It generates broad-group, cell-average microscopic cross sections from ENDF/B basic nuclear data.

MC2-3 handles the complicated resonance self-shielding in fast spectrum systems by directly accounting for the resonance interactions in detail and performing calculations (2082 ultrafine group + 400,000 hyperfine group) on conventional lattice cells or simplified R-Z core models. The resulting microscopic cross sections are used for fast reactor design and analysis calculations.

Applications

• Nuclear fast reactor simulations and analysis

Features

• Code library includes almost all isotopes of the ENDF/B-VII data.
• Resolved resonance self-shielding using the numerical integration of pointwise cross sections based on the narrow resonance approximation
• Unresolved resonance self-shielding using the generalized integral method with the increased number of energy grids
• Anisotropic inelastic scattering
• 1-Dimensional (1-D) transport calculation using ultrafine or hyperfine groups
• Improved equivalence theory for the 1D heterogeneity effect in resonance self-shielding
• Efficient algorithm for solving the hyperfine group transport equation
• Option to use 2-D transport solutions (TWODANT) for group condensation
• Fortran 90/95 memory structure
• Keyword-based input system and built-in data conversion capability

Technical Details/Requirements

Developed using the Compaq Visual Fortran on the Microsoft Windows operating system (OS), the MC2-3 code can be installed and executed on the Windows, Macintosh, Unix and Linux OS environments. The memory requirements depend upon the problem. The current version requires more than 1G byte of memory. The memory management system in the current version does not use scratch files to save memory. Thus, more than 4G byte of memory may be required for large problems with many isotopes, hyperfine groups, and/or one-dimensional geometry. A Fortran compiler is required to compile the included source code. Minor changes may be required for code compilation.

The software is written in Fortran 90/95 and can be run on a variety of operating systems including Unix, Linux, Mac OS and Windows. The software includes comments in the source code and the method/user/programmer manual with several examples. An engineer with neutronics experience can learn to run the code in anywhere from a day to a week.

Argonne’s Battery Life Estimator (BLE) software is a state-of-the-art tool kit for fitting battery aging data and for battery life estimation. It was designed to make life-cycle estimates using two years of aging data.

BLE helps answer key questions on how battery performance will change with calendar age, cycles, internal component aging, cell-to-cell manufacturing variations, summer and winter temperature extremes, differing anode and cathode materials, and electrolyte variations and additives.

The software employs a generalized statistical approach to fit data from accelerated aging experiments to a life equation. The BLE software is different from other curve-fitting routines as it employs robust fitting techniques and estimates battery life by using Monte Carlo techniques (which most generalized curve-fitting software does not consider).

Applications

• Fit battery aging data to life equations
• Estimate battery life

Features

• Easy to learn
• Fast run times
• Easy-to-use graphical user interface
• User guide includes examples and frequently asked questions

Technical Details/Requirements

• Requires PC computer with a Pentium 4 processor, 1 GB of memory and VGA graphics
• Operates on a Windows 2000 or later system and requires Microsoft .NET framework versions 1.1 through 3.5

BatPaC is a software modeling tool designed for policymakers and researchers who are interested in estimating the cost of lithium-ion batteries after they have reached a mature state of development and are being manufactured in high volumes. The tool captures the interplay between the design and cost of these batteries for transportation applications.

BatPaC comes with a library of several lithium-ion battery chemistries and default inputs for all the parameters specified in different manufacturing areas of a factory.

Applications

• Estimates the cost of manufacturing lithium-ion batteries
• Examines trade-offs that result from different user requirements such as power, energy, charging time, etc.

Features

• Supports simulation and design with precise battery mass and dimensions, cost performance characteristics, and battery pack values from bench-scale results
• Calculates battery pack-level quantities by adding together all the battery components that are designed to meet user-defined specifications
• Determines the performance of a given battery chemistry/cell/pack design in batteries for four types of electric vehicle applications

Technical Details/Requirements

• Microsoft Excel-based application

Autonomie is capable of:

• Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), Hardware-in-the-Loop (HIL) and Rapid-Control-Prototyping (RCP)
• Integrating math-based engineering activities through all stages of development
• Mixing and matching models of different levels of abstraction with higher fidelity models where analysis and high-detail understanding is critical

Autonomie provides the environment and standard framework to make an entire engineering organization more efficient through the reuse and sharing of models and methods, and by leveraging modeling and control experts across different areas.

Autonomie gives your organization the capabilities for total control and integration of your multidisciplinary engineering design processes to ensure a quality design that is executed almost flawlessly from the first hardware build.

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Applications

• Automotive engineering
• Any simple or complex engineering application that requires the integration of many systems or subsystems

Features

• Provides for rapid and easy integration of models with varying levels of detail
• User-friendly graphical interface
• Supports customizable workflow
• Links with commercial off-the-shelf software applications for detailed, physically-based models, including GT-Power©, AMESim©, CarSim©, AVL-DRIVE©
• Provides configuration and database management
• Protects proprietary models and processes

Technical Details/Requirements

Autonomie requires:

• Windows XP, Vista
• MATLAB®
• Simulink®
• Stateflow®
• Microsoft .NET 2.0
• 1GB RAM

Licensing Information

Argonne offers first-line technical support as well as on-site training of Autonomie for companies, research institutions and universities.

If you are interested in licensing the Autonomie software, please the complete the Licensing Request Form. If you have questions, please contact autonomie-​licensing@​anl.​gov.

For technical support, please contact autonomie-​help@​anl.​gov.

Argonne provides support for U.S. Government activities at autonomie-​government-​support@​anl.​gov.

The platform, which consists of a set of statistical algorithms to generate wind power point and uncertainty forecasts, can be used for systematic testing and comparison of different computational learning algorithms.

For wind power point forecasting, ARGUS-PRIMA uses concepts from information theoretic learning (ITL) for training a neural network. In tests on real-world data from two large-scale wind farms in the Midwestern United States, results showed distinct advantages of using ITL training criteria as compared to the traditional minimum square error criterion.

For wind power uncertainty forecasting, ARGUS-PRIMA enlists two methods for estimating uncertainty based on kernel density forecasting (KDF). Both KDF algorithms are suitable for online learning. The new algorithms have been tested on datasets from the Eastern Wind Integration and Transmission Study, as well as on two wind farms in the Midwestern United States. Testing shows that the KDF algorithms result in a better match to observed wind power distribution than results obtained using traditional quantile regression.

Applications

• Wind power point forecasting
• Wind power uncertainty forecasting

Features

• Inputs: Can use numerical weather prediction variables, weather observations and power output from wind power farms
• Outputs: wind power predictions (deterministic point forecasts or probability density functions)
• Standard forecast evaluation scores can be calculated

Technical Details/Requirements

Four software environments are used: a PostgreSQL relational database, a C++ neural network library, a kernel density forecast library and supporting algorithm codes. The ARGUS-PRIMA platform consists of source code without an explicit user interface. Users will need to possess considerable programming skills to set up and run the code.