Cephalopods-inspired Self-morphing Skin for Dynamic Antifouling and Turbulence Tripping

Abstract

Approved for Public Release:Biofouling is the undesired accumulation of organic molecules, microorganisms, plants, animals, and thei,r by-products on submerged or partially submerged surfaces in a natural aqueous environment and is a major economic problem for the,marine industry due to increase of hydrodynamic drag, fuel consumption, maintenance and environmental compliance costs, and invasion, of nonindigenous marine species into global ecosystems. Despite substantial research efforts, a cost-effective and durable solution, to control biofouling has eluded scientists. The majority of existing antifouling solutions rely on toxic coatings and the few exis,ting dynamic devices are either expensive to manufacture or powered by cumbersome and heavy power sources. Laminar stalling is anoth,er well-known issue for underwater vehicles and robots operating at high angles of attack due,ow separation. Besides static solutions such as vortex generators, unable to adapt to different operating conditions, the few existi,ng adaptive solutions are mainly based on pneumatic actuators requiring heavy and noisy compressors.I propose to develop a multifunc,tional soft smart skin inspired by cephalopods and actuated by Twisted Spiral Artificial Muscles (TSAMs) to resolve both biofouling,and laminar stall. A working prototype of this skin was developed under ongoing ONR N00014-19-1-2136 and DURIP ONR N00014-20-1-2224,grants. Preliminary results demonstrate the skin s ability to perform texture modulation, biofilm detachment, and turbulence trippin,g. TSAMs can be produced with less than a penny in material costs with inexpensive polymer fibers and provide an actuation of 2000%,with only 0.02 V/mm of input voltage. This new smart skin will overcome the limitations of existing dynamic antifouling and turbulen,ce tripping solutions in terms of portability, ease of manufacturing, cost, and energy requirement. The main goal of this proposal i,s to model, test, control, and optimize the anti-fouling and turbulence tripping performance of the TSAMs-based soft self-morphing s,kin, inspired by cephalopods papillae. The technical approach of the proposed research activity will include: 1. Task I: Anti-foulin,g modeling. A theoretical model will be developed to tailor the properties and performance of the proposed device for different biof,ilm types.2. Task II: Anti-fouling testing and control. The ability of the device to remove bacterial biofilms, diatom-based biofilm,s, and a mixed population biofilm grown in a field environment will be analyzed. 3. Task III: Turbulence tripping modeling. A physic,s-based theoretical model will be developed to relate the location and geometry of the roughness element (i.e., TSAMs) to the transi,tion of the boundary layer and prevention of flow separation and stall.4. Task IV: Turbulence tripping testing and control. Underwat,er tests will be performed to validate the theoretical model and demonstrate the ability to prevent flow separation and stall in und,erwater robots operating at high angle of attack.This device can be attached to the hulls of naval ships and other vessels to promot,e the removal of biofilms, which are responsible for the introduction of invasive species in delicate ecosystems, decreased fuel eff,iciency, and expensive cleaning and maintenance. Additionally, TSAMs can be employed as dynamic roughness elements to prevent lamina,r stalling in underwater robots and vehicles operating at high angles of attack by inducing turbulence tripping. These applications,are highly relevant to the U.S. Navy and scientific progress in the naval research field.

Document Details

Document Type
DoD Grant Award
Publication Date
Feb 08, 2022
Source ID
N000142212021

Entities

People

  • Caterina Lamuta

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Iowa

Tags

Readers

  • Fluid Mechanics and Fluid Dynamics.
  • Nanocomposite Materials Science
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Biotechnology