Multi-scheme comparison and design method for laboratory ventilation system based on Modelica-CFD

Abstract

The invention discloses a multi-scheme comparison design method for laboratory ventilation system based on Modelica-CFD joint simulation, which belongs to the technical field of laboratory ventilation system design, including: determining the laboratory building geometric model, ventilation system equipment parameters, and ventilation system control strategy with energy-saving potential; establishing a laboratory room CFD model, determining the boundary physical quantities that need data exchange in the joint simulation; establishing a ventilation system equipment Modelica model, setting the control strategy logic; performing CFD-Modelica joint simulation based on the FMI standard interface, completing the ventilation efficiency and energy consumption calculation of the laboratory test ventilation system with different control strategy logics in various operating scenarios; comprehensively determining the optimal ventilation system control strategy based on the ventilation efficiency and energy consumption calculation results, and updating the Modelica ventilation system design scheme, and proposing optimization suggestions. The invention uses joint simulation based on the FMI interface, combines parametric modeling technology, CFD algorithm and Modelica system modeling technology, builds a technical scheme of CFD-Modelica joint simulation suitable for laboratory and clean room optimization design, performs multi-objective optimization on the design scheme of the laboratory ventilation system, and improves the design efficiency and quality.

Description

Technical Field

[0001] The present invention belongs to the technical field of laboratory ventilation system design, and more specifically, relates to a multi-scheme comparison and design method for laboratory ventilation systems based on Modelica-CFD joint simulation. Background Art

[0002] The air quality of the laboratory is related to the safety and comfort of the staff. Generally, the laboratory ventilation system requires a large number of exhaust equipment to maintain the indoor air cleanliness requirements. However, compared with the indoor ventilation system of general buildings, the frequent use of ventilation equipment such as fume hoods and exhaust fans in the laboratory will cause huge energy waste, thus affecting the operating costs for decades. Therefore, how to achieve low-energy operation of the ventilation system while ensuring the cleanliness of the laboratory has become one of the key technical issues faced by HVAC engineering designers in the design of ventilation systems.

[0003] At present, numerical simulation technology is often used in the field of HVAC engineering to pre-evaluate the ventilation system scheme in the design stage, which mainly includes the following two aspects: 1) System simulation test of the laboratory variable air volume control system, and optimization of the selection and layout of equipment such as fans and pipes and the control system; 2) Computational Fluid Dynamics (CFD) technology is used to calculate the air exchange efficiency in the three-dimensional laboratory and optimize the indoor layout of vents and fume hoods. However, the numerical simulation analysis content of the above two aspects often involves the fields of HVAC, electrical and automatic control. At present, they are all implemented based on different software platforms, and data exchange cannot be achieved in the design stage. Therefore, joint simulation of indoor air quality and energy consumption is rarely carried out, and it is difficult to accurately evaluate the operating status of indoor ventilation systems in various scenarios during the operation stage. Therefore, the current ventilation system design is usually based on empirical conservative design, and it is difficult to achieve the coordinated optimization of indoor ventilation efficiency and energy consumption at the same time.

[0004] In order to solve the problem of data exchange and coupling of numerical simulation calculations on different software platforms, the FMI (Functional Mock-up Interface) standard was proposed, which supports data exchange and joint simulation of dynamic models through interface files and C code. Therefore, how to apply the joint simulation technology based on the FMI standard to the laboratory ventilation system design process, and how to carry out multi-objective optimization of ventilation systems for future actual operation scenarios in the design stage by combining computational fluid dynamics and building energy system simulation technology is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention proposes a multi-scheme comparison and design method for laboratory ventilation systems based on Modelica-CFD, which adopts a joint simulation method to solve the problem of multi-domain objective optimization in the ventilation system design process.

[0006] To achieve the above object, the present invention provides a multi-scheme comparison and design method for laboratory ventilation system based on Modelica-CFD, comprising:

[0007] S1: Determine the laboratory building geometry model, ventilation system equipment parameter model, and ventilation system control strategy with energy-saving potential in various actual operating scenarios based on the building, equipment manufacturers and HVAC professional data;

[0008] S2: Establish a CFD model of the laboratory room, determine the boundary physical quantities that require data exchange in the joint simulation, establish a Modelica model of the ventilation system equipment system, and set the control strategy logic in various operating scenarios;

[0009] S3: Perform CFD-Modelica joint simulation based on the FMI standard interface, and complete the calculation of ventilation efficiency and energy consumption of laboratory test ventilation systems under various operating scenarios and different control strategy logics based on a CFD-Modelica joint simulation model;

[0010] S4: Determine the optimal ventilation system control strategy based on the ventilation efficiency and energy consumption calculation results, update the Modelica ventilation system model and design scheme, and put forward optimization suggestions.

[0011] In some optional embodiments, before step S1, the method further comprises:

[0012] Optimize the design of the laboratory ventilation system. Optimize the design of the laboratory ventilation system, which means to optimize the ventilation efficiency and energy consumption curve of the ventilation system under the premise of ensuring the ventilation demand. The optimization content includes: equipment selection optimization, specifically, including the selection optimization of equipment such as supply fans, exhaust fans, valves, etc.; ventilation strategy optimization, which refers to the optimization of the start and stop strategy of the ventilation system; optimization of the layout of indoor ventilation equipment, including the layout optimization of fresh air inlets, fume hoods, exhaust hoods and other equipment; variable air volume control optimization, including the parameter optimization of variable air volume control of the ventilation system.

[0013] In some optional embodiments, step S1 comprises:

[0014] Collect the spatial information of the laboratory, preliminarily determine the selection and layout of the ventilation system equipment, and determine the possible control strategies and operating conditions based on the usage requirements. The spatial information of the laboratory includes the room enclosure structure information and indoor equipment layout. The enclosure structure information includes the layout and size of structures such as walls, doors, windows, and air shafts, as well as the parameters of their thermal physical properties such as heat transfer coefficients. The indoor equipment layout mainly includes: the layout of fresh air inlets, the layout of exhaust equipment such as fume hoods and exhaust hoods. The operating requirements of laboratory rooms generally include but are not limited to: indoor air purification requirements to meet the cleanliness requirements in the laboratory; indoor comfort requirements to provide appropriate temperature and relative humidity; operator safety protection to control the spread of harmful substances generated during the operation of the experimenter; indoor negative pressure requirements to maintain the correct airflow direction and pressure gradient.

[0015] In some optional embodiments, step S2 comprises:

[0016] A CFD ventilation calculation model of building rooms is established using XFlow software based on the lattice Boltzmann method. The CFD model specifically includes a grid model corresponding to the indoor building and equipment geometry, turbulence setting parameters, and ventilation system boundary parameters (including the location of the supply and exhaust vents, and the flow rates of the supply and exhaust vents). A Modelica model of the ventilation system is established based on Dymola software. The Modelica model includes the equipment model and control strategy model of the ventilation system, where the control strategy model includes all energy-saving optimization strategy schemes that may be applied to the ventilation system and various operating conditions.

[0017] In some optional implementation schemes, XFlow software is used to establish a CFD model of a building room, which also includes the setting of a data interface, exporting an FMU interface file in xfz format based on the FMI standard, and setting physical variables that require data exchange (Inputs, Outputs) during the joint simulation process, including but not limited to: wind speed input at the inlet boundary of the fresh air duct; negative pressure boundary input of exhaust equipment such as fume hoods and exhaust hoods; output of indoor pollutant concentration; output of indoor average pressure, etc.

[0018] In some optional implementation schemes, Dymola, a multi-physics field system simulation software based on the Modelica language, is used to establish a ventilation system model, including a room system component model and a ventilation system equipment component model; room-ventilation system models under different control strategies are established according to different equipment control schemes; various control strategies and design conditions are input into the same Modelica model and the condition conversion principles during the calculation process are set.

[0019] In some optional implementation schemes, the establishment of the Modelica room system model includes but is not limited to: establishing a heat exchange model between the room and the weather environment; analyzing and simplifying the room space model, including the establishment of wall and window models; setting up an indoor heat source model; and establishing a necessary heat transfer process model for the room.

[0020] In some optional embodiments, the Modelica ventilation system equipment model includes but is not limited to: fans, chillers, heat exchangers, humidification equipment, filters, pipes, tees, mechanical switches, valves, fume hoods, exhaust hoods, etc.

[0021] In some optional implementation schemes, the Modelica logic control strategy includes but is not limited to: control of mechanical switches, valve opening control, manual control of fume hood door height and automatic start and stop conditions; linkage between equipment and delayed start and stop control.

[0022] In some optional implementation schemes, step S3 includes: performing joint simulation based on the FMI standard interface; feeding back the calculation results of the joint simulation to the Modelica model for comparative analysis, so as to optimize the ventilation efficiency and energy consumption in each operating scenario.

[0023] In some optional implementation schemes, multiple operating scenario conditions are completed based on one CFD-Modelica joint simulation model in step S3, which is specifically implemented as follows: the calculation time of each condition is automatically determined through the data exchange of the joint simulation: different control strategy logics are included in a Modelica system simulation model, and each condition is calculated one by one during the joint simulation process, and whether each Modelica calculation condition continues is determined based on the calculation result indicators fed back by CFD. During the calculation process, the indoor air flow in and out and pressure and other indicators of each condition are dynamically monitored to determine whether the monitoring signal of the current condition has reached a stable state. If it has reached a stable state, the calculation of the next condition is automatically entered.

[0024] In some optional implementation schemes, the steps of the joint simulation include: importing an interface file in the xfz format into the Modelica model, and setting control conditions for data exchange and working condition conversion of the joint simulation.

[0025] In some optional implementation schemes, the analysis and optimization include: combining the ventilation efficiency calculation results obtained by the CFD model under different control strategies with the Modelica energy consumption calculation results, analyzing the ventilation times, cleanliness and micro-negative pressure maintenance of the laboratory, adjusting the parameters of the ventilation equipment, and optimizing the equipment selection; analyzing the energy consumption of the ventilation system under different control strategies, and reducing energy consumption by adjusting the variable air volume control parameters; comparing the optimal solutions under different control strategies to obtain the optimal ventilation system design solution.

[0026] In some optional implementation schemes, step S4 includes: making optimization suggestions for equipment selection with reference to the optimized ventilation system design scheme, and determining the control strategy of the ventilation system according to the optimal model.

[0027] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0028] (1) The present invention utilizes CFD technology and the object-oriented open source modeling language Modelica to simulate and evaluate the operating status of the laboratory and its ventilation system in advance based on the FMI standard, which can realize joint simulation of multiple physical fields and improve the reliability and computational efficiency of the results of evaluating the laboratory ventilation efficiency and energy consumption indicators in the design stage.

[0029] (2) The present invention can simulate the operating status of the laboratory ventilation system under different scenarios through joint numerical simulation. By exchanging data between the Modelica model and the CFD model through joint simulation, the ventilation status of the laboratory under different working conditions can be quantitatively evaluated in one joint simulation, which simplifies the traditional numerical simulation process and improves the efficiency of multi-scheme comparison and design. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of a multi-scheme comparison and design method for a laboratory ventilation system based on CFD-Modelica joint simulation provided by an embodiment of the present invention;

[0031] Figure 2 It is a schematic diagram of a basic design scheme of a ventilation system provided by an embodiment of the present invention;

[0032] Figure 3 A three-dimensional model of a laboratory room enclosure structure and indoor ventilation equipment provided by an embodiment of the present invention;

[0033] Figure 4 A second-developed Modelica model of a Venturi valve provided by an embodiment of the present invention;

[0034] Figure 5 is a Modelica ventilation system model provided by an embodiment of the present invention;

[0035] Figure 6 is a pollutant concentration calculation result under different working conditions in an optimization phase provided by an embodiment of the present invention;

[0036] Figure 7 It is a schematic diagram of indoor flow lines of a laboratory after the ventilation system design is optimized according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0038] In order to solve the problem of optimizing the design of the ventilation system in the laboratory design process, the present invention provides an intelligent design method for the laboratory ventilation system based on CFD-Modelica. First, the design scheme information of the laboratory and the operation requirements of the laboratory room are collected, and the ventilation system is designed based on the laboratory and ventilation requirements. Subsequently, CATIA software is used to establish a three-dimensional geometric model of the laboratory and indoor pipelines and equipment, the model is imported into the CFD software X-Flow, the boundary conditions of the model are set, the velocity boundary and the pressure outlet boundary are set for the fresh air inlet and the fume hood, and the FMU interface file in the xfz format with a data exchange interface reserved is exported based on the FMI standard; and the Modelica model of the ventilation system is established by Dymola software, including establishing corresponding equipment models and control models for the equipment and control strategies of the ventilation system, and setting weather environment parameters and human comfort indicators. Then the FMU interface file is imported into the Modelica model, the fresh air and exhaust outlets of the ventilation system are connected to the reserved data interface, and a joint numerical simulation is performed; the calculation results are analyzed, including evaluating the pollutant concentration and energy consumption, and the ventilation system Modelica model is adjusted according to the calculation results until the optimization design requirements are met. Finally, the ventilation system Modelica that meets the optimization requirements is restored to the design plan to obtain the optimized ventilation system design plan. Figure 1 As shown, the method specifically comprises the following steps:

[0039] (1) According to the design information of the laboratory, determine the design parameters and purpose of the laboratory, determine the corresponding ventilation requirements according to the type of laboratory and relevant specifications, determine the room ventilation frequency, indoor ventilation volume, indoor pressure state, the number of air supply and return air ducts, the number of fume hoods and ventilation hoods, and other information, and obtain the basic design plan of the ventilation system;

[0040] (2) According to the geometric parameter information of the laboratory's enclosure structure and the parameter information of the indoor duct layout of the ventilation system, a BIM model of a single laboratory is established based on CATIA, and the indoor space of the laboratory model is set as a closed space; the model is imported into X-Flow, and the Scalar Transport module in X-Flow is used to simulate the diffusion of polluted gases in the laboratory, the fresh air flow rate is set at the fresh air inlet boundary, a negative pressure boundary is set at the exhaust interface of the fume hood, the FMI inputs interface of the ventilation boundary is set, and the FMU file in xfz format is exported;

[0041] (3) Using the open source model library Building based on the Modelica language, a Modelica model of the ventilation system was established. The model was set according to the specific parameters of the room and ventilation system. The necessary special components of the ventilation system, such as Venturi valves and fume hoods, were redeveloped. According to the calculation requirements, the corresponding weather, human comfort model and energy consumption calculation module were introduced. Finally, a Modelica model with multiple modules including weather, room, ventilation system, human comfort, and energy consumption calculation was established.

[0042] (4) Import the FMU interface file into the Modelica model, access the data interface of the fume hood and fresh air inlet in the Modelica model, perform joint simulation, and analyze the results based on the calculation results of the CFD and Modelica models, and modify the ventilation strategy or equipment parameters until the requirements of the optimized design are met;

[0043] (5) Restore the optimized Modelica model to the design scheme, determine the design parameters of the optimization scheme, and propose optimization suggestions for the original design scheme.

[0044] The following is a more specific example of the optimization design of a ventilation system for food and nutrition. Figure 1 As shown in the figure, for the ventilation system design, the specific implementation steps are as follows:

[0045] S1: Determine the enclosure structure size of the laboratory room. The length L, width W and height H are 16.5m, 8.15m and 2.4m respectively. According to the application requirements of the food and nutrition laboratory, the ventilation requirement is determined to be a minimum of 8 air changes per hour, a minimum ventilation volume of 2880 cubic meters per hour, and the indoor pressure needs to be maintained at a slightly negative pressure state within -5pa. Design the corresponding ventilation system according to the ventilation requirements. The design of the ventilation system is as follows: Figure 2 As shown;

[0046] S2: Use CATIA tools to build the enclosure structure and indoor ventilation equipment model of the laboratory room, such as Figure 3As shown, export the model file in STL format, import the model into X-Flow, use the Scalar Transport module in X-Flow to simulate the diffusion of polluted gases in the laboratory, set the fresh air flow rate at the fresh air inlet boundary, set a negative pressure boundary at the exhaust interface of the fume hood, set the FMI inputs interface of the ventilation boundary, and export the FMU file in xfz format;

[0047] To build a Modelica model of the ventilation system in Dymola, first analyze the ventilation system based on the existing Modelica library and perform secondary development on necessary components, such as Venturi valves and fume hoods. Figure 4 As shown, the Modelica model is then established according to the design as shown Figure 5 As shown, the calculation results are tested to ensure the rationality of the calculation results.

[0048] S3: Import the FMU file into the Modelica model, connect the corresponding interface of the ventilation equipment, start the joint simulation, evaluate the indoor pollutant concentration and energy consumption, and optimize the ventilation system model parameters or control strategy according to the needs until the optimization requirements are met. The pollutant concentration changes under different settings are as follows: Figure 6 As shown in the figure, the indoor ventilation airflow diagram of the optimal solution is as follows Figure 7 shown.

[0049] S4: Restore the optimized model to the design plan, and use the adjusted Modelica to correspond to the equipment selection, equipment layout, control strategy and other contents in the design process, and put forward optimization suggestions for the existing design plan.

[0050] In summary, the present invention discloses a multi-scheme comparison and design method for laboratory ventilation system based on Modelica-CFD joint simulation. The present invention utilizes joint simulation based on FMI interface, combines parametric modeling technology, CFD algorithm and Modelica system modeling technology, builds a technical scheme of CFD-Modelica joint simulation suitable for laboratory and clean room optimization design, and performs multi-objective optimization on the design scheme of laboratory ventilation system, thereby improving design efficiency and quality. The method mainly includes the following steps: 1) Determine ventilation requirements based on laboratory enclosure structure and purpose, and perform basic scheme design of ventilation system; 2) Establish Modelica model of building enclosure structure geometry model and basic scheme of ventilation system; 3) Perform joint simulation based on FMI standard interface, analyze and optimize the calculation results of joint simulation until the optimization requirements are met; 4) Restore the optimization model to design scheme, and propose optimization suggestions for existing design scheme. The method aims to realize multi-objective optimization problems involving different physical fields by joint numerical simulation, and propose optimization schemes for laboratory ventilation system design.

[0051] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0052] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-scheme comparison and design method for laboratory ventilation system based on Modelica-CFD, characterized in that: include: S1: Determine the laboratory building geometry model, ventilation system equipment parameter model, and ventilation system control strategy with energy-saving potential in various actual operating scenarios based on the building, equipment manufacturers and HVAC professional data; S2: Establish a CFD model of the laboratory room, determine the boundary physical quantities that require data exchange in the joint simulation, establish a Modelica model of the ventilation system equipment system, and set the control strategy logic in various operating scenarios; S3: Perform CFD-Modelica joint simulation based on the FMI standard interface, and complete the calculation of ventilation efficiency and energy consumption of laboratory test ventilation systems under various operating scenarios and different control strategy logics based on a CFD-Modelica joint simulation model; S4: Determine the optimal ventilation system control strategy based on the ventilation efficiency and energy consumption calculation results, update the Modelica ventilation system model and design scheme, and put forward optimization suggestions; Step S2 includes: The XFlow software based on the lattice Boltzmann method is used to establish the CFD ventilation calculation model of the building room. The CFD model includes the grid model corresponding to the indoor building and equipment geometry, turbulence setting parameters and ventilation system boundary parameters. The ventilation system Modelica model is established based on Dymola software. The ventilation system Modelica model includes the equipment model and control strategy model of the ventilation system. The control strategy model includes all energy-saving optimization strategy schemes and various operating conditions applied to the ventilation system.

2. The method according to claim 1, characterized in that Before step S1, the method further includes: The laboratory ventilation system is optimized to optimize the ventilation efficiency and energy consumption curve of the ventilation system while ensuring the ventilation needs. The optimization contents include: equipment selection optimization, ventilation strategy optimization, indoor ventilation equipment layout optimization and variable air volume control optimization.

3. The method according to claim 2, characterized in that Step S1 includes: Collect the spatial information of the laboratory, preliminarily determine the selection and layout of the ventilation system equipment, and determine the existing control strategies and operating conditions based on usage requirements. The spatial information of the laboratory includes the room enclosure structure information and indoor equipment layout. The operating requirements of the laboratory room include: indoor air purification requirements, indoor comfort requirements, operator safety requirements, and indoor negative pressure requirements.

4. The method according to claim 3, characterized in that XFlow software is used to establish the CFD ventilation calculation model of the building room, which also includes the setting of the data interface, the export of the FMU interface file in xfz format based on the FMI standard, and the setting of the physical variables that require data exchange (Inputs, Outputs) during the joint simulation process, including: wind speed input at the inlet boundary of the fresh air duct, negative pressure boundary input of the exhaust equipment, output of indoor pollutant concentration, and output of indoor average pressure.

5. The method according to claim 4, characterized in that The Modelica model of the ventilation system is established using Dymola, a multi-physics system simulation software based on the Modelica language, including the Modelica room system component model and the Modelica ventilation system equipment component model. According to different equipment control schemes, room-ventilation system models under different Modelica logical control strategies are established. Various control strategies and design conditions are input into the same Modelica model, and the operating condition conversion principle in the calculation process is set. The establishment of the Modelica room system component model includes: establishing a heat exchange model between the room and the weather environment, analyzing and simplifying the room space model, setting the indoor heat source model, and establishing the necessary heat transfer process model of the room. The Modelica ventilation system equipment component model includes: fans, chillers, heat exchangers, humidification equipment, filters, pipes, tees, mechanical switches, valves, fume hoods and exhaust hoods. The Modelica logical control strategy includes: control of mechanical switches, valve opening control, manual control of the door height of the fume hood and automatic start and stop conditions, linkage between equipment, and delayed start and stop control.

6. The method according to claim 5, characterized in that Step S3 includes: Based on the FMI standard interface, joint simulation is carried out, the interface file in xfz format is imported into the Modelica model, the control conditions of data exchange and working condition conversion of the joint simulation are set, and the calculation results of the joint simulation are fed back to the ventilation system Modelica model for comparative analysis, which is used to optimize the ventilation efficiency and energy consumption in each operating scenario.

7. The method according to claim 6, characterized in that In step S3, a variety of operating scenarios are completed based on a CFD-Modelica joint simulation model, including: The calculation time of each working condition is automatically determined through the data exchange of the joint simulation: different Modelica logic control strategies are included in a Modelica system simulation model. During the joint simulation process, each working condition is calculated one by one. The calculation result indicators fed back by CFD are used to determine whether each Modelica calculation condition should continue. During the calculation process, the indoor air flow in and out and pressure indicators of each working condition are dynamically monitored to determine whether the monitoring signal of the current working condition has reached a stable state. If it has reached a stable state, the calculation of the next working condition will be automatically entered.

8. The method according to claim 7, characterized in that In step S3, the ventilation efficiency and energy consumption optimization under each operation scenario includes: The ventilation efficiency calculation results obtained by CFD model under different Modelica logic control strategies and the Modelica energy consumption calculation results are combined to analyze the ventilation times, cleanliness and micro-negative pressure maintenance of the laboratory, adjust the parameters of the ventilation equipment, and optimize the equipment selection; analyze the energy consumption of the ventilation system under different Modelica logic control strategies, and reduce energy consumption by adjusting the variable air volume control parameters; compare the optimal solutions under different Modelica logic control strategies to obtain the optimal ventilation system design solution.

9. The method according to claim 8, characterized in that Step S4 includes: Refer to the optimized ventilation system design plan to make optimization suggestions for equipment selection, and determine the control strategy of the ventilation system based on the optimal model.

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