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motivation.tex
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\begin{frame}
\frametitle{Overview}
\begin{itemize}
\setlength{\itemindent}{1cm}
\item[\textbf{Title:}] Demand-Driven Cycamore Archetypes
\item[\textbf{PI:}] Anthony Scopatz, University of South
Carolina\footnote{Anthony departed academia in year 2
of the project. The PIship was transferred to Travis
Knight at USC}
\item[\textbf{Co-PI:}] Kathryn Huff, University of Illinois at
Urbana-Champaign
\item[\textbf{Start:}] October, 2016
\item[\textbf{End:}] October, 2017
\item[\textbf{Objectives:}] Develop an in situ demand
driven development schedule calculation through
non-optimizing, deterministic-optimizing, and
stochastic-optimizing algorithms as Cyclus archetypes.
Demonstrate these new archetypes in program-supporting
fuel cycle scenarios.
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Quick Statistics}
\begin{block}{Publications Affiliated with this Work}
\begin{itemize}
\setlength{\itemindent}{3cm}
\item[\textbf{Journal Articles}] 3 (2 upcoming)
\item[\textbf{Full Conference Papers}] 3 (2 upcoming)
\item[\textbf{Conference Summaries}] 7
\item[\textbf{Technical Reports}] 2 (1 upcoming)
\item[\textbf{Theses}] 1MS (2 upcoming)
\end{itemize}
\end{block}
\begin{block}{Students Supported}
The funding supported graduate students and
occasional undergraduates at UIUC.
\textbf{Jin Whan Bae} recieved his MS and is now at
ORNL purusing Cyclus usability.
\textbf{Gwendolyn Chee} is writing an MS thesis related
to this work and related work conducted at ANL with Bo Feng.
Undergraduate \textbf{Louis Kissinger} is a
baccalaureate researcher this year in MCS at ANL.
Others include \textbf{Roberto Fairhurst}, \textbf{Gyu
Tae Park}, \textbf{Snehal Chandan}, and \textbf{Aditya
Bhosale}.
\end{block}
\end{frame}
\begin{frame}
\frametitle{Motivation}
% a comment
\begin{block}{Main Objective}
To improve usability of Cyclus for transition scenarios.
\end{block}
\begin{block}{Main Challenge}
Deploying reactors to meet power demand is trivial, and existed
in the earliest versions of Cyclus.
\textbf{Automated, predictive deployment and decommissioning of
other facilities is more complex.} These include mining,
milling, enrichment, fuel
fabrication, reprocessing, and others.
For example, a balanced closed fuel cycle may require ensuring
that there is enough fast reactor fuel for their operation and
may drive deployment of a fleet of light water reactors.
\end{block}
\end{frame}
\begin{frame}
\frametitle{Quick Statistics}
\begin{figure}[htbp!]
\begin{center}
\includegraphics[width=0.9\textwidth]{images/git-stats-issues-ddca.png}
\end{center}
\caption{GitHub issues associated directly with this work.}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Quick Statistics}
\begin{figure}[htbp!]
\begin{center}
\includegraphics[width=0.9\textwidth]{images/git-stats-commits-ddca.png}
\end{center}
\caption{GitHub commits associated directly with this work.}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Quick Statistics}
\begin{figure}[htbp!]
\begin{center}
\includegraphics[width=\textwidth]{images/git-stats-lines-ddca.png}
\end{center}
\caption{Lines of code associated directly with this work.}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Detailed Schedule}
\begin{block}{2016}
\begin{itemize}
\item[$\checkmark$] Literature review of appropriate predictive algorithms.
\item[$\checkmark$] Add stop and restart capabilities to Cyclus (bonus: HPC deployment addition).
\end{itemize}
\end{block}
\begin{block}{2017}
\begin{itemize}
\item[$\checkmark$] Identify and rectify non-algorithmic capability gaps (e.g. specific fuel cycle process archetypes) necessary for transition simulation.
\item[$\checkmark$] Create d3ploy
\item[$\checkmark$] Add toolkit additions related for geospatial information
\item[$\checkmark$] Implement non-optimizing (NO) methods in d3ploy.
\end{itemize}
\end{block}
\end{frame}
\begin{frame}
\frametitle{Detailed Schedule}
\begin{block}{2018}
\begin{itemize}
\item[$\checkmark$] Design numerical experiments (tests) for verifying Deterministic-Optimizing (DO) algorithms in the context of key transitions.
\item[$\checkmark$] Implement Deterministic Optimizing (DO) methods in d3ploy.
\item[$\checkmark$] Design numerical experiments (test) for verifying Stochastic-Optimizing (SO) algorithms in the context of key transitions.
\end{itemize}
\end{block}
\begin{block}{2019}
\begin{itemize}
\item[$\checkmark$] Implement Stochastic Optimizing (SO) methods in d3ploy.
\item[$\checkmark$] Add additional capabilities to the predictive methods. (Buffers, reprocessing complexity handing)
\item[$\checkmark$] Demonstrate and compare the new capability in the context of the evaluation groups the EG 23, 24, 29, 30
\end{itemize}
\end{block}
\end{frame}
\begin{frame}
\frametitle{Detailed Schedule}
\begin{figure}[htbp!]
\begin{center}
\includegraphics[width=\textwidth]{images/github-ddca-proj.png}
\end{center}
\caption{Project management associated with this project.}
\end{figure}
\end{frame}