Barkan Ugurlu

Serena Ivaldi
email: serena.ivaldi@inria.fr

Chapter 35: Free Simulation Software and Library

Keywords: dynamic simulation, physics engine, open source software, humanoid modeling

With the advent of powerful computation technologies and efficient algorithms, simulators became an important tool in most engineering areas. The field of humanoid robotics is no exception; there have been numerous simulation tools developed over the last two decades to foster research and development activities. With this in mind, this chapter is written to introduce and discuss the current-day open source simulators that are actively used in the field. Using a developer-based feedback, we provide an outline regarding the specific features and capabilities of the open-source simulators, with a special emphasis on how they correspond to recent research trends in humanoid robotics. The discussion is centered around the contemporary requirements in humanoid simulation technologies with regards to future of the field.

Introduction

Owing to their anthropomorphic structure, humanoid robots have the potential to replace humans in a variety of different tasks. The main motivation in pursuing human-like robots is that numerous man-made tasks are in fact quite hazardous and risky for human life; therefore, a robotic counterpart might reduce these undesired Barkan factors to a technologically possible minimum. With this goal in mind, research activities on humanoid robotics have sparked in the last two decades.

In order to foster activities in this popular research area, real-time simulators have been actively used for numerous reasons. Fast and flexible prototyping, optimized actuator selection, robotics education, fail-safe validation of controllers and trajectory planners can be listed as some of the distinctive advantages, gained through simulation software. One can simply generate a virtual world in which a simulated humanoid may realize a series of specific tasks that can be difficult to prototype, test and realize directly in the physical world. Simulators allow us to complete the preliminary part of the research before initiating real-world experiments.

Fig. 1 illustrates the fundamental elements regarding a humanoid robot simulation software. The most basic elements are the humanoid model and the environment model. The humanoid model contains the information that is specific to the simulated humanoid in question, such as, number of DoFs (Degrees of Freedom), mechanical parameters, sensors, geometric structure, actuator properties, etc. The environment model consists of the physical properties of the virtual world. Namely, they are gravitational acceleration, terrain type and surface structure, environmental structures (e.g., walls, furniture, tools), extra objects in addition to the simulated humanoid, ground stiffness and friction.

The simulator should be able to compute all the resultant physical quantities that occur due to dynamic interactions between the simulated robot and the virtual environment for a given period of time. Its degree of realism strongly depends on how the developer accurately characterized the robot-environment dynamics as it is run to obtain the result of simulation. Furthermore, a user-specific controller sends command signals to the simulated humanoid, to make it perform the desired motion pattern. It may also evaluate the simulated robot response by means of real-time feedback.

Simulated humanoid and environment responses are usually output via a GUI screen that may include an animation window to observe robot behavior and taskspecific plots to monitor variations of important parameters.

When looking at Fig. 1, one may realize that the simulation design has two intrinsic challenges: i) development of computationally fast and efficient numeric solvers, ii) precise and accurate dynamics algorithms concerning the simulated robot and environment.

Until the mid 1990s, researchers attempted to overcome the first challenge by implementing alternative computation techniques like parallel algorithms [START_REF] Lint | Communication issues in the design and analysis of parallel algorithms[END_REF][START_REF] Lee | Efficient parallel algorithms for robot forward dynamics computation[END_REF][START_REF] Fijany | A class of parallel algorithms for computation of the manipulator inertia matrix[END_REF][START_REF] Mcmillan | Toward super-real-time simulation of robotic mechanisms using a parallel integration method[END_REF][START_REF] Fijany | Parallel o (log n) algorithms for computation of manipulator forward dynamics[END_REF][START_REF] Aghili | A unified approach for inverse and direct dynamics of constrained multibody systems based on linear projection operator: applications to control and simulation[END_REF]. A recent benchmark study can be found in [START_REF] Yamane | Comparative study on serial and parallel forward dynamics algorithms for kinematic chains[END_REF]. Nevertheless, the fast and ever-growing pace of semiconductor technology paved the way for highly powerful computation tools, and therefore, seeking alternative computation techniques has not been a major issue.

While advances in computer technology enabled humanoid roboticists to overcome the first challenge, it is their cumulative research effort that is responsible for the formulation of precise and accurate dynamics algorithms. A milestone study was conducted by Luh et al. to address an online computation scheme for a robotic manipulator [START_REF] Luh | On-line computation scheme for mechanical manipulator[END_REF]. To mention some other major contributions, Featherstone proposed the articulated body inertia method [START_REF] Featherstone | The calculation of robot dynamics using articulated-body inertias[END_REF]. Khatib proposed operational space formulation to map joint space dynamics into task space dynamics [START_REF] Khatib | A unified approach for motion and force control of robot manipulators: The operational space formulation[END_REF]. Rodriguez made use of Kalman filters to implement powerful dynamics algorithms [START_REF] Rodriguez | Kalman filtering, smoothing, and recursive robot arm forward and inverse dynamics[END_REF]. On top of these prior works, various efficient dynamics algorithms have been developed so far, for instance, see [START_REF] Featherstone | A beginner's guide to 6-d vectors (part 1)[END_REF] as an exemplary recent work.

To the best of authors' knowledge, Walker and Orin composed all the necessary elements in a unifying framework and developed one of the first robotic simulators [START_REF] Walker | Efficient dynamic computer simulation of robotics mechanisms[END_REF]. Researchers followed Walker and Orin's footsteps and derived floating-base dynamics [START_REF] Fujimoto | Robust biped walking with force interaction control between foot and ground[END_REF][START_REF] Nakanishi | Inverse dynamics control with floating base and constraints[END_REF][START_REF] Mistry | Inverse dynamics control of floating base systems using orthogonal decomposition[END_REF], so as to provide real-time humanoid simulation software, e.g., see [START_REF] Nakamura | Humanoid robot simulator for the meti hrp project[END_REF][START_REF] Fujimoto | Simulation of an autonomous biped walking robot including environmental force interaction[END_REF][START_REF] Ivaldi | Tools for simulating humanoid robot dynamics: A survey based on user feedback[END_REF].

Dynamic simulation has become a powerful tool for prototyping complex behaviors and study new robot control algorithms, especially for humanoid robots. The robotics teams competing in the DARPA Robotics Challenge made extensive use of such tools, from simulating contacts and whole-body movements to planning complex manipulations. Many different simulation tools have been developed to meet the needs of the researchers. More generally, the number of tools for dynamics simulation has grown substantially in the last years. In this plethora of existing tools, it is necessary for the humanoid robotics community to have a systematic evaluation to assist in choosing which of the available tools is best for their research. However, qualitative and quantitative evaluations are difficult to find.

To this end, Ivaldi et al. investigated the state of the art in dynamics simulation and reported a user-feedback based survey regarding the use of dynamics simulation tools in the robotics research community [START_REF] Ivaldi | Tools for simulating humanoid robot dynamics: A survey based on user feedback[END_REF]. From the survey, it appears that the major requirements for robotics researchers are better physics engines and opensource software.

Despite the numerous tools for simulation, the analysis of the user-based feedback survey in [START_REF] Ivaldi | Tools for simulating humanoid robot dynamics: A survey based on user feedback[END_REF] indicated that there is not a general-purpose simulator which dominates the others in terms of performance or application. However, for humanoid robotics, Gazebo [START_REF] Koenig | Design and use paradigms for gazebo, an open-source multi-robot simulator[END_REF] emerges as the most popular among the open-source projects, while V-Rep and Webots [START_REF] Rohmer | V-rep: A versatile and scalable robot simulation framework[END_REF][START_REF] Michel | Webots: Professional mobile robot simulation[END_REF] are the preferred commercial simulators considering the user-based survey.

With a complementary point of view, this chapter is centered around a developerbased feedback survey to investigate the state-of-the-art in humanoid robotics simulation and software. It particularly aims to provide an outline concerning the special features and characteristics of current-day open source simulators with regards to the present research trends in humanoid robotics.

The chapter is organized as follows. Section 2 addresses the contemporary simulation scenarios and requirements of research on humanoid robotics. Section 3 lists the prominent open-source simulation packages, with a special emphasis on how they correspond to the current-day simulation scenarios. Physics engines are succinctly discussed in section 4 and structurally distinguished from built-in simulation packages. Finally, the chapter is concluded in section 5.

Contemporary Simulation Scenarios and Requirements

In order to portray the current state-of-the-art, the following questions were answered by the simulator developers. The questions were selected in a way so as to see how the simulators correspond to the current-day simulation requirements.

Actuator-Related Requirements

1. Can the simulator incorporate actuator dynamics?

In an electrically-actuated robotic system, actuator dynamics represent the terms that account for rotor inertia, rotor friction, gear ratio, gear efficiency and electrical characteristics. In a hydraulically or pneumatically-actuated robotic system, more complicated actuator modeling may be required to provide a feasible and accurate simulation. Some simulators consider the actuator as a perfect torque source, while others employ exhaustive dynamics model that include actuator parameters.

Can the simulator incorporate passive compliance?

The recent improvements in mechatronics technology allowed the creation of humanoid robots with physically compliant joints. Incorporation of physical compliance introduces numerous advantages [START_REF] Ugurlu | Bipedal hopping pattern generation for passively compliant humanoids: Exploiting the resonance[END_REF]; however, the dynamics become more complicated due the intrinsic passivity. The characterization of passivity within the dynamics framework poses additional challenges to the simulation.

Is actual torque control available?

In some tasks, the robot is required to interact with the environment and exert a certain amount of force while there may be no specific position reference available. To this end, torque control mode is crucial for realizing the task. Simulator packages that surpass the torque control may solely provide position control option, and thus, cannot be utilized for such tasks.

Environment-Related Requirements

Can the simulator allow the creation of custom environments and terrains?

The ability to create custom terrains and environment is a fundamental feature that enables testing the performance of the robot controller in balancing, locomotion and whole-body movement scenarios. For instance, Fig. 2 displays four snapshots from a simulation study, in which the HRP-4 robot (from AIST, Japan) walks forward through a narrow and unstructured path [START_REF] Bouyarmane | Exploring humanoid robot locomotion capabilities in virtual disaster response scenarios[END_REF].

Can the simulator provide a set of different environments?

A set of built-in environment options is a useful asset for simulation packages.

Options may vary from indoor environments, such as furnished houses and humanoid soccer pitches, to outdoor environments, e.g., muddy/rocky areas, sloped surfaces and template terrains.

6. Can the simulator provide a set of tools and objects in the simulated environment? With the recent DARPA Challenge1 , disaster response applications gained importance. Therefore, humanoid robots are expected to accomplish challenging tasks that involve the active use of various tools and objects in the simulated environment, e.g., driving a car, turning a valve, opening/closing doors, wielding a hose. Fig. 3 displays a simulation scenario in which the humanoid Atlas (from Boston Dynamics, US) is ejecting a hose from its socket.

Can the simulator generate perturbations on the robot?

For robustness tests, one may need to perturb the simulated humanoid robot, for example with an external force applied to an arbitrary point on the robot. The ability to generate custom perturbations and add noise is certainly advantageous to test the robustness of the robot control.

Modeling-Related Requirements

Can the simulator incorporate human model?

The latest developments in human-friendly robotics multiplied the possibilities of safe and sound interactions between humanoids and humans. Physical humanhumanoid interaction and cooperation is an active research field that is more and more requiring simulators that can incorporate human models. Human models can be also useful for ergonomy studies, for example to simulate human efforts in whole-body movements.

Can the simulator simulate multiple humanoids?

The first simulators were usually centered around a single robot model, therefore they were unfit for studying scenarios in which multiple humanoids need to cooperate to accomplish a given task, e.g., soccer-playing humanoids. To address the multi-robot scenarios, most of the recent simulators have the capability of simultaneously simulating multiple robots. An example is displayed in 4, where five NAO humanoids are walking in an office environment.

10. Can the simulator incorporate actual humanoid geometry?

The actual robot geometry may not be an essential factor in dynamic simulation of a humanoid, as long as dynamic and kinematic parameters are accurately included. However, one on one characterization of robot-specific humanoid geometry may be required when considering collisions with the environment. If we give an example, Fig. 3 shows a simulation in which the simulator includes the exact robot geometry. On the contrary, Fig. 5 displays a biped robot with its simulation model in which the actual geometry could not be characterized.

11. Can the simulator enable imports from CAD software?

In order to obtain a fast and accurate simulation model, recent simulation pack- ages allow direct imports from CAD software. This feature not only shortens the exhaustive model building process, but also enables the direct insertion of robotspecific properties, such as link parameters, robot geometry, and joint structure.

12. Does the simulator provide common humanoid models?

In recent years, the use of common hardware platforms gained importance in the field. Atlas, Hubo, Darwin-OP, Nao and HRP-4 maybe listed in this category since they are used as test platforms in several laboratories across the world. With this in mind, most simulators are already equipped with the built-in models of common humanoids. For instance, Fig. 3 and Fig. 6 display the simulated Atlas and Hubo robots in Gazebo and K'lampt environments, respectively.

Interfacing and Computation-Related Requirements

13. Can the simulator run multiple simulations sequentially?

In machine learning-based robot control, multiple experiments should be sequentially conducted. It is favorable for a simulator to automate this process, so as to prevent exhaustive and time-consuming manual tasks.

14. Does the simulator have a MATLAB/Simulink Interface? MATLAB/Simulink provides a flexible and convenient environment in synthesizing controllers, due to its extended library, graphical editor and efficient solvers. Therefore, integration of a MATLAB/Simulink interface enables users to incor-Fig. 6 The humanoid robot Hubo is one of the template models in K'lampt [START_REF] Hauser | Fast interpolation and time-optimization with contact[END_REF]. In this study, both the simulated robot and actual robot simultaneously perform balancing on a single leg. The accuracy of the simulator is verified by comparing the actual sensory data and simulated sensory data. Image courtesy of Kris Hauser.

porate these advantages for humanoid robotic simulations.

15. Does the simulator have a ROS Interface? ROS (Robot Operating System) is the most common open-source meta-operating system for robotics, providing numerous tools and libraries for robotic applications. Due to this fact, simulators often provide a ROS interface to benefit the computational advantages it presents.

Open Source Humanoid Robot Simulators

There is a great diversity of open source robot simulators. In [START_REF] Ivaldi | Tools for simulating humanoid robot dynamics: A survey based on user feedback[END_REF], Ivaldi et al. report more than 40 active software projects. This section will provide an overview of the most important open-source simulators that are relevant for humanoid robotics.

Commercial simulators that allows certain groups (academics, students) to use complete simulation features are also included. The following list follows no specific order. ROCOS (Robot Control Simulator) is a dynamic simulator, developed by Y. Fujimoto and A. Kawamura 2 . It includes models of friction and collision between the robot legs and the floor, and it is mainly used for research in multi-modal locomotion, e.g., running, jumping and walking [START_REF] Fujimoto | Simulation of an autonomous biped walking robot including environmental force interaction[END_REF].

OpenHRP3 (Open Architecture Human-centered Robotics Platform version 3) is an integrated software platform for robot simulations. It was developed in Advanced Institute of Science and Technology (AIST), Japan 3 . It consists of several modules, to name a few, a dynamics simulator, a control interface with the robot, a collision detector and several GUI interfaces. It can be used for a seamless simulation and control of the robot [START_REF] Kanehiro | Openhrp: Open architecture humanoid robotics platform[END_REF][START_REF] Yamane | Parallel o(logn) algorithm for dynamics simulation of humanoid robots[END_REF][START_REF] Nakaoka | Constraint-based dynamics simulator for humanoid robots with shock absorbing mechanisms[END_REF].

Robotran is a software that generates symbolic models of multi-body systems, which can be analyzed and simulated in Matlab and Simulink 4 . It is developed by the Center for Research in Mechatronics, Université Catholique de Louvain [START_REF] Fisette | Robotran: Symbolic generation of multi-body system dynamic equations[END_REF].

Gazebo is a multi-robot simulator for outdoor environments, developed by the Open-Source Robotics Foundation [START_REF] Koenig | Design and use paradigms for gazebo, an open-source multi-robot simulator[END_REF] 5 . It is the official software tool for the DARPA Robotics Challenge [START_REF] Aguero | Inside the virtual robotics challenge: Simulating real-time robotic disaster response[END_REF]. It supports multiple physics engines (ODE, Bullet, DART) and, thanks to its modular and plugin-based structure, can be easily extended with new features. For example, a special plugin (gazebo yarp plugin) has been developed for the simulation of humanoid robots based on YARP, namely iCub and COMAN [START_REF] Hoffman | Yarp based plugins for gazebo simulator[END_REF]. In particular, this development is gradually replacing the previous iCub simulator, iCub SIM, that is based on ODE and is used for prototyping behaviors thanks to its seamless interface (the same code runs on the simulator and on the real robot) 6 .

Morse is a generic simulator for academic research, used to simulate small and large indoor and outdoor environments with multiple robots 7 . It provides interfaces for different mobile robots (not legged) such as the PR2 [START_REF] Echeverria | Simulating complex robotic scenarios with morse[END_REF] 8 .

Klamp't (Kris' Locomotion and Manipulation Planning Toolbox

) is an open source, cross-platform software package for modeling, simulating, and planning for complex robots, particularly for manipulation and locomotion tasks 9 . It has been used by members of Team DRC-Hubo in the DARPA Robotics Challenge. The simulator allows the robust rigid body simulation with triangle mesh / triangle mesh collisions. Furthermore, actual objects can be captured using a Kinect sensor and directly integrated to the simulator [START_REF] Hauser | Fast interpolation and time-optimization with contact[END_REF]. In Fig. 6, a simulated Hubo humanoid in Klamp't environment can be observed.

V-Rep is a simulator with multi-robot simulation capability, produced by Coppelia Robotics 10 . It provides an integrated development environment that is based on a distributed control architecture. Each object/model can be separately controlled via an embedded script, a plug-in, a ROS node, a remote API client, or a user-based Fig. 7 The SL software is configured both as a real-time robot controller and as a simulator for the Hydraulic Quadruped HyQ. Image courtesy of Claudio Semini, Istituto Italiano di Tecnologia.

input. Similar to Gazebo, it supports multiple physics engines (ODE, Bullet, Vortex). It is not open-source but it has an academic license that makes it free for use in research and academia [START_REF] Rohmer | V-rep: A versatile and scalable robot simulation framework[END_REF][START_REF] Freese | Virtual robot experimentation platform v-rep: A versatile 3d robot simulator[END_REF]. See Fig. 4, as an example from V-Rep environment, where multiple NAO humanoids are walking in an office environment.

SL is a simulation and control library developed mainly by S. Schaal and his research group. It was primarily developed as a real-time robot controller, and later augmented to be a physical simulator 11 . Therefore, one can compose a script to simulate robot behavior and then directly use the same code for real-time experimentation [START_REF]The SL simulation and real-time control software package[END_REF][START_REF] Billard | Discovering optimal imitation strategies[END_REF]. Fig. 7 displays a scene in which HyQ is being controlled via SL software.

OpenRave is an environment for simulating motion planning algorithms for robotics. It contains several models of industrial robots and targets robotics automation 12 . It provides many command line tools to simulate robots and motion planners. The run-time core is sufficiently compact to be used within controllers [START_REF] Diankov | OpenRAVE: A Planning Architecture for Autonomous Robotics[END_REF][START_REF] Pham | Time-optimal path parameterization for critically dynamic motions of humanoid robots[END_REF].

RBDL (Rigid Body Dynamics Library) is a dynamics library developed by M. Felis, containing efficient implementations of Featherstone's rigid body dynamics algorithms from his book [START_REF] Featherstone | Rigid Body Dynamics Algorithms[END_REF] 13 . It can load robot models from Lua scripts or URDF files.

Drake is not only a simulator but a toolbox for analyzing the dynamics of robots and building control systems for them 14 . It is maintained by R. Tedrake and his Robot Locomotion Group at the MIT Computer Science and Artificial Intelligence Lab. It has been used for the DARPA Robotics Challenge on their Atlas robot [START_REF] Tedrake | Drake -A planning, control, and analysis toolbox for nonlinear dynamical systems[END_REF].

The NTRT Simulator (NASA Tensegrity Robotics toolkit) is a tensegrityspecific simulator built to run on top of the Bullet Physics Engine15 , version 2.82. It can simulate a wide range of multi-body systems, in particular, compliant systems [START_REF] Caluwaerts | Sun-Spiral. Design and control of compliant tensegrity robots through simulation and hardware validation[END_REF].

SCS (Simulation Construction Set) is a Java library for creating simulators 16 , constructed at the Florida Institute for Human and Machine Cognition (IHMC), US [START_REF] Koolen | Design of a momentum-based control framework and application to the humanoid robot atlas[END_REF]. It makes use of the Featherstone's algorithms for rigid body dynamics.

OpenSim is a toolkit for musculoskeletal modeling and dynamic simulation of movement, developed at Stanford University and supported by the US National Institute of Health and by DARPA 17 . It is freely available, open-source, and extensible through user plug-ins. The physics engine of this project is SimBody [START_REF] Delp | Opensim: Open-source software to create and analyze dynamic simulations of movement[END_REF].

Moby is a multi-rigid body dynamics simulator developed by the Positronics Lab at George Washington University 18 . It supports the simulation of deformable bodies, together with their interactions to rigid/articulated bodies) [START_REF] Drumwright | A fast and stable penalty method for rigid body simulation[END_REF].

In order to provide a comprehensive review on these simulators, a survey that included the questions in section 2 was constructed. Inputs of the developers can be viewed in Tables 1234. Particularly, Table 1 displays the simulators' actuator simulation capabilities. Table 2 shows the way they incorporate environment model and objects in the simulated environment. Table 3 depicts their human and humanoid modeling properties Table 4 reveals their interfacing features.

Open Source Physics Engines

Fig. 8 presents a descriptive classification of the dynamics simulation tools. We can distinguish between physics engines (e.g. ODE, Bullet) and more complex software, that we call here "system simulators" (e.g. Gazebo, V-Rep, iCub SIM) that are based on a physics engine.

System simulators make use of physics engines to simulate the dynamics of the bodies in the environment, but also provide additional features, such as sensor simulation, model editors, GUI interfaces and interaction with the operator. In some cases they also provide specific interfaces that facilitate seamless simulation and control of the robot and its virtual character. For example, they emulate the driver interfaces of the real robot in such a way that code running for the simulators can be Tool Incorporates actuator dynamics Simulates passive compliance Has torque control Fig. 8 From [START_REF] Ivaldi | Tools for simulating humanoid robot dynamics: A survey based on user feedback[END_REF]. A practical classification of the simulation tools.

ROCOS × × OpenHRP3 × Robotran × × × Gazebo × Morse × Klamp't × × × V-Rep × × × Gazebo + YARP × SL × × × OpenRAVE × RBDL × × × Drake × × × iCub SIM × × NASA Tens.RT × × × SCS × × × OpenSim × × × Moby × × ×
ROCOS × OpenHRP3 × × Robotran × Gazebo × × × Morse × × × × Klamp't × × V-Rep × × × × Gazebo + YARP × × × × SL × × × × OpenRAVE × × RBDL × × Drake × × × × iCub SIM × NASA Tens. RT × × SCS × × OpenSim × × × × Moby × ×
ROCOS OpenHRP3 × Robotran × Gazebo × × Morse × × × × Klamp't × × × × V-Rep × × × × Gazebo + YARP × × × × SL × × × × OpenRAVE × × RBDL × Drake × × × × iCub SIM × × NASA Tens. RT × SCS × × × × OpenSim × × × Moby × × × ×
ROCOS OpenHRP3 × Robotran × × Gazebo × Morse × × Klamp't × × V-Rep × × × Gazebo + YARP × × SL × × × OpenRAVE × × × RBDL × × Drake × × × iCub SIM × NASA Tens. RT × SCS × OpenSim × × Moby ×
switched to the robot at no cost. This makes them in practice the "official" tools for some platforms; for example, OpenHRP for the HRP robot series [START_REF] Nakaoka | Constraint-based dynamics simulator for humanoid robots with shock absorbing mechanisms[END_REF], SL simulator for Sarcos humanoid [START_REF]The SL simulation and real-time control software package[END_REF], iCub SIM [START_REF] Tikhanoff | Integration of speech and action in humanoid robots: icub simulation experiments[END_REF] and the more recent Gazebo plugin for iCub [START_REF] Hoffman | Yarp based plugins for gazebo simulator[END_REF].

Physics engines can be discriminated by the way they represent rigid-body structures, see Fig. 8. On one hand, we have software tools like ODE which represents joints as constraints between bodies; on the other hand, we have software like XDE, OpenHRP, which make use of parametrized rigid-body dynamics representations, where joints are simply part of the robotics structure.

The second group benefits from the straightforward computation of quantities that recur in robot control, such as Jacobian and joint-space mass matrices and other physical quantities.

The critical difference between the two classes is in the way contact forces are computed. The first class considers contacts forces as bilateral/unilateral constraints, which are added to the list of constraints used to describe the joints; then the same solver is used to find the forces for the global system, including contacts and joints. In the second class, on the contrary, only constraints from the contacts are solved, which notably simplifies the problem. In general, finding the correct contact forces can be burdensome. Contact modeling and simulation is an area of research on its own, which is actively explored improving contact models [START_REF] Hauser | Fast interpolation and time-optimization with contact[END_REF][START_REF] Drumwright | Extensive analysis of linear complementarity problem (lcp) solver performance on randomly generated rigid body contact problems[END_REF] and solvers [START_REF] Todorov | Mujoco: A physics engine for model-based control[END_REF].

The most prominent physics engines may be listed as ODE, PhysX, Bullet, DART.

ODE 19 (Open Dynamics Engine) is an open-source library for simulating rigid body dynamics, with a built-in collision detector. It is one of the most known multipurpose rigid-body physics engines, used in many computer games and embedded in several simulation tools.

PhysX20 is an engine developed by NVIDIA for enabling real-time physics in video-games. It is optimized for GPU (Graphics Processing Unit) computations.

Bullet 21 is another open-source physics library, mostly used for computer graphics and animation. As ODE and NVidia PhysX, it was a game-oriented engine, that enforced joints constraints numerically. The latest release (v.2.82) also supports Featherstone's Articulated Body Algorithm [START_REF] Featherstone | Handbook of Robotics, chapter Dynamics[END_REF] and a Mixed Linear Complementarity Problem. These make it more suitable for robotics applications, since dynamics is solved in joint coordinates and contacts resolution is more stable.

DART 22 (Dynamic Animation and Robotics Toolkit) is another open-source physics engine, used for robotics and computer graphics. For accuracy, it utilizes Lagrange's equations derived from D'Alembert's principle to describe the dynamics of motion.

SimBody 23 is an open-source C++ API that implements Featherstone's algorithms for rigid body mechanics, with the support of different contact models. The supported contact models are one of the interesting features of SimBody; one can model contacts through Hertz contact theory, the elastic foundation model, and can consider Stribeck friction and Hunt and Crossley dissipation models [START_REF] Canudas De Wit | A new model for control of systems with friction[END_REF].

A quantitative comparison among all simulators is very difficult to obtain, as not all simulators may necessarily have the same features of parameters. However, an interesting comparison among physics engine was recently presented by Erez et al. [START_REF] Erez | Simulation tools for model-based robotics: comparison of bullet, havok, mujoco, ode and physx[END_REF], where the authors compared the performances of MuJoCo24 with Bullet, Havok, ODE and PhysX. According to their study, MuJoCo, which is optimized for robot simulation, outperforms the other engines. We report, for the interested reader, the results of their comparisons on a simulated humanoid robot in Fig. 9: from left to right, the first image is the simulated humanoid in contact with the floor, the second image is the plot of the raw speed as thousands of evaluations per second for each engine, while the third image is the plot of speed-accuracy trade-offs. More details can be found in [START_REF] Erez | Simulation tools for model-based robotics: comparison of bullet, havok, mujoco, ode and physx[END_REF].

Concluding Remarks

In this chapter, we provide an outline for the current state of the art in open source humanoid simulation software libraries, with a special emphasis on the simulation requirements for the main ongoing researches. The discussion is centered around a survey that was answered by the developers, where we investigated how the simulators address the needs of recent research trends in the field. Based on the survey inputs, we summarized the listed simulators' special features and capabilities. In addition, a succinct review was addressed regarding the open source physics engines that are at the basis of the current simulators and may be used to build new simulation software. This chapter does not discuss how close to reality is a simulator. Such an assessment should require a detailed and exhaustive study using the real humanoid robots and their simulated models, to assess the so called reality gap. Comparing the actual and simulated sensory output, it is possible to assess the reality gap for a given simulator. For instance, researchers performed simultaneous one-legged balancing experiments on a real Hubo humanoid and its Klamp't model; see Fig. 6. Although the aforementioned study may be enriched with the addition of more dynamic motion patterns (walking, jumping, pushing) for further investigation, it could be taken as a basis for other simulator assessment studies.

The field of simulation software development is quite active and vibrant. More often than not, a new simulator software is presented to the community or additional improvements are announced on the existing ones. Keeping this in mind, this chapter is written to capture the current-day simulation requirements, with a glimpse on the possible future trends in the field.

Indeed, current simulators are quite advanced; however there is room for further improvement in simulating complex systems. For instance, soft actuators, deformable materials and generally soft-bodied robots are still difficult to simulate [START_REF] Largilliere | Real-time Control of Soft-Robots using Asynchronous Finite Element Modeling[END_REF]. The reality gap for soft systems and actuators being larger, it could be interesting to compensate the inaccurate simulation with better control and machine learning techniques; e.g., designing controllers in simulation that are robust to differences to the real robot [START_REF] Del | Robustness to Joint-Torque Tracking Errors in Task-Space Inverse Dynamics[END_REF], or learning the difference between simulation and reality [START_REF] Cully | Robots that can adapt like animals[END_REF], that was recently proven effective also for damaged robots.

Another point to improve for simulators is to increase computational efficiency, such that they can be used in real-time for remote control applications or in modelpredictive control schemes. This a crucial feature that is particularly important for humanoid robots performing complex whole-body dynamic movements [START_REF] Ivaldi | Special issue on whole-body control of contacts and dynamics for humanoid robots[END_REF].

Currently, it is difficult to single out a certain simulator software that is capable of addressing all the requirements. Despite this fact, we strongly believe that this chapter provides guidance to the interested researcher in choosing the right simulator package that suits to his/her specific research objectives.

Fig. 1

 1 Fig.1Fundamental elements of a humanoid robot simulator.

Fig. 2

 2 Fig.2Simulated HRP-4 humanoid is crumbling through an unstructured and narrow path[START_REF] Bouyarmane | Exploring humanoid robot locomotion capabilities in virtual disaster response scenarios[END_REF]. Image courtesy of Karim Bouyarmane.

Fig. 3

 3 Fig. 3 Simulated Atlas humanoid is ejecting a hose from its socket. Image courtesy of DARPA. (http://www.DARPA.mil/)

Fig. 4

 4 Fig. 4 Multiple NAO humanoids are walking in an office environment in V-Rep. In this scenario, one of the robots' vision is reflected to the projector. Image courtesy of Marc Freese, Coppelia Robotics.

Fig. 5 a

 5 Fig.5a) The bipedal robot MARI-3, b) MARI-3's ROCOS model[START_REF] Ugurlu | A unified control frame for stable bipedal walking[END_REF]. The actual robot geometry is not characterized; however, the simulation model is able to represent locomotion behavior of MARI-3.

Fig. 9

 9 Fig.9Image courtesy of Tom Erez, Yuval Tassa and Emanuel Todorov[START_REF] Erez | Simulation tools for model-based robotics: comparison of bullet, havok, mujoco, ode and physx[END_REF]. The first image illustrates the simulated humanoid. The second image shows raw speed as thousands of evaluations per second for each engine. The third image shows the speed-accuracy trade-off in terms of our consistency measure.

  UgurluDept. of Mechanical Engineering, Ozyegin University, 34794 Istanbul, Turkey e-mail: barkan.ugurlu@ozyegin.edu.tr

		Robot and Environment Dynamics
	Command		Environmental
	Signal		Interaction
	User-speci c	Humanoid	Environment
	Controller	Model	Model
	Simulated Sensory
	Information and Robot Response	Simulated Environment Response
		GUI-based Output
		(Animation, data plot)

Table 1

 1 Actuation simulation capabilities.

	Tool	Can create new envi-	Provides a set of differ-	Provides tools in the	Can generate perturba-
		ronments and terrains	ent environments	environment	tions on the robot

Table 2

 2 Simulation of the environment.

	Tool	Incorporates	Simulate multiple	Includes robot ge-	Can import from	Includes common
		human model	humanoids	ometry	CAD	humanoid models

Table 3

 3 Human and humanoid models.

	Tool	Can run multiple simulations	Has a Matlab/Simulink inter-	Has a ROS interface
		in sequence	face

Table 4

 4 Simulation interfaces.

		simulation tools
	physics engines	used in	system simulators
	computations in joint coord. MuJoCo XDE Bullet >= v.2.82	computations in Cartesian coord. (joints as constraints) ODE	seamless robot simulation/control (dedicated) OpenHRP	generic Robotran Gazebo V-Rep
		Bullet < v. 2.82		iCub_SIM
			Gazebo + iCub_plugin

http://www.theroboticschallenge.org/

http://www.kawalab.dnj.ynu.ac.jp

http://www.openrtp.jp/openhrp3/jp/

http://www.robotran.be/

www.gazebosim.org

http://eris.liralab.it/wiki/Simulator README

http://morse-simulator.github.io/

https://github.com/morse-simulator/morse

http://klampt.org

http://www.coppeliarobotics.com

https://www-clmc.usc.edu/sschaal/pub/RSS-SL.key.pdf

http://openrave.org/

http://rbdl.bitbucket.org

http://drake.mit.edu

https://github.com/NASA-Tensegrity-Robotics-Toolkit

https://ihmcrobotics.github.io

http://opensim.stanford.edu

https://github.com/PositronicsLab/Moby

http://www.ode.org/

https://developer.nvidia.com/physx-sdk

http://bulletphysics.org/wordpress/

http://dartsim.github.io/

https://simtk.org/home/simbody/

http://www.mujoco.org/


Chunlin Shi

Yang Liu

Shouli Xuan

Zhiqing Jin

A Soil Water Simulation Model For Wheat Field With Temporary Ditches

Keywords: water, simulation model, wheat field, temporary field ditches

Accurate soil water content simulation is the basis of disaster earlywarning and evaluation about waterlogging and drought. In order to more accurately simulation the water movement in wheat field with temporary field ditches, a two-dimension soil water simulation model was developed in this study. The model included the water movement vertically(up and down) and horizontally( ribbing and ditch), and traditional runoff estimation was replaced by calculating the drainage water from ditches. The model could simulate the comprehensive effect of depth of plow layer, initial soil water content, precipitation intensity and infiltration rate of plow pan layer on runoff. The application of the model in Xinhua city, China showed good agreement between observation with simulation values.

Introduction

Waterlogging is one of the major agrometeorological disasters in wheat production in middle and lower valley of Yangtze river due to consecutive rainfall and weak infiltration of the plow pan because of the prevailing rice-wheat rotation cropping system [START_REF] Li | Waterlogging pretreatment during vegetative growth improves tolerance to waterlogging after anthesis in wheat[END_REF][START_REF] Jin | An early warning system to predict waterlogging injuries for winter wheat in the Yangtze-Huai Plain (WWWS)[END_REF]. The simulation on soil water content could provide support for waterlogging forecast and yield loss evaluation. Now there are many models to simulate the soil water content. In some models, such as CERES-wheat, VSMB, without considering the weak infiltration of plow pan, all excessive water will infiltrate to the next layer. So they could not simulate the waterlogging condition [START_REF] Baier | Soil moisture modeling: Conception and Evaluation of the VSMB[END_REF][START_REF] Ritchie | Description and performance of CERES-wheat:a user-oriented wheat yield mode1[END_REF]. Some models could simulate waterlogging, such as SWATRE [START_REF] Belmans | Simulation model of the water balance of a cropped soil:SWATRE[END_REF], DRAINMOD [START_REF] Cox | Field evaluation of DRAINMOD for predicting waterlogging intensity and drain performance in south-western Australia[END_REF], DHSVM [START_REF] Wigmosta | A distributed hydrology-vegetation model for complex terrain[END_REF], but they always were used to simulate the movement of soil solutions and water management at regional scales, so they ignored the diffusion of water. Lv (1998)simulated the dynamic change of soil water content at Zhejiang province with MARCOS model [START_REF] Jun | Simulation of water balance in crop growth field[END_REF]. Recently, some models based on artificial neural networks have been developed to simulate the soil water content [START_REF] Elshorbagy | On the relevance of using artificial neural networks for estimating soil moisture content[END_REF][START_REF] Si | Modeling soil water content in extreme arid area using an adaptive neuro-fuzzy inference system[END_REF]. Such models could simulate the water content changes, but their explanations in soil water movement is poor and their applicability in easy waterlogging regions have no enough evidences. With the development of drip irrigation in arid regions, some models could simulated the water movement in vertical and horizontal directions [START_REF] Tian | A two-dimensional Richards equation solver based on CVODE for variably saturated soil water movement[END_REF][START_REF] Mohammad N El-Nesr | HYDRUS simulations of the effects of dual-drip subsurface irrigation and a physical barrier on water movement and solute transport in soils[END_REF].

Most of above studies think water movement only at vertical direction. But many temporary field ditches were digged for drainage in wheat production in middle and lower valley of Yangtze river [START_REF] Guo | Wheat crop science and technology in Jiangsu province[END_REF]. So above models maybe cannot accurately express water movement character in easy waterlogging regions. It is necessary to develop a model to simulate the soil water movement which can realize the role of the temporary filed ditches on drainage.

The objective of this paper aims to develop a 2-Dimension model to simulate water movement which will suit to wheat field with temporary ditches at rainy regions. The study will provide the basis for the evaluation of waterlogging disaster and optimization of water management.

Model description

Rice-wheat rotation is a common cropping system in the middle and lower valley of the Yangtze river. Because of rice production, the infiltration rate of the plow pan layer is weak. When the rainfall is enough large in wheat production, the rainfall will be held up at the top of the plow pan and led to waterlogging damage to wheat plants, so in order to drain excess water, some temporary ditches were digged along the plant direction (Fig. 1).

Fig. 1. Sketch map of temporary ditches in wheat field

Water movement character and Equation

Wheat field soil could be divided into three layers, plow layer, plow pan layer and underneath layer (Fig. 2). Rainfall will be absorbed by plow layer soil until its water content reach field capacity, the sparse and porous soil texture in plow layer led the excess rainfall can infiltrate easily to the bottom of the plow layer. But the tighten plow pan layer will led to weak infiltration, the rainfall will accumulate at the bottom of the plow layer, and water content at the bottom of plow layer will increase to saturation condition, thus waterlogging will happen. The saturation water will infiltrate though the plow pan layer or move to the temporary field ditches simultaneously.

Fig. 2. Soil profile for the model

In order to better describe water movement, the soil profile was divided into six layers. The plow layer include two layers, which was determined as the depth of the ditch. Plow pan layer is look as one layer and the underneath layer include three layers. The depth of last layer is changed with the depth of water table.

The movement equation of soil water is listed as below:

Plow Layer 1:

RO RR E i D E i DR P dt i dW D V a        1 1 ) 1 , ( ) 1 , ( ) 1 , ( Plow Layer 2 2 2 ) 2 , ( ) 2 , ( ) 1 , ( ) 2 , ( RR E i D i DR i DR dt i dW D V      Plow Pan Layer ) 3 , ( ) 3 , ( ) 2 , ( ) 3 , ( 3 i D i DR i DR dt i dW D    (1) Underneath Layer1 ) 4 , ( ) 4 , ( ) 3 , ( ) 4 , ( 4 i D i DR i DR dt i dW D    Underneath Layer2 ) 5 , ( ) 5 , ( ) 4 , ( ) 5 , ( 5 i D i DR i DR dt i dW D    Underneath Layer3 ) 6 , ( ) 6 , ( ) 5 , ( ) 6 , ( 6 i D i DR i DR dt i dW D   
where Di is the depth of the ith layer, W(i,j) is the volumn water content at grid (i,j). P is rainfall, Ea is soil evaporation, Ev is crop transpiration. DR(i,j) is drainage amount at grid (i,j), RO is runoff and D (i,j) is the water diffusion at grid (i,j). j is the layer number, and i is the horizontal position for grids.

Precipitation infiltration

In plow layer, similar to other water balance models, we also assume that redundant water will all transfer to next layer when the water content is bigger than filed capacity until the top of the plow pan layer. When the excess precipitation led to soil saturation condition for soil at the bottom of the plow layer, water will move to temporary field ditches and infiltrate through to plow pan layer simultaneously. The infiltration amount (INF) is calculated using equation ( 2):

       0 0 0 ) ( INF DT DT DT a DT 0 0 DT DT DT DT   (2)
where DT is the depth of saturated layer at the bottom of the plow layer, DT 0 is maximum thickness of saturated layer for all infiltration in a day. a is coefficient, which relates to soil texture. The DTo in equation 2 was set to 2mm in this study.

Water diffusion

Water always moves from the positions with high hydraulic potential to those with low hydraulic potential. so when grid (i,j) has difference in water potential with nearby girds, including grids (i-1,j),(i+1,j),(i,j-1) and (i,j-1), water diffusion will happen. The diffusion amount can be expressed as equ.(3):

) /( ) ( ) /( ) ( ) , ( 1 , 1 , 1 , , 1             i i j i j i h i i j i j i h Z Z K Z Z K j i D     ) /( ) ( ) /( ) ( 1 1 , , 1 , 1 ,             i i j i j i V i i j i j i V D D K D D K     (3)
where Kv and Kh are the soil vertical and horizontal unsaturated hydraulic conductivity, respectively. Ψis water potential and the subscribe expresses the position of grids. Di is depth of the ith layer and Zi was horizontal distance of grid i.

Runoff

Because the intensity of rainfall is small during the wheat growth season and the big roughness of wheat field, the surface runoff happens only when the plow layer was statured for the grids on the top of plow layer and the runoff values equal to the precipitation minus soil absorbable water. At the ditches, the water moved to ditches and precipitation minus infiltration amount and soil absorbable water will be leak out as runoff. The soil absorbable water equals to the saturation water content minus current soil water content.

Calculation of evapotranspiration

The potential reference evapotranspiration is calculated as Priestley-Taylor model [START_REF] Ritchie | Description and performance of CERES-wheat:a user-oriented wheat yield mode1[END_REF]:

) 29 ( ) 00437 . 0 00488 . 0 (        T ALBEDO SR K ET S P (4)
where, SR is daily solar radiation (MJ/M 2 /D), ALBEDO was field reflection coefficient. T is mean air temperature,equals to 0.6TM+0.4TN. TM and TN are the maximal and minimal temperatures respectively. K S is temperature coefficient, calculated as below equ [START_REF] Belmans | Simulation model of the water balance of a cropped soil:SWATRE[END_REF].. where,  is the reflection coefficient for bare soil, is set to 0.2.

The potential field evapotranspiration is calculated as equ [START_REF] Wigmosta | A distributed hydrology-vegetation model for complex terrain[END_REF] [START_REF] Jichao | A soil-water balance model under waterlogging condition in winter wheat[END_REF][START_REF] Brission | Agrometeorological soil water balance for crop simulation models[END_REF][START_REF] Paraskevas | Evapotranspiration and Simulation of soil water movement in small area vegetation[END_REF]:

            P C P C C P C ET K ET K L K ET ET 5 . 3 ) 5 . 1 5 ( ) 1 ( 5 5 5 . 1 5 . 1     L L L (7)
where, K C is maximal crop coefficient.

The potential soil transpiration is calculated as

) exp( L ET E P P    
,δis extinction coefficient and set to 0.5 in this study. the crop evaporation is ET C -E P . The actual soil transpiration is:

           0 P F P P P a W W W W E E E P F P F W W W W W W W     (8)
where, W P and W F are soil water content for wilting point and field capacity.

Depth of water table

The depth changed as below equ.(9):

) 6 ( / ) ) 6 , ( ( 2 ) 6 ,
( Z WSD i W K i DR WT V      (9)
where ΔWT is change amount of water table, DR (i,6) is infiltration amount of grid (i,6), Kv is vertical diffuse coefficient, WSD is saturation soil water content for underneath layer. Z( 6) is the depth of 6th layer.

Considering the movement of underneath water, the change of water table could use the mean value ofΔWT at each point.

Model Application

Soil parameter description

Xinhua City located at Jiangsu province is the easy waterlogging area because the elevation is the lowest in Lixiahe plain. According to filed investigation, the depth and its attribution for each layer is list as Table1

  / 3 2 ) (      e V h KS K K    ) ( S e W W  
where, K h and K V are horizontal and vertical unsaturated hydraulic conductivity. KS is saturated hydraulic conductivity. Ψ and Ψe are hydraulic potential and saturated hydraulic potential.

Simulation

Using soil water content and water table observation data in 2012-2013 from Xinhua agrometeorology experiment station (32.95°N, 119.82°E), the model was validated. Because the wheat planted on Oct. 29, 2012, the model simulated from Oct. 20, 2012. The simulated water table change with observed values in good agreement. But the simulated soil water content was lower than observed values during before winter and after wheat heading. The possible reason for this phenomena maybe be the incorrect parameters input (Fig. 3 and Fig. 4). In previous studies, the runoff always was estimated from the CN method proposed by department of agriculture, USA [START_REF] Ritchie | Description and performance of CERES-wheat:a user-oriented wheat yield mode1[END_REF][START_REF] Wigmosta | A distributed hydrology-vegetation model for complex terrain[END_REF]. Such method considered the effect of soil texture, initial soil water content on the runoff. But it could not describe the impact of precipitation intensity and depth of plow layer. In this study, we consider the all above factors, so it is more reasonable.

The water movement always were considered only in vertical direction in previous models [START_REF] Baier | Soil moisture modeling: Conception and Evaluation of the VSMB[END_REF][START_REF] Ritchie | Description and performance of CERES-wheat:a user-oriented wheat yield mode1[END_REF][START_REF] Wigmosta | A distributed hydrology-vegetation model for complex terrain[END_REF]. This paper expanded it to 2-D space which could be suited to most area in wheat field. But at the both sides of wheat field, several connected ditches with different depth makes the simulation of soil water more complex [START_REF] Jun | Simulation of water balance in crop growth field[END_REF]. So further study should be needed to explore water movement wholly in wheat field.

Although there are some other simple models or analytical models to describe the water movement [START_REF] Qian | Development of the universal and simplified soil model coupling heat and water transport[END_REF][START_REF] Shani | Plant response to the soil environment: An analytical model integrating yield, waterm soil type and salinity[END_REF], they always could not suit to different ecological conditions.

Fig. 3 .

 3 Fig. 3. The change of the depth of water table for 2012-2013 wheat production season

Fig. 4 .

 4 Fig. 4. The change of soil water content in layer 1 for 2012-2013 wheat production season

Acknowledgment

This study was funded by the Special Fund for Agro-scientific Research in the Public Interest (201203032), Jiangsu Province Science and Technology Support Program (BE2012391), and the Fund for Independent Innovation of Agricultural Sciences in Jiangsu Province (CX(12)3055).


Xiaojing Tian

Jun Wang
email: jwang@zju.edu.cn

Jutian Yang
email: jutianyang988@163.com

Shien Chen

Zhongren Ma

Study on the Mutton Freshness Using Multivariate Analysis Based on Texture Characteristics

Keywords: Mutton, Freshness, Texture parameters, multivariate analysis

Aiming at discrimination and prediction of mutton freshness by texture profile, the texture parameters of mutton stored at 1℃, 4℃ and Room temperature were analyzed. The analysis methods of Canonical Discriminant Analysis (CDA) and Principal component analysis (PCA) were used to analyze texture parameters of mutton. The results of PCA showed that mutton sample stored at three temperatures clustered into groups according to their freshness, changing along the direction of PC1. Better classification results were found by CDA. The changing trends of mutton freshness were described by Multiple Linear Regression (MLR) and Partial Least Square analysis (PLS), and effective predictive models were found for indices of days stored, TVB-N and pH using texture parameters. With optimum analysis methods, texture parameters could classify and predict freshness of mutton stored at three temperatures. Texture profiles were proved to be a fast and objective tool for the prediction of mutton freshness.

Introduction

It is well known that a country's meat consumption and demands for healthier diets increase reflect its degree of development. Trends for healthier meat could result in increasing demands for meat with high protein, low fat and good taste, such as mutton, which could lead to a diet with better nutritional (lower fat and cholesterol) [START_REF] Park | Moisture, total fat and cholesterol in goat organ and muscle meat[END_REF][START_REF] Schweigert | A summary of the nutrient content of meat: American Meat Institute Foundation[END_REF] and sensorial features (flavor, juiciness, tenderness). The consumption of mutton has a significant increase in China in recent years. However, the shelf life of mutton sold as whole carcasses or as bone-in cubes at retail store is subject to variation of storage temperatures, contamination of microorganisms, enzymes and the processing conditions, which damage the functional, sensoral and nutritional quality of mutton [START_REF] Sonia | Protein and lipid oxidation in Longissimus dorsi and dry cured loin from Iberian pigs as affected by crossbreeding and diet[END_REF]. It is of great importance to monitor the changes of mutton freshness during storage.

Several techniques are used to monitor the deterioration of meat, including the use of microbiological techniques, detection of metabolite concentrations, spectroscopy methods, electronic tongue and electronic nose, and sensorial panels. The microbiological techniques were used to evaluate consequences of bacteria-bacteria interactions [START_REF] Elisabeth | Bacterial spoilage of meat and cured meat products[END_REF][START_REF] Gill | Meat spoilage and evaluation of the potential storage life of fresh meat[END_REF], monitor the spoilage and freshness of meat, and study on the initial microflora and total number of bacteria changes storaged at different temperatures [START_REF] Peng | Study on the initial microflora and its changs of chilling pork meat[END_REF]. For spectroscopy methods, near infrared reflectance spectroscopy (NIR) [START_REF] Grau Raúl | Nondestructive assessment of freshness in packaged sliced chicken breasts using SW-NIR spectroscopy[END_REF], fourier transform near infrared spectroscopy [START_REF] Quansheng | Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy[END_REF] and fluorescence spectroscopy [START_REF] Oto | Non-destructive evaluation of ATP content and plate count on pork meat surface by fluorescence spectroscopy[END_REF] were shown to be effective for meat freshness assessment. The metabolic concentrations of protein, lipids, fat acid, carbohydrates, such as ATP [START_REF] Oto | Non-destructive evaluation of ATP content and plate count on pork meat surface by fluorescence spectroscopy[END_REF], glucose and derived compounds or biogenic amines [START_REF] Aiping | Study On TVB-N Assay for Freshness Index of Chilled Beef by Compound Preseruing Technique[END_REF], detected by spectroscopy methods and physicochemical analysis, were used to express meat freshness. The electronic tongue and electronic nose showed their ability in freshness evaluation of pork [START_REF] Luis | Monitoring of physical-chemical and microbiological changes in fresh pork meat under cold storage by means of a potentiometric electronic tongue[END_REF][START_REF] Musatov | Assessment of meat freshness with metal oxide sensor microarray electronic nose: A practical approach[END_REF] and beef [START_REF] Xuezhen | Discrimination and prediction of multiple beef freshness indexes based on electronic nose[END_REF]. The senses of taste, smell, touch, sight and hearing were used to describe food properties by sensorial panels. The juiciness, tenderness, flavor and color of meat were determined by sensorial panels [START_REF] Jayasingh | Sensory Evaluation of Ground Beef Stored in High-oxygen Modified Atmosphere Packaging[END_REF][START_REF] Salinas | Monitoring of chicken meat freshness by means of a colorimetric sensor array[END_REF]. However, there were few works done on the texture changes along with freshness of meat.

For the physical properties, the texture profiles, as combination of sensory perception, mechanical properties and geometric features, could directly describe the organization status of food texture characteristics in the form of numerical description. Texture Profile Analysis (TPA) [START_REF] Mochizuki | Texture profile analysis[END_REF] is a very useful method for texture analysis. The texture parameters of hardness, springiness, chewiness and cohesiveness were obtained by TPA, and these parameters were used to describe the eating quality of food and freshness assessment of fruits, fishes and meat. The relationships between TPA parameters of meat and sensory characteristics were studied aiming at finding the correlation of texture parameters and sensory analysis [START_REF] Caine | Relationship of texture profile analysis and Warner-Bratzler shear force with sensory characteristics of beef rib steaks[END_REF][START_REF] Ruiz De Huidobro | A comparison between two methods (Warner-Bratzler and texture profile analysis) for testing either raw meat or cooked meat[END_REF]. TPA was used to monitor food quality and control of food quality, food shelf life [START_REF] Xiaojing | Study on freshness of chicken based on texture characteristics[END_REF] and consumer preference studies, modification formulation and processing process [START_REF] Martinez | Texture profile analysis of meat products treated with commercial liquid smoke flavourings[END_REF].

The studies of meat texture were mostly on the differentiation and substitution of sensory perception of raw meat and processed meat products. However, little detailed information is available on quantitative determination of physicochemical parameters of meat freshness, and correlation of texture parameters with physicochemical indexes [START_REF] Herrero | Tensile properties of cooked meat sausages and their correlation with texture profile analysis (TPA) parameters and physico-chemical characteristics[END_REF][START_REF] Ewelina | Sensory quality of selected raw ripened meat products[END_REF], except for the relationship between freshness and elasticity of fish [START_REF] Te | Relationship between the freshness and elasticity of chicken[END_REF] and chicken [START_REF] Zhenxing | The relationship between freshness and elasticity of fish[END_REF].

The potential use of Texture Profile Analysis for detection of mutton freshness was conduncted by texture analyzer in this study. The objectives were: (1) investigation the use of a texture analyzer to monitor the freshness of mutton stored at temperatures of 1℃, 4℃ and room temperature, with the help of multivariate analysis methods, (2) build models for the prediction indicatiors of mutton freshness, (3) develop an objective method for detection of mutton freshness.

2

Experiments and Methods

Experimental Samples

All the meat samples used were obtained from local market. For pretreatment, the fat and connective tissues were removed before detection, and the mutton samples were cut into cubes of 20 mm ×20 mm×15 mm. Cuboids of mutton were stored at 1 ℃, 4 ℃ and room temperature of 20 ±2 ℃, respectively. The samples were brought to room temperature before analyzed by TPA and physichemical methods for freshness indicatiors. Samples detected at the day they were taken to the lab were named d0, samples which were stored for 1 day were named d1, samples which were stored for 2 day were named d2, and so on d3, d4, d5, d6, d7, d8, d9, d10, and d11.

Experimental Methods

The texture analyzer of TMS-Pro ( Food Technology Corporation, USA) , equipped with a 1000 N load cell, was used for texture measurements. The software of V1.13-002 Texture Lab Pro ( Food Technology Corporation, Virginia,USA) was used to acquire and anlyze data. TPA analysis was conduceted according to the method of Tian [START_REF] Xiaojing | Study on freshness of chicken based on texture characteristics[END_REF] with some modifications. All the measurements were carried out with 2 compression cycle test, with cylinder probe of 15 mm diameter, 40% compression of the original sample height. 5 s was left between the two compression cycles. The force-time deformation curves of mutton sample were gained with 1 N trigger force at speed of 60 mm min -1 . The hardness, chewiness, cohesiveness, springiness and gumminess of mutton were calculated from the force-displacement curve for each sample. Eight replicates were measured for TPA parameters of mutton for each treatment.

For tenderness, a digital muscle tenderness meter (C -LM3, Tenovo international co., Limited, China) was used to measure the tenderness of treated mutton samples. Tenderness measurement was evaluated with three replicates by cutting through the sample perpendicular to fibre direction, and expressed in form of shear force. Shear force was calculated as the average shear force of three samples.

As freshness indicator of mutton, content of total volatile basic nitrogen (TVB-N) was detected according to the method of semimicro-Kjeldahl determination in GB_T 5009.44[25] and and pH was detected according to the method in GB/T 9695.5[26]. 3 duplicates were conducted for each sample, and the average of 3 duplicates were used.

For data processing, the texture characteristic parameters of mutton were analyzed by multivariarate data analysis methods to discriminate the samples according to the freshness of mutton. Principal Component Analysis and Canonical Discriminant Analysis were used for visualization of mutton with different freshness using texture parameters. The changing trends of mutton freshness were described by Multiple Linear Regression and Partial Least Square analysis, and the predictive results were compared. For data processing, the software program of the SAS (V8, SAS Institute Inc., Gary, USA) was used.

Results and Discussion

3.1Changes of mutton freshness

The deterioration of mutton samples was monitored by TVB-N, which is the typical indicator of meat freshness. pH of the sample was detected as a reference indicator for mutton freshness. The changing trends of TVB-N and pH of mutton samples stored at 1 ℃, 4 ℃ and room temperature were shown in Fig. 1 and Fig. 2. The value of TVB-N exceeded the limit level of 25 mg/100g at the 2 nd day for samples stored at room temperature, the 8 th day for samples stored at 4 ℃ and 1 ℃. The pH exceeded over the limit level of 6.7 at the 2nd day for samples stored at room temperature, the 7 th day for samples stored at 4 ℃, and the 8 th day for samples stored at 1 ℃. The TVB-N and pH of mutton stored at three different temperatures increased with storage time. Faster changes were observed in higher storage temperature, which is in accordance with the changes of meat freshness by Nan [START_REF] Qingxian | Manual for meat industry[END_REF]. Of all the variables measured, the values of hardness, chewiness, springiness all decreased over days at three storage temperatures. The shear force increased as the freshness of mutton decreased, indicating the tenderness decreased with the increasing of days stored. The higher the storage temperature was, the faster these parameters changed. Same decreasing trends of hardness, chewiness and springiness to storage time were observed for grass carp [START_REF] Rui | Study on texture changes of grass carp frozen at different temperaturea with different treatment[END_REF] and chicken [START_REF] Xiaojing | Study on freshness of chicken based on texture characteristics[END_REF].

Discrimination of mutton freshness by PCA and CDA

For discrimination of mutton freshness changed along storage, PCA and CDA were used to cluster mutton samples. Hardness, chewiness, springiness and cohesiveness, obtained by texture analysis, were used as independent variables to perform the PCA and CDA analysis by SAS V8. The results of PCA for mutton samples stored at three different storage temperatures were shown in Fig. 5. PC1 and PC2 accumulated 90.50%, 94.58% and 92.14% of the total variance for mutton stored at room temperature, 4℃ and 1℃, respectively. As shown in Fig. 5a, with PCA method, mutton samples stored at room temperature clustered into groups according to the days stored, although samples of d2 overlapped with that of d3. The freshness of mutton was clearly discriminated into three groups, with d0 asfresh group, d1 as secondary fresh group, d2, d3 and d4 as deteriorated meat, which were same with freshness level indicated by TVB-N. As shown in Fig. 5b, with PCA method, the mutton samples stored at 4℃ also clustered into three groups according to the freshness of mutton, with samples of d0-d2 in the fresh group, samples of d3-d7 in the secondary fresh group, and samples of d8-d9 into the group of deteriorated meat, although samples of two adjacent days overlapped with each other. The PCA results of mutton samples stored at 1℃ were shown in Fig. 5c, an obvious deteriorating trend was found for clusters of mutton with different freshness, although samples stored at 1℃ overlapped for the adjacent 2 to 3 days.

All the mutton samples were grouped into distinct clusters according to their freshness by the method of PCA, except that some samples of two adjacent days overlapped with each other. What's more, in the decreasing direction of PC1, an obvious deteriorating trend was found for clusters of mutton with different freshness for three storage temperatures. Mutton with different freshness could be discriminated by PCA. The results of CDA for mutton samples stored at three different storage temperatures were shown in Fig. 6. The first two CANs explained 99.18%, 95.94% and 97.77% of the total variance of 100% for mutton stored at room temperature, 4℃ and 1℃, respectively. As shown in Fig. 6a, samples stored at room temperature were grouped into 5 clusters according to the days stored, with d0 as the fresh grade, d1 as the secondary freshness, d2, d3 and d4 as the deteriorated meat. In Fig. 6b and Fig. 6c, similar results were obtained. Mutton samples were grouped into 3 groups according to the freshness indicated by TVB-N value, except that few samples located into the wrong groups. With CDA, better discrimination results were found. In the descending direction of CAN1, a decreasing trend was found for freshness of mutton for three storage temperatures.

Mutton samples stored at three storage temperatures were classified according to their freshness, indicated by TVB-N, by methods of PCA and CDA, and better classification results were found by CDA.

Prediction of mutton freshness using texture characteristic parameters

Aiming at studying the relationship between texture parameters and freshness of mutton, the method of multiple linear regressions (MLR) and partial least square regression (PLS) were used, with texture parameters as input data. The predictive results were compared to find better prediction model. The correlation coefficient of R 2 and root mean square error of RMSE were used to estimate the performance of predictive models. Lower RMSE and larger R 2 always lead to better calibration models.

For data sets containing mutton samples, 72 (6×12) samples for calibration and 24 (2×12) for validation for mutton stored at 1 ℃, 54 (6×9) for calibration and 18 (2 ×9) for validation for mutton stored at 4 ℃, 24 (6×4) for calibration and 8 (2×4) for validation for mutton stored at room temperature, were used to bulid the prediction model for mutton freshness, and the predictive model were validated.

With texture parameters as independent variables, and days stored, TVB-N and pH as dependent variables, the multivariate projection method of PLS was conducted with leave-one-out technique. The accuracy was estimated using R 2 and RMSE. As shown in Table 1, good correlations for calibration data set were found between texture parameters and freshness indicators with R 2 ≥0.890 for TVB-N, R 2 ≥0.891 for pH, R 2 ≥0.941 for days stored at three storage temperatures. The values of R 2 increased with the rising of storage temperature from 1 ℃ to 4 ℃ and then to room temperature. When these models were used to predict mutton freshness for samples in the test set, the lowest R 2 for pH, TVB-N and days stored (0.821, 0.772 and 0.831) were found for samples stored at 4 ℃. In the former studies, chemical parameters were successful predicted by method of PLS [START_REF] Apetrei | Using an e-tongue based on voltammetric electrodes to discriminate among red wines aged in oak barrels or aged using alternative methods: Correlation between electrochemical signals and analytical parameters[END_REF][START_REF] Alisa | Instrumental measurement of beer taste attributes using an electronic tongue[END_REF], When the PLS method was used with texture characteristics, similar results were found that the freshness of mutton stored at different temperatures could be obtained by PLS using texture parameters. The relationship between texture characteristics parameters and freshness indicators of mutton were estabilished by MLR algorithm. Larger R 2 and lower RMSE indicate adequate fits. As shown in table 1, a linear correlation between texture parameters and freshness indicators was illustrated by MLR. Models were obtained with high correlation as R 2 ≥0.885 for TVB-N, R 2 ≥ 0.888 for pH, R 2 ≥0.938 for days stored at three temperatures. For MLR, the values of R 2 increased with the rising of storage temperature. When the MLR models were used to predict samples in test set, the R 2 were all higher than 0.8, except for pH at 4 ℃ (0.772). The MLR model was proved to be effective in the prediction of TVB-N, pH and days stored for mutton samples.

Models built by PLS and MLR showed high ability to predict the content of TVB-N, pH and days stored for mutton. Compared by values of R 2 and RMSE, better results were found by PLS with high correlation for the training and test sets. Freshness indicators, days stored were best predicted, than comes the TVB-N value. The prediction of pH was not so good.

The potential use of texture analyzer to monitor the freshness of mutton stored at different temperatures was evaluated in this study. It was found that texture parameters obtained by TPA analysis could be successfully applied for the monitor of meat deterioration and prediction of freshness indicator (TVB-N, pH, and days stored). Both PCA and CDA showed high ability in the discrimination of mutton samples with different freshness, and the results of CDA were better than that of PCA. The predictive models built by PLS and MLR showed high capacity (R 2 higher than 0.88 for all parameters in calibration data set, higher than 0.77 in validation data set) in mutton freshness prediction based on the texture parameters. The days stored were best predicted, and then comes TVB-N, and pH. What's more, PLS was more effective than MLR when they were used to study the freshness of mutton. The texture analyzer could provide the meat industry with a fast, objective and useful tool for the determination of mutton freshness.

Fig. 1 .

 1 Fig. 1. Trend for pH versus storage time (day) at three temperatures

Fig. 2 .Fig. 3 .

 23 Fig. 2. Trend for TVB-N versus storage time (day) at three temperatures

Fig. 4 .

 4 Fig. 4. Trend for shear force of mutton versus storage time (days) at three storage temperatures

Fig. 5 .

 5 Qualitative identification of mutton freshness by PCA based on the texture profiles for three storage temperatures

Fig. 6 .

 6 Qualitative identification of mutton freshness by CDA based on the texture profiles for three storage temperatures

Table 1 .

 1 Comparison of two predictive methods (PLS and MLR) for TVB-N, pH and days stored for mutton stored at three storage temperatures

	Methods	Storage temperature	Parameters	R 2	Training set RMSEC	R 2	Test set	RMSEP
			TVB-N	0.953	2.05	0.977		2.56
		Room temperature	pH	0.926	0.1	0.913		0.15
			Days stored	0.968	0.25	0.94		0.43
			TVB-N	0.93		1.85	0.821		2.91
	PLS	4 ℃	pH	0.912	0.11	0.772		0.17
			Days stored	0.949	0.66	0.831		1.16
			TVB-N	0.89		2.22	0.87		2.36
		1 ℃	pH	0.891	0.12	0.853		0.14
			Days stored	0.941	0.94	0.923		1.03
			TVB-N	0.949	1.96	0.972		2.39
		Room temperature	pH	0.917	0.1	0.923		0.14
			Days stored	0.962	0.25	0.94		0.43
			TVB-N	0.925	1.85	0.821		2.91
	MLR	4 ℃	pH	0.905	0.11	0.772		0.17
			Days stored	0.945	0.66	0.832		1.24
			TVB-N	0.885	2.21	0.87		2.37
		1 ℃	pH	0.888	0.12	0.853		0.14
			Days stored	0.938	0.94	0.923		1.03

Acknowledgments. Funds for this research was provided by the Changjiang Scholars and Innovative Research Team in University (IRT13091), the Chinese National Foundation of Nature and Science through Project 31560477 and 31160440, the Research Funds for introduced tanlents of Northwest University for Nationalities through Project xbmuyjrc201408.


Wei Ma

Xiu Wang
email: bxiuwang@263.net

Lijun Qi
email: cqilijun@cau.edu.cn

Yanbo Huang
email: dyanbo.huang@ars.usda.gov

A Systematic Method for Quantitative Diameter Analysis of Sprayed Pesticide Droplets

Keywords: quantitative diameter analysis, pesticide droplet, droplet spreading, droplet evaporation, microsyringe

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Pesticides are widely used in crop fields as an effective means of pest control for crop growth [START_REF] Tsay | Evaluation of a pneumatic-shield spraying system by CFD simulation[END_REF]. However, with extensive use of the chemicals, there are serious consequences. For example, soil compaction due to overuse of pesticides could reduce the fertility of soil to a certain extent, and bring yield loses of the grains. Also, atmospheric and water pollutions from pesticides could have serious impact on the health of humans and animals [START_REF] Bykov | System decomposition technique for spray modeling in CFD codes[END_REF]. After years of studies worldwide, it can be determined that the off-target drift of pesticides has become one of major sources of the issues mentioned above [START_REF] Sidahmed | Simulation of spray dispersal and deposition from a forestry airblast sprayer part II: Droplet trajectory model[END_REF]. The studies have been conducting to characterize the pesticide droplet micro behavior on plant leaf surface to reduce the stress caused by the spray drift. How to precisely determine droplet size is one of the research focuses by scientists and engineers of application technology. How to remove bubbles in the spray liquid drops is an issue to impede the study. At present, there is no instrument capable of generating pesticide droplets with specified particle size. There is no report that the instrument can remove the dissolved air bubbles in the liquid drop either [START_REF] Dorr | Spray deposition on plant surfaces: a modelling approach[END_REF]. The droplets are mostly still generated manually [START_REF] Dorr | Minimising Environmental and Public Health Risk of Pesticide Application Through Understanding the Droplet-Canopy Interface[END_REF][START_REF] Dorr | Spray deposition on plant surfaces: a modelling approach[END_REF][START_REF] Forster | A universal spray droplet adhesion model[END_REF][START_REF] Forster | Spray retention models for arable crops[END_REF][START_REF] Forster | Process-driven models for spray droplet shatter, adhesion or bounce[END_REF]. Because of the limited experimental means, the research accuracyabout the diffusion of the pesticide droplets and the evaporation of the single droplet in practice for crop production management is limited. [START_REF] Mao | Spread and rebound of liquid droplets upon impact on flat surfaces[END_REF][START_REF] Martin | Interaction of droplets with a surface impact and adhesion[END_REF][START_REF] Mercer | Process driven models for spray retention by plants[END_REF][START_REF] Mercer | A model for spray droplet adhesion, bounce or shatter at a crop leaf surface[END_REF][START_REF] Mundo | Numerical and experimental investigation of spray characteristics in the vicinity of a rigid wall[END_REF]. This paper is mainly to explore a kind of systematic method for quantitative diameter analysis of sprayed pesticide droplets. Specifically, this study developed a device for generating sprayed pesticide droplet with specified diameters with the method for quantitative diameter analysis of sprayed pesticide droplets to address the issues mentioned above.

2

System and Method

Experimental Device

Fig. 1 is the structural diagrammatic sketch of the device for generating the sprayed pesticide droplets with specified diameters. Because there is no instrument capable of generating pesticide droplets with specific particle size and the studies about the diffusion of the pesticide droplets and the evaporation characteristics of the single droplet have not been developed in practice for crop production management, this study developed a device for generating sprayed pesticide droplets with specified diameter with a sliding component positioned on a track and capable of sliding along the track. One end of the slider is connected with one end of a piston. The other end of the slider is connected with a driving motor through a lead screw. The other end of the piston can move in a droplet generator along with the sliding of the slider and the droplets in the droplet generator are released by a guided pipe. The droplet generator is clamped on a supporting structure. A vibration unit for removing bubbles in the droplet generator is further arranged on the structure. The vibration unit includes an eccentric counterweight module and a buffer module connected with the eccentric counterweight module. The eccentric counterweight module rotates to generate highfrequency vibration, and the vibration is slowed down to the overflow frequency of the bubbles through the buffer module. The buffer module is made of rubber. The minimal propulsion precision of the piston is 0.2mm. The device further contains a control center and a driver. The driver is used for receiving and obtaining instructions of the control center and controlling the driving motor according to control instructions.

The mathematical equation for specifying droplet diameters is:

0.2NS n =αΠD g 3 ( 1
)
where N is the moving distance of the piston; S n is the cross-sectional area of the extrusion droplet piston and D g is the diameter of the droplets.

Experimental Procedure

The method developed in this study can be summarized as the procedure in the following steps:

1. Inject chemicals into the droplet generator;

2. Switch on the driving motor for the lead screw to rotate and release one droplet from the droplet generator through a guided pipe;

3. Determine the diameter of the released droplet and calculate the liquid correction coefficient through the mathematical equation ( 1) with the corresponding diameter of the droplet; and 4. Generating the next droplet with the specified diameter.

Compared with the method of generating droplet by the precision metering pump,this method is based on the spiral movement. The developed device and method in this study have the following advantages:

1.

Step motor receive a certain number of driving pulse signalfrom the control center,and drives the lead screw to rotate;

2. The lead screw drives the slider to a designated position on the guiding track;

3. The exact sizedroplets are generated using adjustable vibration to eliminate bubbles.So research accuracy is improvedfor diffusion law and evaporationproperty of plant leavesdroplets . In the experiments the testing liquid used was rhodamine-B self modulated (concentration is 0.1%).The solution reported in the literature was used to replace the pesticide. Sodium chloride salt solution(2ml packaging, 10%,Guangzhou Pharmaceutical Factory, Guangzhou, China) was used as a calibration..

Results and Discussion

Droplet Generation

The driving motor is connected with the control center through the driver. The driver is used for receiving instructions of the control center and controlling the driving motor to operate according to the control instructions. The control center is a control computer. The computer is connected with the driver through a 232 serial port and used for converting a numerical value of the diameter of the droplets, which is input from the computer, to machine instructions of the device. The instructions are sent by the computer, and the minimal propulsion precision of the piston is 0.2mm.

As shown in Fig. 7, single droplet generation can be precisely controlled by the computer, and the precision error is less than 1.5%. The droplets of different particle sizes are continuously generated quickly in the sequence with the rate up to three droplets per second.

Accuracy Assessment

The droplets measuring instrument is Oxford Laser Imaging Division VisiSizer N60 (Oxford Lasers, Inc., Shirley, MA, USA). The vibration unit can ensure that one droplet released by the device does not contain bubbles, and the droplets can be released one by one with ensured experimental precision. If bubbles are found in chemicals in the operating process, the vibration unit can be used with an exhaust function, and the bubbles in the droplet generator can be automatically removed by extrusion and vibration. In this way the bubbles contained in the droplets can be prevented from affecting the experimental precision. As shown in Fig. 8, the relationship is linearbetween diameter of the droplets and the pulsesof controller driver motor rotation. After a user sets the size and the number of the droplets, the computer will send these parameters to the driver through the serial port, and the driver further completes the next two steps:

1. Automatically calculate the volume of the droplets, which corresponds to the diameter of the droplets; 2. Convert the volume into the extrusion distance required for producing the droplets from the droplet generator.

The control instructions are generated after calculation, and the instructions are sent to the driving motor. The driving motor rotates to drive the slider to move on the track.

According to the instructions of the driver, the slider drives the piston to be slowly inserted into the droplet generator, and the insertion distance is precisely controlled.

The piston of a probe needs to advance 1mm to generate the droplets of 200he. Generally, the minimal advancing precision of the piston is 0.2mm.

Preferably, a calibrated scale is further arranged on the droplet generator. In the volume movement of the chemicals, whether the bubbles exist or not, other problems can be observed very conveniently through scale marks on the calibrated scale. If the bubbles exist, the bubbles can be automatically removed through the operation of the vibration unit, and then the bubbles contained in the droplets can be prevented from affecting the experimental precision.

Method calibration

The system interferences and the error due to bubbles would affect the experimental precision. The droplet is calibrated by continuous generation from large to small droplets, every 3 seconds to generate a different size of droplet.In the different sizees of droplet dripping down the moment, droplet size has been measured by the droplets measuring instrument.And then, in accordance with the droplet size from big to small order by reverse and regression method, the same number and size of droplets were generated.The results of the test can be drawn in Figure 9.Our study indicated that if the control algorithm is calibrated, the system can be with an improved control accuracy up to 97.2%.

.

Conclusions

In this study,a computer-controlled mechanical compression device is used to generate pesticide droplets with specified sizes. The system performance of eliminating air bubbles in extrusionaccuratediameter droplets of pesticide was studied with a large amount of calibration data. Through this study the main conclusions are drawn as follows:

(1) The best value of the driving motor screw feeder controlled by a center motor rotation is 0.2mm. [START_REF] Bykov | System decomposition technique for spray modeling in CFD codes[END_REF]The movement of the piston is affected by the vibration frequency of eccentric counterweight module. The slower the piston moves along with the sliding device, the smaller the error is.

(2)The experimental results indicated that high frequency vibrations were helpful to generatorextrudes a non-stick droplet. Mainly because of the droplet adhesion to the tip, the module can make it fall.

So the method could be better used for studies on droplet evaporation in the tests using water-sensitive paper and other tests of the properties of the pesticides with broad application prospects.

1 :

 1 control center; 2: driver; 3: driving motor; 4: lead screw; 5: base; 6: sliding device; 7: track; 8: piston; 9: droplet generator; 10: support; 11: guided pipe.

Fig. 1

 1 Fig. 1 Illustrative diagram of the device for generating the sprayed pesticide droplets with specified diameters.

Fig. 3

 3 Fig. 3 is the schematic diagram of the developed vibration unit with the enlarged view of the part of the developed droplet generator. Fig. 4 is the work flow of the system.

8: piston; 9 :

 9 droplet generator; 10: support; 11: guide pipe; 12: calibrated scale; 13: buffer module; 14: eccentric counterweight module.

Fig. 3 .

 3 Fig. 3. A schematic diagram of the vibration unit with droplet generator.

Fig. 4 .

 4 Fig. 4. System work flow.

Fig. 7 .

 7 Fig. 7. Droplet generation

Fig. 8 .

 8 Fig. 8.Relationship between diameter of the droplets and the pulse.

Fig. 9 .

 9 Fig. 9. Comparison of the calibration measurement value and set value

Acknowledgment

This research was financially supported by the Special Fund for Agro-scientific Research in the Public Interest, Ministry of Agriculture, China (Grant No. 201303031) and Expert Project Program of Beijing Innovation Team for Fruitsand Vegetables (Grant No. GC-WX2015). The authors would like to thank to Cai Jichen, Research Engineer, and Liu Chuan, Research Assistant, for their effort in acquiring experimental data.