液冷与智算中心日报｜2026-09-19
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，智算中心 CapEx/扩建、电力并网与能源约束、液冷路线（冷板/浸没/两相）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。
ETCInfer: An Energy-efficient Thermal-aware Cooling-joint Scheduler for L…
Large language model (LLM) inference in AI datacenters creates a coupled control problem between GPU serving and facility cooling. …
展开全文
ETCInfer: An Energy-efficient Thermal-aware Cooling-joint Scheduler for LLM Inference in AI Datacenters
- 发布时间
- 2026-09-14
- 作者
- Rui Lu、Rui Ge、Huanghuang Liang、Xiaobo Zhou、Dan Wang
- 主题
- 芯片与算力
摘要
Large language model (LLM) inference in AI datacenters creates a coupled control problem between GPU serving and facility cooling. Raising ambient temperature setpoints can reduce cooling energy and carbon, but also shrinks thermal headroom, induces GPU throttling, and leads to Service-Level-Objective (SLO) violations. In this paper, we study joint cooling--computing control for LLM inference: minimizing per-job GPU-plus-cooling energy while satisfying thermal safety and latency SLO constraints. We present ETCInfer, an energy-efficient, thermal-aware scheduler that selects a pre-job Computer Room Air Conditioner (CRAC) setpoint and adapts per-GPU frequency and micro-batch size during execution. ETCInfer builds compact physics-informed control models by calibrating GPU heat generation, chassis heat dissipation, CRAC power, and prefill/decode latency relations from telemetry. These models estimate hidden thermal states and time-to-throttle, enabling the scheduler to evaluate energy, temperature, and latency before applying an action. We formulate this joint setpoint--frequency--micro-batch control problem as a partially observable Markov decision process and design ETCAdapter, a learning-based controller that minimizes per-job energy under thermal safety and SLO constraints. We implement ETCInfer as a coordination layer over typical inference and cluster management stacks. Evaluation across real-trace simulation and validation experiments shows that ETCInfer reduces total job energy by up to 33.1%, thermal throttle exposure by up to 92.9%, and keeps SLO violation rates below 0.7% even at ambient temperatures up to $48^{\circ}\mathrm{C}$.
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：研究重点指向算力硬件、边缘计算或模型部署对基础设施的牵引。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
Rui Lu, Rui Ge, Huanghuang Liang, 等. ETCInfer: An Energy-efficient Thermal-aware Cooling-joint Scheduler for LLM Inference in AI Datacenters[J/OL]. (2026-09-14)[2026-09-19]. http://arxiv.org/abs/2609.15230v1.
Could Underwater Data Centers Pose a Risk to AI Treaty Verification?
Proposals for international agreements that limit frontier AI development depend on verification, and a central challenge is detect…
展开全文
Could Underwater Data Centers Pose a Risk to AI Treaty Verification?
- 发布时间
- 2026-09-16
- 作者
- James Teague、Ashmita Rajmohan、Yannick Muehlhaeuser
- 主题
- 热管理与液冷
摘要
Proposals for international agreements that limit frontier AI development depend on verification, and a central challenge is detecting undeclared compute facilities used to evade restrictions. Underwater data centers (UDCs) have been suggested as one such evasion vector, but their feasibility at frontier scale and their detectability have not been seriously assessed. We examine current UDC deployments, evaluate construction and maintenance complexity relative to land-based facilities, and analyse the feasibility of a 100,000 H100-equivalent training run underwater. We find that power delivery and cooling are tractable, but interconnect and the hands-on maintenance that large training runs require are severe obstacles - surmountable only by a well-resourced state actor accepting large cost and schedule penalties, and only where concealment, rather than efficiency, is the objective. We then assess detectability through thermal, acoustic, optical and synthetic-aperture-radar (SAR) surveillance. Thermal detection of an operational pod is unlikely outside shallow, calm water; acoustic detection is marginally more effective, but faces limitations in attribution; and optical/SAR monitoring is most powerful during construction and maintenance, when the pressure-vessel fabrication base and the cable-laying fleet create distinctive signatures for AIS-tracking. We conclude that UDCs are a comparatively unlikely evasion route relative to underground or industrially disguised land-based facilities, but the residual risk is non-zero and warrants operationalising the detection modalities discussed.
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
James Teague, Ashmita Rajmohan, Yannick Muehlhaeuser. Could Underwater Data Centers Pose a Risk to AI Treaty Verification?[J/OL]. (2026-09-16)[2026-09-19]. http://arxiv.org/abs/2609.18824v1.
CATS: A Carbon-Aware Task Simulator for Reducing AI Data Center Emissions
The rapid rise of generative AI is accelerating cloud data center expansion, with electricity demand projected to double by 2026. B…
展开全文
CATS: A Carbon-Aware Task Simulator for Reducing AI Data Center Emissions
- 发布时间
- 2026-09-14
- 作者
- Dayuan Chen、Ziliang Zong
- 主题
- 算电协同
摘要
The rapid rise of generative AI is accelerating cloud data center expansion, with electricity demand projected to double by 2026. Because carbon-intensity varies by more than 5.5x across grids and times of day, where and when inference tasks execute significantly affects operational emissions. We address this issue with three aspects in this paper. First, we compile a global alignment dataset unifying 140 operational and planned cloud regions across 8 major providers with five-minute carbon-intensity traces for 145 grid regions from 2022 to 2024, revealing that 50% of current sites lie in medium-to-high carbon-intensity grids, indicating a siting-carbon mismatch and unrealized carbon reduction potential. Second, we develop CATS (Carbon-Aware Task Simulator), a flexible trace-driven framework that profiles six AI inference tasks across multiple GPU types, synthesizes realistic diurnal curve, geographical and task mixes, and SLA constraints, and evaluates spatial and temporal schedulers against two baselines while reporting comprehensive metrics including carbon emissions, energy consumption, runtime, queue delay, and hardware utilization. Third, we quantify achievable CO2 savings under realistic constraints: in a 24-hour trace with 600,000 tasks at fleet utilization of 0.37, spatial shifting reduces CO2 by 38.4% versus speed-first baseline, while temporal shifting yields 16% savings with bounded SLA violations at 3.27%. These results advocate locating future data centers in low carbon-intensity grids and demonstrate that carbon-aware scheduling on today's fleets can achieve substantial operational emissions reduction.
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
Dayuan Chen, Ziliang Zong. CATS: A Carbon-Aware Task Simulator for Reducing AI Data Center Emissions[J/OL]. (2026-09-14)[2026-09-19]. http://arxiv.org/abs/2609.14775v1.
Convective Heat Transfer Optimization for Liquid Cooling Plates Driven by…
The rapid development of liquid-cooled data centers has imposed imperative demands on the performance of liquid cooling plate. The …
展开全文
Convective Heat Transfer Optimization for Liquid Cooling Plates Driven by Field Synergy and Fractal Geometry
- 发布时间
- 2026-09-11
- 作者
- Zixu Han、Peng Zhang
- 主题
- 热管理与液冷
摘要
The rapid development of liquid-cooled data centers has imposed imperative demands on the performance of liquid cooling plate. The density-based topology optimization (TO) is an effective approach to resolving the growing thermal-hydraulic performance requirements of liquid cooling plate. However, existing TO methods can hardly optimize convective heat transfer directly which is the intrinsic heat transfer mechanism, due to the highly complex and evolving structural topologies, varying flow and temperature fields, making it extremely challenging to explicitly describe the heat transfer coefficient and heat transfer area during TO process. A convective heat transfer topology optimization (CTO) method is proposed in this study, where the iteratively evolving heat transfer coefficient is explicitly depicted by the field synergy theory in the thermal objective, and directly described by the velocity and temperature fields without relying on specific geometry. Combined with the explicit depiction of heat transfer area by the fractal geometry theory, a CTO framework is built for a direct optimization of convective heat transfer under both the laminar and turbulent flow conditions. The CTO tends to generate more hierarchical and directional structural topologies in optimization results, which is conducive to reducing low-velocity stagnation zones and improving flow direction in branched channels, achieving enhanced synergy and thermal-hydraulic performance in the optimized liquid cooling plates. Compared with the TO results without incorporation of field synergy theory, the CTO can reduce average temperature rise by 20% while improving the Nusselt number by 15% under laminar flow conditions, and reduce maximum temperature rise by 10.2% and pressure drop by 25% under turbulent flow conditions.
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
Zixu Han, Peng Zhang. Convective Heat Transfer Optimization for Liquid Cooling Plates Driven by Field Synergy and Fractal Geometry[J/OL]. (2026-09-11)[2026-09-19]. http://arxiv.org/abs/2609.12344v1.
Spatial LLM Workload Shifting Needs Foresight: Model Commitment for AI Da…
AI data centers may face power supply shortages during certain periods, requiring operators to shift large language model (LLM) inf…
展开全文
Spatial LLM Workload Shifting Needs Foresight: Model Commitment for AI Data Center Operation under Power Grid Constraints
- 发布时间
- 2026-09-09
- 作者
- Bojun Du、Hongyang Jia、Tonghui Li、Qingchun Hou、Ze Wang、Ershun Du、Ning Zhang
- 主题
- 算电协同
摘要
AI data centers may face power supply shortages during certain periods, requiring operators to shift large language model (LLM) inference workloads spatially to maintain service rates. However, existing workload-shifting methods typically assume that any data center with sufficient computing resources can immediately serve shifted requests, which may lead to infeasible transfers and unserved demand. This letter proposes model commitment (MC), a mixed-integer linear programming framework that jointly schedules model deployment and cross-site request routing under power constraints and electricity-price signals. First, MC formulates the intertemporal coupling introduced by model replica loading. Second, it translates prefill and decode latency requirements into the amount of demand that each replica can serve. Case studies based on real-world data show that MC enables AI data center operators to achieve a 100% service rate under time-varying grid conditions and reduce total operating cost by 29.0%.
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
Bojun Du, Hongyang Jia, Tonghui Li, 等. Spatial LLM Workload Shifting Needs Foresight: Model Commitment for AI Data Center Operation under Power Grid Constraints[J/OL]. (2026-09-09)[2026-09-19]. http://arxiv.org/abs/2609.09787v1.
From Grid to Chip: Power Architecture, Stability, and Flexibility of AI D…
The rapid growth of artificial intelligence (AI) computing is transforming data centers into large, dynamic electrical loads. Their…
展开全文
From Grid to Chip: Power Architecture, Stability, and Flexibility of AI Data Centers
- 发布时间
- 2026-09-10
- 作者
- Yubo Song、Rui Kong、Takuro Umihara、Pooya Davari、Frede Blaabjerg、Subham Sahoo
- 主题
- 算电协同
摘要
The rapid growth of artificial intelligence (AI) computing is transforming data centers into large, dynamic electrical loads. Their deployment is primarily constrained by energy availability and grid-connection capacity, which is further aggravated by the ability of power-delivery architectures, control systems, and computing workloads to operate reliably during fast grid disturbances. This article presents a technological perspective on AI data centers as grid-interactive computing systems. First, it reviews grid-integration bottlenecks, evolving connection policies, grid-code requirements, which has fostered new technological trends via spatio-temporal flexibility available through workload orchestration, cooling systems, on-site resources, and energy storage. Second, it maps the evolution of power-delivery architectures from medium-voltage grid interfaces to chip-level, discussing higher-voltage DC distribution, solid-state transformers, wide-bandgap devices, advanced chip-level power delivery, and liquid cooling. Third, it establishes a three-level stability framework spanning rack-level DC-bus dynamics, facility-level converter interactions, and system-level grid-coupled behavior. The framework connects dominant instability mechanisms, including constant power load effects, impedance interactions, forced oscillations, and operating-mode transitions, with suitable modeling, assessment, and mitigation approaches. Synthesizing these topics, this article highlights grid-to-chip co-design as a central requirement for scalable AI infrastructure, linking computing workloads, power-delivery systems, energy buffers, and grid operation.
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
Yubo Song, Rui Kong, Takuro Umihara, 等. From Grid to Chip: Power Architecture, Stability, and Flexibility of AI Data Centers[J/OL]. (2026-09-10)[2026-09-19]. http://arxiv.org/abs/2609.11649v1.
Grid-Mode-Aware Model Predictive Control of Hybrid Energy Storage Systems…
To facilitate the grid-friendly integration of highly variable AI data center loads, this paper proposes a grid-mode-aware model pr…
展开全文
Grid-Mode-Aware Model Predictive Control of Hybrid Energy Storage Systems for AI Data Center Power Smoothing
- 发布时间
- 2026-09-04
- 作者
- Xin Chen
- 主题
- 算电协同
摘要
To facilitate the grid-friendly integration of highly variable AI data center loads, this paper proposes a grid-mode-aware model predictive control (G-MPC) framework for managing a hybrid energy storage system (HESS) to smooth grid-side power demand. The framework optimally coordinates a battery energy storage system (BESS) and a supercapacitor (SC) by solving a multi-step optimization problem in a receding-horizon manner. In particular, band-pass filter dynamics are directly embedded in the G-MPC formulation to extract and suppress grid-side power components associated with vulnerable grid oscillatory modes, thus mitigating load-induced grid oscillations. The resulting G-MPC optimization jointly minimizes violations of grid-side power-envelope, ramp-rate, and modal-power requirements and the degradation and power-ramping costs of the BESS and SC, while satisfying power limits, state-of-charge limits, and other operational constraints. To enable real-time implementation, a fix-and-re-optimize algorithm is developed to solve each G-MPC problem efficiently while preventing simultaneous charging and discharging. Extensive simulations demonstrate the effectiveness, flexibility, and computational efficiency of the proposed framework. The results also highlight the importance of explicitly suppressing power components associated with vulnerable grid modes, rather than merely reducing overall load variations, to effectively mitigate grid oscillations.
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
Xin Chen. Grid-Mode-Aware Model Predictive Control of Hybrid Energy Storage Systems for AI Data Center Power Smoothing[J/OL]. (2026-09-04)[2026-09-19]. http://arxiv.org/abs/2609.04398v1.
Hosting Capacity Assessment of Data Centers with Voltage Ride-Through Cap…
Large data centers are emerging as concentrated, power-electronic grid loads whose abrupt disconnection or transfer to on-site back…
展开全文
Hosting Capacity Assessment of Data Centers with Voltage Ride-Through Capability in Power Systems
- 发布时间
- 2026-09-03
- 作者
- Pengyu Ren、Wei Sun、Fei Teng
- 主题
- 算电协同
摘要
Large data centers are emerging as concentrated, power-electronic grid loads whose abrupt disconnection or transfer to on-site backup supply during voltage disturbances can remove large demand from the power system, and may create a system-level stability problem. Their interconnection feasibility therefore depends not only on steady-state thermal and voltage limits, but also on whether internal power-conditioning systems can maintain IT service while limiting customer-initiated load reduction. This paper presents a voltage ride-through (VRT)-aware data center and grid co-planning framework that couples transmission-level fault simulation with an internal data center ride-through model. Python-based dynamic simulations generate point-of-interconnection (POI) voltage trajectories under selected network faults, and the resulting waveforms drive an internal model incorporating IT and cooling-load dynamics, DC-link, Uninterruptible Power Supply (UPS) response, and converter apparent power limits. The IEEE 118-bus case study shows that internal VRT capability can become a binding interconnection constraint: steady-state planning alone can overestimate feasible data center capacity, whereas increased UPS converter headroom progressively restores hosting capacity. Under the reduced-order response models studied, the grid-forming mode provides greater ride-through margin than the current-limited grid-following mode under the same network fault conditions. The results further show that VRT constraints can materially change both the total hosting capacity of data centers and its spatial allocation across candidate interconnection buses.
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
Pengyu Ren, Wei Sun, Fei Teng. Hosting Capacity Assessment of Data Centers with Voltage Ride-Through Capability in Power Systems[J/OL]. (2026-09-03)[2026-09-19]. http://arxiv.org/abs/2609.03030v1.
How Data Centers Actually Work
MEP Academy · 检索词：AI datacenter power grid university lecture。适合作为技术背景或研究趋势补充。
展开全文
Reimagine AI data center power from grid to gate
Texas Instruments · 检索词：AI datacenter power grid university lecture。适合作为技术背景或研究趋势补充。
展开全文
The Entire AI Data Center Explained — From Electricity to ChatGPT
Leo Cui, Ph.D., CFA · 检索词：AI datacenter power grid university lecture。适合作为技术背景或研究趋势补充。
展开全文
Why AI Data Centers Are Straining the Power Grid
VECTOR CROWN · 检索词：AI datacenter power grid university lecture。适合作为技术背景或研究趋势补充。
展开全文
ASHRAE ITALY - LIQUID COOLING AND CHALLANGES IN IMPLEMENTATION
ASHRAE Italy · 检索词：data center thermal management seminar。适合作为技术背景或研究趋势补充。
展开全文
智算中心 CapEx/扩建
本期命中 14 条，热度分 49。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
电力并网与能源约束
本期命中 13 条，热度分 39。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
液冷路线（冷板/浸没/两相）
本期命中 3 条，热度分 10。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
产业
产业新闻、技术产品、政策标准、投融资、项目和产业视频。
财报与资本开支观察：NVIDIA Blog 发布相关报道（原文标题：NVIDIA Vera Rubin NVL72 Delivers Leadin…
发布时间：2026-09-16；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
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Virginia’s Loudoun County board…
发布时间：2026-09-19；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
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Hidden costs）
发布时间：2026-09-18；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
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Brazil's President Lula signs R…
发布时间：2026-09-18；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
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Brazil's Eveo plans data center…
发布时间：2026-09-18；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
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：DCD Intelligence: Data center e…
发布时间：2026-09-18；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
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：PGIM sells data center developm…
发布时间：2026-09-18；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
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Enabling the next generation of…
发布时间：2026-09-18；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
展开全文
电力与能源约束观察：Data Center Dynamics 发布相关报道（原文标题：Gazprom partners with Crypto E…
发布时间：2026-09-18；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
展开全文
液冷与热管理进展：Data Center Knowledge 发布相关报道（原文标题：Rack Power Is Rising Fast. Her…
发布时间：2026-09-18；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
展开全文
ACS Door Heat Exchanger Sub Project Updates
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。
展开全文
Advanced Cooling Environments ACF to Coolant Distribution Unit CDU Transi…
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。
展开全文
OCP 2020 Tech Week: ACS Cold Plate: Leak detection and Intervention
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。
展开全文
产业热度指数 9/10
产业热度指数为 9/10：本期自动化检索记录到 18 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。
展开全文
NVIDIA Blackwell/GB200/GB300
今日延续上榜
展开全文
AI 芯片供给与交付
今日延续上榜
展开全文
智算中心 CapEx/扩建
今日延续上榜
展开全文
4. 最新视频观察
How Data Centers Actually Work
专家讲座 · MEP Academy · 检索词：AI datacenter power grid university lecture
在 YouTube 打开Reimagine AI data center power from grid to gate
专家讲座 · Texas Instruments · 检索词：AI datacenter power grid university lecture
在 YouTube 打开The Entire AI Data Center Explained — From Electricity to ChatGPT
专家讲座 · Leo Cui, Ph.D., CFA · 检索词：AI datacenter power grid university lecture
在 YouTube 打开Why AI Data Centers Are Straining the Power Grid
专家讲座 · VECTOR CROWN · 检索词：AI datacenter power grid university lecture
在 YouTube 打开ACS Door Heat Exchanger Sub Project Updates
行业论坛 · Open Compute Project · 检索词：OCP data center cooling workshop
在 YouTube 打开Advanced Cooling Environments ACF to Coolant Distribution Unit CDU Transition
行业论坛 · Open Compute Project · 检索词：OCP data center cooling workshop
在 YouTube 打开OCP 2020 Tech Week: ACS Cold Plate: Leak detection and Intervention
行业论坛 · Open Compute Project · 检索词：OCP data center cooling workshop
在 YouTube 打开ASHRAE ITALY - LIQUID COOLING AND CHALLANGES IN IMPLEMENTATION
专家讲座 · ASHRAE Italy · 检索词：data center thermal management seminar
在 YouTube 打开来源链接区
本次检索说明
- 公开 RSS/Atom：The Register：未检索到符合条件的高相关条目。
- 公开 RSS/Atom：ServeTheHome：未检索到符合条件的高相关条目。
- 公开 RSS/Atom：HPCwire：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 17 条候选；池更新时间 2026-09-19 02:31。
- 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。