Engineering topics

Huguenard, Kimberly

Publications and source records attributed to Huguenard, Kimberly.

3 records

DOE OSTI2024

Experimental validation of a Kalman observer using linearized OpenFAST and a fully instrumented 1:70 model

Abstract To enable real‐time monitoring and control strategies for floating offshore wind turbines, accurate information about the state of the system is needed. This paper details the application of a Kalman filter to the UMaine VolturnUS‐S floating wind platform to provide accurate state estimates in real time using minimal system measurements. The midfidelity nonlinear simulation tool OpenFAST was used to generate the underlying linear state‐space model for the Kalman filter. This linear model and its limitations are demonstrated through comparison with experimental data collected on a 1:70 froude‐scaled model of the floating platform and tower. Using a selection of five measurements from the real system, a Kalman filter was developed to provide estimates for the remaining system states and measurements. These estimates were then validated against the experimental values collected from testing of the scale model. Validation of the Kalman filter produced accurate estimates of surge, heave, and tower base bending moment, measurements of which were not available to the Kalman filter. Performance of the Kalman filter was tested and validated over a range of sea conditions from rated wind speed to storm events and demonstrated robustness in the Kalman filter to maintain accuracy across all operating conditions despite significant error in the underlying linear model for extreme conditions.

17 WIND ENERGY↗

DOE OSTI2024

On Building Predictive Digital Twin Incorporating Wave Predicting Capabilities: Case Study on UMaine Experimental Campaign - FOCAL

The response of floating wind turbines (FWT) are susceptible to stochastic wave variations. For the optimal operation of FWT, a comprehensive understanding of the phaseresolved wave dynamics and the consequential system response is crucial for real-time monitoring and control. A multi-variate, multi-step, long short term memory (MLSTM), a type of recurrent neural network (RNN) is used to capture complex system dynamics for real-time application. Results indicate that the integration of a wave prediction-reconstruction (WRP) model substantially enhances prediction accuracy by 50% on average relative to the baseline model. The improvement is consistent across various wave extremity and prediction horizons, thereby significantly broadening the scope for timely and precise predictive capabilities.

17 WIND ENERGY↗

DOE OSTI2022

Evolving Interior Mixing Regimes in a Tidal River Plume

Microstructure profiling was utilized to estimate vertical mixing (via vertical turbulent buoyancy flux) during a tidal pulse in the interior Merrimack River plume in calm winds. Multiple stratified shear mixing regimes appear and evolve with time. Initially the plume acts as a nearfield jet, with mixing in the plume (plume layer mixing) and over the plume-ambient interface (nearfield interfacial mixing). As the plume grows, interfacial mixing is suppressed offshore of the nearfield as currents slow, diminishing turbulent exchange between plume and shelf. At the end of ebb, ambient tidal currents reverse direction below plume, initiating another mode of internal, interfacial mixing (coined here as tidal interfacial mixing), allowing exchange between plume and ambient waters offshore. This work highlights previously unreported tidally modulated mixing within the near and midfield of a river plume.

54 ENVIRONMENTAL SCIENCES↗