Liquid Cooling and AI Data Center Daily | 2026-09-16
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 2026-09-16.
- Collection window: 2026-09-15 08:00 北京时间 - 2026-09-16 08:00 北京时间.
- Coverage snapshot: 0 industry items; 0 technology items; 8 paper or white-paper items; 8 video signals.
- Current hot topics: 电力并网与能源约束, 液冷路线（冷板/浸没/两相）.
- The heat score is 6/10 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.
Operations, Maintenance, and Industrial Scaling of MW-Class Orbital Data …
Megawatt-class orbital data
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Operations, Maintenance, and Industrial Scaling of MW-Class Orbital Data Centers
- Published
- 2026-08-26
- Authors
- Slava G. Turyshev
- Theme
- AI 运维优化
Abstract
Megawatt-class orbital data
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
Slava G. Turyshev. Operations, Maintenance, and Industrial Scaling of MW-Class Orbital Data Centers[J/OL]. (2026-08-26)[2026-09-16]. https://arxiv.org/abs/2608.27499.
Data Center Life Cycle Co-Design Optimization
Liquid
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Data Center Life Cycle Co-Design Optimization
- Published
- 2026-08-19
- Authors
- Shrenik Jadhav, Vidhyashree Nagaraju, Zheng Liu
- Theme
- 热管理与液冷
Abstract
Liquid
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。
Reference
Shrenik Jadhav, Vidhyashree Nagaraju, Zheng Liu. Data Center Life Cycle Co-Design Optimization[J/OL]. (2026-08-19)[2026-09-16]. https://arxiv.org/abs/2606.15408.
AIDC Microgrid Vulnerability Assessment Under Computing-Power Coordinated…
The rapid growth of large language model (LLM) services is expanding AI
Expand
AIDC Microgrid Vulnerability Assessment Under Computing-Power Coordinated Attacks
- Published
- 2026-08-22
- Authors
- Ze Yu, Hongwei Zhen, Chao Shen, Mingyang Sun
- Theme
- 算电协同
Abstract
The rapid growth of large language model (LLM) services is expanding AI
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。
Reference
Ze Yu, Hongwei Zhen, Chao Shen, 等. AIDC Microgrid Vulnerability Assessment Under Computing-Power Coordinated Attacks[J/OL]. (2026-08-22)[2026-09-16]. https://arxiv.org/abs/2608.10645.
Convective Heat Transfer Optimization for Liquid Cooling Plates Driven by…
The rapid development of liquid
Expand
Convective Heat Transfer Optimization for Liquid Cooling Plates Driven by Field Synergy and Fractal Geometry
- Published
- 2026-09-10
- Authors
- Zixu Han, Peng Zhang
- Theme
- 热管理与液冷
Abstract
The rapid development of liquid
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。
Reference
Zixu Han, Peng Zhang. Convective Heat Transfer Optimization for Liquid Cooling Plates Driven by Field Synergy and Fractal Geometry[J/OL]. (2026-09-10)[2026-09-16]. https://arxiv.org/abs/2609.12344.
Minimizing Grid Interconnection Capacity Requirements for AI Data Centers…
Securing grid interconnection capacity has become a bottleneck for AI
Expand
Minimizing Grid Interconnection Capacity Requirements for AI Data Centers : A Developer-Side Planning Framework with Onsite Resources and Workload Flexibility
- Published
- 2026-08-29
- Authors
- Hassan Zahid Butt, Rida Fatima, Xingpeng Li
- Theme
- 算电协同
Abstract
Securing grid interconnection capacity has become a bottleneck for AI
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
Hassan Zahid Butt, Rida Fatima, Xingpeng Li. Minimizing Grid Interconnection Capacity Requirements for AI Data Centers : A Developer-Side Planning Framework with Onsite Resources and Workload Flexibility[J/OL]. (2026-08-29)[2026-09-16]. https://arxiv.org/abs/2608.29359.
GreenPassport: Request-Level Carbon Accounting for Cross-Border AI Infere…
AI
Expand
GreenPassport: Request-Level Carbon Accounting for Cross-Border AI Inference
- Published
- 2026-09-09
- Authors
- Rui Lu
- Theme
- 热管理与液冷
Abstract
AI
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。
Reference
Rui Lu. GreenPassport: Request-Level Carbon Accounting for Cross-Border AI Inference[J/OL]. (2026-09-09)[2026-09-16]. https://arxiv.org/abs/2609.06784.
From Grid to Chip: Power Architecture, Stability, and Flexibility of AI D…
The rapid growth of artificial intelligence ( AI
Expand
From Grid to Chip: Power Architecture, Stability, and Flexibility of AI Data Centers
- Published
- 2026-09-10
- Authors
- Yubo Song, Rui Kong, Takuro Umihara, Pooya Davari, Frede Blaabjerg, Subham Sahoo
- Theme
- 算电协同
Abstract
The rapid growth of artificial intelligence ( AI
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
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)[2026-09-16]. https://arxiv.org/abs/2609.11649.
Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Co…
This report extends our previous work (Part 1), which introduced an energy
Expand
Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 2
- Published
- 2026-09-10
- Authors
- Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado, Tommaso Salvatori, Germain Haugou, Eric Flamand
- Theme
- 热管理与液冷
Abstract
This report extends our previous work (Part 1), which introduced an energy
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。
Reference
Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado, 等. Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 2[J/OL]. (2026-09-10)[2026-09-16]. https://arxiv.org/abs/2609.11288.
Energy Efficient Data Center Power Design
IEEE Young Professionals · Query: IEEE data center energy efficiency lecture。Useful as technical or research context.
Expand
How The Massive Power Draw Of Generative AI Is Overtaxing Our Grid
CNBC · Query: AI datacenter power grid university lecture。Useful as technical or research context.
Expand
How we're going to power the AI data center buildout | Energy Sec. Chris …
Relentless · Query: AI data center energy conference keynote。Useful as technical or research context.
Expand
Internet of Things IoT
Engineering Funda · Query: IEEE data center energy efficiency lecture。Useful as technical or research context.
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The Outlook for Data Center Power Demand as AI Token Use Grows
Goldman Sachs · Query: AI data center energy conference keynote。Useful as technical or research context.
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Turning Data Centers Into Grid Assets with ON.energy
MCJ · Query: ACM SIGEnergy data center energy talk。Useful as technical or research context.
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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
展开全文
液冷路线（冷板/浸没/两相）
Same-source item from the Chinese report. Verify details against the original linked source: 液冷路线（冷板/浸没/两相）
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Industry
Industry news, products, policy, financing, projects, and market-oriented videos.
Cooling Strategies for Data Center Design and Energy Efficiency with CFD …
SimScale · Query: ASHRAE data center cooling webinar。Useful for product, market, or deployment context.
Expand
2024 ASHRAE Webinar: Adiabatic Solutions for Data Centers
Condair USA/CA · Query: ASHRAE data center cooling webinar。Useful for product, market, or deployment context.
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产业热度指数 6/10
Same-source item from the Chinese report. Verify details against the original linked source: 产业热度指数 6/10
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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
Energy Efficient Data Center Power Design
学术讲座 · IEEE Young Professionals · Query: IEEE data center energy efficiency lecture
Open on YouTubeHow The Massive Power Draw Of Generative AI Is Overtaxing Our Grid
专家讲座 · CNBC · Query: AI datacenter power grid university lecture
Open on YouTubeCooling Strategies for Data Center Design and Energy Efficiency with CFD (ASHRAE 90.4)
标准组织讲座 · SimScale · Query: ASHRAE data center cooling webinar
Open on YouTubeHow we're going to power the AI data center buildout | Energy Sec. Chris Wright & Scott Nolan
学术会议报告 · Relentless · Query: AI data center energy conference keynote
Open on YouTubeInternet of Things IoT
学术讲座 · Engineering Funda · Query: IEEE data center energy efficiency lecture
Open on YouTubeThe Outlook for Data Center Power Demand as AI Token Use Grows
学术会议报告 · Goldman Sachs · Query: AI data center energy conference keynote
Open on YouTubeTurning Data Centers Into Grid Assets with ON.energy
学术讲座 · MCJ · Query: ACM SIGEnergy data center energy talk
Open on YouTube2024 ASHRAE Webinar: Adiabatic Solutions for Data Centers
标准组织讲座 · Condair USA/CA · Query: ASHRAE data center cooling webinar
Open on YouTubeSources
Collection notes
- 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：HPCwire：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 15 条候选；池更新时间 2026-09-16 09:16。
- 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。
- When optional automation services are unavailable, this page uses traceable public sources and conservative rule-based summaries only; unverifiable facts are not filled in.