Малыхина Г.Ф., Гусева А.И., Милицын А.В., Невельский А.С. Ключевые слова Introduction бензина,
нефти, суперкомпьютерном дизельного центре топлива, электрических кабелей выполнено в Политехнического Университета Петра Великого. Суперкомпьютер способен выполнять до 1.2PFLOPS. Суперкомпьютер состоит из двух независимых вычислительных систем “RSC Tornado cluster system” and massively parallel “RSC PetaStream”. Результаты моделирования использованы для формирования целевой функции генетического алгоритма при оптимизации расположения датчиков в судовом помещении. Моделированипе пожара; FDS (Fire Dynamic Simulator); генетический алгоритм; нейронная сеть; обнаружение пожара; мультикритериальные датчики. 1. 2. 3. 4.
Select the optimal arrangement of sensors in the monitored space; Select the model of data collection from the sensors of the system;
Create an intelligent system that decides whether a fire exists; In this article, we will examine all the stages in more detail.
Fires cause great damage to industry, destroying goods and killing people. Forest fires cause irreparable damage to nature. Fires are especially dangerous on the transport because of the difficult of people evacuation. Therefore, the task of developing an early warning system of fire is an actual problem.
To solve the problem of early detection of a fire it is necessary to solve several subtasks:
Collect statistical data, allowing to draw conclusions about the presence or absence of fire in the room under investigation;
solving the problem.
According to the degree of detail which describe thermos-gas-dynamic fire parameters are three types of
deterministic models: integrated, zonal (zone) and field [
The integral method is the easiest among the existing modeling methods. The method consists in the following: the state of the gas environment is estimated based on average values throughout the room. Advantages: easy to calculate. Disadvantages: Only suitable for calculating the volume of large fires, not takes into account the heating and ventilation, unhelpful when there are multiple sources of ignition or at an initial stage of a fire.
The next method of constructing models is zonal. This method involves the separation of the room into two zones: the upper layer, where the products of combustion and the lower layer of the undisturbed air. Unlike integral method, method of zones allows us to determine not only the volume average values depending on the time, but also the distribution thermo-gas-dynamic parameters by the height of the convective column. The two zone boundaries are mobile. The disadvantage of this method is that its application is necessary to know a priori the structure of the flow as well as heat and mass transfer values of all parameters are obtained only on the middle zone. mbm – the rate of change of mass in the extracted volume due to the evaporation of droplets and other factors, –
Law of conservation of momentum: + = , 1 = 1 ( + 2
); , = 1,2,3, +
– the material derivative, " – the rate of heat production per unit volume due to chemical reactions, " – the heat absorption rate due to evaporation, ′ – reflects heat fluxes due to thermal – he mass fraction of the gas component α, – he diffusion coefficient of the gas comp–otnheentherαm,alk
The energy transfer equation: ℎ - the apparent enthalpy, conductivity and radiation: conductivity.
Equation of state: – the average molar mass of the gas mixture. with respect to space and time is used.
The Software ( ℎ )+ ℎ =
+ " − " − ′ + , ′ = − ∇T − ∑ ℎ , ∇ + " ,
, (3) (4) (5) (6) For the numerical solution of equations, an explicit predictor-corrector scheme of the second order of accuracy The most common at the moment are three software products for the field model: Fire Dynamics Simulator (FDS), Kameleon FireEx KFX and SMARTFIRE. The Fire Dynamics Simulator (FDS) is freely distributable and most universal program from these three programs. It allows you to predict the spread of smoke, temperature, carbon monoxide and other dangerous fire factors. FDS simulate fire scenarios using computational fluid dynamics (CFD), optimized for low-speed temperature-dependent flows. This approach is very flexible and can be applied to various fires, from combustion in furnaces to fire on oil tankers. FDS uses a hydrodynamic model to calculate the movement of air currents caused by the fire. For this program solved the Navier-Stokes equations describing the low-speed flows, caused by temperature changes, allowing calculating the propagation of smoke and temperature distribution.
The program for FDS simulations allows parallelizing computations for faster calculation model. Becouse the calculation is carried out within each grid, it is possible to carry out the calculation of each grid on a separate core. Also, we can used to calculate the number of computers connected in a local area network or a network cluster to speed up the work.
FDS supports two standards for parallelizing OpenMP (Open Multi-Processing) and MPI (Message Passing
Interface[
Size of objects in the room made us choose the size of area. In our project it is 1,9x1,9x1,9 cm. If we increase the mesh size it may occur errors and deformation of objects, because all calculations are performed within the FDS rectangular grids. Each object in the model must be rectangular to fit the grid. If the position of the object is not exactly corresponding to the grid, the object automatically moves to the edge of the grid during the simulation. This may adversely affect the accuracy of the calculations. Any object that goes beyond the border grids, clipped to the boundary, and such facilities are not involved in the calculation. Also work with thin lines and objects requires a reduction in the mesh. For optimum accuracy of modeling is desirable to use about one grid cell size in all planes. In accordance with the above, and the size of the selected room total number of cells amounted to 14.5 million. run such applications. We tell you about them.
Several runs with different resource allocation configurations have been conducted to select the best ways to
Use only the OpenMP. It is allows a single computer to run a project with one or more screens on multiple cores. We will use OpenMP on one node of a networked cluster, with one computational grid. During the 48 hours it was modeled around 1 minute real-time fire that has not produced any concrete outcomes;
Only the MPI. This method allows you to run the calculation on several computers in a network, or a network cluster. For this it is necessary to design the area which will be divide into several grids, at least as much as the available CPUs or cores, and each grid is assigned to its own process. Based on the cluster power provided to us, namely four nodes, each of which has two processors with twelve cores, it was decided to divide the model computational grids and 96 computational grids, which corresponds to 96 cores of the entire system. In this way we have divided the load on all cores and reduced computation time. Over 48 hours was modeled almost 4 minutes of real time and obtain more detailed values;
According to the results, we conclude that the use of MPI library reduces the simulation in a several times. Of course, we can combine MPI and OpenMP in the same calculation. If you have multiple computers, and each computer has multiple cores, you can assign a single MPI process for each computer, and use multiple cores on each machine to accelerate grid processing using OpenMP. But this method is still slower than the separation method of computing MPI.
As an example of typical premises, a captain's cabin and a dining room were chosen. An example of a fire development model is shown in Figures 1 and 2 (captain's cabin and dining room respectively).
As already mentioned, the program provides an opportunity to model not only the development of the flame, but also the spread of smoke. Figures 3 and 4 show the spread of smoke for the same premises.
Also, the program allows you to place sensors for temperature, gas concentration and visibility in the premises, which allows you to obtain accurate values of the measured values at given points. This helps to simulate the response of real sensors located at the same points. An example of the gas concentration and temperature sensors located above the ignition source and not far from it is shown in Figures 5-8.
As can be seen from the graphs, the sensors located above the source have a wide range of values.
The simulation allowed us to collect the necessary information on the development of fire in the room, taking into account all its features. To increase accuracy in determining a fire, it is necessary to measure not only the temperature in the room, but also other hazards (concentration of gases, smoke, etc.) algorithm for its application to solve the problem of the optimal arrangement of sensors.
We perform M preliminary fire simulations in the room. We will select the source locations different for – is the reaction time of one set of L sensors to a fire that has arisen;
We will construct a temperature map for each time point and for each variant of M preliminary simulations; Create N random locations of sensors in the room, the number of sensors will take the size of L; For each of the N arrangements, we calculate the function Tmin = ∑ =1 , where = ∑ =1 , where
From all N locations choose 0.01N with the maximum Tmin. These variants are discarded and repeat step 4. The algorithm continues until the final condition is reached;
Multisensory system is better to build on the network technology [
If you use Thread wireless interface it will allows you to build system architecture for a distributed wireless network.
Sensory nodes and portable remotes for controlling the system running directly from the battery, and can easily be displaced in space. The sensors are directly connected to controllers through Thread interface; sensor and control nodes are powered by batteries.
Sensor nodes constitute fully connected (mesh) network. They transmit the results of measurements to the nearest router. Routers collect data and transmit them to the gateway connected to a wired network.
The gateway allows transmitting information to the server database which located in a cloud SCC(Supercomputer center). This information may be used directly on the ship or remotely in a server cloud. 1. 2.
Verification connectivity of multisensory system, including mobile units and controls, and reporting; Verification a mathematical model of combustion process;
We need to construct a network graph to verify connectivity of the system. The nodes of the network are the set of network nodes {S, R, G, Srv, C}. They are includes sensor nodes (S), routers (R), a gateway (G), a cloud computing environment, computer operator (C) Network arcs characterized links between nodes. They have the distance marks (D) and the probability of transmitting data to {P (D)}. The probability of correct data depends on the distance and obstacles.
Example of graph segment network, including the router and sensor nodes, is shown in Figure 10. So as not to overload details the connection pattern is shown only for the node S1.
The set of vertices denote sensor units S1-S6 and the router R1, the set of arcs is characterized by the probability Pij i = 1..6, j = 1..6 of a correct data transfer between blocks. Considering all the paths on the graph connecting node Si and Rj, we obtain the characteristic of network connectivity: 0 ( , )= + (1 − ) , (7) where N is a number of paths, connecting nodes i and j.
The probability of correct data transmission between two nodes are determined theoretically based on the analysis of signal propagation at a frequency of 2.4 GHz and confirmed experimentally in the areas of deployment. ∑ =1, ≠ ≠
The article discussed how to make an intelligent system of fire detection on ships by steps/ First of all how to run simulation on a supercomputer. The choice of method for modeling and simulation programs. A method of calculation model on a supercomputer that enables the correct use of computing resources to achieve the desired result.
Also, in the article considered a method that allows the sensors in the room to be optimally located. It helps to reduce the response time of the fire protection system.
Multisensory fire alarm system allows controlling the temperature, the smoke and the gas simultaneously, that enable detect fire in its early stages. The wireless interface allows deploying and reconfiguring the system rapidly, allows moving the sensors, on the position of the alleged source of fire. Location sensors, their connection is controlled by the software and can be verified on the basis of mathematical models of the object and the system.
The article discussed how to make an intelligent system of fire detection on ships by steps.
First of all how to run simulation on a supercomputer. [
Also, in the article considered a method that allows the sensors in the room to be optimally located. It helps to reduce the response time of the fire protection system.
Multisensory fire alarm system allows controlling the temperature, the smoke and the gas simultaneously, that enable detect fire in its early stages. The wireless interface allows deploying and reconfiguring the system rapidly, allows to move the sensors, on the position of the alleged source of fire. Location sensors, their connection is controlled by the software and can be verified on the basis of mathematical models of the object and the system.
The scientific research was supported by Supercomputer Center "Polytechnic". Authors are grateful for scientific and financial support.
References Об авторах: Малыхина Галина Фёдоровна, доктор технических наук, прроф,несасуочный руководитель кафедры измерительных информационных технологий, института компьютерных наук и технологий, Санкт-Петербургский политехнический университет Петра Велик,оgг_оf_malychina@mail.ru Гусева Алёна Игоревна, аспиранткафедры измерительных информационных технологий, института компьютерных наук и технологий, -ПСаетнекртбургский политехнический университет Петра Великого, alyona-kitty@rambler.ru Милицын Алексей Владимирович, доцентз, аместитель заведующего по общим вопкраофсаемдры измерительных информационных технологий, института компьютерных наук и технологий, Санкт-Петербургский политехнический университеПтетра Великог,оctsp@mail.ru Невельский Артем Сергеевич, магистркафедры измерительных информационных технологий, института компьютерных наук и технологий, -ПеСтаенркбтургский политехнический университет Петра Велик,оaгrоtich@list.ru