
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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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: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
701 S 20TH STREET BIRMINGHAM AL US 35294-0001 (205)934-5266 |
Sponsor Congressional District: |
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Primary Place of Performance: |
AL US 35294-0001 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
S&AS - Smart & Autonomous Syst, EPSCoR Co-Funding |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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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
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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
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