The Design of Crystalline Precursors For the Synthesis of Mn−1AXn Phases and Their Application to Ti3AlC2

A methodology to allow the deliberate design of solid precursors to affect the solid-state synthesis of materials has proven elusive. We have designed a conceptual synthesis route for M _{n +1}AX _n phases that does not involve the usual intermediate phases. Instead, it is proposed that the common structural units within a solid-state precursor M _{n +1}X _n containing vacancy ordering should be the basis for direct synthesis of the desired M _{n +1}AX _n phase. The method is demonstrated to be successful in producing titanium aluminum carbide (Ti₃AlC₂) by the rapid intercalation of Al into TiC_0.67 at 400°–600°C below the conventional synthesis temperature. Time-resolved neutron diffraction at 1 min time-resolution has confirmed the reaction sequence. The vacancy ordering in TiC_0.67 occurred simultaneously to, and appeared to be greatly facilitated by, the ingress of aluminum. There is considerable scope for adaptation of the method to other M _{n +1}AX _n phases.     

Trend Analysis of Maximum Hydrologic Drought Variables Using Mann–Kendall and Şen's Innovative Trend Method

Droughts as destructive climatic extreme events affect natural environment of an area. Identifying droughts has a vital importance in the planning and management of water resource systems of a river basin. This study investigates the trends of maximum hydrologic drought variables, annual maximum duration (AMD), and annual maximum severity (AMS), by using Mann–Kendall, modified Mann–Kendall, and recently developed ?en's innovative trend analysis (ITA) methods. These methods are applied to the AMD and AMS of nine different stations located in Coruh River Basin, Turkey, in which numerous highly critical power plants have recently been constructed, under construction, or planned. Basic Mann–Kendall test indicated no trend in the investigated stations while the modified Mann–Kendall gave significantly decreasing trend for AMS series of station 2304 and for AMD series of station 2321 at 10% significance level. Modified Mann–Kendall and ITA approaches indicate that the AMS of station 2304 show significantly decreasing trend. According to the ITA method, the peak AMS values of the stations 2315 and 2322 have also significantly decreasing trend after 1986. Both modified MK and ITA methods provide increasing trends in drought severities of the stations 2316 and 2323. It should be noted that some droughts or water stress may be occurred in these stations in the future. Modified Mann–Kendall test provides no significant trend for the seven stations while ITA results indicated some positive or negative trends at these stations. The primary points of interest of ?en's ITA method are that it is not subject to any assumption such as serial relationship, non‐normality, sample size, and trends of low, medium, and high data can be seen by this method easily. The study indicates that the ITA method could be simply and successfully used by hydrologist or water resources decision makers or in identifying droughts to prepare efficient management plans. Copyright © 2016 John Wiley & Sons, Ltd.     

Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET₀). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET₀ was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET_0.     

Suspended Sediment Modeling Using Neuro-Fuzzy Embedded Fuzzy c-Means Clustering Technique

The assessment of the suspended sediment (SS) amount in rivers has an importance because it specifically affects the design and operation of numerous hydraulic structures such as dams, bridges, etc. This paper proposes an adaptive neuro-fuzzy embedded fuzzy c-means clustering (ANFIS-FCM) approach for estimating SS concentration. The accuracy of ANFIS-FCM models was compared with classical ANFIS, artificial neural networks (ANNs) and sediment rating curve (SRC). Daily streamflow and SS data from two stations, Muddy Creek near Vaughn and Muddy Creek at Vaughn, operated by the United States Geological Survey were used in the study. Applied models were compared with each other based on root mean square errors and correlation coefficient. Based on comparison, ANFIS-FCM performed superior to the other two models for modeling complex non-linear behavior of the suspended sediment concentration. The ANFIS-FCM model increased the performance (RMSE) of the optimal MLP model by 10 % and 16 % in estimating SSC for the downstream and upstream stations, separately. ANFIS-FCM model provided improvements in performance and parsimonious and took lesser time in calibration than the classical ANFIS model.     

Intermittent Streamflow Forecasting by Using Several Data Driven Techniques

Forecasting intermittent streamflows is an important issue for water quality management, water supplies, hydropower and irrigation systems. This paper compares the accuracy of several data driven techniques, that is, adaptive neuro fuzzy inference system (ANFIS), artificial neural networks (ANNs) and support vector machine (SVM) for forecasting daily intermittent streamflows. The results are also compared with those of the local linear regression (LLR) and the dynamic local linear regression (DLLR). Intermittent streamflow data from two stations, Uzunkopru and Babaeski, in Thrace region located in north-western Turkey are used in the study. The root mean square error and correlation coefficient were used as comparison criteria. The comparison results indicated that the ANFIS, ANN and SVM models performed better than the LLR and DLLR models in forecasting daily intermittent streamflows. The ANN and ANFIS gave the best forecasts for the Uzunkopru and Babaeski stations, respectively.     

Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns

This study develops three neural networks models for estimating daily pan evaporation (PE) in South Korea: multilayer perceptron-neural networks model (MLP-NNM), generalized regression neural networks model (GRNNM), and adaptive neuro-fuzzy inference system (ANFIS). Daily PE was estimated at Daegu and Ulsan stations using temperature-based, radiation-based, sunshine duration-based and merged input combinations under lag-time patterns. Daily evaporation values computed by the models using merged inputs agreed with observed values. Comparison was also made between the neural networks models and multiple linear regression model (MLRM), which showed the superiority of MLP-NNM, GRNNM, and ANFIS over MLRM. It is concluded that the applied neural networks models can be successfully employed for estimating daily PE in South Korea.     

Neutron Diffraction Observations of Ferroelastic Domain Switching and Tetragonal-to-Monoclinic Transformation in Ce-TZP

In-situ neutron diffraction has been used to study the plastic deformation of a tetragonal zirconia polycrystal stabilized with 12 mol% ceria under compressive loads up to 1.6 GPa. The development of significant plastic strain in the ceramic has been found to be due to a combination of ferroelastic switching and the tetragonal-to-monoclinic phase transformation, both beginning at} 1.2 GPa. Evidence of a strong coupling between the two phenomena is present. Both transitions are partially reversed on removal of the load. The linear elastic response of the a and c crystal axes of the parent tetragonal phase suggests that the ferroelastic switching occurs directly by a shear mechanism rather than via a cubic intermediate state. Anisotropic distortion of the tetragonal unit cell, as the critical stress is approached, gives some insight into the shear transformation mechanisms.     

Self-Propagating High-Temperature Synthesis of Ti3SiC2: I, Ultra-High-Speed Neutron Diffraction Study of the Reaction Mechanism

In situ neutron diffraction at 0.9 s time resolution was used to reveal the reaction mechanism during the self-propagating high-temperature synthesis (SHS) of Ti₃SiC₂ from furnace-ignited stoichiometric 3Ti + SiC + C mixtures. The diffraction patterns indicate that the SHS proceeded in five stages: (i) preheating of the reactants, (ii) the α→β phase transformation in Ti, (iii) preignition reactions, (iv) the formation of a single solid intermediate phase in <0.9 s, and (v) the rapid nucleation and growth of the product phase Ti₃SiC₂. No amorphous contribution to the diffraction patterns from a liquid phase was detected and, as such, it is unlikely that a liquid phase plays a major role in this SHS reaction. The intermediate phase is believed to be a solid solution of Si in TiC such that the overall stoichiometry is ∼3Ti:1Si:2C. Lattice parameters and known thermal expansion data were used to estimate the ignition temperature at 923 ± 10°C (supported by the α→β phase transformation in Ti) and the combustion temperature at 2320 ± 50°C.