ALBERT

Description: Uniaxial compressive strength (UCS) and peak strain (PS) are essential indices for studying the mechanical properties of coal and rock masses, and they are closely related to mechanical parameters such as the elastic modulus (E), Poisson’s ratio (υ), cohesion (C) and internal friction angle (Φ) of coal and rock masses. This study took the No. 2-1 coal seam of Zhaogu No. 2 Mine, in Henan Province, China, as the research object. An RMT-150B servo testing machine was used to test all mechanical parameters, including the E, υ, C and Φ of coal and rock masses. Based on the principle of orthogonal testing, Three Dimensions Fast Lagrangian Analysis of Continua (FLAC3D) was used to select E, υ, C, Φ, tensile strength (Rm) and dilation angle (Ψ) as initial participation factors. Using these six parameters and a five-level combination scheme (L25 (56)), the influence of coal mechanical parameters on UCS and PS was investigated, using the software SPSS for stepwise regression analysis, and a uniaxial pressure-resistant regression prediction equation was established. The research showed that, under uniaxial compression conditions, the main parameters controlling UCS of coal masses are C and Φ; conversely, the main parameters controlling PS are E and C. UCS and PS exhibit significant linear relationships with these main controlling parameters. Here, a stepwise regression prediction equation was established through reliability verification analysis using the main controlling parameters. This prediction method produces very small errors and a good degree of fit, thus allowing the rapid prediction of UCS. The precision of the stepwise regression model depends on the number of test samples, which can be increased in the later stages of a design project to further improve the precision of the projection model.

Description: Power transformers are one of the most important devices in electrical networks. The safety operation of the transformers directly affects the reliability of the power system. To diagnose the internal deformation of the transformer as soon as possible is of great significance. As a new technique, sweep frequency impedance (SFI) method has been used to detect the short-circuit fault of the transformer winding. However, the SFI method is still in the early stage and more experimental tests are needed to further demonstrate its accuracy in the detection of other types of winding faults. Therefore, in this paper, the SFI method is investigated to diagnose an open-circuit fault of an onsite transformer. By deeply analyzing the SFI curves and SFI values at power frequency obtained by the SFI test, the open-circuit fault of this transformer winding is determined. Meanwhile, the accuracy of the diagnostic results based on the SFI method is validated by introducing the results of the short-circuit impedance (SCI) and winding resistance measurements. The application of the SFI method on the detection of the open-circuit fault within the transformer winding not only enrich the SFI method research but also provide valuable practical guidance significance for the fault detection.

Description: Coal blending is an effective way to organize and control coal ash fusibility to meet different requirements of Coal-fired power plants. This study investigates three different eutectic processes and explains the mechanism of how coal blending affects ash fusibility. The blended ashes were prepared by hand-mixing two raw coal ashes at five blending ratios, G:D = 10:90 (G10D90), G:D= 20:80 (G20D80), G:D = 30:70 (G30D70), G:D = 40:60 (G40D60), and G:D = 50:50 (G50D50). The samples were heated at 900 °C, 1000 °C, 1100 °C, 1200 °C, and 1300 °C in reducing atmosphere. XRD and SEM/EDX were used to identify mineral transformations and eutectic processes. The eutectic processes were finally simulated with FactSage. Results show that the fusion temperatures of the blended ashes initially decrease and then increase with the blending ratio, a trend that is typical of eutectic melting. Eutectic phenomena are observed in D100, G10D90, and G30D70 in different degrees, which do not appear in G100 and G50D50 for the lack of eutectic reactants. The main eutectic reactants are gehlenite, magnetite, merwinite, and diopside. The FactSage simulation results show that the content discrepancy of merwinite and diopside in the ashes causes the inconsistent eutectic temperatures and eutectic degrees, in turn decrease the fusion temperature of the blended ash and then increase them with the blending ratio.

Description: An experimental study was performed to investigate the effect of subcritical carbon dioxide (CO2) adsorption on mechanical properties of shales with different coring directions. Uniaxial compressive strength (UCS) tests were conducted on shale samples with different CO2 adsorption time at a pressure of 7 MPa and a temperature of 40 °C. The crack propagation and the failure mechanism of shale samples were recorded by using acoustic emission (AE) sensors together with ARAMIS technology. According to the results, samples with parallel and normal bedding angles present reductions of 26.7% and 3.0% in UCS, 30.7% and 36.7% in Young’s modulus after 10 days’ adsorption of CO2, and 30.3% and 18.4% in UCS, 13.8% and 22.6% in Young’s modulus after 20 days’ adsorption of CO2. Samples with a normal bedding angle presented higher brittleness index than that with a parallel bedding angle. The strain distributions show that longer CO2 adsorption will cause higher axial strains and lateral strains. The AE results show that samples with a parallel angle have higher AE energy release than the samples with a normal angle. Finally, samples with longer CO2 adsorption times present higher cumulative AE energy release.

Description: In order to clarify the potential of internal purification methods on medium speed diesel engines to meet the IMO Tier III nitrogen oxide (NOx) emission regulations, combined 1-D engine working cycle simulation and 3-D CFD simulation were conducted to predict the performance and emissions of the engine under different valve close timings, geometric compression ratios, injection timings, and Exhaust Gas Recirculation (EGR) rates. The numerical results show that, as the inlet valve close timing is advanced, NOx is reduced by as much as 27%, but the peak of premixed combustion heat release rate is increased; this can weaken the ability to reduce NOx with the Miller cycle. Moreover, the peak of premixed combustion heat release rate is reduced when the geometric compression ratio is increased to 15.4, and linking with injection timing by delaying 6°CA can further reduce NOx by 55.3% from the baseline. Finally, over 80% NOx reduction can be achieved when the above schemes are combined with over 15% EGR. The NOx and soot can be reduced simultaneously by using moderate Miller cycle combination with moderate EGR, and the results show a large reduction of NOx and moderate reduction of soot. This can be a feasible technical solution to meet Tier III regulations.

Description: To meet Energy Independence and Security Act (EISA) cellulosic biofuel mandates, the United States will require an annual domestic supply of about 242 million Mg of biomass by 2022. To improve the feedstock logistics of lignocellulosic biofuels in order to access available biomass resources from areas with varying yields, commodity systems have been proposed and designed to deliver quality-controlled biomass feedstocks at preprocessing “depots”. Preprocessing depots densify and stabilize the biomass prior to long-distance transport and delivery to centralized biorefineries. The logistics of biomass commodity supply chains could introduce spatially variable environmental impacts into the biofuel life cycle due to needing to harvest, move, and preprocess biomass from multiple distances that have variable spatial density. This study examines the uncertainty in greenhouse gas (GHG) emissions of corn stover logistics within a bio-ethanol supply chain in the state of Kansas, where sustainable biomass supply varies spatially. Two scenarios were evaluated each having a different number of depots of varying capacity and location within Kansas relative to a central commodity-receiving biorefinery to test GHG emissions uncertainty. The first scenario sited four preprocessing depots evenly across the state of Kansas but within the vicinity of counties having high biomass supply density. The second scenario located five depots based on the shortest depot-to-biorefinery rail distance and biomass availability. The logistics supply chain consists of corn stover harvest, collection and storage, feedstock transport from field to biomass preprocessing depot, preprocessing depot operations, and commodity transport from the biomass preprocessing depot to the biorefinery. Monte Carlo simulation was used to estimate the spatial uncertainty in the feedstock logistics gate-to-gate sequence. Within the logistics supply chain GHG emissions are most sensitive to the transport of the densified biomass, which introduces the highest variability (0.2–13 g CO2e/MJ) to life cycle GHG emissions. Moreover, depending upon the biomass availability and its spatial density and surrounding transportation infrastructure (road and rail), logistics can increase the variability in life cycle environmental impacts for lignocellulosic biofuels. Within Kansas, life cycle GHG emissions could range from 24 g CO2e/MJ to 41 g CO2e/MJ depending upon the location, size and number of preprocessing depots constructed. However, this range can be minimized through optimizing the siting of preprocessing depots where ample rail infrastructure exists to supply biomass commodity to a regional biorefinery supply system.

Description: Brine-dependent recovery, which involves injected water ionic composition and strength, has seen much global research efforts in the past two decades because of its benefits over other oil recovery methods. Several studies, ranging from lab coreflood experiments to field trials, indicate the potential of recovering additional oil in sandstone and carbonate reservoirs. Sandstone and carbonate rocks are composed of completely different minerals, with varying degree of complexity and heterogeneity, but wettability alteration has been widely considered as the consequence rather than the cause of brine-dependent recovery. However, the probable cause appears to be as a result of the combination of several proposed mechanisms that relate the wettability changes to the improved recovery. This paper provides a comprehensive review on laboratory and field observations, descriptions of underlying mechanisms and their validity, the complexity of the oil-brine-rock interactions, modeling works, and comparison between sandstone and carbonate rocks. The improvement in oil recovery varies depending on brine content (connate and injected), rock mineralogy, oil type and structure, and temperature. The brine ionic strength and composition modification are the two major frontlines that have been well-exploited, while further areas of investigation are highlighted to speed up the interpretation and prediction of the process efficiency.

Description: With the high wind penetration in the power system, accurate and reliable probabilistic wind power forecasting has become even more significant for the reliability of the power system. In this paper, an instance-based transfer learning method combined with gradient boosting decision trees (GBDT) is proposed to develop a wind power quantile regression model. Based on the spatial cross-correlation characteristic of wind power generations in different zones, the proposed model utilizes wind power generations in correlated zones as the source problems of instance-based transfer learning. By incorporating the training data of source problems into the training process, the proposed model successfully reduces the prediction error of wind power generation in the target zone. To prevent negative transfer, this paper proposes a method that properly assigns weights to data from different source problems in the training process, whereby the weights of related source problems are increased, while those of unrelated ones are reduced. Case studies are developed based on the dataset from the Global Energy Forecasting Competition 2014 (GEFCom2014). The results confirm that the proposed model successfully improves the prediction accuracy compared to GBDT-based benchmark models, especially when the target problem has a small training set while resourceful source problems are available.

Description: This paper explores the mechanism of convective heat transfer enhancement in a new perspective. In this paper, a new parameter called heat convection velocity is proposed based on the field synergy principle. It is defined as the velocity projection on the temperature gradient vector and reflects the magnitude of the velocity component that contributes to heat convection. Three typical cases are taken into consideration to investigate the influence factors of Nusselt number theoretically. The results indicate that the Nusselt number can be enhanced by increasing the mean heat convection velocity and the dimensionless mean temperature difference. Through theoretical analysis, three suggestions are found for designing heat transfer enhancement components: (a) the overall synergetic effect should be improved; (b) the fluid with lower temperature gradient should be guided to the region where the temperature gradient is higher; (c) temperature distribution should be an interphase distribution of hot and cold fluid. Besides, the heat convection velocity is used to investigate the mechanism of convective heat transfer in the smooth tube. It is found that the increase of Nusselt number is due to the increase of heat convection velocity. In addition, according to design suggestions, a new insert is invented and inserted in the circular tube. With heat convection velocity analysis, it is found that there is much potential of increasing heat convection velocity for enhancing the convective heat transfer in the circular tube.

Description: In this paper, a statistical cloud model was proposed for optimal design of the proportional integral derivative (PID) controllers used in current control of vehicle-to-grid connected inverter systems with PID parameters. By collecting the effective control factors and noise factors from a cloud data base, the cloud model can minimize both the reactive power and the total harmonic distortion for the single-phase full-bridge vehicle-to-grid connected system. The multi-objective optimal solution is obtained by using statistical fuzzy-based response surface methodology with multiple performance characteristics index. The testing results showed the validity of the proposed cloud model. It is verified that the statistical cloud model can increase the performance of the single-phase full-bridge vehicle-to-grid connected system in practical vehicle-to-grid applications in the Internet of Things.