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Alumina coating which has excellent corrosion resistance to lead–bismuth eutectic (LBE) is very promising to be used in future LBE cooling system. In this paper, the Al2O3/Fe-Al gradient coating is prepared on the surface of SIMP steel assisted by ionic liquid aluminum electroplating and subsequent heat treatments and selective oxidation. The protective Al2O3 layer is composed of much γ-phase and a small amount α-phase. The Fe-Al transition layer is mainly composed of FeAl alloy phase. After being exposed for 600 h to LBE at 600 °C, the coating still maintains intact and dense structure and good compatibility to the substrate, effectively preventing the substrate steel from the corrosion attack of LBE. While obvious LBE attack can be observed in the compared bare steel. Finally, to verify the possibility of this approach preparing Al2O3/Fe-Al coating on complex geometries, we have also successfully prepared this sort of coating on the inner surface of a steel tube, indicating that this coating has a great application potential in the future.

Abstract This paper first investigates emergency transportation for power recovery in post‐disaster. The problem is formulated as a mixed‐integer linear programming model called vehicle routing problem with charging relief (VRPCR). The battery state of charge (SoC) implies the working hours that the battery can provide. The goal is to make a set of shelters charge before the battery SoC of shelters reaches the minimum bound over time. To this end, a two‐stage algorithm is developed to deal with the problem. In stage I, a reduced road network is obtained from a leading road network by the A‐star search algorithm. Subsequently, to determine the order of power delivery with charging operations at shelters by enhanced genetic algorithm (EGA) in stage II. To evaluate this strategy, the detailed complexity analysis of the three algorithms and results tested on a realistic disaster scenario shows the performance of the A‐star search algorithm for VRPCR that outperforms the Dijkstra and Floyd algorithms. In addition, the EGA is applied to Solomon's benchmarks compared with the state‐of‐the‐art heuristic algorithms, which indicates a better performance of EGA. A real case obtained from a disaster scenario in Ichihara City, Japan is also conducted. Simulation results demonstrate that the method can achieve satisfactory solutions.

This paper presents a feedback control design scheme for internal combustion engines with the exhaust gas recirculation and the variable valve timing. A second-order nonlinear dynamical model is presented for describing the transient behaviour of the engine. According to a given operating point of the engine coordinated by the speed and the torque, seeking approach for the optimal set-point with respect to the cost function of fuel consumption is shown that provides the equilibrium of the dynamic model. Then, a state feedback control law is constructed based on Lyapunov stability theory to render the closed-loop error system converge to zero. Finally, experiment validation results conducted on a full-scaled engine control test-bench are demonstrated.

This paper addresses an energy consumption optimization problem with consideration of drivability improvement for series-parallel plug-in hybrid electric vehicles. Consumption of fuel and electricity are chosen as the cost function of the optimization, and the acceleration rate is involved in the constraint for improving drivability. Then, a two-stage optimization scheme is proposed, which consists of the long-term off-line optimization and the short-term on-line optimization. In the former, the entire route is dealt with as previously known, and the optimal mode switching and engine power are provided by solving the optimization problem, and in the latter, the short-term local optimization in the fashion of receding horizon is used to obtain optimality regarding the actual route situation where the vehicle-to-everything-based driver's demand prediction is involved instead of requiring previous information of the future horizon. The proposed controller was evaluated in the case of a long driving situation including the city and the motorway that required a high-power acceleration. The performance was improved in consideration of the drivability, and the fuel economy was optimized in comparison to the charge depleting and charge sustaining strategy.

The helium effects in Chinese developed CN-G01 beryllium are important issues for its use in nuclear energy systems. In this work, the CN-G01 beryllium samples were irradiated with helium ions to fluences of 5.0 × 1016 ions/cm2 to 1.0 × 1018 ions/cm2 at room temperature and investigated by techniques of scanning electron microscopy (SEM), transmission electron microscopy (TEM) and nano-indentation. It was found that the irradiation induced hardening of beryllium and the nano-hardness of the samples increased with increasing fluence of 5.0 × 1016 ions/cm2 to 1.0 × 1017 ions/cm2. When the fluence reached 5.0 × 1017 ions/cm2 and 1.0 × 1018 ions/cm2, helium irradiation induced serious surface blistering and its burst. TEM observation found that helium bubbles in the damage peak region became visible when the fluence reached 1.0 × 1017 ions/cm2. With increasing fluence, helium bubbles became larger and connected into large cracks. The underlying physical mechanisms are discussed based on the helium behavior at low temperatures and the contributions of helium induced defects. This work will provide some new understanding on the irradiation resistance of CN-G01 beryllium and the helium effects in beryllium at low temperatures.

Medium-entropy alloys (MEAs) are prospective structural materials for emerging advanced nuclear systems because of their outstanding mechanical properties and irradiation resistance. In this study, the microstructure and mechanical properties of three new single-phase body-centered cubic (BCC) structured MEAs (Zr40Nb35Ti25, Zr50Nb35Ti15, and Zr60Nb35Ti5) before and after irradiation were investigated. It is shown that the yield strength and elongation after fracture at room temperature are greater than 900 MPa and 10%, respectively. Three MEAs were irradiated with 3 MeV Fe11+ ions to 8 × 1015 and 2.5 × 1016 ions/cm2 at temperatures of 300 and 500 °C, to investigate the irradiation-induced hardening and microstructure changes. Compared with most conventional alloys, the three MEAs showed only negligible irradiation hardening and even softening in some cases. After irradiation, they exhibit somewhat surprising lattice constant reduction, and the microstructure contains small dislocation loops. Neither cavities nor precipitates were observed. This indicates that the MEAs have better irradiation resistance than traditional alloys, which can be attributed to the high-entropy and lattice distortion effect of MEAs.

This paper presents a control design approach of efficiency optimization while providing the desired torque under the constraint of combustion variation for gasoline engines with EGR. Mainly, the first phase of control design is the equilibrium point optimization under the chance constraint of IMEP probability distribution with a scenario approach, and the second phase is nonlinear dynamic feedback stabilization control design for improving transient performance of the state regulation. The experimental validations demonstrate that the control scheme can achieve stable combustion and fast transient control performance.

In this paper, an on-board optimal control problem for diesel engines with lean NOx trap (LNT) is investigated. First, a two-order LNT model based on mass conservation and energy conservation is constructed. Then, the optimal control problem is formulated as a continuous receding horizon problem under dynamical model constraint and descretized into a nonlinear programming problem by using the multiple shooting method. A sequential quadratic programming approach is employed to derive a numerical solution. Finally simulations are conducted under a standard driving cycle and a random driving cycle with comparison to a dynamic programming based control scheme in MATLAB/Simulink platform. Simulation results verify the effectiveness of the proposed control scheme.

In this work, congruent LiTaO3 crystals are irradiated by heavy ions Fe, Xe, and Bi respectively at fluence range of 1 × 1012 ~ 2 × 1015 cm−2. The effects of heavy ions irradiation are studied by using optical transmission spectroscopy, Rutherford backscattering, optical microscopy, X-ray diffraction.Heavy ion irradiation presents a unique effect in transmission spectra which is quite opposite to the previous reported results of light ions irradiation. The latter could often cause a significant optical absorption in visible light region. The former, however, not only induce no optical absorption, but can enhance transmittance over a broad spectral range (340–2500 nm). It is found that the discrepancy may originate from different damage process during ions irradiation. Light ion irradiation can create numerous of simple defects (like oxygen vacancies) serving as color centers which can cause an optical absorption. However, direct amorphization dominates the damage process induced by heavy ions, during which it hardly produces simple defects. Moreover, due to direct heavy ions bombardment, special textures are formed on the surfaces of the samples which serve as antireflection structures and enhance optical transmissions.

Combustion control is a significant topic for achieving high efficiency and low emissions of internal combustion engines. Recently, in-cylinder pressure sensor-based closed-loop control strategies have become the preferred solution. However, their practical applications in automotive industries are limited due to the intensive acquisition of pressure series for a whole cycle and subsequent calculation of combustion indicators. This paper proposes a method for in-cylinder pressure information extraction and combustion phase estimation of spark ignition (SI) engines based on pressure measurements at several points coordinated by the crank angle. First, nonlinear dynamics analysis is introduced to analyze the system of in-cylinder pressure evolution, which is proved to be a deterministic nonlinear dynamic system with chaotic characteristics. Then, a 3-dimensional system state variable is determined to replace the pressure series during combustion. Second, with the determined system state variable, the in-cylinder pressure series during combustion and the combustion phase are learned and estimated by a machine learning method, namely, extreme learning machine (ELM). As a result, only pressure measurements at 3 points and ELM estimation models are required, instead of intensive data acquisition and calculation. The experimental validations carried out on a gasoline engine test bench have proved that the reconstruction and estimation results are accurate and that the method can perform well in real-time combustion control.

This paper proposes a control scheme based on chance-constrained optimization for spark-ignition engines to ensure transient torque tracking performance with improving thermal efficiency under chance-constraint for combustion phase. Firstly, the optimal equilibrium operating points are obtained by solving a chance-constrained optimization problem offline based on scenario approach. Then, linear quadratic regulator is applied to control the engine operate at the optimal equilibrium operating points under certain torque demand and engine speed. The proposed method is experimentally validated on a commercial gasoline engine, and the results demonstrate the method's performance.

The pure tungsten was implanted with 500 keV helium (He) ions to a fluence of 1.0 × 1017 ions/cm2 at RT and 800 °C. After the implantation, cross-sectional transmission electron microscope (TEM) and nano-indentation measurements were used to investigate the He bubbles and hardness profiles induced by He implantation. Visible He bubbles were observed only in the damage peak region at RT but in the whole damage region at 800 °C. The size of He bubbles at 800 °C increased with increasing depths. With increasing the implantation temperature from RT to 800 °C, the size of He bubbles in the damage peak regions increased. The nano-indentation tests indicated that hardening was induced by He implantation at RT and 800 °C. The implantation induced peak hardness values were the same at both temperatures, but the hardness peaks moved to the surface with increasing temperatures. The formation of He bubbles and the contributions of their distribution and sizes to the hardness were discussed.

This paper deals with the HEV real-time energy management problem using deep reinforcement learning with connected technologies such as Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I). In the HEV energy management problem, it is important to run the engine efficiently in order to minimize its total energy cost. This research proposes a policy model that takes into account road congestion and aims to learn the optimal system mode selection and power distribution when considering the far future by policy-based reinforcement learning. In the simulation, a traffic environment is generated in a virtual space by IPG CarMaker and a HEV model is prepared in MATLAB/Simulink to calculate the energy cost while driving on the road environment. The simulation validation shows the versatility of the proposed method for the test data, and in addition, it shows that considering road congestion reduces the total cost and improves the learning speed. Furthermore, we compare the proposed method with model predictive control (MPC) under the same conditions and show that the proposed method obtains more global optimal solutions.

Reduced-activativon ferritic/martensitic (RAFM) steels are prospective structural materials for fission/fusion nuclear applications because their radiation and swelling resistance outperforms their austenitic counterparts. In radiation environments with a high production rate of helium, such as fusion or spallation applications, these materials suffer from non-negligible swelling due to the inhibited recombination between vacancy and interstitial-type defects. In this work, swelling in helium-implanted Eurofer 97 steel is investigated with a focus on helium production rates in a wide range of helium/dpa ratios. The results show virtually no swelling incubation period preceding a steady-state swelling of about 2 × 10−4%/He-appm/dpa. A saturation of swelling above 5000 He-appm/dpa was observed and attributed to helium bubbles becoming the dominant sinks for new vacancies and helium atoms. Despite a relatively low irradiation temperature (65 ± 5 °C) and a rather high concentration of helium, transmission electron microscope (TEM) results confirmed a microstructure typical of ferritic/martensitic steels exposed to radiation environments with high production rates of helium.

This paper presents an in-cylinder pressure pegging algorithm based on cyclic polytropic coefficient learning for combustion engines. In order to take the cycle-to-cycle variation of the polytropic coefficient into account in the incylinder measurement, an iterative learning algorithm is proposed to provide cyclic estimation of the polytropic coefficient and then with the estimation cyclic compensation method is proposed for the offset of in-cylinder pressure measurement. A comparative study of the proposed algorithm, the least-squares method (LSM) with a fixed polypropic coefficient and the nonlinear least-squares method (NLSM) with a variable polytropic coefficient is conducted using the simulated pressure data. Experimental validations are conducted on a six-cylinder gasoline engine at a motored condition and a steady fired operation condition.

This paper presents an optimal control design approach for LNT aftertreatment system in diesel engines. First, based on the observation of physics of the system, a dynamical model is constructed to represent the NOx reduction behavior under the air-fuel ratio changes. Then, the optimal control problem is formulated as a nonlinear dynamics-constrained optimization problem with a cost function that targets the NOx reduction with trade-off between fuel consumption and emission reduction. A numerical approximation algorithm is proposed to solve the optimization problem. Finally, the validation results with the simulation model with the identified physical parameters are illustrated to compare with the conventional rule-based approach.

In this paper, a map-based optimal energy management strategy is proposed to improve the consumption economy of a plug-in parallel hybrid electric vehicle. In the design of the maps, which provide both the torque split between engine and motor and the gear shift, not only the current vehicle speed and power demand, but also the optimality based on the predicted trajectory of vehicle dynamics are considered. To seek the optimality, the equivalent consumption, which trades off the fuel and electricity usages, is chosen as the cost function. Moreover, in order to decrease the model errors in the process of optimization conducted in the discrete time domain, the variational integrator is employed to calculate the evolution of the vehicle dynamics. To evaluate the proposed energy management strategy, the simulation results performed on a professional GT-Suit simulator are demonstrated and the comparison to a real-time optimization method is also given to show the advantage of the proposed off-line optimization approach.

This paper demonstrates an energy management method using traffic information for commuter hybrid electric vehicles. A control strategy based on stochastic dynamic programming (SDP) is developed, which minimizes on average the equivalent fuel consumption, while satisfying the battery charge-sustaining constraints and the overall vehicle power demand for drivability. First, according to the sample information of the traffic speed profiles, the regular route is divided into several segments and the statistic characteristics in the different segments are constructed from gathered data on the averaged vehicle speeds. And then, the energy management problem is formulated as a stochastic nonlinear and constrained optimal control problem and a modified policy iteration algorithm is utilized to generate a time-invariant state-dependent power split strategy. Finally, simulation results over some driving cycles are presented to demonstrate the effectiveness of the proposed energy management strategy.

The aim of this paper is to explore the model calibration method consisting of the effective design of experiment (DoE) and the model selection for DoE. D-optimality is exploited as the cost function, and the optimal experiment design for the model is calculated by Fedorov's exchange method. An actual implementation example for air-mass flow model of gasoline engines is carried out. The model is chosen in the fashion of minimum cAIC criteria with the preliminary experiment for the purpose of the application of DoE. The experiment result with a full-scale engine test bench is validated to confirm that the proposed method can improve the accuracy of the calibrated model. The comparison of the proposed method with the conventional mesh experiment design by the calibration result is shown.

In four stroke internal combustion engines, optimization of engine performance with air-fuel ratio close to stoichiometric condition is still a challenging task specially in transient operation due to cycle-to-cycle coupling of combustion phenomena and gas dynamics in cylinder. In this paper, the cycle-to-cycle in-cylinder gas dynamics coupling model based air-fuel ratio control using the generalized predictive control law has been discussed and validated in which the input parameters of the discrete time model are updated on cyclic event based. With the discrete time model, a Kalman filter-based state variables such as total fuel mass, unreacted air and residual burnt gas are estimated and used to calculated the in-cylinder air-fuel ratio which reflect the cycle-to-cycle coupling effects of residual gas mass. Then based on model, a controller is designed to achieve the air-fuel control. Apart from this, the control performances of generalized predictive controller and PI controller have been compared. Finally, experimental validation results are demonstrated to show the effectiveness of proposed control scheme that is conducted on a full-scaled gasoline engine test bench.