Search

• Efficiently extracts low-dimensional features of flow fields using an autoencoder, effectively eliminating high-dimensional redundancy and noise. • Achieves high-resolution steady-state flow field and temperature field data under complex internal and external disturbances within 5 seconds, without requiring prior physical knowledge. • Predicts flow conditions for 15 real-world scenarios, outperforming the Mixing-length zero-equation turbulence model in accuracy and matching the precision of the standard k-epsilon turbulence model in certain specific cases. • Encodes boundary conditions using one-hot encoding, enabling prediction of indoor flow and temperature distributions based solely on boundary and spatial information. • Establishes two sets of variable parameters, allowing the model to learn from simple to complex scenarios. The prediction of flow fields is crucial in both scientific research and engineering, yet traditional Computational Fluid Dynamics (CFD) calculations demand substantial computational resources and time. Recently, deep learning has gained attention in fluid simulation for its suitability in nonlinear problems, but its application in practical three-dimensional building simulations remains limited. This study proposes a deep learning framework based on a non-intrusive reduced-order model for rapid predictions of 3D multi-physical fields. The framework involves three key steps: (1) using a convolutional autoencoder to extract low-dimensional features from similar flow fields; (2) incorporating geometric parameters and boundary conditions of the target flow field, computing a Hadamard product with extracted features; and (3) reconstructing the predicted flow field through an up-sampling network. Two model scenarios (case-3v and case-6v) with varying boundary conditions were validated using actual residential environments. Comparisons with CFD simulations and on-site measurements showed that the model could predict wind speed, temperature, and other parameters in under 5 seconds, with accuracy comparable to zero-equation models. This approach offers a rapid and efficient solution for indoor flow prediction in ventilation research and early engineering design. [ABSTRACT FROM AUTHOR] more...

Cavitation is a classic physical phase transition phenomenon accompanied by complex mass transfer. In order to obtain accurate simulation results of vortex cavitation, demanding computing resources are required. This paper introduces a new fast prediction (FP) cavitation model based on the assumptions of the Rankine vortex and the grid resolution theory, aiming to improve cavitation prediction accuracy when grid resolution is insufficient. Additionally, the low-cost advantage of the new model could help researchers quickly assess vortex cavitation in practical engineering. The effectiveness of the FP model is validated through simulations of a Rankine vortex flow and a gap flow, respectively. The main conclusions are as follows: Based on the theoretical solution of Rankine vortex flow, it is observed that grid resolution is positively correlated with the accuracy of vortex cavitation prediction. The requirement for grid resolution in the vortex center region is much greater than at the boundary region. Numerical simulation results using the new FP and the classic Schnerr-Sauer (SS) cavitation models were compared in the cavitation flow. In comparison, the FP model successfully predicts the vortex cavitation with only 3.5% of the computational resources of the SS model. • FP cavitation model enhances the accuracy of vortex cavitation prediction. • Grid resolution impacts cavitation prediction accuracy, mainly near vortex center. • FP model predicts vortex cavitation accurately with 3.5% of traditional model's cost. [ABSTRACT FROM AUTHOR] more...

• A fast prediction model for the key parameters of FEBA is developed using the COSINE code and ANNs. • The input features for prediction model are selected through the sensitivity analysis. • The accuracy of the COSINE code is verified and it is employed to generate a dataset for the training of ANNs. • MHP model has better prediction performance compared with SVR and MLP. Numerical techniques have emerged as an essential tool for operators and designers to preemptively acquire key parameters in accidents analysis. However, due to insufficient experience, it is difficult for them to obtain satisfactory numerical results. Moreover, the uncertainty analysis and quantification necessitate the simulation of a substantial number of samples, which requires a significant amount of computational time. Therefore, the development of a fast prediction model becomes imperative. In this work, a prediction model based on the in-house COSINE subchannel code and Multi-Head Perceptron (MHP) is developed. The COSINE subchannel code is employed to provide data sets for training neural networks. Firstly, the numerical results of COSINE subchannel code are compared with experimental data to ensure the accuracy of data sets. Secondly, input features for neural networks are selected by evaluating the impact of input parameters on numerical results, and a series of simulations is carried out to generate data sets. Then, a comparative analysis was conducted between the Multi-Layer Perceptron (MLP) and Support Vector Regression (SVR) models, and the MLP model performs better. Subsequently, the MLP was compared with the MHP, demonstrating the advantage of MHP model. Based on this, the predictions are conducted using the MHP model and the distribution of key parameters is compared with that obtained by COSINE subchannel code. The results illustrate that developed MHP model is an efficient tool for predicting key parameters during the reflooding phase. [ABSTRACT FROM AUTHOR] more...

High-precision numerical simulation for solving unsteady flow fields is both time- and labor-intensive. Additionally, it is challenging to directly apply to multidisciplinary design fields, such as multi-variable optimization and stability prediction. In this study, a multi-scale mixed information-driven hybrid deep neural network (HDNN) model is proposed to realize fast prediction of unsteady flow fields. The proposed model contains two modules: (a) an HDNN comprising convolutional, convolutional gated recurrent unit, and deconvolutional layers for the modeling of the spatio-temporal dynamics of the flow fields; and (b) a multi-scale mixed loss (MSML) function to evaluate the localized prediction error and feature similarity between the predicted and actual flow fields at different spatial scales. We constructed two datasets, one comprising the flow around a two-dimensional cylinder and the other comprising the flow past an airfoil, respectively. These datasets were utilized for model validation. Experimental results demonstrated that the model predictions aligned well with the computational fluid dynamics simulation results. Furthermore, the computation time of the model is only 10.5% of that required for CFD. We further demonstrated the efficacy of the HDNN model and MSML function through ablation experiments. • A novel model for rapid prediction of unsteady flow fields is proposed. • A loss function called multi-scale mixed loss is proposed. • The proposed model efficiently predicts unsteady flows with reasonable accuracy. • The proposed model can continuously predict unsteady flows based on its prediction. [ABSTRACT FROM AUTHOR] more...

• Design of the ROR model framework based on partial differential operators. • Extraction of low-dimensional flow field features using an autoencoder. • Combination of flow field and spatial features in low-dimensional space using a cross-fitting algorithm. • Elimination of high-dimensional redundancies and noise, reducing dataset quality requirements. • Fast high-fidelity flow field data acquisition without prior physical knowledge. The real-time prediction of flow fields has scientific and engineering significance, although it is currently challenging. To address this issue, we propose a nonintrusive supervised reduced-order machine learning framework for flow-field reconstruction, referred to as ROR, to achieve real-time flow-field prediction. The model predicts a signed distance function of the domain and uses a typical flow field as feature extraction objects. Utilizing a cross-fit method, it efficiently combines these features, enabling rapid prediction of the full-order flow field. During the model validation phase, we assess the performance of our model by reconstructing steady-state two-dimensional indoor flows in different room layouts. The results indicate that our model accurately predicts the flow field in the target indoor layout within a short timeframe (approximately 5 s) and demonstrates robustness. To delve deeper into the model performance, we discuss the specific parameters of the model framework and test the effectiveness of the flow-field reconstruction under different air supply modes, with the results showing a mean squared error (MSE) of less than 1.5 %. Additionally, we compare our model with the fourier neural operator (FNO) model and find that it exhibited superior performance with the same number of training steps. The outcomes of this study not only bear significant theoretical implications for the field of flow-field prediction but also provide robust support for practical engineering applications. [ABSTRACT FROM AUTHOR] more...

Rail transportation, as a critical component of social development, plays a pivotal role during earthquake relief efforts. To ensure the safety and comfort of post-earthquake travel, it is essential to accurately predict the earthquake-induced dynamic irregularities of the track. In this study, 120 seismic waves are chosen from the PEER strong earthquake database based on various site classifications. A three-dimensional coupled vibration model of the train-track bridge, considering seismic damage, is developed. This research also involves the creation of a database for earthquake-induced track dynamic irregularities, and these irregularities are then simplified using variational mode decomposition. The Kolmogorov-Smirnov method is used to test the distribution of these simplified samples. Furthermore, a calculation method based on the probability guarantee rate is introduced for the earthquake-induced simplified dynamic irregularities. A sine function is employed to fit the dynamic irregularity curve. The study also examines the impact of site classification on earthquake-induced irregularities and provides a parameter table for the simplified fitting curve of earthquake-induced irregularities, using a 95% guarantee rate for various site types. This table aids in the rapid prediction of earthquake-induced track dynamic irregularities. The findings indicate that earthquake-induced dynamic irregularities are primarily characterized by a single peak component. This single peak component can serve as a simplified dynamic irregularity. This single peak component also exhibits pronounced sinusoidal characteristics suitable for sinusoidal function fitting. The peak value of the simplified fitting curve for dynamic irregularity rises with the increasing characteristic period of ground motion. Notably, the peak value for the class IV site's simplified fitting curve is significantly greater than that for the class I site. • The influence of site conditions on track dynamic irregularities is studied. • A simplification of dynamic irregularities by utilizing VMD is introduced. • A calculation method for the simplified fitting curve of irregularity is presented. • The simplified fitting curve parameter table of dynamic irregularity is provided. [ABSTRACT FROM AUTHOR] more...

The thermal simulation of oil pipeline transportation is crucial for ensuring safe transportation of pipelines and optimizing energy consumption. The prediction of the soil temperature field is the key to the thermal calculation for the non-isothermal batch transportation of the buried pipeline, while the standard numerical simulation of the soil temperature field is time-consuming. Coupling with a data-driven Bayesian neural network and mechanism-informed partial differential equation, an efficient and robust prediction model of soil temperature field is proposed to dynamically adapt the spatio-temporal changes of boundary conditions. Based on the soil temperature field predicted by the proposed model, the oil temperature at the outlet of the pipeline is further obtained, which is compared with that from the field data and the standard numerical simulation. It is found that the former is in good agreement with the latter two, verifying the proposed model. However, the calculation of the proposed model only takes 10.59 s, which is 29.53 times faster than the standard numerical simulation. Moreover, the predicted error of the proposed model only changes by 0.12 % (from 3.05 % to 3.17 %) when the training data decreases from 100 % to 2.2 %, which is lower than that of two data-driven surrogate models. [ABSTRACT FROM AUTHOR] more...

• Constructing a fast prediction model of urban temperature based on WRF and TFT. • Retaining the atmospheric dynamic characteristics in urban temperature prediction. • WRF-TFT model effectively predicting urban temperature with mean error of 0.8°C. • Fast prediction model providing guidance for urban risk management and regulation. Urban Heat Island (UHI) poses a significant challenge to the sustainable development of global cities. It is of great importance to efficiently characterize the spatiotemporal distribution of urban temperatures for UHI mitigation strategies, such as urban ecosystem planning and control. Numerical Weather Prediction (NWP) methods are used to obtain the urban temperature distribution. However, NWP requires significant hardware resources and long computation time. The development of artificial intelligence approaches have been applied in expediting the weather forecasting, yet their forecasting precision remains significantly inferior to that of NWP. Hence, this study aims to propose a hybrid fast prediction model, considering the accuracy of WRF (Weather Research and Forecasting) and efficiency of Temporal Fusion Transformer (TFT) neural networks. By integrating high-precision temperature time series boundaries generated by WRF into TFT, this method (WRF-TFT) is able to realize the rapid predictions of urban temperature distributions (around 15 times faster compare to WRF) while maintaining the physical characteristics of atmospheric dynamics. With this method, we also conducted for future temperature forecast for cities. It is estimated that the temperature can exceed 35 °C more than 12 hours per day in July 2050. This hybrid model facilitates swift acquisition of urban temperature trends, providing a crucial basis for urban risk management and planning. [ABSTRACT FROM AUTHOR] more...

• Markov chain model combined with CFD for multi-zone space proposed. • Exposure levels and deposition rates of particulate pollutants analyzed. • Proposed model may be of practical use for obtaining real-time predictions. Understanding the distribution and spread of indoor airborne contaminants between rooms in a multi-zone space is crucial for protecting human health and indoor environmental control. Predicting the transmission of suddenly released particulate pollutants from one room to another in a rapid and precise manner is important for reducing or even eliminating the risk of cross contamination between occupants. In this study, we developed a modified Markov chain model that considers the effect of gravity to predict the dispersion and deposition of aerosol particles in a two-zone chamber. To perform particle phase simulations, the proposed model couples the data obtained from a steady-state flow field using the computational fluid dynamics (CFD) software Fluent with some codes that we developed in the MATLAB environment. Two examples based on previously reported experimental data were used to validate the proposed model. The results indicate that the proposed model is suitable for modeling the dynamic processes where airborne particles are released from constant and pulsed contaminant sources in multi-zone spaces with reasonable accuracy and computational efficiency. After analyzing particle exposure in the two-zone chamber, we found that the accumulated exposure level in the zone with the constant contaminant source always had a higher rate of increase than the other zones. However, the accumulated exposure level for the pulsed (instantaneous) source was inversely proportional to the air exchange rate for all zones. Compared with traditional CFD models and multi-zone models, our proposed model can balance the accuracy and efficiency when predicting the spread of particulate contaminants in multi-zone spaces by choosing appropriate grid resolution (s). [ABSTRACT FROM AUTHOR] more...

• Rapid large-scale image regression is possible on a single GPU. • A deep regression model can predict subtle longitudinal deformations. • Image regression captures correlations between deformations and clinical measures. • Algorithmic efficiency facilitates rapid analysis of the ADNI-1/2 datasets (n > 6000). Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets. [ABSTRACT FROM AUTHOR] more...

• A Lagrangian-Markov chain model with coarse grids was proposed for fast prediction. • Detailed procedures and parameter determination approaches were provided. • The proposed model was tens to hundreds of times faster than the existing models. Fast calculation of person-to-person particle transport is essential for accelerating the evaluation and design of air distribution for reducing the risk of infection. This study developed a Lagrangian-Markov chain model with coarse grids for fast prediction of person-to-person particle transport in complex indoor environments. Detailed procedures and parameter determination approaches were developed. The proposed Lagrangian-Markov chain model was first validated with experimental data in two real-life cases of person-to-person particle transport, one in an aircraft cabin and the other in a COVID-19 isolation ward. The computing speed of the proposed model was then compared with the flux-based Markov chain, Eulerian, and Lagrangian models. The results show that the proposed Lagrangian-Markov chain model can predict person-to-person particle transport reasonably well in real-life cases with complex geometry and airflow fields. In terms of computing speed, the proposed Lagrangian-Markov chain model with coarse grids can be tens to hundreds of times faster than the three existing models for the two evaluation cases. With its fast computing speed, the Lagrangian-Markov chain model can be applied in the fast design of air distribution for real-life complex indoor environments. [ABSTRACT FROM AUTHOR] more...

Abstract Thermophoresis is considered as one of the major causes leading to deposition of suspended submicron aerosols with the presence of temperature gradient. Prompt obtaining of information about particle deposition and spatial distribution is crucial to environment control. This study proposed a modified Markov chain model to include the effect of thermophoresis. A steady-state flow field and a constant temperature field are firstly established by using Computational Fluid Dynamics (CFD) tools. Then the data of flow and temperature fields are exported out as data files, which will be used by the proposed model to realize the particle phase simulation. A horizontal steady-state laminar duct flow case is used to validate the model. Results show that the proposed model is able to efficiently predict thermophoresis induced particle deposition and dispersion with reasonable accuracy. Detailed information also indicate that the particle deposition efficiency is dependent on both temperature gradients and particle diameters. However, the beginning of the particle escape (if any) is irrelevant to those two parameters. The highest deposition efficiency on the deposition surface is found to gradually increase over time and the location of highest deposition efficiency (peak) moves along with the main flow. The average speed of the moving peak is inversely proportional to temperature gradient, but the ultimate deposition efficiency is still proportional to temperature gradient. The proposed model is deemed a practical method in predicting aerosol particle deposition/transport as well as contaminant control in non-isothermal environment. Highlights • A modified Markov chain model is proposed to include thermophoresis. • Thermophoresis induced particle deposition and dispersion are efficiently predicted. • Deposition efficiency depends on both temperature gradient and particle diameter. • Location of the highest deposition efficiency on surface is identified. • The proposed method can be used to characterize other thermal process like fouling. [ABSTRACT FROM AUTHOR] more...

As potential carriers of hazardous pollutants, airborne particles may deposit onto surfaces due to gravitational settling. A modified Markov chain model to predict gravity induced particle dispersion and deposition is proposed in the paper. The gravity force is considered as a dominant weighting factor to adjust the State Transfer Matrix, which represents the probabilities of the change of particle spatial distributions between consecutive time steps within an enclosure. The model performance has been further validated by particle deposition in a ventilation chamber and a horizontal turbulent duct flow in pre-existing literatures. Both the proportion of deposited particles and the dimensionless deposition velocity are adopted to characterize the validation results. Comparisons between our simulated results and the experimental data from literatures show reasonable accuracy. Moreover, it is also found that the dimensionless deposition velocity can be remarkably influenced by particle size and stream-wise velocity in a typical horizontal flow. This study indicates that the proposed model can predict the gravity-dominated airborne particle deposition with reasonable accuracy and acceptable computing time. [ABSTRACT FROM AUTHOR] more...

Fast prediction of indoor temperature distribution is valuable to recognize the status and assist in rapid decision making and heat source control. Prediction using a superposition theorem based on the assumption of fixed flow field is an alternative method of performing fast prediction. However, little research revealed the methods’ reliability based on fixed flow field, and previous prediction methods primarily focused on the temperature at a steady state. In this paper, an algebraic expression was established to predict the temperature distribution based on the definition of transient accessibility indices for temperature. The prediction accuracy was mainly verified using a numerical method with 14 cases. It was concluded: (1) the proposed expression can perform fast prediction once the transient accessibility indices are prepared in advance; (2) the fixed flow field adopted in the proposed method should be built by a thermal scenario considering the heat source at a certain intensity, rather than a scenario with no heat source. The accuracy is acceptable for positions outside the heat source area; (3) there is no significant effect of the choice of supply air temperature utilized in building the fixed flow field on prediction accuracy. The research on prediction accuracy is helpful for a reasonable application of the proposed method in real projects. [ABSTRACT FROM AUTHOR] more...

In emergency events where hazardous heavy gases are injected into supply air, the rapid prediction of heavy gas dispersion is significantly important. The linear superposition relationship based on a fixed flow field offers the advantage of fast predictions; however, the buoyancy due to the density difference destabilizes the flow field. In this work, the applicability of a linear relationship based on transient accessibility index in predicting heavy gas dispersion delivered from supply air was studied. The dimensionless transient concentrations predicted by the linear model were compared with those of CFD (computational fluid dynamics) simulation. The numerical results from two heavy gases, carbon dioxide (CO 2) and hydrogen sulfide (H 2 S); two supply mass fraction concentrations, 4E-4 and 4E-2; and two air distributions, ceiling supply side down return (CSD) and side up supply side down return (SUSD), were analyzed. The results showed that the flow field and heavy gas concentrations at a low supply concentration of 4E-4 were slightly different from those of the passive gas. The air jet exhibited sinking characteristics at a high supply concentration of 4E-2. Significant prediction deviations using the linear model mainly occurred at a few positions surrounding the supply air jet and in the upper space for the CSD and SUSD. An acceptable accuracy was achieved with average deviations ranging from 7.8%–15.5%. High heavy gas density and heavy gas concentration in supply air increased the prediction deviation. This study provides support for the rapid assessments of emergency scenarios in the context of ventilation decisions. • Applicability of linear superposition relationship to heavy gas was verified. • Dispersion of heavy gas delivered by supply air was assessed. • The air jet exhibited sinking characteristics at a high supply air concentration. • High gas density and supply concentration increased the prediction deviation. • An acceptable accuracy was achieved with deviations ranging from 7.8%–15.5%. [ABSTRACT FROM AUTHOR] more...

This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Fourier transform near-infrared (FT-NIR) spectroscopy with principal component regression (PCR) and partial least squares regression (PLS) to build hardwood prediction models. Wet chemistry analysis coupled with high performance liquid chromatography (HPLC) was employed to obtain the chemical composition of these samples. Spectra loadings were studied to identify key wavenumber in the prediction of chemical composition. NIR–PLS and FTIR–PLS performed the best for extractives, lignin and xylose, whose residual predictive deviation (RPD) values were all over 3 and indicates the potential for either instrument to provide superior prediction models with NIR performing slightly better. During testing, it was found that more accurate determination of holocellulose content was possible when HPLC was used. Independent chemometric models, for FT-NIR and ATR-FTIR, identified similar functional groups responsible for the prediction of chemical composition and suggested that coupling the two techniques could strengthen interpretation and prediction. [ABSTRACT FROM AUTHOR] more...

Fast predicting the transient effect of an initial condition is crucial for determining effective room ventilation strategies. However, traditional CFD methods conduct time-consuming iterative calculations. In previous studies based on superposition theory, the similarity condition, i.e., the proportional relationships of concentrations among different positions are the same between actual initial condition and initial condition adopted in calculating index AIC (accessibility of initial condition) or TAIC (transient accessibility of initial condition), is required, which is difficult to meet. In this paper, an algebraic expression is established for transient effect of an arbitrary initial condition. To establish the expression, the room is divided into a certain number of zones, and the initial concentration in each zone is assumed to be uniform, whereas the concentration outside the zone is zero (the so-called sub-initial condition). By calculating TAIC of each sub-initial condition in advance, the transient effect of initial condition can be obtained by superposition theory. From an analysis of cases with different initial conditions, the following conclusions can be made: (1) the expression has the same accuracy as a CFD simulation for the condition that initial contaminant distribution in each zone is uniform; (2) a longer predicting time, larger number of zones and more uniform initial distribution in each zone help to improve accuracy; (3) between 12 and 140 zones are suggested for the study in consideration of both accuracy and computing costs. The proposed method may be useful in cases where fast prediction is required, such as emergency ventilation and on-line control. [ABSTRACT FROM AUTHOR] more...

Abstract: In this paper, we proposed a fast prediction algorithm for mode decision and motion estimation (ME) based on the similar macro block (FPSM). The so called similar macro block (MB) has the similar motion information on MB level in AVS-P2 codec. FPSM could efficiently eliminate some redundancy operations of mode decision, ME and rate distortion optimization (RDO) for MBs. What''s more, it could be conjunction with any other excellent algorithms on mode decision, ME and RDO to speed up the codec. Experiments show that, for those video sequences with little motion, the encoding time could be saved up to 60% with little loss of PSNR. Although we limit our discussion FPSM in AVS-p2, our method can also be used in other video coding standards. [Copyright &y& Elsevier] more...

Abstract: In this paper, we describe the principle, technique, and steps of fast predicting the outburst risk before coal un-covering in shaft and cross-cut, including the selection of indexes for the prediction, fast test of gas pressure, coring technique for obtaining an whole coal core, and the determination of outburst parameters. Using this technique, we can finish testing the original gas pressure of coal within 20 h, making it possible to accurately predict the outburst risk before coal un-covering in shaft and cross-cut in 3~5 days. In addition, we can, according to the prediction result, give out the residual pressure and the smallest pre-drainage rate of gas. The comparison of prediction data among more than 10 shafts and cross-cuts shows that the index of initial gas relief expansion energy can more accurately reflect the outburst risk of coal seams. [Copyright &y& Elsevier] more...

Fast prediction tools for turbine cooling performance have been demanded by industry for decades to support the iterative design process and the comprehensive response analysis and sensitivity analysis. This study aimed at establishing a comprehensively evaluated deep learning-based data modeling tool for the design of gas turbine blades. The geometry focused on was an air-cooled blade with ribbed channels and film cooling holes, which deformed globally within a wide range of geometrical parameters. A Conditional Generative Adversarial Network was constructed to model the distribution of the internal heat transfer coefficient and the external adiabatic film cooling effectiveness under any in-range geometry and boundary conditions. A series of single-point tests, response analysis, and sensitivity analysis were conducted using the trained model and compared with the Computational Fluid Dynamics results to comprehensively evaluate the model performance. The results showed that the model provided accurate predictions for cooling performance distributions, and also possessed the ability to obtain reasonable response and sensitivity. This study was a successful case of using deep learning approaches to model complex heat transfer problems. For practical applications, the proposed model could serve as an aid to designers to reduce the design burden. • The object of study is cooling blades rather than simple geometries. • The Conditional Generative Adversarial Network is used for modeling. • The 2-D distribution of cooling performance can be predicted quickly. • The model is able to obtain reasonable response and sensitivity. [ABSTRACT FROM AUTHOR] more...

Numerous state-of-art CFD (Computational Fluid Dynamics) studies have shown their validity and feasibility in engineering applications but still lack prediction efficiency. It is of great potential to apply artificial intelligence (AI) on the basis of CFD considering their fast development. Thus, the data-dimension reduction of CFD can be very important for the efficiencies of database construction, training and storage. Our previously developed linear low-dimension ventilation model (LLVM) is able to convert high-resolution CFD data into low-dimension grid levels, facilitated the use of fast prediction for ventilation online control. However, limitation still exists considering the dilemma of prediction speed and accuracy, e.g., case of a larger building space. This is due to the neglect of volume contribution ratio from single mesh as well as correlations of cells information when using uniform low-dimension methods. Therefore, we proposed a self-adaptive non-uniform low-dimension model for the data conversion but using lower dimension size with acceptable accuracy. The open-source CFD platform OpenFOAM was used for the package development, called self-adaptive low-dimension tool (LDT), including two modules, i.e., 'non-uniform dividing' and 'self-update'. Error index was defined considering the contribution ratio of individual mesh volume. A series of cases were carried out for demonstration and evaluation. It is found that the proposed model is able to largely improve the data accuracy but with smaller dimension requirement compared to uniform dividing method (e.g., with comparable error index around 16.5% when using zone numbers of 80 for non-uniform and 210 for uniform). Moreover, the self-update module enables users to efficiently and automatically identify the optimal low-dimension zone numbers. This work can be of great importance for the application of CFD-AI techniques. Image 1 • Development of a self-adaptive low-dimension model for database construction towards CFD-AI use. • Two modules proposed: 'non-uniform dividing' for dimension reduction, 'self-update' to identify low-dimension zone numbers. • An error index defined considering the contribution ratio of individual mesh volume. • Models integrated into an open-source package based on OpenFOAM platform, called self-adaptive Low-Dimension Tool (LDT). [ABSTRACT FROM AUTHOR] more...