Suspended sediment load prediction using sparrow search algorithm-based support vector machine model.

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash-Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

Carbon nanofibers/liquid metal composites for high temperature laser ultrasound.

As the demand for clean energy becomes greater worldwide, there will also be an increasing demand for next generation nuclear power plants that incorporate advanced sensors and monitoring equipment. A major challenge posed by nuclear power plants is that, during normal operation, the reactor compartment is subjected to high operating temperatures and radiation flux. Diagnostic sensors monitoring such structures are also subject to temperatures reaching hundreds of degrees Celsius, which puts them at risk for heat degradation. In this work, the ability of carbon nanofibers to work in conjunction with a liquid metal as a photoacoustic transmitter was demonstrated at high temperatures. Fields metal, a Bi-In-Sn eutectic, and gallium are compared as acoustic mediums. Fields metal was shown experimentally to have superior performance over gallium and other reference cases. Under stimulation from a low fluence 6 ns pulse laser at 6 mJ/cm2 with 532 nm green light, the Fields metal transducer transmitted a 200 kHz longitudinal wave with amplitude >5.5 times that generated by a gallium transducer at 300 °C. Each high temperature test was conducted from a hot to cold progression, beginning as high as 300 °C, and then cooling down to 100 °C. Each test shows increasing signal amplitude of the liquid metal transducers as temperature decreases. Carbon nanofibers show a strong improvement over previously used candle-soot nanoparticles in both their ability to produce strong acoustic signals and absorb higher laser fluences up to 12 mJ/cm2.

Flood discharge prediction using improved ANFIS model combined with hybrid particle swarm optimisation and slime mould algorithm.

Due to the disastrous socio-economic impacts of flood hazards and estimated rise of its occurrences in the near future, there has been an increase in the importance of flood prediction worldwide. Artificial intelligence (AI) models have contributed significantly by giving cost-effective solutions for simulating physical processes of flood events and improving accuracy in prediction over the last few decades. This paper presents a novel conjoint model to forecast river flood discharge (QFD) considering data from four gauging stations of River Brahmani, Odisha India. The developed hybridised metaheuristic algorithm, i.e. ANFIS-PSOSMA, improves exploration capability of Slime mould algorithm (SMA) by integrating it with particle swarm optimisation (PSO). Performance of novel hybrid model is assessed by utilising quantitative statistical measures like the coefficient of correlation (R2), Nash-Sutcliffe Model Efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE). The proposed hybrid ANFIS model using optimisation algorithm provided the best performance values with NSE of 0.9952, R2 of 0.9946, RMSE of 0.0485, and MAE of 0.0265 during training and NSE of 0.9736, R2 of 0.9731, RMSE of 8.4236, and MAE of 4.3197 during testing at Jenapur gauging station, indicating the prospective of utilising the developed models in forecasting flood discharge. The present study's importance lies in integrating several input parameters, and AI algorithms have been utilised for developing flood prediction model. In addition, the attained results indicated that combining the optimisation algorithms with ANFIS enhanced its performance in modelling monthly flood discharge time series.

Prediction of Flood Discharge Using Hybrid PSO-SVM Algorithm in Barak River Basin.

A crucial necessity in integrated water resource management is flood forecasting. Climate forecasts, specifically flood prediction, comprise multifaceted tasks as they are dependant on several parameters for predicting the dependant variable, which varies from time to time. Calculation of these parameters also changes with geographical location. From the time when Artificial Intelligence was first introduced to the field of hydrological modelling and prediction, it has produced enormous attention in research aspects for additional developments to hydrology. This study investigates the usability of support vector machine (SVM), back propagation neural network (BPNN), and integration of SVM with particle swarm optimization (PSO-SVM) models for flood forecasting. Performance of SVM solely depends on correct assortment of parameters. So, PSO method is employed in selecting SVM parameters. Monthly river flow discharge for a period of 1969 - 2018 of BP ghat and Fulertal gauging sites from Barak River flowing through Barak valley in Assam, India were used. For obtaining optimum results, different input combinations of Precipitation (Pt), temperature (Tt), solar radiation (Sr), humidity (Ht), evapotranspiration loss (El) were assessed. The model results were compared utilizing coefficient of determination (R2) root mean squared error (RMSE), and Nash-Sutcliffe coefficient (NSE). The most important results are highlighted below.•First, the inclusion of five meteorological parameters improved the forecasting accuracy of the hybrid model.•Second, model comparison specifies that hybrid PSO-SVM model executed superior performance with RMSE- 0.04962 and NSE- 0.99334 compared to BPNN and SVM models for monthly flood discharge forecasting.•Third, applied optimization algorithm has easy implementation, simple theory, and high computational efficacy. Results revealed that PSO-SVM could be utilised as an improved alternate method for flood forecasting as it provided a higher degree of reliability and accurateness.

Quenching measurements of Kr 5p[3/2]2 ← ← 4p6 1S0 electronic transition using absorption spectra.

Quenching rate is an important parameter to include in fluorescence measurements that are aimed at quantifying the thermochemical field of a reacting flow. Traditionally, the quenching measurements were obtained at low pressures using the direct measurements of quenching times followed by a linear scaling to the desired pressure. This approach, however, cannot account for the possible deviation from the linear pressure scaling at elevated pressures due to three and multi-body collisions. Furthermore, the best accuracy on the quenching rate is obtained with ultra-short pulse lasers that are typically not readily available. This study offsets these limitations by demonstrating a new approach for making direct quenching measurements at atmospheric conditions and using nanosecond lasers. The quenching measurements are demonstrated in a krypton-perturber system, and the 5p[32]2←←4p 6 1 S 0 two-photon electronic transition is accessed. A theoretical construct is presented that relates the absorption spectral parameters and the integrated fluorescence signal to the quenching rate, referenced to a given species and conditions. Using this formulation, the relative quenching rates for different perturber species, namely, air, C H 4, C 2 H 4, and C O 2, are reported as measured at 1 atm and 300 K. As such, the present technique is limited to the measurement of the relative quenching rate, unlike the previous studies where absolute quenching rates are measured. Nonetheless, when the reference quenching rate is independently measured, the relative quenching rates can be converted to absolute values.

Mixture fraction measurement in turbulent non-premixed MILD jet flame using Rayleigh scattering.

Turbulent combustion of jet flames in a hot diluted coflow of combustion products is conducive to the transition from conventional flamelet combustion to a regime of moderate or intense low oxygen dilution (MILD) combustion, which is commonly characterized by a very low emission and noise. MILD combustion is also characterized by distributed combustion where the net heat release is positive across the entire combustion domain. The turbulence/chemistry interactions in this regime that determine the flame structure, local temperature, and species distribution critically depend on the mixture fraction and scalar dissipation fields. However, there are no experimental tools to measure the mixture fraction field in a distributed (MILD) combustion regime. The present work offsets this limitation by demonstrating a Rayleigh scattering-based approach to measure mixture fraction in a turbulent ethylene MILD combustion zone. 1D counterflow flame simulations enabled mapping the locally calibrated Rayleigh scattering fields to mixture fractions in the fuel-rich regions. This approach also shows very low sensitivity to the local temperature and composition. Overall, the results provide compelling evidence that the distributed heat release does not significantly impact the turbulent processes of the flow-field for the conditions examined. The measurement uncertainty from this approach and its extension to more complex fuels are also discussed. The present technique is limited to mildly turbulent, fully MILD/distributed flame with representative scalar dissipation rates.

Pressure scaling of the collisional broadening parameters of Kr 4p6S01→→5p[3/2]2 transition.

Pressure scaling of collisional broadening parameters of krypton (absorber) 4p6S01→→5p[3/2]2 transition centered at 107.3 nm in the presence of nitrogen N2 (perturber) is investigated. The absorption spectrum in the vicinity of the transition is obtained from the two-photon excitation scan of krypton in the presence of the perturber at different prescribed pressures varying from a few torrs to 10 atm. The absorption spectra reveal noticeable asymmetry at atmospheric pressure, and the asymmetry becomes increasingly pronounced with pressure; however, the absorption spectra at sub-atmospheric pressures tested are symmetric. The absorption spectra are fitted with synthetic asymmetric Voigt profiles across all pressures, wherein the asymmetry parameter is varied to capture the asymmetry at different pressures. The collisional shift (δC), the symmetric equivalent collisional full width at half maximum (wC,0), and the asymmetry parameter (a) are determined from the synthetic fits at various pressures. All the parameters are observed to vary linearly with pressure over the entire range of the pressure values tested. The mechanisms that cause the asymmetry in the absorption spectra are also discussed.

Temperature dependence of collisional broadening and shift for the Kr 4p6S01→5p[3/2]2 electronic transition.

Temperature scaling of collisional broadening parameters for krypton (absorber) 4p6S01→5p[3/2]2 electronic transition centered at 107.3 nm in the presence of major combustion species (perturber) is investigated. The absorption spectrum in the vicinity of the transition is obtained from the fluorescence due to the two-photon excitation scan of krypton. Krypton was added in small amounts to major combustion species such as CH4, CO2, N2, and air, which then heated to elevated temperatures when flowed through a set of heated coils. In a separate experimental campaign, laminar premixed flat flame product mixtures of methane combustion were employed to extend the investigations to higher temperature ranges relevant to combustion. Collisional full width half maximum (FWHM) (wC) and shift (δC) were computed from the absorption spectrum by synthetically fitting Voigt profiles to the excitation scans, and their corresponding temperature scaling was determined by fitting power-law temperature dependencies to the wC and δC data for each perturber species. The temperature exponents of wC and δC for all considered combustion species (perturbers) were -0.73 and -0.6, respectively. Whereas the temperature exponents of wC are closer to the value (-0.7) predicted by the dispersive interaction collision theory, the corresponding exponents of δC are in between the dispersive interaction theory and the kinetic theory of hard-sphere collisions. Comparison with existing literature on broadening parameters of NO, OH, and CO laser-induced fluorescence spectra reveal interesting contributions from non-dispersive interactions on the temperature exponent.

Temperature dependence of collisional broadening and shift for the Kr 4p6${S}_{0}

This publisher's note corrects the author listing in Appl. Opt.59, 1438 (2020)APOPAI0003-693510.1364/AO.380102.