DAWS

The project Development of Advanced Wing Solutions (DAWS) funded by UKRI (https://gtr.ukri.org/projects?ref=46357) includes a subproject at the University of Liverpool addressing uncertainty quantification.

Public facing website (framework): https://riskinstitute.uk/daws/

Reports

Generic synopsis: https://docs.google.com/document/d/1LjeS4TJxUZ1iUJUMq3m0t-dYwtOY4mUrcjwJ9r1snTs/edit#

Specific framework: https://docs.google.com/document/d/1weeY_V_kXM4RoYvNBjA7-S2Yw3idOJz99MiEwDcfi5Y/edit#

Report on V&V: https://www.overleaf.com/project/61b094da52a9894d7da65856

Report on uncertainty propagation: https://www.overleaf.com/project/621cb2d45609e881449f7820

Others

Planned design book: https://docs.google.com/document/d/1NTMea2VH7vd1nc92XOWb2SZSa1yNzgx1iUlsIDu5qwc/edit#

Planning document: https://docs.google.com/document/d/1BG8WX5fE8vNqj3TY7Mok8yUyJkgjPC2kT4GBuiHujGE/edit

This page: https://sites.google.com/site/decoderringofuncertaintyterms/daws

Framework (generic)

see https://sites.google.com/site/uncertaintycalculus/

see Uncertainty matters, Purpose of uncertainty analysis

see https://www.osti.gov/servlets/purl/1116373

see https://www.nafems.org/events/nafems/2016/simulation-governance-vvuq/

see https://cstools.asme.org/csconnect/FileUpload.cfm?View=yes&ID=58268

see https://link.springer.com/referenceworkentry/10.1007%2F978-3-319-12385-1_44

see https://www.sandia.gov/saw/SAW%20Images%20and%20Publications/NAFEMS_2015.pdf

see https://core.ac.uk/download/pdf/194986967.pdf

see https://smartuq.com/about/services/digital-learning/registration/

see https://www.cpe.vt.edu/vavsc/supplemental.html

see https://www.osti.gov/servlets/purl/1368873

see https://cswarm.nd.edu/research/vvuq-experimental-physics/index.html

Framework (specific)

see https://ctr.stanford.edu/turbulence-vv-and-uq-analysis-multi-scale-complex-system

Uncertainty propagation

Risk Calc blog https://sites.google.com/site/riskcalcblog/

Model calibration

Verification & validation

Engineering design

See further links at the bottom of this page.

Google Drive folder for DAWS

https://drive.google.com/drive/folders/1p4SihtdHLCO0oGOH7fq97c-3aL7kMeSe

https://drive.google.com/drive/folders/1p4SihtdHLCO0oGOH7fq97c-3aL7kMeSe?ths=true

Short DAWS framework document, DAWS Synopsis on Uncertainty Quantification

https://docs.google.com/document/d/1LjeS4TJxUZ1iUJUMq3m0t-dYwtOY4mUrcjwJ9r1snTs/edit#

Long DAWS framework document, DAWS Uncertainty Framework

https://docs.google.com/document/d/1weeY_V_kXM4RoYvNBjA7-S2Yw3idOJz99MiEwDcfi5Y/edit#

Python code to do calculations for DAWS framework document, DAWS_framework.ipynb

https://colab.research.google.com/drive/1dZ8M5q6Bw10T7rOCi1FspdCJrRz98JL6#scrollTo=56iYTDf7AuoD

Additional material for the Long DAWS framework document, Second framework document ideas

https://docs.google.com/document/d/1NTMea2VH7vd1nc92XOWb2SZSa1yNzgx1iUlsIDu5qwc/edit#

Schemes for UQ software, The second mouse gets the cheese

https://docs.google.com/document/d/1MaTgD5-UYbhUQvBe5-pLlc6c8nzf1wNOhhPRn7S1-Ts/edit

Python package "Uncertainties"

https://pythonhosted.org/uncertainties/

John Denker's website "Uncertainty as Applied to Measurements and Calculations"

http://www.av8n.com/physics/uncertainty.htm

Bromberger article about vagueness

https://web.stanford.edu/group/cslipublications/cslipublications/bromberger-corpus/Vagueness-Ambiguity.pdf

Tannenbaum lecture on "Judgement Extremity and Accuracy under Epistemic vs. Aleatory"

https://www.youtube.com/watch?v=zBIOIhXIEGo

https://drive.google.com/drive/folders/1KOtqtshDA8kX2lpCDbApSjOdrYDPUBqZ

Discussion of NUSAP in Dewulf & Biesbroek article "Nine lives of uncertainty in decision-making: strategies for dealing with uncertainty in environmental governance"

https://www.tandfonline.com/doi/full/10.1080/14494035.2018.1504484#:~:text=Epistemic%20uncertainty%20refers%20to%20uncertainty,the%20human%20state%20of%20mind.&text=Because%20multiple%20frames%20of%20reference,a%20'state%20of%20society'.

Backup copy of long DAWS framework document, Copy of DAWS Uncertainty Framework

https://docs.google.com/document/d/1F5Wh2OEcD-MLSXha5BJ1t7e4o4sza9Afa6HkSkAVli8/edit

My Drive > DAWS

https://drive.google.com/drive/folders/15eC3yyky-kGxc1XNvrWLg_pBVsgnjzjB

https://drive.google.com/drive/folders/1KOtqtshDA8kX2lpCDbApSjOdrYDPUBqZ

My Google Drive

https://drive.google.com/drive/my-drive?ths=true

Other introductions

There are many competing introductions to UQ.

We were thinking that our effort for DAWS would expand upon the NAFEMS fliers.

See also http://wwwmaths.anu.edu.au/~jakeman/QuantifyingUncertainty/home.html.

See also https://www.osti.gov/servlets/purl/1456596 "Sandia Capabilities for Uncertainty Quantification" by Brian M. Adams (2016).

We should likewise be in some kind of communion with other pedagogically useful attempts, for instance, Swiler et al. (2009) [see page below].

Should we address other methods such as polynomial chaos expansion (PCE), or at least say why we don't?

https://digital.library.unt.edu/ark:/67531/metadc1014164/m2/1/high_res_d/993614.pdf

Several PCE references:

http://tonyohagan.co.uk/academic/pdf/Polynomial-chaos.pdf

https://digital.library.unt.edu/ark:/67531/metadc1014164/m2/1/high_res_d/993614.pdf

https://maths-people.anu.edu.au/~jakeman/QuantifyingUncertainty/home.html

https://maths-people.anu.edu.au/~jakeman/QuantifyingUncertainty/Tutorials/tutorial.html

http://www.cfd4aircraft.com/ecerta_workshop/Cooper.pdf

https://digital.library.unt.edu/ark:/67531/metadc1014164/m2/1/high_res_d/993614.pdf

Red-Horse, J.R. and Benjamin, A.S.. "A probabilistic approach to uncertainty quantification with limited information" Reliability Engineering & System Safety 85: 183--190. Abstract: Many safety assessments depend upon models that rely on probabilistic characterizations about which there is incomplete knowledge. For example, a system model may depend upon the time to failure of a piece of equipment for which no failures have actually been observed. The analysts in this case are faced with the task of developing a failure model for the equipment in question, having very limited knowledge about either the correct form of the failure distribution or the statistical parameters that characterize the distribution. They may assume that the process conforms to a Weibull or log-normal distribution or that it can be characterized by a particular mean or variance, but those assumptions impart more knowledge to the analysis than is actually available. To address this challenge, we propose a method where random variables comprising equivalence classes constrained by the available information are approximated using polynomial chaos expansions (PCEs). The PCE approximations are based on rigorous mathematical concepts developed from functional analysis and measure theory. The method has been codified in a computational tool, AVOCET, and has been applied successfully to example problems. Results indicate that it should be applicable to a broad range of engineering problems that are characterized by both irreducible and reducible uncertainty.

Bruno Sudret 2008. Global sensitivity analysis using polynomial chaos expansion. Reliability Engineering & System Safety 93: 964-979. Abstract: Global sensitivity analysis (SA) aims at quantifying the respective effects of input random variables (or combinations thereof) onto the variance of the response of a physical or mathematical model. Among the abundant literature on sensitivity measures, the Sobol’ indices have received much attention since they provide accurate information for most models. The paper introduces generalized polynomial chaos expansions (PCE) to build surrogate models that allow one to compute the Sobol’ indices analytically as a post-processing of the PCE coefficients. Thus the computational cost of the sensitivity indices practically reduces to that of estimating the PCE coefficients. An original non intrusive regression-based approach is proposed, together with an experimental design of minimal size. Various application examples illustrate the approach, both from the field of global SA (i.e. well-known benchmark problems) and from the field of stochastic mechanics. The proposed method gives accurate results for various examples that involve up to eight input random variables, at a computational cost which is 2–3 orders of magnitude smaller than the traditional Monte Carlo-based evaluation of the Sobol’ indices.