This DG2 formulation achieves well-balancedness for all discharge coefficients in the presence of irregular, piecewise-planar topography with wetting and drying ( Kesserwani et al., 2018). While no exact solution is available, DG2, FV1 and ACC predictions of water depth and velocity agree closely with existing industrial model results (Fig. 4.10 and 4.11 in Néelz and Pender, 2013). ACC and DG2 water depth predictions are almost identical at all gauge points (Fig. 6a–d). FV1 predictions are nearly identical, except that the wave front is slightly smoother and arrives several minutes earlier than ACC or DG2, as seen at point 5 (Fig. 6c) and point 6 (Fig. 6d). Observed free-surface elevation hydrographs are calculated from Environment Agency measurements of water depth and riverbed elevation above mean sea level ( Environment Agency, 2020). When the scheme administrator starts to pay the member’s benefits, they need to satisfy themselves that there is no lifetime allowance charge due. If the lifetime allowance charge is due a scheme administrator needs to know how much is due. A scheme administrator is jointly and severally liable to pay the lifetime allowance charge where the event triggering the lifetime allowance test is not a post-death benefit crystallisation event. A scheme administrator also has to report the member’s liability to the lifetime allowance charge to HMRC on the accounting for tax (AFT) return. Guidance on reporting and paying the lifetime allowance charge can be found at PTM162000. ACC solver scalability falls somewhat below perfect scaling, with a 16-fold increase in CPU cores only yielding a 7-fold decrease in runtime (Fig. 9).

To assess the computational scalability of the multi-core CPU solvers, the test is run using 1–16 CPU cores, while FV1-GPU and DG2-GPU are run on a single GPU device. Timings of the rising and falling limbs are well-predicted by all three solvers for the majority of hydrographs. At coarser grid resolutions, river levels are overpredicted and the difference between base flow and peak flow levels is underpredicted. 2 These findings are consistent with those of Xia et al. ( 2019). Hydrograph inaccuracies are primarily due to DEM coarsening, which artificially smooths river channel geometries, reducing the elevation difference between riverbed and riverbank. Consequently, the terrain elevation at gauging points on the 40 m DEM is between 1.12 and 6.06 m higher than the same points on the 10 m DEM, depending on the local river channel geometry. These terrain elevation errors are shown in Table 2, which are calculated as the difference in local element-average topography elevations between the 40 m DEM and 10 m DEM. Spatially adaptive solvers ( Kesserwani and Sharifian, 2020; Özgen-Xian et al., 2020) and non-uniform meshing techniques ( Kolega and Syme, 2019) offer another alternative to improve flow predictions by selectively capturing fine-scale channel geometries, and such methods are under development for inclusion in a future LISFLOOD-FP release. In summary, all solvers predicted similar water depth and velocity hydrographs, though ACC experienced a short period of numerical instability in a localised region where the Froude number exceeded the limit of the local inertia equations. Choné, G., Biron, P. M., Buffin‐Bélanger, T., Mazgareanu, I., Neal, J. C., and Sampson, C. C.: An assessment of large‐scale flood modelling based on LiDAR data, Hydrol. Process., 35, e14333, https://doi.org/10.1002/hyp.14333, 2021.higher reduction in the number of elements (Fig. 6a). With ε = 10 - 3 , the ratios become 21, 7.6 and 4.7 times, respectively. The available at: https://developer.nvidia.com/blog/cuda-pro-tip-write-flexible-kernels-grid-stride-loops/ (last access: 2~June~2021), Similar to runtime measurements presented earlier in Sect. 3.1.3, the GPU solvers become more efficient on grids with a larger number of elements: in this test, DG2-GPU is 1.8 times faster than DG2-CPU at the standard resolution of Δ x=50 m, becoming 2.5 times faster at the finest resolution of Δ x=10 m; similarly, FV1-GPU is between 1.2 times and 5.1 times faster than FV1-CPU. The department’s accounting system descriptions were designed for internal use and not external publication. We are working to make the data and descriptions easier to understand. We hope these notes will help in the meantime. General points On each grid, the water depth cross section is measured along the centre of the domain (Fig. 7). DG2, FV1 and ACC cross-sectional profiles at the standard grid spacing of Δ x=5 m agree well with industrial model results (Fig. 4.13 in Néelz and Pender, 2013). Differences are most apparent in the vicinity of the wave front, near x=400 m.

The representation of these flood defences could be improved by adopting the recently developed LISFLOOD-FP levee module ( Wing et al., 2019; Shustikova et al., 2020) 3 or by implementing a spatially adaptive multi-resolution method that selectively refines the grid resolution around river channels and other fine-scale features ( Kesserwani and Sharifian, 2020). Applications development – an IS/IT term for work to develop software and systems to support DWP business If the pension credit is transferred into an existing defined benefits or cash balance arrangement protection will be lost if as a result the member’s rights in the arrangement are increased by an amount that exceeds the relevant percentage - see PTM093510 and PTM093600. The downscaling procedure adopted here exploits the full, DG2 piecewise-planar solution by constructing the piecewise-planar maximum water depth on the Δ x=40 m grid and then sampling at the element centres on the higher-resolution grid. Maximum flood extents are obtained for ACC and FV1 runs at resolutions of Δ x=40, 20, and 10 m; due to its relatively high runtime cost, DG2-GPU is only run at Δ x=40 m only with flood maps at 20 and 10 m being inferred by downscaling the 40 m solution.

There are multiple strategies for choosing a scaling factor that is appropriate for a given FP8 tensor: Since the new DG2 and FV1 solvers are purely two-dimensional and parallelised for multi-core CPU and GPU architectures, the new solvers do not currently integrate with the LISFLOOD-FP sub-grid channel model ( Neal et al., 2012 a) or incorporate the CPU-specific optimisations available to the ACC solver ( Neal et al., 2018). The piano is light, round and pleasant, without seeing the keyboard, it's hard to believe that it is a digital piano, at least in Live. Obviously listening headphones, it becomes easier. This piano is trs fragile: the mcanique (toggle the indoor keys) noisy, is in a plastic couraging aging trs bad because it becomes brittle = everything dglingue ! (3 Repair Tool in a short time and the problem persisted)-trs touch is weird, little prcis, and impossible to play notes RPTES soon! Deprecated-keyboard for pianists' playing really "-

This accuracy of the non-uniform solver predictions at ε = 10 - 3 can be confirmed by comparing the results in Fig. 18, which and 10 m (left to right), using (a) ε = 10 - 4 and (b) ε = 10 - 3 . Green parts flag flooded areas predicted by At such locations, hydrograph predictions can be improved by running the model with an ensemble of possible sampling positions within a 100 m radius of each gauging station and then choosing the best fit between predictions and observations. to the number of elements on the finest grid, here 2 3 × 2 3=64 threads, are launched. All these threads start at the elementHence, gauging station coordinates must be adjusted to ensure model results are measured in the channel. Here, a simple approach is adopted to manually reposition each gauging station based on the finest-resolution DEM, with the amended positions given in Table 2.