A novel function-structure concept network construction and analysis method for a smart product design system

The rapid growth of data and the requirement of designers to track massive data to obtain design stimuli have posed challenges to conceptual design, thereby promoting the development of data-driven design. Concept networks precisely capture design information from a large volume of unstructured and heterogeneous textual data and saliently decrease time and labor cost for designers to read texts, which creates new opportunities for developing a smart product design system. To advance data-driven design, this study proposes the novel function-structure concept network (FSCN) construction method, which combines sentence parsing and word/phrase extraction to integrate functional and structural information. Furthermore, a network analysis method is proposed to explore design information associations that contain both explicit and implicit associations together and thereby recommend them simultaneously to designers as inspirational stimuli to support design ideation. This approach can enhance designers' capabilities to build associations between design information, conceive new design ideas during conceptual design, and increase creativity for solving design problems. The proposed FSCN construction and analysis method can be used as an auxiliary tool to visualize associations among design information so as to inspire idea generation in the early stage of conceptual design. An illustrative example was used to validate the practicability of the proposed methodology. The code of the proposed method is available at https://github.com/KWflyer/FSCN.     

A skeletal n-butane mechanism with integrated simplification method

A new skeletal mechanism of n-butane is developed for describing its ignition and combustion characteristics applicable over a wide range of conditions: initial temperature 690–1430 K, pressure 1–30 atm, and equivalence ratio 0.5–2.0. Starting with a detailed chemical reaction kinetic model of 230 species and 1328 reactions (Healy et al., Combust. Flame, 2010), the directed relation graph method is applied as the first step to derive a semi-detailed mechanism with 134 species. Then, the reaction path analysis in conjunction with temperature sensitivity analysis is used to remove the redundant species and reaction paths simultaneously under the condition of low-temperature and moderate-to-high temperatures, respectively. Finally, a skeletal n-butane mechanism consisting of 86 species and 373 reactions can be obtained. Mechanism validation indicates that the new developed skeletal mechanism is in good agreement with the detailed mechanism in predicting the global ignition and combustion characteristics. The new skeletal mechanism is further validated using extensive available literature data including rapid pressure machine ignition delay time, shock-tube ignition delay time, laminar flame speed, and jet-stirred reaction oxidation, covering a large range of temperatures, pressures, and equivalence ratios. The comparison results demonstrate that a satisfactory agreement between predictions and experimental measurements is achieved.     

Wheel slip control for antispin acceleration via dynamic spark advance

One of the problems that might occur during acceleration is wheel spin. While the wheels are spinning, the driving force on the tires reduces considerably and the vehicle cannot speed up as desired. It may even become very difficult to control the vehicle under these conditions. The acceleration characteristics of a vehicle can be improved without changing its physical capabilities with a suitable engine control algorithm that has no additional sensor inputs. This paper presents several control strategies for the wheel slip control problem. The torque output of an engine is modulated to prevent the wheel spin caused by rapid increases in the throttle input. The spark time is varied dynamically to adjust the engine torque and therefore the problem is often referred to as dynamic spark advance (DSA). Variable structure control theory is used for the controller design purposes and simulation results are provided.     

On the rate of growth and extent of the steady damage accumulation phase in repeated impact tests

In the paper, data of repeated impact tests performed on seven laminates of different lay-up and thickness are used to illustrate how the damage index (DI), a damage variable recently introduced by the authors to monitor the range of the penetration process in thick laminates, can be applied in case of repeated impact tests to get information on the rate of initial steady damage accumulation as well as on the onset of severe damage modes.Curves of the rate of growth of the DI in the steady phase (ΔDI/ΔN) vs. the normalised impact energy (ratio of the impact energy E_i and the laminate penetration threshold P_n) show no significant damage accumulation besides initial specimen indentation for impact energies below 0.2P_n. For intermediate levels of impact energy, repeated impact tests are characterised by an initial region of steady damage accumulation followed by an abrupt change in the rate of damage growth a few impacts before laminate perforation. For higher impact energies (E_i/P_n > 0.4–0.45), no phase of steady damage accumulation is present, suggesting that severe damage mechanisms take place from the very first impacts. Values of the DI at the end of the steady phase (DI_unsteady) are shown to be rather peculiar to each laminate regardless of the impact energy used in the tests and therefore may be used to get a first indication of the laminate performance to repeated impacts. The extent of the steady phase may also be used to compute the total energy absorbed by the laminate in the steady damage accumulation phase (E_aTOT__steady).     

A novel negative stiffness magnetic spring design for vehicle seat suspension system

By employing a couple of columnar magnets with a specific arrangement, a negative stiffness magnetic spring (NSMS) is designed, modelled and then experimentally validated in this paper. Such a magnetic spring with negative stiffness can be used for a semi-active seat suspension to achieve vibration reduction on vehicle drivers. Extensive simulation analysis and experimental validation based on static and vibration responses reveal that NSMS brings the beneficial low dynamics stiffness for seat suspension and reduces the resonance frequency vibration during the vertical excitation without compromising loading capacity. Besides, a semi-active seat suspension system with the NSMS can achieve excellent performance of low-frequency vibration suppression (around 28.8 % acceleration reduction under random excitation).     

Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem

Solution of optimal power flow (OPF) problem aims to optimize a selected objective function such as fuel cost, active power loss, total voltage deviation (TVD) etc. via optimal adjustment of the power system control variables while at the same time satisfying various equality and inequality constraints. In the present work, a particle swarm optimization with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power systems. The proposed approach is examined and tested on modified IEEE 30-bus and IEEE 118-bus test power system with different objectives that reflect minimization of fuel cost or active power loss or TVD. The simulation results demonstrate the effectiveness of the proposed approach compared with other evolutionary optimization techniques surfaced in recent state-of-the-art literature. Statistical analysis, presented in this paper, indicates the robustness of the proposed ALC-PSO algorithm.     

An adaptive differential evolution algorithm with an aging leader and challengers mechanism

An adaptive differential evolution algorithm with an aging leader and challengers mechanism, called ADE-ALC, is proposed to solve optimization problems. In ADE-ALC algorithm, the aging mechanism is introduced into the framework of differential evolution to maintain diversity of the population. The key control parameters are adaptively updated based on given probability distributions which could learn from their successful experiences to generate the promising parameters at the next generation. One of the two local search operators is randomly selected to generate challengers which are beneficial for increasing the diversity of population. Finally, the effectiveness of the ADE-ALC algorithm is verified by the numerical results of twenty-five benchmark test functions.     

Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks

To solve the prediction problem of proton exchange membrane fuel cell (PEMFC) remaining useful life (RUL), a novel RUL prediction approach of PEMFC based on long short-term memory (LSTM) recurrent neural networks (RNN) has been developed. The method uses regular interval sampling and locally weighted scatterplot smoothing (LOESS) to realize data reconstruction and data smoothing. Not only the primary trend of the original data can be preserved, but noise and spikes can be effectively removed. The LSTM RNN is adopted to estimate the remaining life of test data. 1154-hour experimental aging analysis of PEMFC shows that the prediction accuracy of the novel method is 99.23%, the root mean square error (RMSE) and mean absolute error (MAE) is 0.003 and 0.0026 respectively. The comparison analysis shows that the prediction accuracy of the novel method is 28.46% higher than that of back propagation neural network (BPNN). Root mean square error, relative error (RE) and mean absolute error are all much smaller than that of BPNN. Therefore, the novel method can quickly and accurately forecast the residual service life of the fuel cell.     

Adaptive comprehensive learning particle swarm optimization with cooperative archive

Comprehensive learning particle swarm optimization (CLPSO) enhances its exploration capability by exploiting all other particles’ historical information to update each particle’s velocity. However, CLPSO adopts a set of fixed comprehensive learning (CL) probabilities to learn from other particles, which may impair its performance on complex optimization problems. To improve the performance and adaptability of CLPSO, an adaptive mechanism for adjusting CL probability and a cooperative archive (CA) are combined with CLPSO, and the resultant algorithm is referred to as adaptive comprehensive learning particle swarm optimization with cooperative archive (ACLPSO-CA). The adaptive mechanism dividing the CL probability into three levels and adjusting the individual particle’s CL probability level dynamically according to the performance of the particles during the optimization process. The cooperative archive is employed to provide additional promising information for ACLPO-CA and itself is updated by the cooperative operation of the current swarm and archive. To evaluate the performance of ACLPSO-CA, ACLPSO-CA is tested on CEC2013 test suite and CEC2017 test suite and compared with seven popular PSO variants. The test results show that ACLPSO-CA outperforms other comparative PSO variants on the two CEC test suites. ACLPSO-CA achieves high performance on different types of benchmark functions and exhibits high adaptability as well. In the end, ACLPSO-CA is further applied to a radar system design problem to demonstrate its potential in real-life optimization.