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コンクリートライニング岩盤空洞における地下エネルギー貯蔵:熱-水-力学挙動、シーリング性能、CAESから水素への移行

Underground Energy Storage in Concrete-Lined Rock Caverns: Thermo-Hydro-Mechanical Behavior, Sealing Performance, and the CAES-to-Hydrogen Transition (原題)

Khan Waqar, Md Zakaria, Md Kayda Millat, Muhammad Yousuf, Rabiul Awal Sayeed

Scientia. Technology, science and society.📚 査読済 / ジャーナル2026-10-08#水素Origin: CN対象セクター: power
DOI: 10.59324/stss.2026.3(10).01
原典: https://doi.org/10.59324/stss.2026.3(10).01

🤖 gxceed AI 要約

日本語

155件の研究を統合し、コンクリートライニング岩盤空洞(LRC)における圧縮空気貯蔵(CAES)および地下水素貯蔵(UHS)の熱-水-力学(THM)挙動とシーリング性能をレビューした。圧力-応力-界面の分類体系を提案し、C40ライニングは内圧6〜8MPaで引張限界に達する一方、EGCは約12MPaまで延伸可能と示す。減圧時の逆間隙水圧勾配がシーリング破壊の支配機構であり、0.5〜1.0MPaで剥離が開始する。CAESから水素への移行は設計問題を質的に変え、鋼ライナーの脆化と熱変動幅の拡大(空気40〜60℃に対し60〜90℃)をもたらす。

English

This review synthesizes 155 studies on thermo-hydro-mechanical (THM) behavior and sealing performance of concrete-lined rock caverns (LRCs) for compressed air energy storage (CAES) and underground hydrogen storage (UHS). It proposes a pressure-stress-interface taxonomy, showing conventional C40 linings reach tensile capacity at 6-8 MPa internal pressure while engineered geopolymer composites extend this to ~12 MPa. Reverse pore-pressure gradients during depressurization govern sealing failure, with debonding initiating at 0.5-1.0 MPa differential pressure. The CAES-to-hydrogen transition qualitatively changes the design problem via steel embrittlement and wider thermal excursions (60-90°C vs 40-60°C for air).

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本は再生可能エネルギーの大量導入と電力系統の安定化が課題であり、長期大規模貯蔵技術としてのCAESや水素貯蔵はGX推進に直結する。特に水素社会実装を見据えた地下貯蔵の設計・安全基準は、国内の水素政策やインフラ整備に示唆を与える。

In the global GX context

Underground hydrogen storage and CAES are critical for balancing variable renewables at scale, aligning with global net-zero pathways and energy security goals. The review's focus on sealing failure mechanisms and material limits informs engineering standards for hydrogen infrastructure, relevant to EU and US decarbonization strategies.

👥 読者別の含意

🔬研究者:THMカップリングとシーリング破壊メカニズムの定量的レビューとして、地下貯蔵のモデル検証や新材料開発の基礎を提供する。

🏢実務担当者:CAESや水素貯蔵プロジェクトの設計・リスク評価において、ライニング材料選定や圧力管理の指針となり得る。

🏛政策担当者:地下エネルギー貯蔵の安全基準や許認可制度を検討する際、シーリング性能や長期モニタリングの重要性を示唆する。

📄 Abstract(原文)

Balancing variable renewable generation is driving demand for underground storage at large scale and long duration. Concrete-lined rock caverns (LRCs) provide a geographically flexible, engineered option for compressed air energy storage (CAES) and, more recently, for underground hydrogen storage (UHS), operating at internal gas pressures of roughly 4–30 MPa. Cyclic injection and withdrawal subject the composite lining to coupled thermo-hydro-mechanical (THM) loading that bears on structural integrity and on gas tightness alike. This review assembles quantitative evidence from 155 eligible studies, 44 of which were examined field by field and normalized to a common set of descriptors. A pressure–stress–interface taxonomy is proposed to separate internal gas pressure, concrete hoop stress and rock–lining contact pressure, quantities that are frequently reported interchangeably in megapascals. Cross-study tables covering 27 cavern designs indicate that conventional C40 linings reach their tensile capacity in hoop stress at internal pressures of about 6–8 MPa, whereas engineered geopolymer composites (EGC) extend that limit toward 12 MPa. Reverse pore-pressure gradients generated during depressurization emerge as the governing mechanism of sealing failure, with debonding reported to initiate at differential pressures of roughly 0.5–1.0 MPa. A CAES-to-hydrogen transferability matrix indicates that changing the working fluid alters the design problem rather than rescaling it, chiefly through embrittlement of steel liners and a wider thermal excursion per cycle, 60–90 °C against 40–60 °C for air. Few caverns at full scale have published long-term monitoring records, and this remains the principal obstacle to model validation; physics-informed neural networks are considered as one route to making use of the sparse field data that are available.

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