Andy Wilkins

The fluid–solid heat transfer coefficient in porous media is not uniquely defined. Its magnitude depends fundamentally on whether the solid temperature is treated as a phase-averaged bulk value or an interface-averaged surface value. Engineering Nusselt–Reynolds correlations assume negligible intra-particle thermal gradient with small particle Biot numbers (Bi << 1), making the solid temperature constant throughout the entire single grain. When this assumption breaks down, intra-grain thermal gradients develop and the two temperature definitions for the solid phase diverge. Yet, this distinction is rarely acknowledged when engineering correlations are imported into continuum-scale local thermal non-equilibrium formulations. To quantify this inconsistency, we combine granular-scale laboratory heat transport experiments with experimentally informed two-dimensional numerical simulations across 36 configurations. Grain diameters range from 5 to 30 mm and Darcy velocities from 3 to 23 m d$^{-1}$, yielding Reynolds numbers between 0.5 and 21.5. The low thermal conductivity of the glass beads (1 W~m$^{-1}$~K$^{-1}$) ensures non-negligible Biot numbers (Bi > 0.1), creating a controlled regime in which the two definitions of the solid temperature diverge. We set Bi = 0.1 as the grain-isothermal threshold, beyond which the lump capacitance method begins to cause significant error. All investigated configurations exceed the isothermal threshold of 0.1. When the two solid temperature definitions are compared, interface-averaged Nusselt numbers exceed phase-averaged values by factors of 3.6 to 17.5 and remain effectively time-invariant. However, phase-averaged coefficients exhibit pronounced transient decay toward smaller steady-state magnitudes, reflecting intra-grain conduction resistance. Existing empirical correlations show systematic bias within the configuration range with this coefficient definition, underestimating Nusselt numbers at Reynolds numbers below 7.5 and overestimating above it. Based on the results, we suggest the phase-averaged heat transfer coefficient for solid materials with Bi > 0.1, as it is consistent with the phase-averaged formulation in the model and captures the intra-grain thermal gradient. These findings demonstrate that physically consistent continuum-scale modelling requires explicit alignment between the solid temperature definition in the constitutive law and that in the governing energy equations. These results can be interpreted for groundwater applications, since the Biot-number framework provides a transferable quantitative diagnostic. Practitioners can evaluate Bi for their aquifer material and flow conditions: Bi < 0.1 validates engineering correlations; Bi > 0.1 signals that a phase-averaged coefficient is required. The grain-size thresholds reported here (15 mm for glass beads) represent order-of-magnitude estimates that would shift toward coarser sizes in 3D configurations due to reduced intra-grain conduction resistance. The exact 3D thresholds remain to be validated in future studies.

Mozzie enables simulation of the lifecycle and spatial spread of mosquitoes. Mozzie can be used to assess risks associated with disease-control strategies at local, regional or continental scales. Most particularly, strategies involving genetic alterations of mosquitoes to eliminate malaria, are of prime interest.

Background: The dynamics of vector borne disease transmission depend on the abundances of vectors. The dominant malaria vector species complex of Anopheles gambiae sensu lato is a target of vector control strategies designed to reduce and eliminate malaria transmission. The three most widely distributed dominant malaria vectors within the species complex are An. arabiensis, An. coluzzii and An. gambiae sensu stricto . Methods: Previous studies across the extent of the species complex range have been restricted to annual predictions of species occurrence or relative abundance. This study incorporated relative abundance data at the species level and abundance data at the species complex level to estimate and predict daily equilibrium vector abundances of each species. Statistical models with interpretable parameters were used to gain insight into how factors such as meteorological conditions, insecticide treated net use, and human density affect the spatial and temporal predictions. The statistical models were used to predict daily equilibrium vector abundance that is an important factor in indices of malaria transmission such as the basic reproduction number and vectorial capacity. Results: Meteorological factors, such as precipitation and relative humidity, and human factors, such as population density and insecticide treated nets, were important predictors of these three malaria vector species in Africa. Insecticide treated net use was negatively associated with per capita vector abundance of the An. gambiae species complex and interacted with year to suggest an a negative effect on the ratio of An. gambiae s.s . to An. arabiensis at the end of the prediction period that extended from 2002 to 2020. A predicted increasing interannual trend for An. coluzzii was potentially caused by changing species identification methods. Conclusion: The predicted equilibrium abundances of the three species showed potentially high levels of geographical overlap, niche overlap, and evidence for stable coexistence despite substantial interspecific competition. Improving collection of longitudinal species abundance data across the spatial range of the An. gambiae species complex will facilitate future explorations of causal hypotheses that relate vector abundance to control measures, malaria interventions and meteorological conditions.

Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the “FluidFlower”), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations.

Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the "FluidFlower"), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations.

Coal seam permeability is a critical factor in coal seam gas extraction and gas outburst control. In Australian coal mines, coal seam permeability is normally estimated using a packer test or drill stem test. In contrast, Chinese coal mines generally estimate a parameter called the “gas conductivity coefficient” by measuring natural gas flow rates from an underground borehole drilled through a coal seam. With this method, it has been frequently reported that the permeability of many Chinese coal seams is between 0.0001 mD and 0.01 mD, which is extremely low compared to that of Australian coal seams (1–100 mD). It is therefore natural to wonder how closely the Chinese method measures permeability. Resolving this question will allow knowledge and experience in outburst management to be shared between Australian and Chinese coal mines. This question is investigated by the numerical modelling of gas desorption and flow through a seam of known permeability and by using the model’s borehole gas flow rate to estimate the permeability using the Chinese method. A total of 126 simulations were run with various input reservoir parameters. The results suggest that the Chinese method estimates permeability at an accuracy of 85% to 100%, which is adequate for mine pre-drainage design and outburst control. For the high diffusion rate (e.g., high gas content and short desorption time) and low Darcy flow rates (e.g., low permeability), these errors are reduced.