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For saving fuel and extending the fuel cells (FC)/battery lifetime, this paper proposes a real-time cost-optimal predictive energy management strategy of FC hybrid electric vehicles based on continuation/general minimal residuals (C/GMRES) algorithm. To ensure the preferable continuation for algorithm application, the continuity method is proposed for accurate model approximations. The external penalty method is employed to transform the inequality constraints as an equivalent index cost. Then, the continuous and unconstrained model predictive control problem is reformulated, and the C/GMRES algorithm is proposed for real-time optimization. Given the output, the expected FC control command can be decided by the designed postprocessing rules. The performance of the proposed strategy is validated under simulations and hardware-in-the-loop (HIL) tests. The results manifest that the proposed strategy can effectively save the total cost for the predictive horizon of 5s–60s even when the neural network-based predictive velocity is adopted. Besides, compared with the interior point method, the proposed C/GMRES algorithm achieves similar solving effects while exhibiting more than 100 times computing efficiency. In addition, the execution time of the proposed strategy in each step is less than 1.2 ms under HIL tests, indicating its real-time applicability. • State of health for fuel cells and battery are modeled and optimized. • Continuity method of health-aware system model is proposed. • Real-time optimization algorithm is proposed for this highly nonlinear problem. • Results about horizon length and predictive errors are gained and discussed. • HIL test verifies the control effects and real-time applicability. [ABSTRACT FROM AUTHOR]

Thermal runaway propagation of the power battery pack is an essential factor affecting the safety of electric vehicles. The commonly adopted propagation inhibition methods mainly include adding heat insulation materials and enlarging battery spacing, which could cause problematic heat dissipation and lower the system energy density. Herein, an innovative battery thermal management system composed of non-uniform thermal conductivity phase change materials and assisted liquid cooling is proposed. Combining the phase change materials with high and low thermal conductivity balances heat transfer and heat insulation requirements. The cooling performance and the ability of thermal runaway propagation mitigation of the proposed schemes are numerically studied. The results show that the proposed strategy can meet the heat dissipation requirements under normal operation and control the thermal runaway in a safe range by transferring the heat generated from the battery thermal runaway in the set direction. The maximum battery temperature and the temperature difference are 38.1 °C and 2.1 °C, respectively, under 3C discharge. Under thermal runaway conditions, the strategy successful confines the thermal runaway propagation within the middle row. The maximum battery temperature in other rows can be controlled under the irreversible thermal runaway reaction temperature of 200 °C. Further study found that increased thermal conductivity benefits the battery heat dissipation and reduces the risk of thermal runaway. However, it propagates faster and broader once the thermal runaway is triggered. In comparison, the decrease of thermal conductivity is beneficial to the mitigation of propagation but may reduce the overall heat dissipation of the battery module. This study can provide a new way to solve the contradiction between battery temperature control and thermal runaway spread suppression. • An innovative strategy is proposed to mitigate thermal runaway propagation. • Non-uniform phase change materials with different thermal conductivity are adopted. • The thermal runaway generated heat can be limited to transfer in a specific direction. • A suitable combination of high and low thermal conductivity for PCM is necessary. • Temperature control and thermal runaway suppression can be achieved simultaneously. [ABSTRACT FROM AUTHOR]

The employment of readily obtainable parameters, including voltage and temperature, derived from the battery management system facilitates the real-time evaluation of lithium-ion batteries health status. However, the complex aging mechanisms and noise interference inherent in lithium-ion batteries present substantial challenges to accurate estimation. Hence, this research proposes a multi-model fusion approach utilizing Gramian Angular Field encoding to accurately predict the health status of lithium-ion batteries. Battery voltage and temperature data are acquired through system aging experiments. The Gramian Angular Field-based image encoding transforms these data into images, accentuating the subtle characteristics of the data and thereby enhancing its recognizability by neural networks. Subsequently, two-dimensional Convolutional Neural Networks are employed to extract features from the images, followed by one-dimensional Convolutional Neural Networks to reduce the dimensions of the feature matrix. The battery health status is then predicted utilizing Long Short-term Memory Networks. The culmination is the evaluation of the model performance through the application of quantitative error metrics. The study results show that the method can pinpoint the prediction error to within 2%, significantly improving accuracy over traditional prediction methods. It proves the great potential of direct data, such as voltage and temperature, in predicting battery health. • Predicting lithium-ion battery health state based on multi-easily accessible data. • The temperature and voltage data are converted into 2D images by applying GAF method. • Feature extraction and SOH prediction using CNN-LSTM fusion models. [ABSTRACT FROM AUTHOR]

This paper proposes a predictive energy management strategy of FC PHEV based on PMP and co-state boundaries. The model predictive control (MPC) problem is established and transformed as a two-point-boundary-value one by PMP theory, and the physical constraints of FC power, FC power varying rate, and battery current, are merged by methodical derivatives. To gain the accurate co-state boundaries, the Karush-Kuhn-Tucker condition, for the first time, is employed to derive the general expressions, and a correction method is developed to modify the co-state boundaries for effectiveness guarantees. By inputting the feedback SOC, power demand, and the unified constraint, the shrunken enclosed range between the developed co-state boundaries can be determined in real time, thereby benefiting the efficient co-state calibration. Based on the co-state bounds, the concise but highly effective heuristic rules are proposed to calibrate the co-state online iteratively, and an analytical method is proposed to fast find the optimal solution of Hamilton function. The global optimality of the proposed strategy for the addressed MPC problem is also strictly proved. The validations and sensitivity analysis for the initial state of charge (SOC), the SOC reference, the predictive velocity accuracy, and the horizon length, are implemented under simulations, and the hardware-in-the-loop (HIL) experiments are carried out to verify the effectiveness of the proposed strategy under on-board environment. The results yield that, the proposed strategy can implement the timely and high-efficiency co-state updates, the smooth control commands, the expected SOC tracking effects, and improve the fuel economy. Additionally, <0.5 ms per sample is spent for the predictive horizon length 40 under HIL tests, indicating the real-time applicability of proposed strategy. • General expressions of co-state bounds are theoretically derived by KKT condition. • Designed analytical method fast minimizes Hamilton function with global optimality. • Heuristic rules by modified co-state bounds can efficiently decide co-state online. • Good co-state calibration stability favors robustness to reference and predicted speed. • HIL tests of designed method show desired fuel savings and high computing efficiency. [ABSTRACT FROM AUTHOR]

The demand for high energy density in lithium-ion battery packs for electric vehicles poses a challenge to maintaining its optimum operating temperature while reducing the risk of thermal runaway (TR) propagation. This study proposes a novel hybrid TR propagation mitigation system that balances heat transfer and thermal insulation requirements using low and high thermal conductivity phase change materials (PCM), heat pipes (HP), and air-cooling. The design and optimization of such a mitigation system are complex due to the many design parameters involved. The Adaptive-Kriging-High dimensional model representation (Adaptive-Kriging-HDMR) is used to establish a surrogate model of the system, and the sensitivity of the system's design parameters is evaluated with the maximum battery temperature and the system weight as the targets, thereby improving the efficiency of model calculation and reducing the dimension of optimization parameters. Then, the design of the sensitive parameters is optimized using an extended elitist non‐dominated sorting genetic algorithm (E-NSGA-II) multi-objective optimization algorithm. The results show that the modeling difficulty and optimization calculation time are significantly reduced by using a surrogate model. The calculation time for a single surrogate model only takes a few seconds instead of several hours for the original three-dimensional heat transfer and flow calculation. The thermal conductivity of high thermal conductivity-PCM, the distance between battery and low thermal conductivity-PCM, the battery spacing, and the HP length significantly affect the system. The optimized system substantially reduces the overall weight of the battery system while ensuring its good heat dissipation capability. In the case of TR in a single battery, the system succeeds in limiting the TR propagation to the same row, with the maximum battery temperature in the second row being only 64.3 °C, well below the battery TR trigger point. Under more severe conditions, such as TR occurring in two batteries simultaneously, the maximum battery temperature in the second row is 155.5 °C, and no TR spreads to the adjacent row. This study provides a rapid and effective method for designing a TR propagation mitigation system. It can serve as a reference for the engineering design and optimization of battery thermal management systems. [ABSTRACT FROM AUTHOR]

With the aging population, osteoporosis, which leads to poor fusion, has become a common challenge for lumbar surgery. In addition, most people with osteoporosis are elderly individuals with poor surgical tolerance, and poor bone quality can also weaken the stability of internal fixation. This study compared the fixation strength of the bilateral traditional trajectory screw structure (TT-TT), the bilateral cortical bone trajectory screw structure (CBT-CBT), and the hybrid CBT-TT (CBT screws at the cranial level and TT screws at the caudal level) structure under different bone mineral density conditions. A finite element (FE) analysis study. Above all, we established a healthy adult lumbar spine model. Second, under normal and osteoporotic conditions, three transforaminal lumbar interbody fusion (TLIF) models were established: bilateral traditional trajectory (TT-TT) screw fixation, bilateral cortical bone trajectory (CBT-CBT) screw fixation, and hybrid cortical bone trajectory screw and traditional trajectory screw (CBT-TT) fixation. Finally, a 500-N compression load with a torque of 10 N/m was applied to simulate flexion, extension, lateral bending, and axial rotation. We compared the range of motion (ROM), adjacent disc stress, cage stress, and posterior fixation stress of the different fusion models. Under different bone mineral density conditions, the range of motion of the fusion segment was significantly reduced. Compared to normal bone conditions, the ROM of the L4–L5 segment, the stress of the adjacent intervertebral disc, the surface stress of the cage, and the maximum stress of the posterior fixation system were all increased in osteoporosis. Under most loads, the ROM and surface stress of the cage and the maximum stress of the posterior fixation system of the TT-TT structure are the lowest under normal bone mineral density conditions. However, under osteoporotic conditions, the fixation strength of the CBT-CBT and CBT-TT structures are higher than that of the TT-TT structures under certain load conditions. At the same time, the surface stress of the intervertebral fusion cage and the maximum stress of the posterior fixation system for the two structures are lower than those of the TT-TT structure. Under normal bone mineral density conditions, transforaminal lumbar interbody fusion combined with TT-TT fixation provides the best biomechanictability. However, under osteoporotic conditions, CBT-CBT and CBT-TT structures have higher fixed strength compared to TT-TT structures. The hybrid CBT-TT structure exhibits advantages in minimal trauma and fixation strength. Therefore, this seems to be an alternative fixation method for patients with osteoporosis and degenerative spinal diseases. This study provides biomechanical support for the clinical application of hybrid CBT-TT structure for osteoporotic patients undergoing TLIF surgery. [ABSTRACT FROM AUTHOR]

A data-driven multi-objective predictive control approach is proposed to increase the power production and reduce fatigue loads on a wind farm level using evolutionary optimization. The FLORIS (FLOw Redirection and Induction in Steady-state) tool is employed to characterize the wake characteristics within a wind farm and generate necessary data for data-driven prediction. A data driven wind farm predictor (WFP) is then constructed by using the turbine yaw angles as inputs and the wind farm power and thrust load as outputs under different inflow wind speeds and wind directions. Based on the WFP, a constrained optimization problem is formulated and the multi-objective predictive controller (MOPC) is designed based on wake steering and evolutionary optimization while considering the yaw angle control constraints. Extensive design experiments are conducted under various wind speeds and wind directions, and the results indicate that the wind farm thrust can be reduced by up to 12.96% while the wind farm power production can be well maintained at almost the same level by using the proposed control in comparison with a conventional model predictive control. The yaw angles optimized from the proposed control are more responsive and active in tuning the wind farm power production and thrust load mitigation than that in the conventional control method. • Designing a data-driven multi-objective predictive controller. • Designing a data driven wind farm predictor. • Providing data from the FLORIS for the multi-objective optimization of yaw angles. [ABSTRACT FROM AUTHOR]

Thermal Runaway Propagation (TRP) of lithium-ion battery packs has serious hazards. However, the TRP prediction is challenging because of the substantial uncertainty and hard-to-acquire data. To solve this problem, a fuzzy system and multi-task CNN-LSTM method are proposed to predict TRP multiple steps ahead. The TRP dataset is constructed by 25 sets of experiments and 130 sets of simulations. The uncertain SoC, charging and discharging conditions, and thermal runaway (TR) trigger points are considered in both experiments and simulations. Then, the fuzzy system is introduced to reason about the TR probability of the battery and optimized by a sparrow search algorithm (SSA). A multi-task CNN-LSTM model is proposed to extract fuzzy and physical information by employing a convolutional neural network (CNN) and multiple long short-term memory (LSTM) neural networks, respectively, and output the temperature of multiple cells simultaneously. Finally, the models are evaluated in the simulation and experimental validation sets with different window lengths and time resolutions. The results show that the fuzzy information significantly improves the prediction accuracy of the method, with a coefficient of determination (R2) of 98.48% for the 3s prediction horizon and 97.27% for the 18s prediction horizon in the experimental validation set. • A data-driven method for predicting thermal runaway propagation is developed. • Uncertain initial SoCs, charging or discharging, and TR points are considered. • A fuzzy system optimized by SSA is introduced to reason about uncertainty. • A multi-task CNN-LSTM model is proposed for simultaneous temperature prediction. [ABSTRACT FROM AUTHOR]

With an interest to enhance the aroma of palm kernel oil (PKO), Viscozyme L, an enzyme complex containing a wide range of carbohydrases, was applied to alter the carbohydrates in palm kernels (PK) to modulate the formation of volatiles upon kernel roasting. After Viscozyme treatment, the content of simple sugars and free amino acids in PK increased by 4.4-fold and 4.5-fold, respectively. After kernel roasting and oil extraction, significantly more 2,5-dimethylfuran, 2-[(methylthio)methyl]-furan, 1-(2-furanyl)-ethanone, 1-(2-furyl)-2-propanone, 5-methyl-2-furancarboxaldehyde and 2-acetyl-5-methylfuran but less 2-furanmethanol and 2-furanmethanol acetate were found in treated PKO; the correlation between their formation and simple sugar profile was estimated by using partial least square regression (PLS1). Obvious differences in pyrroles and Strecker aldehydes were also found between the control and treated PKOs. Principal component analysis (PCA) clearly discriminated the treated PKOs from that of control PKOs on the basis of all volatile compounds. Such changes in volatiles translated into distinct sensory attributes, whereby treated PKO was more caramelic and burnt after aqueous extraction and more nutty, roasty, caramelic and smoky after solvent extraction. [ABSTRACT FROM AUTHOR]

• The physical effects of battery correlated equivalent circuit model parameters. • Low and medium-frequency EIS sensitive to battery aging. • Combined circuit parameters and gated recurrent unit to estimate battery SoH. • Considering the effect of ambient temperature and SoC on EIS. Electrochemical impedance spectroscopy (EIS) is one of the critical techniques to characterize the state of health (SoH) of lithium-ion batteries, which has different performances under different SoH and dynamic working conditions. However, the identification parameters of full-frequency EIS will consume too much computing time. Considering that low and medium-frequency EIS plays a dominant role in battery aging, the present paper constructs a simplified equivalent circuit (SECM) based on low and medium-frequency EIS. Based on the fitted SECM parameters, three SECM parameters are selected as features by grey correlation analysis. Combined with the ambient temperature and SoC under dynamic conditions, the Gated recurrent unit neural network estimates the SoH of lithium-ion batteries. The proposed method has good accuracy and robustness for different SoC states and ambient temperature variations. This method's error values are less than 2 %, which proves the critical value of low and medium-frequency EIS signals in battery management systems. [ABSTRACT FROM AUTHOR]

• A multi-mode and multi-task data-driven method is proposed to predict thermal runaway propagation in battery modules. • A thermal runaway propagation simulation model is developed to supplement the thermal runaway experimental data. • The proposed thermal propagation prediction model is validated in 18,650 batteries with different chemical compositions. • The proposed warning strategy is effective for different state of charge and different model window lengths. Thermal runaway (TR) propagation is triggered in a battery pack by abnormalities such as a cell fire or explosion, which leads to severe consequences. Predicting the TR propagation is challenging due to the complex, high non-linearity, and uncertain disturbances of TR. This paper establishes an electro-thermal coupling simulation model of TR propagation to supplement experimental data and public datasets for model training and verification. Then, a data-driven fusion model named Multi-Mode and Multi-Task Thermal Propagation Forecasting Neural Network (MMTPFNN) is established quantitative advance multi-step prediction of TR propagation in Li-ion battery modules, and a temperature-based TR propagation grading warning strategy is proposed. The TR propagation is mainly influenced by the thermal characteristics of surrounding batteries, and the temperature distribution in the entire battery module is of great significance to the prediction of TR propagation. Herein, the model is presented by using the thermal image and the discrete operating data of cells. Furthermore, because TR is a small probability event, obtaining the thermal image of the battery module requires additional system memory and computational resources. A switching strategy of the prediction model is established to improve the applicability of the model with the temperature threshold of 60 °C. When the battery is in a safe temperature range (below 60 °C), the long short-term memory (LSTM) model is run to predict the battery temperature. Once the battery temperature is detected above 60 °C, the thermal image is captured, and the MMTPFNN model is run to predict the TR propagation. In the validation section, different network structures are discussed, and different time resolutions and different window settings of the MMTPFNN are compared. Finally, the early warning strategy with three alert levels is introduced, and the effectiveness of the warning strategy with different window settings and initial SoCs is further discussed. [ABSTRACT FROM AUTHOR]

This study investigated the impact of protease pre-treatment of coconut kernels (CK) on the flavour modification of the resultant coconut oil (CO). The effects of four proteases (Alcalase, Flavourzyme, Neutrase and Protamex) pre-treatment on volatile modification of CO was screened and Flavourzyme was chosen for detailed investigation. Flavourzyme was found to release not only free amino acids but also simple sugars, increasing the contents of soluble sugars and free amino acids in CK by 1.5- and 1.3-fold, respectively, compared to the untreated control. The increments of these thermal reaction precursors accelerated the Maillard reaction and caramelisation during kernel roasting, resulting in the rise of volatile compounds in obtained CO by 2.1-, 1.4- and 1.4-fold with light, medium and dark kernel roastings, respectively. Besides, volatile profiles of CO extracted from Flavourzyme treated CK differed significantly from those of the untreated control by having increments in furans and pyranones but decreases in furanones and pyrazines. Principal component analysis (PCA) revealed that flavour of CO from Flavourzyme treated CK upon roasting was highly correlated with furans and pyranones. Pre-treatment by Flavourzyme of CK prior to oil production is a potential strategy to modulate and enhance the flavour of obtained CO and hence to widen its application in food industry. [Display omitted] • Protease modified the flavour of coconut oil (CO) obtained from coconut kernels (CK). • Flavourzyme treated CK released more precursors for Maillard reaction and caramelisation. • Flavourzyme treated coconut kernels increased in furans and pyranones. [ABSTRACT FROM AUTHOR]

The existing researches of electric vehicle (EV) drive system mainly focus on the motor itself, without considering the EV energy consumption and battery life simultaneously. Thence, in this paper, a control method of EV drive system based on its energy consumption and battery life is proposed. Combining the EV driving state with the motor control, the relationship between the velocity and acceleration and the optimal dq -axis currents is established. Then, a drive system global control strategy for EV based on optimized acceleration curve is proposed, in which the maximum torque per ampere (MTPA) control is used below motor base speed and the voltage closed-loop feedback flux weakening control is used above motor base speed. Furthermore, the proposed control strategy is verified by both MATLAB/Simulink simulation and experiment. The simulation and experimental results show that compared with original working conditions, the energy consumption per kilometer and the percentage of battery capacity loss per kilometer of the global control strategy based on optimized acceleration curve are both reduced, which verifies the feasibility and effectiveness of the proposed control strategy. • A drive system global control strategy for electric vehicle is proposed. • The relationship between acceleration curve and motor control is established. • The MTPA control is used below motor base speed. • The voltage closed-loop flux weakening control is used above motor base speed. • The proposed control strategy is verified by both simulation and experiment. [ABSTRACT FROM AUTHOR]

To offer references for ventilation and load fluctuation of propeller in actual seaway, this paper presents a RANS (Reynolds-Averaged Navier-Stokes equations) investigation on open propeller dynamic loads in regular head waves at variant submergence depths, from the perspective of the coupled oblique-flow and free-surface effect. Discussions are made concerning the dynamic integral loads and single-blade loads (both axial and in-plane), and instantaneous single-blade loads at different azimuthal positions. CFD (Computational Fluid Dynamics) verification and validation are conducted based on an experiment in calm water and regular head waves conducted in a circulating water channel. As conclusions, under fully-submerged condition, single-blade load variations are related solely to the unsteady oblique-flow environment, with shifts of azimuthal positions corresponding to the maximum and minimum thrust and torque due to inflow non-uniformity. Under periodical-emerging condition, high-frequency integral-load fluctuations are identified by both simulation and experiment, resulting from the enhanced propeller-surface interactions due to upward wake inclination; this enhanced propeller-surface interaction also causes serious ventilation, abrupt rises or drops of axial single-blade loads, and destruction and dissipation of propeller vortex system. Furthermore, the self-induction effect on the load distribution non-uniformity is found to be unobvious in regular waves due to the unsteady and non-uniform inflow environment. • Investigation on the propeller dynamic loads in waves considering the coupled free-surface and oblique-flow effect. • As the periodically-emerging propeller approaches the wave crest, high-frequency thrust fluctuation and vortex destruction are identified. • Under full submergence, minor shifts of azimuthal positions of maximum/minimum single-blade loads occur. • Self-induction effect is found to be unobvious in regular waves. [ABSTRACT FROM AUTHOR]

• A hybrid thermal management system with PCM, HP and liquid cooling is proposed. • A surrogate model is established based on Adaptive-Kriging-HDMR method. • Four sensitive factors are determined through global sensitivity analysis. • The optimized system is evaluated in the condition of cycling and thermal runaway. The hybrid thermal management scheme for lithium-ion battery combining the advantages of various thermal management strategies has been widely adopted. However, due to the complex influence parameters involved in the hybrid thermal management system, its optimal design has become a difficult problem. In this study, a hybrid thermal management system based on phase change material (PCM), liquid cooling, and heat pipe is first designed, and then a precise and reliable numerical heat transfer model is established. In order to optimize the system and improve the optimization efficiency, the Adaptive-Kriging-High dimensional model representation (HDMR) method is used to construct a surrogate model of the thermal management system, and the influencing factors sensitivity analysis and optimization design of the hybrid thermal management system are also conducted. The results show that the thermal conductivity of PCM, the thickness of PCM, the length of heat pipe and the velocity of inlet water have a significant influence on the maximum temperature and temperature difference of the battery system. According to the optimization design of these four factors based on the multi-objective particle swarm optimization (MOPSO), it is found that the optimized thermal management system has the best ability to dissipate heat and maintain temperature uniformity as compared to the original design. In addition, this optimization system has the ability to prevent thermal runaway propagation under the condition of thermal abuse conditions. With these prominent performances, the proposed method is expected to provide insights into the engineering design and optimization of the battery thermal management system for electric vehicle. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic load characteristics of a near-surface open propeller in regular head waves based on a model experiment in a circulating water channel, with the aid of captured free-surface profiles. The influences of periodical variation of submergence depth and inflow velocity on the dynamic load characteristics are systematically investigated, and the thrust hysteresis in periodical emergence condition is quantitatively evaluated. As conclusions, a critical instantaneous submergence depth is identified for this propeller in regular waves at a certain advance ratio; above this value, the dynamic load characteristics are not dominated by the propeller-surface interactions. For a periodically emerged propeller, the influence of the periodical variation of inflow velocity on the dynamic load characteristics is more significant as either submergence depth or wave height increases. With increasing wave period, the wave-caused load fluctuation amplitude increases in a more obvious manner in periodical emergence condition, but decreases less obviously in full submergence condition. Regarding the thrust hysteresis, it takes approximately 5 revolutions for this propeller in full submergence to regain full thrust. Lastly, the thrust fluctuation and loss due to slipstream inclination is considered responsible for thrust hysteresis, which happens as the previously emerged propeller heads into full submergence. • A systematical discussion of dynamic load in waves considering the periodical variation of submergence depth and inflow velocity. • A high-resolution camera is used to capture the free-surface deformation and ventilation. • A critical instantaneous submergence depth is identified at a certain advance ratio. • A quantitative evaluation of the minimum revolutions in full submergence for this propeller to regain full thrust. • Tip-vortex related ventilation due to upward slipstream inclination is considered as also responsible for the thrust hysteresis. [ABSTRACT FROM AUTHOR]

• An adaptive backstepping controller is proposed to extract the maximum marine current energy. • The uncertainty and disturbance can be well estimated and compensated in the controller. • An adaptive update law is designed to compensate for the system parameter uncertainties. The renewable energy from marine currents is highly promising as a clean, reliable and predictable energy source for the next-generation electricity generation. Therefore, the paper focuses on the design and implementation of the high-efficiency control for maximizing the marine current power generation and hence an adaptive backstepping controller with uncertainty and disturbance estimation is proposed for a generic horizontal marine turbine. The turbine design principle and dynamics modelling are presented and then the control problem is formulated. Consequently, the controller is designed to be composed of a marine turbine speed control loop and a q-axis current control loop while the uncertainty and disturbance is estimated and compensated. A swell filter is also incorporated into the control loop to smooth generator power fluctuations. The stability of the proposed control is verified via the Lyapunov synthesis and all the tracking errors are guaranteed to converge to zero asymptotically. The proposed control is verified and the test results indicate that the generator produces obviously more power (up to 30% more power) when using the proposed control in comparison with a conventional backstepping control. [ABSTRACT FROM AUTHOR]

• A method for estimating lithium batteries' state of health (SoH) is proposed. • Transformation of EIS data into 2D pseudo-images. • Unsupervised feature extraction using VGG16 neural network. • Integration of feature extraction and SoH in one framework. Electrochemical impedance spectroscopy (EIS) is a key technique characterizing the batteries' State of Health (SOH). The extraction of features from the limited EIS information for SOH estimation relies heavily on the researcher's prior knowledge. This study proposes a method to enhance the EIS feature information and perform unsupervised feature extraction to estimate the SOH. First, the EIS data is transformed into images using Gramian angular field, which enhances the data features. Next, the images were subjected to unsupervised feature extraction using the VGG16 neural network framework. Finally, the unsupervised feature extraction and SOH prediction were integrated into a neural network framework to achieve end-to-end training and prediction. The experimental results show that the proposed method's SOH estimation error is less than 2%, and its accuracy is improved by 55.6% compared to its benchmark model; the feasibility of unsupervised feature extraction is demonstrated, overcoming the drawbacks of artificially performed feature extraction. [ABSTRACT FROM AUTHOR]

A novel hydro-kite like ocean energy converter is proposed in this paper to harness both ocean current and wave energy. The proposed ocean energy converter is typically designed to consist of a hydro-kite like kite floating or submerged in the ocean and an energy conversion unit set on a ship. The ocean wave or current energies can be generated by pulling a pre-tensioned flexible tether that connects the hydro-kite-like kite and the energy conversion unit. The general dynamics of the novel ocean energy converter is basically described and modelled based on the dynamics of key components under both ocean wave and current conditions. The feasibility and effectiveness of the proposed ocean energy converter are evaluated through computational fluid simulations under both fully submerged ocean current and semi-submerged ocean wave conditions. The simulation results verify that the hydro-kite can be used to generate power from both ocean current and wave conditions and the design parameters of the hydro-kite such as the angle of attack, draft should be optimally controlled or designed to maximize the ocean energy generations under different ocean current speeds, wave periods and heights. [ABSTRACT FROM AUTHOR]

In the study, the effects of in-cylinder steam injection (ICSI) on the liquefied petroleum gas (LPG) engine were investigated in consideration of the impacts of steam on the in-cylinder combustion, to improve its fuel economy and reduce NO. First, a quasi-dimensional model of a baseline engine is established. Then, based on the model, the effects of ICSI on the engine in-cylinder combustion, performance and NO are discussed. After that, according to the impacts of ICSI on the engine, the influences of ignition timing on the engine with ICSI are further analyzed. Finial, with optimum ignition timing, the potentials of ICSI for the engine fuel savings and NO reduction are evaluated. The results show that the laminar flame speed of LPG is decreased by steam. ICSI leads to remarkable increases of flame development angle and rapid burning angle. By adjusting ignition timing, the combustion phase of the engine with ICSI is changed. With optimum ignition timing, engine performance and NO characteristic are remarkably improved by ICSI. At all engine speeds, compared with the baseline engine, with ICSI and optimum ignition timing, the torque is increased by 0.8–6.6%, the BSFC is decreased by 0.6–6.4% and the NO is reduced by 54.0–61.7%. • With ignition timing adjustment, ICSI is further investigated on a LPG engine. • The impacts of steam on the in-cylinder combustion are considered. • Fitting formula of laminar flame speed of propane with steam dilution is presented. • With ICSI and optimum ignition timing, BSFC is reduced by 0.6–6.4%. • NO emission is reduced by 54.0–61.7% at various speeds. [ABSTRACT FROM AUTHOR]

[Display omitted] • Integrating excellent mechanical and electrochemical properties into conductive hydrogel sensors are very valuable. • A general method is proposed for fabricating anisotropic hydrogels via tensile remodeling followed by ion crosslinking. • Highly oriented structure greatly improves its mechanical and sensing capabilities. • Tensile stress and conductivity of parallel-oriented hydrogels are increased to 47.26 MPa and 292.64 mS/m. • Anisotropic hydrogel sensors can monitor human movements and combine with Morse code for information transmission. Combining superior mechanical and electrochemical capabilities into conductive hydrogel sensors has attracted considerable attention in the field of flexible electronics. However, traditional hydrogels with randomly oriented polymer networks often exhibit isotropic mechanical and electrical properties, whereas the magic of anisotropy is universal in nature. Inspired by the anisotropic structure-dependent properties of muscles, this study develops a general and simple method for fabricating anisotropic ultra-high strength conducting hydrogels via tensile remodeling followed by ion coordination crosslinking. Highly oriented polymer networks of anisotropic hydrogels greatly improve their mechanical and sensing capabilities. The tensile stress and conductivity of the PVA-PAA/Fe3+ hydrogels along the parallel direction are significantly increased to 47.26 MPa and 292.64 mS/m, which are considerably greater than that of the vertical-oriented hydrogels (12.42 MPa and 169.09 mS/m). Moreover, the gauge factors (GF) of the parallel-oriented hydrogels are higher than that of the vertical-oriented hydrogels, demonstrating enhanced strain sensing performance. Anisotropic hydrogel sensors can monitor various human movements in real-time and integrate with Morse code for information decoding and transmission. This research work is expected to accelerate the burgeoning pace of conductive hydrogel sensors in soft electronics. [ABSTRACT FROM AUTHOR]

Owing to the prevalence of chronic inflammation and its related disorders, there is a demand for novel therapeutic agents capable of preventing or suppressing inflammation. Natural products (NPs) are well established as an important resource for drug development and provide an almost infinite array of molecular entities. Sulfur-containing NPs (i.e., NPs containing one or more sulfur atoms) are abundant throughout nature, from bacteria to animals. The aim of this review was to survey the emerging evidence on role of sulfur-containing NPs, such as glutathione, garlic-derived sulfur compounds, Epipolythiodioxopiperazines (EPTs), Isothiocyanates (ITCs), and Ergothioneine (EGT), in the control of inflammation and to determine the possible underlying mechanisms. A discussion of how hydrogen sulfide (H 2 S), an endogenous gaseous signaling molecule, links sulfur-containing NPs and their anti-inflammatory action is also performed. This review may help to further the development of sulfur-based compounds by providing a guide for structure-activity relationship-based modification for use in modern medicinal chemistry. However, as this field is still in its infancy, the review is concluded by an overview of the progression of these promising entities as therapeutic agents. Unlabelled Image [ABSTRACT FROM AUTHOR]

An integrated motion simulation and dynamic modeling method is proposed, which takes into account the mutual influences of hull, propeller and rudder. Using Computational Fluid Dynamics (CFD) in a single simulation, all hydrodynamic coefficients, including those for the rudder and propeller, can be determined. Taking SUBOFF model as the object, the dynamics model was established based on the simplified equation of six degrees of freedom motion in space. The hydrodynamic coefficients were identified by using sliding time window and normal distribution methods. The results show that in comparison with the experimental hydrodynamic coefficients, the error of the predicted hydrodynamic coefficients is very small, and the force and moment curves fitted by dynamic equation are very close to those monitored by CFD simulation. This proves the reliability of the established dynamic model and the correctness of the parameter identification method. • The method of motion definition is updated to make the 6-DOF motion interval data cover the whole interval range. • The method of parameter identification is updated to solve the problem of dynamic model divergence in simulation. • The improved CFD simulation strategy is used to realize the interaction of hull, propeller and rudder. • Comparing the results of the hull simulation with those of the integrated propeller-rudder-hull simulation. [ABSTRACT FROM AUTHOR]

The repair of large bone defects is still a challenge in clinical orthopedics. The combination of composite materials and 3D printing is expected to produce ideal bone defect scaffolds. This research studied the combined polylactic acid (PLA), nano-hydroxyapatite (n-HA), and lithium (Li) bone tissue scaffold via 3D printing. The n-HA/PLA/Li composite scaffold was proposed by screening the optimal proportion, biocompatibility, and osteoinductive activity. The degradation and mechanical properties of the proposed specimens were evaluated. Osteogenesis and vasculogenesis were detected in vitro , and the regulatory pathways were explored to screen the doping amount of lithium ions. In vivo, the PLA/n-HA/Li composited scaffold was used for the beagle dog heterotopic and rabbit femur orthotopic osteogenesis studies. Results indicated that the mechanical strength of PLA/n-HA/Li composited scaffold decreased with the increase of the n-HA ratio. The 30% n-HA scaffold group showed a good comprehensive performance of printability and mechanical strength. Based on the osteoinductive activity, 0%, 0.5%, and 1% lithium-doped scaffolds were studied and screened. The 1% lithium group exhibited the best degradation, osteogenesis, and angiogenesis properties in vitro than other groups and could induce ectopic osteogenesis and in situ osteogenesis in vivo. This PLA/n-HA/Li composite biomaterial shows good printability, biocompatibility, degradability, and osteogenic inducibility, it has application potential in the personalized repair of large bone defects. [ABSTRACT FROM AUTHOR]

• Three different structures of the near-nozzle spray are classified. • The injection frequency has an influence on the spray tip formation. • More droplets can be obtained BEOI, but the droplet number decreases AEOI. It is demonstrated that the fuel deposit has a significant influence on the NOx and particular matter (PM) emissions, especially with the consideration of the more and more strict emission regulations issued by the governments, which results in that the near-nozzle spray has been received the increased attention, recently. In this study, the near-nozzle spray characteristics at the initial and end stages were investigated by a high-resolution camera with a laser as the light source. At the initial stage, microscopic behaviors of the near-nozzle spray were observed at upstream, midstream and downstream, respectively. Moreover, the injection frequency was changed to check the effect on the near-nozzle spray morphology. Next, the micro spray length, width and angle were obtained from the processed images. While, at the end stage, the near-nozzle spray evolutions were observed and characterized at 0.5, 0.3, 0.2 and 0.1 ms before the end of injection (BEOI), the end of injection (EOI), 0.05, 0.1, 0.15 and 0.2 ms after the end of injection (AEOI), respectively. Furthermore, the droplet area, spray area without droplets, droplet number and averaged diameter were calculated. Results show that three structures of the near-nozzle spray at the initial stage can be classified as mushroom, steeple and cylinder shapes. And the injection frequency has an influence on their formations owing to the residual fuel and sucked air AEOI. Moreover, although more droplets can be identified at the end stage with time, it does not indicate the better atomization. On the contrary, these droplets AEOI are responsible for the injector deposit in the real engine. [ABSTRACT FROM AUTHOR]