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Tubular thin-walled structure has attracted more and more attention because of its light weight and energy absorption characteristic, and has been widely applied to the fields of aerospace and auto...

X-ray diffractometer, field emission scanning electron microscope (SEM, Hitachi S-4800), laser confocal micro-region Raman spectrometer and vibration sample magnetometer were used to systematically study the effects of polyethyleneimine concentrations and exposure time on the morphology and size of ZnO nanowire arrays. The photoelectric property and the relationship between the morphology of nanowire arrays and ferromagnetism at room temperature were also analyzed. Under 15 min exposure time, when the polyethyleneimine concentration is 2.25 g / L, the obtained ZnO nanowire array film exhibits the smallest size, the optimal density and vertical orientation. According to the study of luminescence and room temperature magnetism, it is shown that the optical and ferromagnetic property are related to the variation tendency of oxygen defects and surface defects of the ZnO nanowires.

In the process of nickel-based alloy machining, chips are easy to bond on the tool surface, which weaken the tool performance and make the tool wear. Therefore, it is very important to study the mechanism of tool bond wear in the process of nickel-based superalloy machining. In order to reflect the wear process of the tool from the perspective of micro cutting, the molecular dynamics simulation model for cutting Ni–Fe-Cr-Co–Cu nickel-based alloy with SiC tool was established. The Morse potential functions between the tool and workpiece are calculated, and the simulation results are analyzed visually. It is found that the bond wear is the main wear form of tool in the process of cutting nickel-based alloy, and the wear processes are divided into three stages: contact, adhesion and shedding. The stress and strain in the cutting area are calculated and it is found that the bond occurs when the tool-workpiece extrusion is strong. Through the calculation of radial distribution function and formation energy, it is found that Ni-Si compound is formed on the tool surface, and the newly generated Ni-Si compound reduces the tool performance compared with the silicon carbide structure. This study provides a more complete microscopic explanation of the tool wear mechanism, which is helpful to find a method to prolong tool life.

The study of surface effects on quasi-periodical vibration is significant because of its promising applications in sensors of nano/micro-electro-mechanical systems (N/MEMS). In this study, the surface effects parameters are adopted from the atomistic calculations. The governing equation of the nanobeam considering the nonlinear curvature and surface effects is established. Considering the first two modes, the Garlerkin method is used to translate the partial differential equation into ordinary differential equations. The multi-level residue harmonic balance method (RHBM) with two time variables is developed to solve these ODEs. The influences of surface and aspect ratio on the two frequencies and corresponding amplitudes are analyzed. Both of the frequency and amplitude discrepancies of the nanobeam to the counterparts of the classical beam become greater when the height of the beam shrinks at nanoscale. For the beam with the same height, both of the first- and second-frequencies decrease with the increase of the aspect ratio, and for the beams with the same aspect ratio, they decrease with the increase of the height. The solution of RHBM fits well with the molecular dynamics and Runge–Kutta numerical simulations. The study provides insight into the mechanism of the nonlinear dynamics of nanostructures, and shed light on quantitative design of the elements in N/MEMS, sensors, actuators, and resonators.

Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy; a multi-stream neural network that extracts the hidden features of vision and V2I information; and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach., Accepted by the IEEE Transactions on Intelligent Transportation Systems

Oxygen-consuming fuel tank inerting technology is a novel technology with a simple structure and a high efficiency for tank explosion suppression based on flameless catalytic combustion. To...

Hundreds of processor cores or modules are integrated into a single chip. The traditional bus or crossbar is challenged by bandwidth, scalability, and silicon area, and cannot meet the requirements of high end applications. Network-on-chip (NoC) has become a very promising interconnection structure because of its good scalability, predictable interconnect length and delay, high bandwidth, and reusability. However, the most available packet routing NoC may not be the perfect solution for high-end heterogeneous multi-core real-time systems-on-chip (SoC) because of the excessive latency and cache cost overhead. Moreover, circuit switching is limited by the scale, connectivity flexibility, and excessive overhead of fully connected systems. To solve the above problems and to meet the need for low latency, high throughput, and flexibility, this paper proposes PCCNoC (Packet Connected Circuit NoC), a low-latency and low-overhead NoC based on both packet switching (setting-up circuit) and circuit switching (data transmission on circuit), which offers flexible routing and zero overhead of data transmission latency, making it suitable for high-end heterogeneous multi-core real-time SoC at various system scales. Compared with typically available packet switched NoC, our PCCoC sees 242% improved performance and 97% latency reduction while keeping the silicon cost relatively low.

High-occupancy vehicle (HOV) lane performance degradation has become more prevalent in many regions because of the growing travel demand and the increasing number of HOV lane-eligible vehicles. Conventional capacity expansion strategies, such as adding a second HOV lane, can be a promising solution. However, they can be difficult in areas where there is little room left to add new travel lanes in both directions. In that case, adding a contraflow HOV lane could be a good compromise, especially if the peak travel demands in the HOV lanes are tidal. In this work, we study the impact of adding a contraflow HOV lane on a section of the I-215 freeway, which connects two major cities in Riverside County, California. Two alternative designs, “full contraflow” and “partial contraflow” HOV lanes, are evaluated in the traffic microsimulation environment. The evaluation results show that in the case of the full contraflow HOV lane design, the average delay during peak hours in the southbound direction of the freeway would be reduced by 76% compared with the scenario with no additional HOV lane. The implementation of the full contraflow HOV lane to supplement the existing HOV lanes would also increase the average speed in the currently degraded HOV lane from 37.8 to 55.0 mph, which is significantly above 45 mph, the speed threshold for HOV lane performance degradation.

Active magnetic ranging (AMR) is a detection technology in relief well projects for determining the subsurface location of the blowout well by applying active excitations to the formation. In this paper, the detection principle of AMR is introduced and the three-dimensional numerical modeling is realized based on FEM. Several measures are adopted to improve the flexibility and efficiency of the model generation and matrix decomposition. Besides, the detection performance of AMR with different tool configurations and in different formation structures are also investigated. And the influence of tool parameters and formation parameters on the response characteristics is analyzed and summarized, which provides support for the practical application and design optimization of AMR in relief well operations.

The recently synthesized monolayer diamond-diamane has proved to possess excellent mechanical and electrical properties, and it holds great potential in the field of nano-mass sensors. Herein, a molecular dynamics (MD) simulation is employed to systematically investigate the vibration response of the diamane nanoribbon (DNR) for the mass inspection. The results show that under different attached masses, the natural frequency of DNR is about three times of that of the bilayer graphene nanoribbon (BGNR) with the same size. The edge flatness of the DNR can be maintained during the vibration process, while the edge of the BGNR will warp in the initial state. Increasing the pre-strain can significantly increase the natural frequency of the DNR, leading to a higher response sensitivity of the DNR. In addition, the DNR has a higher mass resolution than the BGNR, and can detect smaller attached mass. The position of the attached mass in the resonator has a significant effect on the detection response. When the attached mass is near the center of the resonator, the frequency shift reaches the maximum value, and then it rapidly decreases to zero when the attached mass is close to the edge of DNR. Finally, the attached mass has no obvious effect on the quality factor of the DNR, and its value is stable between 105and 106orders of magnitude. The theoretical results demonstrate the accuracy of the MD results. The MD simulations reveal that the DNR has important implications as a resonant material for nano-mass sensor in the future.

Hybrid composite materials, which contain more than one type of reinforcing fiber, have been gaining ever-increasing popularity. They can keep superior mechanical properties while greatly reducing ...

Composite shells with complex geometry are widely used in aerospace structures. Due to the complexity of geometry and curvilinear fiber path, the analysis and optimization based on finite element analysis (FEA) for complex variable-stiffness (VS) shells is extremely time-consuming. By comparing with FEA, isogeometric analysis (IGA) exhibits higher prediction efficiency of buckling load. In this work, the formula of geometric stiffness matrix for complex VS shells is derived for the first time based on degenerated shell method using IGA, which is the basis of performing linear buckling analysis. Then, a new variable curvature quasi-linear function (VCQLF) to describe curvilinear fiber path is proposed, which can further expand the design space of VS shells. After that, two frameworks for shape optimization of complex shells are put forward and then compared, and it is found that the one based on LOFT function can provide representative control variables of shape and effectively reduces the number of design variables for complex shells. Finally, a novel collaborative optimization framework of fiber path and shell shape using IGA is established. By comparison of traditional methods, it is demonstrated that the proposed framework can greatly improve the efficiency of optimization and fully explore the buckling load of complex VS shells.

Chemical vapor transport deposition (CVTD) is an effective method for preparing large tungsten coatings for space thermionic reactors. In this study, a high-density, high-work-function polycrystalline tungsten coating was prepared using a WCl6 transport agent in a concentric tube-type closed transport system. The relationship between the kinetics and the microstructures of the CVTD polycrystalline tungsten coating at the substrate temperature of 1593 K-1793 K and system pressure of 15.93 Pa-106.8 Pa was studied, which provided a basis for the preparation of high-quality tungsten coatings. At a low temperature or a low pressure, the activation energy was approximately 2 kJ/mol, the deposition rate was almost independent of the temperature changes, and the control mechanism was mass transport limited. The tungsten coating had nodules on the surface with pores in the grain boundaries and grew preferentially along . At a high temperature and a high pressure, the apparent activation energy was approximately 90 kJ/mol, the value of order was approximately 1, and the control mechanism in this process range was surface limited. The tungsten coating exhibited a hexagonal pyramidal structure, and the growth direction was preferred along . The average work function of the tungsten coating prepared at a temperature of 1673 K and a system pressure of 106.80 Pa was as high as 5.20 eV.

The surrogate-based optimization of hierarchical stiffened composite shells against buckling is a typical multimodal and multivariables optimization problem. To improve the computational efficiency...

Connected vehicle (CV) technology has great potential to improve the performance of today’s advanced driver assistance systems in terms of safety, energy efficiency, and driving comfort. The aim of this paper is to develop a specific CV application that assists with lane selection, i.e., finding the best travel lane in terms of travel time based on predicted lane-level traffic states. In this paper, a spatial-temporal model (ST-model) was developed, which utilizes spatial and temporal information of road cells to predict future traffic states. This information was used by the proposed lane selection assistance application to select an optimal lane sequence for the application-equipped vehicle. A comprehensive simulation-based evaluation was then conducted under various scenarios, e.g., with different traffic volumes, penetration rates of communication-capable vehicles, and information update cycles. The evaluation results reveal several interesting findings, including: 1) the proposed ST-model outperforms the basic estimation model in terms of traffic state prediction accuracy; 2) travel times of application-equipped vehicles can be reduced by up to 8% with the use of the proposed lane selection assistance application when compared with the baseline, under various traffic scenarios; 3) the application can be effective in the early deployment stage of CV technology, where the penetration rate of communication-capable vehicles is still low; and 4) the potential conflict risk of application-equipped vehicles is reduced, although the application is mainly designed for mobility benefits, due to the more strategic and informed lane changes suggested by the proposed application.

For cylindrical shells under axial compression, the essence of initial geometric imperfections is the superposition of local out-of-plane deformations of various forms, which may facilitate the development of buckling deformations, thus leading to a significant knockdown of the load-carrying capacity. It is very challenging for existing methods to provide an accurate prediction of the lower bound on a load-carrying capacity before the structure is fabricated. Therefore, it is crucial to find a type of assumed imperfection that will allow us to approximate lower bounds for shells in the design stage. Five 1-m-diameter unstiffened shells, termed W1-W5, are designed, analysed and tested. The measured imperfection approach, single-perturbation load approach (SPLA), worst multiple-perturbation load approach (WMPLA), and a Combined Approach for measured imperfections and superimposed radial point load imperfections are compared with test results. The results show that the SPLA-based methods produce higher KDFs than the test results and are sensitive to the distribution of the measured imperfections. In contrast, the KDFs predicted by the WMPLA and the Combined Approach are similar to one another and very close to the test results. From the comparison results, it can preliminarily be concluded that the WMPLA is able to envelop the small- and large-amplitude measured imperfections, which has the potential to predict a rational lower bound on the buckling loads of unstiffened cylindrical shells. The WMPLA should be extended to the design of other types of thin-walled structures with caution, because the manufacturing signature may be distinctly changed for different processes, and the buckling tests of other types of structures would be carried out in future study.

Composite materials continue to grow in popularity within the aerospace industry as the preferred material for manufacturing large airframe structures. However, the popularity of composite materials has also led to the increase in composite waste. As the popularity of composite materials continues to grow, the proper management and recycling of these composite waste materials becomes increasingly crucial to the sustainability of the environment. In order to investigate potential recycling techniques for composite waste, a team of Purdue University School of Aviation and Transportation Technology (SATT) faculty and students teamed up to investigate the characteristics of 3D printed recycled carbon fiber. A prototype 3D printed recycled carbon fiber part was used for the study. Through the use of microscopy and ImageJ image analyzing software, the researchers were able to determine the void content, fiber volume fraction, and fiber orientation of the prototype 3D printed recycled carbon fiber part and identified potential improvements to the 3D printing process in order to improve the 3D printed part’s characteristics.

Battery electric vehicles (BEVs) are a critical pathway towards achieving energy independence and meeting greenhouse and criteria pollutant gas reduction goals in the current and future transportation sector. Emerging connected and automated vehicle (CAV) technologies further open the door for developing innovative applications and systems to leverage vehicle efficiency and substantially transform transportation systems. Therefore, we present a simulation study of various BEV types and compare the performance when driving on real-road drive cycles to highly optimized eco-driving cycles using advanced CAV technologies. The results demonstrate that eco-driving has a high potential to reduce energy consumption for all types of BEVs considered. The investigated BEVs include a compact vehicle, a transit city bus, and a Class 7 delivery truck. The impact of eco-driving on conventional vehicles was also compared to comparable BEVs. Compared to the BEVs, eco-driving provides a larger reduction in the conventional vehicle's braking energy loss, and also provides conventional vehicles with greater reductions in the engine mechanical energy output but the fuel savings did not show a consistent trend among all the conventional vehicle types. As part of the study, a comprehensive EV powertrain model was developed to account for key EV components and powertrain configurations.

Using connected vehicle technology, a number of eco-approach and departure (EAD) strategies have been designed to guide vehicles through signalized intersections in an eco-friendly way. Most of the existing EAD applications have been developed and tested in traffic-free scenarios or in a fully connected environment, where the presence and behavior of all surrounding vehicles are detectable. In this paper, we describe a prediction-based EAD strategy that can be applied toward more realistic scenarios, where the surrounding vehicles can be either a connected or non-connected. Unlike highway scenarios, predicting speed trajectories along signalized corridors is much more challenging due to disturbances from signals, traffic queues, and pedestrians. Based on vehicle activity data available via inter-vehicle communication or onboard sensing (e.g., by radar), we evaluate three state-of-the-art nonlinear regression models to perform short-term speed forecasting of the preceding vehicle. It turns out radial basis function neural network outperformed both Gaussian process and multi-layer perceptron network in terms of prediction accuracy and computational efficiency. Using signal phase and timing information and the predicted state of the preceding vehicle, our prediction-based EAD algorithm achieved better fuel economy and emissions reduction in urban traffic and queues at intersections. Results from the numerical simulation using the next generation simulation data set show that the proposed prediction-based EAD system achieve 4.0% energy savings and 4.0% – 41.7% pollutant emission reduction compared with a conventional car following strategy. Prediction-based EAD saves 1.9% energy and reduces criteria pollutant emissions by 1.9% – 33.4% compared with an existing EAD algorithm without prediction in urban traffic.

For cylindrical shell structures under axial compression, it is crucial to provide high-fidelity knockdown factors (KDFs) in preliminary design for aerospace and civil structures. In this paper, numerical studies of buckling response for composite cylindrical shell with geometric imperfections and embedded delamination imperfections are performed to predict the lower-bound buckling loads. Results indicate that composite shells with single dimple-shape geometric imperfection exhibit similar lower bound trend and buckling behavior as those with embedded delamination imperfection. It is found that the lower-bound buckling loads are much less conservative than the corresponding design recommendation from NASA SP-8007. And the effect of geometric imperfections can envelope that of delamination imperfections. Therefore, the worst multiple perturbation load approach (WMPLA) is performed to find the worst combination of dimple-shape geometric imperfections to predict the lower-bound buckling load, and the efficient global optimization (EGO) is employed to improve the computational efficiency of WMPLA. It is demonstrated that the improved WMPLA can provide an improved KDF by examples in open literature. Based on the improved KDFs, it is possible to increase the load-bearing efficiency of composite structures in practical engineering.

Reliability-based design optimization (RBDO) is a powerful tool to handle the influence of various uncertainties during optimization. However, unbearable computation cost is one of the largest barriers for its application, especially for the finite element method (FEM)-based RBDO. In this paper, an efficient and accurate RBDO framework is established based on isogeometric analysis (IGA) for complex engineering problems. Furthermore, an enhanced step length adjustment (ESLA) iterative algorithm and a second-order reliability method-based stepped-up sequential optimization and reliability assessment approach (SSORA-SORM) are proposed to boost the efficiency of RBDO. According to the situation of iteration process, the step length of search the most probable target point can be adaptively updated by the proposed criterion to improve the robustness in ESLA. In the proposed framework, the analytical first-order sensitivity is derived based on IGA in the optimization process to substitute the time-consuming finite difference method. The robustness, accuracy and efficiency of proposed methods are verified via several numerical benchmarks. Besides, three complex IGA-based examples demonstrate that the proposed method is able to save much computational cost without losing accuracy, which is inherently suitable for the RBDO of complex engineering problems.

A full understand of the influential mechanism concerning electricity consumption has a crucial political reference significance for the implementation of energy conservation and consumption reduction policies. To solve the lack of correlation between the empirical results of the research regions in present researches, this paper takes China's electricity consumption from 1995 to 2016 as the research subject, uses Multi-period ST-LMDI model by setting a unified reference province as a benchmark for different regions in China to completely decompose the changes in China's electricity consumption, and further analyzes the influence mechanism of China's electricity consumption changes by combining the characteristics of the departments and regions. As the result, it's found that the economic growth has a strong impetus for power consumption while the technological progress can effectively curb it. Meanwhile, results of studying electricity consumption from the perspective of industry and region show that the economic structure and consumption intensity have obviously different influences in the eastern, central and western regions. So when it comes to formulate the policy for electricity development and reform, the characteristics of industrial structure as well as the regional differences must be taken into consideration.

Microscopic vehicle emissions models have been well developed in the past decades. Those models require second-by-second vehicle trajectory data as a key input to perform vehicle energy/emissions estimation. Due to the omnipresence of mobile sensors such as floating cars, real-world vehicle trajectory data can be collected in a large scale. However, most large-scaled mobile sensor data in practice are sparse in terms of sampling rate due to the consideration in implementation cost. In this paper, a new modal activity framework for vehicle energy/emissions estimation using sparse mobile sensor data is presented. The valid vehicle dynamic states are identified including four driving modes, named acceleration, deceleration, cruising, and idling. The best valid vehicle dynamic state with the largest probabilities is selected to reconstruct the second-by-second vehicle trajectory between consecutive sampling times. Then vehicle energy/emissions factors are estimated based on operating mode distributions. The proposed model is calibrated and validated using the Next Generation Simulation’s dataset, and shows better performance in vehicle energy/emissions estimation compared with the linear interpolation model. Sensitivity analysis is performed to show the model accuracy with different time intervals. This paper provides a new methodology for vehicle energy/emissions estimation and extends the application area of sparse mobile sensor data.

The connected vehicle eco-approach and departure (EAD) application for signalized intersections has been widely studied and is deemed to be effective in terms of reducing energy consumption and both greenhouse gas and other criteria pollutant emissions. Prior studies have shown that tangible environmental benefits can be gained by communicating the driver with the signal phase and timing (SPaT) information of the upcoming traffic signals with fixed time control to the driver. However, similar applications to actuated signals pose a significant challenge due to their randomness to some extent caused by vehicle actuation. Based on the framework previously developed by the authors, a real-world testing has been conducted along the El Camino Real corridor in Palo Alto, CA, USA, to evaluate the system performance in terms of energy savings and emissions reduction. Strategies and algorithms are designed to be adaptive to the dynamic uncertainty for actuated signal and real-world traffic. It turns out that the proposed EAD system can save 6% energy for the trip segments when activated within DSRC ranges and 2% energy for all trips. The proposed system can also reduce 7% of CO, 18% of HC, and 13% of NOx for all trips. Those results are compatible with the simulation results and validate the previously developed EAD framework.

Processing tough hydrogels into sophisticated architectures is crucial for their applications as structural elements. However, Digital Light Processing (DLP) printing of tough hydrogels is challenging because of the low-speed gelation and toughening process. Described here is a simple yet versatile system suitable for DLP printing to form tough hydrogel architectures. The aqueous precursor consists of commercial photoinitiator, acrylic acid, and zirconium ion (Zr

The hierarchical stiffened shell is a promising aerospace structure configuration with high load-carrying capacity, however, it is challenging to fully explore its optimal load-carrying efficiency. Therefore, a bi-level optimization framework is proposed for hierarchical stiffened shells. In the first level of the optimization framework, a parallel computing numerical-based smeared stiffener method (NSSM) is first introduced for the fast prediction of critical buckling load and mode, by combining the numerical implementation of asymptotic homogenization (NIAH) method with the Rayleigh-Ritz method. Then, a large-scale Latin hypercube sampling (LHS) is performed in the entire design space based on NSSM, and a set of competitive sampling points is collected from the Pareto front of LHS results according to a screening criterion of load-carrying efficiency. In the second level, a surrogate-based optimization using radial basis function (RBF) technique is performed based on generated competitive sampling points with high load-carrying efficiency. Finally, detailed comparisons between optimal results of the proposed optimization method based on the competitive sampling method and the traditional surrogate-based optimization method based on the RBF technique and the LHS sampling method are made from the viewpoint of computational efficiency and global optimizing ability. Spending an approximate computational time, the optimal buckling result of the proposed method increases by 23.7% than that of the traditional method. In order to achieve an approximate global optimization result, the proposed method is able to reduce the computational time by 74.4% than the traditional method. By evaluating competitive sampling results, it can also be concluded that the partial global buckling mode and global buckling mode are most dominant buckling modes for hierarchical stiffened shells with the thick skin and closely-spaced stiffeners, which are prone to obtain a higher load-carrying efficiency.

This paper presents an isogeometric analysis (IGA) based design method to address the stress-constrained topology optimization problem of plane stress and bending of thin plates. Based on the popular SIMP model, the integrated framework of geometry modeling, structural stress analysis and optimization is established. Owing to the geometry exactness and high-order continuity between elements, the IGA improves the computational accuracy of stress, and thus enhances the credibility of optimum design. Meanwhile, the obvious zigzag boundaries are avoided in the optimized results, and the stress function of IGA maintains the continuity for a relatively coarse discretization . Moreover, the IGA-SIMP method can easily meet the requirement of C 1 -continuity for the Kirchhoff plate formulations, which also facilitates the stress analysis and sensitivity calculation. To overcome the convergence difficulty of highly nonlinear stress aggregation constraint, two STM (stability transformation method)-based stabilization schemes combining with the P-norm function for global stress constraint are developed to achieve the stable iterations and acceptable designs. Finally, representative examples illustrate the effectiveness and convenience of the proposed approach. It is indicated that the IGA-SIMP method shows superior performance for solution accuracy and efficiency, and the local stress level is well controlled.

This paper systematically summarized the technical state of art and research results on the motion error of a linear guideway, corrected some misconceptions, and further clarified the relationship between the straightness error of the guide rail itself and the motion error of the linear stage. Moreover, a new method based on parallel mechanism is provided to study the motion errors of the linear guide pair. The basic idea is to abstract the structural relationship between the stage and the guide rail into a 4-bar parallel mechanism. Thus, the stage can be considered as a moving platform in the parallel mechanism. Its motion error analysis is also transferred to moving platform position analysis in the parallel mechanism. The straightness motion error and angular motion error of the stage can be analyzed simultaneously by using the theory of parallel mechanism. Some experiments were conducted on the linear guideway of a self-developed parallel coordinate measuring machine. The experimental data and analysis verify the feasibility and correctness of this method.

The structural efficiency of stiffened panels can be significantly improved by utilizing curvilinear stiffeners because of their outstanding design flexibility. However, the explosion of design variables poses a stiff challenge to the design of layouts of such structures. In this study, a novel layout optimization method is proposed for curvilinearly stiffened panels based on deep learning-based models, which enables their intelligent design. Unlike traditional methods, the image-based structural layout, which is characteristic of curvilinear stiffener paths, is employed as a design variable, and convolutional neural networks (CNNs) are used to extract the layout features from the curvilinear stiffeners and construct a surrogate model between layout features and structural performance. Subsequently, sub-optimization is performed using the constructed CNN to obtain new designs and correspondingly update the dataset. As the trained CNN does not require input data to exhibit the same number of stiffeners present in the training data, the proposed optimization framework can be used to address layout designs of stiffeners with variable numbers. Numerical examples demonstrate that the proposed intelligent optimization framework significantly improves the optimization efficiency compared to traditional models. It also indicates the extraordinary promise of deep learning-based methods in the field of engineering optimization.

Black phosphorus (BP) is a novel two-dimensional nanostructure with wide potential applications in such areas as nanoresonators and nanosensors. In this study, we concentrate on the role of the bending stiffness of the BP monolayer in its mechanical performances, including tension, compression, buckling and bending. Firstly, the stress–strain curve and Young’s modulus of the single layer black phosphorus (SLBP) nanoribbon with different chiral structures are obtained in the tension process via the molecular dynamics (MD) simulation. Next, the loading behavior of the SLBP nanoribbon during compression is simulated via MD. It was found that the bending stiffness of the nanoribbon has an essential effect on its postbuckling behaviors, and an empirical formula is proposed which can accurately depict the postbuckling process. Eventually, the bending properties of chiral SLBP nanoribbons are explored via the MD simulation, and the modified expression of the bending stiffness can better predict its large deflection. These findings are beneficial for us to fully understand mechanical responses of BP, which hold implications in engineering new materials and devices at nanoscale.