基于多元统计技术的铀矿蚀变信息高光谱模型

介绍了一种基于数理统计分析技术的铀矿蚀变信息高光谱建模技术,它利用了高光谱数据细微、丰富的光谱信息,根据岩石和蚀变的光谱特征与物质成分的关系,通过矿物中特定离子或离子基团的诊断性吸收谷的特征参数反演岩矿的物质成分,从而综合识别蚀变信息,将为航空和航天成像光谱遥感的应用开辟一个矿物蚀变信息识别和区分的研究途径。     

GF-5星载AHSI和航空高光谱矿物填图对比——以新疆雪米斯坦地区为例

卫星和航空遥感平台获取的高光谱数据各有技术优势,在经济成本上也有很大差异。在数据处理和提取矿物信息技术方法上面既有一致性,也有不同点。本文以新疆雪米斯坦地区GF-5星载AHIS高光谱数据和CASI/SASI航空高光谱数据为数据源,基于不同的空间尺度和光谱尺度高光谱数据矿物填图做对比分析。结果显示,AHIS高光谱数据可以从空间大尺度上提取与成岩和成矿紧密相关的多种矿物信息,但由于空间分辨率较低,识别的矿物种类较少;CASI/SASI航空高光谱数据空间分辨率较高,且根据蚀变矿物特征吸收峰位置差异,识别出的矿物种类更加丰富。两种高光谱数据提取的矿物在空间分布上一致性较好,可以满足地质勘查的一般需求,但考虑到航空数据源获取成本问题,在未来地质应用工作中需要寻找一种兼顾两者优点的高光谱遥感数据获取方式。     

钻探岩心高光谱数据管理与分析系统的构建

钻探岩心编录一直是地质勘探中至关重要的环节,高光谱探测技术的发展为岩心分析提供了一种新的编录技术。为了使获得的岩心资料能永久保存且便于使用和分析,笔者开发了钻探岩心高光谱数据管理与分析系统。通过该系统使用者能方便管理、查看钻探岩心数据,快速提取、分析岩心矿物的光谱特征,为后期矿物的识别奠定了基础。     

基于Hyperion数据的岩矿蚀变信息的特征峰提取法

介绍了一种基于光谱曲线特征峰提取的岩石蚀变信息提取方法。对于目前Hyperion数据的空间分辨率所得到的混合光谱,用该方法对精细矿物的识别尚难做到,但可以用于半定量提取矿物蚀变信息。文中提出的方法作为一种航天高光谱数据挖掘手段,目的在于探索基于高光谱数据的蚀变信息提取方法,为高精度、高空间分辨率的航空或未来航天高光谱数据的处理抛砖引玉、探索先行。     

新一代元素俘获谱测井仪(ECS)及其应用

元素俘获谱测井仪(ECS)是斯伦贝谢公司研发的新一代地球化学元素测井技术,近年来已在我国投入商用.它的显著特点是不但可以确定粘土矿物含量,而且可以确定粘土矿物类型.本工作在介绍该仪器结构、测井原理以及氧化物闭合模型求解矿物的原理的基础上,通过一定的闭合模型,探讨定量求取矿物类型和含量,以及碳酸盐岩、蒸发岩、砂岩和泥质等各种矿物成分的方法,总结了它在岩石骨架属性、渗透率估算、煤层识别、沉积相带划分、油藏工程压裂设计等方面的应用.     

基于逆向质量守恒模拟识别地浸淡化过程中的水岩反应机制

为探讨新疆十红滩砂岩铀矿原位地浸开采前的淡化过程中溶浸矿体与注入淡水所发生的水岩反应，本文利用浸泡试验分析浸泡前后的水化学组分，通过逆向质量守恒模拟分析淡化过程中水岩反应机制。结果表明，砂岩型矿石蒸馏水浸泡过程中以盐岩溶解为主，硫酸盐、碳酸盐次之，硅酸盐溶解最少，说明该矿石淡化过程中由于矿物溶解，会释放出大量易溶离子，阻碍地下水淡化，其中蒸发盐岩是主要影响矿物。浸泡过程中浸出液总溶解固体(TDS)升高约1.27 g/L,模拟结果表明主要发生白云石、长石、石膏、盐岩和赤铁矿的溶解及白云母和方解石的沉淀，黏土矿物中高岭石、钙蒙脱石、伊利石均存在溶解的可能，沉淀以高岭石和钙蒙脱石为主，白云石与二氧化碳的溶解和方解石的沉淀使得溶液中重碳酸根离子浓度发生变化，长石与黏土矿物的溶解和云母的沉淀使得溶液中硅浓度发生变化。本研究可为识别地浸采铀发生的水岩反应和矿物沉淀堵塞提供技术支撑。     

加利福尼亚/内华达死谷北部地区航空可见光/红外成像光谱仪测量数据分析

作为航空可见光/红外成像光谱仪(AVIRIS)评价项目的一部分,1989年5月在死谷部分地区应用航空可见光/红外224道成像光谱仪(工作波长范围:0.41—2.45μm)进行了飞行。通过地面光谱及“经验线定标”技术,把测量数据转换成反射率,从影像上提取出反射光谱,并与野外和实验室光谱数据进行比较,以便识别矿物,如绢云母、(细粒白云母)、方解石、白云石、赤铁矿和针铁矿,用影像光谱的二进制编码来产生表示这些矿物的空间分布及铁氧化物与其他矿物共生的影像图,该图像与常规地质图及若干次野外工作填制的蚀变图吻合较好,不过也显示了几个原先未填出的碳酸盐岩露头及需要进一步进行野外填图的地区。     

预测和寻找超大型内生金属矿床的地质-地球化学标志(以金矿床为例)

讨论了预测最获利的金属矿床的基本问题,即构成世界矿物原料储量基础的特别巨大的内生矿物原料产地。研究了超大型金矿床采用的预测方法,因为金在成矿过程中聚集成一般品位的矿床与它的克拉克值相比要高出10~3～10~4倍,聚集成超大型矿床需高出10~6～10~7倍。情况表明,应采用航天热测量以填制确定长期活动的成矿系统的内生活动性源和查明在其范围内超大型金属矿床的构造部位。超大型金属矿床的预测不要求区分出具体的远景地段,而是要求在矿区或矿结级别上去识别可能达到这种金属量规模的矿床的成矿系统。     

印度安得拉邦地区以白钨矿作为Ramagiri绿岩带中找金的普查工具

Ramagiri金矿田位于印度Peninsular南部的太古代绿岩带内。金矿化产于绿岩带的中心部位,具体产在一套变安山质成分的高片理化的片状建造中的灰石英体内。矿带与围岩的伴生矿物包括黄铁矿、白钨矿和砷黄铁矿;次要副矿物包括黄铜矿、铁白云石、菱铁矿、绿帘石和电气石。印度Peninsular南部的太古代绿岩带内见有金与白钨矿的共生组合。据此,研究了白钨矿在Ramagiri金矿田内的分布及其在金矿勘探中识别目标方面的应用的可能性。沿6条横剖面以均匀的间距采集了676个土壤样品,经过陶冼,将每一样品中的白鸡矿颗位数目进行目测,对2条测线进行了金的分析。初步调查结果表明,白钨矿异常显示出,它与金异常有密切的空间关系,所以,可以利用它在详细的地质和地球化学调查中识别和圈定勘探目标。在进行区域性金矿的初勘阶段,该方法可提供一种能圈定详测区的较快速的和经济有效的手段。     

内华达州Humboldt县Sleeper矿床富矿石中二氧化硅和金的结构——胶体的证据和浅成低温成矿过程的推断

内华达州Humboldt县Sleeper矿床的带状金-银富矿脉具有类似于National地区和日本Hishikari矿床这类浅成低温矿脉系的结构和矿物学特征。这些矿脉所有富矿的形成深度约小于500m。其特征是:(1)Au/Ag比相对高(典型地>1);(2)银金矿是金矿化阶段中的主要矿石矿物,其含量要比共生的硫化物和硫盐矿物多得多;(3)作为主要脉石矿物的细粒二氧化硅普遍存在。早期的研究认为,胶体二氧化硅和金粒对Sleeper矿床富矿石中产生富金条带是很重要的。原生二氧化硅性质和银金矿沉淀物的识别及其沉淀期后结晶史对于了解Sleeper矿床富矿脉内金的沉淀过程尤为重要。     

Mssbauer 谱仪数据接口的改进

对Ｍｏｓｂａｕｅｒ谱仪数据接口进行了改进。通过简化电路，以软件实现硬件的功能。在测量中省却了单道分析器，使测量系统更为简单、可靠和高效。     

人工神经网络在预测流动沸腾曲线中的应用

选用20世纪60年代以来的实验数据，应用人工神经网络分析入口欠热度、质量流速、压力等主要参数对沸腾曲线的影响。在整个传热区内，热流密度随入口欠热度的增加而增大；在过渡沸腾和膜态沸腾区，热流密度随质量流速的增加而增加；压力起重要的作用，除膜态沸腾区外，增加压力能强化传热。除泡核沸腾外，稳态和瞬态的流动沸腾曲线的差异很小。     

Mssbauer study of the orientation of the magnetic moments in Fe-based nanocrystalline alloys

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Application of an Artificial Neural Network in Reactor Thermohydraulic Problem: Prediction of Critical Heat Flux

A new method for predicting Critical Heat Flux (CHF) with the Artificial Neural Network (ANN) method is presented in this paper. The ANNs were trained based on three conditions: type I (inlet or upstream conditions), II (local or CHF point conditions) and III (outlet or downstream conditions). The best condition for predicting CHF is type II, providing an accuracy of ±10%. The effects of main parameters such as pressure, mass flow rate, equilibrium quality and inlet subcooling on CHF were analyzed using the ANN. Critical heat flux under oscillation flow conditions was also predicted.     

Numerical simulation and artificial neural network modeling of natural circulation boiling water reactor

Numerical simulation of natural circulation boiling water reactor is important in order to study its performance for different designs and under various off-design conditions. Numerical simulations can be performed by using thermal-hydraulic codes. Very fast numerical simulations, useful for extensive parametric studies and for solving design optimization problems, can be achieved by using an artificial neural network (ANN) model of the system. In the present work, numerical simulations of natural circulation boiling water reactor have been performed with RELAP5 code for different values of design parameters and operational conditions. Parametric trends observed have been discussed. The data obtained from these simulations have been used to train artificial neural networks, which in turn have been used for further parametric studies and design optimization. The ANN models showed error within ±5% for all the simulated data. Two most popular methods, multilayer perceptron (MLP) and radial basis function (RBF) networks, have been used for the training of ANN model. Sequential quadratic programming (SQP) has been used for optimization.     

Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. Applications of artificial neural network

Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study. The input parameters of the ANN are pressure, mean mass flow rate, relative amplitude, inlet subcooling, oscillation period and the ratio of the heated length to the diameter of the tube, L/D. The output is a nondimensionalized factor F, which expresses the relative CHF under oscillation conditions. Based on the trained ANN, the influences of principal parameters on F for FC were analyzed. The parametric trends of the CHF under oscillation obtained by the trained ANN are as follows: the effects of pressure below 500 kPa are complex due to the influence of other parameters. F will increase with increasing mean mass flow rate under any conditions, and will decrease generally with an increase in relative amplitude. F will decrease initially and then increase with increasing inlet subcooling. The influence curves of mean mass flow rate on F will be almost the same when the period is shorter than 5.0 s or longer than 15 s. The influence of L/D will be negligible if L/D>200. It is found that the minimum number of neurons in the hidden layer is a product of the number of neurons in the input layer and in the output layer.     

Analysis of CHF in saturated forced convective boiling on a heated surface with impinging jets using artificial neural network and genetic algorithm

In this paper, a three-layer Back Propagation (BP) algorithm artificial neural network (ANN) for predicting critical heat flux (CHF) in saturated forced convective boiling on a heated surface with impinging jets was trained successfully with a root mean square (RMS) error of 17.39%. The input parameters of the ANN are liquid-to-vapor density ratio, ρl/ρv, the ratio of characteristic dimension of the heated surface to the diameter of the impinging jet, L/d, reciprocal of the Weber number, 2σ/ρ_lu²(L − d), and the number of impinging jets, N_j. The output is dimensionless heat flux, qco/ρvHfgu. Based on the trained ANN, the influence of principal parameters on CHF has been analyzed as follows. CHF increases with an increase in jet velocity and decreases with an increase in L/d and N_j. CHF increases with an increase in pressure at first and then decreases. Besides, a new correlation was generalized using genetic algorithm (GA) as a comparison with ANN to confirm the advantage of ANN.     

An optimization on strontium separation model for fission products (inactive trace elements) using artificial neural networks

In this study, an experimental design using artificial neural networks for an optimization on the strontium separation model for fission products (inactive trace elements) is investigated. The goal is to optimize the separation parameters to achieve maximum amount of strontium that is separated from the fission products. The result of the optimization method causes a proper purity of Strontium-89 that was separated from the fission products. It is also shown that ANN may be establish a method to optimize the separation model.     

Instability localization with artificial neural networks (ANNs)

The aim of this piece of research is to investigate the potential of artificial neural networks (ANNs) for tackling the problem of instability localization. The instability is modeled by a variable strength absorber (point-source) in a two-dimensional bare reactor model with a one neutron-energy group. The proposed approach constitutes an exercise in simplicity in that: (1) an arbitrarily simplified model is employed for ANN training and validation; (2) few training and validation patterns of low complexity are utilized; (3) the ANN inputs are derived directly from the neutron noise signals, the proposed location of instability is given on-line via an uncomplicated combination of ANN outputs; (4) the ANN architecture is independent of the number of possible locations of instability. In fact, unlike previous approaches which employ hundreds of outputs (one for each fuel assembly), only two ANN outputs are employed representing the X- and Y-coordinates (location) of instability; (5) the responses of only a few detectors are employed; (6) a measure of confidence in the prediction is assigned. The results of ANN testing, which is performed on patterns from both actual and simplified models, are reported and analyzed.     

Application of artificial intelligence techniques in modeling and control of a nuclear power plant pressurizer system

In pressurized water reactor (PWR) nuclear power plants (NPPs) pressure control in the primary loops is fundamental for keeping the reactor in a safety condition and improve the generation process efficiency. The main component responsible for this task is the pressurizer. The pressurizer pressure control system (PPCS) utilizes heaters and spray valves to maintain the pressure within an operating band during steady state conditions, and limits the pressure changes during transient conditions. Relief and safety valves provide overpressure protection for the reactor coolant system (RCS) to ensure system integrity. Various protective reactor trips are generated if the system parameters exceed safe bounds. Historically, a proportional-integral-derivative (PID) controller is used in PWRs to keep the pressure in the set point, during those operation conditions. The purpose of this study is two-fold: first, to develop a pressurizer model based on artificial neural networks (ANNs); secondly, to develop fuzzy controllers for the PWR pressurizer modeled by the ANN and compare their performance with conventional ones. Data from a 2785 MWth Westinghouse 3-loop PWR simulator was used to test both the pressurizer ANN model and the fuzzy controllers. The simulation results show that the pressurizer ANN model responses agree reasonably well with those of the simulated power plant pressurizer, and that the fuzzy controllers have better performance compared with conventional ones.