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This study aims to evaluate blast loads on and the response of submerged structures.

An arbitrary Lagrangian–Eulerian method is developed to model fluid–structure interaction (FSI) problems of close-in underwater explosions (UNDEX). The “fluid” part provides the loads for the structure considers air, water and high explosive materials. The spatial discretization for the fluid domain is performed with a second-order vertex-based finite volume scheme with a tangent of hyperbola interface capturing technique. The temporal discretization is based on explicit Runge–Kutta methods. The structure is described by a large-deformation Lagrangian formulation and discretized via finite elements. First, one-dimensional test cases are given to show that the numerical method is free of mesh movement effects. Thereafter, three-dimensional FSI problems of close-in UNDEX are studied. Finally, the computation of UNDEX near a ship compartment is performed.

The difference in the flow mechanisms between rigid targets and deforming targets is quantified and evaluated.

Cavitation is modeled only approximately and may require further refinement/modeling.

The results demonstrate that the proposed numerical method is accurate, robust and versatile for practical use.

Better design of naval infrastructure [such as bridges, ports, etc.].

To the best of the authors’ knowledge, this study has been conducted for the first time.

A common observation made when computing chemically reacting flows is how central processing unit (CPU)-intensive these are in comparison to cold flow cases. The update of tens or hundreds of species with hundreds or thousands of reactions can easily consume more than 95% of the total CPU time. In many cases, the region where reactions (combustion) are actually taking place comprises only a very small percentage of the volume. Typical examples are flame fronts propagating through a domain. In such cases, only a small fraction of points/cells needs a full chemistry update. This leads to extreme load imbalances on parallel machines. The purpose of the present work is to develop a methodology to balance the work in an optimal way.

Points that require a full chemistry update are identified, gathered and distributed across the network, so that work is evenly distributed. Once the chemistry has been updated, the unknowns are gathered back.

The procedure has been found to work extremely well, leading to optimal load balance with insignificant communication overheads.

In many production runs, the procedure leads to a reduction in CPU requirements of more than an order of magnitude. This allows much larger and longer runs, improving accuracy and statistics.

The procedure has allowed the calculation of chemically reacting flow cases that were hitherto not possible.

To the authors’ knowledge, this type of load balancing has not been published before.

It is of paramount importance to ensure safe and fast evacuation routes in cities in case of natural disasters, environmental accidents or acts of terrorism. The same applies to large-scale events such as concerts, sport events and religious pilgrimages as airports and to traffic hubs such as airports and train stations. The prediction of pedestrian is notoriously difficult because it varies depending on circumstances (age group, cultural characteristics, etc.). In this study, the Ensemble Kalman Filter (EnKF) data assimilation technique, which uses the updated observation data to improve the accuracy of the simulation, was applied to improve the accuracy of numerical simulations of pedestrian flow.

The EnKF, one of the data assimilation techniques, was applied to the in-house numerical simulation code for pedestrian flow. Two cases were studied in this study. One was the simplified one-directional experimental pedestrian flow. The other was the real pedestrian flow at the Kaaba in Mecca. First, numerical simulations were conducted using the empirical input parameter sets. Then, using the observation data, the EnKF estimated the appropriate input parameter sets. Finally, the numerical simulations using the estimated parameter sets were conducted.

The EnKF worked on the numerical simulations of pedestrian flow very effectively. In both cases: simplified experiment and real pedestrian flow, the EnKF estimated the proper input parameter sets which greatly improved the accuracy of the numerical simulation. The authors believe that the technique such as EnKF could also be used effectively in other fields of computational engineering where simulations and data have to be merged.

This technique can be used to improve both design and operational implementations of pedestrian and crowd dynamics predictions. It should be of high interest to command and control centers for large crowd events such as concerts, airports, train stations and pilgrimage centers.

To the authors’ knowledge, the data assimilation technique has not been applied to a numerical simulation of pedestrian flow, especially to the real pedestrian flow handling millions pedestrian such as the Mataf at the Kaaba. This study validated the capability and the usefulness of the data assimilation technique to numerical simulations for pedestrian flow.

The purpose of this paper is to shed some light into the equivalency of grid size and spatial and temporal orders of accuracy for turbulent flows.

This paper compared several finite difference schemes on various meshes and various orders of accuracy for the canonical Taylor–Green vortex testcase.

A remarkable empirical result observed is that the accuracy of eighth-order schemes on grids of size 2 h was equivalent to second-order schemes on grids of size h. This was the case when going from 100³ to 200³ as well as from 200³ to 400³. In all these cases, kinetic energy, vorticity and numerical dissipation were compared in detail.

The results obtained were carried out using finite difference solvers. It remains to be seen if similar behaviors are also observed for other numerical schemes.

These (surprising) findings should be of interest to the larger community.

The purpose of this study is to compare interpolation algorithms and deep neural networks for inverse transfer problems with linear and nonlinear behaviour.

A series of runs were conducted for a canonical test problem. These were used as databases or “learning sets” for both interpolation algorithms and deep neural networks. A second set of runs was conducted to test the prediction accuracy of both approaches.

The results indicate that interpolation algorithms outperform deep neural networks in accuracy for linear heat conduction, while the reverse is true for nonlinear heat conduction problems. For heat convection problems, both methods offer similar levels of accuracy.

This is the first time such a comparison has been made.

The purpose of this study is to determine the possibility of an accurate assessment of the spatial distribution of material properties such as conductivities or impedances from boundary measurements when the governing partial differential equation is a Laplacian.

A series of numerical experiments were carefully performed. The results were analyzed and compared.

The results to date show that while the optimization procedure is able to obtain spatial distributions of the conductivity k that reduce the cost function significantly, the resulting conductivity k is still significantly different from the target (or real) distribution sought. While the normal fluxes recovered are very close to the prescribed ones, the tangential fluxes can differ considerably.

At this point, it is not clear why rigorous mathematical proofs yield results of convergence and uniqueness, while in practice, accurate distributions of the conductivity k seem to be elusive. One possible explanation is that the spatial influence of conductivities decreases exponentially with distance. Thus, many different conductivities inside a domain could give rise to very similar (infinitely close) boundary measurements.

This implies that the estimation of field conductivities (or generally field data) from boundary data is far more difficult than previously assumed when the governing partial differential equation in the domain is a Laplacian. This has consequences for material parameter assessments (e.g. for routine maintenance checks of structures), electrical impedance tomography, and many other applications.

This is the first time such a finding has been reported in this context.

The purpose of this paper is to determine the reason for the discrepancy in estimated and observed damage caused by fragmenting charges in closed environments.

A series of carefully conducted physical and numerical experiments was conducted. The results were analyzed and compared.

The analysis shows that for fragmenting charges in closed environments, dust plays a far larger role than previously thought, leading to much lower pressures and damage.

In light of these findings, many assumptions and results for fragmenting charges in closed environments need to be reconsidered.

This implies that for a far larger class of problems than previously estimated it is imperative to take into consideration dust production and its effect on the resulting pressures.

This is the first time such a finding has been reported in this context.

Limitations in space and city planning constraints have led to the search for alternative shock mitigation devices that are architecturally appealing. The purpose of this paper is to consider a compromise solution which consists of partially open, thick, bending-resistant shapes made of acrylic material that may be Kevlar- or steel-reinforced. Seven different configurations were analyzed numerically.

For the flow solver, the FEM-FCT scheme as implemented in FEFLO is used. The flowfields are initialized from the output of highly detailed 1-D (spherically symmetric) runs. Peak pressure and impulse are stored and compared. In total, seven different configurations were analyzed numerically.

It is found that for some of these, the maximum pressure is comparable to usual, closed walls, and the maximum impulse approximately 50 percent higher. This would indicate that such designs offer a blast mitigation device eminently suitable for built-up city environments.

Future work will consider fully coupled fluid-structure runs for the more appealing designs, in order to assess whether such devices can be manufactured from commonly available materials such as acrylics or other poly-carbonates.

This would indicate that such designs offer a blast mitigation device eminently suitable for built-up city environments.

This is the first time such a semi-open blastwall approach has been tried and analyzed.

Develop a method for the optimal placement of sensors in order to detect the largest number of contaminant release scenarios with the minimum amount of sensors.

The method considers the general sensor placement problem. Assuming a given number of sensors, every release scenario leads to a sensor input. The data recorded from all the possible release scenarios at all possible sensor locations allow the identification of the best or optimal sensor locations. Clearly, if only one sensor is to be placed, it should be at the location that recorded the highest number of releases. This argument can be used recursively by removing from further consideration all releases already recorded by sensors previously placed.

The method developed works well. Examples showing the effect of different wind conditions and release locations demonstrate the effectiveness of the procedure.

The method can be used to design sensor systems for cities, subway stations, stadiums, concert halls, high value residential areas, etc.

The method is general, and can be used with other physics‐based models (puff, mass‐conservation, RANS, etc.). The investigation also shows that first‐principles CFD models have matured sufficiently to be run in a timely manner on PCs, opening the way to optimization based on detailed physics.

During a routine benchmarking and scalability study of CFD codes for typical large-scale wind engineering runs, it was observed that the resulting loads for buildings varied considerably with the number of parallel processors employed. The differences remained very small at the beginning of a typical run, and then grew progressively to a state of total dissimilitude. A “butterfly-effect” for such flows was suspected and later confirmed. The paper aims to discuss these issues.

A series of numerical experiments was conducted for massively separated flows. The same geometry – a cube in front of an umbrella – was used to obtain the flowfields using different grids, different numbers of domains/processors, slightly different inflow conditions and different codes.

In all of these cases the differences remained very small at the beginning of a typical run, they then grew progressively to a state of total dissimilitude. While the mean and maximum loads remained similar, the actual (deterministic) instantiations were completely different. The authors therefore suspect that for flows of this kind a “butterfly effect” is present, whereby even very small (roundoff) errors can have a pronounced effect on the actual deterministic instantiation of a flowfield.

This implies that for flows of this kind the CFD runs have to be carried out to much larger times than formerly expected (and done) in order to obtain statistically relevant ensembles.

For practical calculations this implies running to much larger times in order to reach statistically relevant ensembles, with the associated much higher CPU time requirements.

This is the first time such a finding has been reported in the numerical wind engineering context.