RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.
RAVEN is designed to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. RAVEN includes the following major capabilities:
The RAVEN statistical analysis framework can be employed for several types of applications:
RAVEN provides a set of basic and advanced capabilities that ranges from data generation, data processing and data visualization. More information can be found at RAVEN Wiki.
MonteCarlo sampling
Grid sampling
Stratified Sampling
Factorial design
Response surface design
Generalized Polynomial Chaos (gPC) with sparse grid collocation (SGC)
Generalized Polynomial Chaos (gPC) with sparse grid collocation (SGC) using the High Dimensional Model Representation expansion (HDMR)
General combination of the above sampling strategies
Generic interface with external codes
Custom code interfaces (third-party software(s) currently available:
Custom ad-hoc external models (build in python internally to RAVEN)
More information on this project is available at the RAVEN website.
This project is supported by Idaho National Laboratory.
The following plugin repositories are officially supported by RAVEN.
These plugin repositories are available without restriction to RAVEN users:
TEAL
module: provides economic analysis.HERON
module: provides workflow generation and dispatching models for performing stochastic technoeconomic analysis of systems of components interconnected by the resources they produce and consume.SR2ML
module: provides safety, risk and reliability analysis tools.LOGOS
module: provides computational capabilities to optimize plant resources such as maintenance optimization and optimal component replacement schedule by using state-of-the-art discrete optimization methods.FARM
module: is designed to solve the supervisory control problem in Integrated Energy System (IES) project. FARM utilizes the linear state-space representation (A,B,C matrices) of a model to predict the system state and output in the future time steps, and adjust the actuation variable to avoid the violation of implicit thermal mechanical constraints.BayCal
module: is aiming at inversely quantifying the uncertainties associated with simulation model parameters based on available experiment data. BayCal tries to resolve two critical issues existing in the Bayesian inference: 1) high-dimensional experimental data (such as time series observations at multiple locations), 2) expensive computational simulations. These issues have been studied and resolved in literature, but there is not yet a complete toolkit to resolve these issues in an efficient and automatic way. BayCal automatizes the process by coupling with RAVEN, utilizes artificial intelligence algorithms to automatically construct surrogate models for the expensive computational simulations and dimensionality reduction techniques to significantly reduce the number of simulations for convergence.POEM
module: is a platform for optimal experiment management, powered with automated machine learning to accelerate the discovery of optimal solutions, and automatically guide the design of experiments to be evaluated. POEM currently supports 1) random model explorations for experiment design, 2) sparse grid model explorations with Gaussian Polynomial Chaos surrogate model to accelerate experiment design ,3) time-dependent model sensitivity and uncertainty analysis to identify the importance features for experiment design, 4) model calibrations via Bayesian inference to integrate experiments to improve model performance, and 5) Bayesian optimization for optimal experimental design. In addition, POEM aims to simplify the process of experimental design for users, enabling them to analyze the data with minimal human intervention, and improving the technological output from research activities. POEM leverages RAVEN (a robust platform to support model explorations and decision making) to allow for large scalability and reduction of the computational costs and provides access to complex physical models while performing optimal experimental design.DOVE
module: The Dispatch Optimization Variable Engine (DOVE) is software tool written in python, developed at Idaho National Laboratory that provides an easily accessible API to performing dispatch analysis for integrated energy system configurations.Idaho National Laboratory is a cutting edge research facility which is a constantly producing high quality research and software. Feel free to take a look at our other software and scientific offerings at:
Primary Technology Offerings Page
Supported Open Source Software
Raw Experiment Open Source Software
Unsupported Open Source Software
Files in crow/contrib, src/contrib and framework/contrib are third party libraries that are not part of Raven and are provided here for covenience. These are under their own, seperate licensing which is described in those directories.
Raven itself is licensed as follows:
Copyright 2016 Battelle Energy Alliance, LLC
Licensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.