Use Of Computational Fluid Dynamics Research Articles

Solar Dryers: Technical Insights and Bibliometric Trends in Energy Technologies

This review article provides a comprehensive analysis of the technical advancements and research trends in solar drying technologies for agricultural products. The study encompasses various innovations in energy storage systems, including phase change materials (PCMs) and the use of computational fluid dynamics (CFD) for optimizing the drying process. Through a bibliometric analysis of 126 scientific papers published between 1984 and 2024, five major research clusters were identified: energy generation, heat transfer, thermal storage, simulation modeling, and the integration of hybrid systems. The results demonstrate a marked increase in scientific output over the past decade, emphasizing a growing interest in the sustainable use of solar energy for drying applications. Key findings highlight that while PCM-based storage solutions significantly enhance the thermal stability of dryers, the high implementation costs and technical complexities limit their adoption, especially in small-scale operations. Similarly, CFD models have proven effective in optimizing air and temperature distribution within dryers; however, their performance is hindered by real-world fluctuations in solar radiation and humidity levels. To address these limitations, future research should focus on the development of cost-effective PCM materials and the improvement of CFD models for dynamic environmental conditions. The review concludes by emphasizing the importance of interdisciplinary collaboration in the design and application of these technologies, recommending the inclusion of real-world case studies to better illustrate the practical implications and economic benefits of solar drying technologies for agricultural production.

Flotation separation of lithium–ion battery electrodes predicted by a long short-term memory network using data from physicochemical kinetic simulations and experiments

Anode and cathode active materials from spent lithium–ion batteries may be recovered and potentially used in new batteries to promote recycling and resource circulation. Froth flotation was applied to pristine active materials and the black mass obtained from pretreated spent batteries. Flotation kinetics was simulated with the use of computational fluid dynamics and surface chemistry. Bubble surface coverage and entrainment in the flotation kinetics model were selected and optimized by systematic fitting to experimental data. Entrainment influences the recovery and grade of the active materials. The optimized flotation kinetics model was used for generating additional data that, along with the fitted data, were used to train a deep learning neural network. The trained network was validated using anode–cathode and black mass flotation experiments, and its predictions showed a maximum residual error of 0.18 ± 0.11 recovery. The simulation framework was developed into a desktop application that predicts the flotation behavior of active materials. It provides information for estimating results following operational and physicochemical changes and for optimizing flotation processes. Finally, recovered anode active materials from black mass were selected for coin cell tests. The coulombic efficiency of these coin cells was initially lower (86.8 %) than that of cells made with pristine graphite particles (98.4 %).

Computational fluid dynamics; a new diagnostic tool in giant intracerebral aneurysm treatment

Giant intracerebral aneurysms (GIA) comprise up to 5 % of all intracranial aneurysms. The indirect surgical strategy, which leaves the GIA untouched but reverses the blood flow by performing a bypass in combination with proximal parent artery occlusion is a useful method to achieve spontaneous aneurysm occlusion. The goal of this study was to assess the utility of computational fluid dynamics (CFD) in preoperative GIA treatment planning. We hypothesise that CFD simulations will predict treatment results.A fluid-structure interaction (FSI) CFD investigation was performed for the entire arterial brain circulation. The analyses were performed in three patient-specific CT angiogram models. The first served as the reference geometry with a C6 internal carotid artery (ICA) GIA, the second a proximal parent artery occlusion (PAO) and virtual bypass to the frontal M2 branch of the middle cerebral artery (MCA), and the third a proximal PAO in combination with a temporal M2 branch bypass. The volume of “old blood”, flow residence time (FRT), dynamic viscosity and haemodynamic changes were also analysed.The “old blood” within the aneurysm in the bypass models reached 41 % after 20 cardiac cycles while in the reference model it was fully washed out. In Bypass 2 “old blood” was also observed in the main trunk of the MCA after 20 cardiac cycles. Extrapolation of the results yielded a duration of 4 years required to replace the “old blood” inside the aneurysm after bypass revascularization. In both bypass models a 7-fold increase in mean blood viscosity in the aneurysm region was noted.Bypass revascularization combined with proximal PAO favours thrombosis. Areas prone to thrombus formation, and subsequently the treatment outcomes, were accurately identified in the preoperative model. Virtual surgical operations can give a remarkable insight into haemodynamics that could support operative decision-making.

Computational fluid dynamics simulating of the FDA benchmark blood pump with different coefficient sets and scaler shear stress models used in the power-law hemolysis model.

Hemolysis is the most important issue to consider in the design and optimization of blood-contacting devices. Although the use of Computational Fluid Dynamics (CFD) in hemolysis prediction studies provides convenience and has promising potential, it is an extremely challenging process. Hemolysis predictions with CFD depend on the mesh, implementation method, coefficient set, and scalar-shear-stress model. To this end, an attempt was made to find the combination that would provide the most accurate result in hemolysis prediction with the commonly cited power-law based hemolysis model. In the hemolysis predictions conducted using CFD on the Food and Drug Administration (FDA) benchmark blood pump, 3 different scalar-shear-stress models, and 5 different coefficient sets with the power-law based hemolysis model were used. Also, a mesh independence test based on hemolysis and pressure head was performed. The pressure head results of CFD simulations were compared with published pressure head of the FDA benchmark blood pump and a good agreement was observed. In addition, results of CFD-hemolysis predictions which are conducted with scalar-shear-stress model and coefficient set combinations were compared with experimental hemolysis data at three operating conditions such as 6-7L/min flow rates at 3500rpm rotational speeds and 6L/min at 2500rpm. One of the combinations of the scalar-shear-stress model and the coefficient set was found to be within the error limits of the experimental measurements, while all other combinations overestimated hemolysis.

The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems

The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems

Physics guided machine learning modelling of compressor stall flutter

Modern aircraft engines need to meet ever more stringent requirements that greatly increase the complexity of design, which strives for enhanced performance, reduced operating costs, emissions and noise simultaneously. The drive for performance leads to the development of thin, lightweight, highly loaded fan and compressor blades which are increasingly more prone to incur high, sustained vibratory stresses and aeroelastic problems such as flutter. The current practice employs preliminary design tools for flutter that are often based on empiricism or simplified analytical models, requiring extensive use of computational fluid dynamics to verify aeroelastic stability. As the industry moves to new designs, fast and accurate prediction tools are needed. In this work, data-driven techniques are employed to model the aeroelastic response of compressor blades. Machine learning has been applied to a plethora of engineering problems, with particular success in the field of turbulence modelling. However, conventional, black-box data-driven methods based on simple input parameters require large databases and are unable to generalise. In this work a combination of machine learning techniques and reduced order models is proposed to address both limitations at the same time. Previous knowledge of flutter is introduced in the physics guided framework by formulating relevant, steady state input features, and by injecting results from low-fidelity analytical models. The models are tested on several unseen cascades and it is found that training on even a single geometry yields accurate results. The models developed here allow flutter prediction of fan and compressor flutter stability based on the steady state flow only without a need for any CPU intensive unsteady simulations. Hence, one can predict flutter stability of a given blade for different mechanical properties (mode shape, frequency) at near zero additional cost once the mean flow is known.

RMSE POWER COEFFICIENT OF WIND TURBINES WITH INVERSE TOQUE-SPEED CONTROL

The Blade Element Momentum (BEM) method stands out among various turbine modelling techniques due to its integration of airfoil aerodynamics with momentum theory, allowing detailed force calculations on blade elements. While traditional methods like BEM and Wake Models are efficient for initial turbine design, advancements in computational power have enabled the use of Computational Fluid Dynamics (CFD) for more accurate simulations. Although these models are precise, they are computationally intensive, leading to the continued use of simpler empirical methods, such as the static power coefficient approach, in power system studies. Future research aims to validate and implement dynamic estimation models to account for wind variability and turbine control dynamics. Traditional constant torque-speed control strategies maintain a constant generator speed by using a fixed electromagnetic torque command and adjusting the pitch angle when wind speed exceeds the rated value. However, if the pitch control does not respond quickly enough, rotor acceleration can occur, leading to increased power beyond the generator’s rating. To mitigate this, the pitch control mechanism must react promptly to adjust the turbine's torque profile, ensuring convergence to the rated operating point and avoiding prolonged generator overload. To reduce the burden on the pitch-control system, an inverse torque-speed control approach is proposed, where the electromagnetic torque is dynamically adjusted based on real-time rotor speed data. This method aims to achieve rated power operation with improved responsiveness and reduced stress on the pitch-control mechanism.

Computational investigation and optimization of the bulb turbine for ultra-low head application

The Indian River, which is celebrated for its breathtaking beauty and diverse heritage of cultures, has an extensive amount of potential for producing green hydroelectric electricity. 7.5 % of the world's energy came from renewable sources in 2022, with India making up 36 % of the share and growing. India has nearly reached the 5 GW objective for small hydro projects and has reached 68 % of its 175 GW renewable energy ambition. At 90 % efficiency, bulb turbines are hydroelectric turbines that produce power from water, making them a perfect choice for high flow rates and low water head conditions. In order to optimize bulb turbines, this research investigates the use of computational fluid dynamics (CFD) to assess variables including the number of guiding vanes, the angle of the draft tube, and the design of the blades. According to studies, efficiency is increased when guiding vanes increase. The geometry of guiding vanes also has a significant impact on performance. Draft tubes, which restore kinetic energy from water entering the turbine, can enhance efficiency with proper design. Adding anti-cavitation capabilities to runner blades improves performance even further. CFD models verify these conclusions, with little variations from the field data. By fine-tuning important parameters such as 14 guiding vanes and adjusting the draft tube angle to 2.5°, the turbine's efficiency jumped from 87 % to 97 %.

Procedure of Combustion Chamber Airflow Rate Distribution

Combustion systems are the least amenable of all gas turbine components to analyze. Among the literature overview made, it was realized that even though significant steps have been made in improving the combustor design procedure via the use of computational fluid dynamics, much of the design process still relies upon empirically derived rules. These rules include the calculation procedure of the required airflow rate by each zone of the combustion chamber to attain a suitable gas temperature, high values of combustion efficiency, low concentrations of pollutant species together with the determination of liner geometry that matches the chamber required performance goals with the constraints imposed by the engine dimensions [1,2,3, and 4]. The main target of this research work is to identify the proper procedure to distribute a predetermined airflow rate in annular type combustor and to generalize an effective calculation method that formulate and solve the problems in as much simplified and accurate manner as possible. The combustor dimensions and airflow rates in each zone is found in reference [5] and shown in Figure 1. It is designed with central vaporizing unit to deliver 516.3 KW of power with a geometrical constraint of 142 mm & 140 mm overall length and casing diameter, respectively, while the airflow rate is 0.8 kg/sec and the fuel flow rate is 0.012 kg/sec [5, 6].

Numerical Calculation and Direct Measurement of Local Hot Spot Temperatures in Transformer Clamping Plates

A verified multiphysics model is developed to simulate local hot spot temperatures in clamping structures. Stray field losses in clamping plates are obtained with the use of nonlinear impedance boundary condition through finite element method (FEM) while hot spot temperatures are simulated with the use of computational fluid dynamics (CFD). This multiphysics coupling is validated through direct measurements of local hot spot temperatures that were carried out with fiber optic temperature probes in real transformer. The local hot spot temperatures obtained with the simulations were in good agreement with the measured values, thus verifying overall approach and indirectly the calculation of local stray field losses.

Numerical Investigation of Performance and Flow Characteristics of Francis Turbine in Part Load Condition

Abstract Due to the complexity of the flow phenomena inside Francis turbines, it is very time-consuming and expensive to determine their performance and flow characteristics under different operating conditions through model tests and field measurements. The use of computational fluid dynamics (CFD) to improve turbine design and performance is a more cost-effective alternative to traditional methods of forecasting flow characteristics in different flow domains across the whole turbine channel. The performance and flow characteristics of the turbine under various part load working circumstances are studied by numerical simulations employing the shear stress transfer SST (k − ω) turbulence model. Four different operating conditions from 50%,70%,80% and 100% of the maximum and minimum power were analyzed, and the flow characteristics across the entire turbine channel were investigated in detail. The study results shows the calculated efficiencies of four operating condition have good agreement with measured ones, and the efficiency differences between numerical simulation and measurement are below 0.44%. Visualization of velocity and pressure in the whole Francis turbine shows that output torque, output power, and performance are in good agreement with experiment results. This study’s findings can be utilized to improve the design, efficiency, and hydraulic stability of Francis turbines.

Effects and optimization of airflow on the thermal environment in a data center

In this research, the escalating energy consumption challenges in data centers are addressed by optimizing airflow organization designs. Through the use of computational fluid dynamics (CFD) simulations, three different airflow strategies were evaluated and improved: underfloor precision air conditioning, inter-column air conditioning, and backplane air conditioning. These cooling systems, which are usually considered in isolation, were compared in a comprehensive manner to get a full picture of their efficiency and effectiveness. The findings reveal that the implementation of cold aisle containment (CAC) or hot aisle containment (HAC) significantly improves air supply efficiency (ASE) and reduces the supply heat index (SHI), leading to a more uniform temperature distribution and enhanced cooling performance. Specifically, the ASE increased from 65.69% to 85.57% and 90.25% for underfloor precision air conditioning and from 71.29% to 92.16% and 92.17% for inter-column air conditioning, with corresponding reductions in SHI. The backplane cooling system offered consistent ambient temperatures throughout the room, eliminating thermal hotspots without the need for aisle containment. This study offers a comparative analysis of different airflow organization schemes, highlighting the benefits of aisle containment in precision and inter-column air conditioning and the suitability of backplane air conditioning for high-density cooling without the need for traditional aisle separation. The results are crucial for informing energy-efficient cooling strategies in data center design and operation.

Abstract 3021: An Examination Of Emerging Techniques Forquantification Of Systemic-to-pulmonary Collateral Vessel Burden Using Imaging Modalities

Introduction: The quantification of systemic-to-pulmonary collateral vessels (SPCs) and their effects on hemodynamics is a subjective standard that has been intriguing for physicians due to the implications they could have; in cases where there is significant collateral burden, occlusion of these SPCs may be desirable, though this can present with a risk of stroke due to errant embolization particles. No benchmark method of SPC flow assessment has been established. Quantitative analysis of systemic and pulmonary flow values is possible through the use of computational fluid dynamics (CFD). CFD models can provide advanced and accurate analysis of cardiovascular hemodynamics providing more information beyond routine imaging. Hypothesis: Current methods for quantifying SPC burden are inadequate and could be improved through the use of additional CFD analysis. Methods: A PubMed and Science Direct search was performed. Select inclusion and exclusion criteria were applied to search results. Results: The search resulted in a total of 155 unique articles. Of these, 94 articles were found to meet inclusion criteria, and 54 articles were settled on after reviewing articles for further exclusion criteria. Seven articles discussed the use of CT and catheterization for quantifying collateral burden while 43 described using MRI to provide flow values. Eight articles mentioned the use of CFD to quantify hemodynamics in patients with SPCs, and 25 articles provided specific formulas for collateral flow. Conclusions: Using cardiac magnetic resonance velocity mapping is currently regarded as the best method for quantifying SPC burden. The incorporation of CFD analysis with flow data acquired with CMR may be useful in simulating the effects of SPC burden prior to any intervention as well as in predicting the effects of SPC occlusion on patient hemodynamics.

Research and numerical assessment of design and construction errors in the swimming pool facility structures

Swimming pools are characterized by high humidity, high temperature, an aggressive environment caused by disinfection processes, high energy consumption, and extensive technical infrastructure. As a result, these facilities are at high risk of damage, including structural damage, which can have catastrophic consequences in extreme cases. Adequate ventilation plays a critical role in natatoria. The technical condition of swimming pool facilities should be regularly inspected to ensure that they are safe for the public. Various techniques can be used for this purpose. In the present study, a numerical analysis of ventilation performance and an assessment of the risks associated with design and construction errors in the ventilation system were conducted in a swimming pool building with a volume of 116,280.7 m3 and a surface area of 20,188.6 m2 in north-eastern Poland. Two scenarios were analyzed: the performance of the originally designed ventilation system and the performance of the ventilation system installed in the examined facility. The analyses were conducted with the use of computational fluid dynamics (CFD) tools in the FloVent program. Air distribution was determined by measuring ventilation parameters and conducting smoke tests in analyzed natatorium, and the obtained data were consistent with the results of the numerical analysis. The analyzed scenarios were compared to identify the risks associated with inadequate ventilation in swimming pools. A significant decrease in air supply led to local increases in air velocity and worsened thermal comfort parameters in the real-world facility. In addition, the decrease in air supply induced changes in air distribution and prevented air streams from reaching all parts of the natatorium. Some areas were inadequately ventilated, and condensed water vapor settled on the building’s glass facade and the roof, posing a direct threat to structural elements. The results of the numerical analysis were congruent with on-site measurements, which indicates that CFD tools are highly useful for assessing ventilation systems in swimming pools. These tools can be used to analyze ventilation performance and optimize the proposed solutions already at the design stage. The applied tools enable designers to eliminate construction errors and optimize a building’s structural safety, performance, and energy efficiency.

Modeling the Interaction of Proximal Fragments for Destructive Atmospheric Entry Analysis

The destructive atmospheric entry process is distinguished by the formation of several fragments due to the severe aerothermal loads encountered during the descent. The proximity of debris leads to shock wave interactions and collisions between bodies, influencing the overall dynamics of the fragments and affecting the prediction of demise and ground impact location. To account for these effects, the use of computational fluid dynamics is introduced to resolve the shock interaction patterns by employing a quasi-steady approach, and the use of a simple rigid-body collision model is introduced to represent the dynamics of fragments in the cloud of debris. The quasi-steady approach is assessed and validated by comparative analysis with a few studies in the literature. One of these examples is the rebuilding of the VKI’s experiment of a free-flying ring crossing a shock wave generated by the presence of a stationary cylinder. The impact of explicitly modeling collision dynamics in the trajectory of fragments and ground spread distance is validated by considering a set of relevant test cases from the literature and tested them for destructive atmospheric reentry of the Attitude Vernier Upper Module, the launcher VEGA upper stage.

An in-silico analysis of hydrodynamics and gas mass transfer characteristics in scale-down models for mammalian cell cultures

Bioprocess scale-up and technology transfer can be challenging due to multiple variables that need to be optimized during process development from laboratory scale to commercial manufacturing. Cell cultures are highly sensitive to key factors during process transfer across scales, including geometric variability in bioreactors, shear stress from impeller and sparging activity, and nutrient gradients that occur due to increasing blend times. To improve the scale-up and scale-down of these processes, it is important to fully characterize bioreactors to better understand the differences that will occur within the culture environment, especially the hydrodynamic profiles that will vary in vessel designs across scales. In this study, a comprehensive hydrodynamic characterization of the Ambr® 250 mammalian single-use bioreactor was performed using time-accurate computational fluid dynamics simulations conducted with M-Star computational fluid dynamics software, which employs lattice-Boltzmann techniques to solve the Navier-Stokes transport equations at a mesoscopic scale. The single-phase and two-phase fluid properties within this small-scale vessel were analyzed in the context of agitation hydrodynamics and mass transfer (both within the bulk fluid and the free surface) to effectively characterize and understand the differences that scale-down models possess when compared to their large-scale counterparts. The model results validate the use of computational fluid dynamics as an in-silico tool to characterize bioreactor hydrodynamics and additionally identify important free-surface transfer mechanics that need to be considered during the qualification of a scale-down model in the development of mammalian bioprocesses.

Analysis of Solar Water Heater with Modification Absorber Plate Integrated Thermal Storage

In various countries worldwide, solar water heater systems (SWHs) are utilized as devices that harness solar energy as a primary energy source. This study investigates the performance of SWHs numerical simulation by integrating phase change material (PCM) paraffin wax onto an absorber plate collector at the bottom for thermal storage. This study presents the thermal performance of a SWHs that uses an absorber plate with PCM for thermal energy storage. In this test, four variations of the model tested, namely a) standard flat plate (SFP), b) standard flat plate with PCM storage thickness 10mm (SFP+PCM 10mm), c) standard flat plate with PCM storage thickness 7mm (SFP+PCM 7mm), and d) standard flat plate with PCM storage thickness 4mm (SFP+PCM 4mm), were investigated using numerical simulation. Initially, an analytical investigation was conducted to examine the material qualities of paraffin wax utilised for phase change material (PCM) storage. The SWH systems were subsequently imported and simulated under three different levels of continuous solar radiation: 400 W/m2 700 W/m2 and 1000 W/m2. The simulation incorporates the use of computational fluid dynamics (CFD) tools. The results of the study revealed that the collector of the SWH system, which utilised an absorber plate containing phase change material (PCM) storage, demonstrated exceptional performance. Models with a thickness of 7mm PCM or SFP+PCM 7mm have the highest efficiency compared to other models with an efficiency value of 64%. There is a 3-4% increase in efficiency with variations in models that use PCM thermal storage compared to models that do not use PCM thermal storage.

A computational fluid dynamics (CFD) modeling in a new design of closed greenhouse

Closed greenhouses are crucial buildings for agriculture in controlled environments because they offer the best growing conditions for crops and shield them from outside influences. Researchers can now better optimize design parameters for increased crop output and energy efficiency by simulating airflow and temperature distribution inside closed greenhouses with the use of computational fluid dynamics (CFD) modeling. We examine the temperature distribution and airflow patterns inside the greenhouse under various environmental conditions using CFD simulations. Our findings show that, in comparison to traditional greenhouse constructions, the novel design greatly improves temperature uniformity and lowers energy use. Moreover, the greenhouse's thermal insulation design minimizes heat loss during the colder months, enhancing energy efficiency overall. We offer important insights into how design changes affect airflow dynamics and thermal performance in enclosed greenhouses by utilizing CFD modeling. Our research highlights how effective CFD modeling can be in maximizing crop yields and achieving sustainable agricultural practices through greenhouse design optimization. The integration of novel design components for improved energy efficiency and crop yield is a feasible outcome of this research, which advances the field of closed greenhouse technology overall. The research highlights the value of using CFD modeling to inform the design of next-generation closed greenhouse systems and has important ramifications for sustainable agriculture methods and greenhouse management techniques. The goals were to assess how well various heating/cooling systems maintained the ideal environmental conditions for plant growth. A verified CFD model was used to run the simulations, which took into account a number of variables including the shape of the greenhouse, the outside environment, and the interior heat sources. Important discoveries include understanding temperature gradients, airflow patterns, and possible areas for environmental management enhancement are presented in this paper. Results showed that the species mass transfer of vapor (H2o) will vary over time.

Effect of hydrocyclone design in microplastics-water separation by using computational fluid dynamics simulations

Nowadays microplastics have become a significant environmental concern, impacting both freshwater and marine ecosystems. In response to this issue, extensive research has been conducted to explore methods for separating microplastics from water. One area of investigation involves the use of computational fluid dynamics (CFD) models to study hydrocyclones and their intricate flow patterns. In this study, CFD was systematically used to examine various hydrocyclone designs with different geometrical parameters, including the ratios of cylindrical diameter to underflow diameter and overflow diameter, the cone to cylindrical height ratio, and the inlet angle. The efficiency of these hydrocyclones has been evaluated based on key performance metrics, including microplastics recovery percentage, water split percentage, and pressure drop. The results of this study have revealed noteworthy findings. Moreover, statistical analysis, specifically the analysis of variance (ANOVA), was employed to determine the significance of various geometric parameters on microplastics separation within the hydrocyclone. As a result of this comprehensive investigation, an optimized hydrocyclone design was proposed. This optimized hydrocyclone featured an underflow diameter to diameter of the cylindrical ratio of 0.264, an overflow diameter to diameter of the cylindrical ratio of 0.3, a cone to cylindrical height ratio of 13.22:70.78, and a tangential inlet. This configuration achieved a remarkable 76% microplastics recovery, a 52% water split, and a pressure drop of 82,340 Pa.

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support.

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in, e.g., the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. This paper consists of three distinct parts-an introduction to and review of the field of computational rhinology, a review of the published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of this paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.