一点小进展，庆贺一下。

 

http://blog.csdn.net/duanbingnan/archive/2007/10/30/1856105.aspx

 

一、预处理的由来：
在C++的历史发展中，有很多的语言特征（特别是语言的晦涩之处）来自于C语言，预处理就是其中的一个。C++从C语言那里把C语言预处理器继承过来（C语言预处理器，被Bjarne博士简称为Cpp，不知道是不是C Program Preprocessor的简称）。

二、常见的预处理功能：
预处理器的主要作用就是把通过预处理的内建功能对一个资源进行等价替换，最常见的预处理有：文件包含，条件编译、布局控制和宏替换4种。
文件包含：#include 是一种最为常见的预处理，主要是做为文件的引用组合源程序正文。
条件编译：#if,#ifndef,#ifdef,#endif,#undef等也是比较常见的预处理，主要是进行编译时进行有选择的挑选，注释掉一些指定的代码，以达到版本控制、防止对文件重复包含的功能。
布局控制：#progma，这也是我们应用预处理的一个重要方面，主要功能是为编译程序提供非常规的控制流信息。
宏替换： #define，这是最常见的用法，它可以定义符号常量、函数功能、重新命名、字符串的拼接等各种功能。

三、预处理指令：
预处理指令的格式如下：
# directive tokens
#符号应该是这一行的第一个非空字符，一般我们把它放在起始位置。如果指令一行放不下，可以通过\进行控制，例如：
#define Error if(error) exit(1) 等价于
#define Error \
if(error) exit(1)
不过我们为了美化起见，一般都不怎么这么用，更常见的方式如下：
# ifdef __BORLANDC__
if_true<(is_convertible<Value,named_template_param_base>::value)>::
template then<make_named_arg, make_key_value>::type Make;
# else
enum { is_named = is_named_parameter<Value>::value };
typedef typename if_true<(is_named)>::template
then<make_named_arg, make_key_value>::type Make;
# endif
下面我们看一下常见的预处理指令：
#define 宏定义
#undef 未定义宏
#include 文本包含
#ifdef 如果宏被定义就进行编译
#ifndef 如果宏未被定义就进行编译
#endif 结束编译块的控制
#if 表达式非零就对代码进行编译
#else 作为其他预处理的剩余选项进行编译
#elif 这是一种#else和#if的组合选项
#line 改变当前的行数和文件名称
#error 输出一个错误信息
#pragma 为编译程序提供非常规的控制流信息
下面我们对这些预处理进行一一的说明，考虑到宏的重要性和繁琐性，我们把它放到最后讲。

四、文件包含指令：
这种预处理使用方式是最为常见的，平时我们编写程序都会用到，最常见的用法是：
#include <iostream>         //标准库头文件
#include <iostream.h>         //旧式的标准库头文件
#include "IO.h"             //用户自定义的头文件
#include "../file.h"         //UNIX下的父目录下的头文件
#include "/usr/local/file.h" //UNIX下的完整路径
#include "..\file.h"         //Dos下的父目录下的头文件
#include "\usr\local\file.h" //Dos下的完整路径
这里面有2个地方要注意：
1、我们用<iostream>还是<iostream.h>?
我们主张使用<iostream>，而不是<iostream.h>,为什么呢？我想你可能还记得我曾经给出过几点理由，这里我大致的说一下：首先，.h格式的头文件早在98年9月份就被标准委员会抛弃了，我们应该紧跟标准，以适合时代的发展。其次，iostream.h只支持窄字符集，iostream则支持窄/宽字符集。
还有，标准对iostream作了很多的改动，接口和实现都有了变化。最后，iostream组件全部放入namespace std中，防止了名字污染。
2、<io.h>和"io.h"的区别？
其实他们唯一的区别就是搜索路径不同：
对于#include <io.h> ，编译器从标准库路径开始搜索
对于#include "io.h" ，编译器从用户的工作路径开始搜索
五、编译控制指令：
这些指令的主要目的是进行编译时进行有选择的挑选，注释掉一些指定的代码，以达到版本控制、防止对文件重复包含的功能。
使用格式，如下：
1、
#ifdef identifier
your code
#endif
如果identifier为一个定义了的符号，your code就会被编译，否则剔除
2、
#ifndef identifier
your code
#endif
如果identifier为一个未定义的符号，your code就会被编译，否则剔除
3、
#if expression
your code
#endif
如果expression非零，your code就会被编译，否则剔除
4、
#ifdef identifier
your code1
#else
your code2
#endif
如果identifier为一个定义了的符号，your code1就会被编译，否则yourcode2就会被编译
5、
#if expressin1
your code1
#elif expression2 //呵呵，elif
your code2
#else
your code3
#enif
如果epression1非零，就编译your code1，否则，如果expression2非零，就编译your code2，否则，就编译your code3

其他预编译指令
除了上面我们说的集中常用的编译指令，还有3种不太常见的编译指令：#line、#error、#pragma，我们接下来就简单的谈一下。
#line的语法如下：
#line number filename
例如：#line 30 a.h 其中，文件名a.h可以省略不写。
这条指令可以改变当前的行号和文件名，例如上面的这条预处理指令就可以改变当前的行号为30，文件名是a.h。初看起来似乎没有什么用，不过，他还是有点用的，那就是用在编译器的编写中，我们知道编译器对C++源码编译过程中会产生一些中间文件，通过这条指令，可以保证文件名是固定的，不会被这些中间文件代替，有利于进行分析。
#error语法如下：
#error info
例如：#ifndef UNIX
#error This software requires the UNIX OS.
#endif
这条指令主要是给出错误信息，上面的这个例子就是，如果没有在UNIX环境下，就会输出This software requires the UNIX OS.然后诱发编译器终止。所以总的来说，这条指令的目的就是在程序崩溃之前能够给出一定的信息。
#pragma是非统一的，他要依靠各个编译器生产者，例如，在SUN C++编译器中：
// 把name和val的起始地址调整为8个字节的倍数
#progma align 8 (name, val)
char name[9];
double val;
//在程序执行开始，调用函数MyFunction
#progma init (MyFunction)
预定义标识符
为了处理一些有用的信息，预处理定义了一些预处理标识符，虽然各种编译器的预处理标识符不尽相同，但是他们都会处理下面的4种：
__FILE__ 正在编译的文件的名字
__LINE__ 正在编译的文件的行号
__DATE__ 编译时刻的日期字符串，例如： "25 Dec 2000"
__TIME__ 编译时刻的时间字符串，例如： "12:30:55"
例如：cout<<"The file is :"<<__FILE__"<<"! The lines is:"<<__LINE__<<endl;

预处理何去何从
如何取代#include预处理指令，我们在这里就不再一一讨论了。
C++并没有为#include提供替代形式，但是namespace提供了一种作用域机制，它能以某种方式支持组合，利用它可以改善#include的行为方式，但是我们还是无法取代#include。
#progma应该算是一个可有可无的预处理指令，按照C++之父Bjarne的话说，就是："#progma被过分的经常的用于将语言语义的变形隐藏到编译系统里，或者被用于提供带有特殊语义和笨拙语法的语言扩充。”
对于#ifdef，我们仍然束手无策，就算是我们利用if语句和常量表达式，仍然不足以替代她，因为一个if语句的正文必须在语法上正确，满足类检查，即使他处在一个绝不会被执行的分支里面。

最近有机会和南方一家民营中小企业做了交流。总体而言这家企业还算不错，老总受过很好的教育，在商海里面也摸爬滚打了很长时间，和国外公司也有联系。比起一般的“农民”企业水平要高。企业的目标是在国际上创立自己的品牌。无奈“有心杀贼，无力回天”。中国的技术力量不是一般般的薄弱，聘请高水平人员恐怕也不是这类公司所能承受的，而一般的工程技术人员尽管有经验，但是缺乏独立设计的思路和方法，这样的公司距离自主设计创新还有很长的路要走。

很早就听一个朋友评价西工大的电磁流变做的好。来了西工大以后有机会接触了课题组的老师——一位很有风度的先生，年纪不算大。有一次发现这位老师居然没有住在学校的“小高层”（西工大“高级”人物的集聚地），很是诧异。

这次在校车上无意听到他和另外一位老师的谈话，他的话令我很感动：给钱做工作，不给钱照样做工作！比起其他一些学者，可算是高风亮节了。

西工大的理学院在学校并不算是强势的学院，可是那里的老师自己贷款搞科研（去年Nature上介绍了他们的工作），海龟副教授和别人合用一个16个人的办公室却仍然很乐观（他们的工作上了国际一个挺好杂志的封面）。

很敬佩这些人。

本打算把FLUENT的后台执行功能调通以后，可以远程提交作业，并关闭本地机。搞了半天，发现，原来不能关，一关程序跟着死！

不过总算搞通了BATCH提交作业——可以预先设定计算参数，存在input文件里，而不用不停跟踪程序。

BATCH操作：

BATCH是FLUENT 提供的无图形界面启动功能，它将把FLUENT计算过程产生的文件存储在文件内，为不同线程通过远程登录用户监视计算过程。当前就用了很简单的功能：

;读入数据

rc temp.cas
rd temp.dat

;非定常问题求解
/solve/dual-time-iterate
10
500

;输出
/file/write-case-data fin.cas
exit
yes

 

一个网站上的例子

rc def.cas 
rd def.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def10.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def20.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def30.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def40.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def50.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def60.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def70.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def80.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def90.dat
solve/set/time-step 0.01
solve/set/reporting-interval 20
solve/dual-time-iterate 10
it 100
wd def100.dat
exit

 

The equilibrium distributions F_i^(0) will be calculated by maximizing the entropy for given constrains which for the case under consideration are the mass and momentum density.

From: http://lcni.uoregon.edu/~mark/Stat_mech/thermodynamic_entropy_and_information.html

 

The connection between thermodynamic entropy and information

    The bottom line is that thermodynamic entropy is best understood not as a property or macroscopic state of matter (like mass, temperature, or pressure), but as a lack of knowledge of the detailed configuration of matter. In particular, thermodynamic entropy is a measure of our lack of information about the microstate of a closed system of matter near equilibrium. To make this concrete, I'll compare two similar simple systems, one of particles and one of bits. Although the concept of entropy in classical thermodynamics was elucidated long before information theory was developed, thermodynamic entropy can be viewed as a straight-forward application of information theory to a physical problem.

    There are many other fine discussions of this topic, but few that strip it down to a simple example. A more in-depth, but more technical, discussion of the same topic is at Entropy in thermodynamics and information theory. But this discussion, and others have the same bottom line, with only a variation of language:

The 'message' in thermodynamics, the microstate of a physical system, will never be communicated as it is inaccessible to observation or transmission. A good diagram illustrating this idea of "physical information" is in M. P. Franks paper "Physical limits of Computing".

Background: What are particles and bits? What the heck is "log", and what does it have to do with this topic?

    Consider a perfectly insulated 2-D box of simple particles. The macrostate of an ideal gas can be specified by the total energy E, number of particles N and volume V. There are a large but finite number of possible microstates that are all consistent with this system's single, and unchanging, macro-state:

Ludwig Boltzmann's leap of imagination was that the number of possible microstates, Ω, was finite, and in some sense a particle's state is discrete. But it wasn't until quantum mechanics was developed that this was clarified and shown to be strictly true. Henri Poincairé and others showed that such an ideal particle system would necessarily cycle through all possible microstates, and that each would be visited with equal probability. The same holds for all practical purposes in real physical systems; no state or group of states is favored.

    Any one of these microstate is equally likely to be the actual microstate (near equilibrium) and we have no way of knowing which is the actual microstate. And we never will. This lack of information is not because we haven't examined the system closely; it reflects the inaccessibility of this information near equilibrium. But we can count how many microstates are possible.

    The thermodynamic entropy, S, for this case is:

Boltzmann's form of this equation is S = k ln(Ωp), where Boltzmann's constant has SI units of JK-1. Because thermodynamic entropy is dependent on the energy and temperature of of the system, it was convenient to use this proportionality constant if these variables are measured or derived.   An alternative, used here, is to normalize thermodynamic variables such that the proportionallity constant is defined as 1. From Entropy in thermodynamics and information theory: "The presence of Boltzmann's constant k in the thermodynamic definitions is a historical accident, reflecting the conventional units of temperature. It is there to make sure that the statistical definition of thermodynamic entropy matches the classical entropy of Clausius, thermodynamically conjugate to temperature. For a simple compressible system that can only perform volume work, the first law of thermodynamics becomes But one can equally well write this equation in terms of what physicists and chemists sometimes call the 'reduced' or dimensionless entropy, σ = S/k, so that Just as S is conjugate to T, so σ is conjugate to kT (the energy that is characteristic of T on a molecular scale)." Writing the equation in this way doesn't change thermodynamics, or its expression in information theoretic terms.

   
    This statistical measure of thermodynamic entropy quantifies the uncertainty about which microstate is occupied. The higher the number of equally probable possibilities, the more uncertainty. Near equilibrium the system has a maximum entropy, because there are the most possible microstates near equilibrium. For example, there are very few possibilities for all the particles clumped in one corner of our insulated box but many possible ways they can be roughly evenly distributed across the box.

    Compare this with a set of 2-D 4 x 4 arrays of bits (images in this case, each one a kind of message), each with the same macro-state specified by the number of bits (N = 16, represented by black or white squares). Note that the number of bits, N, is the same in each instance, although all combinations of black and white are in the set.  If an acquaintance is to send you an image/message of this form (a 16-bit email, for example), and you have no prior information about which image/message is to be sent, then each of a countable number (65,536) of images/messages is equally probable.

    The information theory entropy (Shannon entropy), H, for this case is defined as:

    The entropy H quantifies the uncertainty about what message is to be received. The higher the number of equally probable possibilities, the more entropy. The image/message has a maximum of entropy before it is received. But after it is received and read, there is no longer any uncertainty; there is only one possible microstate, the image/message itself; Ω_p = 1 and H = 0.

    If a single one of these arrays is received as an image/message, the information, I, contained in the image/message is:

If the microstates are not equally probable, these formulas for S, H and I need to be modified. They become weighted sums over all possible states, where the probability of each state is the weighting factor. See Entropy in thermodynamics and information theory.

    The probability of this particular image/message being sent is 1/Ω_s. The larger the number of possibilities, the more uncertainty is resolved, or entropy reduced, when the particular image/message is received. Information is a measure of how much an image/message (an observed microstate) tells us, by comparison with the number of other messages it could have been (those consistent with the image/message's macrostate).

Here's the math. For this 16 bit message with 65,536 possibilities, a single message contains I = -log2(1/65,536) bits = -log2(2-16 ) bits = 16 bits. This is the amount the message was "surprising", or how much our uncertainty (entropy) was reduced -- it could have been a lot of things but it was this singular message. But this result -- 16 bits of information is contained in the message -- is not surprising for this simple example; we knew we were to be sent 16 bits and when we received the message we found out what each of the 16 bits was.

    H and I might seem redundant because the formulas are similar. But H does not equal I. Entropy refers to the uncertainty of an unknown message, and information refers to the probability of a known message occurring by chance alone.

More accurately, entropy is a measure of uncertainty due to the unknown part of a message/particle system, and information is a measure of reduction of uncertainty due to the known part of a message/particle system.

Information gained is equal to entropy lost. Information and entropy are two sides of the same probabilistic coin. While a flipped coin is spinning in the air the entropy H is one bit (an unknown heads or tails), and the information I is zero. When it lands and is observed, the entropy H is zero, and the information I is one bit (a known heads or tails).

    S and H (thermodynamic and Shannon entropy) are equivalent, in that S is directly proportional to H, and this is because the same conditions hold for both systems. S is reserved for thermodynamics, but H can can be applied to any statistical system. As Shannon and Weaver wrote:

    The entropy S is a state function of a thermodynamic system, but it can't be directly measured like pressure and temperature (see measuring entropy). There is no entropy-meter; entropy must be infered by varying the state of a system near equlibrium and observing how other thermodynamic variables (pressure, temperature, etc.) respond. This is one reason why the statistical mechanics interpretation of entropy is so important:

    It might seem like this statistical interpretation of matter can cause matter to be "influenced" by our knowledge, or lack of knowledge, of its microstates. What does information or knowledge about microstates have to do with how a steam engine works! But this train of thought is a result of a misperception of microscopic states in nature. Which microstate a particle system is in is irreducibly (inherently) uncertain, in same sense that the position and momentum of individual particles are uncertain (Heisenberg's uncertainty principle). The fact that entropy almost always increases or stays the same (the second law of thermodynamics) is a statistical statement about the uncertainty of a particle system's microstate.

The fact that entropy sometimes can and does decrease is often glossed over in discussions of, and even the statement of, the second law of thermodynamics. The usefulness of the second law (it's explanatory power) is due to how frequently entropy doesn't measureably increase for any large number of particles. For even small macroscopic systems with a small number of possible states (e.g. > 1,000 particles each with >10 possible states and >101,000 total possible states), it is highly improbable (p <<< 1/2) that a measureable increase of entropy (e.g. a fractional increase of 1/1,000) will occur in the (current) lifetime of the universe (~1010 years). Almost is good enough for physics too.

    James Clerk Maxwell's thought experiment Maxwell's demon is an example of the importance of observability/uncertainty in discussing the second law. The experiment's resolution, that the demon can't cheat the second law because she can't observe the microstate without altering it, highlights the importance of observability/uncertainty in physics.






[To Do:  Show how these ideas can be extended to easily percieved messages, particulary images.]
53,754 bit (184 x 289) images ( 2⁵³⁷⁵⁴ possible images ). Each pixel is represented by one bit, black or white. These three are particular images, not arbitrary selections from all possible 184 x 289 one-bit images :

Low algorithmic complexity "simple" (one of very few possible low information images) High "image available energy" (non-random intensity gradient) Farthest from equilibrium

Medium algorithmic complexity "complex" (one of a few possible medium information images) Medium "image available energy" Far from equilibrium

High algorithmic complexity "random" (one of many possible nearly random images) Low "image available energy" Near equilibrium

改革开放卅十年最富有教育成果之一的是中国人懂得了以下几点：

    1、所谓铁饭碗，不是一辈子在一个地方吃饭，而是一辈子到哪里都有饭吃。
    2、把每一天的日子能过好，就是不容易；把每一件简单的事能做好，就是不简单；把每一件平凡的事能做好，就是不平凡。
    3、人生的最佳成果是在阳世上潇洒走一回；生存的最终目的是舒坦，生命的最低纲领是平安；生活的最高境界是宽容，相处的最高境界是尊重。
    4、从崇高到荒唐只有半步之遙，从荒唐到崇高却是举步维艰。
    5、所谓生老病死：生得要好，老得要慢，病得要晚，死得要快。
    6，傲不可长，欲不可纵，乐不可极，志不可移。
    7、不与富交己不贫，不与贵交已不贱。
    8、世上只有想不通的人，没有走不通的路。
    9、能力就像一张支票，除非把它兑换成现金，否则毫无价值。
    10、人生如戏，戏如人生；人生在世，无非是让别人笑笑，偶尔笑笑别人。

 

花了将近一个礼拜，把两台机器的系统重新装了一遍。第一次练手，没有装一些程序开发的软件包——导致FLUENT没法并行。后来又整个重装，把软件包补上。现在带上了gcc，FLUENT也能并行了。只是MPICH是请钟老师装的。听说需要编译，我就没有管。现在，暂时要用到的东西都安装完毕。抽空可以开始做老刘的项目——老刘要求现在的结果可以在合同的基础上再进一步，到时候回报的时候给领导留个好印象。这段时间除了需要搞定LB方法意外，就是这个事情。

今年自然基金没有通过，主要的意见是研究基础欠缺——也在意料之中。对于我来讲，问题是新的，方法也是新的，一次“命中”目标的几率不是很大。所以，要抓紧这段时间把LB方法再研究一下，争取发几篇文章。明年的把握大一点。

作为LINUX练手，还需要把新校区的老PC搬回来，修理的同时，可以用来装LINUX，毕竟分区之类的东西钟老师也没有试过。如果搞定，将来可以节省很多时间。

今天收到了班里同学的贺卡。感觉上，这个班要比前面带过的班要“开窍”一点。因为深切的感觉到中国学生缺乏独立自主的意识。我在开始带本科课程的时候就试着尽量给学生“自决”的机会，其中也包括让他们决定自己是否可以到课。去年的班通过一年的训练，基本上有1/5的学生可以很好的适应——自我感觉也不错了。我的同班同学已经有很多不搞力学了，没有兴趣的同学在及格的基础上，可以多留一点时间做一些自己感兴趣的事情。今年航海学院这个班，接受的能力似乎比较强，刚提了几次，就有很多同学课后过来问问题，效果挺好。从贺卡上看得出来，他们也感觉到了我上课方法的不同，也愿意和我一起尝试这一做法。

其实，我的做法在科大也不算什么创新，记得大一下半学期的高数老师就没有对到课率提要求，我只不过照搬到这里。所不同的是，自我感觉在科研上还有一点心得，所以经常在课上花很多时间给学生讲写论文、做课题。希望一些科大的做法可以成功移植到这里。

对Linux一窍不通。通过朋友指点，设置成功；同时，恢复了以前的一些设置（自己瞎调，结果把机器搞得狂慢），系统基本正常。

设置很简单，输入"gdmsetup"，进入“登陆窗口首选”菜单，

激活“XDMCP”，将远程选项设成“与本地相同”，在“安全”里面允许管理员远程登录。

一切就OK了。用EXCEED访问很方便。

前些天，自己调的时候，把机器名改了，和其他地方的设置可能有冲突，结果机器很慢，今天调回来了。一切正常！

经过半个多月的忙碌，新校区的工作场所终于基本收拾完了，现在各个部门已经开始运转。今天联系了网络中心，服务态度比较好，更换固定IP的工作也没有多费周折。从新校区远程登录老校区计算机也很顺利。

办公室有点挤，但是毕竟是自己窝，而且再也不用“凑合”着过了。更别说我对这方面的工作条件要求也不是很高。其他有些老师好像有点意见，我还好。

接下来就是落实实验室。这段时间要把电雾化实验开展起来。原来联系的本科生这前半个月要去实习，而且训练还需要一段时间，所以决定亲自搞，否则不知道要拖到什么时候。现在需要考虑的是实验室的布置和改造，估计院里面还是老态度，不能指望。栽跟头多了，浑身都是老茧了。

新校区总体来讲不错，空气要比市里面干净，也算是依山傍水。天气好的时候看看蓝天、秦岭，总是勾起在温哥华的回忆。如果不是每天要去食堂挤着买饭，也还真有点采菊东篱下，悠然见南山的感觉。等到将来安定下来了，可以抽空让大家过来度个周末什么的。

来日方长，慢慢干吧。

办公室空调今天可以安装完毕，机架也搭起来了，现在等着联网，调试。

办公室没有想象的大，几个大沙发一放就塞满了，看将来怎么调整吧。

眼看着就要“搬家”了。这几天需要整理一下。内部网络已经基本上调试好了。基本思路是，远程登录管理节点（windows的远程控制提供了一个很好的接口）。这样在校内就能通过管理节点控制内部网的服务器，进行计算了。将来办公室搬到新校区以后，不必每天都花2个多小时在路上，在家也可以干。剩下的就是是否能在老校区访问新校区IP的问题了——这是我无法控制的。

在调试过程中，也遇到了一些麻烦。由于一个不知道的原因，某些机器无法控制、而其他机器却可以。换了操作系统也没办法。幸亏最近由partner帮忙配的机器可以用。因为显卡配的是9600的，本来打算留在这里将来调试GPGPU用的，现在就搬过去做管理节点了。

2x4core服务器计算速度的问题好像是解决不了——一个2x4core的机器居然和2x2core的性能差不多——可能是CPU本身的性能问题。好在现在至少已经过了从无到有的阶段，将来配机器要谨慎。配完以后要做测试，积累经验，在硬件方面只懂个皮毛会浪费很多money。

接下来就是准备攻LBM。今年的自然基金开来是没戏了，不过确实也是基础太薄弱；即没有很强的空化方面的经验，LBM也是现学现卖。现在很多同行工作都很有进展，在这个行业混饭吃也不容易。LBM的问题也不少，昨天看了一篇文献，提到LBM在处理大密度比的问题时也有技术障碍，被打击了一下。不过还是需要试了再说。好歹发几篇文章，“东方不亮，西方亮”也行。这几天先看看文献，研究一下SC模型，做个算例测试一下。ESP实验则要等到开学，学生过来了再开展，争取在学报上发表一、两篇文章。

周围的人经常提醒注意劳逸结合，我想是对的。尽管自我感觉前一阶段的“高强度”工作没多大影响，但还是要注意。另外，抽空也要练练英语，时间长了有时候已经反应不过来了。上次老板过来，提到是否要给导游“tip”，居然没明白。丢了一下人。

程序调通、材料写好。休息、休息一下。