KR20090081537A - System and methods to predict fire and smoke propagation - Google Patents

Abstract

A system and a method for predicting propagation of fire and smoke are provided to establish escaping flow route in real time by providing information for determining smoke flow route and flow time. A plurality of peripheral condition detecting positions are established in one section. A smoke flow route is established between a pair of positions. A database as to specific information and smoke flow route is established. Peripheral condition at each position is detected. The position specific information and smoke route associated with the information are derived. Time based smoke flow value is established in real time on the basis of the smoke flow value. A smoke flow route originating from two or more activated detectors(22,24) and one or more smoke propagation routes are established.

Description

SYSTEM AND METHODS TO PREDICT FIRE AND SMOKE PROPAGATION}

The present invention relates to a regional monitoring and alarm system. More specifically, the present invention relates to such a system in which an evacuation route to the exit is established while the zone is monitored.

In multi-story buildings for residential, business or commercial purposes, zonal monitoring systems, for example fire detection / monitoring systems, are very commonly required. Such a system can provide information for initiating an alarm condition, such as initiating a fire condition, to a given floor, or zone, of a multi-story building. This information can be linked to a fire alarm control panel or control unit that can be located in the fire lobby so that it can be easily accessed by a first responder.

In addition to being able to detect and identify fire conditions, it has been found useful to provide people located in the vicinity of these conditions, such as evacuation routes, to help them fall off from the vicinity of the alarm condition. It has also been found to be useful to provide flexible evacuation or escape routes through an adaptive evacuation system that can respond dynamically to fire conditions.

Adaptive evacuation systems offer the potential to help residents who have difficulty determining emergency exits. With awareness of the location of fire and smoke barriers in buildings, these systems plan for safe routes and communicate them to residents. The basic adaptive evacuation system receives information about smoke and heat detectors that are currently active from the fire panel. Paths and emergency exits located in the vicinity of the active detector are assumed to be unsafe and closed for evacuation. Evacuation planning is done with the rest of the “safe” path. Determining the situation, predicting the progress of water and smoke, and then identifying unsafe roads for evacuation are important steps in an adaptive evacuation system.

Processes or methods for predicting fire and smoke propagation paths may be useful, which may then be sent as input to the evacuation path planning algorithm. Smoke propagation methods are complicated by severe limitations on processing capacity and speed of conventional topical control panels. This limits the use of a high fidelity smoke propagation model. An effective approach to smoke spread prediction that requires small real-time computation by an available fire control panel would be desirable.

Processes or methods for predicting fire and smoke propagation paths may be useful, which may then be sent as input to the evacuation path planning algorithm. Smoke propagation methods are complicated by severe limitations on processing capacity and speed of conventional topical control panels. This limits the use of a high fidelity smoke propagation model. An effective approach to smoke spread prediction that requires small real-time computation by an available fire control panel would be desirable.

Systems and methods are provided for establishing a smoke flow path and time of flow between a plurality of ambient condition detectors. This information can be used to establish a dynamically changing evacuation flow route in real time.

While embodiments of the invention take many forms, specific embodiments thereof are shown in the drawings and will be described in detail herein.

In the first method of practicing the present invention, the location of each detector and the typical smoke diffusion time between a pair of adjacent detectors are stored. Typical smoke diffusion times between adjacent detectors are estimated by off-line smoke propagation models or by historical data or experiments. Nearly all processing can be performed in advance and offline. The propagation time can then be stored in an alarm control unit. When the detector is activated due to fire or smoke, the smoke propagation path and detector active time sequence are retrieved from the reservoir and used. Since propagation information is pre-built, stored, and obtained, very little computational load is added to the alarm control circuit and processor for smoke propagation prediction.

In alternative embodiments, methods and data required to predict smoke propagation time between adjacent detectors, or compartments, may be stored. The smoke propagation path can then be predicted based on real time data. Real-time smoke propagation prediction processing may be performed that includes only a small number of compartments. This method requires much smaller computational loads than in the conventional building fire / smoke model that includes all the compartments in each iteration.

Thus, in the first method, almost all smoke propagation path predictions in the fire control panel are precomputed and stored in the database before being used. Smoke propagation paths are obtained in real time from the reservoir. Path processing, unlike conventional propagation processing, which requires the use of computational power to solve existing modeling equations, adds little computational load to the fire control unit. This processing almost always goes beyond the capabilities of conventional fire control units.

In an alternative method, fire propagation is predicted between active compartment structures. In this embodiment, in almost all cases, especially in the early stages of a fire, the compartment containing the active detector and its adjacent compartments, ie a small number of total compartments, can be processed to determine the propagation time. Thus, when solving a set of simultaneous equations for an entire network, or zone, all at once, much smaller computational power is required than in conventional propagation processing methods where it is necessary to include all compartments in each iteration.

The nodes of the processing of the present invention are based on detectors rather than rooms or other building compartments as in the prior art models. Methods for predicting smoke propagation between adjacent nodes, or detectors, may use real-time data from the detectors.

The results of the processing of the present invention may be displayed visually or stored locally on a disk drive / database for use by a first responder. The results can also be entered into one or more evacuation routes from the zone, or by adaptive evacuation route software to facilitate the development of the route. Such routes may be identified in an audible, visually, or both manner in the relevant sub-zone monitored.

1 shows a system 10, ie an environmental condition monitoring system, such as a fire alarm system according to the invention. In FIG. 1, the system 10 is installed and monitoring zone R is shown.

The zone R may comprise an interior space of a multi-storey building with multiple floors. Alternatively, the system 10 can be installed in one single floor zone, without limitation.

System 10 communicates via wired medium 14, or wireless medium 16, with multiple detectors (eg, 20, 22) that are installed throughout zone R in a manner known to those skilled in the art. And an alarm control unit 12.

Multiple detectors 22, 24 (22 are wired in communication with control unit 12 and 24 are in wireless communication via medium 14) detect the environmental condition being deployed in zone R, Smoke, fire, or heat sensors as will be appreciated. It will be appreciated by those skilled in the art to select the installation location of these detectors, and generally one or more detectors are located in one compartment (room, staircase, corridor, etc.). Each detector 22, 24, which can be thought of as a node of a network of the system 10, communicates with the control unit 12 and status information, such as one or more detected environmental conditions in the immediate vicinity. Can be sent.

Control unit 12, which may be implemented in part through one or more programmable processors 12a capable of executing a plurality of control programs or software 12b, prerecorded on a computer readable medium, As is known, in response to signals or information received from various detectors 22 and 24, it is possible to determine the developed alarm state. In addition, the control unit 912 may be located in the vicinity of the zone R, but may also be moved out of the zone R and communicate with the detectors 22, 24 via a computer network such as the Internet.

System 10 may include storage unit and database software 12c that may be accessed by processor 12a and control software 12b. The control software 12b may also include an adaptive evacuation route, or route determination software, that responds to the deferral path and transmits time information as will be described later. This information is displayed on the graphical display 14 for one or more of the original responders and evacuators. The output device 26, which is connected to the circuit 12 via the medium 28, is a person who places audio, visual or audiovisual evacuation path information within the zone R based on the method to be discussed below. Can be provided to

2 shows a plan view R1 of the zone R in which the system 10 is installed. For example, nodes, or detectors 22-i, ..., -p, are distributed throughout the zone, some of which are located in subzone R1 of FIG.

In one aspect of the invention, the smoke propagation time between each detector pair (e.g., 22-i and 22-j in FIG. 2) (detector 22-i appears to have entered an alert state) Can be judged. This determination may be made offline using an off-line smoke propagation model, or historical data.

3 shows the smoke propagation path between a pair of detectors, or nodes, in the form of arrows. For example, when the detector, or node 22-i, enters an alert state, a smoke propagation path, i. E. An arrow, with detector 22-j may be formed, i.e., path 30a is formed. Likewise, for the detector 22-j, a number of further smoke propagation paths 30b, c, d, e, f may be formed, and if desired, nodes 22-i and 22-k. The smoke propagation transit time between can be established.

Thus, a process may be executed to identify off-line propagation time between each node or pair of detectors, all data being stored in database 12c for later use, for example one of the detectors, For example, if the detector 22-i is in an alarm state, it can be retrieved by the control unit 12 or other processor. Given a pre-stored attribute of smoke propagation time, on establishing a path and elapsed time of smoke flow from the detector 22-i to another portion of the zone R1, on the control unit or circuit 12 There is little or no additional computational load added.

Each detector located in zone R is associated with a node and a data structure representing the various characteristics of the node (102). For each node, a number of information is stored (104), such as detector identification number, type, location, status, and other related information. All adjacent pairs of nodes are determined (as best seen in FIG. 3) (106).

For each adjacent detector, or pair of nodes, one arrow may be assigned if the smoke propagation has a single direction. If the smoke propagation has both directions between the nodes, two arrows may be assigned 108. For each arrow, the smoke propagation time between associated detectors is established or estimated 110. Finally, data for each arrow is stored in database 12c, for example the identifier of the starting node, the identifier of the end node, as well as the deferred propagation time calculated along the arrow (112).

Processing 100 establishes data that forms nodes, arrows, and smoke propagation times along all available paths in zone R. This information can be stored, for example, in a disk drive on a computer readable medium, or in the control unit 12 of the database 12c. The control unit 12 can then access the information directly, or can access it using a processor in communication with the control unit 12.

Details of the data structure in which the information is stored in the disk drive or database 12c are not limited by the present invention.

The processing 200 of FIG. 5 may be configured to respond to one or more of the detectors 22-i and 22-j that have entered an alarm state, as shown by the detector of FIG. 3, or the node 22-i. Reflect one processing embodiment of 10). The method 200 may be performed on a periodic basis, and it is not necessary to respond to any given detector, or node, exiting the alarm condition.

The state of the next detector Si is checked at time Ti (202). When activated, all arrows starting at Si are identified (204). As described in processing 100 above, all these arrows are pre-stored in one or more of the disk drive and database 12c.

For all the arrows starting at Si, the end node Sj is identified. The propagation propagation time Tj is then obtained, for example, from the disk drive database 12c (206). For each arrow starting at Si and ending at Sj, the path from node, or detector Si to node, or detector Sj is labeled as one smoke propagation path during time Ti + Tij at time Ti. Detector Sj is designated as active (208). The predicted time limit is compared with the sum of Ti + Tij (210). If the prediction time limit is low, propagation is stopped 212 at the end node of this arrow. Otherwise, node Sj replaces node Si and time is reset (214).

Following interruption of propagation 212, a determination is made (216) whether or not all arrows started at Si have been examined. Otherwise, the next arrow starting at Si is identified (218). If so, the node active time sequence in the smoke propagation path is output (220).

The output node active time sequence and smoke propagation path are then made available for use by the adaptive evacuation system (eg, software 12b) in response to the fire condition and establishing a safe route for evacuation. The information may be stored in one or more files of database 12c or may be provided visually.

Preferably, as mentioned above, since the processing 100 is performed non-real time, offline, such processing by the control unit 12 is unnecessary when an alarm condition is detected at a particular node or detector. . Thus, since the smoke propagation time between the sensors has been previously established and stored in the disk drive and database 12c, processing 200 can quickly establish the smoke propagation path and sensor activation time sequence.

It may be expected that the additional computational load required by the control unit 12 to perform the processing 200 in consideration of the pre-stored propagation path and sensor activation time sequence is limited or minimal. This is very different from conventional processors for predicting fire and smoke propagation paths that require sufficient computational resources.

Unlike the processing 100, 200 described above, alternative processing to be discussed later is performed between a small number of active nodes, or detectors, unlike all nodes or detectors on a given layer, or region, being used at a time. Predict smoke propagation.

Many of the components of the processing 300 are the same as the processing 100 described above and identified with the same identification number. Processing 100 includes component 302. For each node or detector, an output indicator is stored that indicates the type and location of the sensor as well as the status and temperature, smoke concentration, and the like. After executing component 108 of method 300, a smoke propagation time is estimated for each arrow based on real-time readings, or outputs, from each detector, using a specified equation, or processor. 304. Thereafter, for each arrow, a method for estimating the propagation propagation time along the arrow, as well as the start node identifier and end node identifier, is stored 306.

Those skilled in the art will appreciate that a variety of processors are available that can be used to estimate the smoke propagation time following one arrow. No details of this processor are limited by the present invention.

Processing 400 illustrates the use of real-time detector information in establishing a smoke propagation path and propagation time estimate based on the information associated with an active detector or node. Many of the components of the processing 400 are identical to the components described above with respect to the processing 200 and have been given the same identification numbers. First, the state of the next detector Si at time Ti is determined, and the current output marker, or value, is obtained. Then, for each arrow starting at the detector, or node Si, an end node Sj is identified and deferred using a pre-selected equation, or processing, with a real-time sensor output (output only at the active state detector). The propagation time Tij is established or calculated (404). Preferred processing of the smoke propagation time is shown in FIG. 8.

From the smoke propagation path, a sensor activation time sequence due to smoke propagation and other related information may be sent to the evacuation route planning processor 220.

8 illustrates example delay propagation time determination processing 500. It will be understood that various methods may be used to establish the deferral propagation time between nodes, within the spirit and scope of the present invention. For example, the heat release rate, or smoke release rate, at the node of interest, or in the compartment containing the detector, may be a sensor-driven fire model based on detector output and compartment structure. -driven fire model) can be determined (502). The active compartment structure may be configured to include an active detector, or a compartment having a node, as well as its adjacent compartment and a passage therebetween (504). Conventional smoke propagation models may be used 506 to predict how one or more of the temperature and concentration of the smoke varies over time along various compartments.

Thereafter, for example, a node or detector indicating a temperature or smoke concentration may be updated (508). A determination is made as to whether any other node, or detector, is in alarm and activated. If so, these activated nodes, or detectors, are identified, and the compartments containing them and their adjacent compartments are added to the active compartment structure (512). Otherwise, an estimate of smoke propagation time following the arrow can be determined 514.

Those skilled in the art will recognize that in addition to sensor-driven fire models, the zone model equations can be implemented in a variety of configurations. The following is to explain the equations, not to limit the invention.

Key equations for sensor-driven fire models (SDFM):

과

and

Where Q _c is the conductive heat release rate (kW), Q is the total heat release rate (kW), H1 is the ceiling height (m) from above the fire surface, and r is the plume centerline Distance from m, ΔTcj is the ceiling jet temperature K, T _∞ is the ambient temperature K and χ _r is the radial fraction of the fuel.

Further details can be found in W.D. Available from David and G.P.Forney's NISTIR 6705, A Sensor-Driven Fire Model, Version 1.1, National Institute of Standards and Technology, Gaithersburg, MD, January 2001.

Key equations of the Consolidated Model of Fire Growth and Smoke Transport:

Table 1. Conventional Zone Model Equations

Where mi is the total mass (kg) of layer i and the lower symbol i is in the two-zone model as the CFAST model, one zone of the zone model, such as the upper smoke layer zone (U), or the lower cool. Represents the air layer zone (L), P is the pressure (Pa), Cp is the heat capacity of air at constant pressure (J / (kgK)), Cv is the heat capacity at constant volume (J / (kgK)) is the ratio of the heat capacity of the air at a constant pressure (Cp) and a constant volume (Cv), V is the volume (m 3), h is the enthalpy (J / kg), and E is the internal energy (J / kg). , P is the air density (kg / m 3), and R is the universal gas constant (J / (kgK)).

For more details, see W.W. Jones, R.D. Peacock, G.P. Forney, P.A. Reneke's NIST Special Publication 1026, CFAST-Consolidated Model of Fire Growth and Smoke Transport (version 6) Technical Reference Guide, Gaithersburg, MD, December 2005.

If there is more than one detector detected as active in one building, each detector active state is processed by processing 200, or processing 400, with a smoke propagation path and time sequence for the future activity of another detector. Will begin the prediction of. By various methods, multiple predictions originating from several different detectors can be combined into one prediction. One example of the combination method is that after the combination takes the value of the earliest active time of the same detector along the path in multiple predictions starting from the activity of several different detectors, the combined smoke propagation path is one of the aforementioned multiple predictions. It consists of all the paths that appear in at least one, and the active time of the detector along the path.

1 is an overall view of a system embodying the present invention.

2 is part of a plan view of a zone showing an exemplary node, or detector.

3 shows various smoke paths and corresponds to the zone of FIG. 2.

4 is a flow chart showing multiple nodes, arrows, and smoke propagation time, offline between nodes or detectors.

FIG. 5 is a flow chart for predicting a node or detector active time sequence and smoke propagation path based on information obtained from the data established by the flow chart of FIG. 4.

6 is a flow diagram of FIG. 4 taking into account real-time sensor or detector output labels, or readings such as smoke concentration, temperature, and the like.

FIG. 7 is a flow chart variant of the flow chart of FIG. 5 wherein the smoke propagation time is dynamically established in real time for the active detector or node as well as adjacent detectors or nodes.

FIG. 8 illustrates preferred delay propagation time processing in response to a real time signal, or output, from each detector or node.

Claims (12)

Establishing a number of ambient condition sensing locations in one zone, Establishing a smoke flow path between the selected pair of locations, Establishing a database of specific information and associated smoke flow paths, Detecting a surrounding condition developing at each location and obtaining, in real time, the location specific information and a smoke path associated therewith; Based on the smoke flow values, establishing a time-based smoke flow value in real time for one or more smoke paths from each location, Establishing one or more smoke propagation paths, combined from a plurality of predictions of time and smoke flow paths starting from two or more activated detectors, and Outputting the smoke path information and each time based on the smoke flow value; Method comprising a. 2. The method of claim 1, wherein establishing the time-based smoke flow in real time comprises establishing a time-based smoke flow value for a plurality of smoke paths starting at each location. The method of claim 1, wherein establishing at least one time-based smoke flow value in real time comprises: retrieving each pre-stored flow value between each location and another selected location, respectively. And executing one or more of the specified methods for calculating a time-based flow value between the location of and another selected location. 4. The method of claim 3, wherein the retrieving comprises obtaining a plurality of pre-stored time based flow values starting at each location and another location. 4. The method of claim 3, wherein executing includes calculating a selected plurality of time based flow values starting at each location and extending through the other plurality of locations. Multiple ambient condition detectors, and A control component connected to the detector, the control component being responsive to a condition indicating an indicia from one or more of the detectors, obtaining one or more pre-stored smoke flow patterns and complying with some or all of the patterns The control component for establishing one or more smoke flow time intervals System comprising a. 7. A graphical display device as claimed in claim 6, wherein the control component visually provides the diffused smoke projected from the vicinity of the selected detector. System comprising a. The database of claim 6 comprising a representation of a smoke flow pattern. System comprising a. 9. The control software of claim 8, wherein the control software is executed by the component to evaluate a condition representing an indicia from one or more detectors, respond to a selected condition, and obtain a smoke flow pattern starting at the detector. System comprising a. 10. The smoke flow of claim 9, wherein the control software calculates, for a smoke flow pattern, a pre-stored flow time interval, or calculates a corresponding flow time interval between selected detectors. System for establishing a time interval. 12. The system of claim 10, wherein the control software generates one or more evacuation routes in response to the smoke flow pattern and associated flow time intervals. 12. The system of claim 11, wherein the detector is selected from smoke detectors, heat detectors, fire detectors and gas detectors.

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