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The adaptive foam reticulation technique combines the foam reticulation and freeze casting methodologies of fabricating bone reparative scaffolds to offer a potential alternative to autografts. For the first time this paper studies the effect of processing on the mechanical properties and in-vitro cell growth of controllably generating a hierarchical structure of macro- (94 ± 6 to 514 ± 36 μm) and microporosity (2–30 μm) by the inclusion of camphene as a porogen during processing. Scaffolds were produced with porogen additions of 0–25 wt%. Porosity values of the structures of 85–96% were determined using the Archimedes technique and verified using X-ray Computed Tomography. The strength of the hydroxyapatite scaffolds, 5.70 ± 1.0 to 159 ± 61 kPa, correlated to theoretically determined values, 3.71 ± 0.8 to 134 ± 12 kPa, calculated by the novel incorporation of a shape factor into a standard equation. Fibroblast (3T3) and pre-osteoblast (MC3T3) cell growth was found to be significantly (P < 0.005) improved using 25 wt% porogen. This was supported by increased levels of alkaline phosphatase and was thought to result from greater dissolution as quantified by increased calcium levels in incubating media. The combination of these properties renders adaptive foam reticulation-fabricated scaffolds suitable for non-structural bone regenerative applications in non-load bearing bone defects.

The temporal and geographical availability of renewable energy sources is highly variable, which imposes the importance of correct choices for energy storage and energy transport systems. This paper presents a smart strategy to utilize the natural gas distribution grid to transport and store the hydrogen. The goal is twofold: evaluating the capacity limits of the grid to accommodate “green hydrogen” for preset increasing shares of renewable energy sources (RESs) and determining at the same time the optimal mix of wind, photovoltaic (PV), biomethane and power-to-gas systems that minimizes the investment and operation costs. To this end, the energy supply system of an entire country is modelled and optimized considering the real characteristics and pressure levels of the gas grid, which is assumed to be the only storage mechanism of green hydrogen. The operational concept is to fill up the gas grid with hydrogen during the day and with natural gas during the night while always consuming the natural gas-hydrogen blend. Green hydrogen is generated by electrolysers powered by PVs, wind turbines and biomethane power systems. Results of the optimizations showed that: i) as long as the share of RES does not exceed 20%, there is no need to use the gas grid as RES storage system, ii) from 20 to 50% of RES share the gas grid receives the surplus of electricity in the peaks that would be necessary to “complete” the dispatchability of RES electricity, iii) above 50%, the excess of electricity in the peaks has to be used to generate the thermal energy required by the consumers. The gas grid can be used as unique renewable energy carrier and storage system up to 65% of RES share.

MXenes are a quickly growing and extended group of two-dimensional (2D) substances that have earned unbelievable analysis credits for various application areas within different manufacturing areas. Due to novel essential architectural and physicochemical properties shows good properties, such as elevated exterior area, living adaptability, strong electrochemistry, and great hydrophilicity. Given the fast progress within the structure and synthesis of MBNs for water treatment, quick updates on this research field are required to remove toxic substances, such as production approaches and characterization methods for the advantages and constraints of MXenes for pollutant degradation. MXenes are determined as a proposed road toward atmosphere-clean-up machinery to identify and decrease a pattern of hazardous resistant pollutants from environmental forms. Here, in this review article, we have been focused on describing the overview, novel synthesis methods, and characteristics of the MXene-based nanomaterials (MBNs) in the field for removing hazardous contaminants from environmental conditions. In the last, the utilizations of MBNs in water sanitization, organic solvent filtration, antibiotics degradation, pesticide degradation, heavy metals degradation, ions removal, bacterial pathogens degradation, along with the conclusion, challenges, and prospects in this field, have been discussed.

The interaction between the cold-formed steel members and fasteners in a structural assembly often involves complex stress states, material plasticity, significant in-plane deformations and curling, rendering computational modelling of the resulting structural system challenging. In addition, the presence of initial geometric imperfections and the susceptibility of cold-formed steel members to local instabilities adds a further degree of complexity to the numerical modelling. The behaviour of bolted connections between cold-formed steel members has traditionally been assessed based on physical tests, though these can be time consuming and expensive. In this paper, a practical numerical approach to the simulation of bolted cold-formed steel connections, capturing all the key behavioural features including bolt preload, slippage and bearing, is introduced. Starting from a typical shell finite element (FE) model of a cold-formed steel plate, a small number of solid elements is introduced around the bolt holes, to allow accurate replication of the bolt-ply interaction, with contact pairs defined between the bolts and the surrounding material. An explicit dynamic solver is then employed to solve the geometrically and materially nonlinear problem, both with and without bolt slippage. Validation of the FE model is conducted using benchmark tests reported in the literature. It is shown that the proposed numerical method can accurately capture the structural behaviour of bolted cold-formed steel connections and cold-formed steel built-up sections, while being computationally efficient and numerically stable; the proposed approach is therefore recommended for the numerical modelling of cold-formed steel systems assembled using fasteners.

Machine learning and in particular deep learning techniques have demonstrated the most efficacy in training, learning, analyzing, and modelling large complex structured and unstructured datasets. These techniques have recently been commonly deployed in different industries to support robotic and autonomous system (RAS) requirements and applications ranging from planning and navigation to machine vision and robot manipulation in complex environments. This paper reviews the state-of-the-art with regard to RAS technologies (including unmanned marine robot systems, unmanned ground robot systems, climbing and crawler robots, unmanned aerial vehicles, and space robot systems) and their application for the inspection and monitoring of mechanical systems and civil infrastructure. We explore various types of data provided by such systems and the analytical techniques being adopted to process and analyze these data. This paper provides a brief overview of machine learning and deep learning techniques, and more importantly, a classification of the literature which have reported the deployment of such techniques for RAS-based inspection and monitoring of utility pipelines, wind turbines, aircrafts, power lines, pressure vessels, bridges, etc. Our research provides documented information on the use of advanced data-driven technologies in the analysis of critical assets and examines the main challenges to the applications of such technologies in the industry.

This paper provides an insight into the involvement of two PhD students in the PARTICLE VIBRATION project, a multiphase fluid experiment, also known as, “Thermovibrationally-driven Particle self-Assembly and Ordering mechanisms in Low grAvity” (T-PAOLA) to be launched on the International Space Station by the end of 2022. The project aims to identify self-organization phenomena in dispersed phase flows when vibrations are applied to the system. It will therefore underpin the development of new contactless particle manipulations and materials processing strategies. In this short paper, the work of two PhD candidates, working within the T-PAOLA project framework, is discussed. In doing so, the various research activities undertaken are highlighted, both experimental and numerical, as is the peripheral or supporting research being undertaken by both students in order to expand the scope of the project and identify new lines of enquiry regarding convection-based control mechanisms

The flavoring process ensures the quality of cigarettes by endowing them with special tastes. In this process, the flavoring liquid is atomized into particles by a nozzle and mixed with the tobacco in a rotating drum. The particle size of the flavoring liquid has great influence on the atomization effect; however, limited research has addressed the quantitation of the liquid particle size in two-phase nozzle flow. To bridge this research gap, the authors of this study employed numerical and experimental techniques to explore the quantitative analysis of particle size. First, a simulation model for the flavoring nozzle was established to investigate the atomization effect under different ejection pressures. Then, an experimental test is carried out to compare the test results with the simulation results. Lastly, the influencing factors of liquid particle size in two-phase nozzle flow were analyzed to quantify particle size. The analysis results demonstrated that there was a cubic correction relationship between the simulation and experiment particle size. The findings of this study may provide a reliable reference when evaluating the atomization effect of flavoring nozzles.

The influence of the cohesive zone length on the crack driving force is quantified and analyzed in a representative system of particles dispersed in a matrix of a composite material. For heterogeneous material systems, e.g. particulate composites, it is known that as a crack approaches the particles, the crack driving force may increase (shielding) or decrease (anti-shielding) depending on the relative stiffness of the particles. These results have been established in numerous studies using the classical linear elastic fracture mechanics approach (LEFM). The cohesive zone method (CZM) introduces a length scale parameter, referred to as the cohesive zone (or fracture process zone) length scale, into the formulation of fracture mechanics. It is generally established that fracture mechanics predictions using the CZM are similar to those obtained using LEFM in the limit case where the process zone is very small relative to a suitable characteristic dimension of the problem. However, the influence of the length scale parameter has not been clearly demonstrated for crack propagation in a heterogeneous material system, especially when the cohesive zone length is not negligible. By considering a simple crack-particle-matrix system, it is shown that, in addition to the elastic properties, the process zone length scale parameter exhibits a critical influence on the crack driving force. For this study, the concept of configurational forces is utilized and the eXtended Finite Element Method (XFEM) is employed as a tool to simulate crack propagation. Through numerical simulations, it is shown that (i) the magnitude of the driving force vector directly depends on the length scale parameter and (ii) the direction of the driving force is largely influenced by the presence of a cohesive zone. This, in turn, alters the crack trajectory in the particulate system if the criterion for the direction of crack propagation depends on the orientation of the driving force vector. Towards this end, two different criteria for direction of crack propagation, namely maximum principal stress and maximum energy dissipation, are compared in the presence of a cohesive zone and the results are reported. The study reveals the crucial influence of the inherent length scale associated with the cohesive zone method when applied to crack propagation in particulate composite systems and elucidates important differences when comparing predictions from distinct theories of fracture mechanics.

Needle biopsy is an essential part of modern clinical medicine. The puncture accuracy and sampling success rate of puncture surgery can be effectively improved through virtual surgery. There are few three-dimensional puncture (3D) models, which have little significance for surgical guidance under complicated conditions and restrict the development of virtual surgery. In this paper, a 3D simulation of the muscle tissue puncture process is studied. Firstly, the mechanical properties of muscle tissue are measured. The Mooney-Rivlin (M-R) model is selected by considering the fitting accuracy and calculation speed. Subsequently, an accurate 3D dynamic puncture model is established. The failure criterion is used to define the breaking characteristics of the muscle, and the bilinear cohesion model defines the breaking process. Experiments with different puncture speeds are carried out through the built in vitro puncture platform. The experimental results are compared with the simulation results. The experimental and simulated reaction force curves are highly consistent, which verifies the accuracy of the model. Finally, the model under different parameters is studied. The simulation results of varying puncture depths and puncture speeds are analyzed. The 3D puncture model can provide more accurate model support for virtual surgery and help improve the success rate of puncture surgery.

Circular economy is an upward trending notion that has drawn worldwide attention of policymakers, industry administrators, environmentalist as well as academic researchers. Though there are several tools developed for monitoring the material recovery, a very few number of research have been conducted to integrate circular economy principles with end-of-life (EOL) management strategies. This paper proposes an EOL-driven circular economy framework for the management of materials flow so as to extend the lifetime of materials through improved durability as well as to provide more social, economic and environmental benefits through less material waste. A case study from the agricultural waste industry is presented in order to test the model and validate its performance. The results show that the proposed framework has a good potential for small and medium enterprises (SME) advances.

There exist two basic formulations of static shakedown theorem for bounded kinematic hardening (KH) model in literature. Whether the two formulations are equivalent, whether the conditions of loading-path-independence are satisfied, and how to employ the shakedown theorem to obtain the safety margin of realistic engineering structures under cyclic loading are some key issues to be solved and addressed. This paper presents theoretical aspects of the static shakedown theorem of KH and proposes a direct approach for shakedown analysis of KH structures. The presented direct approach allows for both the two shakedown formulations and considers the simultaneous influences of multiple load vertices. The two formulations are suitable for plastic models with different hardening laws and determine different shakedown load boundaries in the region of incremental collapse. Instead of dealing with a complicated problem of mathematical programming, the presented approach performs several linear elastic finite element iterative calculations, which ensures its high computational efficiency. Furthermore, the direct approach is implemented into Abaqus software platform so that it becomes a general tool to handle large-scale shakedown problems of engineering structures with complex geometry and loading conditions. An illustrative sample with analytical solution and three numerical examples from power plant engineering are adopted to validate these theoretical aspects and to present the potential of the direct approach.

The application of polyethylene pipes and equipment in the natural gas networks is continuously increasing due to their competitive weight and cost compared to metallic materials. Electrofusion welding is an effective and fast approach for the production of polyethylene joints with high safety and endurance. However, recently intermittent failures have reported in underground polyethylene piping networks. Although the failure frequencies are low, but disasters could happen due to the failure in gas pipelines as they usually buried in populated areas. In this study a combination of Failure Mode and Effects Analysis (FMEA), and empirical methods were used to identify main damage mechanisms incorporated to intermittent failures of polyethylene natural gas networks. After performing the FMEA process, based on the obtained risk ranking, three most critical damage mechanisms, including improper scraping, lipid contaminations, and humidity existence in weld zones were investigated experimentally to determine their practical severity. According to empirical evaluations, improper scraping was the most severe damage mechanism, followed by the contaminated welding surfaces during the weld construction.

The advances of many future engineering applications rely on effective cooling techniques. Beyond the traditional thermal management solutions, the design potential of unsteady impingement cooling is still under-explored. As a combined experimental and numerical study, this paper reports new findings on high-amplitude intermittent impingement cooling with controlled unsteady patterns. Specifical attention was paid on the intermittent flow close time ratio. The experimental work involved unsteady cooling performance measurement with a small-scale water tunnel system. Unsteady Reynolds Averaged Navier-Stokes Simulation (URANS) was conducted to illustrate the unsteady flow physics, and to evaluate the cooling performance at a wider range of flow conditions (average Reynolds number 2800 < Rem < 10000, pulsating frequency 0.1 Hz < f < 2 Hz, close time ratio 0.2< γ < 0.8). Both experimental and numerical data confirm a remarkable improvement of overall cooling efficiency by high-amplitude intermittent impingement flow. Especially around the wall jet region, the enhancement can reach as high as 50%. The generation and interaction of vortex rings break the development of thermal boundary layer, and enhance the generation of near wall turbulence, especially for the wall jet region. Saving in coolant consumption with high-amplitude intermittent impingement cooling technique in practice is also demonstrated. The novel concept presented in this paper can be applied to a wide range of applications including electronic cooling, deicing, gas turbine blade cooling, etc.

In the present article, a new thermal plasma-aided process is proposed and analysed that utilises alumina/aluminium particles to dissociate steam/carbon dioxide blends into high-quality synthetic fuel. The proposed system utilises two reactors namely a synthetic fuel reactor and a thermal plasma particle regenerator following the chemical looping gasification principle. In the former, the gas blend reacts with aluminium particles to produce hydrogen-enriched synthetic fuel and alumina. While in the latter, the alumina is dissociated into oxygen and reduced aluminium. Using thermochemical equilibrium analysis, it was identified that the proposed system can offer a self-sustaining factor of up to 0.18, thermodynamic and exergy efficiency of 0.38 and 0.68, respectively. The system was integrated with photovoltaic energy and a solar share of ≤ 0.5 (with low-capacity battery storage ≤ 4 MWh) and > 0.5 (with high-capacity battery storage < 5 MWh) was obtained at photovoltaic capacity ∼ 3–5 MW. The thermodynamic conditions of 1273 K < T < 1573 under lean oxygen conditions and T > 6373 K were identified for the fuel and plasma reactors, respectively. The calculated syngas quality was > 2 for the selected thermodynamic conditions. The integration of the system with photovoltaic energy showed that the installed capacity of photovoltaic panels and battery storage are intertwined representing a trade-off trend. The optimum storage capacity of 4–6 MWh and photovoltaic installation capacity of 3–5 MWe was calculated for the localised production scale of 775 tonnes/year. The proposed system offers resiliency against the continuous operation in both centralised and decentralised arrangements. Based on this proof of concept, the viability of the thermal plasma-aided process for remote small-scale applications was discussed by benchmarking how it can meet the requirements well known for remote gas-to-liquid compact plants, producing gasoline out of syngas.

This paper introduces a robust Bayesian particle filter that can handle epistemic uncertainty in the measurements, dynamics, and initial conditions. The robust filter returns robust bounds on the output quantity of interest, rather than a crisp value. Particles are generated with an importance sampling technique and propagated only one time during the estimation process. The proposal distribution is constructed by running a parallel unscented Kalman filter to drive particles in regions of high expected likelihood and achieve a high effective sample size. The bounds are then computed by an inexpensive tuning of the importance weights via numerical optimization. A Branch & Bound algorithm over simplexes with a Lipschitz bounding function is employed to achieve guaranteed convergence to the lower and upper bounds in a finite number of steps. The filter is applied to the robust computation of the collision probability of SENTINEL 2B with a FENGYUN 1C debris in different operational instances, all characterized by a mix of aleatory and epistemic uncertainty on initial conditions and observation likelihoods.

The purpose of this study was to develop a closed-loop machine vision system for wire electrical discharge machining (EDM) process control. Excessive wire wear leading to wire breakage is the primary cause of wire EDM process failures. Such process interruptions are undesirable because they affect cost efficiency, surface quality, and process sustainability. The developed system monitors wire wear using an image-processing algorithm and suggests parametric changes according to the severity of the wire wear. Microscopic images of the wire electrode coming out from the machining zone are fed to the system as raw images. In the proposed method, the images are pre-processed and enhanced to obtain a binary image that is used to compute the wire wear ratio (WWR). The input parameters that are adjusted to recover from the unstable conditions that cause excessive wire wear are pulse off time, servo voltage, and wire feed rate. The algorithm successfully predicted wire breakage events. In addition, the alternative parametric settings proposed by the control algorithm were successful in reducing the wire wear to safe limits, thereby preventing wire breakage interruptions.

The paper presents a numerical investigation on a floating offshore wind turbine (FOWT) in a complex marine environment consisting of winds and waves. It takes account of the aerodynamics, hydrodynamics of FOWT system and their interactions simultaneously using a hybrid model, qaleFoam, which combines a fully nonlinear potential (FNPT) solver with a two-phase Navier-Stokes (NS) solver using a domain decomposition approach. The qaleFoam is validated by comparing its predictions with experimental and numerical results available in the public domain, and then is applied to model the FOWT in a unidirectional focused wave accompanying with a uniform wind. The results reveal a significant interaction between the aerodynamic and hydrodynamic responses of the FOWT in such condition. Moreover, it demonstrates that the most extreme response of the FOWT may not occur at the highest wave. This implies the importance of identifying the survival condition of the FOWT in normal operational sea state.

This paper presents a one-way coupling method based on the open-source computational fluid dynamics tool OpenFOAM. This hybrid method that couples the fully nonlinear potential theory (FNPT)-based quasi-arbitrary Lagrangian–Eulerian finite element method (QALE-FEM) with the viscous flow method is applied to simulate the forward movement and motion of a trimaran in stern waves. With this hybrid method and the corresponding solver QaleFOAM, the linear and nonlinear incident waves are generated by the external domain of FNPT-based QALE-FEM. The waves propagate to the internal domain by a transition zone. The interaction between the wave and trimaran model in the internal domain is simulated by the viscous flow method. The validation of the wave generation is carried out first. Then, the motion of the trimaran model in stern waves is simulated. Finally, by changing the forward speed and the wave parameters, the amplitude and time history are obtained to analyze the trimaran's motion characteristics in stern waves.

Periodically distributed wall-mounted hot blocks with a cubic shape located at the bottom of a layer of liquid with a free top interface tend to create patterns in their surrounding fluid that are reminiscent of the classical modes of Marangoni–Rayleigh–Bénard convection. Through direct numerical solution of the governing equations in their complete three-dimensional unsteady and nonlinear formulation, we investigate this specific subject giving much emphasis to understanding how ensemble properties arise from the interplay of localized effects. Through the used numerical framework, we identify the emerging planforms and connect the statistics of the associated heat transport mechanisms with the spatially averaged behaviour of the underlying thermal currents. In some cases, all these features can be directly mapped into the topography at the bottom of the layer. In other circumstances, these systems contain their own capacity for transformation, i.e. intrinsic evolutionary mechanisms are enabled, by which complex steady or unsteady patterns are produced. It is shown that self-organization driven by purely surface-tension or mixed buoyancy–Marangoni effects can result in ‘quantized states’, i.e. aesthetically appealing solutions that do not depend on the multiplicity of wall-mounted elements.

Recent design and test concepts for small and medium fixed wing unmanned air vehicles (UAV) have considered new technologies of variable spanwise lift control for high-speed manoeuvring. In addition, solar powered, ultra-large wing span concepts for high altitude long endurance flights, need to be tested against gusts of variable strength along the span for their safe deployment. To address those questions in wind tunnel studies, a new concept of bio-inspired active flaplets has been developed, which can act as localised lift or gust control on aerofoils. Spanwise distributed actuators at the trailing edge are used for controlled opening and closing of the individual flaps. Causing an initial local suction and then successive blowing into the wake region, in the form of a tangential jet. This modifies the flow around the trailing edge and therefore the local circulation distribution. Furthermore, it interrupts the wake region during the time period when the tangential jet blowing happens. The segmented control regions can act independently of each other and allow for more complex control scenarios addressing localised separation cells or local gust alleviation. In addition, the concept can also be used to test the performance of aerofoils under complex inflow conditions, such as spanwise varying gusts. This can be achieved when the model is placed upstream of a wing and used as gust control unit at zero angle of attack (AoA). Disturbance patterns such as spanwise traveling waves, induced by individual flap deployment, or random artificial gust generation at different span locations can be achieved with arbitrary space-time control. The present paper covers the design, manufacture and testing of the concept with some initial results for the induced flow field when placed in a water tunnel for further research.

Residual stress measurement using the ultrasonic method is based on the acoustoelasticity law, which ‎states that the Time-of-Flight (ToF) of an ultrasonic wave is affected by the stress field. Traditionally, single-element ultrasonic transducers are used for residual stress measurement. In ‎this paper, a Phased Array Ultrasonic Testing (PAUT) system is used and the single ‎element transducers are replaced by 5 MHz and 10 MHz arrays with 8 and 16 elements, respectively. The 10 MHz transmitter array can generate 16 ultrasonic waves ‎and each of them can be received by any of the 16 elements of the 10 MHz receiver array. ‎Therefore, a matrix of 16 × 16 acoustic paths can potentially be generated. Each of these 256 LCR paths ‎is different from the others (i.e., different distance or different position of the travel path in the ‎material) whereby 256 ToFs can be generated. This is anticipated to increase the measurement ‎accuracy in comparison with the traditional setup in which only two acoustic paths can be generated by using three single element transducers. In this paper, a feasibility study is ‎conducted to investigate the requirements of a residual stress measurement system using the PAUT method. An advanced processing algorithm is also developed to analyse Full Matrix ‎Capture (FMC). Based on the preliminary results, some variations between different acoustic paths are ‎measured which prove that the effect of the residual stress on the ultrasonic wave is detectable using ‎the PAUT system.‎ Furthermore, the potential of this system for robotic residual stress measurement is discussed.

The fuel spray process is of utmost importance to internal combustion engine design as it dominates engine performance and emissions characteristics. While designers rely on computational fluid dynamics (CFD) modeling for understanding of the air-fuel mixing process, there are recognized shortcomings in current CFD spray predictions, particularly under super-critical or flash-boiling conditions. In contrast, time-resolved optical spray experiments have now produced datasets for the three-dimensional liquid distribution for a wide range of operating conditions and fuels. By utilizing such a large amount of detailed experimental data, the machine learning (ML) techniques have opened new pathways for the prediction of fuel sprays under various engine-like conditions. The ML approach for spray prediction is promising because (1) it does not require phenomenological spray models, (2) it can provide time-resolved spray data without time-stepping simulation, and (3) its evaluation has only a tiny fraction of the computational cost of a CFD simulation. In this study, an Artificial Neural Network (ANN) was applied for gasoline spray prediction under realistic engine conditions. Experimental data obtained under seven different fuels and three ambient conditions, totaling 21 different cases, were fed into a training procedure to investigate fuel effects on spray morphology. The quantitative validation results showed that the ANN is capable of predicting spray performance with nine input features, including fuel properties and ambient conditions. The ANN model fully trained on the experimental dataset showed greater accuracy in capturing the details of plume dynamics especially under flash-boiling conditions than the current state-of-the-art CFD model. While the ANN model cannot yet function or replace CFD in a full engine simulation, the ANN can be used now as a convenient design tool incorporating vast physical conditions.

There is a limited data on VHCF for structural steels and their weldments for >107 cycles. Unalloyed low-carbon steel S275JR+AR (EN 10025) is a common structural material for the components made for the minerals and mining applications. The purpose of this research is an investigation of the gigacycle domain for S275JR+AR grade that is intended to work for years at normal frequencies (10-20 Hz) of loading with low stress amplitudes. The work focuses on ultrasonic fatigue testing of the steel in both as-manufactured and pre-corroded conditions. As the heating is a massive challenge for ultrasonic fatigue testing especially in the case of structural steels attributed with a pronounced frequency effect, temperature control arrangement is crucial for proper implementation of testing. The frequency effect is assessed by comparing the fatigue test data at 20kHz and conventional frequency of 15Hz. Its contribution is found to be significant because there is no overlap between the stress ranges of interest. The ultrasonic fatigue data is intended to be applied to the fatigue assessments of the equipment operating at normal frequency for 1010 cycles. Therefore, the effect of frequency sensitivity is quantified by calculating the difference in terms of stress amplitude between corresponding SN curves.

Optimal charge scheduling of electric vehicles in solar-powered charging stations based on day-ahead forecasting of solar power generation is proposed in this paper. The proposed algorithm’s major objective is to schedule EV charging based on the availability of solar PV power to minimize the total charging costs. The efficacy of the proposed algorithm is validated for a small-scale system with a capacity of 3.45 kW and a single charging point, and the annual cost analysis is carried out by modelling a 65 kWp solar-powered EV charging station The reliability and cost saving of the proposed optimal scheduling algorithm along with the integration and the solar PV system is validated for a charging station with a 65 kW solar PV system having charging points with different charging powers. A comprehensive comparison of uncontrolled charging, optimal charging without solar PV system, and optimal charging with solar PV system for different vehicles and different time slots are presented and discussed. From the results, it can be realized that the proposed charging algorithm reduces the overall charging cost from 10–20% without a PV system, and while integrating a solar PV system with the proposed charging method, a cost saving of 50–100% can be achieved. Based on the selected location, system size, and charging points, it is realized that the annual charging cost under an uncontrolled approach is AUS $28,131. On the other hand, vehicle charging becomes completely sustainable with net-zero energy consumption from the grid and net annual revenue of AUS $28,134.445 can be generated by the operator. New South Wales (NSW), Australia is selected as the location for the study. For the analysis Time-Of-Use pricing (ToUP) scheme and solar feed-in tariff of New South Wales (NSW), Australia is adopted, and the daily power generation of the PV system is computed using the real-time data on an hourly basis for the selected location. The power forecasting is carried out using an ANN-based forecast model and is developed using MATLAB and trained using the Levenberg–Marquardt algorithm. Overall, a prediction accuracy of 99.61% was achieved using the selected algorithm.

Electrochemical impedance spectroscopy is often used for biomolecular detection based on the interaction of a molecule with a receptor functionalised electrode surface and consequent impedance change. Though its performance is well established, there is still a need for improved sensitivity and specificity, especially when attempting to detect nucleic acids from clinical samples with minimal amplification steps. Localised heating is a potential approach for improving nucleic hybridisation rates and reducing non-specific interactions, and thereby producing high sensitivity and selectivity. The aim of the study was therefore to develop a microheater surrounding Au thin film electrodes, an integrated hybrid chip, for detecting genes of Mycobacterium tuberculosis with enhanced sensitivity. The performance of the integrated hybrid chip was determined using the changes in the charge transfer resistance (R ct) upon DNA hybridisation using probe sequences for M. tuberculosis. Heat transfer within the system was simulated by using COMSOL Multiphysics as a mathematical modelling tool. When a temperature of 50 °C was applied to the microheater during DNA hybridisation steps, R ct values (which were indicative of DNA–DNA hybridisation) increased 236% and 90% as opposed to off-chip non-heated experiments and off-chip heated experiments. It is concluded from these observations that the microheater indeed can significantly improve the performance of the nucleic acid hybridisation assay and paves the way for the development of highly sensitive and specific integrated label-free biosensors.

This study describes the impact of postprocessing methods on the calculated parameters of tip vortices of a wind turbine model when tested using particle image velocimetry (PIV). Several vortex identification methods and differentiation schemes are compared. The chosen methods are based on two components of the velocity field and their derivatives. They are applied to each instantaneous velocity field from the dataset and also to the calculated average velocity field. The methodologies are compared through the vortex center location, vortex core radius and jittering zone.\ud \ud Results show that the tip vortex center locations and radius have good comparability and can vary only a few grid spacings between methods. Conversely, the convection velocity and the jittering surface, defined as the area where the instantaneous vortex centers are located, vary between identification methods.\ud \ud Overall, the examined parameters depend significantly on the postprocessing method and selected vortex identification criteria. Therefore, this study proves that the selection of the most suitable postprocessing methods of PIV data is pivotal to ensure robust results.

A large deployment of energy storage solutions will be required by the stochastic and non-controllable nature of most renewable energy sources when planning for higher penetration of renewable electricity into the energy mix. Various solutions have been suggested for dealing with medium- and long-term energy storage. Hydrogen and ammonia are two of the most frequently discussed as they are both carbon-free fuels. In this paper, the authors analyse the energy and cost efficiency of hydrogen and ammonia-based pathways for the storage, transportation, and final use of excess electricity from an offshore wind farm. The problem is solved as a linear programming problem, simultaneously optimising the size of each problem unit and the respective time-dependent operational conditions. As a case study, we consider an offshore wind farm of 1.5 GW size located in a reference location North of Scotland. The energy efficiency and cost of the whole chain are evaluated and compared with competitive alternatives, namely, batteries and liquid hydrogen storage. The results show that hydrogen and ammonia storage can be part of the optimal solution. Moreover, their use for long-term energy storage can provide a significant, cost-effective contribution to an extensive penetration of renewable energy sources in national energy systems.

Flows of thermal origin and heat transfer problems are central in a variety of disciplines and industrial applications. The present book entitled Thermal Flows consists of a collection of studies by distinct investigators and research groups dealing with different types of flows relevant to both natural and technological contexts. Both reviews of the state-of-the-art and new theoretical, numerical and experimental investigations are presented, which illustrate the structure of these flows, their stability behavior, and the possible bifurcations to different patterns of symmetry and/or spatiotemporal regimes. Moreover, different categories of fluids are considered (liquid metals, gases, common fluids such as water and silicone oils, organic and inorganic transparent liquids, and nano-fluids). This information is presented under the hope that it will serve as a new important resource for physicists, engineers and advanced students interested in the physics of non-isothermal fluid systems; fluid mechanics; environmental phenomena; meteorology; geophysics; and thermal, mechanical and materials engineering.

Time-resolved soot and PAH formation from gasoline and diesel spray pyrolysis are visualized and quantified using diffuse back illumination (DBI) and laser induced fluorescence (LIF) at 355 nm, respectively, in a constant-volume vessel at 60 bar from 1400 to 1700 K for up to 30 ms. The delay, maximum formation rate, and yield of soot and PAHs are compared across fuels and temperatures and correlated with the yield sooting indices on either the mass or mole basis. The delays generally decrease with increasing temperature, and the formation rates of both PAHs and soot generally increase with temperature. The apparent PAH-LIF yield may decrease with temperature due to PAH growth and conversion into larger species, signal trapping, and thermal quneching. Soot yield generally increases with temperature. The mass-based YSI correlates reasonably well with soot delay, but YSI does not correlate well with soot yield. The mass-based YSI is a more appropriate predictor of sooting propensity than the mole-based YSI.

This work takes an empirical and evidence-based approach in the development of a resorption thermal transformer. It presents the initial modelling conducted to understand key performance parameters (coefficient of performance and specific mean power) before discussing a preliminary design. Experimental results from large temperature jump and isosteric heating tests have identified the importance of heat transfer in ammonia-salt systems. Both the heat transfer resistance between the salt composite adsorbent and the tube side wall, and the heat transfer from the heat transfer fluid to the tube side wall are key to realising resorption systems. The successful performance of a laboratory-scale prototype will depend on the reduction in these heat transfer resistances, and improvements may be key in future prototype machines. A sorption reactor is sized and presented, which can be scaled for length depending on the desired power output. The reactor design presented was derived using data on reaction kinetics constants and heat of reaction for calcium chloride reacting with ammonia that were obtained experimentally. The data enabled accurate modelling to realise an optimised design of a reactor, focusing on key performance indicators such as the coefficient of performance (COP) and the system power density. This design presents a basis for a demonstrator that can be used to collect and publish dynamic data and to calculate a real COP for resorption system.

One limitation of social robots has been the ability of the models they operate on to infer meaningful social information about people's subjective perceptions, specifically from non-invasive behavioral cues. Accordingly, our paper aims to demonstrate how different deep learning architectures trained on data from human-robot, human-human, and human-agent interactions can help artificial agents to extract meaning, in terms of people's subjective perceptions, in speech-based interactions. Here we focus on identifying people's perceptions of their subjective self-disclosure (i.e., to what extent one perceives to be sharing personal information with an agent). We approached this problem in a data-first manner, prioritizing high quality data over complex model architectures. In this context, we aimed to examine the extent to which relatively simple deep neural networks could extract non-lexical features related to this kind of subjective self perception. We show that five standard neural network architectures and one novel architecture, which we call a Hopfield Convolutional Neural Network, are all able to extract meaningful features from speech data relating to subjective self-disclosure.

Following the success of the first edition of Robo-Identity, the second edition will provide an opportunity to expand the discussion about artificial identity. This year, we are focusing on emotions that are expressed through speech and voice. Synthetic voices of robots can resemble and are becoming indistinguishable from expressive human voices. This can be an opportunity and a constraint in expressing emotional speech that can (falsely) convey a human-like identity that can mislead people, leading to ethical issues. How should we envision an agent's artificial identity? In what ways should we have robots that maintain a machine-like stance, e.g., through robotic speech, and should emotional expressions that are increasingly human-like be seen as design opportunities? These are not mutually exclusive concerns. As this discussion needs to be conducted in a multidisciplinary manner, we welcome perspectives on challenges and opportunities from variety of fields. For this year's edition, the special theme will be "speech, emotion and artificial identity''.

Graded electrodes for Li-ion batteries aim to exploit controlled variations in local electrode microstructure to improve overall battery performance, including reduced degradation rates and increased capacity at high discharge rates. However, the mechanisms by which grading might deliver performance benefit, and under what conditions, are not yet fully understood. A Li-ion battery electrochemical model (a modified Doyle-Fuller-Newman type model capable of generating impedance functions) is developed in which local microstructural changes are captured in order to understand why and when graded electrodes can offer performance benefits. Model predictions are evaluated against experimental electrochemical impedance data obtained from electrodes with micro-scale, controlled variations in microstructure. A region locally enriched with carbon at the electrode/current collector interface is shown to significantly reduce the overpotential distribution across the thickness of a LiFePO4-based Li-ion battery cathode, resulting in a lower charge transfer resistance and impedance. The insights gained from the LiFePO4-based electrodes are generalised to wider design principles for both uniform and graded Li-ion battery electrodes.

This paper aims to address the wind-farm power tracking problem, which requires the farm's total power generation to track time-varying power references and therefore allows the wind farm to participate in ancillary services such as frequency regulation. A novel preview-based robust deep reinforcement learning (PR-DRL) method is proposed to handle such tasks which are subject to uncertain environmental conditions and strong aerodynamic interactions among wind turbines. To our knowledge, this is for the first time that a data-driven model-free solution is developed for wind-farm power tracking. Particularly, reference signals are treated as preview information and embedded in the system as specially designed augmented states. The control problem is then transformed into a zero-sum game to quantify the influence of unknown wind conditions and future reference signals. Built upon the $H_\infty$ control theory, the proposed PR-DRL method can successfully approximate the resulting zero-sum game's solution and achieve wind-farm power tracking. Time-series measurements and long short-term memory (LSTM) networks are employed in our DRL structure to handle the non-Markovian property induced by the time-delayed feature of aerodynamic interactions. Tests based on a dynamic wind farm simulator demonstrate the effectiveness of the proposed PR-DRL wind farm control strategy.

Proppant transport in underground fractures plays a key role in mitigating sand screen-out and enhancing the stimulated production for hydraulic fracturing. The effects of field pumping schedules, however, are not fully studied. We investigate the effect of slickwater-alternate-slurry injection on proppant transport at field-practical scales. A new hybrid approach is proposed to directly connect experimental studies with field operations, which consists of observation experiments, calculations, and deep learning (DL) workflow. The experiments reveal that the alternate injection induces the unexpected proppant ridge. The modified calculations (considering the ridge height) are proposed to extract features for training the DL algorithm. The workflow predicts the downhole pressure (mainly governed by proppant injection) for error analyses. Approximately 20.2% of the error is eliminated by considering the proppant ridge, thus demonstrating its effect on proppant injection. The predictions are significantly improved in early and late periods of fracturing operations when the fracture is initially created and highly filled. The sensitivity analysis suggests that the pump rate may dominate the ridge height compared with other hydraulic parameters. The study of proppant ridge complements the mechanisms of proppant transport, which is essential for controlling fracturing pressure and boosting the proppant injection.

Welding of copper and aluminium has been problematic with tradition fusion method. The friction stir welding (FSW) was employed to butt weld the T2 copper and 1061 aluminium alloy plates. The welding parameters were planned by the orthogonal experiment design method. The samples obtained in experiments were investigated in the aspects of microstructure, tensile strength, fatigue performance and micro-hardness. Sound weld with the best tensile strength and fatigue life was produced under the welding parameters of rotation speed, 1100 rpm and welding speed 50 mm min −1. The tensile strength was 193.16 MPa, 85% of the base aluminium material. Three intermetallic compounds of CuAl, Cu 3Al and Cu 9Al 4 were detected at the copper–aluminium interface in X-ray diffraction analysis.

In diesel engines, double-layer multi-holes nozzles contribute significantly in making spray injection uniform in both the circumferential and axial directions; they further ensure that minimal or no interactions are encountered among the spray jets emerging from the nozzle holes and positively affect fuel atomisation and enhance mixing during engine operation. In this study, the variation in internal flow characteristics and spray patterns from the upper and the lower layer nozzle holes were investigated experimentally and computationally. A double-layer 8-hole heavy-duty diesel engine injector nozzle was utilised for the characterisation of hole-to-hole variation on spray formation. The actual nozzle geometry was derived from X-ray scans obtained at the third generation X-ray imaging and biomedical beamline station in SSRF, revealing all geometrical differences between the individual injection holes. The momentum fluxes from each holes were obtained together with spray tip penetration under non-evaporating conditions. These data were used to validate the computational fluid dynamics (CFD) model suitable to describe the relevant flow processes. Initially, an Eulerian-Eulerian two-phase flow model was utilised to predict the internal nozzle flow under cavitating conditions. This model was weakly coupled with a Lagrangian spray model predicted the subsequent atomisation and penetration of all individual spray plumes. The results show that cavitation development within the upper layer holes is more intense than those formed within the lower layer nozzle holes; this is leading to higher injection rates from the lower layer nozzle holes that they also exhibit less cycle-to-cycle variations in the observed spray patterns.

The majority of facilities installed in offshore oil and gas fields during the 1980s and 1990s were designed to operate in ‘normal’ conditions. However, during the operational life of the fields, some new high pressure/high temperature (HPHT) wells may be discovered and tied back to older facilities. Operating these facilities beyond their design parameters in harsh environments may lead to catastrophic failures, resulting in significant economic losses and environmental problems. Managing the risks associated with failure of ageing subsea facilities in HPHT environments is considered as a very complex and critical task. To overcome such challenge, there is a need for development of decision-making methods that are capable of estimating precisely the risks associated with HPHT conditions as well as prioritising the risk mitigation and remediation strategies. This paper aims to propose an integrated risk management framework – based on Failure Mode and Effects Analysis (FMEA) approach and a hybrid Multi-Criteria Decision Analysis (MCDA) model – for evaluating the risks and prioritising mitigation strategies over the extended lifetime of subsea facilities in HPHT environments. For the purpose of illustrating the model, a case study of subsea manifold and flowlines is provided and the results are evaluated and discussed. Our findings indicate that the proposed approach offers significant improvement to the classical risk management processes applied to subsea oil and gas facilities as it can assist asset managers, risk analyst, regulators and policy makers with a decision model which considers both subjective (qualitative) judgements and objective (quantitative) evaluation measures.

The continuous cost reduction of wind turbines has consolidated the competitiveness of wind energy. With the increasing installation of wind turbines in seismic-prone regions, it is likely that earthquakes will strike farms in operation. A practical approach to predict the dynamic behavior of a wind turbine under simultaneous seismic and operational wind loads is investigated in this work. The combined action can be determined by analyzing the wind and the seismic-induced responses separately. However, an accurate definition of the aerodynamic damping is required for this purpose and there are few experimental studies on the additional damping source. A 1/100-scaled wind turbine model was designed and the aerodynamic damping of the model was identified. Subsequently, the ground motion was applied in the model by means of a shake table and the combined wind/earthquake response that was measured experimentally was compared with the response predicted by several combination rules. A numerical study using the FAST analysis package for wind turbines was also conducted to complement the experiments with fully-coupled simulations that include aeroelastic effects. This work provides a necessary experimental reference for structural engineers to use adequate aerodynamic damping and load combination methods for the seismic analysis of wind turbines in operation.

This paper presents a novel unknown input observer (UIO) integrated extended finite impulse response (EFIR) estimator (UIOEFIR) and its application for an effective sensor fault tolerant control of an electro-hydraulic-actuator (EHA). The proposed estimator exploits the UIO structure in the EFIR filter. Thus, it requires only a small number of historical data (N) whilst ensuring threefold: i) Sensor fault and system-state estimation accuracy under time-correlated noise ii) The number of estimator-design-parameters is significantly minimized. iii) Robust residual generation. A Lyapunov-stability-based theory is carried out to study its convergence condition. Next, an EHAbased test rig has been setup and sensor FTC is performed by carrying this estimator as a part of fault diagnosis algorithm to evaluate its performance by both simulation and realtime experiments. Results highlight that under optimal setting (N = Nopt), the estimator performance is near-accurate to the very-well-developed Extended Kalman Filter-based unknown input observer in an undisturbed condition but significantly outperformed while dealing with time-correlated noise under the same control environment. The estimator also shows its robustness under below-optimal setting (downgrading Nopt by 50%.) while performing in real-time sensor fault-tolerant control.

Owing to the increasing penetration of renewable power generation, the modern power system faces great challenges in frequency regulations and reduced system inertia. Hence, renewable energy is expected to take over part of the frequency regulation responsibilities from the gas or hydro plants and contribute to the system inertia. In this article, we investigate the feasibility of frequency regulation by the offshore hydrostatic wind turbine (HWT). The simulation model is transformed from NREL (National Renewable Energy Laboratory) 5-MW gearbox-equipped wind turbine model within FAST (fatigue, aerodynamics, structures, and turbulence) code. With proposed coordinated control scheme and the hydrostatic transmission configuration of the HWT, the `continuously variable gearbox ratio' in turbulent wind conditions can be realised to maintain the constant generator speed, so that the HWT can be connected to the grid without power converters in-between. To test the performances of the control scheme, the HWT is connected to a 5-bus grid model and operates with different frequency events. The simulation results indicate that the proposed control scheme is a promising solution for offshore HWT to participated in frequency response in the modern power system.\ud

The smoothed-particle hydrodynamics (SPH) method was introduced to simulate the quartz glass grinding process with a single grain under micro-nano scale. To investigate the mechanism of brittle–ductile transition, such factors as the machining depth, grinding force, maximum equivalent stress, and residual stress were analyzed. The simulation results indicate that quartz glass can be machined in a ductile mode under a certain condition. In this paper, the occurrence and propagation of cracks in quartz glass at different grinding depths (0.1–1 μm) are observed, and the critical depth of brittle–ductile transformation is 0.36 μm. At different grinding depths, the grinding force ratio is greater than 1. When the cutting depth is 0.4 μm, the crack propagation depth is about 1.2 μm, which provides a basis for the prediction of subsurface damage depth. In addition, the correctness of the simulation result was verified by carrying out scratch experiments of varying cutting depth on optical quartz glass.

Due to high local cooling rates and non-equilibrium directional solidification conditions, selective laser melting (SLM) processed metals exhibit microstructural and textural features significantly different from the conventionally processed ones. The evolution of crystallographic orientations in a maraging stainless steel (commercially known as stainless steel CX) sample fabricated by the SLM process was studied through experimental and modelling approaches Electron backscattering diffraction analysis showed that the dominant texture components in martensite and austenite phases are || building direction and || building direction, respectively. Texture simulation indicated that the formation of crystallographic orientations in the studied sample is the result of two consecutive phase transformations, from initially solidified delta ferrite phase with dominant cube fiber texture to austenite and austenite to martensite.

This work aims to study the influence of the tool rotational speed and tool traverse speed on dissimilar friction stir butt welds on 3 mm thick AA5083 to commercially pure copper plates. Complex microstructures were formed in the thermo-mechanically affected zone, in which a vortex-like pattern and lamellar structures were found. Several intermetallic compounds were identified in this region, such as Al2Cu, Al4Cu9 and these developed an inhomogeneous hardness distribution. The highest ultimate tensile strength of 203 MPa and joint efficiency of 94.8% were achieved at 1400 rpm tool rotational speed and 120 mm/min traverse speed. Placing the softer material (aluminium) on the advancing side produced an excellent metallurgical bond with no requirement for tool offsetting.

Fast and accurate in-line areal surface topography measuring instruments are required to control the quality of microscale manufactured components, without significantly slowing down the production process. Full-field areal optical surface topography measurement instruments are promising for in-line or on-machine measurement applications due to their ability to measure quickly, to access small features and to avoid surface damage. This paper presents the development and integration of a compact optical focus variation sensor for on-machine surface topography measurement mounted on to a hybrid ultraprecision machine tool. The sensor development is described and a case study involving the on-machine dimensional measurement of the depth of hydrophobic microscale features, including microchannels and micro-dimples, is presented. Comparisons of results between the on-machine measurements obtained by the developed sensor and a desktop focus variation microscope are presented and discussed. The comparison results show that the developed focus variation sensor is able to perform on-machine dimensional measurement of microscale features within sub-micrometre accuracy.

This paper discusses cooperative path-planning and tracking controller for autonomous vehicles using a distributed model predictive control approach. Mixed-integer quadratic programming approach is used for optimal trajectory generation using a linear model predictive control for path-tracking. Cooperative behaviour is introduced by broadcasting the planned trajectories of two connected automated vehicles. The controller generates steering and torque inputs. The steering and drive motor actuator constraints are incorporated in the control law. Computational simulations are performed to evaluate the controller for vehicle models of varying complexities. A 12-degrees-of-freedom vehicle model is developed and is subsequently linearised to be used as the plant model for the linearised model predictive control-based tracking controller. The model behaviour is compared against the kinematic, bicycle and the sophisticated high-fidelity multi-body dynamics CarSim model of the vehicle. Vehicle trajectories used for tracking are longitudinal and lateral positions, velocities and yaw rate. A cooperative obstacle avoidance manoeuvre is performed at different speeds using a co-simulation between the controller model in Simulink and the high-fidelity vehicle model in CarSim. The simulation results demonstrate the effectiveness of the proposed method.

This paper investigates the dynamic modeling and robust control of an underactuated floating wind turbine for vibration suppression. The offshore wind turbine is equipped with a tuned mass damper on the floating platform. The Lagrange's equation is employed to establish the limited degree‐of‐freedom dynamic model. A novel disturbance observer‐based hierarchical sliding mode control system is developed for mitigating loads of the underactuated floating wind turbine. In the proposed control scheme, two prescribed performance nonlinear disturbance observers are developed to estimate and counteract unknown disturbances, where the load induced by wave is considered as a mismatched disturbance while the load caused by wind is treated as a matched disturbance. The hierarchical sliding mode controller regulates the states of such an underactuated nonlinear system. In particular, the first‐order sliding mode differentiator is used to avoid the tedious analytic computation in the sliding mode control design. The stability of the whole closed‐loop system is rigorously analyzed, and some sufficient conditions are derived to guarantee the convergence of the states for the considered system. Numerical simulations deployed on both the design model and the National Renewable Energy Laboratory 5‐MW wind turbine model are provided, which demonstrate great effectiveness and strong robustness of the proposed control scheme.\ud \ud

Significance Wirelessly controlled, multitasking soft devices active in aqueous environments are highly required for applications in microfluidics and organ-on-a-chip and as medical devices. Inspired by marine organisms, we present an approach to achieve such devices by utilizing stimuli-responsive material assemblies capable of untethered object manipulation in an enclosed aqueous environment. Our soft robot assembly integrates a magnetically controlled stem with a light-responsive gripper with unmatched speed, insensitivity to contaminants, and high control of actuation underwater at low light intensities. The independent device segments can be orthogonally controlled to realize different tasks such as attracting, capturing, and releasing targets in an aqueous environment, demonstrating the significance of actuator assemblies in the fabrication of multifunctional soft devices operating underwater., The development of light-responsive materials has captured scientific attention and advanced the development of wirelessly driven terrestrial soft robots. Marine organisms trigger inspiration to expand the paradigm of untethered soft robotics into aqueous environments. However, this expansion toward aquatic soft robots is hampered by the slow response of most light-driven polymers to low light intensities and by the lack of controlled multishape deformations. Herein, we present a surface-anchored artificial aquatic coral polyp composed of a magnetically driven stem and a light-driven gripper. Through magnetically driven motion, the polyp induces stirring and attracts suspended targets. The light-responsive gripper is sensitive to low light intensities and has programmable states and rapid and highly controlled actuation, allowing the polyp to capture or release targets on demand. The artificial polyp demonstrates that assemblies of stimuli-responsive materials in water utilizing coordinated motion can perform tasks not possible for single-component devices.

Forming limit curves characterise “mean” failure strains of sheet metals. Safety levels from the curves define the deterministic upper limit of the processing and part design window, which can be small for high strength, low formability materials. Effects of heterogeneity of plastic deformation, widely accepted to occur on the microscale, are neglected. Marciniak tests were carried out on aluminium alloys (AA6111-T4, NG5754-O), dual-phase steel (DP600) and mild steel (MS3). Digital image correlation was used to measure the effect of heterogeneity on failure. Heterogeneity, based on strain variance, was modelled with the 2-component Gaussian mixture model, and a framework was proposed to (1) identify the onset of necking and to (2) re-define formability as a probability to failure. The results were “forming maps” in major-minor strain space of contours of constant probability (from probability,P= 0 toP= 1), which showed how failure risk increased with major strain. The contour bands indicated the unique degree of heterogeneity in each material. NG5754-O had the greatest width (0.07 strain) in plane strain and MS3 the lowest (0.03 strain). This novel characterisation will allow engineers to balance a desired forming window for a component design with the risk to failure of the material.

The facile and efficient reaction of graphene oxide (GO) and 2-Ureido-4[1H]-pyrimidinone (UPy), an isocyanate terminated 4-site hydrogen-bonding moiety, produces a functionalised GO (f-GO) that readily self-assembles into a freestanding nacre-like film using a vacuum filtration process. The reaction of UPy with GO occurs predominately via the epoxide and hydroxyl groups on the GO which was confirmed from a combination of Fourier-transform infrared (FTIR), Raman and X-ray photoelectron spectroscopy (XPS) and 13C Solid State Nuclear Magnetic Resonance (SSNMR) measurements. The nacre-like films obtained were typically 50µm to 100µm thick, from cross-section scanning-electron microscopy (SEM) imaging. The GO d-spacing (X-ray diffraction, XRD) increased with increasing UPy content from 0.934nm to 1.45nm, resulting in porous films with reduced tortuosity to oxygen, carbon dioxide and water. However, at higher UPy content reduced tortuosity is, balanced with the ability of UPy dimers to readily dissociate and exchange with water. The tensile strength and tensile toughness of the GO nacre-like film increased by up to 470% and 1100% and the maximum strain by a factor of ~2, for the film with the highest UPy content. These improvements are achieved through a mechanism of extension and unfolding of the linear chain of six carbon atoms in UPy, enhancing strain under tensile loading permitting the platelets to slide more before failure. This work highlights the impact of enhanced interlayer interactions via hydrogen bonding in producing polymer-free nacre-like films.