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• An experimental dataset of flow boiling heat transfer in mini channels with pin fins was built. • A transfer learning framework was proposed to predict the heat transfer coefficient. • The effects of different factors on heat transfer coefficient prediction accuracy was identified. Flow boiling in mini channels with micro pin fins is a promising heat sink technique to achieve high-efficient aircraft thermal management. The accurate prediction of its heat transfer coefficient is critical for the practical design of two-phase heat exchanger based on mini channels with micro pin fins. Previous investigation shows that heat transfer coefficient prediction accuracy of the machine learning method is generally better than that of conventional empirical correlations. However, the machine learning method cannot guarantee its prediction accuracy among different data domains. To extend the application region of the conventional machine learning method, a transfer learning framework was proposed in present study. First of all, an experimental system was built to acquire test data from different sample domains (i.e., the diamond pin fins with different geometries). Then a conventional machine learning model was developed based on the deep learning method. Furthermore, the developed deep learning model was adjusted with transfer learning process, and the performance of these two kinds of models (i.e., conventional machine learning model and transfer learning model) was comprehensively evaluated. Results showed that the conventional machine learning model had a good prediction accuracy with an overall deviation of 4.11 % but was only confined in the same data domain as training data. Differently, the transfer learning model with 70 % data set from the new domain can achieve appropriate prediction in the new domains with deviation of 4.28 %. The results of this paper demonstrated that the conventional machine learning model can extend into different domains with reasonable prediction accuracy through transfer learning frameworks. It will benefit the practical design of high-efficiency heat exchangers for aircraft thermal management. [ABSTRACT FROM AUTHOR]

In this paper, a numerical model of high-temperature proton exchange membrane fuel cell (HT-PEMFC) was developed, in which the thermal and electrical properties were treated as temperature dependent. Based on the numerical simulation, the needed training data was acquired and used for the development of data-driven model via the artificial neural network (ANN) algorithm. The developed data-driven model was then used to predict the performance of HT-PEMFC. The simulation results indicated that the deviation of ANN prediction was less than 2.48% compared with numerical simulation. The effects of various influential factors, such as the geometry size of the gas flow channel, the thickness of the membrane and the operating temperature, could be predicted easily by using the ANN model. The ANN model prediction results showed that the more compact fuel cell and the higher operating temperature improved the performance of HT-PEMFC. The proposed ANN model and the parameters study will contribute to the further design and operation of HT-PEMFC. • A numerical model of HT-PEMFC was developed with temperature dependent property. • An ANN based data-driven model was built for HT-PEMFC performance prediction. • The deviation between the ANN model and numerical model was less than 2.48%. • The effects on the geometry and the operating temperature were evaluated by ANN model. • The compact design and high operating temperature improved the HT-PEMFC performance. [ABSTRACT FROM AUTHOR]

Highlights • A CCHP system primed by Maisotsenko combustion turbine cycle was proposed. • Performance comparison with that based on conventional gas turbine cycle was made. • Dynamic system simulations were conducted using the TRNSYS platform. • Different climate regions were considered with the results compared. • The loading profiles affected the relative performances of the two CCHP systems. Abstract Buildings are the significant primary energy consumption and pollutant emission contributors in a modern city. Distributed multi-generation systems such as combined cooling, heating and power (CCHP) systems are considered an alternative to fulfil the energy saving requirements in the building sectors. In this study, a CCHP system primed with Maisotsenko combustion turbine cycle (MCTC) was considered with its performance compared with that primed with a simple gas turbine with recuperator (SGTR). With dynamic models of the CCHP systems constructed using TRNSYS, a design optimisation was conducted using genetic algorithm to determine the system component size and number based on the best year-round cost saving. A study case which consisted of building complexes in a district scale was used for investigation under two climatic conditions. The simulation results showed that the relative performances of both CCHP systems depended on the load profiles which differed from the situations when both prime movers were used solely for power generation. In a heating-dominated application, the performance of the MCTC-primed CCHP system was better than the SGTR-primed one only during the summer period and that the year-round performance was inferior to the latter one. Last but not the least, a parameter study was conducted which showed the benefit reduction of CCHP system with the decreasing of public grid price and increasing fuel gas price. [ABSTRACT FROM AUTHOR]

• Up-to-data dew-point evaporative cooling techniques were comprehensively reviewed. • The recent applications in demand-side and supply-side were summarized. • Research limitations in temperature region and film distribution were discussed. • Future research directions with the use of renewable energy were highlighted. Dew-point evaporative cooling (DPEC) is a high-efficiency heat and mass transfer technique and has been widely used in various industries. The suitable and efficient application of this technology is helpful for energy conservation and can bring great benefits for the fulfillment of carbon–neutral development. In this paper, a comprehensive review of DPEC techniques was conducted through the introduction of its current applications and limitations. First, the theories behind DPEC together with the existing modelling methods and experimental tests were presented after a brief introduction of its research significance. Then, recent advances of DPEC techniques from both demand-side and supply-side applications were described in detail, such as Heating Ventilation Air Conditioning system, gas turbine and other special designs. The research gap between actual carbon–neutral applications and current techniques and potential future research directions were also discussed and finally, some conclusive remarks were outlined. This review concludes the current main characteristics of DPEC techniques and indicates the possible further investigation directions. The inadequate investigation in high temperature region or un-even distribution water film together with future renewable energy combination are highlighted, which is expected to guide the future development of dew-point evaporative coolers and serve the carbon–neutral strategies. [ABSTRACT FROM AUTHOR]

The adoptions of mini-channel and nanofluid are potential technologies to improve the heat transfer capability. The present study experimentally investigated the flow and thermal performance of EG/water based ZnO nanofluid inside a mini-channel with serrated fins with an equivalent diameter of 1.59 mm. EG/water mixture with a mass fraction of 40%/60% served as the base fluid. Nanofluids with a volumetric concentration of 0.75 vol% and 1.5 vol% were prepared. The thermophysical properties of viscosity, density and thermal conductivity were measured. It reveals that although the adding of nanoparticles is able to improve the thermal conductivity by 4.2% on average, the viscosity also enlarges remarkably by up to 18.9%. The measured density and thermal conductivity can be predicted accurately by existing formulations. As all correlations underestimate the viscosity, a new one is proposed for the fabricated nanofluid with a small Mean Absolute Relative Deviation (MARD) of 0.1%. The Nusselt number can be averagely improved by 10.6% and 13.2% for the 0.75 vol% and 1.5 vol% nanofluid with a corresponding sacrifice of friction factor augmentation of 31.2% and 47.3% respectively due to the significant augmentation of viscosity, which results in a relatively small heat transfer coefficient enhancement ratio of 0.92–1.09 under the same pumping power. In addition, the heat transfer enhancement shows a nonlinear increment with the concentration and a higher relative enhancement is found at a lower concentration. This indirectly indicates that the heat transfer does not always increase with the concentration. Even though some published correlations present acceptable predictions for the tested flow and thermal performance of EG/water based ZnO nanofluid, the newly developed Backpropagation Artificial Neural Networks (BP-ANNs) show even better prediction accuracies for Nusselt number and friction factor with the MARDs of 0.39% and 0.35% respectively. Our study is expected to give valuable data sources and inspiration for future investigation on hydrodynamic and thermal behavior of nanofluids. • Flow and heat transfer behavior of ZnO-EG/water nanofluid were studied. • Thermal properties of nanofluid were measured and compared with correlations. • A new correlation for viscosity was proposed for ZnO-EG/water nanofluid. • Empirical Nusselt number correlations were evaluated by measured data. • Novel BP-ANNs were proposed to predict Nusselt number and friction. [ABSTRACT FROM AUTHOR]

• The flow and thermal performance of ZnO/water nanofluid were investigated. • Impacts of channel size, concentration and Reynolds number were identified. • A novel GA-optimized BP-ANN was proposed for Nusselt number prediction. • CFD studies with single-phase, mixture and various turbulence models were conducted. The thermal and flow characteristics of ZnO/water nanofluid in two multiport mini-channels were experimentally and numerically studied. Nanofluids with volumetric concentrations of 0.75 and 1.5% were used. Experimentally, the influences of concentration, Reynolds number (100–3750) and channel size (1.22 and 1.42 mm) on performance were identified. A novel Genetic Algorithm-optimized Backpropagation-Artificial Neural Network (GA-optimized BP-ANN) was proposed for Nusselt number prediction. Numerically, the performance using single-phase and mixture model with different turbulence models were evaluated. Results reveal that the nanofluids show better heat transfer performance and higher pressure drop than that of water. Additionally, the improvement is more obvious in laminar/turbulent transition region at a higher concentration. A better heat transfer performance is observed in a smaller channel after laminar flow region. For thermal performance factor, enhancement only appears at higher Reynolds numbers after flow transition. The most remarkable enhancements are nearly 1.3 and 1.48 for the two channels at the Reynolds numbers of 1600 and 1430, respectively. The developed GA-optimized BP-ANN shows an extremely high prediction accuracy with a Mean Absolute Relative Deviation (MARD) of 2.70%. Numerically, the single-phase model combined with the Lam-Bremhorst model exhibits better simulation results for nanofluid in mini-channel with a MARD of 9.0% than the mixture model. [ABSTRACT FROM AUTHOR]

• Both flow boiling and condensation in mini channels with segregated fins were investigated; • Machine learning based performance prediction models were developed; • The influential factors of different dimensionless parameters were analyzed. Two phase flow boiling and condensation heat transfer in mini channels are promising technologies in handling the challenges of modern thermal management in various fields. In this paper, experimental systems were developed to generate data for flow boiling and condensation of refrigerant R134a in two mini channels with serrated fins under various working conditions. Based on these data, machine learning based Artificial Neural Network (ANN) models were trained to predict the heat transfer performance for both flow boiling and condensation. The proposed ANN models predict the heat transfer coefficient satisfactorily with a Mean Absolute Relative Deviation (MARD) of 11.41% and 6.06% for boiling and condensation respectively compared with experimental data. The test results also indicate that using the most important parameters rather than all available parameters is able to formulate a reasonable ANN model. It also reveals that the developed ANN model can be transferred in a different dataset from the same domains but shows an unacceptable performance for different domains. The proposed ANN model can illuminate future study on two phase flow and be used in practical heat exchanger design, which will benefit the advanced thermal management system. [ABSTRACT FROM AUTHOR]

Isocitrate dehydrogenase 1 (IDH1), which catalyzes the conversion of isocitrate to α-ketoglutarate, is one of key enzymes in the tricarboxylic acid cycle (TCA). Hotspot mutation at Arg132 in IDH1 that alters the function of IDH1 by further converting the α-ketoglutarate(α-KG) to 2-hydroxyglutarate (2-HG) have been identified in a variety of cancers. Because the IDH1 mutations occur in a significant portion of gliomas and glioblastomas, it is important that IDH1 inhibitors have to be brain penetrant to treat IDH1-mutant brain tumors. Here we report the efforts to design and synthesize a novel serial of mutant IDH1 inhibitors with improved activity and the blood-brain barrier (BBB) penetration. We show that compound 5 exhibits good brain exposure and potent 2-HG inhibition in a HT1080-derived mouse xenograft model, which makes it a potential preclinical candidate to treat IDH1-mutant brain tumors. Image 1 • A series of 24 new mutant IDH1 inhibitors have been synthesized and tested. • Compound 5 exhibits excellent inhibitory activity both in the enzyme and in cells. • Compound 5 is demonstrated good pharmacodynamics and pharmacokinetics properties. • Compound 5 exhibits higher exposure than AG-120 in the brain and tumor. • Compound 5 is effective in inhibiting 2-HG in HT1080 xenograft tumor samples. [ABSTRACT FROM AUTHOR]

• The merit of humidified gas turbine cycle was investigated. • Two air saturator designs were considered for performance comparison. • Detailed heat and mass transfer analysis were employed for the air saturators. • Parametric studies were conducted under different operating conditions. • The Maisotsenko combustion turbine cycle offered higher energy efficiency. The urge for more energy-efficient power plant systems to relieve the energy crisis triggers the design for more advanced power cycles. The humidified gas turbine cycle is considered one of the potential choices, and the air saturator performance is critical to the energy merit of such kind of system. To understand more, the performances of humidified gas turbine cycles with two types of air saturator designs were compared in this study. Type 1 air saturator was a hybrid design which combined an indirect evaporative cooler with a Maisotsenko cycle while Type 2 was a conventional indirect evaporative cooler. Detailed heat and mass transfer analysis was taken into account in the air saturator modelling. Through system simulations, it was found that all the humidified gas turbine cycle systems offered higher system efficiencies than a simple gas turbine system with recuperator. Besides, parametric studies were conducted which highlighted the effects of system inlet air temperature, turbine inlet temperature, water injection rate, and part-load ratio on the performances of the different humidified gas turbine cycle designs. The employment of Type 1 air saturator offered 9.34% and 23.55% enhancement in the system efficiencies as compared to those using Type 2 air saturator under the design and 50% part-load ratio conditions respectively. This reinforced the benefit of applying Maisotsenko cycle to the air saturator design of humidified gas turbine cycle for the enhancement of system efficiency. [ABSTRACT FROM AUTHOR]

• Design optimization and two-stage control strategy of the CCHP system were conducted. • CCHP system performance was investigated in two different climatic regions. • The performance of different control strategies was compared. Combined cooling, heating and power (CCHP) is a promising energy supply technology to improve the overall energy utilization efficiency. It provides an opportunity to achieve low or zero carbon energy supply with tactful control strategies. In this study, the two-stage model predictive control (MPC) strategy with combined rolling optimization and real-time adjustment is demonstrated to be highly effective for CCHP system application. In most previous studies, the MPC strategy was performed under a given design configuration, which was not optimally constructed. In real practice, the system configuration, the installed equipment capacity, and the control strategy altogether affect the CCHP system performance. Both the system design optimization and the two-stage MPC strategy were conducted in our numerical analysis through a hypothetical case study. Our simulation results illustrated that the equipment selection affects the running cost of the CCHP system, and the desirable solution is well linked to the building load profile. The effect of forecasting error on the two-stage MPC was also investigated. The results showed that although the two-stage MPC strategy is able to give better performance than the traditional strategies in most cases, poor performance may still exist when the load forecasting error is more than 8.8%. [ABSTRACT FROM AUTHOR]

The actual operation of turbomachinery is inevitably affected by multi-source uncertainties. Such uncertainties are detrimental to the performance and reliability of energy systems. Based on graph learning methods, this work aims to provide a convenient and effective approach for aerodynamic robust optimization of turbomachinery. A radial inflow turbine is taken as the research target and Dual Graph Neural Network (DGNN) regression model is constructed for flow field prediction and performance discrimination. By comparing the accuracy and time consumption, the advantages of DGNN over classical surrogate models and computational fluid dynamics (CFD) are clarified. The proposed model is integrated into uncertainty quantification and aerodynamic robust optimization. The effect of multi-source uncertainties on performance is quantified. The stochastic response of flow fields is also obtained conveniently through DGNN. Robust optimization is performed for power and efficiency, respectively. The power robust optimization improves the power by 1.52% and reduces the standard deviation of power by 15.45%. The efficiency robust optimization achieves an efficiency improvement of 1.76% (increment) and an efficiency standard deviation reduction of 36.82%. The proposed approach is an efficient and competitive choice for uncertainty quantification and robust optimization. The present work contributes to constructing the digital twin of turbomachinery systems. • A new complete approach for aerodynamic robust optimization. • A graph deep learning model is contained for flow fields and performance prediction. • Our model performs better than classical surrogate models. • The effect of multi-source uncertainties is quantified. • The proposed approach can effectively solve aerodynamic robust optimization problems. [ABSTRACT FROM AUTHOR]

Carbonate tidal delta system used to be put much less attention on unlike the counterparts in siliciclastic systems, and its unique architecture influences the reservoir heterogeneity and corresponding development strategy significantly. This study aims to recognize and characterize the carbonate tidal delta system of Mishrif Formation in Southeast Iraq. Based on detailed observation of cores from 5 wells and integrated analysis on well-loggings and 3D seismic, five lithofacies associations were recognized: (1) subtidal mudstone to wackestone (LF-1); (2) back-margin massive wackestone to packstone (LF-2); (3) high-energy bioclastic grainstone (LF-3); (4) swamp carbonaceous mudstone interbedded with bioclastic wackestone (LF-4); and (5) incision brown mudstone to wackestone with chert nodular (LF-5). A detailed description of the recognized lithofacies and facies association of the system is given in this work, together with an interpretation of the corresponding depositional setting. The architecture of the reservoir bodies has been reconstructed in detail and placed within a high-frequency sequence-stratigraphic framework. The studied interval belongs to the highstand system tract (HST) of a single composite sequence and comprises two shallowing-upward high-frequency sequences. The internal architecture of the tidal delta system is the result of complex tidal-dominated hydrodynamics. Facies and diagenetic partitioning with each high-frequency sequences are primary controls on pore types and reservoir properties. Original lithofacies texture and subsequent meteoric dissolution resulted in regional barriers and high permeable streaks that created a complex internal architecture of reservoir bodies. The shape of reservoir bodies generally follows those of the lithofacies, but with the addition of intercalated thin flow barriers. • The flood tidal delta system develops different types of lithofacies. • The architecture of the system is analyzed within high-frequency sequence framework. • The stratigraphic architecture and diagenetic alternation complicate the reservoir heterogeneity. • Compartmentalization of the reservoir is caused by the presence of barriers and the tidal channel. [ABSTRACT FROM AUTHOR]

• Local condensation heat transfer in smooth channels and pin fin array is presented. • The effect of operating parameters on the heat transfer characteristics was analyzed. • Sensitivity analysis is performed of heat transfer to identify the critical parameters. • Condensation heat transfer increases with heat flux in the range of vapor quality above 0.4. In this paper, an experimental study of flow condensation was carried out of R134a in a multiport mini channel and a mini channel with pin fin array. The former comprised 20 parallel rectangular channels with an equivalent diameter of 0.64 mm. The latter is a narrow rectangular mini channel containing 10 rows of diamond pin fins with a staggered configuration, and height and longitudinal/transverse pitch of 0.5 mm and 2 mm respectively. To acquire the local value of heat flux and heat transfer coefficient, air jet impingement cooling devices were adopted to condense the vapor of refrigerants. The effects of vapor quality, mass flux, heat flux, and saturation pressure on flow condensation heat transfer coefficient were investigated with the operating conditions: vapor quality from 1 to 0, mass flux from 160 to 450 kg/(m2s), heat flux from 10.0 to 39.8 kW/m2, and saturation pressure from 600 to 1500 kPa. The experimental results indicated that the pin fin array significantly improves the condensation heat transfer coefficient up to nearly 186 % compared to the multiport mini channel in the high vapor quality region. For both channels, the local heat transfer coefficient increases with an increase in vapor quality, mass flux, and heat flux whereas decreases with increase in saturation pressure. The influence of heat flux and mass flux on heat transfer coefficient was more pronounced in the high vapor quality region than in the low vapor quality region. A sensitivity analysis was performed at different vapor quality levels. The most influential parameters in the smooth channel and the pin fin array are saturation pressure and mass flux, respectively. The relative contribution of heat flux is more obvious in the high vapor quality region. Some of the existing correlations have good prediction performance for the smooth channel experimental data in this paper, and the corresponding MAD is within 20 %. However, their deviations are much greater for the applications of pin fin array. [ABSTRACT FROM AUTHOR]