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  • Journal article
    Xu Y, Cartwright B, Advincula L, Myant C, Stokes JRet al., 2021,

    Generalised scaling law for soft contact tribology: Influence of load and asymmetric surface deformation

    , Tribology International, Vol: 163, ISSN: 0301-679X

    Finite Element Analysis is used to investigate surface deformation associated with traction and normal forces within soft contact tribology (SCT). Significant deviation from the Hertzian contact model occurs, including asymmetric deformation in soft substrates. A cavity is predicted in front of the contact that has a characteristic length scale significantly larger than the film thickness expected in lubricated SCT. This cavity may facilitate fluid entrainment and squeeze out dynamics in boundary-mixed lubrication regimes that would explain many anomalous results in SCT studies. Analysis of contact mechanics reveals scaling parameters for the boundary-mixed and EHL regimes in SCT, which are used to generalise Stribeck friction curves onto a “single” curve as a function of a modified Hersey number at varying loads.

  • Journal article
    Reza Attar H, Li N, Foster A, 2021,

    A new strategy for developing component design guidelines for aluminium alloy corners formed through cold and hot stamping processes

    , Materials & Design, Vol: 207, ISSN: 0264-1275

    In recent sheet metal forming research, efforts have been largely focused on determining optimum processing parameters, while intuitive guidelines to efficiently develop feasible component geometries are rarely considered. Consequently, there are currently no suitable design support tools that can guide component designers using the most recent, and therefore unfamiliar, sheet metal manufacturing technologies. This paper aims to address this bottleneck by proposing a strategy for the creation of early stage manufacturing design guidelines for the common limiting design requirement of deep corners. Aluminium alloys formed under both cold, and elevated temperature working conditions are considered. A new methodology to simplify the analysis of complex viscoplastic behaviour of aluminium alloys at elevated temperatures into an equivalent strain hardening response is presented. The effects and trends of the deep corner geometry and simplified material hardening characteristics on the post-form thinning distribution are identified. New equation sets are proposed which model the identified trends and enable the development of intuitive design maps. Following the approach proposed in this paper, an awareness of the available design envelope can be created at the early stages of a design process to guide component design, material, and manufacturing process selection decisions for deep corner geometries.

  • Journal article
    Yang S, Zhang Z, Cao R, Wang M, Cheng H, Zhang L, Jiang Y, Li Y, Chen B, Ling H, Lian Y, Wu B, Liu Xet al., 2021,

    Implementation for a cloud battery management system based on the CHAIN framework

    , Energy and AI, Vol: 5, Pages: 100088-100088, ISSN: 2666-5468

    An intelligent battery management system is a crucial enabler for energy storage systems with high power output, increased safety and long lifetimes. With recent developments in cloud computing and the proliferation of big data, machine learning approaches have begun to deliver invaluable insights, which drives adaptive control of battery management systems (BMS) with improved performance. In this paper, a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed, with the composition and function of each link described. Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network (CHAIN) framework to provide multi-scale insights, more advanced and efficient algorithms can be used to realize the state-of-X estimation, thermal management, cell balancing, fault diagnosis and other functions of traditional BMS system. The battery intelligent monitoring and management platform can visually present battery performance, store working-data to help in-depth understanding of the microscopic evolutionary law, and provide support for the development of control strategies. Currently, the cloud-based BMS requires more effects on the multi-scale integrated modeling methods and remote upgrading capability of the controller, these two aspects are very important for the precise management and online upgrade of the system. The utility of this approach is highlighted not only for automotive applications, but for any battery energy storage system, providing a holistic framework for future intelligent and connected battery management.

  • Journal article
    Kalossaka LM, Sena G, Barter LMC, Myant Cet al., 2021,

    Review: 3D printing hydrogels for the fabrication of soilless cultivation substrates

    , Applied Materials Today, Vol: 24, Pages: 1-16, ISSN: 2352-9407

    The use of hydrogels in academic research is fast evolving, and becoming more relevant to real life applications across varying fields. Additive Manufacturing (AM) has paved the way towards manufacturing hydrogel substrates with tailored properties which allow for new functionalities and applications. In this review, we introduce the idea of fabricating hydrogels as bioreceptive structures to be used as soilless cultivation substrates. AM is suggested as the fabrication process to achieve structures with features similar to soil. To evaluate this, we first review hydrogel fabrication processes, highlighting their key differences in terms of resolution, printing speed and build volume. Thus, we illustrate the examples from the literature where hydrogels were 3D printed with microorganisms such as algae. Finally, the challenges and future perspectives of printing soilless cultivation substrates are explored.

  • Conference paper
    Cursi F, Kormushev P, 2021,

    Pre-operative offline optimization of insertion point location for safe and accurate surgical task execution

    , Prague, Czech Republic, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)

    In robotically assisted surgical procedures thesurgical tool is usually inserted in the patient’s body througha small incision, which acts as a constraint for the motionof the robot, known as remote center of Motion (RCM). Thelocation of the insertion point on the patient’s body has hugeeffects on the performances of the surgical robot. In this workwe present an offline pre-operative framework to identify theoptimal insertion point location in order to guarantee accurateand safe surgical task execution. The approach is validatedusing a serial-link manipulator in conjunction with a surgicalrobotic tool to perform a tumor resection task, while avoidingnearby organs. Results show that the framework is capable ofidentifying the best insertion point ensuring high dexterity, hightracking accuracy, and safety in avoiding nearby organs.

  • Conference paper
    Cursi F, Bai W, Kormushev P, 2021,

    Kalibrot: a simple-to-use Matlab package for robot kinematic calibration

    , Prague, Czech Republic, International Conference on Intelligent Robots and Systems (IROS) 2021

    Robot modelling is an essential part to properlyunderstand how a robotic system moves and how to controlit. The kinematic model of a robot is usually obtained byusing Denavit-Hartenberg convention, which relies on a set ofparameters to describe the end-effector pose in a Cartesianspace. These parameters are assigned based on geometricalconsiderations of the robotic structure, however, the assignedvalues may be inaccurate. The purpose of robot kinematiccalibration is therefore to find optimal parameters whichimprove the accuracy of the robot model. In this work wepresent Kalibrot, an open source Matlab package for robotkinematic calibration. Kalibrot has been designed to simplifyrobot calibration and easily assess the calibration results. Besidecomputing the optimal parameters, Kalibrot provides a visualization layer showing the values of the calibrated parameters,what parameters can be identified, and the calibrated roboticstructure. The capabilities of the package are here shownthrough simulated and real world experiments.

  • Journal article
    Choi I, Ho N, Morris R, Harvey SB, Calvo RA, Glozier Net al., 2021,

    The impact of communicating personal mental ill-health risk: A randomized controlled non-inferiority trial

    , Early Intervention in Psychiatry: the development, onset and treatment of emerging mental disorders, Vol: 15, Pages: 932-941, ISSN: 1751-7885

    AimRisk algorithms predicting personal mental ill‐health will form an important component of digital and personalized preventive interventions, yet it is unknown whether informing people of personal risk may cause unintended harm. This trial evaluated the comparative effect of communicating personal mental ill‐health risk profiles on psychological distress.MethodsAustralian participants using a mood‐monitoring app were randomly allocated to receiving their current personal mental ill‐health risk profile (n = 119), their achievable personal risk profile (n = 118) or to a control group (n = 118) in which no risk information was communicated, in a non‐inferiority trial design. The primary outcome was psychological distress at four‐weeks as assessed on the Kessler Psychological Distress Scale.ResultsThere was high attrition in the trial with 64% of data missing at follow up. Per‐protocol (completer) analysis found that the lower bounds of the confidence intervals of the estimated mean change of the current risk (m = 0.19, 95% CI: −2.59‐ 2.98) and achievable risk (m = −0.09, 95% CI: −2.84 to 2.66) groups were within the non‐inferiority margin of the control group's mean at follow up. Supplementary intention‐to‐treat analysis using Multivariate Imputation by Chained Equations (MICE) found that 98/100 imputed datasets of the current risk profile group, and all imputed datasets of the achievable risk profile group showed non‐inferiority to the control group.ConclusionsThis study provides preliminary support that providing personal mental health risk profiles does not lead to unacceptable worsening of distress compared to no risk feedback, although this needs to be replicated in a fully powered RCT.

  • Journal article
    Wang X, Zhu L, Sun L, Li Net al., 2021,

    Optimization of graded filleted lattice structures subject to yield and buckling constraints

    , Materials & Design, Vol: 206, Pages: 1-17, ISSN: 0264-1275

    To reduce the stress concentration and ensure structural safety for lattice structure designs, in this paper, a new optimization framework is developed for the optimal design of graded lattice structures, innovatively integrating fillet designs as well as yield and buckling constraints. Both relative strut radii and fillet parameters are defined as design variables, for BCC and PC lattices. Numerical homogenization is employed to characterize the effective elastic constants and yield stresses of the lattice metamaterials. Metamaterial models are developed to represent the relationships between the metamaterial effective properties and lattice geometric variables. Yield and buckling constraints, based on modified Hill’s yield criterion as well as Euler and Johnson buckling formulae respectively, are developed as functions of lattice geometric variables. A new optimization framework is proposed with both yield and buckling constraints integrated. A case study on minimizing the compliance of a Messerschmitt-Bolkow-Blohm beam, composed of either BCC or PC lattices, is conducted. The yield and buckling constraints guarantee the structural safety of the optimized lattice beams. The optimized beams composed of filleted lattices, compared with non-filleted lattices in the corresponding type, show reduced proportions subject to high modified Hill’s stress (

  • Conference paper
    Wang K, Saputra RP, Foster JP, Kormushev Pet al., 2021,

    Improved energy efficiency via parallel elastic elements for the straight-legged vertically-compliant robot SLIDER

    , Japan, 24th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines

    Most state-of-the-art bipedal robots are designed to be anthropomorphic, and therefore possess articulated legs with knees. Whilstthis facilitates smoother, human-like locomotion, there are implementation issues that make walking with straight legs difficult. Many robotshave to move with a constant bend in the legs to avoid a singularityoccurring at the knee joints. The actuators must constantly work tomaintain this stance, which can result in the negation of energy-savingtechniques employed. Furthermore, vertical compliance disappears whenthe leg is straight and the robot undergoes high-energy loss events such asimpacts from running and jumping, as the impact force travels throughthe fully extended joints to the hips. In this paper, we attempt to improve energy efficiency in a simple yet effective way: attaching bungeecords as elastic elements in parallel to the legs of a novel, knee-less bipedrobot SLIDER, and show that the robot’s prismatic hip joints preservevertical compliance despite the legs being constantly straight. Due tothe nonlinear dynamics of the bungee cords and various sources of friction, Bayesian Optimization is utilized to find the optimals configurationof bungee cords that achieves the largest reduction in energy consumption. The optimal solution found saves 15% of the energy consumptioncompared to the robot configuration without parallel elastic elements.Additional Video: https://youtu.be/ZTaG9−Dz8A

  • Journal article
    Wang H, Liu H, Ding Z, Li Net al., 2021,

    Experimental and constitutive modelling studies of semicrystalline thermoplastics under solid-state stamp forming conditions

    , Polymer, Vol: 228, Pages: 1-17, ISSN: 0032-3861

    Experimental characterisation and constitutive modelling studies on the thermomechanical behaviour of thermoplastics, under solid-state stamp forming conditions, are required for understanding and optimising the stampforming process. In this paper, two semicrystalline thermoplastics, Polyamide 6 (PA6, or Nylon 6) and PolyEther-Ether-Ketone (PEEK) are studied via uniaxial tensile tests at temperatures between their glass transitiontemperatures (Tg ) and melting temperatures (Tm), and at different strain rates (0.001–50 /s). The temperatureand strain rate effects are analysed quantitatively to further understand the thermomechanical response of thesesemicrystalline thermoplastics. The results show that temperature has significant effects on the thermomechanical behaviour of thermoplastic polymers, while the strain rate effects are relatively marginal in theinvestigated strain rate range. In addition, a new physically-based viscoelastic-viscoplastic constitutive model isproposed to simulate the thermomechanical behaviour of both materials. The model shows good predictionaccuracy on tensile stress responses and provides an insight into the microstructural evolution of the semicrystalline thermoplastics; thus, it can be used to analyse solid-state stamp forming of pure semicrystallinethermoplastics and thermoplastic polymer matrix composites (TPMCs).

  • Journal article
    Moschella M, Ferraro P, Crisostomi E, Shorten Ret al., 2021,

    Decentralized Assignment of Electric Vehicles at Charging Stations Based on Personalized Cost Functions and Distributed Ledger Technologies

    , IEEE INTERNET OF THINGS JOURNAL, Vol: 8, Pages: 11112-11122, ISSN: 2327-4662
  • Conference paper
    Minto L, Haller M, Haddadi H, Livshits Bet al., 2021,

    Stronger privacy for federated collaborative filtering with implicit feedback

    , 15th ACM Conference on Recommender Systems

    Recommender systems are commonly trained on centrally collected userinteraction data like views or clicks. This practice however raises seriousprivacy concerns regarding the recommender's collection and handling ofpotentially sensitive data. Several privacy-aware recommender systems have beenproposed in recent literature, but comparatively little attention has beengiven to systems at the intersection of implicit feedback and privacy. Toaddress this shortcoming, we propose a practical federated recommender systemfor implicit data under user-level local differential privacy (LDP). Theprivacy-utility trade-off is controlled by parameters $\epsilon$ and $k$,regulating the per-update privacy budget and the number of $\epsilon$-LDPgradient updates sent by each user respectively. To further protect the user'sprivacy, we introduce a proxy network to reduce the fingerprinting surface byanonymizing and shuffling the reports before forwarding them to therecommender. We empirically demonstrate the effectiveness of our framework onthe MovieLens dataset, achieving up to Hit Ratio with K=10 (HR@10) 0.68 on 50kusers with 5k items. Even on the full dataset, we show that it is possible toachieve reasonable utility with HR@10>0.5 without compromising user privacy.

  • Conference paper
    Yu Z, SMHadi S, Hasitha W, Childs P, Nanayakkara Tet al., 2021,

    A method to use reservoir computing in a whisker sensor for terrain identification by mobile robots

    , 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems

    This paper shows analytical and experimental evidence of using the vibration dynamics of a compliant whisker for accurate terrain classification during steady state motion of a mobile robot. A Hall effect sensor was used to measure whisker vibrations due to perturbations from the ground. Analytical results predict that the whisker vibrations will have a dominant frequency at the vertical perturbation frequency of the mobile robot sandwiched by two other less dominant but distinct frequency components. These frequency components may come from bifurcation of vibration frequency due to nonlinear interaction dynamics at steady state. Experimental results also exhibit distinct dominant frequency components unique to the speed of the robot and the terrain roughness. This nonlinear dynamic feature is used in a deep multi-layer perceptron neural network to classify terrains. We achieved 85.6% prediction success rate for seven flat terrain surfaces with different textures.Index Terms— Robotic whiskers, Surface identification, multi-layer perceptron, Modal analysis.

  • Journal article
    Zhou H, Xu Q, Nie Z, Li Net al., 2021,

    A study on using image-based machine learning methods to develop surrogate models of stamp forming simulations

    , Journal of Manufacturing Science and Engineering, Pages: 1-41, ISSN: 1087-1357

    In design for forming, it is becoming increasingly significant to develop surrogate models of high-fidelity finite element analysis (FEA) simulations of forming processes, to achieve effective component feasibility assessment as well as process and component optimizations. However, surrogate models using traditional scalar-based machine learning methods (SBMLMs) fall short on accuracy and generalizability. This is because SBMLMs fail to harness the location information available from the simulations. To overcome this shortcoming, the theoretical feasibility and practical advantages of innovatively applying image-based machine learning methods (IBMLMs) in developing surrogate models of sheet stamp forming simulations are explored in this study. To demonstrate the advantages of IBMLMs, the effect of the location information on both design variables and simulated physical fields is firstly proposed and analyzed. Based on a sheet steel stamping case study, a Res-SE-U-Net IBMLM surrogate model of stamping simulations is then developed and compared with a baseline multi-layer perceptron (MLP) SBMLM surrogate model. The results show that the IBMLM model is advantageous over the MLP SBMLM model in accuracy, generalizability, robustness, and informativeness. This paper presents a promising methodology in leveraging IBMLMs as surrogate models to make maximum use of information from stamp forming FEA results. Future prospective studies that are inspired by this paper are also discussed.

  • Conference paper
    Rakicevic N, Cully A, Kormushev P, 2021,

    Policy manifold search: exploring the manifold hypothesis for diversity-based neuroevolution

    , Proceedings of the 2021 Genetic and Evolutionary Computation Conference

    Neuroevolution is an alternative to gradient-based optimisation that has thepotential to avoid local minima and allows parallelisation. The main limitingfactor is that usually it does not scale well with parameter spacedimensionality. Inspired by recent work examining neural network intrinsicdimension and loss landscapes, we hypothesise that there exists alow-dimensional manifold, embedded in the policy network parameter space,around which a high-density of diverse and useful policies are located. Thispaper proposes a novel method for diversity-based policy search viaNeuroevolution, that leverages learned representations of the policy networkparameters, by performing policy search in this learned representation space.Our method relies on the Quality-Diversity (QD) framework which provides aprincipled approach to policy search, and maintains a collection of diversepolicies, used as a dataset for learning policy representations. Further, weuse the Jacobian of the inverse-mapping function to guide the search in therepresentation space. This ensures that the generated samples remain in thehigh-density regions, after mapping back to the original space. Finally, weevaluate our contributions on four continuous-control tasks in simulatedenvironments, and compare to diversity-based baselines.

  • Journal article
    Nissim L, Butt H, Gao L, Myant C, Hewson Ret al., 2021,

    Role of protein concentration on transient film thickness in synovial fluid lubricated joints

    , Biotribology, ISSN: 2352-5738

    A computational model of protein aggregation lubrication has been developed for predicting transient behaviour in lubricated prosthetics. The model uses an advection-diffusion equation to simulate protein transport in order to map concentration changes throughout the contact and inlet zones of an elasto-hydrodynamic contact. Concentration increases lead to exponential increase in fluid viscosity giving rise to lubricating film thicknesses an order of magnitude larger than would be expected using conventional elasto-hydrodynamic theory. The model parameters have been calibrated such that good agreement in transient film thickness is achieved with observed experimental results.KeywordsProtein aggregation lubrication; Elasto-hydrodynamic lubrication; Prostheses

  • Conference paper
    Mo F, Haddadi H, Katevas K, Marin E, Perino D, Kourtellis Net al., 2021,

    PPFL: privacy-preserving federated learning with trusted execution environments

    , Mobile Systems, Applications, and Services conference, Publisher: ACM, Pages: 94-108

    We propose and implement a Privacy-preserving Federated Learning (PPFL)framework for mobile systems to limit privacy leakages in federated learning.Leveraging the widespread presence of Trusted Execution Environments (TEEs) inhigh-end and mobile devices, we utilize TEEs on clients for local training, andon servers for secure aggregation, so that model/gradient updates are hiddenfrom adversaries. Challenged by the limited memory size of current TEEs, weleverage greedy layer-wise training to train each model's layer inside thetrusted area until its convergence. The performance evaluation of ourimplementation shows that PPFL can significantly improve privacy whileincurring small system overheads at the client-side. In particular, PPFL cansuccessfully defend the trained model against data reconstruction, propertyinference, and membership inference attacks. Furthermore, it can achievecomparable model utility with fewer communication rounds (0.54x) and a similaramount of network traffic (1.002x) compared to the standard federated learningof a complete model. This is achieved while only introducing up to ~15% CPUtime, ~18% memory usage, and ~21% energy consumption overhead in PPFL'sclient-side.

  • Journal article
    Ford E, Edelman N, Somers L, Shrewsbury D, Lopez Levy M, van Marwijk H, Curcin V, Porat Tet al., 2021,

    Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners

    , BMC Medical Informatics and Decision Making, Vol: 21, ISSN: 1472-6947

    BACKGROUND: Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. METHODS: We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. RESULTS: We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. CONCLUSIONS: Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.

  • Journal article
    Farajzadeh Khosroshahi S, Yin X, Donat C, McGarry A, Yanez Lopez M, Baxan N, Sharp D, Sastre M, Ghajari Met al., 2021,

    Multiscale modelling of cerebrovascular injury reveals the role of vascular anatomy and parenchymal shear stresses

    , Scientific Reports, Vol: 11, ISSN: 2045-2322

    Neurovascular injury is often observed in traumatic brain injury (TBI). However, the relationship between mechanical forces and vascular injury is still unclear. A key question is whether the complex anatomy of vasculature plays a role in increasing forces in cerebral vessels and producing damage. We developed a high-fidelity multiscale finite element model of the rat brain featuring a detailed definition of the angioarchitecture. Controlled cortical impacts were performed experimentally and in-silico. The model was able to predict the pattern of blood–brain barrier damage. We found strong correlation between the area of fibrinogen extravasation and the brain area where axial strain in vessels exceeds 0.14. Our results showed that adjacent vessels can sustain profoundly different axial stresses depending on their alignment with the principal direction of stress in parenchyma, with a better alignment leading to larger stresses in vessels. We also found a strong correlation between axial stress in vessels and the shearing component of the stress wave in parenchyma. Our multiscale computational approach explains the unrecognised role of the vascular anatomy and shear stresses in producing distinct distribution of large forces in vasculature. This new understanding can contribute to improving TBI diagnosis and prevention.

  • Conference paper
    Attar HR, Zhou H, Li N, 2021,

    Deformation and thinning field prediction for HFQ® formed panel components using convolutional neural networks

    , International Deep-Drawing Research Group Conference (IDDRG 2021), Publisher: IOP Publishing, Pages: 1-11, ISSN: 1757-8981

    The novel Hot Forming and cold die Quenching (HFQ®) process can provide cost-effective and complex deep drawn solutions through high strength aluminium alloys. However, the unfamiliarity of the new process prevents its widescale adoption in industrial settings, while accurate Finite Element (FE) simulations using the most advanced material models take place late in design processes and require forming process expertise. Machine learning technologies have recently been proven successful in learning complex system behaviour from representative examples and have the potential to be used as design support tools for new forming technologies such as HFQ®. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate to predict the deformation and thinning fields for variable deep drawn geometries formed using HFQ® technology. A dataset based on deep drawn geometries and corresponding FE results is generated and used to train the model. The results show that near indistinguishable full field predictions in real time are obtained from the surrogate when compared with HFQ® simulations. This technique can be adopted in industrial settings to aid in both concept and detailed component design for complex-shaped panel components formed under HFQ® conditions, without underlying knowledge of the forming process.

  • Report
    Brophy K, Davies S, Olenik S, Cotur Y, Ming D, Van Zalk N, O'Hare D, Guder F, Yetisen AKet al., 2021,

    The future of wearable technologies

    , Briefing Paper
  • Journal article
    Zhu T, Wills RGA, Lot R, Ruan H, Jiang Zet al., 2021,

    Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting

    , APPLIED ENERGY, Vol: 292, ISSN: 0306-2619
  • Journal article
    He L, Tan X, Suzumori K, Nanayakkara Tet al., 2021,

    A method to 3D print a programmable continuum actuator with single material using internal constraint

    , Sensors and Actuators A: Physical, Vol: 324, ISSN: 0924-4247

    Soft continuum robots require differential control of channel pressure across several modules to trace 3D trajectories at the tip. For current designs of such actuators, sheathing is required to prevent radial expansion when the chambers are pressurized. With the recent development of soft materials additive manufacturing, 3D printing has become a promising fabrication method for soft continuum robots. However, most current designs for continuum actuators are based on molding, which are not designed for 3D printing. This paper proposes an internal constraint-based soft continuum actuator for single material 3D printing, with tunable design parameters to render pre-defined motions. The internal constraint method maximizes the superiority of the rapid prototyping solution in terms of customizing the soft continuum actuators with high fabrication speed and design freedom. The internal constraints come in the form of internal beam elements that not only limit the undesired radial expansion (up to ∼14% of conventional design) but also allows the actuator to be pressurized at a higher driving pressure (up to ∼160%) and higher maximum bending angle (up to ∼320%) compared to conventional no-beam design. By tuning the design parameter q (determined by the number of constraint beams n per radial cross-section and the number of such sections k along the axial direction), we can render the actuator to the desired movement under specific driving pressure p. We show numerical simulation and hardware experiment results for a soft actuator to achieve specified bending and twisting motions following this design approach.

  • Journal article
    Schimpe M, Varela Barreras J, Wu B, Offer GJet al., 2021,

    Battery degradation-aware current derating: an effective method to prolong lifetime and ease thermal management

    , Journal of The Electrochemical Society, Vol: 168, Pages: 1-13, ISSN: 0013-4651

    To ensure the safe and stable operation of lithium-ion batteries in battery energy storage systems (BESS), the power/current is de-rated to prevent the battery from going outside the safe operating range. Most derating strategies use static limits for battery current, voltage, temperature and state-of-charge, and do not account for the complexity of battery degradation. Progress has been made with models of lithium plating for fast charging. However, this is a partial solution, does not consider other degradation mechanisms, and still requires complex optimization work, limiting widespread adoption. In this work, the calendar and cycle degradation model is analysed offline to predetermine the degradation rates. The results are integrated into the current-derating strategy. This framework can be adapted to any degradation model and allows flexible tuning. The framework is evaluated in simulations of an outdoors-installed BESS with passive thermal management, which operates in a residential photovoltaic application. In comparison to standard derating, the degradation-aware derating achieves: (1) increase of battery lifetime by 65%; (2) increase in energy throughput over lifetime by 49%, while III) energy throughput per year is reduced by only 9.5%. These results suggest that the derating framework can become a new standard in current derating.

  • Journal article
    Saputra RP, Rakicevic N, Kuder I, Bilsdorfer J, Gough A, Dakin A, Cocker ED, Rock S, Harpin R, Kormushev Pet al., 2021,

    ResQbot 2.0: an improved design of a mobile rescue robot with an inflatable neck securing device for safe casualty extraction

    , Applied Sciences, Vol: 11, Pages: 1-18, ISSN: 2076-3417

    Despite the fact that a large number of research studies have been conducted in the field of searchand rescue robotics, significantly little attention has been given to the development of rescue robotscapable of performing physical rescue interventions, including loading and transporting victims toa safe zone—i.e. casualty extraction tasks. The aim of this study is to develop a mobile rescue robotthat could assist first responders when saving casualties from a danger area by performing a casualty extraction procedure, whilst ensuring that no additional injury is caused by the operation andno additional lives are put at risk. In this paper, we present a novel design of ResQbot 2.0—a mobilerescue robot designed for performing the casualty extraction task. This robot is a stretcher-type casualty extraction robot, which is a significantly improved version of the initial proof-of-concept prototype, ResQbot (retrospectively referred to as ResQbot 1.0), that has been developed in our previous work. The proposed designs and development of the mechanical system of ResQbot 2.0, as wellas the method for safely loading a full body casualty onto the robot’s ‘stretcher bed’, are describedin detail based on the conducted literature review, evaluation of our previous work and feedbackprovided by medical professionals. To verify the proposed design and the casualty extraction procedure, we perform simulation experiments in Gazebo physics engine simulator. The simulationresults demonstrate the capability of ResQbot 2.0 to successfully carry out safe casualty extractions

  • Conference paper
    Frazelle C, Walker I, AlAttar A, Kormushev Pet al., 2021,

    Kinematic-model-free control for space operations with continuum Manipulators

    , USA, IEEE Conference on Aerospace, Publisher: IEEE, Pages: 1-11, ISSN: 1095-323X

    Continuum robots have strong potential for application in Space environments. However, their modeling is challenging in comparison with traditional rigid-link robots. The Kinematic-Model-Free (KMF) robot control method has been shown to be extremely effective in permitting a rigid-link robot to learn approximations of local kinematics and dynamics (“kinodynamics”) at various points in the robot's task space. These approximations enable the robot to follow various trajectories and even adapt to changes in the robot's kinematic structure. In this paper, we present the adaptation of the KMF method to a three-section, nine degrees-of-freedom continuum manipulator for both planar and spatial task spaces. Using only an external 3D camera, we show that the KMF method allows the continuum robot to converge to various desired set points in the robot's task space, avoiding the complexities inherent in solving this problem using traditional inverse kinematics. The success of the method shows that a continuum robot can “learn” enough information from an external camera to reach and track desired points and trajectories, without needing knowledge of exact shape or position of the robot. We similarly apply the method in a simulated example of a continuum robot performing an inspection task on board the ISS.

  • Conference paper
    Tassell C, Aurisicchio M, 2021,

    A Systems Thinking Framework Integrating Circular Behaviour Research

    , PLATE
  • Journal article
    Whitehouse S, Myant C, Cann PM, Stephens Aet al., 2021,

    Fluorescent imaging of razor cartridge/skin lubrication

    , SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES, Vol: 9, ISSN: 2051-672X
  • Journal article
    Han J, Jiang P, Childs PRN, 2021,

    Metrics for Measuring Sustainable Product Design Concepts

    , ENERGIES, Vol: 14
  • Journal article
    Ojha M, Wu B, Deepa M, 2021,

    Cost-Effective MIL-53(Cr) metal–organic framework-based supercapacitors encompassing fast-ion (Li+/H+/Na+) conductors

    , ACS Applied Energy Materials, Vol: 4, Pages: 4729-4743, ISSN: 2574-0962

    A chromium-based low-cost metal–organic framework (MOF) cathode, MIL (Matériaux de l′Institut Lavoisier)-53(Cr), is coupled with a bioderived porous carbon (BPC) anode, produced from abundantly available agricultural waste betel nut shells in an asymmetric supercapacitor, for the first time. The impact of the electrolyte on the electrochemical behavior of an asymmetric BPC//MIL-53(Cr) supercapacitor was assessed by constructing cells with the following electrolytes: proton-conducting camphorsulfonic acid (CSA), Li+-ion-conducting solutions of LiClO4, Na+-ion-conducting sodium poly(4-styrene sulfonate) solution, and ionic liquid (IL:1-butyl-1-methyl-pyrrolidinium trifluoromethanesulfonate)-based solutions. The aqueous H+-ion-based CSA electrolyte shows a superior ionic conductivity (270 mS cm–1) and an enhanced transport number (0.96), carries larger ionic currents, and retains high conductivity even at subambient temperatures, clearly outperforming all the other Li+/Na+/IL electrolytes. The BPC/aqueous CSA or LiClO4/MIL-53(Cr) supercapacitors show enhanced storage performances, with the H+ cell having a specific capacitance of 70 F g–1 and energy and power density maxima of 9.7 Wh kg–1 and 0.25 kW kg–1 and enduring 104 cycles. A detailed account of the dependence of the electrolyte cation/anion- and solvent-type on electrochemical charge storage provides a basis for adapting these design principles to developing high-performance MOF-based supercapacitors.

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