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1. Gravitational wave memory and the wave equation

   https://doi.org/10.1088/1361-6382/ac7203  

   Garfinkle, David ( June 2022 , Classical and Quantum Gravity)

   Abstract Gravitational wave memory and its electromagnetic analog are shown to be straightforward consequences of the wave equation. From Maxwell’s equations one can derive a wave equation for the electric field, while from the Bianchi identity one can derive a wave equation for the Riemann tensor in linearized gravity. Memory in both cases is derived from the structure of the source of those wave equations. 

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2. Wave prediction using wave rider position measurements and NARX network in wave energy conversion

   https://doi.org/10.1016/j.apor.2018.10.016  

   Desouky, Mohammed A.A. ; Abdelkhalik, Ossama ( January 2019 , Applied Ocean Research)
3. Accelerating Atmospheric Gravity Wave Simulations Using Machine Learning: Kelvin‐Helmholtz Instability and Mountain Wave Sources Driving Gravity Wave Breaking and Secondary Gravity Wave Generation

   https://doi.org/10.1029/2023GL104668  

   Dong, Wenjun ; Fritts, David_C ; Liu, Alan_Z ; Lund, Thomas_S ; Liu, Han‐Li ; Snively, Jonathan ( August 2023 , Geophysical Research Letters)

   Gravity waves (GWs) and their associated multi‐scale dynamics are known to play fundamental roles in energy and momentum transport and deposition processes throughout the atmosphere. We describe an initial machine learning model—the Compressible Atmosphere Model Network (CAM‐Net). CAM‐Net is trained on high‐resolution simulations by the state‐of‐the‐art model Complex Geometry Compressible Atmosphere Model (CGCAM). Two initial applications to a Kelvin‐Helmholtz instability source and mountain wave generation, propagation, breaking, and Secondary GW (SGW) generation in two wind environments are described here. Results show that CAM‐Net can capture the key 2‐D dynamics modeled by CGCAM with high precision. Spectral characteristics of primary and SGWs estimated by CAM‐Net agree well with those from CGCAM. Our results show that CAM‐Net can achieve a several order‐of‐magnitude acceleration relative to CGCAM without sacrificing accuracy and suggests a potential for machine learning to enable efficient and accurate descriptions of primary and secondary GWs in global atmospheric models.

    

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4. Wave energy conversion under constrained wave-by-wave impedance matching with amplitude and phase-match limits

   https://doi.org/10.1016/j.apor.2019.101858  

   Korde, Umesh A. ( September 2019 , Applied Ocean Research)
5. Wave-by-Wave Control of a Wave Energy Converter with Deterministic Wave Prediction

   Korde, Umesh A. ( September 2017 , European Wave and Tidal Energy Conference)

   This paper discusses wave-by-wave near-optimal control of a wave energy device in irregular waves. A deterministic propagation model is used to predict the wave elevation several seconds into the future at the device location. Two prediction approaches are considered. The first is based on a time series being measured over an advancing time window at a particular up-wave location. This approach is here utilized in long-crested irregular waves. The second approach uses successive snapshots of wave elevation measurements over an up-wave area. This approach is found more convenient for multi-directional waves, and is here applied in a bi-directional wave irregular wave field. A small, heaving vertical cylinder reacting against a deeply submerged (i.e. assumed to undergo negligible oscillations) mass is studied under wave-by-wave control. The non-causal feedforward control force required for optimum velocity under a swept-volume constraint is based on the past, current, and predicted wave elevation at the device. Results for time-averaged converted power and displacement/force maxima are obtained for a range of irregular wave conditions. Also presented in addition are energy conversion results with a feedback-alone control force using a multi-resonant control technique. 

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