Welcome to the April 2013 edition of AMBER Update, the electronic newsletter for the AMBER software tool that allows users to build their own deterministic and probabilistic dynamic compartment models to represent the migration, degradation and fate of contaminants in environmental and engineered systems.

AMBER 5.6 Released

Quintessa is committed to the long-term development and support of AMBER. Software upgrades are provided annually to users with software maintenance agreements. AMBER 5.6 is the latest upgrade, which was finalised in December 2012 and distributed in January 2013.

A key new feature in AMBER 5.6 is the capability to refer to values evaluated for one item in the expression used for another item in the same parameter. This is described as 'self-referencing' and is particularly useful when inputting functions that should provide a mass balance over a nameset (index). For example, users will be able to index water flows over compartments and use the product of the water flow from one compartment as an input to the water flow through another within the same parameter.

Other new features include:

  • A new log file that is maintained when AMBER is run in batch mode, enabling users to see the history of messages reported by AMBER when running in batch mode.
  • A new date and time output for batch mode, which helps to trace when automated outputs/reports are generated in batch mode and supports quality assurance checking.
  • Refinement of the way in which the Laplace solver handles very small amounts, increasing the accuracy of results even further.

A demonstration version of AMBER 5.6 is available to download for free from here. Please contact us if you have any questions about AMBER or about how to obtain or upgrade a licence.

Recent AMBER Applications

AMBER continues to be applied to a wide range of assessment studies. These include:

  • Use on behalf of the Nuclear Waste Management Organization (NWMO) to assess post-closure safety of a proposed Deep Geologic Repository (DGR) for Ontario Power Generation's (OPG) low and intermediate level radioactive waste in Canada. Regulatory submission documentation is available online, including supporting reports on the evaluation of post-closure safety for a Normal Evolution Scenario and for Disruptive Scenarios using AMBER.
  • Application of AMBER to long-term modelling of contamination in small rural communities resulting from the Fukushima Daiichi nuclear power station accident on behalf of JAEA. An AMBER model has been developed that enables a range of remediation options to be assessed for individual sites, based on measured levels of contamination, its distribution and patterns of land-use.
  • Use as the modelling 'engine' behind the PRISM code for probabilistic modelling of radionuclide and heavy metal deposition from the atmosphere to UK agricultural systems on behalf of the Food Standards Agency. Dynamic soil, plant and animal models are used to simulate contaminant behaviour through to calculating potential concentrations in plant and animal food stuffs.
  • Use by ANDRAD in support of safety assessments for the proposed near-surface disposal of low and intermediate level radioactive waste at the Saligny site in Romania.
  • Use in support of safety assessment calculations for developing International Atomic Energy Agency (IAEA) guidance concerning the potential disposal of disused sealed radioactive sources in narrow diameter boreholes.
  • Use by SoGIN to support the screening process for identifying potential sites for the near-surface disposal of low and intermediate level radioactive waste in Italy.

Please don't hesitate to contact us if you have any questions about AMBER and its application.

Hints and Tips for Using AMBER

Easily Change Parameter Type or Multiplicity

As with any modelling code, implementing a model in AMBER is often an iterative process. A useful feature in AMBER is the capability to easily change (i) the multiplicity (indexing) for a parameter or (ii) from a standard (input) to an observer (result) parameter by right-clicking on the Parameter window and selecting the appropriate option. See Section of the Reference Guide for more information.

Annotating Cases

One of the powerful features in AMBER is the transparency with which models can be implemented and explored, as nothing is hidden from the user. Models can be edited or explored either via the AMBER interface or via the text based case file, which provides expert users with significant flexibility. Another feature that aids in communicating models is the ability to add comments or descriptions to almost every input. These can be used to explain specific entries for data or expressions and/or to provide comments/quality assurance notes when checking model implementation. See Section 4.4 of the Reference Guide for more information.

Optimising Complex Models

We aim to provide users with the maximum flexibility to implement their own, fully auditable, time-dependent and probabilistic compartment models in AMBER. There is no in-built limit to the size and complexity of AMBER models. Calculation times tend to increase linearly with the size and complexity of a model. It is possible to refine the way in which models are implemented to help reduce calculation times. This is particularly helpful for large and complex or probabilistic models. Tips for creating efficient models include:

  • Avoid repetitively introducing the same expression into AMBER parameters with multiplicity (indexing), as AMBER will evaluate the expression each time it is encountered. It is better to include the expression as a default entry in another parameter with the same multiplicity and reference this parameter this instead. This way, AMBER will only evaluate the expression once.
  • Mappings in large models can generate large arrays of data that need to be manipulated within expressions. Calculation times can be improved if expressions involving large mappings are rearranged to become more efficient, for example, by avoiding intermediate results which depend on both the input and output namesets for a mapping.

There are two outputs that are available when running AMBER in batch mode that are particularly useful in optimising models, these report the amount of processor time used on each parameter within a case (EVALUATION-TIMING) and the amount of memory used for each parameter (MEMORY-REPORT). See Section 12 of the Reference Guide for information about running AMBER in batch mode.

New Face

Rebecca Shaw joined Quintessa from a PhD in chemical engineering at Cambridge University in Autumn 2012 and is helping Russell Walke and Peter Robinson to support AMBER. Rebecca is assisting in the provision of technical and administrative support to AMBER users, as well as supporting Quintessa's safety assessment projects involving AMBER.