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Mostafa Momen

from Houston, TX
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Related to:
Abdul Momen, 50Abdul Momen, 66Kabeer MomenMaryam M Momen, 38Kavon K Momen, 38
Connected to:
Ali MomenKhandker N Momen, 52Safiyy Momen, 39Maria Momene, 63Rachel A Momenee, 35Betty H Momenian, 45Firouz Momeni, 66Hossein Momeni, 51Jack Momeni, 63Lida Momeni, 36

Mostafa Momen Phones & Addresses

  • Houston, TX
  • Kissimmee, FL
  • Princeton, NJ

Resumes

Resumes

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Postdoctoral Fellow

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Location:
Princeton, NJ
Industry:
Research
Work:
Stanford University
Postdoctoral Fellow
Education:
The Data Incubator 2017 - 2017
Princeton University 2012 - 2017
Doctorates, Doctor of Philosophy, Philosophy, Environmental Engineering Massachusetts Institute of Technology 2014 - 2015
Doctorates, Doctor of Philosophy, Philosophy, Mechanical Engineering Sharif University of Technology 2008 - 2012
Bachelors, Environmental Engineering
Skills:
Matlab
Civil Engineering
Structural Analysis
Microsoft Office
Autocad
Hydraulics
Microsoft Excel
Structural Engineering
Turbulence
Finite Element Analysis
Engineering
Hydrology
Cad
Sap2000
Modeling
Concrete
Engineering Design
Microsoft Project
Autocad Civil 3D
Environmental Fluid Mechanics
Steel Design
Etabs
Water
Project Engineering
Water Resources
Ms Project
Geophysical Fluid Dynamics
Large Eddy Simulation
Construction
Construction Management
Surveying
Environmental Engineering
Steel Structures
Feasibility Studies
Project Estimation
Bridge
Geotechnical Engineering
Highways
Drainage
Deep Learning
Machine Learning
Python
Data Science
Natural Language Processing
Languages:
English
Persian
Turkish
Arabic
Certifications:
Program Graduate
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Mostafa Momen

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Publications

Us Patents

System And Method For Performing Wind Forecasting

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US Patent:
20180062393, Mar 1, 2018
Filed:
Mar 28, 2016
Appl. No.:
15/557610
Inventors:
- Princeton NJ, US
Mostafa Momen - Princeton NJ, US
Assignee:
Trustees of Princeton University - Princeton NJ
International Classification:
H02J 3/38
G01W 1/10
H02K 7/18
H02P 9/48
G05B 13/04
Abstract:
A system and method for performing novel wind forecasting that is particularly accurate for forecasting over short-term time periods, e.g., over the next 1-5 hours. Such wind forecasting is particularly advantageous in wind energy applications. The disclosed method is anchored in a robust physical model of the wind variability in the atmospheric boundary layer (ABL). The disclosed method approach leverages a physical framework based on the unsteady dynamics of earth's atmosphere, and drives forecasting as a function of previously-observed atmospheric condition data observed at the same location for which a wind forecast is desired.
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Mostafa Momen from Houston, TX Get Report