The nasal cavity of the bearded seal: An effective and robust organ for retaining body heat and water.

We report the effects of varying physiological and other properties on the heat and water exchange in the maxilloturbinate structure (MT) of the bearded seal (Erignathus barbatus or Eb) in realistic environments, using a computational fluid dynamics (CFD) model. We find that the water retention in percent is very high (about 90 %) and relatively unaffected by either cold (-30 °C) or warm (10 °C) conditions. The retention of heat is also high, around 80 % . Based on a consideration of entropy production by the maxilloturbinate system, we show that anatomical and physiological properties of the seal provide good conditions for heat and water exchange at the mucus lining in the seal's nasal cavity. At normal values of tidal volume and maxilloturbinate (MT) length, the air temperature in the MT reaches the body temperature before the air has left the MT channels. This confers a safety factor which is expected to be helpful in exercise, when ventilation increases.

CT analysis of frontal recess air cell and fluid dynamics simulation of frontal sinus in people with different frontal sinus development after Draf1-3 surgery.

OBJECTIVE: To explore the effects of Draf1-3 on frontal sinus airflow and frontal sinus irrigation in people with different frontal sinus development METHODS: The development of the frontal sinus and the distribution of the frontal recess cells were evaluated by CT scan in 150 adults (300 sides). The airflow changes into the frontal sinus and frontal recess after Draf were analyzed by Fluent software under a steady state and quiet inspiratory state. Nasal irrigation after Draf in adults with well-developed frontal sinus was simulated using 120 mL saline at a rate of 12 mL/s in a position at 45° to observe the changes in transient flow distribution. RESULTS: The moderately developed type of the frontal sinus was the most common. The airflow patterns in the frontal sinus and frontal recess in the moderate development group were laminar, while several large vortexes were formed between the frontal sinus and frontal recess in the well-development group. The Draf exerted more significant effects on the patterns, pressure, and velocity of the airflow in the frontal sinus and frontal recess in the well development group than in the moderate development group. The volume fraction of saline in the frontal sinus increased significantly from Draf1 to Draf3, and the time required for a complete infiltration of saline in the frontal sinus mucosa was significantly reduced. CONCLUSIONS: Draf1-3 has different effects on the airflow field of the frontal sinus with different developmental types; and Draf1-3 can significantly improve the postoperative flushing of the frontal sinus.

Wall Shear Stress (WSS) Analysis in Atherosclerosis in Partial Ligated Apolipoprotein E Knockout Mouse Model through Computational Fluid Dynamics (CFD).

Atherosclerosis involves an inflammatory response due to plaque formation within the arteries, which can lead to ischemic stroke and heart disease. It is one of the leading causes of death worldwide, with various contributing factors such as hyperlipidemia, hypertension, obesity, diabetes, and smoking. Wall shear stress (WSS) is also known as a contributing factor of the formation of atherosclerotic plaques. Since the causes of atherosclerosis cannot be attributed to a single factor, clearly understanding the mechanisms and causes of its occurrence is crucial for preventing the disease and developing effective treatment strategies. To better understand atherosclerosis and define the correlation between various contributing factors, computational fluid dynamics (CFD) analysis is primarily used. CFD simulates WSS, the frictional force caused by blood flow on the vessel wall with various hemodynamic changes. Using apolipoprotein E knockout (ApoE-KO) mice subjected to partial ligation and a high-fat diet at 1-week, 2-week, and 4-week intervals as an atherosclerosis model, CFD analysis was conducted along with the reconstruction of carotid artery blood flow via magnetic resonance imaging (MRI) and compared to the inflammatory factors and pathological staining. In this experiment, a comparative analysis of the effects of high WSS and low WSS was conducted by comparing the standard deviation of time-averaged wall shear stress (TAWSS) at each point within the vessel wall. As a novel approach, the standard deviation of TAWSS within the vessel was analyzed with the staining results and pathological features. Since the onset of atherosclerosis cannot be explained by a single factor, the aim was to find the correlation between the thickness of atherosclerotic plaques and inflammatory factors through standard deviation analysis. As a result, the gap between low WSS and high WSS widened as the interval between weeks in the atherosclerosis mouse model increased. This finding not only linked the occurrence of atherosclerosis to WSS differences but also provided a connection to the causes of vulnerable plaques.

Computational fluid dynamics modeling of coronary artery blood flow using OpenFOAM: Validation with the food and drug administration benchmark nozzle model.

Cardiovascular disease (CVD), a global health concern, particularly coronary artery disease (CAD), poses a significant threat to well-being. Seeking safer and cost-effective diagnostic alternatives to invasive coronary angiography, noninvasive coronary computed tomography angiography (CCTA) gains prominence. This study employed OpenFOAM, an open-source Computational Fluid Dynamics (CFD) software, to analyze hemodynamic parameters in coronary arteries with serial stenoses. Patient-specific three-dimensional (3D) models from CCTA images offer insights into hemodynamic changes. OpenFOAM breaks away from traditional commercial software, validated against the FDA benchmark nozzle model for reliability. Applying this refined methodology to seventeen coronary arteries across nine patients, the study evaluates parameters like fractional flow reserve computed tomography simulation (FFRCTS), fluid velocity, and wall shear stress (WSS) over time. Findings include FFRCTS values exceeding 0.8 for grade 0 stenosis and falling below 0.5 for grade 5 stenosis. Central velocity remains nearly constant for grade 1 stenosis but increases 3.4-fold for grade 5 stenosis. This research innovates by utilizing OpenFOAM, departing from previous reliance on commercial software. Combining qualitative stenosis grading with quantitative FFRCTS and velocity measurements offers a more comprehensive assessment of coronary artery conditions. The study introduces 3D renderings of wall shear stress distribution across stenosis grades, providing an intuitive visualization of hemodynamic changes for valuable insights into coronary stenosis diagnosis.

Hybrid bioreactor built-in with fixed bio-carriers for denitrification with low C/N ratio: Hydrodynamic optimization and microbial divergence.

Hydrodynamics played an important role in the design and operation of bioreactors for wastewater treatment. In this work, an up-flow anaerobic hybrid bioreactor built-in with fixed bio-carriers was designed and optimized using computational fluid dynamics (CFD) simulation. The results indicated that the flow regime involving with vortex and dead zone was greatly affected by the positions of water inlet and bio-carrier modules. The ideal hydraulic features were obtained when the water inlet and bio-carrier modules located 9 cm and 60 cm above the bottom of reactor. Using the optimum hybrid system for nitrogen removal from wastewater with low carbon-to-nitrogen ratio (C/N = 3), the denitrification efficiency could reach 80.9 ± 0.4%. Illumina sequencing of 16S rRNA gene amplicons revealed that the microbial community divergence occurred among the biofilm on bio-carrier, the suspended sludge phase and the inoculum. Especially, the relative abundance of denitrifying genera Denitratisoma in the biofilm of bio-carrier reaches 5.73%, 6.2 times higher than that in the suspended sludge, implying the imbedded bio-carrier was conductive to enrich the specific denitrifiers to polish the denitrification performance with low carbon source. This work provided an effective method for the design optimization of bioreactor based on CFD simulation, and developed a hybrid reactor with fixed bio-carrier for nitrogen removal from wastewater with low C/N ratio.

Dispersion of sneeze droplets in a meat facility indoor environment - Without partitions.

Spreading patterns of the coronavirus disease (COVID-19) showed that infected and asymptotic carriers both played critical role in escalating transmission of virus leading to global pandemic. Indoor environments of restaurants, classrooms, hospitals, offices, large assemblies, and industrial installations are susceptible to virus outbreak. Industrial facilities such as fabrication rooms of meat processing plants, which are laden with moisture and fat in indoor air are the most sensitive spaces. Fabrication room workers standing next to each other are exposed to the risk of long-range viral droplets transmission within the facility. An asymptomatic carrier may transmit the virus unintentionally to fellow workers through sporadic sneezing leading to community spread. A novel Computational Fluid Dynamics (CFD) model of a fabrication room with typical interior (stationary objects) was prepared and investigated. Study was conducted to identify indoor airflow patterns, droplets spreading patterns, leading droplets removal mechanism, locations causing maximum spread of droplets, and infection index for workers along with stationary objects in reference to seven sneeze locations covering the entire room. The role of condensers, exhaust fans and leakage of indoor air through large and small openings to other rooms was investigated. This comprehensive study presents flow scenarios in the facility and helps identify locations that are potentially at lower or higher risk for exposure to COVID-19. The results presented in this study are suitable for future engineering analyses aimed at redesigning public spaces and common areas to minimize the spread of aerosols and droplets that may contain pathogens.

Application of HIPEC simulations for optimizing treatment delivery strategies.

INTRODUCTION: Hyperthermic IntraPEritoneal Chemotherapy (HIPEC) aims to treat microscopic disease left after CytoReductive Surgery (CRS). Thermal enhancement depends on the temperatures achieved. Since the location of microscopic disease is unknown, a homogeneous treatment is required to completely eradicate the disease while limiting side effects. To ensure homogeneous delivery, treatment planning software has been developed. This study compares simulation results with clinical data and evaluates the impact of nine treatment strategies on thermal and drug distributions. METHODS: For comparison with clinical data, three treatment strategies were simulated with different flow rates (1600-1800mL/min) and inflow temperatures (41.6-43.6 °C). Six additional treatment strategies were simulated, varying the number of inflow catheters, flow direction, and using step-up and step-down heating strategies. Thermal homogeneity and the risk of thermal injury were evaluated. RESULTS: Simulated temperature distributions, core body temperatures, and systemic chemotherapeutic concentrations compared well with literature values. Treatment strategy was found to have a strong influence on the distributions. Additional inflow catheters could improve thermal distributions, provided flow rates are kept sufficiently high (>500 mL/min) for each catheter. High flow rates (1800 mL/min) combined with high inflow temperatures (43.6 °C) could lead to thermal damage, with CEM4310 values of up to 27 min. Step-up and step-down heating strategies allow for high temperatures with reduced risk of thermal damage. CONCLUSION: The planning software provides valuable insight into the effects of different treatment strategies on peritoneal distributions. These strategies are designed to provide homogeneous treatment delivery while limiting thermal injury to normal tissue, thereby optimizing the effectiveness of HIPEC.

Performance Optimization of a Microfluidic Virus Detection Cartridge: A Numerical and Experimental Study.

Detection and imaging of viruses in a complex solution is particularly significant for virology and requires a comprehensive understanding of biosensors. While lab-on-a-chip systems are used in virus detection as biosensors, analysis and optimization of these systems are especially challenging due to the size of the system to be used in the certain application. The system of interest for virus detection is required to be cost efficient and is also needed to be able to easily operable with a simple setup. Moreover, the detailed analysis of these microfluidic systems should be made with precision in order to predict the capabilities and the efficiency of the system accurately. This paper reports on the use of a common commercial computational fluid dynamics (cfd) software for the analysis of a microfluidic lab-on-a-chip virus detection cartridge. This study evaluates the problems commonly encountered during microfluidic applications of cfd softwares particularly in the area of reaction modeling of the antigen-antibody interaction. cfd analysis is later validated and combined with experiments to optimize the amount of dilute solution used in the tests. Thereafter, the geometry of the microchannel is also optimized and optimal test conditions are set for a cost efficient and effective virus detection kit using light microscopy.

AI-Enabled IoT Framework for Leakage Detection and Its Consequence Prediction during External Transportation of LPG.

An accident during the transport of liquefied petroleum gas (LPG) via a tanker vehicle leads to the leakage of a flammable substance, causing devastation. In such a situation, the appropriate action with the shortest possible delay can minimize subsequent losses. However, the decision-making mechanism remains unable to detect the occurrence of an accident and evaluate its extent within the critical time. This paper proposes an automatic framework for leakage detection and its consequence prediction during the external transportation of LPG using artificial intelligence (AI) and the internet of things (IoT). An AI model is developed to predict the probable consequences of the accident in terms of the diameter of risk contours. An IoT framework is proposed in which the developed AI model is deployed in the edge device to detect any leakage of gas during transportation, to predict its probable consequences, and to report it to the remotely located disaster management team for initiating appropriate action. A prototype of the proposed model is built and its performance is successfully tested. The proposed solution would significantly help to identify efficient disaster management techniques by allowing for quick leakage detection and the prediction of its probable consequences.

Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm.

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement.

Case Study: Impacts of Air-Conditioner Air Supply Strategy on Thermal Environment and Energy Consumption in Offices Using BES-CFD Co-Simulation.

Due to energy constraints and people's increasing requirements for indoor thermal comfort, improving energy efficiency while ensuring thermal comfort has become the focus of research in the design and operation of HVAC systems. This study took office rooms with few people occupying them in Wuhan as the research object. The EnergyPlus-Fluent co-simulation method was used to study the impact of 12 forms of air distribution on the thermal environment and air-conditioner energy consumption. The results indicate that 3 m/s supply air velocity and 45° supply air angle are more suitable for the case model in this study. The EnergyPlus-Fluent co-simulation method used in this paper provides a reference for the study of indoor environments in offices with few people occupying them.

AI-Guided Computing Insights into a Thermostat Monitoring Neonatal Intensive Care Unit (NICU).

In any healthcare setting, it is important to monitor and control airflow and ventilation with a thermostat. Computational fluid dynamics (CFD) simulations can be carried out to investigate the airflow and heat transfer taking place inside a neonatal intensive care unit (NICU). In this present study, the NICU is modeled based on the realistic dimensions of a single-patient room in compliance with the appropriate square footage allocated per incubator. The physics of flow in NICU is predicted based on the Navier-Stokes conservation equations for an incompressible flow, according to suitable thermophysical characteristics of the climate. The results show sensible flow structures and heat transfer as expected from any indoor climate with this configuration. Furthermore, machine learning (ML) in an artificial intelligence (AI) model has been adopted to take the important geometric parameter values as input from our CFD settings. The model provides accurate predictions of the thermal performance (i.e., temperature evaluation) associated with that design in real time. Besides the geometric parameters, there are three thermophysical variables of interest: the mass flow rate (i.e., inlet velocity), the heat flux of the radiator (i.e., heat source), and the temperature gradient caused by the convection. These thermophysical variables have significantly recovered the physics of convective flows and enhanced the heat transfer throughout the incubator. Importantly, the AI model is not only trained to improve the turbulence modeling but also to capture the large temperature gradient occurring between the infant and surrounding air. These physics-informed (Pi) computing insights make the AI model more general by reproducing the flow of fluid and heat transfer with high levels of numerical accuracy. It can be concluded that AI can aid in dealing with large datasets such as those produced in NICU, and in turn, ML can identify patterns in data and help with the sensor readings in health care.

Computational Fluid Dynamics (CFD) Model for Analysing the Role of Shear Stress in Angiogenesis in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease characterised by an attack on healthy cells in the joints. Blood flow and wall shear stress are crucial in angiogenesis, contributing to RA's pathogenesis. Vascular endothelial growth factor (VEGF) regulates angiogenesis, and shear stress is a surrogate for VEGF in this study. Our objective was to determine how shear stress correlates with the location of new blood vessels and RA progression. To this end, two models were developed using computational fluid dynamics (CFD). The first model added new blood vessels based on shear stress thresholds, while the second model examined the entire blood vessel network. All the geometries were based on a micrograph of RA blood vessels. New blood vessel branches formed in low shear regions (0.840-1.260 Pa). This wall-shear-stress overlap region at the junctions was evident in all the models. The results were verified quantitatively and qualitatively. Our findings point to a relationship between the development of new blood vessels in RA, the magnitude of wall shear stress and the expression of VEGF.

"Simulation of medical goggles to stop airborne transmission of viruses: computational fluid dynamics in ergonomics".

This paper presents a position statement on combining computational fluid dynamics (CFD) and ergonomics to guide the design of personal protective equipment (PPE). We used CFD to simulate 36 exposure scenarios of an infected patient sneezing at different distances and different angles while facing either the front or the side of a healthcare worker with or without goggles. The results show that medical goggles indeed block most droplets from the outer surface, but many droplets still deposit on the bottom edge (especially at the nose), inside the air holes and on the side edge. However, the edges of medical goggles have fitment problems with people in different regions, and the air holes do not function as filters and cannot prevent fine droplets from entering the interior and contacting the eyes. Our research demonstrates the feasibility of studying the design of PPE for airtightness and protection by means of CFD.Practitioner summary: Computational fluid dynamics can quickly and efficiently reflect the airtightness design problems of PPE. A model was developed using CFD to examine the protective effect of medical goggles in preventing the airborne transmission of viruses. The model demonstrates the feasibility of using CFD to solve ergonomic problems.Abbreviations: CFD: computational fluid dynamics; PPE: personal protective equipment; WHO: the World Health Organisation; COVID-19: coronavirus disease 2019; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; OSHA: the Occupational Safety and Health Administration; CDC: the Centres for Disease Control; FEM: finite element method; 3M: Minnesota Mining and Manufacturing Corporation; SPH: smoothed particle hydrodynamics; AROM: active range of motion; DPM: discrete phase model; PISO: pressure implicit with splitting of operators; VR: virtual reality; AR: augmented reality.

Evaluation of infection probability of Covid-19 in different types of airliner cabins.

According to the World Health Organization (https://covid19.who.int/), more than 651 million people have been infected by COVID-19, and more than 6.6 million of them have died. COVID-19 has spread to almost every country in the world because of air travel. Cases of COVID-19 transmission from an index patient to fellow passengers in commercial airplanes have been widely reported. This investigation used computational fluid dynamics (CFD) to simulate airflow and COVID-19 virus (SARS-CoV-2) transport in a variety of airliner cabins. The cabins studied were economy-class with 2-2, 3-3, 2-3-2, and 3-3-3 seat configurations, respectively. The CFD results were validated by using experimental data from a seven-row cabin mockup with a 3-3 seat configuration. This study used the Wells-Riley model to estimate the probability of infection with SARS-CoV-2. The results show that CFD can predict airflow and virus transmission with acceptable accuracy. With an assumed flight time of 4 h, the infection probability was almost the same among the different cabins, except that the 3-3-3 configuration had a lower risk because of its airflow pattern. Flying time was the most important parameter for causing the infection, while cabin type also played a role. Without mask wearing by the passengers and the index patient, the infection probability could be 8% for a 10-h, long-haul flight, such as a twin-aisle air cabin with 3-3-3 seat configuration.

Accelerated sequences of 4D flow MRI using GRAPPA and compressed sensing: A comparison against conventional MRI and computational fluid dynamics.

PURPOSE: To evaluate hemodynamic markers obtained by accelerated GRAPPA (R = 2, 3, 4) and compressed sensing (R = 7.6) 4D flow MRI sequences under complex flow conditions. METHODS: The accelerated 4D flow MRI scans were performed on a pulsatile flow phantom, along with a nonaccelerated fully sampled k-space acquisition. Computational fluid dynamics simulations based on the experimentally measured flow fields were conducted for additional comparison. Voxel-wise comparisons (Bland-Altman analysis, L 2 $$ {L}_2 $$ -norm metric), as well as nonderived quantities (velocity profiles, flow rates, and peak velocities), were used to compare the velocity fields obtained from the different modalities. RESULTS: 4D flow acquisitions and computational fluid dynamics depicted similar hemodynamic patterns. Voxel-wise comparisons between the MRI scans highlighted larger discrepancies at the voxels located near the phantom's boundary walls. A trend for all MR scans to overestimate velocity profiles and peak velocities as compared to computational fluid dynamics was noticed in regions associated with high velocity or acceleration. However, good agreement for the flow rates was observed, and eddy-current correction appeared essential for consistency of the flow rates measurements with respect to the principle of mass conservation. CONCLUSION: GRAPPA (R = 2, 3) and highly accelerated compressed sensing showed good agreement with the fully sampled acquisition. Yet, all 4D flow MRI scans were hampered by artifacts inherent to the phase-contrast acquisition procedure. Computational fluid dynamics simulations are an interesting tool to assess these differences but are sensitive to modeling parameters.

Understanding Ozone Transport and Deposition within Indoor Surface Boundary Layers.

Ozone-initiated oxidation reactions on indoor surfaces meaningfully alter the chemical composition of indoor air and human exposure to air toxins. Ozone mass transport within the indoor surface boundary layer plays a key role in ozone-surface reaction kinetics. However, limited information is available on detailed ozone transport dynamics near realistic, irregular indoor surfaces. This paper presents a research framework to study the underlying mechanisms of ozone reactions with realistic indoor surfaces based on microscope scanning of surface material and detailed Computational Fluid Dynamics (CFD) simulation. The study results show that indoor surface topography can meaningfully affect ozone mass transport within a surface boundary layer, thereby modulating near-surface ozone concentration gradient and surface uptake. The results also reveal that the effective indoor surface area available for ozone reaction varies with indoor air speed and turbulent air mixing within the boundary layer. The detailed dynamic behaviors of ozone reactions with realistic indoor surfaces provide insights into the implications of pollutant-surface interactions on indoor chemistry and air quality.

Numerical investigation on indoor environment decontamination after sneezing.

More than 320 million people worldwide were affected by SARS-CoV-2 or COVID-19, which already caused more than 5.5 million deaths. COVID-19 spreads through air when an infected person breathes, coughs, or sneezes out droplets containing virus. Emerging variants like Omicron with positivity rate of 16 (highest among others) present a greater risk of virus spread, so all types of indoor environments become critically important. Strategically adopted Heating Ventilation and Air Conditioning (HVAC) approach can significantly reduce the virus spread by early removal of contaminated aerosolized droplets. We modeled different HVAC configurations to characterize the diffusion of contaminated droplets cloud through Computational Fluid Dynamics (CFD) simulations of sneeze in standard hospital room as indoor scenario. Injection of saliva droplets with characteristics of exhaled air from lungs was applied to mimic real sneeze. CFD simulations have been performed for three HVAC configurations at two Air Change per Hour (ACH) rates; 6 and 15 ACH. For the first time, use of air curtain at low flow rate has been examined. Simulations provide high fidelity spatial and temporal droplets cloud diffusion under different HVAC configurations, showing spread in room indoor environment up to 360 s. Over 92% of ejected sneeze mass is removed from room air within seconds while the remaining 8% or less becomes airborne with droplets (<50 µm size) and tends to spread uniformly with regular HVAC configuration. Low-speed air curtain accelerates decontamination by efficiently removing aerosolized 1-50 µm size droplets. Study investigates role of droplets removal mechanisms such as escape, evaporation, and deposition on surfaces. Interestingly, results show presence of contaminated droplets even after 5 min of sneeze, which can be effectively removed using low-speed air curtain. Study finds that high ventilation rate requirements can be optimized to modify earlier and new hospital designs to reduce the spread of airborne disease.

Airplane cabin mixing ventilation with time-periodic supply: Contaminant mass fluxes and ventilation efficiency.

Airplane cabin ventilation is essential to ensure passengers' well-being. The conventional ventilation method is mixing ventilation with a statistically steady supply, which, according to former studies, has reached its limits regarding, for example, the ventilation efficiency. However, the effect of a statistically unsteady (time-periodic) supply on the mixing ventilation efficiency has remained largely unexplored. This research uses computational fluid dynamics (CFD) with the large eddy simulation (LES) approach to study isothermal time-periodic mixing ventilation in a section of a single-aisle airplane cabin model, in which the air exhaled by the passengers functions as (passive) contaminants. Two time-periodic supply strategies are evaluated. The induced time-periodic airflow patterns promote an efficient delivery of fresh air to the passenger zone and affect the passengers' expiratory plumes. This results in increased mean contaminant mass fluxes, causing a strong reduction of the mean contaminant concentrations in the passenger zone (up to 23%) and an increased contaminant extraction from the cabin. Mean velocities increase with up to 55% but remain within the comfortable range. It is shown that the ventilation efficiency improves; that is, the contaminant removal effectiveness and air change efficiency (in the full cabin volume) increase with up to 20% and 7%, respectively.

Sonar Dome Geometry Design Using CFD to Reduce Ship Resistance at Cruise Speed.

The objective of this study is to design the hull-mounted sonar dome of a ship. The goal is to reduce the ship total resistance and improve the flow field around the sonar dome for the ship design speed. OpenFOAM 6 was applied to analyze the viscous flow around the ship bow and then optimize the sonar dome geometry. The length, width and depth of the original geometry were maintained. Only the local geometry was fine-tuned considering the back slope and front tip by using Rhinoceros 6. The verification and validation was performed for the original hull form against towing tank resistance data. The grid independence was checked for the optimal design in different design stages. To ensure less influence on the interior equipment installation and to be able to re-use the non-steel dome part, the best resistance reduction is almost 2%. With a larger allowance of shape deformation, the maximal reduction could reach slightly higher than 3%. The flow field is improved for smaller flow separation and vortex, and less fluid nose in sonar detection is expected. The main reason of the resistance reduction is the decrease of the pressure component. In conclusion, a sonar dome design procedure is proposed, and an optimal geometry is suggested.