代写program、代做Java/c++设计编程

项目预算：开发周期：发布时间：要求地区：

– mkdir build
– cd build
– cmake ..
– make -j
– make install
You must follow these instructions to build and install using CMake, otherwise CMake will
not be able to find FFTW3 when you try to build your project.
Reporting Errors
Contact the lecturer directly if you believe that there is a problem with the starting code, or the build process
in general. To make error reporting useful, you should include in your description of the error: error messages,
operating system version, compiler version, and an exact sequence of steps to reproduce the error. Questions
regarding the clarity of the instructions are allowed. Any corrections or clarifications made after the initial
release of this coursework will be written in the errata section and announced.
Assignment: A Universe in a Box
Important: Read all these instructions before starting programming. In particular, some marks are awarded
for a reasonable git history (as described in Part 3), that should show what you were thinking as you
developed.
0.2 Overview: N-body Simulations and Particle-Mesh Methods
This assignment will ask you to produce a simulation of particles subject to gravitational forces. This is
known as an “n-body simulation”. Please read through this background section carefully, even if you are
familiar with the subject area, as this contains the models that you will have to implement and introduces
notation that will be used throughout the assignment text.
N.B. In order to keep diagrams clear, illustrations will use a 2D grid. You should keep in mind however that
this will be a three dimensional simulation.
Don’t worry about the underlying physics, we’ll describe the mathematical process that needs
to be implemented in detail.
The basic idea of an gravitational n-body simulation is that:
• We have some space in which there are many particles
• We start with some intitial distribution of particles; in our case this they will be uniform randomly
distributed in a box (a unit cube).
• We have np particles.
• Every particle has the same mass, mp.
• Particles have individual positions and velocities.
• The time evolution of the system is broken up into equal time steps.
• We perform the following process in a loop, starting at t = 0 and ending at t = tmax:
– Calculate the acceleration of all particles due to gravity
– Update the position and velocity of all particles
– Increase the time t by the time-step t.
Furthermore, we’ll choose our coordinate system so that:
• Particles exist in a box, with sides of size W.
• Coordinates ˛r in the box will be relative to the size of the box, so particle positions will always
been within the unit cube: x, y, z œ [0, 1).
– The actual distance between two particles is therefore W˛r
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– This allows us to model an expanding universe simply by updating the value of W, without
updating the positions of each particle.
– Particles with a fixed position as W increases are moving apart from each other in line with the
overall expansion of the universe; this is why these coordinates are sometimes known as co-moving
coordinates.
– We only keep track of what’s called the peculiar velocity of each particle: the velocity the particle
has when the expansion is ignored.
We will label particles from 0 to (np≠1); every particle has its own position (˛ri), velocity (˛vi), and acceleration
(˛ai). Position, velocity, and acceleration for a given particle are all three dimensional vectors with an x, y,
and z component.
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0.3 Notation
We will define notation as we introduce it throughout this assignment, but the notation used is summarised
here for reference.
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0.3.1 Mathematical notation:
• ˛r is used for a point/vector in real space coordinates
• x, y, z are the x, y, z components of the ˛r vector
• ˛
k is used for a point/vector in frequency space coordinates.
• kx, ky, kz are the x, y, z components of the ˛
k vector
• k2 is used for ˛
k · ˛
k
• f(˛r) is a function in real space coordinates, while ˜f(˛
k) is the frequency space representation.
• FT(f) is used for the Fourier transform from real space (˛r) to frequency space (˛
k).
– ˜f(˛
k) = FT(f(˛r))
• FT≠1( ˜f) is used for the inverse Fourier transform from frequency space (˛
k) to real space (˛r)
– f(˛r) = FT≠1( ˜f(˛
k))
0.3.2 Physical functions:
• fl(˛r): the density
• (˛r): the gravitational potential
• ˛g(˛r): the gravitational field; this is the acceleration felt by a particle at ˛r
0.3.3 Simulation properties:
• W: width of the box
• nc: the size of each dimension of the mesh in cells
– The mesh is an nc ◊ nc ◊ nc grid
• Nc: the total number of cells in the mesh, Nc = n3
c
• wc: the width of a cell
• (i, j, k) is used for indexing the mesh with separate x, y, and z indices (i, j, and k respectively)
• µ is used for indexing the mesh with a single index
– µ = k + nc(j + nci)
• np: the number of particles
• mp: the mass of a particle
• ˛ri: the position of particle i
– xi: the x-position of particle i
– yi: the y-position of particle i
– zi: the z-position of particle i
– xi, yi, zi œ [0, 1)
• ˛vi: the velocity of particle i
• ˛ai: the acceleration of particle i
• t: the time coordinate (begins at 0)
• t: the time-step, the time dierence between one simulation step and the next
• tmax: the finish time of the simulation
• F: the expansion factor per timestep.
0.4 Updating the particles
We can update a particle’s velocity using its current acceleration, and we can update a particle’s position by
using its velocity. We will take a very simple approach to this update known as Euler integration.
˛vi(t) = ˛vi(t ≠ t) + ˛ai(t)t
˛xi(t) = ˛xi(t ≠ t) + ˛vi(t)t
Note that this formulation has implied an ordering: we update the velocity first, and then update the position
using the new velocity values. This order is specified to avoid ambiguity in the implementation rather than
for theoretical reasons.
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This method is not the most accurate, but it is fast and simple to implement. In order to perform this update
we need to be able to calculate the acceleration at a given time step for every particle, which is dependent on
the relative positions of all of the particles.
We will treat the space as periodic, so that if a particle moves outside of the box it reappears on the other
side.
0.5 Calculating Accelerations: Particle-Particle and Particle-Mesh methods
0.5.1 Particle-Particle
The simplest approach is to calculate the acceleration on each particle due to every other particle directly. If
there are np particles, labeled 0...(np ≠ 1), then the acceleration experienced by particle i due to all other
particles is:
˛ai = ≠q
j”=i
mp
|˛rj≠˛ri|2 rˆji,
where rˆji is the unit vector pointing to particle j from particle i, and mp is the mass of a particle. (We have
also ignored a factor of Newton’s constant, G, which we can set to 1 by a choice of units. This keeps the
mathematical structure as bare as possible.)
This calculation would need to be done for all n particles. This approach is known as the Particle-Particle
(PP) method, since it calculates forces between every pair of particles directly. There are unfortunately a
few problems with this approach:
1. The acceleration function is singular where rij = 0, and thus we can get extreme behaviour (or
even inf/nan issues) when particles are very close. This would need to mitigated by so called “force
softening”.
2. This algorithm rapidly becomes very slow as the number of particles increases (O(n2) due to pair-wise
approach).
3. We would like to simulate an infinite universe, so how do we calculate interactions more distant than
the size of the box?
0.5.2 Particle-Mesh
Another approach, which we shall take in this project, is to use a Particle-Mesh (PM) method. ParticleMesh is so called because it still has particles in a space, but now we also keep track of discretised functions
on the space, which are represented on a mesh. The mesh is just a gridding of our 3D space: our space is
split up into a finite number of cells, and a function can be represented by a value in each cell.
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The mesh is a 3D uniform grid of cells; the grid is nc cells wide, high, and deep. This means that the width
of a cell, wc, is given by:
wc = W
nc ,
and the volume of a cell is:
Vc = w3
c .
Note that wc is the physical width of the cell, not the coordinate width. The width of a cell in comoving
coordinates ˛r is always 1
nc , since the coordinates are scaled so that the box is a unit cube. We need the
physical width and volume of the cell in order to calculate physical quantities like gradients and densities.
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To represent a function, we need to assign a value to every cell in the mesh. To avoid doing the pair-wise
acceleration function, we want to convert the distribution of particles into a density function fl. This density
function can then be transformed into a gravitational potential, which can then be used to calculate the
gravitational field (the acceleration that a particle experiences due to gravity at a given point in space).
0.5.2.1 The density function
The density function, fl, in a given cell is calculated by counting the particles within that cell and using the
following formula:
fl = M
Vc = pcmp
Vc ,
where pc is the number of particles in the cell, and M = pcmp is the total mass of inside the cell.
0.5.2.2 The potential function
The potential, , is related to the density by the following formula:
Ò2 = 4fifl,
where again we have ignored G. Calculating the potential involves solving this dierential equation, which
can be converted to a simpler equation using a Fourier transform (FT). Don’t worry about this process: we
will be using a library to perform the FT itself. All you need to know is:
1. Fourier transforms convert our function in real space f(˛r) to a function in a reciprocal frequency space
˜f(˛
k) where k is related to the reciprocal of r. For example they might convert between time and
frequency, or distance and wavenumber.
2. Fourier transforms can be used to solve some kinds of dierential equations.
3. The frequency representation makes functions in finite spaces inherently periodic. This is ideal for our
circumstances as we are treating our box as periodic as an approximation to an infinite space with
infinite particles. We will therefore be solving the above equation with periodic boundary conditions
i.e. f(x + W) = f(x).
4. Our functions have a discrete representation on an nc ◊ nc ◊ nc mesh; the discrete Fourier transform
that we will use will likewise give us a discrete representation on an nc ◊ nc ◊ nc mesh. In real space
each cell in the mesh corresponds to a dierent position (˛r), whereas in frequency space each cell
corresponds to a dierent wavenumber (˛
k).
After applying Fourier transforms to both sides of our equation, we have the following frequency space
expression relating the potential and the density function:
≠k2˜(˛
k)=4fifl˜(˛
k),
where k2 = ˛
k · ˛
k. This can be straightforwardly solved since it is not a dierential equation:
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˜(˛
k) = ≠4fi
k2 fl˜(˛
k),
which just involves scaling our existing fl˜(˛
k) in each cell in our mesh by a factor of 4fik≠2. (See the section
on FFTW for how to find the value of ˛
k for each cell of the mesh in frequency space.) Once we have obtained
˜(˛
k) we can get the potential in real space (˛r) by performing an inverse Fourier transform.
(˛r) = FT≠1(˜(˛
k)) = FT≠1( ≠4fi
k2 FTfl(˛r))
The sequence of operations computationally is then:
1. Find the density function fl(˛r)
2. Apply a fourier transform to the density function to get fl˜(˛
k)
3. Recale fl˜(˛
k) to get ˜(˛
k)
4. Apply an inverse fourier transform to ˜(˛
k) to get the real space potential (˛r)
0.5.2.3 The gravitational field
In order to calculate the acceleration of a particle we need the gravitational field, which is the negative of the
gradient of the potential.
˛g(˛r) = ≠Ò˛ ˛(r)
The gradient is calculated using a simple finite dierence method, defined by the following formula:
df
dx ¥ f(x+x)≠f(x≠x)
2 .
As with the density and potential, the gravitational field function is defined on the mesh with indices (i, j, k);
unlike the density and potential however the gravitational field at any given point is a 3D vector.
To calculate the gradient in a given cell in the mesh, we take the value of the potential in the neighbouring
cells, and approximate the gradient using the formula:
gx(i, j, k) = ≠ (i+1,j,k)≠(i≠1,j,k)
2wc
gy(i, j, k) = ≠ (i,j+1,k)≠(i,j≠1,k)
2wc
gz(i, j, k) = ≠ (i,j,k+1)≠(i,j,k≠1)
2wc
Remember that the functions in the box are periodic, so indices should wrap around if they go below 0 or
above nc ≠ 1.
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Figure 1: Examples of finding neighbouring cells in the x direction on a periodic mesh; right shows how
indices wrap around.
To get the acceleration of particle i at position ˛ri we simply need to get the value of the gravitational field at
this point:
˛ai = ˛g(˛ri).
Since ˛g is defined on our mesh like all our other functions, we simply need to find which cell in the mesh the
position ˛ri falls in and then get the value of ˛g there.
0.6 Making the Universe Expand
In reality the universe doesn’t just stay one size, but it expands over time. As the universe expands, our
particles move with it: remember that we are using co-moving coordinates so that positions are always
relative to the size of the box. Co-moving coordinates means:
• Positions and velocities are relative to the size of the box.
• The coordinates of the particle positions don’t change when the box changes size W. This is because
particles move apart as the universe expands.
• The velocities need to scale down when the box expands. The velocities that we keep track of are
the motion of the particles on top of the background expansion, and these shouldn’t increase along with
the size of the box.
To model a universe which expands we need to do two things:
1. Scale W by a factor F, so W(t + t) = W(t) ◊ F
2. Divide the velocities of all particles by F
Remember that other variables can depend on W, like the physical width of a cell wc = W
nc , and calculations
like the density and gradient rely on the physical size of a cell. During each simulation time step
you will need to make sure that you are using an up to date value for W and other variables
derived from it.
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0.7 Summary: the Simulation Algorithm
The full simulation involves setting an initial states and looping over just five functions. It can be summarised
as follows:
1. Set up np particles with coordinates in the unit box.
• Initial positions are usually uniform random and initial velocities are 0.
2. Define the width of the box W, the expansion rate F, and the width of the mesh in cells nc.
3. Starting at t0, apply the following loop until t>tmax:
• Calculate density from particle positions (counts in cells)
• Calculate the potential:
– Forward FT applied to density
– Solve for potential (frequency space)
– Inverse FT to get (real space) potential
• Calculate gradient to get gravitational field (acceleration)
• Update particles positions and velocities
• Scale the box (scale up W and scale down velocity)
0.8 The FFTW Library
This project will make use of the very common FFTW Library. The documentation for this library can be
confusing, so we will explain how to use the relevant parts of the library. The most important thing that we
need to understand is how data is formatted going into and out of these functions, and how data gets scaled
by these functions.
We will describe the use of the FFTW library only as it applies to the specific functions and data structures
that we use in this assignment. These statements should not be taken as fully general and may not hold for
other methods in the FFTW library which have dierent properties!
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All of the following types and methods are available in the fftw3.h header file.
0.8.1 Data order and ˛
k values
FFTW can perform multi-dimensional Fourier transforms, but the data is provided as a one-dimensional
array, which means we must adopt some ordering for multi-dimensional data.
We can convert the 3D indices (i, j, k) to a flat index µ using the following expression:
µ = k + nc(j + nci),
where i is indexing along the x axis, j along the y axis, and k along z.
• We will usually write the three dimensional indexing (i, j, k) when describing what needs to be done in
this assignment, as it is easier to understand the geometry of the problem in this way.
• You will need to use this conversion to fill out your FFTW buers in practice and to understand the
arrangement of data in memory.
• This data ordering is used for both input and output buers.
In order to calculate the potential, we need to solve an equation with ˛
k in it,
˜(˛
k) = ≠4fi
k2 fl˜(˛
k).
Or, treating our functions as indexed on the mesh, we could write:
˜(i, j, k) = ≠ 4fi
k2(i,j,k)fl˜(i, j, k)
This means that we need to associate a value of ˛
k with each cell for our frequency space function.
The ˛
k components (kx, ky, kz) all range over the values [0, 1
W , 2
W , ..., nc≠1
W ]. The value of ˛
k at indices (i, j, k)
is:
˛
k(i, j, k)=( i
W , j
W , k
W ).
0.8.2 Scaling
The Fourier transforms computed by FFTW are not normalised. For a normalised FT we would have:
FT≠1(FT(f(x))) = f(x).
However, using the fast Fourier transform as defined in FFTW, we have the following relation instead
FFTW≠1(FFTW(f(x)) = (2nc)3f(x),
for a 3D Fourier transform. (More generally, it is scaled by 2ni where ni is the number of cells in each
dimension.) Since our method involves a forwards and inverse transform, we pick up this extra factor of 8n3
c
along the way, which requires the elements in the buer to be rescaled. We’ll take care of this scaling when
we calculate the potential .
0.8.3 FFTW input / output buers
The FFT method that we will use takes arrays with elements of the type fftw_complex, which is included in
the header. This is just an alias for the type double[2], so we can access the real and imaginary
parts using code such as the following:
#include
#include
int main()
{
fftw_complex z;
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//set real part
z[0] = 1.5;
//set imaginary part:
z[1] = 0.2;
//print complex number
std::cout << z[0] << "+" << z[1] << "i" << std::endl;
return 0;
}
FFTW has its own methods for allocating a buer in memory, which should be used whenever using the
library.
The following line allocates an array called buffer to contain N_elements values of type fftw_complex. The
type of buffer is fftw_complex *, which is a pointer i.e. it is implemented as a C-style array.
fftw_complex * buffer = (fftw_complex *) fftw_malloc(sizeof(fftw_complex) * N_elements);
Note that this does not initialise the elements in the buer.
• The FT has separate input and output buers, and both must be allocated before the FT is called.
• Whatever is in the output buer will be rewritten with the result of the FT.
• The input and output buers are the same size.
Buers which are allocated with fftw_malloc must also be freed using fftw_free, for example:
fftw_complex * buffer = (fftw_complex *) fftw_malloc(sizeof(fftw_complex) * N_elements);
fftw_free(buffer);
0.8.4 FFTW plans and executing the Fourier transform
FFTW uses “plans” to describe how the Fourier transform that needs to be performed. Plans determine how
many dimensions the data has (ours is three dimensional), what the input and output buers will be, and
whether the transform is a forward or inverse Fourier transform. The plans do not need to be re-made every
time you want to perform the transform: you can re-use a plan as many times as you like as long as
these properties don’t change.
The plans have type fftw_plan, and are declared, allocated, and deallocated as follows:
Declaration and Allocation:
The forward plan is initialised using the fftw_plan_dft_3d function with the following arguments:
fftw_plan forward_plan = fftw_plan_dft_3d(n_x,
n_y,
n_z,
input_buffer,
output_buffer,
FFTW_FORWARD,
FFTW_MEASURE);
• n_x, n_y, and n_z are the size of the three dimensions. These should all be nc since we are using a box
with an equal number of cells in each direction.
• input_buffer is the function to which you want to apply the forward transform. This is a fftw_complex
* type array, and is a real space function f(˛r).
• output_buffer is where you stored the result of the transform. This is also a fftw_complex * type
array, and is a frequency space function ˜f(˛
k).
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• FFTW_FORWARD and FFTW_MEASURE are constants defined in fftw3.h. You should keep these as they are!
The inverse plan is initialised similarly:
fftw_plan inverse_plan = fftw_plan_dft_3d(n_x,
n_y,
n_z,
input_buffer,
output_buffer,
FFTW_BACKWARD,
FFTW_MEASURE);
• Note that FFTW_FORWARD has been changed to FFTW_BACKWARD
• The input buer should be a frequency space representation g˜(˛
k)
• The output buer is a real space representation g(˛r)
Deallocation:
Both plans need to be deallocated using the fftw_destroy_plan function:
fftw_destroy_plan(forward_plan);
fftw_destroy_plan(inverse_plan);
1 Simulating Particles in a Box (60 marks)
In the first part of this assignment you’ll create a serial (not parallelised) particle-mesh simulation. We will
specify the representation of some functions due to the need to interface with the FFTW library, but for the
most part selecting data structures will down to you, along with appropriate naming and code organisation.
You should think carefully about your use of functions and classes to structure your code.
You should aim to make your code as performant as you can, but still clear, safe, and well structured. You
may assume that optimisations will be turned on to a high level (-O3) for a release build. (If debugging you
should usually keep the optimisations o, unless you are explicitly using the debugger to see how the compiler
has optimised the code.)
We will explicitly ask you to write a Simulation class and a number of functions, but you can, and should,
add further classes and function where you feel that they are appropriate and useful.
1.1 The Initial Repository
Start by familiarising yourself with the starter repository.
There are multiple folders which contain source files:
1. lib for library code (this is where the bulk of your simulation code will be developed).
• include contains library headers.
2. apps for application code.
3. test for test code.
4. benchmarks for benchmarking (timing) code.
You will find in the source files:
1. A skeleton Simulation class that you will need to complete.
2. Some partially coded tests that you will need to adapt to use your classes / data structures.
3. A method for converting density functions to images (.pbm format)
4. A method for calculating a radial correlation function, which will be used to calculate summary statistics
later on and which you may need to adapt to work with your data structures.
You should pay attention to the CMakeLists.txt files as well.
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1.2 Representing Particles (8 marks)
It is up to you to decide how to represent your particles. You will need to be able to represent a large number
of them (in the millions).
• Each particle must have its own position (˛r) and velocity (˛v), which are three dimensional.
• Positions are always measured relative to the size of the box. This means that each coordinate of
the position is 0 Æ ri < 1, regardless of the width of the box.
• All particles have the same mass, which is constant throughout the simulation.
The initial collection of particles will need to be passed in to the Simulation class. You need to be able to
create custom initial states as well as random ones.
• You must be able to straightforwardly create a custom distribution of particles, for example one particle
in the middle of the box. This will be important for constructing test cases.
– You must be able to set the position of every particle individually, but you may assume that the
initial velocities are all zero.
• You must also write a function or constructor which creates a random distribution of particles. In this
case:
– The position of each particle must be uniform random (between 0 and 1) for each dimension (x, y,
z).
– The velocity of each particle must be zero in all dimensions.
– The seed for the random number generator must be settable. This is vital for reproducability.
TASKS:
1. Decide on your particle implementation and write any class or function definitions that you need.
2. In Responses.md explain the choices that you have made, and point out where the relevant code
can be found. You should consider both ease of use and performance expectations. (You may wish to
come back to this response later if you decide to try more than one approach.)
1.3 The Simulation Class (7 marks)
In your library code you will find a class called Simulation. This class will be used to represent a single
simulation which can be run, including holding relevant data like the state of the particles in the simulation,
the timestep, the length of the simulation (tmax), and the various functions defined on the mesh.
• You may want to build this class representation as you work through the assignment, adding member
variables as you encounter new things to be represented.
• You may make any functions in this class that you want to test public, in order to make
constructing tests and benchmarks practical. For data, or functions which do not require direct
tests themselves, you should make normal use of access specifiers.
TASKS
1. You should add a constructor to your Simulation class which takes the following information:
• The total time tmax.
• The time step t.
• The initial collection of particles.
• The width of the box W.
• The number of cells wide that the box is, nc.
– Remember that this is just the size of the box in one dimension; the total number of cells in
the 3D space will be Nc = n3
c .
• The expansion factor F.
– This is the factor that the universe will expand by each timestep.
You should use your constructor to set and initialise any member variables that you want to keep in the
class. Below is some advice on what data structures are needed for the Fourier transform part of the
code. You should consult also the section of the Background about the FFTW library and its usage.
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FFTW Buers and Plans
Your simulation class will internally use FFTW as a library for calculating the fast Fourier transforms
necessary for this approach. These methods require storing both the input and output buers, which are the
discrete representations of our density and potential functions on the mesh, and FFTW “plans”.
1.3.0.1 Buers
You will need three data buers for the FT parts:
• Two for real space functions (density fl(x) and potential (x)).
– In principle we could use just one buer, since we don’t need to use fl and at the same time, but
using two will help clarity as it prevents ambiguity over what is contained in the buer at any
given time. This also allows us to keep both the density and potential functions until the end of
an iteration, which can be useful for outputs and debugging. If you are worried about your total
memory usage however, you can use just one buer.
• One for frequency space values. This will only be used during the calculation of the potential function,
and will be used to represent both fl˜(k) and ˜(k). After the forward transform, this buer will contain fl˜.
This buer then gets modified (in place) to represent ˜, before being input into the inverse transform.
– Using one buer here is less confusing because it only needs to be used within one single function.
If we were interested in using the frequency space representations elsewhere, and memory usage
was not a major concern, then we might consider splitting this into two buers as well.
Each of the three buers will be of the size n3
c .
1.3.0.2 Plans
You will need to use two plans: one for the forward transform and one for the inverse transform.
• The forward transform must take the real space density buer as input and the frequency space buer
as output.
• The inverse transform must take the frequency space buer as input and the real space potential buer
as output.
1.4 Calculating the density function (7 marks)
The density function, as with all functions in our 3D space, is defined on our nc ◊ nc ◊ nc mesh. The density
function is defined by a single value in each cell of this 3D gridding, and is stored in one of our fftw_complex
* buers. This buer is a contiguous piece of memory, i.e. all n3
c values (of type fftw_complex) are stored in
sequential memory addresses.
Our mesh is divided into cells with indices (i, j, k) where:
• i ranges over 0, 1, . . . , nc ≠ 1 and represents cell indices in the x coordinate,
• j ranges over 0, 1, . . . , nc ≠ 1 and represents cell indices in the y coordinate,
• k ranges over 0, 1, . . . , nc ≠ 1 and represents cell indices in the z coordinate.
Since our coordinate system is scaled so that x, y, z are always between 0 and 1, the cell at indices (i, j, k)
covers coordinates in 3D space where:
• 1
nc i Æ x < 1
nc (i + 1),
• 1
nc j Æ y < 1
nc (j + 1),
• 1
nc k Æ z < 1
nc (k + 1).
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The corresponding index µ of the buer is:
µ = k + nc(j + nci).
The value of in the density function for a given cell with indices (i, j, k) is equal to the number of particles
whose position falls within that cell, multiplied by mp
Vc where Vc = w3
c is the volume of the cell. (N.B. The
volume of the cell Vc is calculated using wc = W
nc i.e. does not use scaled coordinates. As the
size of the box changes, the volume of a cell also changes.)
TASKS
1. Write a function in your Simulation class to calculate the density function and fill in the buer.
• The imaginary part of every value in the density buer should be set to 0 since the density
is a real function.
• Bear in mind that this function will be called every time step.
2. Test your density function. You should check that:
• All imaginary components are 0.
• Density is everywhere 0 if there are no particles.
• Density is correct when a single particle is present.
– Only a single cell should therefore have non-zero density. You should check that the value and
index of this is correct.
• Density is correct when multiple particles are present.
1.5 Calculating the Potential (7 marks)
In order to calculate the potential we need to make use of our Fourier transform.
Calculating the potential involves 3 steps:
1. Apply the forward Fourier transform (input: fl(x), output: fl˜(k))
2. Apply scaling to the frequency space representation (see below).
3. Apply the inverse Fourier transform (input: ˜(k), output: (k))
Applying the forward/inverse transform simply requires calling fftw_execute (defined in fftw3.h) with the
correct plan:
fftw_execute(forward_plan);
// ...
// manipulate the frequency space buffer
// ...
fftw_execute(inverse_plan);
After the FFT is complete the output buer will contain the non-normalised transformed density function
(see the FFTW section in the Background). We can use this to calculate the potential by scaling this buer
by k≠2.
The ordering of elements in the frequency space representation is the same as the real space array. I