Award Abstract # 1849264
S&AS:INT:COLLAB: Aerodynamic Intelligent Morphing System (A-IMS) for Autonomous Smart Utility Truck Safety and Productivity in Severe Environments

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: UNIVERSITY OF ALABAMA AT BIRMINGHAM
Initial Amendment Date: April 19, 2019
Latest Amendment Date: April 19, 2019
Award Number: 1849264
Award Instrument: Standard Grant
Program Manager: James Donlon
jdonlon@nsf.gov
 (703)292-8074
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: May 1, 2019
End Date: April 30, 2023 (Estimated)
Total Intended Award Amount: $651,260.00
Total Awarded Amount to Date: $651,260.00
Funds Obligated to Date: FY 2019 = $651,260.00
History of Investigator:
  • Vladimir Vantsevich (Principal Investigator)
    vvantsevich@wpi.edu
  • Nasim Uddin (Co-Principal Investigator)
  • Roy Koomullil (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Alabama at Birmingham
701 S 20TH STREET
BIRMINGHAM
AL  US  35294-0001
(205)934-5266
Sponsor Congressional District: 07
Primary Place of Performance: University of Alabama at Birmingham
AL  US  35294-0001
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): YND4PLMC9AN7
Parent UEI:
NSF Program(s): S&AS - Smart & Autonomous Syst,
EPSCoR Co-Funding
Primary Program Source: 01001920DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 046Z, 9150
Program Element Code(s): 039Y00, 915000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Utility trucks are the first responders in areas of extreme weather situations for tasks such as rescuing people from disaster areas, cutting trees to restore traffic, and repairing electric posts and restoring power. This study establishes a scientific framework for maintaining the productivity and safety of emergency response vehicles while eliminating accidents. This is implemented via a novel integrated framework to monitor and predict weather conditions and feed that information into intelligent mechanisms that autonomously shape the aerodynamic surfaces of utility trucks. The project includes recruitment efforts and activities to integrate high-school students, as well as students from multiple cultures, and disciplines into autonomy research. This project is expected to contribute new scientific knowledge and engineering techniques for next generation transportation infrastructure resiliency, and to facilitate economic growth in the state of Alabama.

Unlike conventional approaches, the A-IMS will integrate model-free shape-morphing learning mechanisms with model-based interactive design to manage air-fluid flows, based on the road conditions, meteorology, speed limit, wind speed, and direction. This potentially transformative framework for A-IMS will: (1) bring new perspectives of learning to enhance the adaptability and intelligence in natural-engineering systems that leverage physical and information processes; (2) establish an integrated design framework for hazardous environments to achieve resilience, and productivity through integrated adaptation of morphological properties while also mitigating the effects of potentially adversarial learning agents that can exist in the cloud; (3) investigate the interactive physical components of the A-IMS, that will simultaneously operate in two different mediums of multi-phase fluids, and solids (i.e., the air/fluid and road). The A-IMS framework will be evaluated through hardware/software implementation, as well as in real-world conditions in the unique test conditions available at Wall of Wind at Florida International University. The project's education and outreach component include integrated research and education plans that will lead to technology transfer and summer camps with a special focus on reaching out to underrepresented minorities and women.

This award is jointly funded by the Division of Information and Intelligent Systems in the Directorate for Computer & Information Science & Engineering and the Established Program to Stimulate Competitive Research (EPSCoR)in the Office of Integrative Activities.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

(Showing: 1 - 10 of 12)
AbdelLate, A.I. and Uddin, Nasim "Baseline-free damage detection in bridges using acceleration records with the application of Laplacian" ASCE International Conference on Transportation & Development 2020, Seattle, Washington, USA , 2020 https://doi.org/ Citation Details
Jesse Paldan, Vladimir Vantsevich "4x4 Hybrid Electric Vehicle vs. Fully Electric Vehicle Mobility in Drastically Changing Terrain Conditions" Proceedings of the 3rd International Conference of IFToMM Italy , 2020 https://doi.org/ Citation Details
KUTSYK, Andriy "A Real-Time Model of Locomotion Module DTC Drive for Hardware-In-The-Loop Implementation" PRZEGLD ELEKTROTECHNICZNY , v.1 , 2021 https://doi.org/10.15199/48.2021.06.11 Citation Details
Patel, Parth Y. and Happawana, Gemunu and Vantsevich, Vladimir V. and Boger, David and Harned, Chris "Morphing Capabilities and Safe Operation Zone of the Utility Truck Boom Equipment" IMECE2020 Proceedings , 2020 https://doi.org/10.1115/IMECE2020-24283 Citation Details
Patel, Parth Y. and Jannoi, Thannathorn and Zou, Wenhui and Vantsevich, Vladimir and Koomullil, Roy "Aerodynamic Analysis of the Utility Truck With the Morphing Boom Equipment" Aerodynamic Analysis of the Utility Truck With the Morphing Boom Equipment , v.2 , 2022 https://doi.org/10.1115/FEDSM2022-88368 Citation Details
Patel, Parth Y and Jia, Hua and Vantsevich, Vladimir V and Koomullil, Roy "Modelling Effect of Rain on Aerodynamic Performance of the Ahmed Body" AIAA SCITECH 2022 Forum , 2021 https://doi.org/10.2514/6.2022-0335 Citation Details
Patel, Parth Y. and Krishnamurthy, Chandramouli and Clausman, Gavin and Vantsevich, Vladimir and Koomullil, Roy "Modelling Effect of Rain on the External Aerodynamics of the Utility Truck with the Morphing Boom Equipment: Computations and Wind Tunnel Testing" Modelling Effect of Rain on the External Aerodynamics of the Utility Truck with the Morphing Boom Equipment: Computations and Wind Tunnel Testing , 2023 https://doi.org/10.2514/6.2023-1761 Citation Details
Patel, Parth Y. and Vantsevich, Vladimir and Happawana, Gemunu and Harned, Chris and Boger, David "Dynamic Formulation of the Utility Truck with the Morphing Boom Equipment" SAE Technical Paper Series , v.1 , 2022 https://doi.org/10.4271/2022-01-0917 Citation Details
Patel, Parth Y. and Vantsevich, Vladimir V and Whitson, Jordan "Utility Truck: Morphing Boom Equipment for Terrain Mobility" 20th International Conference and 9th Americas Conference of ISTVS 2021 , 2021 Citation Details
Vantsevich, Vladimir and Gorsich, David and Paldan, Jesse R. and Letherwood, Michael "A Virtual Driveline Concept to Maximize Mobility Performance of Autonomous Electric Vehicles" SAE Technical Paper Series , v.1 , 2020 https://doi.org/10.4271/2020-01-0746 Citation Details
Vantsevich, Vladimir and Paldan, Jesse "A Generalized Approach to Virtual Driveline Systems for E-Vehicle Operation Improvements" 27th IAVSD Symposium on Dynamics of Vehicles on Roads and Tracks , 2021 Citation Details
(Showing: 1 - 10 of 12)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

  Utility trucks are the first responders in areas of extreme climate and severe weather situations. Thus, it is crucial to create an advanced and effective scientific framework for maintaining productivity and safety while eliminating accidents. Towards that direction, the "Aerodynamic Intelligent Morphing Systems (A-IMS) for Autonomous Smart Utility Truck Safety and Productivity in Severe Environments" project aimed to establish a novel and integrated framework to monitor and predict weather conditions and feed such information into reinforcement learning mechanisms that enabled to shape the utility truck autonomously. This integration includes the model-free shape-morphing learning mechanisms to manage air-fluid flows based on road conditions, meteorology, speed limit, wind speed, and direction.

  The ultimate goal of the A-IMS is to provide autonomous morphing capabilities based on, (i) intelligent and secure awareness on the current state of the autonomous truck's aero- and road- dynamics, roadway, terrain characteristics, meteorology, and multi-hazardous environments, (ii) intelligent estimation of the in-coming multi-hazardous environments, reasoning, decision making on "to go" or "not to go" through unknown environmental conditions and, if the decision "to go" is made, then, (iii) intelligent morphing of the entire Autonomous Smart Utility Truck (ASUT).

  The project was organized into three research thrusts and the evaluation plan, which complement each other, as provided in the Figure. Specifically, in Thrust 1, the main thrust, we investigated the learning mechanisms that integrate all the project components, which includes (i) knowledge gaining with augmented real-time weather, tire-road, truck powertrain data parametrization, intelligent database, (ii) adaptive, secure, smart autonomy, with self-learning, self-healing, self-improvement, (iii) intelligent awareness of aero-road/terrain truck, and (iv) reasoning, and decision making which captures the safe zone.

  In Thrust 2, we analyzed the model-based design to counterbalance, mitigate, and manage the aerodynamic forces and moments of the hazardous environments (wind, rain, or snow), thus, holding the truck within the safe zone. First, the Discrete Phase Model based computational methodology was evaluated and authenticated to estimate the effect of rain on the aerodynamic performance. Second, a novel approach, a method, and mathematical models were developed to investigate the morphing and inverse dynamics of the truck's boom equipment and truck multi-body combinations in on- and off-road conditions. Moreover, a novel comprehensive study of the morphing system is investigated, which utilizes the active aerodynamics and truck dynamics to manage the truck tire-road forces and the aerodynamic multi-phase forces, thus, improving its stability and safety under critical weather conditions. Finally, two smart morphing structures, (i) the morphable boom equipment and (ii) the morphing device, are considered in this morphing system. It was concluded that by combining morphing boom equipment and the proposed designed morphing device, it is possible to manage aero- and road/off-road-dynamic forces and moments to keep the truck within the specified safe zone in the hazardous weather conditions.

  Furthermore, in Thrust 3, we investigated (i) the morphing geometric characteristics, (ii) the fluid flows around the morphing truck, and (iii) the fields of the 3D-tire-road/terrain forces. The actual multi-phase hazardous weather and roadway environments were investigated by utilizing statistics data from several States and new Computational Fluid Dynamics methods.

  In evaluation plan, the outcomes of the three thrusts were integrated and rigorously evaluated through mixed software/hardware simulations in virtual and real hazardous conditions. The experiments were conducted at the Wall of Wind Experimental Facility, which can generate wind speeds up to 70 m/s. The experimental work was aimed to verify and validate the new CFD mathematical models developed in Thrust 3 and to confirm that the CFD model agrees well with the previous analytical/experimental results in truck aerodynamics.

  In the presence of multi-hazardous weather and roadway conditions, the model-free learning design gets information from the Traffic Data Acquisition (TDA) and the Truck Monitoring and Warning System (TMW). The TDA receives weather and roadway information from existing traffic control systems, and the TMW obtains the truck-aero-surface information from non-co-located sensors of the physical smart morphing system and the model-based design of Thrust 2. The integration forms an intelligent database for experience replay that is further utilized in Thrust 1. Thus, the safe zone was estimated (i) by the A-IMS/TMW for the current weather conditions of the truck and (ii) by the TDA for in-coming conditions. This TDA data were used to make the decision of "to go" or "not to go" into the in-coming weather conditions. If the decision "to go" is made, then intelligent morphing of the entire ASUT is autonomously done to manage the aerodynamic flows.

  In conclusion, the A-IMS project successfully developed a methodology to predict the safe operating conditions for the utility truck using model-based design by controlling the aerodynamic forces and moments in hazardous environments. Integrating the proposed aerodynamic morphing capabilities with autonomous systems and traffic & weather data offers a promising direction for future advancements in the utility truck industry, benefiting both operators and society.  

 

 


Last Modified: 08/04/2023
Modified by: Vladimir V Vantsevich

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page