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Cleaned up makefile, started presentation

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Connor
2022-03-19 13:39:02 -06:00
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8
LaTeX/approach.tex
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@@ -114,7 +114,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{flowcharts/nlp}
\includegraphics[width=\textwidth]{LaTeX/flowcharts/nlp}
\caption{A flowchart of the TPBVP Solution Approach}
\label{nlp}
\end{figure}
@@ -176,7 +176,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{fig/laguerre_plot}
\includegraphics[width=\textwidth]{LaTeX/fig/laguerre_plot}
\caption{Example of a natural trajectory propagated via the Laguerre-Conway
approach to solving Kepler's Problem}
\label{laguerre_plot}
@@ -202,7 +202,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{fig/spiral_plot}
\includegraphics[width=\textwidth]{LaTeX/fig/spiral_plot}
\caption{An example trajectory showing that classic continuous-thrust orbit
shapes, such as this orbit spiral, are easily achievable using a Sims-Flanagan
model}
@@ -320,7 +320,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{flowcharts/mbh}
\includegraphics[width=\textwidth]{LaTeX/flowcharts/mbh}
\caption{A flowchart visualizing the steps in the monotonic basin hopping
algorithm}
\label{mbh_flow}

50
LaTeX/presentation.tex Normal file
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@@ -0,0 +1,50 @@
\documentclass{beamer}
\usetheme{Luebeck}
\definecolor{color1}{HTML}{3A4040}
\definecolor{color2}{HTML}{F5F2F8}
\definecolor{color3}{HTML}{B65D4E}
\definecolor{color4}{HTML}{B6AD96}
\definecolor{color5}{HTML}{A96041}
\setbeamercolor*{structure}{bg=color3,fg=color3}
\setbeamercolor*{palette primary}{fg=color1,bg=color4}
\setbeamercolor*{palette secondary}{fg=color1,bg=color2}
\setbeamercolor*{palette tertiary}{fg=color1,bg=color2}
\setbeamercolor*{palette quaternary}{fg=color1,bg=color3}
\setbeamercolor{alerted text}{fg=color1,bg=color3}
\setbeamercolor{titlelike}{bg=color2,fg=color1}
\setbeamercolor*{titlelike}{bg=color2,fg=color1}
\setbeamercolor{frametitle}{bg=color1,fg=color2}
\setbeamercolor{background canvas}{bg=color2,fg=color2}
\title{Designing Optimal Low-Thrust Interplanetary Trajectories}
\subtitle{Utilizing Monotonic Basin Hopping}
\author{Richard Connor Johnstone}
\institute{University of Colorado -- Boulder}
\date{\today}
\begin{document}
\begin{frame}
\titlepage
\end{frame}
\section{Introduction}
\begin{frame} \frametitle{First Frame}
\begin{itemize}
\item Item 1
\item Item 2
\end{itemize}
\end{frame}
\section{Introduction}
\section{Introduction}
\section{Introduction}
\section{Introduction}
\end{document}

18
LaTeX/results.tex
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@@ -128,7 +128,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{fig/EMS_plot}
\includegraphics[width=0.9\textwidth]{LaTeX/fig/EMS_plot}
\caption{Depictions of the faster Earth-Mars-Saturn trajectory found by the
algorithm to be most efficient; planetary ephemeris arcs are shown during the phase
in which the spacecraft approached them}
@@ -137,7 +137,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{fig/EMS_plot_noplanets}
\includegraphics[width=0.9\textwidth]{LaTeX/fig/EMS_plot_noplanets}
\caption{Another depiction of the EMS trajectory, without the planetary ephemeris
arcs}
\label{ems_nop}
@@ -156,14 +156,14 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{fig/EMS_thrust_mag}
\includegraphics[width=0.9\textwidth]{LaTeX/fig/EMS_thrust_mag}
\caption{The magnitude of the unit thrust vector over time for the EMS trajectory}
\label{ems_mag}
\end{figure}
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{fig/EMS_thrust_components}
\includegraphics[width=0.9\textwidth]{LaTeX/fig/EMS_thrust_components}
\caption{The inertial x, y, and z components of the unit thrust vector over time for
the EMS trajectory}
\label{ems_components}
@@ -212,7 +212,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{fig/EMJS_plot}
\includegraphics[width=0.9\textwidth]{LaTeX/fig/EMJS_plot}
\caption{Depictions of the slower Earth-Mars-Jupiter-Saturn trajectory found by the
algorithm to be the second most efficient; planetary ephemeris arcs are shown during
the phase in which the spacecraft approached them}
@@ -221,7 +221,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{fig/EMJS_plot_noplanets}
\includegraphics[width=0.9\textwidth]{LaTeX/fig/EMJS_plot_noplanets}
\caption{Another depiction of the EMJS trajectory, without the planetary ephemeris
arcs}
\label{emjs_nop}
@@ -236,14 +236,14 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{fig/EMJS_thrust_mag}
\includegraphics[width=0.9\textwidth]{LaTeX/fig/EMJS_thrust_mag}
\caption{The magnitude of the unit thrust vector over time for the EMJS trajectory}
\label{emjs_mag}
\end{figure}
\begin{figure}[H]
\centering
\includegraphics[width=0.9\textwidth]{fig/EMJS_thrust_components}
\includegraphics[width=0.9\textwidth]{LaTeX/fig/EMJS_thrust_components}
\caption{The inertial x, y, and z components of the unit thrust vector over time for
the EMJS trajectory}
\label{emjs_components}
@@ -304,7 +304,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{fig/c3}
\includegraphics[width=\textwidth]{LaTeX/fig/c3}
\caption{Plot of Delta IV and Atlas V launch vehicle capabilities as they relate to
payload mass \cite{c3capabilities} from Vardaxis, et al, 2007 }
\label{c3}

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LaTeX/thesis.tex
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@@ -1,4 +1,4 @@
\documentclass[defaultstyle,11pt]{thesis}
\documentclass[defaultstyle,11pt]{LaTeX/thesis}
\usepackage{graphicx}
\usepackage{amssymb}
@@ -61,7 +61,7 @@
\begin{document}
\input macros.tex
\input LaTeX/macros.tex
\input LaTeX/introduction.tex
@@ -77,8 +77,6 @@
\bibliographystyle{plain}
\nocite{*}
\bibliography{thesis}
\appendix
\bibliography{LaTeX/thesis}
\end{document}

18
LaTeX/trajectory_design.tex
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@@ -37,7 +37,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.65\textwidth]{fig/2bp}
\includegraphics[width=0.65\textwidth]{LaTeX/fig/2bp}
\caption{Figure representing the positions of the bodies relative to each other and
the center of mass in the two body problem}
\label{2bp_fig}
@@ -148,7 +148,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.8\textwidth]{fig/kepler}
\includegraphics[width=0.8\textwidth]{LaTeX/fig/kepler}
\caption{Geometric representation of auxiliary circle}\label{aux_circ}
\end{figure}
@@ -297,7 +297,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.8\textwidth]{fig/patched_conics}
\includegraphics[width=0.8\textwidth]{LaTeX/fig/patched_conics}
\caption{Patched Conics Example Figure}
\label{patched_conics_fig}
\end{figure}
@@ -396,7 +396,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.8\textwidth]{fig/flyby}
\includegraphics[width=0.8\textwidth]{LaTeX/fig/flyby}
\caption{Velocity changes during a gravity assist}
\label{grav_assist_fig}
\end{figure}
@@ -470,7 +470,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.8\textwidth]{fig/lamberts}
\includegraphics[width=0.8\textwidth]{LaTeX/fig/lamberts}
\caption{Visualization of the possible solutions to Lambert's Problem}
\label{type1type2}
\end{figure}
@@ -587,7 +587,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{fig/porkchop}
\includegraphics[width=\textwidth]{LaTeX/fig/porkchop}
\caption{A sample porkchop plot of an Earth-Mars transfer}
\label{porkchop}
\end{figure}
@@ -652,7 +652,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=0.6\textwidth]{fig/sft}
\includegraphics[width=0.6\textwidth]{LaTeX/fig/sft}
\caption{Example of an orbit raising using the Sims-Flanagan Transcription with 7
Sub-Trajectories}
\label{sft_fig}
@@ -739,14 +739,14 @@
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{fig/low_efficiency}
\includegraphics[width=\textwidth]{LaTeX/fig/low_efficiency}
\caption{Graphic of an orbit-raising with a low efficiency cutoff}
\label{low_efficiency_fig}
\end{figure}
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{fig/high_efficiency}
\includegraphics[width=\textwidth]{LaTeX/fig/high_efficiency}
\caption{Graphic of an orbit-raising with a high efficiency cutoff}
\label{high_efficiency_fig}
\end{figure}

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LaTeX/trajectory_optimization.tex
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@@ -105,7 +105,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{fig/single_shoot}
\includegraphics[width=\textwidth]{LaTeX/fig/single_shoot}
\caption{Single shooting over a trajectory arc}
\label{single_shoot_fig}
\end{figure}
@@ -130,7 +130,7 @@
\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{fig/multiple_shoot}
\includegraphics[width=\textwidth]{LaTeX/fig/multiple_shoot}
\caption{Visualization of a multiple shooting technique over a trajectory arc}
\label{multiple_shoot_fig}
\end{figure}

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Makefile
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@@ -1,68 +1,39 @@
OPTIONS = markdown+yaml_metadata_block+smart
NOTES = $(wildcard prelim_notes/*.md)
NOTES_PDFS = $(patsubst %.md,%.pdf,$(NOTES))
THESIS = LaTeX/thesis.tex
SRC_DIR = LaTeX/
THESIS_SRC_NAMES = thesis.tex thesis.bib approach.tex conclusion.tex introduction.tex \
results.tex trajectory_design.tex trajectory_optimization.tex
THESIS_SRC = $(addprefix $(SRC_DIR)/,$(THESIS_SRC_NAMES))
THESIS_PRES_SRC_NAMES = presentation.tex
THESIS_PRES_SRC = $(addprefix $(SRC_DIR)/,$(THESIS_PRES_SRC_NAMES))
THESIS_PRES = presentation.pdf
THESIS_PDF = thesis.pdf
BUILD_DIR = temp/
all: $(THESIS_PDF) $(NOTES_PDFS)
all: $(THESIS_PDF) $(THESIS_PRES)
$(NOTES_PDFS): $(NOTES)
pandoc \
--variable mainfont="Roboto" \
--variable monofont="Fira Code" \
--variable fontsize=11pt \
--variable geometry:"top=1in, bottom=1in, left=1in, right=1in" \
--variable geometry:letterpaper \
-f markdown $< \
-o $@
$(BUILD_DIR):
mkdir -p $(BUILD_DIR)
thesis_pdf/:
mkdir $@
$(BUILD_DIR)/$(THESIS_PDF): $(BUILD_DIR) $(THESIS_SRC)
xelatex --output-directory $(BUILD_DIR) $(SRC_DIR)/thesis
bibtex $(BUILD_DIR)/thesis
xelatex --output-directory $(BUILD_DIR) $(SRC_DIR)/thesis
xelatex --output-directory $(BUILD_DIR) $(SRC_DIR)/thesis
$(THESIS_PDF): $(THESIS) LaTeX/thesis.bib
mkdir -p temp
cp -r LaTeX/fig .
cp -r LaTeX/flowcharts .
cp -r LaTeX/thesis.tex .
cp -r LaTeX/macros.tex .
cp -r LaTeX/thesis.bib .
cp -r LaTeX/thesis.cls .
xelatex thesis
bibtex thesis
xelatex thesis
xelatex thesis
rm -rf fig
rm -rf flowcharts
cp thesis.pdf temp/.
rm -rf macros.tex
rm -rf thesis.*
cp temp/thesis.pdf .
rm -rf temp
$(BUILD_DIR)/$(THESIS_PRES): $(BUILD_DIR) $(THESIS_PRES_SRC)
xelatex --output-directory $(BUILD_DIR) $(SRC_DIR)/presentation
$(THESIS_PDF): $(BUILD_DIR)/$(THESIS_PDF)
cp $(BUILD_DIR)/thesis.pdf .
$(THESIS_PRES): $(BUILD_DIR)/$(THESIS_PRES)
cp $(BUILD_DIR)/presentation.pdf .
.PHONY: clean revise
clean:
rm -rf $(THESIS_PDF)
rm -rf $(NOTES_PDFS)
rm -rf thesis_pdf
rm -rf $(THESIS_PRES)
rm -rf $(BUILD_DIR)
revise: $(THESIS) LaTeX/thesis.bib
mkdir -p temp
cp -r LaTeX/fig .
cp -r LaTeX/flowcharts .
cp -r LaTeX/thesis.tex .
cp -r LaTeX/macros.tex .
cp -r LaTeX/thesis.bib .
cp -r LaTeX/thesis.cls .
xelatex thesis
bibtex thesis
xelatex thesis
xelatex thesis
rm -rf fig
rm -rf flowcharts
cp thesis.pdf temp/.
rm -rf macros.tex
rm -rf thesis.*
cp temp/thesis.pdf .
final: $(THESIS_PDF) $(THESIS_PRES)
rm -rf $(BUILD_DIR)

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best_missions
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@@ -1,3 +0,0 @@
./archive/EMMJS_2021-12-03T17:38:37.546/mission
./archive/EMS_2021-12-12T16:01:04.514/mission
./archive/EVMS_2021-12-14T16:31:21.927/mission

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email_to_commitee.md
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Dr. Scheeres,
I'm currently working on finishing up my Master's Thesis. The topic will be a routine for optimizing
Interplanetary Low-Thrust Trajectories, combining approaches for flyby optimization and low-thrust
trajectory arc optimization. I think that your research interests would make you an ideal fit for a
place on my committee, so I would like to ask if you have any availability to be on my committee
this semester? Currently, I am targeting finish up sometime around March 12th, so it would probably
be a defense in late March or early April, but I have a little room for flexibility if that doesn't
work.
If you think you may be able to sit on my committee then just let me know and I can begin the
process of determining everyone's availabilities and finding the most appropriate defense date.
Thanks!
Connor Johnstone

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ipopt.out
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prelim_notes/paper_notes.md
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@@ -1,102 +0,0 @@
# Notes on Research Papers
## Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers
- For the most part this paper isn't *that* relevant.
- It seems to be a low-fidelity method, or at least, it's fidelity is kind of hard to pin down,
since it uses a neural net.
- However, the neural net is an interesting concept.
- And in fact, this paper advocates for a concept of using the neural net as a predictor. So, say,
given a leg of a particular journey, as in the Englander paper, could I use this technique rather
than Sims-Flanagan transcription in the outer loop?
- Something to consider as an alternative to the method proposed in Englander
- Could also be used to generate initial guesses for the single-shooting methods that I'll have to
use if I use an indirect optimization method.
## Design and optimization of interplanetary low-thrust trajectory with planetary aerogravity-assist maneuver
- I didn't realize that this paper is specifically talking about *aero*gravity assists. I don't
think that level of complication is necessary for this paper. I'm going to stick with gravity
assists that don't get into atmospheric effects.
## Orbital and Angular Motion Construction for Low Thrust Interplanetary Flight
- This one actually isn't even about optimization. Not relevant
- To be honest, I don't actually understand it very well anyway
## A Rapid Estimation for Interplanetary Low-Thrust Trajectories Using Support Vector Regression
- This is another machine-learning approach
- It uses a different approach that I'm not that familiar with (Support Vector Regression)
- It looks like this is a form of regression similar to linear regression, I suppose being used
by the machine learning algorithm for predicting optimal trajectories
- However, it seems like everything that applies in the neural net paper probably apply here as well
- This could be an alternative for predicting optimal trajectories over certain legs of the journey
## Automated Solution of the Low-Thrust Interplanetary Trajectory Problem
- This is the Englander paper I mentioned earlier. It seems highly relevant as they're essentially
doing what I'd like to do: producing an automated method for high-level interplanetary low-thrust
mission design including the flybys.
- I need to look up MALTO and GALLOP (established tools that do this)
- This paper also open sources it's code: available [here](https://opensource.gsfc.nasa.gov/projects/emtg/index.php)
- This paper formulates the problem as a *Hybrid Optimal Control Problem*. This requires some
further research by me, but from the paper it seems to be a way of optimizing two seperables
subproblems where one of the subproblems exists as a sub-loop of the other problem (for instance
optimizing flybys in a high-level loop and particular planet-to-planet trajectories as a sub-loop of
that problem). This apparently works because the "outer-loop" uses discrete variables while the
"inner-loop" uses continuous variables.
- The outer loop is based on the "null-gene" transciption from another Englander paper and uses a
genetic algorithm.
- I'm not going to go too deep here into the details of the GA. But there's an entire paper on
it
- The paper does mention that it lends itself well to parallelization, which is true. Kubernetes
cluster?
- The inner loop uses Sim-Flanagan transcription combined with Monotonic Basin Hopping, a method for
single-shooting without initial guesses
- Sims-Flanagan transcription is where you discretize the flight arc into many smaller time
steps and the thrust applied is approximated as an impulsive thrust in the middle of each time
step.
- SFT is considered to be a "medium-fidelity" approach
- For this solver, the trajectory betweens these points is produced as a solution to Kepler's
problem, which is basically just the analytical solution to the 2BP, so that no derivatives or
orbit-propagation is needed, for speed.
- One thing I noted about this approach is that it doesn't seem to include a possibility for
"coasting arcs" or throttling anything less than 100% (though the modeling of what 100% means is
quite thorough), so perhaps we're missing some fidelity there?
- I think SNOPT is used to optimize these "inner-loops". This should be pretty fast since it
just uses Kepler's eq
- The MBH method eliminates the need to solve Lambert's problem for initial guesses. This allows
for a more robust analysis of the search base (if there are global optima further from the local
optima near lambert's solution) but might be slower? I'm not sure.
- The technique is kind of weird, but I suppose it works.
- This paper uses a hierarchy of events starting with the overall *mission*, which separates into
*journies*, which, in the example I'm pursuing will be Earth -> Neptune and Neptune -> Earth (if
applicable, but probably just the first one). Then these journies are further divided into
*phases*, which include each planet -> planet leg. The number of phases and the identities of the
planets are chosen by the algorithm.
- The paper goes into some length to determine what the launch C3, propulsion, power, and ephemeris
modeling are. This is all very useful, but as far as I can tell it's pretty typical, so I won't note
too much about it.
- However, it does mention that SPICE presented some challenges for using a preferable method of
parallelization. As an alternative, the paper mentions that FIRE could be used instead for
ephemeris. Which might be worth looking into.
- The paper also include pseudocode, which is nice
## Automated Mission Planning via Evolutionary Algorithms
- This is another Englander paper that gives more details on the outer-loop GA. Useful for details.
## Multi-Objective Low-Thrust Interplanetary Trajectory Optimization Based on Generalized Logarithmic Spirals
- This is the first paper I looked at. It's actually quite similar to the Englander paper, but I
think not quite as good
- Again, it formulates the problem as an HOCP.
- However, the "inner-loop" for this problem is an optimization of generalized logarithmic spirals.
I don't think this is a very high fidelity method.
- The outer step uses collocation and an NLP optimizer (looks like it actually might just feed the
guesses into GALLOP, which I assume uses SNOPT, though, to be honest, I can't find much on it from
a quick search)
- I'm leaning toward Englander's approach over this one, perhaps with an alternative being to use
one of the machine-learning approaches from above for the inner loop instead

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prelim_notes/plan.md
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@@ -1,37 +0,0 @@
# Plan After Literature Review
After reading the papers in the Google Drive (see `paper_notes.md`) I've come up with the following
plan:
I think that I'd like to follow an approach similar to what I saw in Englander and Morante. However,
I think it makes more sense to follow the Englander approach for the specific optimizers being used
in the inner and outer loops. Specifically this means:
- Set up the problem as a Hybrid Optimal Control Problem (HOCP) with an inner and an outer loop
- The outer loop will determine the number and identities of the flybys and optimize using a Binary
Genetic Algorithm described by Englander
- The inner loop in Englander uses Sims-Flanagan Transcription optimized using monotonic basin
hopping. This seems like a good approach. I'd like to use this approach, but also consider using,
as an alternative method for comparison, one of the machine-learning algorithms from the other
papers.
- There are a number of other details including modeling launch C3, power, thrust, and ephemeris.
For all of these I'll use either the exact approach from Englander or a similar approach. There
exists an option to use alternatives to SPICE for ephemeris, but I think the parallelization
problems that SPICE poses can be solved in other ways.
- Specifically, I'd like to build this program using a micro-service architecture. This could
allow for deployment using Kubernetes clusters. This will handle the parallelization (by
running multiple inner loop micro-services at once) and allow for simpler use in production
environments if that's ever needed, as kubernetes has a robust integration with most
web-hosting services. This also allows for flexible scalability if improved speed is ever
needed.
## Open Questions
- Are there other inner and outer loop optimization approaches I should consider?
- I didn't see a ton of information regarding the specific parameters of the orbital flybys outside
of the number and the identities of the planets. I need to figure out whether Englander approaches
that optimization in the inner or the outer loop
- I would like to use a fast language for this, and ideally one that I could benefit from learning
about (by this I mean, preferably not C++, as I've used that before -- though it does integrate
well with tools like SPICE, so it is a good option). Preferably Rust, as it has the fastest server
libraries, which I don't expect to have a huge overhead, but is something to consider.

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prelim_notes/technical_plan.md
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@@ -1,76 +0,0 @@
# Technical Plan
## Notes
- Each of these steps will be completed first in Julia, as I can use that to get
to a working point really quickly. Then, I'll redo the efforts in Rust inside
of docker containers, providing increased robustness and speed. I'll provide
expected time for completion after each item in the form (x/y) where x is the
number of hours I expect to take to write the Julia code and y is the number
of hours I expect to take to write the Rust code.
## Stages of Development
1. First I need to set up the inner loop. The inner loop is a Sims-Flanagan transcription
single-shooting algorithm, optimized using monotonic basin-hopping.
a. I've constructed single-shooting algorithms before, and the Sims-Flanagan
transcription is just a really simple notation for approximating low-thrust
arcs. So first I'll need to set up a very simple Sims-Flanagan single
shooter. I don't expect this to take long. I will write a simple
single-shooting algorithm that takes in a start and end state (for now... in
stage 2b below I'll update this to only need the J200 time (for ephemeris
lookup) and the velocity) and solves Kepler's equation to satisfy the
continuity condition. For the rust code, this will require some considerable
initial setup of the docker containers and such, though. (6/20)
b. Next I'll need to set up the monotonic basin-hopping algorithm. I've
written similar optimization algorithms in my spacecraft trajectory
optimization course, but none that use basin-hopping. However, it's more
or less just a genetic algorithm, so it should be simple to set up in a
very general way. The function I'm optimizing can be seen as a system of
inputs (initial and final states) and outputs (mass used, or some other
more complicated measure of optimality), so I can follow many very
descriptive accounts online of this type of genetic algorithm. (12/18)
2. Then I'll need to set up the outer loop. This will require optimizing over
inputs (selection of flybys) by calling the inner loop to optimize each leg
and summing these by way of some cost function. These flyby selections will
be optimized using the genetic algorithm described in the first Englander
paper.
a. First I'll set up a very simple version of the outer loop, that
doesn't optimize anything. It will take in a selection of flybys and
simply call the inner loop for each of the flybys, outputting some cost
function. This shouldn't take long, but at this point I'm adding time
because the inner loop may take some time to call (8/12).
b. There are also some other inputs to this algorithm, that won't change from
problem to problem, but will take some effort to calculate. These include
initial launch C3, ephemeris, problem constraints, etc. SPICE and basic
modeling can be used for most of this, but I should include some time for
incorporating SPICE via C-bindings, which should be relatively easy in
both Julia and Rust (in fact, a wrapper exists at least in Julia
already). As a less high fidelity backup, I've also modeled ephemeris by
polynomials before, so I can do that again, if I find incorporating SPICE
is taking too long (16/16)
c. Next, I'll write the genetic algorithm in a very general way, as before.
This is a different GA (a Binary Genetic Algorithm, suited for discrete
problems), but the algorithm is described in-depth in the first Englander
paper and I also can refer to the source code from the second Englander
paper. In the first paper, he mentions that he used Matlab's
implementation. It seems Julia also has an implementation, so this should
be simple in Julia, but I will include significant extra time in Rust, in
case that is more difficult (6/32)
d. From there, calling my outer-loop implementation from the GA should be
simple. However, I will include extra time for the rust implementation,
because I will also have to consider spinning up extra kubernetes
instances for each inner and outer loop. (6/32)
3. From here, the work is more or less done. There will be some finalizing to
do, including setting up the Kubernetes cluster for the Rust implementation.
I've not set up a Kubernetes cluster before, but I have set up docker-compose
setups for Rust code, so I would anticipate an extra 10 hours being needed
for transitioning that. I will also have to provide a sort of wrapper
function that parses the inputs and spins up/calls each lower loop of this
optimizer. That should be really simple, but I'm including some slop time in
this category to account for administrative issues, particularly in setting
up the deployment of the cluster (12/32)
## Summary
So, in total, that comes out to 66 hours of coding for the initial Julia implementation and then another 162 hours for the re-implementation in Rust using docker + kubernetes and the deployment of the cluster. At a rate of 20 hours/week, which seems reasonable, as I will have my job to account for, but I am finished with classes, that comes out to roughly 3.5 weeks to finish the first implementation and then 8 weeks to finalize. I think, if anything, that's very conservative, particularly on the Rust side, as I've got a decent amount of experience in web-based deployment, so I shouldn't have as much trouble with Kubernetes and Docker as I've outlined. But it's also worth noting that the Julia implementation is probably perfectly proficient for a Master's Thesis. If the Rust work is too much, then I can write the thesis on the Julia implementation.

13
readme.md
View File

@@ -2,10 +2,7 @@
This will be a repository for my code while I work on my thesis. The general idea is to generate a
method for automatically producing optimal low-thrust trajectories (including optimizations of the
planetary flybys) for a sample mission to Neptune.
For now, it will just be a location for storing my notes as I come up with a plan. I will update
this readme as I flush out the plan a little better and then as I begin producing code.
planetary flybys) for a sample mission to Saturn.
## Dependencies
@@ -37,5 +34,11 @@ To produce the pdfs, simply run:
```bash
make
```
### Julia Code
They will go in the build directory
To produce final pdfs:
```bash
make final
```