Towards an improved understanding of tidal turbine dynamics in a turbulent marine environment
Towards an improved understanding of tidal turbine dynamics in a turbulent marine environment
批准号:
1706358
负责人:
Arindam Banerjee
金额:
$30.88万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2024-02-29
中文摘要
可再生能源技术为化石和核燃料发电厂提供了无污染和可持续的替代方案。该研究项目的目的是更好地了解潮汐涡轮机与周围流体环境之间的相互作用。潮汐涡轮机是一种相对较新的可再生能源设备,可以将河流或潮汐中流动的水的运动转化为能量。这项研究的重点是解决几个需要克服的突出科学挑战,以便开发和部署这些新的设备。除了科学发现之外,该研究项目的教育部分还包括通过研究、教学和推广活动培训下一代工程师和专业人员。外展部分包括与利哈伊大学S外展办公室和北安普顿社区学院合作开展STAR(准备好的学生)计划,该计划针对经济和学业上处于不利地位和/或处于危险之中的8至12年级学生。潮汐涡轮机技术的长期成功对社会产生了重大影响。除了美国大陆,这些技术的商业化可能会直接影响世界上很大一部分人口,因为大多数大型人口中心要么靠近主要河流,要么靠近海岸线。本研究项目的实验设计是出于需要将近尾流和远尾流物理与涡轮机在较高水平的自由气流湍流下的性能相耦合。采用主动网格的湍流产生技术正被用来模拟水力和自然的水流条件。诊断包括用于系统分析的反作用力矩-推力传感器和用于流场测量的时间分辨立体粒子图像测速仪。在考虑潮汐发电场(类似于风力发电场)的设计时,详细了解尾流的演变是至关重要的。在河流中设计最优的农场布局是具有挑战性的,因为由于航行要求,提取电力的有用区域可能被限制在很小的占地面积内。现有的工程分析没有完全捕捉到由于这种紧密排列的布局或相关的环境影响而产生的非线性相互作用。为了加强科学影响,科学界正在提供新的数据集,以便对潮汐涡轮机周围流动的高级计算流体力学模型进行验证和验证。
英文摘要
Renewable energy technologies offer the promise of non-polluting and sustainable alternatives to fossil and nuclear-fueled power plants. The objective of this research project is to better understand the interactions between a tidal turbine and the surrounding fluid environment. Tidal turbines are a relatively new class of renewable energy devices that convert the motion of flowing water in rivers or tides into energy. This research is focused on addressing several outstanding scientific challenges that need to be overcome so that these novel devices can be developed and deployed. In addition to the scientific discoveries, the educational component of this research project involves training the next generation of engineers and professionals through research, teaching, and outreach activities. The outreach component involves partnering with Lehigh University?s Office of Outreach and Northampton Community College in the STAR (Students That Are Ready) program, which targets economically and academically disadvantaged and/or at-risk 8th to 12th grade students. The social impact of the long-term success of tidal turbine technology is significant. In addition to the continental United States, commercialization of these technologies may directly impact a major fraction of the world population as the majority of large population centers are located either close to major rivers or coastlines. The experimental design in this research project is motivated by the need to couple near and far wake physics to turbine performance at elevated levels of free-stream turbulence. Turbulence generation techniques using active grids are being used to mimic hydraulic and natural in-stream conditions. Diagnostics include a reaction torque-thrust sensor for system analysis and time-resolved stereo particle image velocimetry for flow-field measurements. A detailed understanding of the evolution of the wake is vital when considering design of a tidal farm (similar to wind farms). Designing an optimal farm layout in rivers is challenging as the useful zone for power extraction may be restricted to a small footprint due to navigational requirements. Existing engineering analysis do not fully capture the non-linear interactions that arise due to such closely packed layouts or the associated environmental impact. To enhance the scientific impact, the new data sets are being made available to the scientific community to allow validation and verification of advanced computational fluid dynamics models of flow around tidal turbines.
期刊论文(7)
专著(0)
科研奖励(0)
会议论文
登录
查看更多内容
DOI:
10.1016/j.renene.2021.05.152
发表时间:
2021
期刊:
Renewable Energy
影响因子:
8.7
作者:
[P. Modali;A. Vinod;A. Banerjee]
通讯作者:
P. Modali;A. Vinod;A. Banerjee
DOI:
10.1016/j.apenergy.2019.113639
发表时间:
2019-07
期刊:
Applied Energy
影响因子:
11.2
作者:
[A. Vinod;A. Banerjee]
通讯作者:
A. Vinod;A. Banerjee
DOI:
--
发表时间:
2019
期刊:
Italy
影响因子:
--
作者:
[Modali, Pranav K, Banerjee, Arindam]
通讯作者:
Banerjee, Arindam
DOI:
10.1016/j.renene.2021.05.026
发表时间:
2021-09
期刊:
Renewable Energy
影响因子:
8.7
作者:
[A. Vinod;Cong Han;A. Banerjee]
通讯作者:
A. Vinod;Cong Han;A. Banerjee
DOI:
10.36688/imej.1.41-50
发表时间:
2018-09
期刊:
International Marine Energy Journal
影响因子:
--
作者:
[P. Modali;Nitin Kolekar;A. Banerjee]
通讯作者:
P. Modali;Nitin Kolekar;A. Banerjee
共 7 条
NRT - Stakeholder Engaged Equitable Decarbonized Energy Futures
-
批准号:2244162
-
项目类别:Standard Grant
-
资助金额:$299.87万
-
财政年份:2023
-
负责人:Arindam Banerjee
-
依托单位:
Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting
-
批准号:2130835
-
项目类别:Continuing Grant
-
资助金额:$38.52万
-
财政年份:2021
-
负责人:Arindam Banerjee
-
依托单位:
III: Small: Stochastic Algorithms for Large Scale Data Analysis
-
批准号:2131335
-
项目类别:Continuing Grant
-
资助金额:$50.0万
-
财政年份:2021
-
负责人:Arindam Banerjee
-
依托单位:
PFI-TT: Advancing the Technology Readiness of Pylon Fairings for Tidal Turbines
-
批准号:1919184
-
项目类别:Standard Grant
-
资助金额:$25.0万
-
财政年份:2019
-
负责人:Arindam Banerjee
-
依托单位:
III: Small: Stochastic Algorithms for Large Scale Data Analysis
-
批准号:1908104
-
项目类别:Continuing Grant
-
资助金额:$50.0万
-
财政年份:2019
-
负责人:Arindam Banerjee
-
依托单位:
Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting
-
批准号:1934634
-
项目类别:Continuing Grant
-
资助金额:$38.52万
-
财政年份:2019
-
负责人:Arindam Banerjee
-
依托单位:
III: Medium: Collaborative Research: Bayesian Modeling and Inference for Quantifying Terrestrial Ecosystem Functions
-
批准号:1563950
-
项目类别:Continuing Grant
-
资助金额:$72.4万
-
财政年份:2016
-
负责人:Arindam Banerjee
-
依托单位:
CAREER: Transition to Turbulence and Mixing for Rayleigh Taylor Instability with Acceleration Reversal
-
批准号:1453056
-
项目类别:Standard Grant
-
资助金额:$50.0万
-
财政年份:2015
-
负责人:Arindam Banerjee
-
依托单位:
BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data
-
批准号:1447566
-
项目类别:Standard Grant
-
资助金额:$35.7万
-
财政年份:2014
-
负责人:Arindam Banerjee
-
依托单位:
EAGER: Collaborative Research: Learning Relations between Extreme Weather Events and Planet-Wide Environmental Trends
-
批准号:1451986
-
项目类别:Standard Grant
-
资助金额:$10.0万
-
财政年份:2014
-
负责人:Arindam Banerjee
-
依托单位:
RI: Small: Finding Patterns in Complex Data with Probablistic Graphical Models
-
批准号:1422557
-
项目类别:Standard Grant
-
资助金额:$45.0万
-
财政年份:2014
-
负责人:Arindam Banerjee
-
依托单位:
TWC: Medium: Collaborative: HIMALAYAS: Hierarchical Machine Learning Stack for Fine-Grained Analysis of Malware Domain Groups
-
批准号:1314560
-
项目类别:Standard Grant
-
资助金额:$25.25万
-
财政年份:2013
-
负责人:Arindam Banerjee
-
依托单位:
Buoyancy Driven Turbulence Beyond Self-Similar Equilibrium
-
批准号:1305512
-
项目类别:Standard Grant
-
资助金额:$18.98万
-
财政年份:2012
-
负责人:Arindam Banerjee
-
依托单位:
CAREER: Combinatorial Online Learning and its Applications
-
批准号:0953274
-
项目类别:Continuing Grant
-
资助金额:$49.58万
-
财政年份:2010
-
负责人:Arindam Banerjee
-
依托单位:
Buoyancy Driven Turbulence Beyond Self-Similar Equilibrium
-
批准号:0967672
-
项目类别:Standard Grant
-
资助金额:$28.56万
-
财政年份:2010
-
负责人:Arindam Banerjee
-
依托单位:
RI: Small: Statistical Modeling of Dynamic Covariance Matrices
-
批准号:0916750
-
项目类别:Standard Grant
-
资助金额:$45.5万
-
财政年份:2009
-
负责人:Arindam Banerjee
-
依托单位:
III-COR-Small: Multi-Relational Data Clustering with Probabilistic Mixture Models
-
批准号:0812183
-
项目类别:Standard Grant
-
资助金额:$39.96万
-
财政年份:2008
-
负责人:Arindam Banerjee
-
依托单位:
海外基金