發(fā)布時(shí)間：2018-08-31 18:09

【摘要】：泡沫金屬是目前能夠同時(shí)抑制瓦斯爆炸火焰波和壓力波的新型阻隔爆材料,影響其阻隔爆效果的因素眾多,而測試實(shí)驗(yàn)過程費(fèi)用高,周期長。為減少實(shí)驗(yàn)周期的材料損耗,優(yōu)化泡沫金屬參數(shù)組合,提高實(shí)驗(yàn)效率,采用人工神經(jīng)網(wǎng)絡(luò)方法對(duì)不同參數(shù)組合的泡沫金屬阻隔效果進(jìn)行了預(yù)測研究。結(jié)果表明:BP神經(jīng)網(wǎng)絡(luò)適用泡沫金屬阻隔爆效果預(yù)測。當(dāng)BP網(wǎng)絡(luò)采用10個(gè)神經(jīng)元,傳遞函數(shù)選擇"logsig"、"purelin",網(wǎng)絡(luò)達(dá)到最優(yōu),預(yù)測壓力和溫度的最大衰減率平均誤差分別為13%和4%。研究表明人工神經(jīng)網(wǎng)絡(luò)可以用于泡沫金屬阻隔爆效果的預(yù)測。
[Abstract]:Foam metal is a new type of flame barrier material which can simultaneously suppress the flame wave and pressure wave of gas explosion at present. There are many factors that affect the flame wave and pressure wave of gas explosion, but the cost of test process is high and the period is long. In order to reduce the material loss in the experiment cycle, optimize the combination of foam metal parameters and improve the experimental efficiency, the artificial neural network method was used to predict the barrier effect of different parameter combinations of foam metal. The results show that the 1: BP neural network is suitable for predicting the effect of foam metal explosion barrier. When the BP network adopts 10 neurons, the transfer function selects "logsig" and "purelin", and the network reaches the optimum. The average error of the maximum attenuation rate of predicting pressure and temperature is 13% and 4%, respectively. The results show that the artificial neural network can be used to predict the effect of foam metal explosion barrier.
【作者單位】： 黑龍江科技大學(xué)安全工程學(xué)院;中國礦業(yè)大學(xué)安全工程學(xué)院;黑龍江科技大學(xué)理學(xué)院;黑龍江龍煤鶴崗礦業(yè)有限責(zé)任公司俊德煤礦;
【基金】：黑龍江省自然科學(xué)青年基金資助項(xiàng)目(QC2015054) 國家安全監(jiān)督管理總局2016重大事故防治關(guān)鍵技術(shù)科技項(xiàng)目(heilongjiang-0001-2016AQ)
【分類號(hào)】：TD712.7

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