Geothermal energy potential is usually discussed in the context of conventional or engineered systems and at the scale of an individual reservoir. Whereas exploration for conventional reservoirs has been relatively ea...Geothermal energy potential is usually discussed in the context of conventional or engineered systems and at the scale of an individual reservoir. Whereas exploration for conventional reservoirs has been relatively easy, with expressions of resource found close to or even at the surface, exploration for non-conventional systems relies on temperature inherently increasing with depth and searching for favourable geological environments that maximise this increase. To utilitise the information we do have, we often assimilate available exploration data with models that capture the physics of the domi- nant underlying processes. Here, we discuss computational modelling approaches to exploration at a regional or crust scale, with application to geothermal reservoirs within basins or systems of basins. Target reservoirs have （at least） appropriate temperature, permeability and are at accessible depths. We discuss the software development approach that leads to effective use of the tool Underworld. We explore its role in the process of modelling, understanding computational error, importing and exporting geological knowledge as applied to the geological system underpinning the Guangdong Province, China.展开更多
Uncertainty quantification(UQ)is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated.Much of the microscopic world of atoms and molecules has remaine...Uncertainty quantification(UQ)is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated.Much of the microscopic world of atoms and molecules has remained immune to these developments but due to the fundamental problems of reproducibility and reliability,it is essential that practitioners pay attention to the issues concerned.Here aUQstudy is undertaken of classical molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters,which affect key quantities of interest,including material properties and binding free energy predictions in drug discovery and personalized medicine.Using scalable UQ methods based on active subspaces that invoke machine learning and Gaussian processes,the sensitivity of the input parameters is ranked.Our analyses reveal that the prediction uncertainty is dominated by a small number of the hundreds of interaction potential parameters within the force fields employed.This ranking highlights what forms of interaction control the prediction uncertainty and enables systematic improvements to be made in future optimizations of such parameters.展开更多
基金supported by the Australian Government infrastructure investment in AuScope Simulation and Modelling and the NeCTAR Geology to Geophysics eResearch toolsupported by the National Computational Infrastructure grant "p67-Energy driven understanding of deep geological resources" and the NeCTAR Research Cloud grant "Underworld_NeCTAR_ Cloud_Flow" executed at the R@CMon,the Research Cloud node at Monash University
文摘Geothermal energy potential is usually discussed in the context of conventional or engineered systems and at the scale of an individual reservoir. Whereas exploration for conventional reservoirs has been relatively easy, with expressions of resource found close to or even at the surface, exploration for non-conventional systems relies on temperature inherently increasing with depth and searching for favourable geological environments that maximise this increase. To utilitise the information we do have, we often assimilate available exploration data with models that capture the physics of the domi- nant underlying processes. Here, we discuss computational modelling approaches to exploration at a regional or crust scale, with application to geothermal reservoirs within basins or systems of basins. Target reservoirs have （at least） appropriate temperature, permeability and are at accessible depths. We discuss the software development approach that leads to effective use of the tool Underworld. We explore its role in the process of modelling, understanding computational error, importing and exporting geological knowledge as applied to the geological system underpinning the Guangdong Province, China.
基金funding support from(i)the UK EPSRC for the UK High-End Computing Consortium(EP/R029598/1)the Software Environment for Actionable&VVUQ-evaluated Exascale Applications(SEAVEA)grant(EP/W007762/1)+5 种基金the UK Consortium on Mesoscale Engineering Sciences(UKCOMES grant no.EP/L00030X/1)the Computational Biomedicine at the Exascale(CompBioMedX)grant(EP/X019276/1)(ii)the UK MRC Medical Bioinformatics project(grant no.MR/L016311/1)(iii)the European Commission for EU H2020 CompBioMed2 Center of Excellence(grant no.823712)EU H2020 EXDCI-2 project(grant no.800957)We made use of SuperMUC-NG at Leibniz Supercomputing Center under project COVID-19-SNG1,and the ARCHER2 UK National Supercomputing Service under the SEAVEA grant(EP/W007762/1).
文摘Uncertainty quantification(UQ)is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated.Much of the microscopic world of atoms and molecules has remained immune to these developments but due to the fundamental problems of reproducibility and reliability,it is essential that practitioners pay attention to the issues concerned.Here aUQstudy is undertaken of classical molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters,which affect key quantities of interest,including material properties and binding free energy predictions in drug discovery and personalized medicine.Using scalable UQ methods based on active subspaces that invoke machine learning and Gaussian processes,the sensitivity of the input parameters is ranked.Our analyses reveal that the prediction uncertainty is dominated by a small number of the hundreds of interaction potential parameters within the force fields employed.This ranking highlights what forms of interaction control the prediction uncertainty and enables systematic improvements to be made in future optimizations of such parameters.
