液冷与智算中心日报｜2026-07-17
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。
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to L…
Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these fr…
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How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization
- 发布时间
- 2026-07-09
- 作者
- Xinyi Wu、Siyuan Liu、Ali Jadbabaie
- 主题
- 算电协同
摘要
Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-uniformly. We study what determines this frequency usage and propose a data-centered explanation: RoPE frequencies are selected to match the relative-distance structure of the training data. Viewing each frequency as a positional lens, we formalize a field-resolution tradeoff and show that, for a data-induced dependency profile of width $W$, the optimal frequency scales as $1/W$. This frequency-matching principle explains controlled observations on synthetic and text-based data, and suggests that the mid-low frequency bands observed in language models arise from the multi-scale dependency structure of natural language. We further connect frequency selection to position-interpolation-based length generalization: scaling frequencies down expands the effective field while reducing resolution. This helps when longer-context dependencies are approximate dilations of those seen during training, but can fail when relevant dependencies do not scale with context length. Empirically, we show that natural language exhibits approximate self-similarity across positional scales, explaining why test-time frequency scaling can support long-context generalization. Overall, our results identify a data-driven mechanism behind emergent RoPE frequency usage and show that long-context generalization depends on two forms of scale matching: between learned frequencies and training-time dependencies, and between frequency scaling and how those dependencies extend to longer contexts.
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
Xinyi Wu, Siyuan Liu, Ali Jadbabaie. How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization[J/OL]. (2026-07-09)[2026-07-17]. http://arxiv.org/abs/2607.07678v1.
Storage as a Transmission Asset (SATA) for Large-Load Congestion Relief
Hyperscale data centers and other large concentrated loads can impose substantial new demand on existing transmission networks. If …
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Storage as a Transmission Asset (SATA) for Large-Load Congestion Relief
- 发布时间
- 2026-07-06
- 作者
- Abanish Tiwari、Chandan Chaudhary、Yansong Pei、Mohammed Ben-Idris、Joydeep Mitra
- 主题
- AI 运维优化
摘要
Hyperscale data centers and other large concentrated loads can impose substantial new demand on existing transmission networks. If import corridors lack sufficient transfer capability, operators may need to curtail load, delay interconnection, or reinforce the network to maintain reliable service. An energy storage system (ESS) deployed as a storage-as-transmission asset (SATA) offers a non-wires alternative by providing operator-directed support to constrained import corridors. However, the operating-level reliability value of SATA dispatch remains insufficiently quantified. This paper evaluates operator-directed SATA using a day-ahead DC optimal power flow that co-optimizes generation, ESS dispatch, and load curtailment across Monte Carlo scenarios of demand and generator availability. Operating reliability is assessed using expected energy not served (EENS), loss-of-load hours (LOLH), and the conditional value at risk (CVaR) of daily unserved energy. Congestion-price and flow-sensitivity metrics are used to identify the limiting corridor and storage location. The interconnection is then screened to determine whether SATA is suitable, reinforcement is required, or storage would provide little transmission value. Results show that operator-directed SATA reduces average unserved energy, loss-of-load exposure, and tail risk compared with deploying the same ESS for pure arbitrage. These results demonstrate that the operating designation of storage is a primary driver of its transmission value.
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
Abanish Tiwari, Chandan Chaudhary, Yansong Pei, 等. Storage as a Transmission Asset (SATA) for Large-Load Congestion Relief[J/OL]. (2026-07-06)[2026-07-17]. http://arxiv.org/abs/2607.04545v1.
AI-Driven Thermal Mapping and Management in 3D Integrated Photonic Circui…
Photonic Integrated Circuits (PICs) are advancing high-performance computing, data centers, and sensing, yet three-dimensional (3D)…
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AI-Driven Thermal Mapping and Management in 3D Integrated Photonic Circuits
- 发布时间
- 2026-06-25
- 作者
- Liton Kumar Biswas、Katayoon Yahyaei、Shajib Ghosh、M Shafkat M Khan、Himanandhan Reddy Kottur、Rayhane Ghane-Motlagh、Mahdi Nikdast、Navid Asadizanjani
- 主题
- 热管理与液冷
摘要
Photonic Integrated Circuits (PICs) are advancing high-performance computing, data centers, and sensing, yet three-dimensional (3D) PICs introduce critical thermal management challenges due to high-density bonding and heterogeneous materials. Traditional methods like thermal microscopes and in-package sensors yield sparse data, limiting full thermal profile visibility. This paper presents a dual-method solution combining an AI-driven thermal modeling framework with a design-based heuristic approach. The AI method integrates sparse sensor data with design layer and density information to predict multilayer temperature variations, while the heuristic approach uses localized material properties, design layout, component geometries, and sensor coordinates to refine thermal estimations in specific regions. A 2D thermal map of a 3D PIC is generated by interpolating sensor data and adjusting for local thermal resistivity using comparative analysis between design regions. The heuristic method complements the AI model, improving estimation accuracy without extensive training data. Together, these methods offer a scalable, accurate solution for real-time thermal mapping and design-time simulation, enabling reliable thermal management in next-generation 3D photonic systems.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Liton Kumar Biswas, Katayoon Yahyaei, Shajib Ghosh, 等. AI-Driven Thermal Mapping and Management in 3D Integrated Photonic Circuits[J/OL]. (2026-06-25)[2026-07-17]. http://arxiv.org/abs/2607.07711v1.
WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs
Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing thes…
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WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs
- 发布时间
- 2026-07-03
- 作者
- Mauricio Fadel Argerich、Jonathan Fürst、Marta Patiño-Martínez
- 主题
- 芯片与算力
摘要
Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination. While some predictive models exist, they still require profiling data and struggle to generalize to hardware unseen during training. To address this, we introduce \textit{WattGPU}, featuring two predictive models for mean GPU power draw and Inter-Token Latency (ITL). Our approach leverages only publicly available LLM metadata and GPU specifications, eliminating the need for hardware access or profiling while enabling generalization to unseen NVIDIA server-grade GPUs and LLMs. We evaluate our models using rigorous leave-one-GPU-out and leave-one-LLM-out cross-validation on a dataset of 42 open-source LLMs (0.1B--27B parameters) and 8 GPUs under both offline and server scenarios. The mean power draw model achieves a median absolute percentage error of $\leq3.4\%$ for offline and $\leq13.5\%$ for server scenarios on unseen GPUs, while the latency model achieves $\leq8.5\%$ in server mode, both maintaining strong GPU ranking correlations for server scenarios (Kendall $τ\geq0.76$). Compared to standard physically grounded baselines -- Load-Scaled Thermal Design Power (TDP) for power draw and roofline for latency -- our models reduce median absolute percentage error by approximately 4$\times$ on unseen LLM-GPU combinations for server scenarios or approximately 2$\times$ for completely unseen GPUs. WattGPU's data and code are publicly available at https://github.com/maufadel/wattgpu.
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
Mauricio Fadel Argerich, Jonathan Fürst, Marta Patiño-Martínez. WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs[J/OL]. (2026-07-03)[2026-07-17]. http://arxiv.org/abs/2607.02391v1.
Grid-Interactive Thermal Management of AI Data Centers via Contextual Dis…
Thermal management in AI data centers is increasingly challenged by bursty workloads and uncertain heat generation. To prevent ther…
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Grid-Interactive Thermal Management of AI Data Centers via Contextual Distributionally Robust Optimization
- 发布时间
- 2026-07-01
- 作者
- Jiachen Shen、Jian Shi、Yijie Yang、Chenye Wu、Dan Wang、Ju Bin Song、Zhu Han
- 主题
- 算电协同
摘要
Thermal management in AI data centers is increasingly challenged by bursty workloads and uncertain heat generation. To prevent thermal violations, existing cooling strategies either enforce conservative, rigid bounds that severely limit grid responsiveness, or rely on forecast-driven controllers that perform poorly under AI workload uncertainty and distribution shifts. To overcome the above challenges, this paper proposes a Contextual Distributionally Robust Optimization (CDRO) framework for grid-interactive cooling control. Unlike standard DRO with fixed ambiguity sets, the proposed approach dynamically adapts the Wasserstein radius using real-time AI and grid context. This safely shrinks uncertainty bounds during stable regimes, unlocking deep demand-side flexibility. Theoretically, we formulate the control as an infinite-dimensional inf-sup problem, derive an exact tractable reformulation for the Wasserstein worst-case expected-cost term, and then derive a tractable conservative deterministic counterpart for the Distributionally Robust Conditional Value at Risk (DR-CVaR) thermal safety constraint. Solved via a scalable nested Alternating Direction Method of Multipliers (ADMM) algorithm, the CDRO controller achieves near-zero thermal violations under extreme workload spikes in high-fidelity EnergyPlus co-simulations. Simultaneously, it reduces the operational cost premium of robustness by approximately 13.7 percentage points relative to standard Min-Max Model Predictive Control (MPC).
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
Jiachen Shen, Jian Shi, Yijie Yang, 等. Grid-Interactive Thermal Management of AI Data Centers via Contextual Distributionally Robust Optimization[J/OL]. (2026-07-01)[2026-07-17]. http://arxiv.org/abs/2607.00099v1.
Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable …
Terrestrial AI training faces an unsustainable energy and water crisis, positioning Orbital Data Centers (ODCs) as a "zero operatio…
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Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable Orbital AI Clusters
- 发布时间
- 2026-06-23
- 作者
- Shuyi Chen、Zhengchang Hua、Nikos Tziritas、Georgios Theodoropoulos
- 主题
- AI 运维优化
摘要
Terrestrial AI training faces an unsustainable energy and water crisis, positioning Orbital Data Centers (ODCs) as a "zero operational carbon" alternative. However, the sub-$10μ\text{s}$ communication latency required for synchronized scientific workloads, such as distributed Large Language Model (LLM) training, forces ODCs into extreme physical density, triggering a critical "Proximity-Thermal Paradox." As these high-density systems scale into Monolithic Structures or Proximity Swarms, they suffer from intense thermal-fluid crosstalk (heat traps in shared cooling loops) and thermal-radiative crosstalk (mutual heating that blocks deep-space cooling radiators). If left unmitigated, this persistent heat stagnation not only triggers severe thermal throttling that degrades training throughput, but also induces severe thermal fatigue, drastically shortening hardware lifespans and generating premature space e-waste. To make orbital AI truly sustainable, this position paper challenges traditional uniform load-sharing. We propose the Thermal-Aware Heterogeneity Thesis, which treats spatial cooling variances as a primary resource management dimension. Building on this, we introduce Thermal-Load Balancing (TLB), a software framework that dynamically migrates these intensive workloads to the coolest available units based on instantaneous fluid temperatures or absorbed radiation. Our analysis demonstrates that TLB resolves thermal bottlenecks to restore Model Flops Utilization (MFU), while simultaneously reducing physical thermal stress. Extending the operational lifespan of orbital hardware is crucial to amortize the massive embodied carbon of rocket launches, outlining a necessary pathway to scale orbital AI without accelerating e-waste.
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
Shuyi Chen, Zhengchang Hua, Nikos Tziritas, 等. Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable Orbital AI Clusters[J/OL]. (2026-06-23)[2026-07-17]. http://arxiv.org/abs/2606.26150v2.
Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructu…
Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debate…
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Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa
- 发布时间
- 2026-06-24
- 作者
- Kai-Hsin Hung、Sumaya Nur Adan、Krupa Suchak、Armita Sadeghian Barzoki、Kofi Yeboah、Mohammad Amir Anwar
- 主题
- 热管理与液冷
摘要
Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debates treat compute primarily as a technical input rather than as an outcome of investment, ownership, and financial control. This paper examines AI infrastructure investment flows across Africa through a systematic analysis of 46 publicly announced projects totalling USD $12.7 billion between 2019 and 2025. Using a value chain framework, we analyze who invests in AI-relevant infrastructure and where investments concentrate. Our findings reveal a highly concentrated landscape dominated by global data center operators, hyperscale technology firms, and development finance institutions, clustering in South Africa, Kenya, Nigeria, and Egypt. We introduce asymmetrical interdependence to describe a structural condition in which capital and physical infrastructure account for 73% of total funding while control remains concentrated in the compute layer among a small number of global technology firms. We argue that compute governance must account for capital flows, ownership, and control, not only geographic access, because these dynamics shape AI compute equity. Infrastructure presence is necessary but insufficient for meaningful governance capacity.
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
Kai-Hsin Hung, Sumaya Nur Adan, Krupa Suchak, 等. Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa[J/OL]. (2026-06-24)[2026-07-17]. http://arxiv.org/abs/2606.28404v1.
AI Data Centers and the Water Use Feedback Loop
AI data centres consume water for cooling, water scarcity constrains siting, and AI tools can improve water system efficiency. Thes…
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AI Data Centers and the Water Use Feedback Loop
- 发布时间
- 2026-06-20
- 作者
- Basit A. Akinade、Amobichukwu C. Amanambu、Jonathan M. Frame、Shaolei Ren
- 主题
- 热管理与液冷
摘要
AI data centres consume water for cooling, water scarcity constrains siting, and AI tools can improve water system efficiency. These dynamics are studied separately yet form a feedback loop. This review formalises the Water and AI Feedback Loop, introduces the Water Consumption Impact index to quantify community-scale utility burden, and demonstrates across ten US sites that burden spans three orders of magnitude, from 0.2% to 134% of host capacity.
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
Basit A. Akinade, Amobichukwu C. Amanambu, Jonathan M. Frame, 等. AI Data Centers and the Water Use Feedback Loop[J/OL]. (2026-06-20)[2026-07-17]. http://arxiv.org/abs/2606.21760v1.
Presentation on Latest in Liquid cooling solutions for Data Centers
ET Edge · 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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The Hottest Building on Earth? The Hidden Science Behind AI Data Centers …
[ MIDAS ] MTS Lab · 检索词：data center thermal management seminar。适合作为技术背景或研究趋势补充。
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Vertiv Investor Conference 2026 | Data Center Liquid Cooling Production S…
i101 · 检索词：data center liquid cooling conference presentation。适合作为技术背景或研究趋势补充。
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Vertiv Investor Conference 2026 | Data Center Liquid Cooling Production S…
i101 · 检索词：data center liquid cooling conference presentation。适合作为技术背景或研究趋势补充。
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SHORTS - WHY WE BOND (Neutral & Ground) Explained in 3 Minutes
Electrician U · 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 4 条，热度分 12。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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产业
产业新闻、技术产品、政策标准、投融资、项目和产业视频。
投融资、财报或公司动态：HPCwire 发布相关报道（原文标题：3M and Microsoft Partner on AI Data Cente…
发布时间：2026-07-17；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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Cooling Solutions for Data Centers
Advanced Cooling Technologies Inc. · 检索词：high performance computing data center cooling workshop。用于补充产业、产品或工程部署观察。
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Solidigm | The Physics of 400G Network Buffering and Storage Tuning
Tech Field Day · 检索词：high performance computing data center cooling workshop。用于补充产业、产品或工程部署观察。
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Testing Station for cooling fluid reservoir caps
MTS — Modern Technology Systems · 检索词：high performance computing data center cooling workshop。用于补充产业、产品或工程部署观察。
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产业热度指数 6/10
产业热度指数为 6/10：本期自动化检索记录到 9 条候选条目，指数按候选条目数量、来源可信度和栏目覆盖度保守计算。
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4. 最新视频观察
Presentation on Latest in Liquid cooling solutions for Data Centers
学术会议报告 · ET Edge · 检索词：data center liquid cooling conference presentation
在 YouTube 打开The Hottest Building on Earth? The Hidden Science Behind AI Data Centers | #CAE #MTS #MidasIT
专家讲座 · [ MIDAS ] MTS Lab · 检索词：data center thermal management seminar
在 YouTube 打开Vertiv Investor Conference 2026 | Data Center Liquid Cooling Production Scales For AI Systems
学术会议报告 · i101 · 检索词：data center liquid cooling conference presentation
在 YouTube 打开Vertiv Investor Conference 2026 | Data Center Liquid Cooling Production Scales For AI Systems
学术会议报告 · i101 · 检索词：data center liquid cooling conference presentation
在 YouTube 打开Cooling Solutions for Data Centers
技术研讨会 · Advanced Cooling Technologies Inc. · 检索词：high performance computing data center cooling workshop
在 YouTube 打开Solidigm | The Physics of 400G Network Buffering and Storage Tuning
技术研讨会 · Tech Field Day · 检索词：high performance computing data center cooling workshop
在 YouTube 打开Testing Station for cooling fluid reservoir caps
技术研讨会 · MTS — Modern Technology Systems · 检索词：high performance computing data center cooling workshop
在 YouTube 打开SHORTS - WHY WE BOND (Neutral & Ground) Explained in 3 Minutes
学术讲座 · Electrician U · 检索词：ACM SIGEnergy data center energy talk
在 YouTube 打开来源链接区
本次检索说明
- 当前自动化环境未配置 Tavily、Bing News 或 SerpAPI 检索密钥；脚本将使用公开 RSS/Atom、公共 arXiv 接口与固定监测源，不会编造产业新闻。
- 公开 RSS/Atom：Data Center Dynamics：检索失败，原因：fetch failed
- 公开 RSS/Atom：The Register：检索失败，原因：fetch failed
- 公开 RSS/Atom：ServeTheHome：检索失败，原因：fetch failed
- 公开 RSS/Atom：Data Center Knowledge：检索失败，原因：fetch failed
- 公开 RSS/Atom：NVIDIA Blog：检索失败，原因：fetch failed
- 论文池：已从本地论文池读取 26 条候选；池更新时间 2026-07-17 07:41。
- YouTube：检索失败，原因：fetch failed
- 视频推荐：当日未形成新候选，按上一日排序池顺延补位。
- 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：fetch failed
- x.ai 论文配图：论文 1 生成失败，已使用内置主题图；原因：fetch failed
- x.ai 论文配图：论文 2 生成失败，已使用内置主题图；原因：fetch failed
- x.ai 论文配图：论文 3 生成失败，已使用内置主题图；原因：fetch failed
- x.ai 论文配图：论文 4 生成失败，已使用内置主题图；原因：fetch failed
- x.ai 论文配图：论文 5 生成失败，已使用内置主题图；原因：fetch failed
- x.ai 论文配图：论文 6 生成失败，已使用内置主题图；原因：fetch failed
- x.ai 论文配图：论文 7 生成失败，已使用内置主题图；原因：fetch failed
- 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…