Cyclus Archetypes
November 17, 2015 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Anthony M. Scopatz, Matthew J. Gidden, Robert W. Carlsen, Robert R. Flanagan, Kathryn D. Huff, Meghan B. McGarry, Arrielle C. Opotowsky, Olzhas Rakhimov, Zach Welch, Paul P. H. Wilson
arXiv ID
1511.05619
Category
cs.SE: Software Engineering
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
The current state of nuclear fuel cycle simulation exists in highly customized form. Satisfying a wide range of users requires model modularity within such a tool. Cyclus is a fuel cycle simulator specifically designed to combat the lack of adaptability of previous generations of simulators. This is accomplished through an agent-based infrastructure and treating time discretely. The Cyclus kernel was developed to allow for models, called archetypes, of differing fidelity and function depending on need of the users. To take advantage of this flexibility, a user must write an archetype for their desired simulation if it does not yet exist within the Cyclus ecosystem. At this stage, a user graduates to the title of archetype developer. Without automation, archetype development is difficult for the uninitiated. This paper presents the framework developed for simplifying the writing of archetypes: the Cyclus preprocessor, or cycpp. cycpp addresses the computer science and software development aspects of archetype development that can be addressed algorithmically, allowing the developer to focus on modeling the physics, social policies, and economics. cycpp passes through the code three times to perform the following tasks: normalizing the code via the C preprocessor, accumulation of notations, and code generation. Not only does this reduce the amount of code a developer must write by approximately an order of magnitude, but the archetypes are automatically validated.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted