Investigations on High-Speed Flash Boiling Atomization of Fuel Based on Numerical Simulations

Flash boiling atomization(FBA)is a promising approach for enhancing spray atomization,which can generate a fine and more evenly distributed spray by increasing the fuel injection temperature or reducing the ambient pressure.However,when the outlet speed of the nozzle exceeds 400 m/s,investigating high-speed flash boiling atomization(HFBA)becomes quite challenging.This difficulty arises fromthe involvement ofmany complex physical processes and the requirement for a very fine mesh in numerical simulations.In this study,an HFBA model for gasoline direct injection(GDI)is established.This model incorporates primary and secondary atomization,as well as vaporization and boilingmodels,to describe the development process of the flash boiling spray.Compared to lowspeed FBA,these physical processes significantly impact HFBA.In this model,the Eulerian description is utilized for modeling the gas,and the Lagrangian description is applied to model the droplets,which effectively captures the movement of the droplets and avoids excessive mesh in the Eulerian coordinates.Under various conditions,numerical solutions of the Sauter mean diameter(SMD)for GDI show good agreement with experimental data,validating the proposed model’s performance.Simulations based on this HFBA model investigate the influences of fuel injection temperature and ambient pressure on the atomization process.Numerical analyses of the velocity field,temperature field,vapor mass fraction distribution,particle size distribution,and spray penetration length under different superheat degrees reveal that high injection temperature or low ambient pressure significantly affects the formation of small and dispersed droplet distribution.This effect is conducive to the refinement of spray particles and enhances atomization.

Effect of Gd on neutron absorption properties and electrochemical corrosion behavior of Zr-Gd alloy in boiling concentrated HNO3

A Zr-Gd alloy with neutron poisoning properties and resistance to boiling concentrated HNO3 corrosion was developed based on a corrosion-resistant Zr-702 alloy to meet the demand for neutron shielding in the closed-loop treatment of spent fuel and the nuclear chemical industry.In this study,1 wt.%,3 wt.%,5 wt.%,7 wt.%,and 9 wt.%Zr-Gd alloys were designed and fabricated with Zr-702 as the control element.The electrochemical behavior of the Zr-Gd alloys in boiling concentrated HNO3 was investigated,and the neutron shielding effect on plate thickness and Gd content was simulated.The experimental results demonstrate that the corrosion resistance of the alloy decreased slightly before~7-9 wt.%with increasing Gd content;this is the inflection point of its corrosion resistance.The alloy uniformly dissolved the Gd content that could not be dissolved in the Zr lattice,resulting in numerous micropores on the passivation coating,which deteriorated and accelerated the corrosion rate.The MCNP simulation demonstrated that when the Gd content was increased to 5 wt.%,a 2-mm-thick plate can shield 99.9%neutrons;an alloy with a Gd content≥7 wt.%required only a 1-mm-thick plate,thereby showing that the addition of Gd provides an excellent neutron poisoning effect.Thus,the corrosion resistance and neutron shielding performance of the Zr-Gd alloy can meet the harsh service requirements of the nuclear industry.

基于Leap Motion手势识别的三维交互系统

随着虚拟交互技术的发展,人们迈入了“体验式经济时代”,消费者越来越关注个性体验,因此,基于Leap Motion手势识别设备,设计了一种三维虚拟室内交互系统。该系统以Unity3D作为开发工具,Leap Motion作为硬件平台,结合C#语言进行脚本的编译,利用3ds Max平台对室内进行场景搭建,通过Unity3D工具将组件整合,设计了七种手势,使用Leap Motion硬件设备对场景中物体进行各种不同的操作。经试验表明,该系统实现了用户与场景中物体的交互能力,可以应用在室内装修和设计等方面,增强人们的体验感与趣味性。

Simulation of single bubble dynamic process in pool boiling process under microgravity based on phase field method

We use the phase field method to track the gas-liquid interface based on the gas-liquid two-phase flow in the pool boiling process,and study the bubble nucleation,growth,deformation,departure and other dynamic behaviors on the heating surface under microgravity.By simulating the correlation between liquid undercooling and bubble dynamics,we find that the bubble growth time increases with the increase of liquid undercooling,but the effect of liquid undercooling on bubble height is not significant.Meanwhile,the gas-liquid-solid three-phase contact angle and the gravity level will also have an effect on the bubble growth time and bubble height.With the increase of the contact angle,the bubble growth time and bubble height when the bubble departs also increase.While the effect of gravity level is on the contrary,the smaller the gravity level is,the larger the bubble height and bubble growth time when the bubble separates.

Comparisons of Structured Surface Floors for Pool Boiling Enhancement at Low Heat Fluxes: Hands-On Learning Setup for Heat Transfer Classroom

Various enhanced surfaces have been proposed over the years to improve boiling heat transfer. This paper introduces an experimental setup designed for boiling demonstration in the graduate-level Heat Transfer course. The pool boiling performance of water under atmospheric pressure of 1.025 bar is investigated by using several structured surfaces at heat fluxes of 28 and 35 kW/m2. Surfaces with holes, rectangular grooves, and mushroom fins are manufactured by an NC-controlled vertical milling machine. The heat flux versus excess temperature graph is plotted by using thermocouple measurements of water and base temperatures of the boiling vessel. The separation, rise, and growth of individual vapor bubbles from the surface during boiling were recorded with a digital camera. The results for the plain surface are compared to the Rohsenow correlation. The enhancement of heat transfer coefficient (h) ranged between 15% - 44.5% for all structured surfaces. The highest heat transfer coefficient enhancement is observed between 41% - 56.5% for holed surface-3 (405 holes) compared to the plain surface. The excess temperature dropped around 29% - 34% for holed surface-3 (405 holes) compared to the plain surface. The heat transfer coefficient increases as the spacing between channels or holes decreases. While the bubbles on holed and mushroomed surfaces were spherical, the bubbles on the flat and grooved surfaces were observed as formless. The suggested economical test design could be appropriate to keep students focused and participating in the classroom.

Experimental and Numerical Analysis of the Influence ofMicrochannel Size and Structure on Boiling Heat Transfer

Computational fluid dynamics was used and a numerical simulation analysis of boiling heat transfer in microchannels with three depths and three cross-sectional profiles was conducted.The heat transfer coefficient and bubble generation process of three microchannel structures with a width of 80μm and a depth of 40,60,and 80μm were compared during the boiling process,and the factors influencing bubble generation were studied.A visual test bench was built,and test substrates of different sizes were prepared using a micro-nano laser.During the test,the behavior characteristics of the bubbles on the boiling surface and the temperature change of the heated wall were collected with a high-speed camera and a temperature sensor.It was found that the microchannel with a depth of 80μm had the largest heat transfer coefficient and shortest bubble growth period,the rectangular channel had a larger peak heat transfer coefficient and a lower frequency of bubble occurrence,while the V-shaped channel had the shortest growth period,i.e.,the highest frequency of bubble occurrence,but its heat transfer coefficient was smaller than that of the rectangular channel.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

Lattice Boltzmann simulation of the effects of cavity structures and heater thermal conductivity on nucleate boiling heat transfer

The boiling heat transfer technology with cavity surfaces can provide higher heat flux under lower wall superheat,which is of great significance for the cooling of electronic chips and microelectromechanical devices.In this paper,the boiling characteristics of the cavity surfaces are investigated based on the lattice Boltzmann(LB)method,focusing on the effects of cavity shapes,sizes,and heater thermal conductivity on the heat transfer performance.The results show that the triangular cavity has the best boiling performance since it has less residual vapor and higher bubble departure frequency than those of the trapezoidal and rectangular cavities.As the cavity size increases,the enhancement of heat transfer by the cavity mouth is suppressed by the heat accumulation effect at the heater bottom.The liquid rewetting process during bubble departure is the reason for the fluctuation of the space-averaged heat flux,and the heater thermal conductivity determines the fluctuation amplitude.The evaporation of liquid in the cavity with high thermal conductivity walls is more intense,resulting in shorter waiting time and higher bubble departure frequency.

The‘New Normal’of Extreme Weather And the Urgency of Transformation“The era of global warming is over and the era of global boiling has arrived.”

Phong,a 42-year-old Vietnamese motorcyclist begins his busy day at 5 a.m.In Hanoi,light motorcycles are an important mode of transportation.Countless motorcycles shuttle through the streets and alleys of the city,transporting parcels,goods,and passengers to various destinations.Phong usually works for 12 hours straight with little rest.However,the unprecedented heat waves this summer had often pushed daytime temperatures to above 40 degrees Celsius,making it extremely difficult for Phong to complete his daily routine.Phong prepared a hat,a wet handkerchief,and more drinking water.He even installed a small umbrella above his mobile phone rack to prevent the phone from overheating in the sun because it is his only source of work.Despite all the equipment,he is still worried.“If I suffer heat stroke or anything else,I will be unable to work,”he said.“I can’t afford it.”

基于Leap motion 的大学物理实验虚拟课堂设计

通过利用手势控制器(Leap Motion)技术,设计并实施一套创新的大学物理虚拟实验课程.通过在Unity3D软件中整合Leap Motion,创建一个大学物理实验测量杨氏模量实验场景,主要是利用C#语言进行开发,通过设计出多个模块,包括主界面设计和基本实验组件来完成物理实验的设计.在Leap Motion官网上下载关于Unity3D的SDK资源包并且导入Unity3D中,用其建立手部模型,实现Leap Motion控制器与Unity3D的手部交互实验系统的设计.人机交互技术的引入不仅可以丰富大学物理实验的教学手段,而且还能提高学生的学科理解和实际操作能力,同时培养其创新思维和科技素养.

Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://github.com/yehaowei/temt.

Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

基于Leap Motion手势交互技术的博物馆数字展示应用研究

随着科技的不断发展,博物馆数字展示已成为呈现文物与历史不可或缺的重要手段之一。在数字化潮流的推动下,Leap Motion技术凭借其卓越的手势交互能力引起了广泛的关注与兴趣。本研究旨在探索如何充分利用Leap Motion手势交互技术,设计并实现一种全新的博物馆数字展示交互系统,以提升观众的参与度和沉浸式体验。本研究采用实验研究方法,开发并实施了一套基于LeapMotion技术的手势交互系统。通过实验设计、设备选择与安装、手势识别与映射、展示内容的创建与集成、以及用户测试与优化等步骤完成研究。其结果表明,该系统能够准确捕捉观众的手部动作,并将其映射到超大屏幕的交互中,显著提升了展示效果和观众的互动体验。手势识别的误差率控制在5%以内,交互响应速度优秀,达到了预期目标。本研究验证了LeapMotion手势交互技术在博物馆数字展示中的有效性和可行性,提供了一种创新的展示方式,为公众呈现出更为丰富、引人入胜的文化体验,为未来博物馆展示的数字化发展方向提供有益的参考与借鉴,以促进整个行业的创新发展。

Multiparameter Numerical Investigation of Two Types of Moving Interactions Between the Deep-Sea Mining Vehicle Track Plate and Seabed Soil:Digging and Rotating Motions

To ensure the safe performance of deep-sea mining vehicles(DSMVs),it is necessary to study the mechanical characteristics of the interaction between the seabed soil and the track plate.The rotation and digging motions of the track plate are important links in the contact between the driving mechanism of the DSMV and seabed soil.In this study,a numerical simulation is conducted using the coupled Eulerian–Lagrangian(CEL)large deformation numerical method to investigate the interaction between the track plate of the DSMV and the seabed soil under two working conditions:rotating condition and digging condition.First,a soil numerical model is established based on the elastoplastic mechanical characterization using the basic physical and mechanical properties of the seabed soil obtained by in situ sampling.Subsequently,the soil disturbance mechanism and the dynamic mechanical response of the track plate under rotating and digging conditions are obtained through the analysis of the sensitivity of the motion parameters,the grouser structure,the layered soil features and the soil heterogeneity.The results indicate that the above parameters remarkably influence the interaction between the DSMV and the seabed soil.Therefore,it is important to consider the rotating and digging motion of the DSMV in practical engineering to develop a detailed optimization design of the track plate.

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

Quantitative analysis of the morphing wing mechanism of raptors:IMMU-based motion capture system and its application on gestures of a Falco peregrinus

This paper presented a novel tinny motion capture system for measuring bird posture based on inertial and magnetic measurement units that are made up of micromachined gyroscopes, accelerometers, and magnetometers. Multiple quaternion-based extended Kalman filters were implemented to estimate the absolute orientations to achieve high accuracy.Under the guidance of ornithology experts, the extending/contracting motions and flapping cycles were recorded using the developed motion capture system, and the orientation of each bone was also analyzed. The captured flapping gesture of the Falco peregrinus is crucial to the motion database of raptors as well as the bionic design.

Pulses in ground motions identified through surface partial matching and their impact on seismic rocking consequence

In seismology and earthquake engineering,it is fundamental to identify and characterize the pulse-like features in pulse-type ground motions.To capture the pulses that dominate structural responses,this study establishes congruence and shift relationships between response spectrum surfaces.A similarity search between spectrum surfaces,supplemented with a similarity search in time series,has been applied to characterize the pulse-like features in pulse-type ground motions.The identified pulses are tested in predicting the rocking consequences of slender rectangular blocks under the original ground motions.Generally,the prediction is promising for the majority of the ground motions where the dominant pulse is correctly identified.

Bioinspired Polarized Optical Flow Enables Turbid Underwater Target Motion Estimation

Underwater target motion estimation is a challenge for ocean military and scientific research.In this work,we propose a method based on the combination of polarization imaging and optical flow for turbid underwater target detection.Polarization imaging can reduce the influence of backscattered light and obtain high-quality images underwater.The optical flow shows the motion and structural information of the target.We use polarized optical flow to obtain the optical flow field and estimate the target motion.The experimental results of different targets under varying water turbidity levels illustrate that our method is realizable and robust.The precision is verified by comparing the results with the precise displacement data and calculating two error measures.The proposed method based on polarized optical flow can obtain accurate displacement information and a good recognition effect.Moving target segmentation based on the Otsu method further proves the superiority of the polarized optical flow under turbid water.This study is valuable for target detection and motion estimation in scattering environments.

A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable devices.Previous work have achieved impressive performance in classifying steady locomotion states.However,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion states.Due to the similarities between the information of the transitions and their adjacent steady states.Furthermore,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as transitions.To address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor intent.Each classifier in these levels is composed of multiple-LSTM layers and an attention mechanism.To introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating block.The method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions states.Experimental results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,respectively.It is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.