Experimental Validation of Nuclear Models Course
Online 300-hour course in experimental validation of nuclear models. 6 modules with JCGM 100:2008 (GUM, Guide to the Expression of Uncertainty in Measurement), JCGM 101:2008 (propagation of distributions by Monte Carlo) and Python (SciPy). Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Design experiments aimed at model validation.
Quantify experimental uncertainty in accordance with the GUM.
Select suitable experimental benchmarks from the NEA databases.
Apply validation metrics between simulation and experiment.
Analyse the representativeness of experiments with respect to the application.
Propagate uncertainties using Monte Carlo methods.
Write an experimental validation report.
Who it's for
- Nuclear model validation engineer wishing to specialise in Experimental Validation of Nuclear Models.
- Reactor physics engineer wishing to specialise in Experimental Validation of Nuclear Models.
- Uncertainty analyst wishing to specialise in Experimental Validation of Nuclear Models.
- Researcher in nuclear experimentation wishing to specialise in Experimental Validation of Nuclear Models.
- Test laboratory engineer wishing to specialise in Experimental Validation of Nuclear Models.
- Licensing engineer wishing to specialise in Experimental Validation of Nuclear Models.
- Engineering or science graduates seeking to enter the nuclear industry.
What you'll work with
- JCGM 100:2008 (GUM, Guide to the Expression of Uncertainty in Measurement)
- JCGM 101:2008 (propagation of distributions by Monte Carlo)
- ISO/IEC 17025 (competence of testing and calibration laboratories)
- ASME V&V 20 (verification and validation in CFD and heat transfer)
- ISO 5725-2 (accuracy of measurement methods)
- OECD/NEA ICSBEP Handbook (criticality benchmarks)
- OECD/NEA IRPhEP Handbook (reactor physics benchmarks)
6 modules · 300 h
1.1The role of experiments in code validation1.2Separate-effects experiments and integral experiments1.3Requirements of a validation experiment1.4Experiment design with prior simulation1.5Measurement chain and metrological traceability1.6Experimental uncertainty according to the GUM1.7Boundary conditions and input data1.8Documentation and archiving of experiments1.9Cooperation between experimenters and analysts1.10Case study: analysis of a validation experiment
2.1Critical experiments and the ICSBEP Handbook2.2IRPhEP reactor physics benchmarks2.3Shielding experiments (SINBAD)2.4Zero-power reactor experiments2.5Reactivity and coefficient measurements2.6Reaction rate distributions and activation foils2.7Isotopic composition of irradiated fuel2.8Sensitivity to nuclear data2.9Biases and similarity to application systems2.10Laboratory: modelling an ICSBEP benchmark in OpenMC
3.1Integral loops: LOFT, PKL and ROSA3.2Separate-effects facilities3.3Facility scaling: the H2TS method3.4Boiling and critical flow experiments3.5Thermal and hydraulic instrumentation3.6OECD/NEA programmes: PSB-VVER and ATLAS3.7Natural convection experiments3.8Containment experiments: PANDA3.9CSNI validation matrices3.10Practice: comparison of a PKL test with RELAP5
4.1Quantitative validation metrics4.2FFTBM frequency-domain error method4.3Comparison with experimental and numerical uncertainty4.4Parameter calibration and Bayesianism4.5Model bias and discrepancies4.6Validation domain and extrapolation4.7Data quality in validation databases4.8Expert judgement and PIRT panels4.9Representativeness and scaling4.10Workshop: calculation of FFTBM metrics
5.1Validation of MCNP and SERPENT 5.2 Validation of Burn-Up Codes 5.3 Validation of TRACE and RELAP5 5.4 CFD Validation in Thermal Mixing 5.5 Fuel Code Validation: FRAPCON 5.6 Severe Accident Experiments: Phébus 5.7 Validation of Codes for Fast Reactors 5.8 Validation for Small Modular Reactors 5.9 Lessons from Blind Exercises 5.10 Case Study: OECD Blind Exercise on T-Junction Mixing
6.1Selection of the phenomenon and the code6.2Review of the experimental database6.3Modelling the experiment6.4Quantification of uncertainties6.5Simulation and comparison6.6Evaluation of validation metrics6.7Analysis of the application domain6.8Conclusions on model adequacy6.9Report and presentation6.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and a dedicated technical tutor. No fixed timetable: students progress at their own pace within the enrolment window, which makes it possible to combine the course with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (SciPy), DAKOTA, SCALE, OriginPro, in free, academic or evaluation versions. Case studies are based on real procedures for comparing models with experimental benchmarks, measurement uncertainty analysis and validation experiment design. Assessment is continuous across modules, plus a final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Design experiments aimed at model validation.
Quantify experimental uncertainty in accordance with the GUM.
Select suitable experimental benchmarks from the NEA databases.
Apply validation metrics between simulation and experiment.
Analyse the representativeness of experiments with respect to the application.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow the reading of technical regulations.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 300 hours, spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
The course uses Python (SciPy), DAKOTA, SCALE and OriginPro. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Check the recognition policy when you enrol.
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