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Debnath, Mithu C.

Publications and source records attributed to Debnath, Mithu C..

Wind Energy Instrumentation Development Roadmap

The instrumentation development roadmap provides a guide on which measurements, and instruments, are important for future field campaigns to meet both the science objects and validation plans for their successful execution. The roadmap ensures the measurements are of sufficient type and resolution to meet science objectives and validation needs essential to the campaigns. The roadmap includes measurement categories ranging from instruments that need to be created to existing instruments essential to the field campaigns. The creation of the instrumentation development roadmap builds upon the previous Atmospheric to Electrons (A2e) validation work and formation of the American WAKE experimeNt (AWAKEN) science objectives.

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Wind Energy Instrumentation Development Roadmap

The current instrumentation for observing the complex flow fields in and around wind plants struggles to match the fidelity of existing simulation tools. As a result, these measurement limitations create a hurdle for validating and assessing the quality of the wind plant numerical models. This roadmap for instrumentation development recommendations was created to offer guidance on narrowing the gap between measurement and simulation fidelity. A process was established to identify where gaps in instrumentation exist for wind energy test campaigns by analyzing the capabilities of instrumentation for capturing the various important phenomena at the necessary resolution for both the science goal and validation objectives. To this end, a multi-disciplinary team of experts on instrumentation, wind energy, and atmospheric science was assembled to identify these significant instrumentation needs. A recommendation for instrumentation to be developed is provided, and the framework developed through this process is expected to be useful to the design of future test campaigns. The mapping tools developed for this process will be distributed as part of a future International Energy Agency Wind Technology Collaboration Program task on instrumentation development.

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Validation of RU-WRF, the Custom Atmospheric Mesoscale Model of the Rutgers Center for Ocean Observing Leadership

The Rutgers University Center for Ocean Observing Leadership (RU-COOL) contracted the National Renewable Energy Laboratory (NREL) to evaluate RU-COOL's atmospheric observation and modeling capabilities for characterizing the New Jersey offshore wind resource. The observational network used by RU-COOL consists of mostly public but some private coastal and offshore buoy-based stations. The core wind resource modeling capability of RU-COOL is a custom setup of the Weather Research and Forecasting (WRF) mesoscale model, referred to in this report as RU-WRF. The most unique feature of RU-WRF and not found in other WRF model setups is the use of custom sea surface temperature (SST) product generated by RU-COOL. This custom product was designed to better capture the unique coastal upwelling and strong storm mixing in the Mid-Atlantic Bight, which other typical SST products are not designed to capture. Funding for the development, maintenance, and use of RU-WRF by RU-COOL is provided by the New Jersey Board of Public Utilities, who also funded the validation work presented in this report. In this validation study, NREL was specifically tasked to: 1) Assess the observational network used by RU-COOL to validate RU-WRF and make recommendations for improvement, 2) Assess methods used by RU-COOL to validate RU-WRF and make recommendations for improvement, 3) Examine the inputs to and setup within RU-WRF, compare against available NREL data sets, and make recommendations for improvement.

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National Wind Technology Center-Characterization of Atmospheric Conditions

Data provided by the instrumentation located at the National Wind Technology Center (NWTC) is compiled below for the purposes of characterizing the atmospheric operating conditions at the National Renewable Energy Laboratories (NREL) wind site. Conditions are presented conditioned on month of the year to show seasonal variation, and based on characteristic quantities seen in the atmospheric flow including, bulk wind speed, direction, and atmospheric stability. All data reported and analyzed in the following document were collected from instrumentation mounted on the 135 m-tall masts erected at the NWTC near Boulder, Colorado. Each of the two met-masts (named M4 and M5) were sited specifically to measure the inflow wind to several of the utility-scale turbines at the NWTC. The construction, operation, and maintenance of the masts has been supported since their commissioning in 2011 by the Department of Energy, Office of Energy Efficiency and Renewable Energy, Wind and Water Power Program. Data from the masts are publicly available for download or basic visualization, hosted on NREL's website (https://nwtc.nrel.gov/135mData). Data from the masts have been used in the past for a wide range of projects including wind turbine power performance measurements, high fidelity model development and validation, and atmospheric science research. An interactive visualization platform has been developed on NREL's Open Energy Information platform and can be used to reproduce many of the graphics included in the current work. This report describes in detail the atmospheric conditions recorded by M5, the inflow mast for the DOE-owned GE 1.5 MW wind turbine. The report focuses on data recorded from August 2012 to May 2018 and includes: an introduction to the wind climate at the NWTC; a physical description of the met masts; a review of installed instrumentation; and data collection, processing, and quality control methods. In addition to a concise description of the instrumentation from the met masts and the data channels they provide, this report characterizes the behavior of the atmospheric resource first in a cumulative sense, discussing mainly the distributions wind speeds, flow directions and thermodynamic properties at several heights along the towers. Periodicity in atmospheric conditions is then detailed through analysis of diurnal and annual cycles of the data streams. Periods of both low turbulence and extreme atmospheric events that are of interest in terms of wind turbine operation and wind energy research are detailed in the final chapter of this report. Low turbulence events are somewhat rare at the NWTC and are of interest from the perspective of validating the operation of new wind turbine and blade designs. Similarly, the NTWC is known for a relatively higher rate of occurrence of extreme weather events.

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Benchmarks for Model Validation based on LiDAR Wake Measurements

Technology development and design decisions in wind energy are often based on results from simulations performed for individual wind turbines or entire wind plants. It is therefore critical to ensure that the models being used for research and industry applications in wind energy be thoroughly validated against measurements. A full-system validation of wind plant simulations must consider the atmospheric inflow, the response of the wind turbines, and their wakes. This task is complicated by the lack of freely available, quality-controlled, high-quality measurements. Here, such measurements are used to offer a validation exercise that can be used to assess the accuracy of models of any fidelity level. When it comes to real-world measurements, the dataset considered herein is simple in terms of terrain but exhibits pronounced diurnal cycles. Instead of a full-scale wind plant, we consider an individual research-scale, utility wind turbine instrumented for power and loads measurements. Three benchmarks are defined, with increasing levels of complexity: near neutral, slightly unstable, and very stable atmospheric stratification. Through comparisons between observations and simulations, the benchmarks provide complementary information about the model performance and its ability to reproduce mean and dynamic wake characteristics. This article describes the measurements and methodology used to define these benchmarks and provides the information required to perform simulations and conduct the model-measurement comparison. The objective is to provide a robust wake model validation exercise open to anyone, which will serve to minimize uncertainty in model validation practices related to varying methodologies across simulation tools and users.

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