Results 131 to 140 of about 18,494 (307)

The 2025 Kamchatka Earthquake and Tsunami: Cascading Impacts and Risks to Japan

open access: yesEarthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT On July 29th, 2025, a large earthquake (Mw 8.8) occurred near the Kamchatka Peninsula. This event caused a tsunami that reached Japan's Pacific coast, even though the source was far away. In this study, we analyze the characteristics of earthquakes and tsunamis and discuss how they relate to the recurrence possibility and risk in Japan.
Ampan Laosunthara   +6 more
wiley   +1 more source

Development of a contra-rotating tidal current turbine and analysis of performance [PDF]

open access: yes, 2007
A contra-rotating marine current turbine has a number of attractive features: nearzero reactive torque on the support structure, near-zero swirl in the wake, and high relative inter-rotor rotational speeds.
Clarke, Joseph Andrew   +9 more
core  

Uneven Impacts of Seawall Heightening on Tsunami Risk Reduction under Rising Sea Levels: Probabilistic Scenarios from the Japan Trench

open access: yesEarthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Disasters exacerbated by climate change have prompted adaptation measures, including seawall heightening, which is an effective approach to protecting coastal areas. However, the combined effect of rising sea levels and tsunamis can create a compound coastal hazard, in which long‐term sea level rise amplifies the impact of tsunamis.
Yushi Miki   +7 more
wiley   +1 more source

New methodologies and scenarios for evaluating tidal current energy potential [PDF]

open access: yes, 2012
Transition towards a low carbon economy raises concerns of loss of security of supply with high penetrations of renewable generation displacing traditional fossil fuel based generation.
Sankaran Iyer, Abhinaya
core  

Spatial Variations in Methane Content and Their Main Controlling Factors of the Deep‐Buried Coalbed in the Nalinhe–Hengshan Area, Ordos Basin

open access: yesEnergy Science &Engineering, EarlyView.
Ash yield is the primary geological factor controlling the spatial variations in the total gas content of the deep‐buried coalbed methane in the Nalinhe‐Hengshan area, Ordos Basin. However, porosity dominates the differential enrichment of the free gas content of the deep‐buried coalbed methane in the Nalinhe‐Hengshan area, Ordos Basin.
Shi Yunhe   +9 more
wiley   +1 more source

Real‐Time Incremental Learning Artificial Neural Networks Maximum Power Point Tracking With Raspberry Pi‐Based Meteorological Data Acquisition

open access: yesEnergy Science &Engineering, EarlyView.
We present a smart solar tracking method using artificial intelligence to improve the efficiency of solar panels. Unlike traditional techniques, our system learns and adapts to changing sunlight conditions, ensuring faster and more reliable power generation for real‐world energy needs.
Rida Amine   +5 more
wiley   +1 more source

A Comprehensive Review of AI‐Powered Energy Systems

open access: yesEnergy Science &Engineering, EarlyView.
The role of Artificial Intelligence (AI) in developing next‐generation energy systems is getting more day by day. Therefore, incorporating AI enables real‐time decision‐making and advanced grid management, which are essential for optimizing the use of intermittent renewable sources like wind and solar power.
Armin Razmjoo   +5 more
wiley   +1 more source

Relating fish distributions to physical characteristics of a tidal energy candidate site in the Banks Strait, Australia

open access: yesInternational Marine Energy Journal, 2020
With the tidal energy industry moving towards commercial-scale developments, it is important to consider potential interactions between tidal energy converters (TECs) and the marine environment prior to the instalment of large-scale TEC arrays. The Banks
Constantin Scherelis   +5 more
doaj  

Hybrid Simulation–Machine Learning Surrogates for Coordinate‐Based Solar and Wind Energy Yield Assessment in Iraq: A Streamlit Decision‐Support Tool

open access: yesEnergy Science &Engineering, EarlyView.
This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem   +3 more
wiley   +1 more source

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