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A multitask learning (MTL) model based on artificial neural networks (ANNs) is proposed in this study to improve the prediction accuracy and physical reliability of marine propeller hydrodynamic performance. The propeller's comprehensive geometric features are used as inputs, and the coefficients of quadratic polynomials for the thrust coefficient (KT) and torque coefficient (10KQ) curves are predicted as outputs. The loss function is customized through a positive gradient penalty of the curves to accelerate training. When the single-task and multitask models were compared, the prediction errors were reduced; KT decreased from 2.61% to 2.07%, 10 KQ decreased from 3.58% to 2.31%, and the efficiency (η) decreased from 3.04% to 2.00%. Non-physical fluctuations in the performance curves were effectively mitigated by the multitask model, yielding predicted curvatures which closely matched the experimental data. Strong generalization was demonstrated when the model was tested on unseen propellers, with deviations of 2.2% for KT, 4.6% for 10 KQ, and 3.8% for η. Finally, the model was applied to optimize the propeller design for a 325,000 ton very large ore carrier ship, where a Pareto front with 58 non-dominant solutions for the maximum speed and fluctuating pressure was successfully generated and effectively verified by the model's test results. The model enhanced the prediction of the propeller performance and contributed to optimization in the propeller's design. [ABSTRACT FROM AUTHOR]

A multitask learning (MTL) model based on artificial neural networks (ANNs) is proposed in this study to improve the prediction accuracy and physical reliability of marine propeller hydrodynamic performance. The propeller’s comprehensive geometric features are used as inputs, and the coefficients of quadratic polynomials for the thrust coefficient (KT) and torque coefficient (10KQ) curves are predicted as outputs. The loss function is customized through a positive gradient penalty of the curves to accelerate training. When the single-task and multitask models were compared, the prediction errors were reduced; KT decreased from 2.61% to 2.07%, 10 KQ decreased from 3.58% to 2.31%, and the efficiency (η) decreased from 3.04% to 2.00%. Non-physical fluctuations in the performance curves were effectively mitigated by the multitask model, yielding predicted curvatures which closely matched the experimental data. Strong generalization was demonstrated when the model was tested on unseen propellers, with deviations of 2.2% for KT, 4.6% for 10 KQ, and 3.8% for η. Finally, the model was applied to optimize the propeller design for a 325,000 ton very large ore carrier ship, where a Pareto front with 58 non-dominant solutions for the maximum speed and fluctuating pressure was successfully generated and effectively verified by the model’s test results. The model enhanced the prediction of the propeller performance and contributed to optimization in the propeller’s design.

In the present paper, the prediction of the performance of a centrifugal pump is investigated based on proper orthogonal decomposition (POD) reduced-order model (ROM). Firstly, the POD method is used to analyze the characteristics of the complex flow field inside the centrifugal pump, especially the eigenvalues of the velocity field and pressure field, and then the ROM of the flow field in the centrifugal pump is established then. After that, the established reduced-order model is used to predict the internal flow field. The results show that the POD mode field can reflect the spatial scale distribution of the flow field. The low-order flow field reflects the large-scale flow field structure, while the high-order flow field reflects the small-scale flow field structure. With the established ROM, the overall pump head (0D, 0-dimensional parameter) can be predicted with the relative error of less than 5%, and the standard root mean square error (normalized root mean square error, NRMSE) of pressure field and velocity field (3D, 3-dimensional parameter) is less than 4% and 8%, respectively. The efficiency and accuracy of both 0D and 3D parameters’ prediction motivate the fast prediction of multi-scale simulation of the large-scale industrial system in the future.

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In modern smart cities, road networks are becoming more and more complicated, resulting in more complex format of graphs. This brings many challenges to the forecasting of traffic flow in road graphs. Most of traditional traffic flow forecasting methods ignored many implicit relationships inside road graphs. And this cannot be well suitable for modern road networks in smart cities. Besides, the operation of smart cities is accompanied with real-time big data stream. The running efficiency of forecasting methods is another important concern. To handle this issue, this paper proposes a graph deep learning-based fast traffic flow forecasting method in urban road networks. Firstly, the theory about graph convolution operations is deduced and can be used as the basis of a graph convolution network (GCN). Then, the whole road network is viewed as a complex road graph, and the GCN is introduced to establish a novel forecasting method for graph-level traffic flow. With roads being regarded as nodes and their relations being regarded as edges, graph-level forecasting can be realized with the use of the proposed method. Experiments are carried out on a standard real dataset to evaluate the proposal. The experimental results show a proper performance of the proposal.

Rapid prediction of urban flooding is an important measure to reduce the risk of flooding and to protect people's property. In order to meet the needs of emergency flood control, this paper constructs a rapid urban flood prediction model based on a machine learning approach. Using the simulation results of the hydrodynamic model as the data driver, a neural network structure combining convolutional neural network (CNN) and long and short-term memory network (LSTM) is constructed, taking into account rainfall factors, geographical data, and the distribution of the drainage network. The study was carried out with the central city of Zhoukou as an example. The results show that after the training of the hydrodynamic model and CNN−LSTM neural network model, it can quickly predict the depth of urban flooding in less than 10 s, and the average error between the predicted depth of flooding and the measured depth of flooding does not exceed 6.50%, which shows that the prediction performance of the neural network is good and can meet the seeking of urban emergency flood control and effectively reduce the loss of life and property. [ABSTRACT FROM AUTHOR]

Efficient prediction of indoor temperature distribution (ITD) is crucial for various building-related applications, particularly in real-time thermal management. Despite efforts from both physics-based and data-driven perspectives, ITD prediction still faces challenges, particularly in balancing prediction accuracy with computational speed and addressing the need for extensive high-quality data in practical applications. To address these challenges, this study develops a hybrid temperature distribution prediction model (HTDPM) that integrates deep learning with physical mechanisms, enabling the effective capture of spatial-temporal dependencies and uncertainties with limited input parameters, achieving a balance between prediction accuracy and computational efficiency. Meanwhile, a novel transfer learning approach is applied to HTDPM (HTDPM-TL), significantly reducing data requirements and enhancing model generalization across various practical scenarios. Besides, orthogonal and randomized experiments are designed to simulate multiple real-world scenarios, thus constructing an extensive source domain dataset. The HTDPM-TL was tested in various real-world scenarios with several baseline methods, demonstrating an average RMSE of 0.794 ∘C, a computational time of 0.289 s, and limited data volume. The computational speed was improved by three orders of magnitude compared to CFD simulations, and the prediction resolution was enhanced threefold compared to traditional data-driven models. These results highlight the potential of HTDPM-TL to achieve an optimal trade-off between prediction accuracy, computational efficiency, and data requirement. • A hybrid mechanism-based and data-driven model for efficient indoor temperature distribution prediction was developed. • A novel transfer learning approach for reducing data requirements and enhancing generalization was introduced. • The computational speed was improved by three orders of magnitude compared to baseline CFD simulations. • The prediction resolution was enhanced threefold compared to traditional data-driven models. [ABSTRACT FROM AUTHOR]

• 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]

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]

• 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]

Mechanical and natural ventilations are effective measures to remove indoor airborne contaminants, thereby creating improved indoor air quality (IAQ). Among various simulation techniques, Markov chain model is a relatively new and efficient method in predicting indoor airborne pollutants. The existing Markov chain model (for indoor airborne pollutants) is basically assumed as first-order, which however is difficult to deal with airborne particles with non-negligible inertial. In this study, a novel weight-factor-based high-order (second-order and third-order) Markov chain model is developed to simulate particle dispersion and deposition indoors under fixed and dynamic ventilation modes. Flow fields under various ventilation modes are solved by computational fluid dynamics (CFD) tools in advance, and then the basic first-order Markov chain model is implemented and validated by both simulation results and experimental data from literature. Furthermore, different groups of weight factors are tested to estimate appropriate weight factors for both second-order and third-order Markov chain models. Finally, the calculation process is properly designed and controlled, so that the proposed high-order (second-order) Markov chain model can be used to perform particle-phase simulation under consecutively changed ventilation modes. Results indicate that the proposed second-order model does well in predicting particle dispersion and deposition under fixed ventilation mode as well as consecutively changed ventilation modes. Compared with traditional first-order Markov chain model, the proposed high-order model performs with more reasonable accuracy but without significant computing cost increment. The most suitable weight factors of the simulation case in this study are found to be (λ1 = 0.7, λ2 = 0.3, λ3 = 0) for second-order Markov chain model, and (λ1 = 0.8, λ2 = 0.1, λ3 = 0.1) for third-order Markov chain model in terms of reducing errors in particle deposition and escape prediction. With the improvements of the efficiency of state transfer matrix construction and flow field data acquisition/processing, the proposed high-order Markov chain model is expected to provide an alternative choice for fast prediction of indoor airborne particulate (as well as gaseous) pollutants under transient flows. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence (AI) technologies have been widely applied in many different fields including in the design, maintenance, and control of aero-engines. The air-cooled turbine vane is one of the most complex components in aero-engine design. Therefore, it is interesting to adopt the existing AI technologies in the design of the cooling passages. Given that the application of AI relies on a large amount of data, the primary task of this paper is to realize massive simulation automation in order to generate data for machine learning. It includes the parameterized three-dimensional (3-D) geometrical modeling, automatic meshing and computational fluid dynamics (CFD) batch automatic simulation of different film cooling structures through customized developments of UG, ICEM and Fluent. It is demonstrated that the trained artificial neural network (ANN) can predict the cooling effectiveness on the external surface of the turbine vane. The results also show that the design of the ANN architecture and the hyper-parameters have an impact on the prediction precision of the trained model. Using this established method, a multi-output model is constructed based on which a simple tool can be developed. It is able to make an instantaneous prediction of the temperature distribution along the vane surface once the arrangements of the film holes are adjusted.

In this work, a model is proposed for the computation of unbalanced magnetic pull (UMP) in PM machines. This model is suitable for use in dynamic simulations. However, the aim of this research is not dynamic modeling of the PM machines. The main idea is to provide a fast model to be integrated into the dynamic models of PM machines. To reduce the required computational burden, the UMP of PM machines is expressed as a second-order algebraic vector function of the stator currents. The function parameters are estimated by post-processing of the results of some magneto-static simulations. These parameters are functions of the machine geometrical data and the rotor position. The role of these parameters in the computation of the machine UMP is the same as the role of the machine inductances in computation of the electromagnetic torque. To investigate the capability of the developed model, the UMP of a concentric fractional-slot surface PM machine with diametrically asymmetric stator windings and the UMP of an eccentric PM-inset machine are predicted. The obtained results are compared in terms of the consumed time and the accuracy by means of finite element analysis. [ABSTRACT FROM AUTHOR]

Field evaluation of the strength properties of jointed rock masses is a challenging task in geotechnical engineering. Typically, laboratory tests using small jointed specimens have difficulty determining the strength parameters of jointed rock masses due to the scale dependence of discontinuities and because the tests are expensive and time-consuming. Fast and continuous estimation of the unconfined compressive strength σcm of a jointed rock mass directly using drilling via a deep convolutional neural network (CNN) is a novel and practical field investigation method. This paper presents a CNN framework that includes (1) obtaining a training dataset; (2) determining the unconfined compressive strength σcm via a rock mass quality rating (RMQR) system; (3) training the CNN model; and (4) validating the results using tunnel engineering calculations. A comparison of the CNN predictive results with the true values suggests that the CNN makes good predictions across a wide range of unconfined compressive strengths σc of intact rock, especially for high RQD values. Due to the joint orientation, the unconfined compressive strength σcm of a jointed rock mass cannot be reliably determined using the σcm/σc ∼ RQD relation. By incorporating the physical variables of RQD and σc, which are known to affect the unconfined compressive strength σcm of a jointed rock mass, into the CNN, the proposed CNN model can provide better predictions than the regular CNN model. All the results predicted by the physics-informed CNN are within the accepted error range of 10%. This method is applied to the excavation of the Huangshan Tunnel in the Hanjiang-to-Weihe River Project of China and is verified as reliable via comparative studies with previous works. Thus, the proposed method represents fast and efficient prediction of the strength of jointed rock masses in rock engineering. [ABSTRACT FROM AUTHOR]

A new dynamic predict method for solving the problem of a wearable body temperature measuring equipment’s poor dynamic response characteristics and a data fitting method of body temperature under experimental conditions are proposed in this paper. The method is based on the law of conservation of energy, Taylor expansion in respect of time and the idea of finite difference. The predictive equations of predict temperature with respect to predict coefficient and known time nodes’ temperature are established, the body temperature can be quickly obtained by measuring the first few minutes’ temperature. The predicted results are well verified by experimental data. And the possible influencing factors for prediction are analyzed, which include difference scheme, initial temperature, body temperature, time constant and temperature difference. This method allows the body temperature measurement equipment with lower price get higher performance, which greatly improved the practicability of temperature sensors that measurement process satisfy the first order inertia link. [ABSTRACT FROM AUTHOR]

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]

• 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]

• 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]

• 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]

• 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]

This paper proposes a novel fast open circuit voltage prediction approach for Lithium-ion battery, which is potential to facilitate a convenient battery modeling and states estimation in the energy storage system. Open circuit voltage measurement suffers from a long relaxation time (several hours, even days) to reach the thermodynamic equilibrium of the battery. On the basis of the feedback control theory, the proposed multiple correction approach utilizes the constrained nonlinear optimization of the power function in each curve fitting step. The voltage measurement in a short period is divided into several segments to correct the voltage prediction multiple times with the feedback errors after each curve fitting. The similarity between the shape of the power function and the variation of the terminal voltage during the relaxation time is utilized. The proposed method can speed up the time-consuming open circuit voltage measurement and predict the open circuit voltage with high accuracy. Experimental tests on a LiFePO4 battery prove the validation and effectiveness of the proposed method in accurately predicting the open circuit voltage within a very short relaxation time (less than 15 min). [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rapid prediction of a transient rise in a contaminant concentration attributed to a sudden release of a contaminant is crucial for decision-making in relation to deployment of emergency ventilation. The superposition theorem for a fixed flow field is an alternative approach that has been used to accomplish fast prediction. In this study, the accuracy of a superposition method was investigated based on a novel method to obtain the initial condition using a limited number of sensors. Sensors were placed at the centre of each divided zone to record the initial zonal concentration, and the prediction was performed in accordance to an algebraic expression that uses the measured concentrations and the index of transient accessibility of the sub-initial condition (TASIC) as inputs. The analysis of these numerical cases has led to the following conclusions: (1) the prediction accuracy is improved when the prediction time is longer, and when there are more zones and less nonuniformity for the sub-initial conditions, (2) the relative prediction deviations are larger when the initial condition is measured using sensors rather than those elicited based on the predicted initial conditions, and (3) the use of eight sensors is appropriate for the studied case in accordance to sensor cost and accuracy. The method is helpful for fast decision-making when the initial conditions are unpredictable. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

In order to predict acoustic radiation from a structure in waveguide, a method based on wave superposition is proposed, in which the free-space Green's function is used to match the strength of equivalent sources. In addition, in order to neglect the effect of sound reflection from boundaries, necessary treatment is conducted, which makes the method more efficient. Moreover, this method is combined with the sound propagation algorithms to predict the sound radiated from a cylindrical shell in waveguide. Numerical simulations show the effect of how reflections can be neglected if the distance between the structure and the boundary exceeds the maximum linear dimension of the structure. It also shows that the reflection from the bottom of the waveguide can be approximated by plane wave conditionally. The proposed method is more robust and efficient in computation, which can be used to predict the acoustic radiation in waveguide. [ABSTRACT FROM AUTHOR]

The directional pattern of sound waves scattered from an object insonified by a plane wave can be efficiently predicted using the Fourier diffraction theorem (FDT). This is achieved by sampling a circle in the discrete Fourier transform of the object/medium distribution. However, the FDT-based approach under the first-order Born approximation is only applicable to weak scattering. To improve the prediction accuracy and expand the method's scope of applications, we introduce a second-order correction term to the solution, which is obtained by taking the first-order scattered waves as secondary incident sources, and calculate the 'scattering' in the same way as in the first-order FDT-based approach. Adding the resulting correction term to the directional pattern based on the first-order Born approximation, the second-order prediction is obtained. Numerical results show that the proposed method can provide improved directional patterns of the scattered waves, and the range of applicability is significantly expanded. [ABSTRACT FROM AUTHOR]

In this study, a novel approach is proposed for fast prediction of dynamic voltage instability occurrence (as a short term phenomenon and/or a long term one) and voltage stability stiffness of the system, against load disturbances. The main contribution of this paper is in introducing a procedure for generating novel features to be applied to a pattern classifier, by which dynamic voltage stability status of a power system can be predicted. The proposed feature generation procedure only needs measured pre‐disturbance variables and disturbance severity provided by phasor measurement units as inputs whereas a set of output variables are derived from an unconstrained power flow program. Since the proposed method does not need any measured post disturbance data, the prediction task can be performed just after the disturbance. Thus, corrective actions can be executed in a short time after the disturbance to inhibit voltage instability. Moreover as no measured post‐disturbance data are needed, the proposed method can also be employed in preventive procedures for voltage stability enhancement and/or decreasing possibility of voltage instability occurrence. Training a decision tree based classifier with the proposed features and testing the method on a modified version of Nordic32 test system, the simulation results have demonstrated that the proposed method effectively predicts the status of dynamic voltage stability in the test system. [ABSTRACT FROM AUTHOR]

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]

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]

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]

This paper describes a Decision Support System for Disaster Management (DSS-DM) to aid operational and strategic planning and policymaking for disaster mitigation and preparedness in a less-developed infrastructural context. Such contexts require a more flexible and robust system for fast prediction of damage and losses. The proposed system is specifically designed for earthquake scenarios, estimating the extent of human losses and injuries, as well as the need for temporary shelters. The DSS-DM uses a scenario approach to calculate the aforementioned parameters at the district and sub-district level at different earthquake intensities. The following system modules have been created: clusters (buildings) with respect to use; buildings with respect to construction typology; and estimations of damage to clusters, human losses and injuries, and the need for shelters. The paper not only examines the components of the DSS-DM, but also looks at its application in Besiktas municipality in the city of Istanbul, Turkey. [ABSTRACT FROM AUTHOR]

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]

The utilization of buoyancy-driven natural ventilation in atrium buildings during transitional seasons helps create a healthy and comfortable indoor environment by bringing fresh air indoors. Among other factors, the air flow rate is a key parameter determining the ventilation performance of an atrium. In this study, a grey box modeling method is proposed and a prediction model is built for calculating the buoyancy-driven ventilation rate using three openings. This model developed from Bruce's neutral height-based formulation and conservation laws is supported with a theoretical structure and determined with 7 independent variables and 4 integrated parameters. The integrated parameters could be estimated from a set of simulated data and in the results, the error of the semi-empirical predictive equation derived from CFD (computational fluid dynamics) simulated data is controlled within 10%, which indicates that a reliable predictive equation could be established with a rather small dataset. This modeling method has been validated with CFD simulated data, and it can be applied extensively to similar buildings for designing an expected ventilation rate. The simplicity of this grey box modeling should save the evaluation time for new cases and help designers to estimate the ventilation performance and choose building optimal opening designs. [ABSTRACT FROM AUTHOR]