Fluid-driven robotic systems typically use bulky and rigid power supplies, considerably limiting their mobility and flexibility. Although various forms of low-profile soft pumps have been demonstrated, they either are limited to specific working fluids or generate limited flow rates or pressures, making them ill-suited for widespread robotics applications. In this work, we introduce a class of centimeter-scale soft peristaltic pumps for power and control of fluidic robots. An array of high power density robust dielectric elastomer actuators (DEAs) (each weighing 1.7 grams) were adopted as soft motors, operated in a programmed pattern to produce pressure waves in a fluidic channel. We investigated and optimized the dynamic performance of the pump by analyzing the interaction between the DEAs and the fluidic channel with a fluid-structure interaction finite element model. Our soft pump achieved a maximum blocked pressure of 12.5 kilopascals and a run-out flow rate of 39 milliliters per minute with a response time of less than 0.1 second. The pump can generate bidirectional flow and adjustable pressure through control of drive parameters such as voltage and phase shift. Furthermore, the use of peristalsis makes the pump compatible with various liquids. To illustrate the versatility of the pump, we demonstrate mixing a cocktail, powering custom actuators for haptic devices, and performing closed-loop control of a soft fluidic actuator. This compact soft peristaltic pump opens up possibilities for future on-board power sources for fluid-driven robots in a variety of applications, including food handling, manufacturing, and biomedical therapeutics.

Material extrusion additive manufacturing (ME-AM) techniques have been recently introduced for core–shell polymer manufacturing. Using ME-AM for core–shell manufacturing offers improved mechanical properties and dimensional accuracy over conventional 3D-printed polymer. Operating parameters play an important role in forming the overall quality of the 3D-printed manufactured products. Here we use numerical simulations within the framework of computation fluid dynamics (CFD) to identify the best combination of operating parameters for the 3D printing of a core–shell polymer strand. The objectives of these CFD simulations are to find strands with an ultimate volume fraction of core polymer. At the same time, complete encapsulations are obtained for the core polymer inside the shell one. In this model, the deposition flow is controlled by three dimensionless parameters: (i) the diameter ratio of core material to the nozzle, $d/D$; (ii) the normalised gap between the extruder and the build plate, $t/D$; (iii) the velocity ratio of the moving build plate to the average velocity inside the nozzle, $V/U$. Numerical results of the deposited strands’ cross-sections demonstrate the effects of controlling parameters on the encapsulation of the core material inside the shell and the shape and size of the strand. Overall we find that the best operating parameters are a diameter ratio of $d/D=0.7$, a normalised gap of $t/D=1$, and a velocity ratio of $V/U=1$.

As electric vehicles (EVs) gain market dominance, ensuring safety during the battery usage is crucial. This paper presents a new thermal management approach to address the battery heat accumulation challenge through a novel combination of composite phase change material (CPCM) with liquid cooling systems. An optimised hybrid cooling model is developed to evaluate the proposed battery thermal management system (BTMS) under high-temperature and high-power conditions. Benchmark studies are conducted to assess the impact of inlet position, inlet flow rate, and flow channel distribution on the cooling performance to achieve a uniform temperature distribution within the battery. The optimised BTMS, consisting of a five-cell battery pack, demonstrates a maximum temperature of 41.15 °C and a temperature difference of 4.89 °C in a operating condition at 36 °C with a discharge rate of 3 C. The BTMS outperforms the initial model, reducing the maximum temperature by 1.5%, temperature difference by 5%, and liquid fraction by 13%, with a slight (1.3%) increase in weight. The cooling performance is most efficient at a liquid flow rate of 0.1 m/s, minimising energy consumption. The proposed BTMS with CPCM-3 is also sufficient enough to keep the battery pack under a thermal runaway event. Overall, the theoretical simulation highlights the BTMS’s ability to effectively control battery temperatures and temperature differences, ensuring safe operation during high-temperature and high-power conditions in practical EV usage.

Biosensors are favored devices for the fast and cost-effective detection of biological species without the need for laboratories. Microfluidic integration with biosensors has advanced their capabilities in selectivity, sensitivity, controllability, and conducting multiple binding assays simultaneously. Despite all the improvements, their design and fabrication are still challenging and time-consuming. The current study aims to enhance microfluidic-integrated biosensors’ performance. Three different functional designs are presented with both active (with the help of electroosmotic flow) and passive (geometry optimization) methods. For validation and further studies, these solutions are applied to an experimental setup for DNA hybridization. The numerical results for the original case have been validated with the experimental data from previous literature. Convection, diffusion, migration, and hybridization of DNA strands during the hybridization process have been simulated with finite element method (FEM) in 3D. Based on the results, increasing the velocity on top of the functionalized surface, by reducing the thickness of the microchamber in that area, would increase the speed of surface coverage by up to 62%. An active flow control with the help of electric field would increase this speed by 32%. In addition, other essential parameters in the fabrication of the microchamber, such as changes in pressure and bulk concentration, have been studied. The suggested designs are simple, applicable and cost-effective, and would not add extra challenges to the fabrication process. Overall, the effect of the geometry of the microchamber on the time and effectiveness of biosensors is inevitable. More studies on the geometry optimization of the microchamber and position of the electrodes using machine learning methods would be beneficial in future works.

The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for the first time, attempts to use sensitivity analysis to screen the design variables and achieve an efficient optimisation design with a large number of original design variables. Specifically, the sensitivity analysis method was proposed to screen a certain number of optimisation variables, reducing the computational complexity while ensuring the efficiency of the optimisation process. A combination of the Generalised Regression Neural Network (GRNN) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to construct surrogate models and solve the optimisation problem. The optimisation model integrates these techniques to balance structural performance and weight reduction. The optimisation results demonstrate a significant reduction in battery box weight while maintaining structural integrity. Therefore, the proposed approach in this study provides important insights for achieving high-efficiency multi-objective optimisation of battery box structures.