1,723 research outputs found
High-performance shape memory composites with intrinsic heating capabilities
Shape morphing structures have played a significant role within the field of aerospace for more than a century. While the shape morphing aerostructures of the past and present have depended on hinges and motors to achieve morphing, their future is expected to rely on smart materials and structures that have intrinsic shape morphing capabilities.
One such smart material, that has previously been developed at Imperial College London, is the carbon fibre reinforced epoxy polymer (CFRP) composite with thermoplastic (TP) interleaves. These interleaved composites exhibit controllable stiffness (CS) and shape memory (SM) capabilities under suitable thermal conditions. While these interleaved composites showed excellent shape morphing capabilities, they had several drawbacks. These composites showed poor flexural modulus and through-thickness shear strength compared to the epoxy-based non-interleaved CFRP. These composites also used an oven to achieve the high temperatures required to exhibit the CS and SM capabilities.
This thesis describes studies conducted to mitigate these drawbacks. In the first study described in this thesis, the source of the premature through-thickness shear failure in TP interleaved CFRP composites was discovered to be the low shear strength of the polystyrene (PS) interleaves used in previous works. It was then demonstrated that replacing PS with Poly(styrene-co-acrylonitrile) (SAN) could improve the through-thickness shear strength of the interleaved composites to be almost as high as that of pristine CFRP. Furthermore, the SAN-interleaved CFRP laminates also exhibited excellent CS and SM capabilities.
In the next study described in this thesis, it was demonstrated that the flexural modulus of TP interleaved CFRP composites can be substantially improved by two different methods- (i) reducing the thickness of the TP interleaves, and (ii) introducing reinforcements within the TP interleaves.
The following study describes how intrinsic heating capability was achieved in TP interleaved CFRP composites, through resistive heating of heater elements such as stainless steel (SS) meshes and woven carbon fabric (WCF) embedded within the layup of the composite. This intrinsic heating strategy was used to supply the temperature necessary for the corresponding composites to exhibit CS and SM capabilities. As a result, these intrinsically heated TP interleaved CFRP composites exhibited successful out-of-oven morphing capabilities.
In the final study described in this thesis, composite structures that were initially flat in their as-cured state, but were capable of deployment into planar and curved meshes were designed. Finite element numerical models were used to predict the deployment capabilities of these composite structures. Finally, the deployable composite mesh structures were manufactured and characterised.Open Acces
Multi-objective resource optimization in space-aerial-ground-sea integrated networks
Space-air-ground-sea integrated (SAGSI) networks are envisioned to connect satellite, aerial, ground,
and sea networks to provide connectivity everywhere and all the time in sixth-generation (6G) networks. However, the success of SAGSI networks is constrained by several challenges including
resource optimization when the users have diverse requirements and applications. We present a
comprehensive review of SAGSI networks from a resource optimization perspective. We discuss
use case scenarios and possible applications of SAGSI networks. The resource optimization discussion considers the challenges associated with SAGSI networks. In our review, we categorized
resource optimization techniques based on throughput and capacity maximization, delay minimization, energy consumption, task offloading, task scheduling, resource allocation or utilization,
network operation cost, outage probability, and the average age of information, joint optimization (data rate difference, storage or caching, CPU cycle frequency), the overall performance of
network and performance degradation, software-defined networking, and intelligent surveillance
and relay communication. We then formulate a mathematical framework for maximizing energy
efficiency, resource utilization, and user association. We optimize user association while satisfying
the constraints of transmit power, data rate, and user association with priority. The binary decision
variable is used to associate users with system resources. Since the decision variable is binary and
constraints are linear, the formulated problem is a binary linear programming problem. Based on
our formulated framework, we simulate and analyze the performance of three different algorithms
(branch and bound algorithm, interior point method, and barrier simplex algorithm) and compare
the results. Simulation results show that the branch and bound algorithm shows the best results,
so this is our benchmark algorithm. The complexity of branch and bound increases exponentially
as the number of users and stations increases in the SAGSI network. We got comparable results
for the interior point method and barrier simplex algorithm to the benchmark algorithm with low
complexity. Finally, we discuss future research directions and challenges of resource optimization
in SAGSI networks
Characterisation and State Estimation of Magnetic Soft Continuum Robots
Minimally invasive surgery has become more popular as it leads to less bleeding, scarring, pain, and shorter recovery time. However, this has come with counter-intuitive devices and steep surgeon learning curves. Magnetically actuated Soft Continuum Robots (SCR) have the potential to replace these devices, providing high dexterity together with the ability to conform to complex environments and safe human interactions without the cognitive burden for the clinician. Despite considerable progress in the past decade in their development, several challenges still plague SCR hindering their full realisation. This thesis aims at improving magnetically actuated SCR by addressing some of these challenges, such as material characterisation and modelling, and sensing feedback and localisation.
Material characterisation for SCR is essential for understanding their behaviour and designing effective modelling and simulation strategies. In this work, the material properties of commonly employed materials in magnetically actuated SCR, such as elastic modulus, hyper-elastic model parameters, and magnetic moment were determined. Additionally, the effect these parameters have on modelling and simulating these devices was investigated.
Due to the nature of magnetic actuation, localisation is of utmost importance to ensure accurate control and delivery of functionality. As such, two localisation strategies for magnetically actuated SCR were developed, one capable of estimating the full 6 degrees of freedom (DOFs) pose without any prior pose information, and another capable of accurately tracking the full 6-DOFs in real-time with positional errors lower than 4~mm. These will contribute to the development of autonomous navigation and closed-loop control of magnetically actuated SCR
Designing LMPA-Based Smart Materials for Soft Robotics Applications
This doctoral research, Designing LMPA (Low Melting Point Alloy) Based Smart Materials for Soft Robotics Applications, includes the following topics: (1) Introduction; (2) Robust Bicontinuous Metal-Elastomer Foam Composites with Highly Tunable Mechanical Stiffness; (3) Actively Morphing Drone Wing Design Enabled by Smart Materials for Green Unmanned Aerial Vehicles; (4) Dynamically Tunable Friction via Subsurface Stiffness Modulation; (5) LMPA Wool Sponge Based Smart Materials with Tunable Electrical Conductivity and Tunable Mechanical Stiffness for Soft Robotics; and (6) Contributions and Future Work.Soft robots are developed to interact safely with environments. Smart composites with tunable properties have found use in many soft robotics applications including robotic manipulators, locomotors, and haptics. The purpose of this work is to develop new smart materials with tunable properties (most importantly, mechanical stiffness) upon external stimuli, and integrate these novel smart materials in relevant soft robots. Stiffness tunable composites developed in previous studies have many drawbacks. For example, there is not enough stiffness change, or they are not robust enough. Here, we explore soft robotic mechanisms integrating stiffness tunable materials and innovate smart materials as needed to develop better versions of such soft robotic mechanisms. First, we develop a bicontinuous metal-elastomer foam composites with highly tunable mechanical stiffness. Second, we design and fabricate an actively morphing drone wing enabled by this smart composite, which is used as smart joints in the drone wing. Third, we explore composite pad-like structures with dynamically tunable friction achieved via subsurface stiffness modulation (SSM). We demonstrate that when these composite structures are properly integrated into soft crawling robots, the differences in friction of the two ends of these robots through SSM can be used to generate translational locomotion for untethered crawling robots. Also, we further develop a new class of smart composite based on LMPA wool sponge with tunable electrical conductivity and tunable stiffness for soft robotics applications. The implications of these studies on novel smart materials design are also discussed
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