Modeling to connect big data and decision making

Numerical Simulation · Big Data · Artificial Intelligence · Uncertainty Quantification · Optimization · Crowd sourcing · Sensor Network

Research Vision

Thanks to Artificial Intelligence, we can extract a huge volume of data from the society and natural environment to an extraordinary level of details, velocity, and variety. However, it is still difficult to use the data to support decision-making. We believe the key is numerical modeling, which can effectively integrate data, transform data into information, and make predictions to guide decision making. Our research group aims to address two critical challenges to achieve the goal.

1) Data-Model Interface: how to enrich the big data source and develop a transforming interface to improve model reliability and accuracy.

2) Model-based Decision Making: how to inform decision making taking the advantage of the fast speed and high resolution of the model with large scale supercomputing.

Our lab has a wide spectrum of research topics, including coastal resilience, urban floods, wind energy, aquaculture, multiphase flows, sediment transport, nano-microfluidics, etc.

Grants and Works

New Grant for 5G interuption on Weather and Climate Monitoring/forecasting

WHIRLab received an NSF research grant to examine the impact of 5G leakage on weather forecasting. More. (9/1/2021)

New Project Funded for Storm Surge Warning System

WHIRLab received a new research grant from NJ Transit to design and develop a storm surge warning system to forecast and inform flooding in a railway station in NJ.(7/26/2021)

Reconfigurable Array of High-Efficiency Hydrokinetic Energy Harvesting

WHIRLab received new funds from US DOE to spearhead the environmental impact of tidal energy harvesting.(2/23/2021)

A new grant to support the NJ DOT for climate-resilient transportation systems

WHIRLab with a Rutgers team to receive a grant from NJ DOT to work with NJDOT/NJDEP to develop planning tools to enhance climate resilience in transportation planning (4/15/2020)

How to save 95% computational time? A new paper about DRESSA is published

We published a new paper to completely describe the DRESSA method and applied to two practical problems. Please check it here: Efficient dimension reduction and surrogate-based sensitivity analysis for expensive models with high-dimensional outputs and How to save 95% of computational load of numerical modeling in examining the coastal protection decision scenarios (2/1/2020)

WHIRLab received two more grants

WHIRLab received 1) NJ Water Resources Resesarch Institute Grant for the project of "Security and Sensitivity of Hydrological Model Forecasting to the Disruption of Sensor Networks" and 2) a contract award from Colgate-Palmolive Company for the project of "Theoretical and numerical study on the biological fluid flow in dentinal tubules" (12/11/2019)

First federal grant awarded!!

Department of Energy Announced $1.3 million to fund our project of "Computationally Efficient Atmospheric-Data-Driven Control Co-Design Optimization Framework with Mixed-Fidelity Fluid and Structure Analysis". The project is led by Dr Onur Bilgen at Mechanical Engineering, Rutgers (09/20/2019)

New Grants Awarded

Recently, WHIRL lab received three grants: The Rutgers Raritan River Consortium Mini-grant, Rutgers Research Council Grant, and Rutgers Energy Institute Undergraduate Research Opportunity Program Grant! These will allow us to do AI, Offshore Wind, and Coastal Flood research. (06/03/2019)

AI-supported High Tide Flooding Monitoring

High-tide Flooding is challenging to monitor because the existing observation infrastructure is limited in coverage or updating frequency such as gauges and satellite sensing. Crowdsourcing is considered the most promising solution but extracting high-quality data is difficult. We are developing an AI-supported monoplotting tool that brings us to a new level of data mining from social media images. This week, we presented our preliminary results on the high tide flood of Newport Beach, CA in July 2020 in the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities. This is the first Behzad's conference presentation. Congrats! More details are at here (11/10/2020)

WHIRLab cracks the 5G's impact on weather forrecasting

5G may have an unexpected impact on our daily life: less accurate weather forecasting. I'm excited to contribute to this study and look forward to developing solutions for the problem with the Rutgers WIN Lab. More details at here(10/15/2020)

Edenville Dam collapse shown in new 3D video made at Rutgers University

We recently used computer vision to 3D reconstruct the Edenville dam collapse. Please check the news report at here (6/28/2020)

WHIRLab contributed to inventing a new device to fight deadly infections

Tiny device could help professionals diagnose and fight deadly infections: New Device Quickly Detects Harmful Bacteria in Blood (3/23/2020)

Turbulence Characteristics and Mass Transport in the Near-Wake Region of an Aquaculture Cage

Aquaculture is booming in the world. Its environmental impact on the coastal development is still poorly understood. We published a new study to experimentally analyze the turbulent transport characteristics in the wake zone of the fish cage. The study is published in Water (2/23/2021).

Nature Human Behaviour Paper published

Twitter announced it would remove the precise geotagging feature in tweets. In addition to protecting the location privacy of users, this change also affects human behavior studies based on geotagged tweets. We discussed the potential impact of Twitter’s decision and how researchers can respond to this change. Very excited to publish in Nature journals for the first time. Thank you for the great collaboration, Yingjie Hu! The paper is at here (11/2/2020)

Satellite-based Ocean Wave Estimate

David presented in this week's virtual meeting of NSF EarthCube, Thursday June 18th. We are investigating how SAR satellite imagery can be used to provide an estimate of significant wave height. Congrats to the first conference presentation of David! (6/18/20)

Shreya Patil received funding from the Project SUPER program

Project SUPER (Science for Undergraduates: A Program for Excellence in Research) is a STEM-focused enrichment program that offers undergraduate women the opportunity to actively participate in academic research. She is working on data-driven recognition of chemical structures using AFM microscopy collaborating with ExxonMobile. Good job, Shreya! (06/20/2020)

How to better manage ash in engine?

WHIRLab joined a group to review the recent advances in managing ash in engines: The origin, transport, and evolution of ash in engine particulate filters (4/1/2020)

Coupling wind turbines and ocean-atmosphere modeling

Dr. Wang presented in the 2020 Ocean Sciences Meeting about offshore wind energy: Multi-scale Interaction between Wind Turbines and Coastal Processes: coupling OpenFAST with a regional coupled air-sea modeling system (2/16/2020)

New advances in Wave Energy Design

A new study to improve wave energy conversion is published: Hydrodynamic performance of an offshore-stationary OWC device with a horizontal bottom plate: Experimental and numerical study (12/11/2019)

A new CFD book published!

Our new CFD book has been published! Get it from here: Computational Fluid Dynamics: Applications in Water, Wastewater, and Stormwater Treatment (06/30/2019)

Invited talk at the Sustainable Urban Subsurface Systems Workshop

Dr. Wang gave an invited talk in the Sustainable Urban Subsurface Systems Workshop at New York University (06/25/2019)

Dr. Wang gave an invited talk at Princeton University

Dr. Wang was invited to give a presentation at the symposium of Building the Future: Smart Cities & New Technological Frontiers. (05/06/2019)

Co-organized a GeoAI session in the AAG Annual Meeting

We co-organized the GeoAI and Deep Learning Symposium: Geo-Text Data Analytics in the AAG Annual Meeting, Washington DC, 2019. (04/03/2019)

A Deformable Nano-sieve

We invented a new nanofluidic device that uses deformation to sieve particles of different sizes. The discovery has been published in Nanotechnology DOI. (06/03/2019)

Twitter + Citizen Science + AI = improved flood data collection

A new data-driven analysis method reveals hidden patterns: the different coastal hydrodynamics responses to tides, sea-level rises, and storm surges.

Can a city combat Sea-level Rise alone? A study reveals higher sea-level rise requires wider range of social collaboration.

Which CFD code is better to simulate annular reactors, OpenFOAM or Ansys Fluent? A comparative study.

Big data has a potential to improve the preparation of urban drainage area planning.

Modeling to create the next-generation low-energy drip irrigation.

More past research can be found at the publication section.