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[AlgorithmMAIN 3-DIMENSIONAL CFD-PROGRAM

Description:

 

#//u(i,j)        x方向的速度u;或者θ方向速度
#//u(i,j)        y方向的速度v;或者径向速度ur'h\K
#//pc(i,j)       压力修正 p'[OS
#//p(i,j)        压力p-=5-+
#//p(i,j)        密度ρBk
#//p(i,j)        扩散系数γQ"G48E
#//t(i,j)        温度Tn7/bKr
#//ake(i,j)        湍流脉动能量k971E
#//dis(i,j)      动能的耗散率ε4
 
//amut(i,j)     湍动扩散系数+
//gen(i,j)      湍流能量的生成率S
//f(i,jnf)     不同的φ变量M
//lsolve(nf)   1,求解变量f(i,j,nf)  8
//lprint(nf)   1,打印变量f(i,j,nf)   ~f
//lblk(nf)     1,对变量f(i,j,nf)应用块修正b5|{@U
//mode         选择坐标系的变量. 目前只支持mode=1.
mode=1 直角坐标系(x~y)BMI
mode=2 圆柱坐标系(r~z)^pg>)
mode=3 极坐标(r~θ)   Z^r[LE
solve子程序中求解变量f(i,j,nf)的重复扫描次数 b
变量fi,j,nf)的字符性标题 |L;
 
//xl    计算区域在x方向上的宽度)?
//yl    计算区域在y方向上的宽度@ob
//l1     x方向上主控制体的网格数。也是x方向上压力节点位置的最后一个i值。
//m1   y方向上主控制体的网格数。也是y方向上压力节点位置的最后一个j值。
//dt           时间步长 △t
//第三部分网格设定的变量
//x(i)        节点位置的x
//xu(i)       主控制容积在x方向的界面位置,即速度u(i,j)所在位置
//xdif(i)     差值 x(i)-x(i-1)
 //xcv(i)      主控制容积在x方向上的宽度
//xcvs(i)     速度u(i,j)的控制容积在x方向上的宽度
//y(j)        节点位置的y
//yv(j)       主控制容积在y方向的界面位置,即速度v(i,j)所在位置
//ydif(j)     差值 y(j)-x(j-1)
//ycv(j)      主控制容积在y方向上的宽度
//ycvs(j)     速度v(i,j)的控制容积在y方向上的宽度
////r(i,j)      主网络节点的半径r
////rmn(j)      在速度v(i,j)所在处的半径r之值
////sx (j)      主网格节点位置y(j)x方向上的标尺因子
//sxmn(j)     在界面位置yv(j)x方向的尺度因子
       //上面四个变量应用于非直角坐标系的情况
//xcvi(i,j)   xcv(i) 中与u(i,j)的控制容积相覆盖的部分
//xcvi(i,j)   xcv(i) 中与u(i+1,j)的控制容积相覆盖的部分
//ycvr(j)     主控制容积垂直于x方向的面的面积
//ycvrs(j)    速度v(i,j)的控制容积垂直于x方向的面的面积
//arx(j)      x方向相垂直的控制容积的面积
//arxj(j)     arx(j)中与速度v(i,j)的控制容积相覆盖的部分
//arxjp(j)    arx(j)中与速度v(i,j+1)的控制容积相覆盖的部分
//arxjarjxp实际上对应于x方向上的ycviycvip
 
//第四部分差分方程系数设定的变量
//con(i,j)    离散方程中的常数项b,在子程序gamsor中又作为存储sc的单元
//aip(i,j)    系数ae
//aim(i,j)    系数aw
//ajp(i,j)    系数an
//ajm(i,j)    系数as
//ap(i,j)     系数ap,在在子程序gamsor中又作为存储sp的单元
//flow         穿过控制容积界面的质量流率
//diff         扩散传导性 D
//acof         DIFLOW子程序计算的量,它给出了对流与扩散作用的联合影响
 
//第五部分求解差分方程过程中的变量求解
//du(i,j)     影响u(i,j)de
//dv(i,j)     影响v(i,j)dn
//pv(j)       用于计算主网络节点i,j上的质量流率的ρvr的插值因子:计算式如下: //fvp(j)      说明同上
//fx(i)       用于计算主控制容积界面(即速度u(i,j)所在处)的密度
 
//            rhom的插值因子,计算式如下:
 
//fxm(i)      说明同上
//pt(i)pt(j) tdma中的转换系数(消元过程中)l
//qt(i)qt(j) tdma中的转换系数(消元过程中)
//第六部分 index变量
int nf;               //nf           标明不同φ变量的下标值
int nfmax;         //nfmax        设有存储单元的nf的最大值
int np;              //(nfmax)      p(i,j)实际为f(i,j,nfmax)
int nrho;           //(nfmax+1)    rho(i,j)实际为f(i,j,nfmax)
int ngam;         //(nfmax+2)    gam(i,j)实际为f(i,j,nfmax+2)n
int l2;              //l2           (l1-1)
int l3;              //l3           (l1-2)
int m2;           //m2           (m1-1)
int m3;           //m3           (m1-2) 2
int ist;                    //ist          i的第一个内节点值`
int jst;                    //jst          j的第一个内节点值
int iter;                   //iter         不稳态问题的步进计数
int last;                   //last         用户所规定的最大迭代计数
int iter1;             //iter1        一个时间点求解setup2( )的迭代次数
double time;              //time         不稳态问题中的时间t
int ipref;              //ipref        压力参考结点的i
int jpref;                //jpref        压力参考结点的j=/
 
//第七部分其它变量
double rhocon;           //rhocon       密度为常数的问题中的ρ
int lstop;                 //lstop        =1时,停止计算
double  smax;            //smax         p'方程中的质源的最大值

double  ssum;            //ssum         p'方程中的质源的代数和


Platform: | Size: 11439 | Author: tanglincn | Hits:

[matlabTVL1_HCS_v1

Description: % May 2010 % This matlab code implements TVL1 based Hybrid Compressive Sensing using LSQR. % Only suitable the small scale data due to the costly storage and computation. % % A - M x N measurement matrix: random sampling alone or hybrid sampling (random sampling and low resolution sampling) % % gamma - weight for L1-norm of the diagonal gradients % % tol - tolerance for stopping criterion. % - DEFAULT 1e-3 if omitted. % % maxIter - maximum number of iterations % - DEFAULT 50, if omitted. %Original TVL1 based Hybrid Compressive Sensing problem: % min ||gx ||_1 + ||gy ||_1 +\gamma *(||gxy ||_1 + ||gyx ||_1) % s.t. A*I = b and I >=0 %It can be solved using PDCO as follows % min c*x s.t. Phi * x =B % x = [I; gx; -gx; gy; -gy; gxy; -gxy; gyx; -gyx] % Phi and B enforce the measurement (A) and constrant among the elements of x; % Xianbiao Shu, May 2010. Questions? xshu2@uiuc.edu; % Copyright: Computer Vision and Robotics Lab, University of Illinois, Urbana-Champaign % Acknowledgement: Primal-Dual interior method for Convex Objectives (PDCO) % http://www.stanford.edu/group/SOL/software/pdco.html
Platform: | Size: 917110 | Author: li123kai@126.com | Hits:

[CommunicationMultiuserOFDM_0_1

Description: 多载波OFDM系统仿真,共有8个文件 capavsgamma.m capavsusernum.m chtry.m rheesub.m shenpowerallo.m tdma_cap_cal.m waterfilling.m waterfillingPowerAllo.m-OFDM system simulation, a total of eight papers capavsgamma.m capavsusernum.m chtry.m rheesub.m shenpowerallo.m tdma_cap_cal.m waterfilling.m waterfillingPowerAllo.m
Platform: | Size: 10240 | Author: 王洁 | Hits:

[CSharpgamma

Description: 影像处理 工具 应用在gamma 上的使用
Platform: | Size: 1485824 | Author: jeff | Hits:

[Special Effectsgamma_MAP

Description: gamma_MAP滤波函数文件 可以对图像进行gamma_MAP滤波-gamma_MAP filter function file can be gamma_MAP image filtering
Platform: | Size: 1024 | Author: 李鹏坤 | Hits:

[OtherDigital.Video.And.HDTV.Algorithms.And.Interfaces.r

Description: 英文原版。介绍了数字视频处理的基本概念,视频采样,压缩,编码和协议标准等。值得收藏-With the help of hundreds of high quality technical illustrations, this book presents the following topics: * Basic concepts of digitization, sampling, quantization, gamma, and filtering * Principles of color science as applied to image capture and display * Scanning and coding of SDTV and HDTV * Video color coding: luma, chroma (4:2:2 component video, 4fSC composite video) * Analog NTSC and PAL * Studio systems and interfaces * Compression technology, including M-JPEG and MPEG-2 * Broadcast standards and consumer video equipment
Platform: | Size: 3288064 | Author: ilotusr | Hits:

[Special Effectsgama_amend

Description: 相机中都需要进行伽马校正,本实验中通过程序caculate_curve.m读入图片0中的灰度值,通过计算得到伽马变换的伽马系数,然后利用伽马系数进行反变换。-Both the need for gamma camera calibration, the experimental procedure caculate_curve.m read into the picture through the gray value of 0, obtained by calculating the gamma-gamma transform coefficients, then use the gamma coefficient inverse transform.
Platform: | Size: 123904 | Author: 孟希羲 | Hits:

[File Operatedigama

Description: digama.m calculates DIGAMMA ( X ) = d ( LOG ( GAMMA ( X ) ) ) / dX psi_values.m returns some values of the Psi or Digamma function for testing. timestamp.m prints out the current YMDHMS date as a timestamp. asa103_test.m, a sample calling program. asa103_test01.m, tests DIGAMA. asa103_test_output.txt, the output file. -digama.m calculates DIGAMMA ( X ) = d ( LOG ( GAMMA ( X ) ) )/dX psi_values.m returns some values of the Psi or Digamma function for testing. timestamp.m prints out the current YMDHMS date as a timestamp. asa103_test.m, a sample calling program. asa103_test01.m, tests DIGAMA. asa103_test_output.txt, the output file.
Platform: | Size: 4096 | Author: Jafar Hasan | Hits:

[EditorAddisonWesleyErichGammaDesignPatterns

Description: This .rar file includes the ebook(pdf) of the Design Patterns: Elements of Reusable Object-Oriented Software book written by Erich Gamma / Richard Helm / Ralph Johnson / John M. Vlissides from Addison-Wesley series. The examples are implemented in C-This .rar file includes the ebook(pdf) of the Design Patterns: Elements of Reusable Object-Oriented Software book written by Erich Gamma/Richard Helm/Ralph Johnson/John M. Vlissides from Addison-Wesley series. The examples are implemented in C++
Platform: | Size: 1792000 | Author: luci | Hits:

[matlabForcedPendulum

Description: This simulink model simulates the damped driven pendulum, showing it s chaotic motion. theta = angle of pendulum omega = (d/dt)theta = angular velocity Gamma(t) = gcos(phi) = Force omega_d = (d/dt) phi Gamma(t) = (d/dt)omega + omega/Q + sin(theta) Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method. Chaos can be seen for Q=2, omega_d=w/3. The program outputs to Matlab time, theta(time) & omega(time). Plot the phase space via: plot(mod(theta+pi, 2*pi)-pi, omega, . ) Plot the Poincare sections using: t_P = (0:2*pi/omega_d:max(time)) plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . ) System is described in: "Fractal basin boundaries and intermittency in the driven damped pendulum" E. G. Gwinn and R. M. Westervelt PRA 33(6):4143 (1986) -This simulink model simulates the damped driven pendulum, showing it s chaotic motion. theta = angle of pendulum omega = (d/dt)theta = angular velocity Gamma(t) = gcos(phi) = Force omega_d = (d/dt) phi Gamma(t) = (d/dt)omega+ omega/Q+ sin(theta) Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method. Chaos can be seen for Q=2, omega_d=w/3. The program outputs to Matlab time, theta(time) & omega(time). Plot the phase space via: plot(mod(theta+pi, 2*pi)-pi, omega, . ) Plot the Poincare sections using: t_P = (0:2*pi/omega_d:max(time)) plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . ) System is described in: "Fractal basin boundaries and intermittency in the driven damped pendulum" E. G. Gwinn and R. M. Westervelt PRA 33(6):4143 (1986)
Platform: | Size: 8192 | Author: Mike Gao | Hits:

[matlabdengyi

Description: CONSX—这是一个主要子程序,调用其它于程序及输出中间结果 * !* CHECK—检查所有的点是否满足约束条件,对违背约束的点进行校正 * !* CENTR—计算中心点 * !* FUNC —目标函数,由用户提供 * - PROGRAM COMPLEX PARAMETER(N=3,M=4,K=6) DIMENSION X(K,M),R(K,N),F(K),G(M),H(M),XC(N) INTEGER GAMMA OPEN(4,FILE= COMPDATA ) READ(4,*)ITMAX,IPRINT,ALPHA,BETA,GAMMA,DELTA READ(4,*)(X(1,J),J=1,N) IX=2097151 YFL=0. DO 100 II=2,K DO 100 JJ=1,N CALL RANDU(IX,YFL) R(II,JJ)=YFL 100 CONTINUE OPEN(2,FILE= COMPDAT.OUT ,STATUS= UNKNOWN ) WRITE(2,10) 10 FORMAT(//,10X, COMPLEX PROGRAM OF BOX ) WRITE(2,11)N,M,K,ITMAX,IPRINT,ALPHA,BETA,G
Platform: | Size: 21504 | Author: wangmeng | Hits:

[Mathimatics-Numerical algorithmssvm4

Description:  -s svm类型:SVM设置类型(默认0)   0 -- C-SVC   1 --v-SVC   2 – 一类SVM   3 -- e -SVR   4 -- v-SVR   -t 核函数类型:核函数设置类型(默认2)   0 – 线性:u v   1 – 多项式:(r*u v + coef0)^degree   2 – RBF函数:exp(-r|u-v|^2)   3 –sigmoid:tanh(r*u v + coef0)   -d degree:核函数中的degree设置(针对多项式核函数)(默认3)   -g r(gama):核函数中的gamma函数设置(针对多项式/rbf/sigmoid核函数)(默认1/ k)   -r coef0:核函数中的coef0设置(针对多项式/sigmoid核函数)((默认0)   -c cost:设置C-SVC,e -SVR和v-SVR的参数(损失函数)(默认1)   -n nu:设置v-SVC,一类SVM和v- SVR的参数(默认0.5)   -p p:设置e -SVR 中损失函数p的值(默认0.1)   -m cachesize:设置cache内存大小,以MB为单位(默认40)   -e eps:设置允许的终止判据(默认0.001)   -h shrinking:是否使用启发式,0或1(默认1)   -wi weight:设置第几类的参数C为weight*C(C-SVC中的C)(默认1)   -v n: n-fold交互检验模式,n为fold的个数,必须大于等于2--s svm_type : set type of SVM (default 0) 0-- C-SVC 1-- nu-SVC 2-- one-class SVM 3-- epsilon-SVR 4-- nu-SVR -t kernel_type : set type of kernel function (default 2) 0-- linear: u *v 1-- polynomial: (gamma*u *v+ coef0)^degree 2-- radial basis function: exp(-gamma*|u-v|^2) 3-- sigmoid: tanh(gamma*u *v+ coef0) 4-- precomputed kernel (kernel values in training_instance_matrix) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in kernel function (default 1/k) -r coef0 : set coef0 in kernel function (default 0) -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default 100) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1) -b
Platform: | Size: 17408 | Author: little863 | Hits:

[matlabBureau

Description: The Nakagami distribution or the Nakagami-m distribution is a probability distribution related to the gamma distribution. It has two parameters: a shape parameter μ and a second parameter controlling spread, ω.
Platform: | Size: 20480 | Author: ahmad | Hits:

[matlabar_g

Description: PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model imposing stability restrictions using Gibbs sampling y = b0 + y(t-1) b1 + y(t-2) b2 +,...,y(t-k) bk + E, E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T), V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)- PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model imposing stability restrictions using Gibbs sampling y = b0+ y(t-1) b1+ y(t-2) b2+,...,y(t-k) bk+ E, E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T), V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)
Platform: | Size: 3072 | Author: Jack | Hits:

[matlabstartval.m

Description: Function to compute starting valus for a multiple-regime SmoothTransition Regression (STR). The algorith is based on a grid search over the parameters gamma and c.
Platform: | Size: 1024 | Author: canopus | Hits:

[Post-TeleCom sofeware systemsFSO_Analysis

Description: 自由空间光通信系统在大气湍流信道下的3个主要性能指标分析,即平均误码率、中断概率和平均信道容量。其中大气湍流采用gamma-gamma湍流信道衰退模型。3个M文件为平均误码率与平均信噪比之间的关系(BER_SNR.m),中断概率(OutageP.m),以及平均信道容量与平均信噪比之间的关系(C_SNR.m)。程序可直接运行。-The analysis of 3 key performance indicators of Free-Space Optical communication system in atmospheric turbulent channel, namely the average bit error rate, outage probability and the average channel capacity. The atmospheric turbulence is modeled by the gamma-gamma turbulent channel fading model. Three M-files are the relationship between the average bit error rate and the the average SNR (BER_SNR.m), outage probability (OutageP.m), the relationship between the average channel capacity and the average SNR (C_SNR.m).The programs can run directly.
Platform: | Size: 2048 | Author: | Hits:

[matlabFirefly_Algorithm

Description: % Firefly Algorithm by X S Yang (Cambridge University) % % Usage: firefly_simple([number_of_fireflies,MaxGeneration]) % eg: firefly_simple([12,50]); % % ======================================================== % % This is a demo for 2D functions; for higher dimenions, % % you should use fa_ndim.m or fa_mincon.m % % Parameters choice: % Gamma should be linked with scales. Otherwise, the FA % % the efficiency will be significantly reduced because % % the beta term may be too small. % % Similarly, alpha should also be linked with scales, % % the steps should not too large or too small, often % % steps are about 1/10 to 1/100 of the domain size. % % In addition, alpha should be reduced gradually % % using alpha=alpha_0 delta^t during eteration t. % % Typically, delta=0.9 to 0.99 will be a good choice.
Platform: | Size: 1024 | Author: hichem22 | Hits:

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