Simple Efficiency Model Research Articles

Capability assessment of conventional and data-driven models for prediction of suspended sediment load.

Information about suspended sediment concentration (SSC) in the stream is vital for sustainability of water conservation and erosion control planning, designing and monitoring. In this research, prediction of SSC has been done using artificial neural network (ANN), support vector regression (SVR) and multi-linear regression (MLR) models. Performance evaluation of developed models has been carried out on the basis of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (CE) and pooled average relative error (PARE). Cross-correlation function (CCF) validated that gamma test (GT) is an appropriate tool for the selection of most responsive input variables. On the basis of GT and CCF, GT-6 model was selected as the model with most effective input variables, with the values of gamma, standard error and V-ratio as 0.0643, 0.00583 and 0.2570, respectively. The ANN (6-3-1) model performed better than the other single and double hidden layered ANN models with the values of r, RMSE, CE and PARE as 0.939, 0.0063g/l, 85.17 and 0.0160, respectively. The performance of the SVR model was found better with the values of r, RMSE, CE and PARE as 0.906, 0.018g/l, 79.09 and 0001, respectively, but slightly poor than the selected ANN (6-3-1) model. The values of r, RMSE, CE and PARE were found as 0.899, 0.0312g/l, 65.15 and - 0.0031, respectively, in the case of MLR model. The present study revealed that among the ANN, SVR and MLR models, the ANN model with a single hidden layer is most suitable for observed SSC. The present study offers the simple efficient model to estimate the suspended sediment concentration in the stream with minimum error using limited data set.

Field-programmable gate arrays-based Morris-Lecar implementation using multiplierless digital approach and new divider-exponential modules

Simulation, modeling, and hardware implementation of different parts of central nervous system (CNS) are very attractive research areas in terms of neuromorphic engineering aspects. Considering the neuronal parts of the human brain, the hardware implementation of neural models of spiking neural networks should be evaluated from preparing a simple efficient model and optimal hardware implementation. In this paper, Morris-Lecar (ML) model as a biological neuronal model with nonlinear differential equations and hyperbolic functions is discussed. This original model with non-linear complex functions needs high-computational high-cost hardware implementation on Field-Programmable Gate Arrays (FPGA). This paper presents a new divider module and exponential term (2X) to provide low-cost and high-speed implementation of original ML called Division-Based ML (DBML). DBML model suggests simpler equations using division-based exponential terms, logical shifts and simple arithmetic operations. Thus, high-performance neuronal design is presented following original ML model in terms of time domain and different dynamic behaviors.

General-Purpose and Scalable Internal-Combustion Engine Model for Energy-Efficiency Studies

Hybrid powertrains that combine electric machines and internal-combustion engines offer substantial opportunities to increase the energy efficiency and minimize the exhaust emissions of vehicles and nonroad working machines. Due to the wide range of applications of such powertrains, simulation tools are used to evaluate and compare the energy efficiency of hybrid powertrains for application-specific working cycles in virtual environments. Therefore, the accurate modeling of the powertrain components of a hybrid system is important. This paper presents an agile calculation tool that can generate realistic fuel consumption data of a scalable diesel engine. This method utilizes a simple efficiency model of the combustion and crank train friction model to generate the fuel consumption map in the operating area of a typical diesel engine. The model parameters are calibrated to produce accurate fuel consumption data in the initial phase of system-level simulations. The proposed method is also validated by using three real engine datasets, and the comparison of results is presented.

Dual Laser Beam Processing of Semiconducting Thin Films by Excited State Absorption.

We present a unique dual laser beam processing approach based on excited state absorption by structuring 200 nm thin zinc oxide films sputtered on fused silica substrates. The combination of two pulsed nanosecond-laser beams with different photon energies—one below and one above the zinc oxide band gap energy—allows for a precise, efficient, and homogeneous ablation of the films without substrate damage. Based on structuring experiments in dependence on laser wavelength, pulse fluence, and pulse delay of both laser beams, a detailed concept of energy transfer and excitation processes during irradiation was developed. It provides a comprehensive understanding of the thermal and electronic processes during ablation. To quantify the efficiency improvements of the dual-beam process compared to single-beam ablation, a simple efficiency model was developed.

Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements

This paper tested the energy outputs of different types of PV modules and evaluated the accuracies of different simplistic PV module power prediction models. A test rig was developed in Hong Kong to assess the PV power outputs of ten PV modules. The solar radiation, ambient temperature and power generation are recorded. The evaluated models include simple efficiency model, temperature correction model and one-diode model. The results show that the mono-Si PV module is the highest in terms of the annual energy outputs per unit area, and a-Si PV modules is the lowest. The simple efficiency model overestimates the power output of all types of PV modules for more than 10% except for a CdTe PV module. The one-diode model demonstrated the highest accuracy for mono-Si and poly-Si PV modules. The accuracies of the evaluated models are low for thin-film PV technologies. Although the mean bias error of the one-diode model is larger than 10% for the thin-film PV modules (expect for one CdTe PV module), the one-diode model has the highest accuracy among the three models. Further studies should be conducted to investigate the energy performance of thin-film PV modules and then improve their prediction accuracy.

Charge separation in organic solar cells: Effects of Coulomb interaction, recombination and hole propagation

Bulk heterojunction (BHJ) organic photovoltaic cells are analysed within a simple efficient model that includes the important physical properties of such photovoltaic systems. In this model, in contrast with most of the previous studies, we take into account the motion of both the electron and the hole in the separation process at the donor-acceptor interface. We theoretically examine the exciton dissociation yield under the influences of charge Coulomb interaction and non-radiative recombination. We find that the electron-hole local Coulomb attraction and charge carriers' coupling parameters play an important role in the system performance and in the optimal energy conversion efficiency of the BHJ photocell. We show that the fixed-hole models tend to underestimate the yield.

Towards Achieving the Full Clinical Potential of Proton Therapy by Inclusion of LET and RBE Models.

Despite increasing use of proton therapy (PBT), several systematic literature reviews show limited gains in clinical outcomes, with publications mostly devoted to recent technical developments. The lack of randomised control studies has also hampered progress in the acceptance of PBT by many oncologists and policy makers. There remain two important uncertainties associated with PBT, namely: (1) accuracy and reproducibility of Bragg peak position (BPP); and (2) imprecise knowledge of the relative biological effect (RBE) for different tissues and tumours, and at different doses. Incorrect BPP will change dose, linear energy transfer (LET) and RBE, with risks of reduced tumour control and enhanced toxicity. These interrelationships are discussed qualitatively with respect to the ICRU target volume definitions. The internationally accepted proton RBE of 1.1 was based on assays and dose ranges unlikely to reveal the complete range of RBE in the human body. RBE values are not known for human (or animal) brain, spine, kidney, liver, intestine, etc. A simple efficiency model for estimating proton RBE values is described, based on data of Belli et al. and other authors, which allows linear increases in α and β with LET, with a gradient estimated using a saturation model from the low LET α and β radiosensitivity parameter input values, and decreasing RBE with increasing dose. To improve outcomes, 3-D dose-LET-RBE and bio-effectiveness maps are required. Validation experiments are indicated in relevant tissues. Randomised clinical studies that test the invariant 1.1 RBE allocation against higher values in late reacting tissues, and lower tumour RBE values in the case of radiosensitive tumours, are also indicated.

Efficiency dynamics on two coupled small-world networks

We investigate the effect of clusters in complex networks on efficiency dynamics by studying a simple efficiency model in two coupled small-world networks. It is shown that the critical network randomness corresponding to transition from a stagnant phase to a growing one decreases to zero as the connection strength of clusters increases. It is also shown for fixed randomness that the state of clusters transits from a stagnant phase to a growing one as the connection strength of clusters increases. This work can be useful for understanding the critical transition appearing in many dynamic processes on the cluster networks.

EFFICIENCY DYNAMICS ON SCALE-FREE NETWORKS WITH COMMUNITIES

The simple efficiency model is developed on scale-free networks with communities to study the effect of the communities in complex networks on efficiency dynamics. For some parameters, we found that the state of system will transit from a stagnant phase to a growing phase as the strength of community decreases.

Simple model of double adiabatic inversion (DAI) efficiency.

Multislice continuous arterial spin labeling (CASL) using alternating single adiabatic inversion (SAI) and double adiabatic inversion (DAI) (ASD) suffers from control inefficiencies that reduce the perfusion signal-to-noise ratio (SNR). A simple analytic model for DAI efficiency (chi) is presented, and compared with simulation and experimental data. The DAI efficiency (defined as the ratio of uninverted to equilibrium spins) depends primarily on the effective adiabaticity (beta') and the inversion time (both determined from the labeling RF field and gradient amplitudes, and flow velocities). In this study, chi behaved like a damped oscillator, starting from 1 at beta'=0 (no spin inversion), dropping to 0.5 at beta'=0.5 (spin saturation), rising again to a peak at beta' approximately 3, and subsequently oscillating at higher beta'. The simple model matched the flow phantom data for beta' < 2, with an accuracy of better than 10%, but it diverged from the flow phantom data as the data oscillated at higher beta'. The relative inversion efficiencies of ASD and simultaneous proximal and distal irradiation (SPDI) were measured in 28 elderly volunteers. The ratio of CASL difference (control-label) signals measured in gray matter (= 1.16 +/- 0.05), and the simple efficiency models (= 1.13 +/- 0.02) had a correlation coefficient of 0.974.

The use of rotating-impeller gas injection in aluminum processing

The three major characteristics of liquid aluminum quality are hydrogen content, alkaline content, and inclusion content. The past 20 years have seen considerable development in rotating gas injectors processes, at different steps of liquid metal processing in the casthouses, designed to address these three quality factors. Simultaneously, a great deal of academic work has been done to understand and model these processes, using knowledge from the fields of chemical engineering, thermodynamics, and fluid dynamics. This article shows how very simple efficiency models can be used to represent the processes, combining first-order kinetics for batch treatment and residence time distribution for in-line systems, leading to realistic efficiency laws. These laws are valid for all quality factors under the same form and involve very few parameters.