Liquid Cooling and AI Data Center Daily | 2026-11-01
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 2026-11-01.
- Collection window: 2026-10-31 08:00 北京时间 - 2026-11-01 08:00 北京时间.
- Coverage snapshot: 8 industry items; 4 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.
Stackelberg Game with Multi-Agent Transformer for ISRS-Aware Resource Re-…
The rapid expansion of generative AI is driving computing infrastructures toward interconnected AI data centers. Multi-band optical…
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Stackelberg Game with Multi-Agent Transformer for ISRS-Aware Resource Re-optimization in Multi-Band Optical Networks
Research perspective (Chinese)
光网络中一项服务的局部调整，可能通过非线性耦合改变其他服务的传输条件。资源重优化应把调整哪些服务与如何联合重分配一起决策，并检查计算开销和收益的边界。
- Published
- 2026-10-10
- Authors
- Jiaxin Liu, Rentao Gu, Miao Zhu, Zeguang Zou, Hui Li, Yuefeng Ji
- Theme
- 热管理与液冷
Abstract
The rapid expansion of generative AI is driving computing infrastructures toward interconnected AI data centers. Multi-band optical networks have become the backbone of such connectivity. To accommodate increasingly dynamic traffic demands, these networks require continual resource re-optimization to improve resource utilization and reduce service blocking. However, multi-band transmission amplifies inter-channel stimulated Raman scattering(ISRS), inducing nonlinear coupling among services. This coupling complicates re-optimization, as local adjustments perturb the global power profile, creating interdependent optimization decisions across services. Moreover, the nonlinear interactions vary with the subset of services selected for re-optimization, and inappropriate selection may reduce re-optimization effectiveness or diminish achievable benefits. To address these challenges, we propose a Stackelberg Game with Multi-Agent Transformer (SG-MAT) approach for ISRS-aware resource re-optimization. SG-MAT employs a leader-follower structure, in which the MAT-based follower models nonlinear coupling and generates coordinated channel reallocations, feeding the utilization improvements to the leader. Using this feedback, the leader identifies critical service subsets, approximating a Stackelberg equilibrium to guide global decisions and enabling consistent and efficient re-optimization. Simulation results show that SG-MAT achieves the highest utilization improvement and reduces the blocking ratio by approximately 30% under moderate traffic loads, with computational complexity comparable to benchmarks.
Chinese interpretation
该研究针对多波段光网络中因ISRS导致的非线性耦合问题，提出了一种结合斯塔克尔伯格博弈与多智能体Transformer的SG-MAT方法。该方法通过领导者-跟随者机制协调信道重分配，近似斯塔克尔伯格均衡以优化全局资源。该研究对于提升互联数据中心的光网络资源利用率和降低服务阻塞具有重要意义，为应对动态流量需求提供了有效的重优化策略。以上判断仅依据摘要，方法细节与适用条件请核验原文。
Reference
Jiaxin Liu, Rentao Gu, Miao Zhu, 等. Stackelberg Game with Multi-Agent Transformer for ISRS-Aware Resource Re-optimization in Multi-Band Optical Networks[J/OL]. (2026-10-10)[2026-11-01]. http://arxiv.org/abs/2610.14830v1.
Capturing Electrical Windows Through Reconfigurable Optical Networks: Tra…
Artificial-intelligence data centers (AIDCs) can provide spatiotemporal load flexibility by migrating training workloads in respons…
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Capturing Electrical Windows Through Reconfigurable Optical Networks: Transfer-aware Scheduling of Multi-Region AI Data Centers
Research perspective (Chinese)
低电价或富余可再生能源只有在检查点及时到达后，才能转化为可用的迁移窗口。跨区域调度应把传输延迟、共享链路和光网络重构纳入可执行性验证。
- Published
- 2026-10-09
- Authors
- Xi Tong, Zijian Li, Xiaoyi Fan, Cong Zhang, Ershun Du, Qin Wang
- Theme
- 算电协同
Abstract
Artificial-intelligence data centers (AIDCs) can provide spatiotemporal load flexibility by migrating training workloads in response to regional electricity prices and renewable-energy (RE) availability. However, transfer-blind scheduling may overestimate migration benefits by overlooking transfer time or prevent training from resuming within renewable-rich or low-price windows when considering transfer time. This paper proposes a transfer-aware compute-electricity-network scheduling framework that coordinates training placement, checkpoint transmission, and optical circuit switching (OCS) to improve local RE utilization and reduce grid-purchase cost. A compact compute-electricity master problem generates candidate schedules using estimated transfer delays. The framework then schedules optical circuits, verifies checkpoint delivery under shared-link capacity constraints, and revises infeasible schedules using updated delay estimates. It integrates day-ahead planning, event-triggered intraday updates, and real-time dispatch. In a five-AIDC benchmark with equal completed work and network resources, the proposed method reduces the real-time grid purchase cost by 12.50% and increases the local RE utilization by 2.724 percentage points relative to static optical operation. Sensitivity and ablation analyses indicate that OCS is particularly beneficial when renewable-rich or low-price windows shift across sites and network bottlenecks hinder timely checkpoint delivery.
Chinese interpretation
本文探讨多区域AI数据中心迁移调度问题，通过协调计算、电力和光网络优化，提升可再生能源利用率并降低成本。以上判断仅依据摘要，方法细节与适用条件请核验原文。
Reference
Xi Tong, Zijian Li, Xiaoyi Fan, 等. Capturing Electrical Windows Through Reconfigurable Optical Networks: Transfer-aware Scheduling of Multi-Region AI Data Centers[J/OL]. (2026-10-09)[2026-11-01]. http://arxiv.org/abs/2610.13061v1.
FALCON: FPGA-Accelerated Latency-Certifiable Occupancy Networks for Camer…
A perception system that must brake for a pedestrian does not need a low average latency. It must deliver an answer before the dead…
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FALCON: FPGA-Accelerated Latency-Certifiable Occupancy Networks for Camera-Based Autonomous Driving
Research perspective (Chinese)
安全关键计算更需要说明最坏情况下何时交付，以及超时会返回什么。周期上界、实测时延与完整流水线性能应分开报告，避免把代理测试的保证外推到整套系统。
- Published
- 2026-10-16
- Authors
- Haohua Que, Mingkai Liu, Handong Yao
- Theme
- 芯片与算力
Abstract
A perception system that must brake for a pedestrian does not need a low average latency. It must deliver an answer before the deadline on every frame, the worst case included. Commodity edge accelerators optimize the mean through dynamic scheduling, OS jitter, thermal throttling, and contention, so their per-frame latency can be measured but not bounded. We build FALCON, a camera-based 3D occupancy front-end on the AMD Kria KR260, an embedded FPGA drawing 4.3 W idle and up to 6.6 W under inference, and make the latency guarantee layered and explicit: programmable-logic (PL) compute is cycle-bounded analytically; inference is a measured envelope over 100k-frame traces; and a watchdog enforces output delivery, publishing the previous output flagged stale when a frame is late. The deadline runs use a fixed-work proxy of six ResNet-50 DPU inferences per frame, not the full occupancy network: across three 100k-frame enforced runs, all 300k proxy frames delivered an output within the 100 ms deadline plus at most 10.2 ms, the worst of the three runs. The full six-camera FlashOcc pipeline composes to about 1.3 s per frame on the board and is not yet a 10 Hz system. The two operations the DPU cannot run, a view-transform gather and a bilinear resize, become fixed-trip-count PL IPs numerically matched to their software references. On the full Occ3D-nuScenes validation set the board reaches mIoU 29.88, 92.8% of an FP32 baseline the DPU cannot run. On a ResNet-50 probe over 100k frames the KR260 tail stays within 1.5 times its median (2.0 times for the six-inference proxy), where Jetson Orin reaches 4.4 times and the worst datacenter GPU run (NVIDIA L4) 23.6 times. The deployment recipe, HLS sources, and raw timing traces are released.
Chinese interpretation
本文研究相机自主驾驶中延迟可验证的占用网络问题，采用FPGA加速实现延迟分层保障机制。通过可编程逻辑计算的周期约束、轨迹测量和看门狗机制，确保处理延迟在可预测范围内。研究显示系统在占用率指标上接近FP32基线，但整体处理速度尚未满足10Hz要求。以上判断仅依据摘要，方法细节与适用条件请核验原文。
Reference
Haohua Que, Mingkai Liu, Handong Yao. FALCON: FPGA-Accelerated Latency-Certifiable Occupancy Networks for Camera-Based Autonomous Driving[J/OL]. (2026-10-16)[2026-11-01]. http://arxiv.org/abs/2610.22143v2.
Unlocking AI Data Center Interconnection Capacity Through Coordinated Gri…
The rapid growth of AI data centers is creating concentrated electricity demands that can exceed available distribution network hea…
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Unlocking AI Data Center Interconnection Capacity Through Coordinated Grid and Data Center Flexibility
Research perspective (Chinese)
可接入容量的瓶颈可能首先在馈线拓扑与接入位置，而不在计算任务可移动多少。投资负载调度或UPS灵活性之前，宜先对照电网侧重构、电压约束和备用要求评估增益。
- Published
- 2026-10-05
- Authors
- Rida Fatima, Xingpeng Li
- Theme
- 算电协同
Abstract
The rapid growth of AI data centers is creating concentrated electricity demands that can exceed available distribution network headroom and delay interconnection. This paper investigates whether capacity can be used effectively by coordinating flexibility on both sides of the interconnection. A day-ahead mixed integer second order cone programming framework maximizes feasible AI data center IT capacity using utility side conservation voltage reduction and network topology reconfiguration, together with data center workload shifting and reserve constrained uninterruptible power supply storage. Background feeder demand uses voltage dependent ZIP models, while the data center is modeled as constant power demand using Training, Inference, and Mixed workload profiles. A two-stage solution first maximizes interconnection capacity and then minimizes feeder losses for the retained capacity, with numerical relaxation screening. Studies on a 116-bus model derived from the IEEE 123-node feeder show that, at the constrained bus-60 connection point, grid side flexibility, driven almost entirely by network reconfiguration, increases feasible IT capacity by approximately 42.5-45.0%, while data center flexibility alone provides approximately 2.1-5.4% gains. Coordinated operation increases feasible IT capacity from approximately 10.5-10.6 MW under BASE operation to 15.3-16.0 MW, corresponding to gains of approximately 45.9-50.8%; the 16.0 MW Inference result is limited by the adopted UPS reserve requirement. Benefits are strongly location dependent and influenced by workload flexibility, deferral duration, PUE, UPS energy and reserve requirements, and feeder loading. Under a combined adverse operating condition, FULL capacity decreases by only 3.4% relative to the nominal Mixed case while retaining substantial additional capacity over BASE.
Chinese interpretation
本文针对AI数据中心高电力需求导致的互联瓶颈问题，提出协调电网与数据中心灵活性的优化方法。通过构建日前规划框架，结合电网侧电压调控与网络重构及数据中心侧工作负载调整，评估互联容量提升效果。研究基于IEEE 123节点模型进行仿真，揭示电网侧灵活性对容量提升的主导作用，并指出协同运行对容量增长的显著影响。以上判断仅依据摘要，方法细节与适用条件请核验原文。
Reference
Rida Fatima, Xingpeng Li. Unlocking AI Data Center Interconnection Capacity Through Coordinated Grid and Data Center Flexibility[J/OL]. (2026-10-05)[2026-11-01]. http://arxiv.org/abs/2610.05502v1.
Trajectory Sensitivity-Based Dynamic Assessment of an AI Data Center Unde…
Artificial Intelligence (AI) data centers are emerging as converter-dominated, parameter-rich electrical systems whose dynamic resp…
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Trajectory Sensitivity-Based Dynamic Assessment of an AI Data Center Under Contingencies
Research perspective (Chinese)
轨迹敏感度可以帮助快速筛选哪些参数更值得关注，但它描述的是给定扰动附近的局部变化。用于运行决策前，应明确扰动范围与参数变化条件，并以非线性仿真复核。
- Published
- 2026-10-07
- Authors
- Arnav Bagga, Saskia A. Putri, Abhishek Banerjee, Xiaofan Wu
- Theme
- 算电协同
Abstract
Artificial Intelligence (AI) data centers are emerging as converter-dominated, parameter-rich electrical systems whose dynamic response depends on fast workload-driven demand variations, tightly regulated data-hall converters, and coordinated on-site sources. Assessing such systems by repeatedly solving nonlinear time-domain models for every outage, load realization, and parameter perturbation is computationally expensive and provides limited insight into response sensitivity. This paper presents a trajectory sensitivity (TS)-based dynamic assessment framework for a self-contained AI data center supplied by a 2 kV DC microgrid with a grid-tied interface. The model includes time-varying data-hall load profiles, a voltage source converter (VSC), battery energy storage systems (BESS) units, supercapacitors, and synchronous-generation-side sources. For each disturbance, firstly, TS are computed along the nonlinear disturbance trajectory, capturing the local effect of source trips, data-hall outages, converter controls, and load ramps. Later, the computed TS data is used to estimate system trajectories under simultaneous parameter perturbations. Validation against full nonlinear ODE simulations with $\pm 15\%$ perturbations shows close agreement. In addition, the TS-based approach reduces computational time of dynamic-response evaluations from seconds to milliseconds, demonstrating its potential as a scalable screening tool for AI data center dynamics.
Chinese interpretation
该研究通过轨迹敏感度分析AI数据中心动态响应，方法适用于含多种电源的系统。评估结果与非线性微分方程仿真一致，计算效率显著提升。
Reference
Arnav Bagga, Saskia A. Putri, Abhishek Banerjee, 等. Trajectory Sensitivity-Based Dynamic Assessment of an AI Data Center Under Contingencies[J/OL]. (2026-10-07)[2026-11-01]. http://arxiv.org/abs/2610.09393v1.
A Critical Review of Grid Integration Challenges and Flexibility Opportun…
The rapid proliferation of cloud computing and artificial intelligence workloads is fundamentally reshaping the magnitude, spatial …
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A Critical Review of Grid Integration Challenges and Flexibility Opportunities for Large-Scale Data Centers
Research perspective (Chinese)
数据中心可靠并网需要把逐时供电、网络可送达性与实际可控能力连起来。研究边际排放、用水或备用责任时，也应说明负荷模型和验证数据能支持到哪一层结论。
- Published
- 2026-10-14
- Authors
- Bibek Dhakal, Manohar Chamana, Biswambhar Gayen, Jatendar Kumar, S M Shams Rahman, Aaqib Peerzada, Ki Yeob Lee, Olatunji M. Adeyanju
- Theme
- 算电协同
Abstract
The rapid proliferation of cloud computing and artificial intelligence workloads is fundamentally reshaping the magnitude, spatial concentration, and electrical characteristics of data center power demand. Data centers differ from conventional commercial loads because they combine high rack densities, converter-dominated interfaces, thermally coupled cooling, uninterruptible power supplies, programmable workloads, and increasingly complex behind-the-meter generation and storage within a single facility. This review examines their grid integration from utility and sustainable-energy perspectives across resource planning, large-load interconnection, distribution feeders, market operation, real-time dynamics, power quality, and asset management. Particular attention is given to distribution-level issues that receive limited treatment in broader energy reviews: bidirectional operation on renewable-rich feeders, harmonic propagation and resonance, ride-through and reconnection, transformer derating, cable ampacity, neutral-conductor heating, protection, and measurement. Static, composite, physics-based dynamic, calibrated, impedance, electromagnetic transient, and hybrid load models are compared according to study purpose and data requirements, and utility coordination platforms, uninterruptible power supply and battery control, workload flexibility, renewable co-location, cogeneration, fuel cells, thermal storage, and clean firm supply are assessed. The central finding is that requested peak demand and annual energy matching alone cannot establish reliable or sustainable integration. Credible assessment must consider hourly and locational supply, network deliverability, marginal emissions, lifecycle and water impacts, backup obligations, rebound, asset health, and verified operating capability.
Chinese interpretation
研究探讨数据中心并网问题，分析其电力特性与电网整合挑战。评估多种模型与灵活性措施，强调可持续性需综合考量多因素。
Reference
Bibek Dhakal, Manohar Chamana, Biswambhar Gayen, 等. A Critical Review of Grid Integration Challenges and Flexibility Opportunities for Large-Scale Data Centers[J/OL]. (2026-10-14)[2026-11-01]. http://arxiv.org/abs/2610.19374v1.
Impact of Data Center Load on Co-Optimized Generation and Transmission Ex…
The rapid growth of hyperscale data centers challenges grid expansion planning, yet existing studies focus largely on operational f…
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Impact of Data Center Load on Co-Optimized Generation and Transmission Expansion Planning with Embedded Unit Commitment
Research perspective (Chinese)
大负荷接入可能把最优扩展方案从输电投资转向发电投资，且运行成本可能主导长期支出。规划比较应联合检查负荷选址、机组组合和区域供能结构，保留算例条件。
- Published
- 2026-10-13
- Authors
- Haoxiang Wan, Xingpeng Li, Jonghwan Kwon
- Theme
- 算电协同
Abstract
The rapid growth of hyperscale data centers challenges grid expansion planning, yet existing studies focus largely on operational flexibility rather than long-term investment decisions. This paper examines how large data center loads sited at multiple buses affect co-optimized Transmission Expansion Planning (TEP) and Generation Expansion Planning (GEP). The problem is formulated as a multi-year, Unit Commitment (UC)-embedded mixed-integer linear program on the modified IEEE 24-bus Reliability Test System (RTS-24) with Electric Reliability Council of Texas (ERCOT)-calibrated renewable energy profiles. A 500 MW data center load, about 17.5% of the base-year peak, is allocated across four buses by voltage level, generation headroom, renewable co-location, and network connectivity. Integrating this load raises the 15-year net present value of total system cost by 1,794 million USD (+61.6%) and the system-average locational marginal price (LMP) by 45.2%, with operating cost dominating capital investment (84.2% versus 15.8%). Renewable curtailment remains negligible: existing renewables are already near full utilization, so the added demand is served almost entirely by incremental thermal generation. Most notably, the load shifts the optimal plan from transmission-only to generation-only, replacing the base-case 230 kV reinforcement with a new 400 MW combined-cycle gas turbine and showing that strategically sited generation can substitute for network reinforcement.
Chinese interpretation
本文探讨数据中心负荷对电网规划的影响，采用改进的IEEE 24节点系统进行分析。研究显示负荷分布影响最优规划方案，可能替代网络强化方案。以上判断仅依据摘要，方法细节与适用条件请核验原文。
Reference
Haoxiang Wan, Xingpeng Li, Jonghwan Kwon. Impact of Data Center Load on Co-Optimized Generation and Transmission Expansion Planning with Embedded Unit Commitment[J/OL]. (2026-10-13)[2026-11-01]. http://arxiv.org/abs/2610.18385v1.
AI Data Centers Meet Electrical Grids: A Review of Power Challenges and C…
Artificial intelligence (AI) workloads are creating large, power-electronic loads whose effects on power systems depend on location…
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AI Data Centers Meet Electrical Grids: A Review of Power Challenges and Coordinated Solutions
Research perspective (Chinese)
每个token更省电，仍可能伴随功率尖峰、扰动或碳排放问题。缓解措施应按扰动时间尺度匹配芯片、机架、储能与电网资源的响应能力。
- Published
- 2026-10-07
- Authors
- Mariam Mughees, Yuzhuo Li, Yize Chen, Yunwei Ryan Li
- Theme
- 算电协同
Abstract
Artificial intelligence (AI) workloads are creating large, power-electronic loads whose effects on power systems depend on location, temporal variability, controllability, and interactions with other loads and resources. Annual electricity consumption alone cannot capture these effects. This review examines how AI data centers affect power systems stability, renewable-energy integration, and low-carbon planning by tracing the power-delivery chain from accelerators and workloads through rack converters, uninterruptible power supplies (UPS), storage, and microgrids to the transmission point of interconnection. Reported oscillatory events and multi-gigawatt load transfers highlight how facility responses can amplify disturbances, while programmable workloads and controllable power converters offer opportunities for demand flexibility. Challenges and mitigation strategies are organized across device, rack, facility, and power systems levels, spanning millisecond-to-year timescales. A bandwidth-matching framework relates disturbances to the response capabilities of mitigation resources. The review distinguishes computational energy efficiency from power systems and sustainability outcomes: lower energy consumption per token does not necessarily reduce peak demand, electrical disturbances, or carbon emissions. It examines how coordinated workload scheduling, grid-interactive UPS systems, storage, high-voltage DC distribution, and flexible interconnection can support reliability, efficiency, and decarbonization. Key research needs include dynamic load models, high-bandwidth telemetry, carbon-and grid-aware scheduling, and mechanisms for valuing flexible computational demand.
Chinese interpretation
本文分析AI数据中心对电网的多维度影响，探讨设备至系统层面的协同解决方案。研究强调计算能效与电网可持续性的区别，提出柔性调度等策略。以上判断仅依据摘要，方法细节与适用条件请核验原文。
Reference
Mariam Mughees, Yuzhuo Li, Yize Chen, 等. AI Data Centers Meet Electrical Grids: A Review of Power Challenges and Coordinated Solutions[J/OL]. (2026-10-07)[2026-11-01]. http://arxiv.org/abs/2610.09060v1.
Keynote Session - From Data to Megawatts: AI and the Future of Energy
Organización Latinoamericana y Caribeña de Energía · Query: AI data center energy conference keynote。Useful as technical or researc…
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WeCan'22: The Software- and AI-Driven Future of Renewables - Shivkumar Ka…
Noman Bashir · Query: ACM SIGEnergy data center energy talk。Useful as technical or research context.
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World’s Largest AI Data Center Announced on DOE Land
U.S. Department of Energy - Oak Ridge · 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.
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Sponsored: One security strateg…
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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：Europe’s AI ambitions are hitt…
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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：Data center mooted in Glasgow b…
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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：Modular data center provider DX…
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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: The new age of contr…
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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：Sabey DC to pilot HVO fuel as d…
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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：A taxonomy of American data cen…
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 2MW（原文标题：PowerBank plans 2MW modu…
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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：DayOne signs MoU with Sembcorp…
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技术与产品进展：The Register 发布相关报道（原文标题：PC chip plunge wipes out a quarter of In…
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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：Where Solid-State Micro-Coolin…
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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：Rethinking the Blind-Mate Conn…
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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AI 算力基础设施动态：ServeTheHome 发布相关报道（原文标题：Aivres NVIDIA Vera Rubin and HGX B30…
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财报与资本开支观察：ServeTheHome 发布相关报道（原文标题：Intel Q3 2026 Earnings Data Center Soa…
Same-source item from the Chinese report. Verify details against the original linked source: 财报与资本开支观察：ServeTheHome 发布相关报道（原文标题：Int…
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Data Center Engineer DISPUTES "Closed Loop" Cooling Claims
Our Community Media · Query: OCP data center cooling workshop。Useful for product, market, or deployment context.
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Data Centers HVAC DESIGN CRITERIA- eBook
HVAC Education · Query: ASHRAE data center cooling webinar。Useful for product, market, or deployment context.
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Webinar Recording: Next Generations – Data Center Cooling Technologies
ASHRAE Pyramids Chapter · Query: ASHRAE data center cooling webinar。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
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AI 芯片供给与交付
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智算中心 CapEx/扩建
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电力并网与能源约束
Same-source item from the Chinese report. Verify details against the original linked source: 电力并网与能源约束
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4. Video signals
Data Center Engineer DISPUTES "Closed Loop" Cooling Claims
行业论坛 · Our Community Media · Query: OCP data center cooling workshop
Open on YouTubeData Centers HVAC DESIGN CRITERIA- eBook
标准组织讲座 · HVAC Education · Query: ASHRAE data center cooling webinar
Open on YouTubeKeynote Session - From Data to Megawatts: AI and the Future of Energy
学术会议报告 · Organización Latinoamericana y Caribeña de Energía · Query: AI data center energy conference keynote
Open on YouTubeWebinar Recording: Next Generations – Data Center Cooling Technologies
标准组织讲座 · ASHRAE Pyramids Chapter · Query: ASHRAE data center cooling webinar
Open on YouTubeWeCan'22: The Software- and AI-Driven Future of Renewables - Shivkumar Kalyanaraman
学术讲座 · Noman Bashir · Query: ACM SIGEnergy data center energy talk
Open on YouTubeWorld’s Largest AI Data Center Announced on DOE Land
学术会议报告 · U.S. Department of Energy - Oak Ridge · 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：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 28 条候选；池更新时间 2026-11-01 00:35。
- 论文中文增强：0 篇通过 Cloudflare 模型校验（Qwen 主模型，GLM 备用；备用 0 篇），复用 8 篇与原文一致的中文缓存；新增 0 篇研究启示。
- AI 分析：已使用 Cloudflare @cf/qwen/qwen3-30b-a3b-fp8 生成中文结构化研判；仅依据列出的来源材料，模型不作为事实来源。