SPECIAL NOTICE
D -- Development and Implementation of the methods and algorithms for dynamic data evaluation of thermodynamic transport properties of binary mixtures and chemical reactions
- Notice Date
- 2/12/2007
- Notice Type
- Special Notice
- NAICS
- 541710
— Research and Development in the Physical, Engineering, and Life Sciences
- Contracting Office
- Department of Commerce, National Oceanic and Atmospheric Administration (NOAA), Mountain Region Acquisition Division, 325 Broadway - MC3, Boulder, CO, 80305-3328, UNITED STATES
- ZIP Code
- 00000
- Solicitation Number
- NB838000703806MY
- Response Due
- 2/27/2007
- Archive Date
- 3/1/2007
- Description
- The U.S. Department of Commerce, National Institute of Standards and Technology (NIST), Time and Frequency Division proposes to negotiate on a sole source basis under the authority of 41 U.S.C. 253(c)(1) with Dr. Vladimir Diky for the services of a Development and implementation of the methods and algorithms for development and improvement of property prediction methods for dynamic data evaluation; development of an automated method of thermophysical experiment planning based on dynamic data evaluation; development of a method for propagation of uncertainty information from thermophysical data to process simulation results; development and implementation of methods for ternary mixture data processing in dynamic data evaluation; development of storage principles and data processing methods for electrolytes, polymers and biological systems at Thermodynamics Research Center (TRC) of the National Institute of Standards and Technology for dynamic data evaluation of thermodynamic and transport properties of binary mixtures and chemical reactions. The work is targeting further enhancement of the Source database, ThermoData Engine (TDE) software, ThermoML standard, and Guided Data Capture software developed and supported by Thermodynamics Research Center (TRC) of the National Institute of Standards and Technology implementing the concept of dynamic data evaluation to provide on-demand recommended thermophysical and thermochemical property data for pure substances, mixtures and chemical reactions. This award is a Base year with 4 option periods. The proposed work includes the following tasks: Development and improvement of property prediction methods for dynamic data evaluation. 1. Analysis of the existing general group classification schemas and definition of a common primary group classification. 2. Analysis of structural elements deployed by prediction methods and development of a structural element recognition algorithm. 3. Resolving inconsistencies in structural elements defined for transport properties. 4. Development of a parametrization procedure for prediction methods. 5. Development of a self-testing procedure for prediction methods. 6. Development of extrapolation principles on large structures. 7. Revision of structure similarity principles for selection of the best method. 8. Revision of uncertainty assessment methods for property prediction. 9. Extension of group definition and re-parametrization of thermophysical property prediction methods. 10. Extension of group definition and re-parametrization of thermochemical and transport property prediction methods. 11. Development of a UNIFAC parametrization procedure. 12. Testing and debugging of a UNIFAC parametrization procedure. Development of an automated method of thermophysical experiment planning based on dynamic data evaluation. 1. Re-parametrization and testing of the prediction methods of ThermoData Engine. 2. Completion of the evaluation of entropies and Gibbs energies of formation for pure compounds. Release of a new version of ThermoData Engine (4.0). 3. Development of algorithm for detection of critical inconsistencies in experimental data for pure compounds. 4. Development of algorithm for detection of inconsistent predictions for pure compounds. 5. Development of an algorithm for automated planning of measurements for selected compounds. 6. Development of algorithm for determination of experimental data responsible for unsuccessful parameterization of prediction methods for pure compounds. 7. Development of algorithm for planning of experiment to measure the properties necessary for determination of missing parameters for prediction methods for pure compound classes. 8. Testing and debugging of the algorithm for experiment planning for pure compounds. 9. Development of algorithm for detection of inconsistent data for mixtures. 10. Development of an algorithm for automated planning of measurements for selected mixtures. 11. Testing and debugging of the algorithm for experiment planning for mixtures. 12. Release of a ThermoData Engine version 5.0 with planning abilities. Development of a method for propagation of uncertainty information from thermophysical data to process simulation results. 1. Analysis of past experience in uncertainty assessment for process simulation. 2. Development of a program for trial process simulation with varied source data. 3. Testing of first-order effects of model uncertainties on immediate results of process simulation. 4. Testing of second-order effects of model uncertainties on immediate results of process simulation. 5. Testing of first-order effects of model uncertainties on results of process optimization. 6. Testing of second-order effects of model uncertainties on results of process optimization. 7. Determination of limiting conditions when the propagation of model uncertainties gives meaningful uncertainties of simulated process parameters. 8. Development of a ?black box? algorithm of uncertainty propagation from models to process simulation results through uncertainties of intermediate property values. 9. Testing and debugging of the uncertainty propagation algorithm. 10. Determination of the applicability of the algorithm to practical tasks. 11. Development of recommendations on the use of the uncertainty propagation algorithm with process simulators. 12. Writing a paper on uncertainty propagation. Development and implementation of methods for ternary mixture data processing in dynamic data evaluation. 1. Development of a C++ class for multi-component mixtures. 2. Development of ThermoData Engine interface for multi-component mixtures. 3. Development of ThermoData Engine file format for multi-component mixtures. 4. Development of a ThermoML parser for multi-component mixtures. 5. Development of C++ classes for representation of multi-component mixture properties. 6. Implementation of UNIFAC model for multi-component mixture properties in ThermoData Engine. 7. Implementation of the basic excess Gibbs energy model for multi-component mixture properties in ThermoData Engine. 8. Development of a liquid-liquid flash procedure for multi-component mixtures in ThermoData Engine. 9. Implementation of model fitting and covariance calculation for multi-component mixtures. 10. Testing and debugging of ThermoData Engine for multi-component mixtures. 11. Validation of ternary mixture data in Source database. 12. Release of a new ThermoData Engine version 6.0. Development of storage principles and data processing methods for electrolytes, polymers and biological systems. 1. Development of an array method for reaction equilibrium data processing. 2. Processing and validation of reaction equilibrium data in source database. 3. Analysis of system identification information and state variables for electrolytes and polymers. 4. Analysis of system identification information for biological systems. 5. Analysis of state variables for biological systems. 6. Development of additional system identification tables and fields for electrolytes, polymers, and biological systems in Source database. 7. Development of additional property codes and state variables for electrolytes, polymers, and biological systems in Source database. 8. Completion of ThermoML format for electrolytes, polymers, and biological systems. 9. Completion of Guided Data Capture software for electrolytes, polymers, and biological systems. 10. Development and implementation of data validation principles for electrolytes. 11. Development of data validation principles for biological systems. 12. Implementation of data validation principles for biological systems. This procurement is being conducted per FAR Part 13, Simplified Acquisition Procedures (NTE $100K). This synopsis is issued for information only. Based on our market research, Dr. Vladimir Diky is the only vendor who can meet the required specifications. No competitive solicitation is planned. Information submitted in response to this notice will be used solely to determine whether or not use of competitive procedures to fulfill this requirement would be in the Government's best interest and must address qualifications pertinent to this requirement. Any questions regarding this notice must be submitted in writing to the attention of Ms. Yates (e-mail preferred) questions need to be received by February 21, 2007. Anticipated award date is February 27, 2007.
- Place of Performance
- Address: 325 Broadway Boulder, CO
- Zip Code: 80305
- Country: UNITED STATES
- Zip Code: 80305
- Record
- SN01230349-W 20070214/070212220230 (fbodaily.com)
- Source
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