Large Eddy simulation of tracer gas dispersion in a cavity

This paper assesses the prediction of inert tracer gas dispersion within a cavity of height (H) 1.0 m, and unity aspect ratio, using large Eddy simulation (LES). The flow Reynolds number was 67 000, based on the freestream velocity and cavity height. The flow upstream of the cavity was laminar, producing a cavity shear layer which underwent a transition to turbulence over the cavity. Three distinct meshes are used, with grid spacings of H / 100 (coarse), H / 200 (intermediate), and H / 400 (fine) respectively. The Smagorinsky, WALE, and Germano-Lilly subgrid-scale models are used on each grid to quantify the effects of subgrid-scale modelling on the simulated flow. Coarsening the grid led to small changes in the predicted velocity field, and to substantial over-prediction of the tracer gas concentration statistics. Quantitative metric analysis of the tracer gas statistics showed that the coarse grid simulations yielded results outside of acceptable tolerances, while the intermediate and fine grids produced acceptable output. Interrogation of the fluid dynamics present in each simulation showed that the evolution of the cavity shear layer is heavily influenced by the grid and subgrid scale model. On the coarse and intermediate grids the development of the shear layer is delayed, inhibiting the entrainment and mixing of the tracer gas into the shear layer, reducing the removal of the tracer gas from the cavity. On the fine grid, the shear layer developed more rapidly, resulting in enhanced removal of the tracer gas from the cavity. Concentration probability density functions showed that the fine grid simulations accurately predicted the range, and the most probable value, of the tracer gas concentration towards both walls of the cavity. The results presented in this paper show that the WALE and Germano-Lilly models may be advantageous over the standard Smagorinsky model for simulations of pollutant dispersion in the urban environment.

The Potential of Harnessing Real-Time Occupancy Data for Improving Energy Performance of Activity-Based Workplaces

Currently, the available studies on the prediction of building energy performance and real occupancy data are typically characterized by aggregated and averaged occupancy patterns or large thermal zones of reference. Despite the increasing diffusion of smart energy management systems and the growing availability of longitudinal data regarding occupancy, these two domains rarely inform each other. This research aims at understanding the potential of employing real-time occupancy data to identify better cooling strategies for activity-based-working (ABW)-supportive offices and reduce the overall energy consumption. It presents a case study comparing the energy performance of the office when different resolutions of occupancy and thermal zoning are applied, ranging from the standard energy certification approach to real-time occupancy patterns. For the first time, one year of real-time occupancy data at the desk resolution, captured through computer logs and Bluetooth devices, is used to investigate this issue. Results show that the actual cooling demand is 9% lower than predicted, unveiling the energy-saving potential to be achieved from HVAC systems for non-assigned seating environments. This research demonstrates that harnessing real-time occupancy data for demand-supply cooling management at a fine-grid resolution is an efficient strategy to reduce cooling consumption and increase workers’ comfort. It also emphasizes the need for more data and monitoring campaigns for the definition of more accurate and robust energy management strategies.

A Fine Grid Tide-Wave-Ocean Circulation Coupled Model for the Yellow Sea: Comparison of Turbulence Closure Schemes in Reproducing Temperature Distributions

The performance of three turbulence closure schemes (TCSs), the generic length scale scheme (GLS), the Mellor–Yamada 2.5 scheme (MY2.5) and the K-profile parameterization scheme (KPP), embedded in the ocean model ROMS, was compared with attention to the reproduction of summertime temperature distribution in the Yellow Sea. The ROMS model has a horizontal resolution of 1/30° and 30 vertical sigma layers. For model validation, root mean square errors were checked, comparing model results with wave and temperature buoy data as well as tidal station data supplied by various organizations within the Republic of Korea. Computed temperature and vertical temperature diffusion coefficients were mainly compared along Lines A (36° N) and B (125° E) crossing the central Yellow Sea, Lines C (32° N) and E (34° N) passing over the Yangtze Bank and Line D off the Taean Peninsula. Calculations showed that GLS and MY2.5 produced vertical mixing stronger than KPP in both the surface and bottom layers, but the overall results were reasonably close to each other. The lack of observational data was a hindrance in comparing the detailed performance between the TCSs. However, it was noted that the simulation capability of cold patches in the tidal mixing front can be useful in identifying the better performing turbulence closure scheme. GLS and MY2.5 clearly produced the cold patch located near the western end of Line E (122° E–122.3° E), while KPP hardly produced its presence. Similar results were obtained along Line D but with a less pronounced tidal mixing front. Along Line C, GLS and MY2.5 produced a cold patch on the western slope of the Yellow Sea, the presence of which had never been reported. Additional measurements near 125° E–126° E of Line C and along the channel off the Taean Peninsula (Line D) are recommended to ensure the relative performance superiority between the TCSs.

Characterization of the Microflow Through 3D Synthetic Niche Microenvironments Hosted in a Millifluidic Bioreactor

Background: Development of new medicines is a lengthy process with high risk of failure since drug efficacy measured in vitro is difficult to confirm in vivo. Intended to add a new tool aiding drug discovery, the MOAB-NICHOID device was developed: a miniaturized optically accessible bioreactor (MOAB) housing the 3D engineered scaffold NICHOID. The aim of our study was to characterize the microflow through the 3D nichoid microenvironment hosted in the MOAB-NICHOID device.Methods: We used computational fluid dynamics (CFD) simulations to compute the flow field inside a very fine grid resembling the scaffold microenvironment.Results: The microflow inside the multi-array of nichoid blocks is fed and locally influenced by the mainstream flow developed in the perfusion chamber of the device. Here we have revealed a low velocity, complex flow field with secondary, backward, or local recirculation micro-flows induced by the intricate architecture of the nichoid scaffold.Conclusion: Knowledge of the microenvironment inside the 3D nichoids allows planning of cell experiments, to regulate the transport of cells towards the scaffold substrate during seeding or the spatial delivery of nutrients and oxygen which affects cell growth and viability.

A semi-implicit multiple-nested grid model for simulation of flow in a tropical storm

ABSTRACT .A three-layer three-dimensional, triply-nested primitive equation model. suitable to simulate tropical storm, has been designed. A grid telescopic technique has been used with a fine grid mesh of 18 km grid length in the centre which is surrounded by a medium mesh of 54 km grid length; this is again surrounded by a course grid mesh of 162 km grid length. Each mesh consists of 32 X 32 array of momentum points enclosing 31 X 31 array of mass points. The variables are staggered in space which reduces the amount of averaging to a minimum and hence improves accuracy. To suppress non-linear instability an improved finite difference scheme has been applied. A two-way interaction method has been adopt to match the solutions between grids of different lengths. To increase the time step for integration, a semi-implicit scheme has been used. The speed of the solution of the system of Helmholtz equations arising out of semi-implicit scheme has been appreciably increased by devising an iterative method. To examine the role of surface friction as postulated by Yamasaki (1977) and forced subsidence as hypothesized by Arnold (1977), Gray (1977) and Yanai (1961) at the initial stage of development of a tropical storm. numerical experiments have been accomplished with this model varying coefficient of surface drag. and specifying heat around the centre of the to disturbance which is considered as the effect of forced subsidence through an analytical function similar to one used by Harrison (1973). The integration was started from a weak barotropic vortex in &r8dient balance en and continued for 48 hours in two cases and 60 hours in one case. It is observed that surface friction may not be an essential factor at the initial stage of development of tropical storm when the vortex is weak. On the other hand, initial development could be initiated by forced subsidence. But in the subsequent stage, surface friction plays an important role to induce mass convergence in the boundary layer and to reduce horizontal of the disturbance. This preliminary experiment has yielded smooth and encouraging results.

Individual Fracture Efficiency Monitoring in Horizontal Wells by Using a New 3d Fine-Grid Temperature Modelling

Profitable development of hard-to-recover reserves often involves drilling of horizontal wells with multistage hydraulic fracturing to increase the oil recovery factor. Usually to monitor the fracture sweep efficiency, pressure transient analysis is used. However, in case of several fractures this method delivers only average hydrodynamic parameters of the well-fracture system. This paper illustrates the value of temperature logging data and demonstrates possibilities of the 3-D thermo-mechanical modelling in evaluating the differential efficiency of multi-stage hydraulic fracturing.

Systematic Modelling of Unstable Displacement

Modelling unstable displacement is a challenge which may lead to large errors in reservoir simulations. Field scale coarse grid simulations therefore need to be anchored to more reliable fine grid models which capture fluid displacement instabilities in a physically correct manner. In this paper, a recently developed approach for accurately modelling viscous fingering has been applied to various types of unstable displacement. The method involves estimation of dispersivity of the porous medium and length scale of the model to determine the required size of the simulation grid cell. Fractional flow theory is then applied to obtain the correct saturation of the injected phase in the unstable fingers formed due to the adverse mobility ratio. Unstable displacement experiments have been history matched using 2D-imaging of in-situ saturation as a calibration of our method, before carrying out sensitivity calculations on the effect of fluid viscosity, and rock heterogeneity. Our modelling approach allows us to carry out simulations using a conventional numerical simulator using elementary numerical methods (e.g. single-point upstreaming). The methods used to model instability (Sorbie et al, 2020) was originally developed for immiscible water/oil systems. The current paper now presents new results applying this approach to unstable gas displacements, where adverse viscosity ratios may be even higher than in water/oil systems. The displacement with injected gas is shown to be influenced by mass exchanges between the gas and oil as the alternating fluids (water and gas) are injected in WAG processes. Swelling of fingers delay the gas front and WAG processes divert the injected gas and improve sweep efficiency. We have also modelled water-oil displacement at adverse mobility and shown the benefit which is obtained by reducing the instability by adding polymers to viscosify the injected water. The impact of rock heterogeneity has different effect depending on buoyancy forces and the degree of crossflow into the high permeable zones. This paper extends our novel approach to modelling the fine scale distribution of the injected fluids in adverse mobility ratio displacements. This approach has now been applied to both, gas/oil and water/oil systems where viscous fingering is present, either at extremely adverse mobility ratios and/or for reservoirs where the permeability field is very heterogeneous.

A Multigrid Dynamic Bidirectional Coupled Surface Flow Routing Model for Flood Simulation

Surface flow routing is an important component in hydrologic and hydrodynamic research. Based on a literature review and comparing the different coupling models (the hydrologic model and hydrodynamic model), a multigrid dynamic bidirectional coupled surface flow routing model (M-DBCM), consisting of diffusion wave equations (DWEs) and shallow water equations (SWEs), is proposed herein based on grids with different resolutions. DWEs were applied to obtain runoff routing in coarse grid regions to improve the computational efficiency, while the DWEs and SWEs were bidirectionally coupled to detail the flood dynamics in fine grid regions to obtain good accuracy. In fine grid zones, the DWEs and SWEs were connected by an internal moving boundary, which ensured the conservation of mass and momentum through the internal moving boundary. The DWEs and SWEs were solved by using the time explicit scheme, and different time steps were adopted in regions with different grid sizes. The proposed M-DBCM was validated via three cases, and the results showed that the M-DBCM can effectively simulate the process of surface flow routing, which had reliable computational efficiency while maintaining satisfactory simulation accuracy. The rainfall runoff in the Goodwin Creek Watershed was simulated based on the proposed M-DBCM. The results showed that the discharge hydrographs simulated by the M-DBCM were closer to the measured data, and the simulation results were more realistic and reliable, which will be useful in assisting flood mitigation and management.

An adaptive spot placement method on Cartesian grid for pencil beam scanning proton therapy

Pencil beam scanning (PBS) proton radiotherapy (RT) offers flexible proton spot placement near treatment targets for delivering tumoricidal radiation dose to tumor targets while sparing organs-at-risk (OAR). Currently the spot placement is mostly based on a non-adaptive sampling (NS) strategy on a Cartesian grid. However, the spot density or spacing during NS is a constant for the Cartesian grid that is independent of the geometry of tumor targets, and thus can be suboptimal in terms of plan quality (e.g., target dose conformality) and delivery efficiency (e.g., number of spots). This work develops an adaptive sampling (AS) spot placement method on the Cartesian grid that fully accounts for the geometry of tumor targets. Compared with NS, AS places (1) a relatively fine grid of spots at the boundary of tumor targets to account for the geometry of tumor targets and treatment uncertainties (setup and range uncertainty) for improving dose conformality, and (2) a relatively coarse grid of spots in the interior of tumor targets to reduce the number of spots for improving delivery efficiency and robustness to the minimum-minitor-unit (MMU) constraint. The results demonstrate that (1) AS achieved comparable plan quality with NS for regular MMU and substantially improved plan quality from NS for large MMU, using merely about 10% of spots from NS, where AS was derived from the same Cartesian grid as NS; (2) on the other hand, with similar number of spots, AS had better plan quality than NS consistently for regular and large MMU.

Technology Focus: Hydraulic Fracturing Modeling (November 2021)

In the past decades, the success of unconventional hydrocarbon resource development can be attributed primarily to the improved understanding of fracture systems, including both hydraulically induced fractures and natural fracture networks. To tackle the fracture characterization problem, several recent papers have provided novel insights into fracture modeling technique. Because of the complex nature and heterogeneity of rock discontinuity, fabric, and texture, the fracture-modeling process typically suffers from limited data availability. Research shows that modeling results reached without interrogation of high-resolution petrophysical and geomechanical data can mislead because the fluid flow is actually controlled by fine-scale rock properties. A more-reliable fracture geometry can be obtained from an enhanced modeling process that preserves the signature from high-frequency data. Advanced techniques to model fracturing processes with proppant transportation and thermodynamics require even more-sophisticated simulation and computation power. When the subsurface is too puzzling to be described by a physical model and existing data, machine learning and artificial intelligence can be adapted as a practical alternative to interpret complex fracture systems. Taking a discrete fracture network (DFN) as an example, a data-driven approach has been introduced to learn from outcrop, borehole imaging, core computed tomography scan, and seismic data to recognize stratigraphic bedding, faults, subseismic fractures, and hydraulic fractures. Input data can be collected by hand, 3D stereophotogrammetry, or drone. When upscaling DFN into a coarse grid for reservoir simulation, deep-learning techniques such as convolutional neuron networks can be used to populate fracture properties into a dual-porosity/dual-permeability model approved to yield high accuracy compared with a fine-grid model. Furthermore, the new techniques greatly extend the application of fracture modeling in the arena of the energy transition, such as in geothermal optimization. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203927 - Numerical Simulation of Proppant Transport in Hydraulically Fractured Reservoirs by Seyhan Emre Gorucu, Computer Modelling Group, et al. SPE 202679 - Deep-Learning Approach To Predict Rheological Behavior of Supercritical CO2 Foam Fracturing Fluid Under Different Operating Conditions by Shehzad Ahmed, Khalifa University of Science and Technology, et al. SPE 203983 - A 3D Coupled Thermal/Hydraulic/Mechanical Model Using EDFM and XFEM for Hydraulic-Fracture-Dominated Geothermal Reservoirs by Xiangyu Yu, Colorado School of Mines, et al.