeon Ham, Yu Min Kim, In Hyeok Choi, Jeong Woo Han
- Theme
- 热管理与液冷
Abstract
The rapid growth of generative AI has intensified the need for efficient heat dissipation in large-scale data centers. To control heat flow, thermal metamaterials with layered structures have been widely used, which impart the anisotropic properties of thermal conductivities. However, the conventional effective medium approximation (EMA) often fails to provide accurate predictions in systems with a high thermal conductivity contrast between adjacent layers embedded in a background medium. Here, we generalize the EMA by introducing two corrective coefficients that extend its validity to regimes where the conventional EMA was previously inapplicable, i.e., high-contrast thermal metamaterials with the background medium. Notably, one of these coefficients that we proposed has the same mathematical form as the Fresnel reflection coefficient in optics. This allows us to interpret the "reflection-like" behavior of heat flow as it penetrates adjacent layers with high thermal contrast. Our findings suggest that heat diffusion, traditionally viewed as a purely dissipative process, can be understood intuitively through the framework of ray optics.
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
Seung Hyeon Ham, Yu Min Kim, In Hyeok Choi, 等. Generalizing Thermal Transport in High-Contrast Metamaterials through Interfacial Fresnel Reflection[J/OL]. (2026-08-26)[2026-08-28]. http://arxiv.org/abs/2608.25499v1.
Steady-State Equivalent Circuit Model for Data Center Loads
Planners currently represent data centers as aggregate constant-PQ or ZIP loads in steady-state interconnection and contingency stu…
Expand
Steady-State Equivalent Circuit Model for Data Center Loads
- Published
- 2026-08-18
- Authors
- Muhammad Hamza Ali, Peng Sang, Hyeon Woo, Hyein Kang, Sungyun Choi, Amritanshu Pandey
- Theme
- 算电协同
Abstract
Planners currently represent data centers as aggregate constant-PQ or ZIP loads in steady-state interconnection and contingency studies. These aggregate models are computationally convenient. However, they obscure the electrical relationship between computational workloads, server utilization, and grid-side demand. They ignore the internal power-electronic conversion stages of IT loads and assume homogeneous workload distributions across the compute clusters. This hides operating-point-dependent converter losses and efficiency variations. We propose a steady-state equivalent-circuit model (ECM) for data centers, which explicitly builds circuit models for IT loads, power supply units, cooling, and auxiliary systems. For power supply units, the equivalent circuit model explicitly represents internal power-electronic conversion stages. For IT loads, we develop a utilization-dependent server power model, and we combine it with loss-aware ECMs of power supply units. This approach captures the grid-side impact of heterogeneous workload distributions while preserving compatibility with conventional power-flow analysis. We evaluate this data center ECM in large-scale transmission power flows, using Monte Carlo simulations under heterogeneous and homogeneous cluster utilization. In comparison with the fixed-efficiency constant-PQ model, the ECM predicts that the most stressed line exceeds its thermal limit in about 30% of Monte Carlo samples. The results further show that homogeneous server utilization overstates line-loading variability by 17%-46% relative to heterogeneous server utilization, depending on the intra-cluster workload correlation.
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
Muhammad Hamza Ali, Peng Sang, Hyeon Woo, 等. Steady-State Equivalent Circuit Model for Data Center Loads[J/OL]. (2026-08-18)[2026-08-28]. http://arxiv.org/abs/2608.17925v1.
Predictive Failure Detection in Network Hardware Using Thermal Imaging an…
Unplanned network hardware malfunctions can interrupt services and result in expensive downtime in data centers. A deep learning-ba…
Expand
Predictive Failure Detection in Network Hardware Using Thermal Imaging and Deep Learning with Sensor Fusion
- Published
- 2026-08-05
- Authors
- Ashly Joseph
- Theme
- 热管理与液冷
Abstract
Unplanned network hardware malfunctions can interrupt services and result in expensive downtime in data centers. A deep learning-based predictive maintenance strategy is presented that utilizes thermal imaging and power sensor data to detect early indicators of equipment breakdown in routers, switches, and servers. A simulated dataset was generated comprising annotated thermal pictures and power readings indicative of three operating states: Normal, Warning, and Critical. Three ImageNet-pretrained convolutional neural network (CNN) models ResNet-50, InceptionV3, and VGG16 were assessed together with a multi-modal CNN-LSTM fusion model that integrates visual and sensor time-series information. Experiments were performed with and without pre-processing procedures, including region-of-interest (ROI) extraction and normalization. In the absence of pre-processing, CNNs attained moderate accuracy (e.g., ResNet-50 at 52%), but ROI-based pre-processing significantly enhanced performance (ResNet-50 accuracy reaching 91%). The CNN-LSTM model attained the greatest accuracy of 94%, with precision and recall approaching 95%, illustrating the effectiveness of multi-modal fusion. The results validate that domain-specific pre-processing and sensor fusion substantially improve early failure prediction, providing a potential foundation for proactive maintenance of network hardware through non-intrusive monitoring.
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
Ashly Joseph. Predictive Failure Detection in Network Hardware Using Thermal Imaging and Deep Learning with Sensor Fusion[J/OL]. (2026-08-05)[2026-08-28]. http://arxiv.org/abs/2608.07582v1.
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
- Published
- 2026-08-20
- Authors
- Saroj Khanal, Geon Roh, Boyu Yao, Abraham Silverman, Dennice Gayme, Charalambos Konstantinou, Jip Kim, Yury Dvorkin
- Theme
- 算电协同
Abstract
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.
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。
Reference
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)[2026-08-28]. http://arxiv.org/abs/2608.19622v1.
Environmental and Economic Implications of Artificial Intelligence Data C…
In this study, we use electricity demand growth, cooling requirements, and backup system operation to evaluate the environmental an…
Expand
Environmental and Economic Implications of Artificial Intelligence Data Centers in the United States
- Published
- 2026-08-11
- Authors
- Johanna Bolaños-Zuñiga, Alberto J. Lamadrid
- Theme
- 算电协同
Abstract
In this study, we use electricity demand growth, cooling requirements, and backup system operation to evaluate the environmental and economic implications of artificial intelligence data centers in the United States. Our results indicate that impacts are not determined solely by facility design, but by the broader electricity, water, and land-use systems in which these facilities operate. Emissions are primarily driven by electricity consumption and therefore depend on marginal generation mixes, transmission constraints, and the spatial and temporal distribution of demand. Analysis further shows that local effects include pressures on water resources, increased noise exposure, and land-use changes, with outcomes varying across regions and infrastructure conditions. The assessment of technological and operational measures shows that improvements in energy efficiency, cooling configurations, and operational strategies can reduce these impacts, although their effectiveness depends on system-level conditions. Evaluation of regulatory and market structures suggests that existing frameworks may not fully account for location- and time-specific externalities. These findings support the need for integrated policy approaches that align data center deployment and operation with electricity system characteristics, water availability, and land-use planning to improve overall environmental and economic performance.
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
Johanna Bolaños-Zuñiga, Alberto J. Lamadrid. Environmental and Economic Implications of Artificial Intelligence Data Centers in the United States[J/OL]. (2026-08-11)[2026-08-28]. http://arxiv.org/abs/2608.09882v1.
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)[2026-08-28]. http://arxiv.org/abs/2607.26560v1.
Sigenergy C&I Series Part 3
Failte Solar - Knowledge Hub · Query: ACM SIGEnergy data center energy talk。Useful as technical or research context.
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Tech leaders debate if massive AI data center spending risks creating a c…
Future Tech Minute · Query: AI data center energy conference keynote。Useful as technical or research context.
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There’s a big pro-AI data center party happening next month in Washington…
Taylor Lorenz · Query: AI data center energy conference keynote。Useful as technical or research context.
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WeCan'22: Brainstorming Session with the Audience - Minghua, George, Davi…
Noman Bashir · Query: ACM SIGEnergy data center energy talk。Useful as technical or research context.
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YouTube video nelgW0WmWTA
YouTube · Query: AI data center energy conference keynote。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
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AI 芯片供给与交付
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Industry
Industry news, products, policy, financing, projects, and market-oriented videos.
AI 算力基础设施动态：NVIDIA Blog 发布相关报道，涉及 72 w（原文标题：With Groq 3 LPX in Full Produ…
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AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：Up to 30x More Work Per Watt: NVIDIA …
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电力与能源约束观察：Data Center Dynamics 发布相关报道，涉及 €5.6bn（原文标题：Schwarz Group commit…
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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：War and peace in the Middle Eas…
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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（原文标题：Salim Group buys out Keppe…
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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: Designing the white …
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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：Privacy-focused email service P…
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 500MW（原文标题：VCI Global announces l…
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 81MW（原文标题：Gateway Capital files t…
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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（原文标题：Nexspace breaks ground on …
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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液冷与热管理进展：Data Center Knowledge 发布相关报道（原文标题：Liquid Cooling Options: RDHx, …
Same-source item from the Chinese report. Verify details against the original linked source: 液冷与热管理进展：Data Center Knowledge 发布相关报道（…
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政策、标准或能效观察：The Register 发布相关报道（原文标题：EPA to drop requirement for public no…
Same-source item from the Chinese report. Verify details against the original linked source: 政策、标准或能效观察：The Register 发布相关报道（原文标题：EP…
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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…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：Data Center Knowledge 发布相关报道…
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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：AWS to deploy 2 million addi…
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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电力与能源约束观察：Data Center Knowledge 发布相关报道，涉及 4 GW（原文标题：Lancium, Nvidia Partn…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：Data Center Knowledge 发布相关报道…
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电力与能源约束观察：Data Center Knowledge 发布相关报道，涉及 6.8 GW（原文标题：PJM’s Power Shortfa…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：Data Center Knowledge 发布相关报道…
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Why AI Data Centers Are Filling With Liquid
LIT Tech · Query: OCP data center cooling workshop。Useful for product, market, or deployment context.
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Data Center and AI Infrastructure in Arizona: A Knowledge Exchange | Citi…
ASU Energy Forward · Query: AI infrastructure datacenter panel discussion。Useful for product, market, or deployment context.
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Data Center and AI Infrastructure in Arizona: A Knowledge Exchange | Util…
ASU Energy Forward · Query: AI infrastructure datacenter panel discussion。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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NVIDIA Blackwell/GB200/GB300
Same-source item from the Chinese report. Verify details against the original linked source: NVIDIA Blackwell/GB200/GB300
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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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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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4. Video signals
Sigenergy C&I Series Part 3
学术讲座 · Failte Solar - Knowledge Hub · Query: ACM SIGEnergy data center energy talk
Open on YouTubeTech leaders debate if massive AI data center spending risks creating a compute overbuild bubble.
学术会议报告 · Future Tech Minute · Query: AI data center energy conference keynote
Open on YouTubeThere’s a big pro-AI data center party happening next month in Washington D.C.
学术会议报告 · Taylor Lorenz · Query: AI data center energy conference keynote
Open on YouTubeWeCan'22: Brainstorming Session with the Audience - Minghua, George, David, and Jay
学术讲座 · Noman Bashir · Query: ACM SIGEnergy data center energy talk
Open on YouTubeWhy AI Data Centers Are Filling With Liquid
行业论坛 · LIT Tech · Query: OCP data center cooling workshop
Open on YouTubeYouTube video nelgW0WmWTA
学术会议报告 · YouTube · Query: AI data center energy conference keynote
Open on YouTubeData Center and AI Infrastructure in Arizona: A Knowledge Exchange | Cities Panel w/ Paul Westerhoff
专家圆桌 · ASU Energy Forward · Query: AI infrastructure datacenter panel discussion
Open on YouTubeData Center and AI Infrastructure in Arizona: A Knowledge Exchange | Utilities Panel with Kelly Barr
专家圆桌 · ASU Energy Forward · Query: AI infrastructure datacenter panel discussion
Open on YouTubeSources
Collection notes
- 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。
- 论文池：已从本地论文池读取 23 条候选；池更新时间 2026-08-28 02:36。
- 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…