Real-time or faster-than-real-time simulation of airflow in buildings

A scalable convolutional neural network approach to fluid flow prediction in complex environments

We evaluate the capability of convolutional neural networks (CNNs) to predict a velocity field as it relates to fluid flow around various arrangements of obstacles within a two-dimensional, rectangular channel. We base our network architecture on a gated residual U-Net template and train it on velocity fields generated from computational fluid dynamics (CFD) simulations. We then assess the extent to which our model can accurately and efficiently predict steady flows in terms of velocity fields associated with inlet speeds and obstacle configurations not included in our training set. Real-world applications often require fluid-flow predictions in larger and more complex domains that contain more obstacles than used in model training. To address this problem, we propose a method that decomposes a domain into subdomains for which our model can individually and accurately predict the fluid flow, after which we apply smoothness and continuity constraints to reconstruct velocity fields across the whole of the original domain. This piecewise, semicontinuous approach is computationally more efficient than the alternative, which involves generation of CFD datasets required to retrain the model on larger and more spatially complex domains. We introduce a local orientational vector field entropy (LOVE) metric, which quantifies a decorrelation scale for velocity fields in geometric domains with one or more obstacles, and use it to devise a strategy for decomposing complex domains into weakly interacting subsets suitable for application of our modeling approach. We end with an assessment of error propagation across modeled domains of increasing size.

Comparative Review of Standardized Incidence Ratio of Nonlymphoid, De Novo Malignancies After Liver Transplant Versus After Kidney Transplant

OBJECTIVES

De novo malignancies are the most common cause of death after solid-organ transplant. Here, we aimed to summarize standard incidence ratios of de novo malignancies after liver and kidney transplant within the same geographical locations, compare these ratios among differenttypes of de novo malignancies after liver and kidney transplant, and elucidate differences in de novo malignancies between liver and kidney transplant recipients.

MATERIALS AND METHODS

We performed a systematic review to identify studies on standard incidence ratios of de novo malignancies after liver and kidney transplant in the United Kingdom, Sweden, South Korea, and Taiwan.

RESULTS

Four articles reported standard incidence ratios of de novo malignancies in 14 016 liver transplant recipients (mean follow-up 4.3 ± 0.7 y) and 48179 kidney transplant recipients (mean follow-up 6.1 ± 2.1 y). Mean ratios of oropharyngeal, pulmonary, colorectal, renal, and breast malignancies were 5.3, 1.6, 1.9, 1.8, and 1.1,respectively, after liver transplant and 3.2, 1.7, 1.5, 17.0, and 1.3, respectively, after kidney transplant. Mean ratios of bladder, cervixuterus, and stomach de novo malignancies were 1.8, 2.0, and 2.9, respectively, after liver transplant and 13.0, 1.9, and 1.9,respectively, after kidney transplant. Mean ratios of prostatic and esophageal malignancies were 1.6 and 1.8 after liver transplant and 1.2 and 1.1 after kidney transplant. Mean ratio of ovarian cancer was 1.2 and 2.9, respectively, after liver and kidney transplant.

CONCLUSIONS

Low-frequency and lower standard incidence ratios were observed for testicular, ovarian and central nervous system malignancies after kidney and liver transplant. Standard incidence ratios of oropharyngeal and hepatic malignancies were higher after liver transplant compared with kidney transplant. After kidney transplant, standardized ration for renal malignancy were 9.4 times and bladder malignancies were 7.2 times higher compared with liver transplant recipients.

Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence

Prediction of indoor airflow distribution often relies on high-fidelity, computationally intensive computational fluid dynamics (CFD) simulations. Artificial intelligence (AI) models trained by CFD data can be used for fast and accurate prediction of indoor airflow, but current methods have limitations, such as only predicting limited outputs rather than the entire flow field. Furthermore, conventional AI models are not always designed to predict different outputs based on a continuous input range, and instead make predictions for one or a few discrete inputs. This work addresses these gaps using a conditional generative adversarial network (CGAN) model approach, which is inspired by current state-of-the-art AI for synthetic image generation. We create a new Boundary Condition CGAN (BC-CGAN) model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter, such as a boundary condition. Additionally, we design a novel feature-driven algorithm to strategically generate training data, with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the AI model. The BC-CGAN model is evaluated for two benchmark airflow cases: an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated box. We also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error criteria. The results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5% relative error and up to about 75,000 times faster when compared to reference CFD simulations. The proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the AI models while maintaining prediction accuracy, particularly when the flow changes non-linearly with respect to an input.

Unsteady Flow Oscillations in a 3-D Ventilated Model Room with Convective Heat Transfer

Improving indoor air quality and energy consumption is one of the high demands in the building sector. In this study, unsteady flow oscillations in a 3-D ventilated model room with convective heat transfer have been studied for three configurations of an empty room (case 1), a room with an unheated box (case 2) and a room with a heated box (case 3). Computational results are validated against experimental data of airflow velocity, temperature and turbulence kinetic energy. For each case, flow unsteadiness is presented by the time history of airflow velocity and temperature at prescribed monitor points and further analyzed using the Fast Fourier Transform technique. For case 1, the flow oscillation is irregular and less dependent on the monitor points. For case 2, the flow oscillation is still irregular but with increased frequency, possibly due to enhanced flow recirculation around the corners of the unheated box. For case 3, a dominant frequency exists, and thermal energy oscillating is higher than flow kinetic energy. Among the three cases, case 3 has the highest dominant frequency in a range of 4.3–4.6 Hz, but the kinetic energy is the lowest at 1.25 m2⁄s. The unsteady flow oscillation is likely due to a high Grashof number and corner flow recirculation for cases 1 and 2, and a combination effect of a high Grashof number, corner flow recirculation and thermal instability (induced by the formation and movement of the thermal plume) for case 3.

Computational Fluid Dynamics Applied to Lubricated Mechanical Components: Review of the Approaches to Simulate Gears, Bearings, and Pumps

The lubrication of the mechanical components reduces friction, and increases the efficiency and the reliability. However, the interaction of moving components with the lubricant leads to power losses due to viscous and inertial effects. Nowadays, the study of lubricant behavior can be carried out through computational fluid dynamics (CFD) simulations. Nevertheless, the modeling of the computational domain within complex mechanical systems (e.g., ordinary, planetary and cycloidal gearboxes, roller bearings, and pumps) requires the exploitation of specific CFD techniques. In the last decades, many mesh-based or meshless approaches have been developed to deal with the complex management of the topological changes of the computational domain or the modeling of complex kinematics. This paper aims to collect and to classify the scientific literature where these approaches have been exploited for the study of lubricated mechanical systems. The goal of this research is to shed a light on the current state of the art in performing CFD analysis of these systems. Moreover, the objective of this study is to stress the limits and the capabilities of the main CFD techniques applied in this field of research. Results show the main differences in terms of accuracy achievable and the level of complexity that can be managed with the different CFD approaches.

Modeling transient particle transport in transient indoor airflow by fast fluid dynamics with the Markov chain method

It is crucial to accurately and efficiently predict transient particle transport in indoor environments to improve air distribution design and reduce health risks. For steady-state indoor airflow, fast fluid dynamics (FFD) + Markov chain model increased the calculation speed by around seven times compared to computational fluid dynamics (CFD) + Eulerian model and CFD + Lagrangian model, while achieving the same level of accuracy. However, the indoor airflow could be transient, if there were human behaviors involved like coughing or sneezing and air was supplied periodically. Therefore, this study developed an FFD + Markov chain model solver for predicting transient particle transport in transient indoor airflow. This investigation used two cases, transient particle transport in a ventilated two-zone chamber and a chamber with periodic air supplies, for validation. Case 1 had experimental data for validation and the results showed that the predicted particle concentration by FFD + Markov chain model matched well with the experimental data. Besides, it had similar accuracy as the CFD + Eulerian model. In the second case, the prediction by large eddy simulation (LES) was used for validating the FFD. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The computational time of the FFD + Markov chain model was 7.8 times less than that of the CFD + Eulerian model.

Real-Time Reconstruction of Contaminant Dispersion from Sparse Sensor Observations with Gappy POD Method

Real-time estimation of three-dimensional field data for enclosed spaces is critical to HVAC control. This task is challenging, especially for large enclosed spaces with complex geometry, due to the nonuniform distribution and nonlinear variations of many environmental variables. Moreover, constructing and maintaining a network of sensors to fully cover the entire space is very costly, and insufficient sensor data might deteriorate system performance. Facing such a dilemma, gappy proper orthogonal decomposition (POD) offers a solution to provide three-dimensional field data with a limited number of sensor measurements. In this study, a gappy POD method for real-time reconstruction of contaminant distribution in an enclosed space is proposed by combining the POD method with a limited number of sensor measurements. To evaluate the gappy POD method, a computational fluid dynamics (CFD) model is utilized to perform a numerical simulation to validate the effectiveness of the gappy POD method in reconstructing contaminant distributions. In addition, the optimal sensor placement is given based on a quantitative metric to maximize the reconstruction accuracy, and the sensor placement constraints are also considered during the sensor design process. The gappy POD method is found to yield accurate reconstruction results. Further works will include the implementation of real-time control based on the POD method.

Modeling transient particle transport by fast fluid dynamics with the Markov chain method

Fast simulation tools for the prediction of transient particle transport are critical in designing the air distribution indoors to reduce the exposure to indoor particles and associated health risks. This investigation proposed a combined fast fluid dynamics (FFD) and Markov chain model for fast predicting transient particle transport indoors. The solver for FFD-Markov-chain model was programmed in OpenFOAM, an open-source CFD toolbox. This study used two cases from the literature to validate the developed model and found well agreement between the transient particle concentrations predicted by the FFD-Markov-chain model and the experimental data. This investigation further compared the FFD-Markov-chain model with the CFD-Eulerian model and CFD-Lagrangian model in terms of accuracy and efficiency. The accuracy of the FFD-Markov-chain model was similar to that of the other two models. For the two studied cases, the FFD-Markovchain model was 4.7 and 6.8 times faster, respectively, than the CFD-Eulerian model, and it was 137.4 and 53.3 times faster than the CFD-Lagrangian model in predicting the steady-state airflow and transient particle transport. Therefore, the FFD-Markov-chain model is able to greatly reduce the computing cost for predicting transient particle transport in indoor environments.

Constructing Markov matrices for real-time transient contaminant transport analysis for indoor environments

Predicting the movement of contaminants in the indoor environment has applications in tracking airborne infectious disease, ventilation of gaseous contaminants, and the isolation of spaces during biological attacks. Markov matrices provide a convenient way to perform contaminant transport analysis. However, no standardized method exists for calculating these matrices. A methodology based on set theory is developed for calculating contaminant transport in real-time utilizing Markov matrices from CFD flow data (or discrete flow field data). The methodology provides a rigorous yet simple strategy for determining the number and size of the Markov states, the time step associated with the Markov matrix, and calculation of individual entries of the Markov matrix. The procedure is benchmarked against scalar transport of validated airflow fields in enclosed and ventilated spaces. The approach can be applied to any general airflow field, and is shown to calculate contaminant transport over 3000 times faster than solving the corresponding scalar transport partial differential equation. This near real-time methodology allows for the development of more robust sensing and control procedures of critical care environments (clean rooms and hospital wards), small enclosed spaces (like airplane cabins) and high traffic public areas (train stations and airports).

State-of-the-art methods for inverse design of an enclosed environment

The conventional design of enclosed environments uses a trial-and-error approach that is time consuming and may not meet the design objective. Inverse design concept uses the desired enclosed environment as the design objective and inversely determines the systems required to achieve the objective. This paper discusses a number of backward and forward methods for inverse design. Backward methods, such as the quasi-reversibility method, pseudo-reversibility method, and regularized inverse matrix method, can be used to identify contaminant sources in an enclosed environment. However, these methods cannot be used to inversely design a desired indoor environment. Forward methods, such as the CFD-based adjoint method, CFD-based genetic algorithm method, and proper orthogonal decomposition method, show the promise in the inverse design of airflow and heat transfer in an enclosed environment. The CFD-based adjoint method is accurate and can handle many design parameters without increasing computing costs, but the method may find a locally optimal design that could meet the design objective with constrains. The CFD-based genetic algorithm method, on the other hand, can provide the global optimal design that can meet the design objective without constraints, but the computing cost can increase dramatically with the number of design parameters. The proper orthogonal decomposition method is a reduced-order method that can significantly lower computing costs, but at the expense of reduced accuracy. This paper also discusses the possibility to reduce the computing costs of CFD-based design methods.