Semi-empirical Modeling Approach Research Articles

Application of a semi-empirical modelling approach to a Two-Stage Rotary CO2 compressor

2-stage Rotary CO2 compressors are currently in use in refrigeration and heat pump applications, including occasionally including vapour injection. The need of correctly assessing the compressor’s efficiency by means of a computationally fast model arises for cycle performance prediction and optimization. In response, an innovation on an existing semi-empirical modelling approach has been introduced in this paper to describe 2-stage Rotary compressors. The model has been validated with experimental data of a CO2 compressor available in open literature, giving maximum errors of around ±3% for mass flow and ±1.82% for power prediction. Calibration of a simplified 1-stage model is also performed to analyse to which extent it is necessary to complexify the model, and to identify key or neglectable modelling elements. It is found that doing this increases the error of the compressor performance prediction. Model analysis has been done to identify the inefficiencies introduced by each physical effect considered.

Semi-empirical growth model of InSbBi grown by molecular beam epitaxy

The molecular beam epitaxy growth of bump-free indium antimonide-bismide (InSbBi) necessitates precise control of group III and V stoichiometry, as well as the growth temperature and rate. In this research, we propose a semi-empirical growth model approach to enhance our understanding of the involved growth kinetics and predict the bismuth (Bi) content and surface quality of InSbBi films. The model combines the impact of growth rate and the maximum incorporation rate of Bi on the Bi content of InSbBi. Our findings indicate that bump-free InSbBi requires a minimum Bi incorporation rate to Bi adatoms arrival rate (or Bi flux) ratio of 0.96, which closely approaches the ideal value of 1.0. By employing this growth model, we can optimize the growth conditions for achieving high-quality InSbBi films with controlled Bi content.

Mapping the mechanical properties of cobalt-based stellite alloys manufactured via blending

ABSTRACT Stellite alloys have good wear resistance and maintain their strength up to ~ 600°C, making them suitable for various industrial applications like cutting tools and combustion engine parts. This investigation was aimed at i) manufacturing new Stellite alloy blends using powder metallurgy and ii) mathematically mapping hardness, yield strength, ductility and impact energy of base and alloy blends. Linear, exponential, polynomial approximations and dimensional analyses were conducted in this semi-empirical mathematical modelling approach. Base alloy compositions similar to Stellite 1, 4, 6, 12, 20 and 190 were used in this investigation to form new alloys via blends. The microstructure was analysed using Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD). Mechanical performance of alloys was conducted using tensile, hardness and Charpy impact tests. MATLAB® coding was used for the development of property maps. This investigation indicates that hardness and yield strength can be linked to the wt.% composition of carbon and tungsten using linear approximation with a maximum variance of 5% and 20%, respectively. Elongation and carbide fraction showed a non-linear relationship with alloy composition. Impact energy was linked with elongation through polynomial approximation. A dimensional analysis was developed by interlinking carbide fraction, hardness, yield strength, and elongation to impact energy.

The design of a point of care FET biosensor to detect and screen COVID-19

Graphene field effect transistor (FET) biosensors have attracted huge attention in the point-of-care and accurate detection. With the recent spread of the new emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the need for rapid, and accurate detection & screening tools is arising. Employing these easy-to-handle sensors can offer cheap, rapid, and accurate detection. Herein, we propose the design of a reduced graphene oxide (rGO) FET biosensor for the detection of SARS-CoV-2. The main objective of this work is to detect the SARS-CoV-2 spike protein antigen on spot selectively and rapidly. The sensor consists of rGO channel, a pair of golden electrodes, and a gate underneath the channel. The channel is functionalized with COVID-19 spike protein antibodies to achieve selectivity, and with metal nanoparticles (MNPs) such as copper and silver to enhance the bio-sensing performance. The designed sensor successfully detects the SARS-CoV-2 spike protein and shows singular electrical behavior for detection. The semi-empirical modeling approach combined with none-equilibrium Green’s function were used to study the electronic transport properties of the rGO-FET biosensor before and after the addition of the target molecules. The sensor’s selectivity is also tested against other viruses. This study provides a promising guide for future practical fabrication.

Calorimetry characterization and crystallization modelling of wax-based mixtures under isokinetic and non-isokinetic cooling

Characterization and modelling of wax crystallization must be carefully addressed to understand wax-oil organogels formation. During casting process of wax-based mixtures, wax crystallization occurs under concomitant non-constant and high cooling rates. In this study, the crystallization kinetics of two representative wax-based materials were studied by power-compensated Differential Scanning Calorimetry (DSC) with moderate-high cooling rates (from − 20 to − 200 °C min−1) in constant cooling rate (isokinetic) conditions and in simplified non-constant cooling rate (non-isokinetic) conditions. Analyses based on the evolution of the mass fraction of solid wax calculated from heat flow (DSC signal) show similar kinetics trend and enthalpy of crystallization for the different isokinetic conditions, but with a significant influence on the supercooling effects. Considering the limits of the classic Avrami kinetics modelling for high aspect ratio crystals and complex mixtures, a semi-empirical modelling approach of non-isokinetic cooling conditions in a differential form is proposed. The modelling shows a good correlation with experimental results.

Incorporating solution alkalinity into a hydraulic reduction model to account for disaggregation and dispersion

The use of alkaline and sodic water for irrigation has increased in recent years due to constrained freshwater sources and a rapid increase in industrial by-product wastewater from industries such as coal seam gas and effluent management from other industrial sources. The use of alkaline sodic water can significantly impact soil condition, in particular soil physical properties. The threshold electrolyte concentration (CTH) is generally used to assess soil structural stability when it is subjected to a solution of a given sodium adsorption ratio and electrolyte concentration. The current disaggregation model commonly used is mainly based on the sodium and calcium system, without considering the adverse effects of alkaline anions (e.g., HCO3–) reducing saturated hydraulic conductivity (Ks). This study aimed to assess the incorporation of HCO3– into the semi-empirical disaggregation model approach to determine CTH. The percentage reduction in Ks increased with increasing solution alkalinity and this reduction rate was dependent on soil type. The results indicated that there is a correlation between the reduction of Ks produced by non-alkaline and alkaline solutions represented as SAR and adjusted SAR (SARadj) for up to 30% Ks reduction. This association confirms that the effect of SAR and SARadj on CTH are similar. Therefore, HCO3– can be incorporated into the current disaggregation model to determine CTH (≤20% Ks reduction) for alkaline irrigation waters when disposed of on lands.

Review of a Semi-Empirical Modelling Approach for Cohesive Sediment Transport in River Systems

In this paper, a review of a semi-empirical modelling approach for cohesive sediment transport in river systems is presented. The mathematical modelling of cohesive sediment transport is a challenge because of the number of governing parameters controlling the various transport processes involved in cohesive sediment, and hence a semi-empirical approach is a viable option. A semi-empirical model of cohesive sediment called the RIVFLOC model developed by Krishnappan is reviewed and the model parameters that need to be determined using a rotating circular flume are highlighted. The parameters that were determined using a rotating circular flume during the application of the RIVFLOC model to different river systems include the critical shear stress for erosion of the cohesive sediment, critical shear stress for deposition according to the definition of Partheniades, critical shear stress for deposition according to the definition of Krone, the cohesion parameter governing the flocculation of cohesive sediment and a set of empirical parameters that define the density of the floc in terms of the size of the flocs. An examination of the variability of these parameters shows the need for testing site-specific sediments using a rotating circular flume to achieve a reliable prediction of the RIVFLOC model. Application of the model to various river systems has highlighted the need for including the entrapment process in a cohesive sediment transport model.

A novel approach for predicting PEMFC in varying ambient conditions by using a transient search optimization algorithm based on a semi-empirical model

<abstract> <p>Proton exchange membrane fuel cell (PEMFC) is an alternate energy source that produces electricity without any adverse effects on the environment. The drawbacks of PEMFC are its short life and its non-linear voltage with loading current. Also, PEMFC is prone to ambient conditions, and its performance varies with different ambient conditions. In this work, the semi-empirical modeling approach has been used to predict the PEMFC voltage accurately. However, when the ambient condition varies, the voltage of PEMFC varies accordingly and consequently the previous parameters of the EMI-empirical model don't produce good results. Previously the voltage variation due to changes in ambient has been predicted with the help of ambient conditions and load resistance, but this model isn't sui for all PEMFCs. In this work, a new method has been proposed where fast and accurate optimization technique such as Transient search optimization (TSO) has been used to optimize parameters when ambient condition varies and predicts the PEMFC voltage accurately and doesn't consume a lot of time. The proposed method will be very helpful in future research for predicting the PEMFC voltage for various PEMFC systems at different ambient conditions. The proposed method has been validated experimentally by performing experiments on n single-cell PEMFC system at normal and high ambient temperature.</p> </abstract>

An expedient semi-empirical modelling approach for optimal bandgap profiling of stoichiometric absorbers: A case study of thin film amorphous silicon germanium for use in multijunction photovoltaic devices

Bandgap energy profiling is applied in a variety of materials for photovoltaic technologies, such as chalcogenides, III–V materials and perovskites. Bandgap profiling of the absorber layer is used to fight the fundamental loss mechanisms imposed by the bandgap energy of the absorber for the maximum voltage and current that a photovoltaic device can generate. The bandgap profile can be affected by a number of profiling strategies, such as the difference between the maximum and minimum bandgap energy, the position of the minimum bandgap energy, the width over which this minimum bandgap energy occurs and the total absorber width. These parameters have a complex effect on output characteristics of a photovoltaic device. Varying multiple parameters at once further increases the complexity, limiting the effectiveness of rigorous physical opto-electrical modelling. In this work we therefore present an expedient semi-empirical approach for the optimal bandgap profiling of stoichiometric absorbers.Using PECVD processed amorphous silicon germanium as a model, we present a unique set of semi-empirical relations that simulate the VOC and JSC of solar cells as a function of the bandgap energy profile. For this model, the influence of deposition conditions such as the relative germane flow rate, the deposition power and substrate temperature on the opto-electrical properties of a-SiGe:H films is first characterized. Opting for the relative germane flow rate to control the bandgap profiling, the experimental results of a large number of solar cells with profiled a-SiGe:H absorber are presented, varying: 1. the absorber thickness, 2. the peak germane flow rate, so minimum bandgap energy, and 3. the introduction of a plateau at the minimum bandgap energy. Using this experimental data and optical simulations, the expedience and effectiveness of the semi-empirical approach is demonstrated.

Optimal Control of a Spark Ignition Engine Including Cold Start Operations for Consumption/Emissions Compromises

This study presents a semi-empirical modeling approach based on an extensive parametric study using a spark-ignition port-injection engine. The experimental results are used to derive engine-out emission models for each regulated pollutant (CO, HC, NOx) as a function of engine operating parameters. Such parameters include engine speed, intake manifold pressure, equivalence ratio, and spark advance. The proposed models provide accurate predictions over a large range of engine operating conditions. The adequate accuracy and low computational burden of the models are promising in the context of optimal control theory. Dynamic programming is applied in order to find the best operating parameters that define trade-off between fuel consumption and emissions over driving cycles.

Battery lifetime prediction and performance assessment of different modeling approaches

SummaryLithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a challenge. In this work, a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cell is used to develop and/or train different capacity fade models to compare output responses. The assessment is conducted for semi-empirical modeling (SeM) approach against a machine learning model and an artificial neural network model. Among all, the nonlinear autoregressive network (NARXnet) can predict the capacity degradation most precisely minimizing the computational effort as well. This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior.

Modeling of Hydrogen Production in an Alkaline Electrolyser System Connected with a Solar Photovoltaic Panel or a Wind Turbine: Case Study; Douala-Cameroon

This article is in the field of research into the storage of renewable energy production. One of the main obstacles to the rapid development of renewable energies is the storage of the energy produced at low cost and with good efficiency. The production of hydrogen from renewable energies is a promising solution. The present work evaluates the potential of hydrogen production by electrolysis from solar photovoltaic and wind renewable energies in the city of Douala in Cameroon. The methodological approach used is based on the semi-empirical modelling approach of an alkaline electrolyser associated with the solar panel or the wind turbine. The simulation results obtained on the MATLAB/Simulink platform show that the average hydrogen production potential is estimated at 0.55 Nm3/h for a PV panel supply, which corresponds to average energy efficiency of 70%, and at 0.675 Nm3/h for a wind turbine supply, which corresponds to average energy efficiency of 84%. These results show the need to promote this technology, whose efficiency can be improved depending on the choice of site.

Interception of radionuclides by planophile crops: A simple semi-empirical modelling approach in case of nuclear accident fallout

Shortly after an atmospheric release, the interception of radionuclides by crop canopies represents the main uptake pathway leading to food chain contamination. The food chain models currently used in European emergency decision support systems require a large number of input parameters, which inevitably leads to high model complexity. In this study, we have established a new relationship for wet deposited radionuclides to simplify the current modelling approaches. This relationship is based on the hypothesis that the stage of plant development is the key factor governing the interception of radionuclides by crops having horizontally oriented leaves (planophile crops). The interception fraction (f) and the leaf area index normalized (fLAI) and mass normalized (fB) interception fractions were assessed for spinach (Spinacia oleracea) and radish (Raphanus sativus) at different stages of plant development and for different contamination treatments and plant densities. A database of 191 f values for Cs-137 and Th-229 was built and complemented with existing literature covering various radionuclides and crops with similar canopy structure. The overall f increased with the plant growth, while the reverse was observed for fB. The fLAI significantly decreased by doubling the contaminated rainfall deposited. Fitting a multiple linear regression to predict the f value as a function of the standing biomass (B), and the radionuclide form (anion and cation) led to a better estimation of the interception (R2 = 81%) than the ECOSYS-87 model (R2 = 35%). Hence, the simplified modelling approach here proposed seems to be a suitable risk assessment tool as fewer parameters will minimize the model complexity and facilitate the decision-making procedures in case of emergencies, when countermeasures need to be identified and implemented promptly.

Suppression of nucleate boiling in upward two-phase annular flow: A direct measurement using modern diagnostics

In heat exchangers employing flow boiling, e.g., boiling water reactors and industrial boilers, the heat transfer mechanism will transition from nucleate boiling to forced convective evaporation as the flow pattern transition from bubbly to annular flow at relatively high steam quality. Such thermal transition is called suppression of nucleate boiling (SNB). The occurrence of SNB affects the local heat transfer coefficient, the stability of liquid film, as well as the characteristics of entrained liquid droplets in the gas core. Despite its importance, there has been hitherto few direct measurements of the SNB conditions. Furthermore, the existing prediction approaches of SNB are only approximate, since they are based on extrapolation of empirical heat transfer correlations valid for nucleate boiling and forced convective evaporation regimes, rather than SNB mechanism. The objective of this study is to, experimentally and theoretically, fill the gap in understanding SNB phenomenon, using a modern set of diagnostics and a semi-empirical modeling approach. We leveraged synchronized infrared thermometry and an electrical conductance-based liquid film thickness sensor to investigate the details of the SNB phenomenon with high spatial and temporal resolutions. Such advanced diagnostics measure two crucial boundary conditions for SNB, i.e., the distribution of the temperature and heat flux on the heated wall, as well as the thickness of the liquid film. Such direct measurement revealed a clear dependency of SNB heat flux and wall superheat on both steam quality and mass flux. The experimental database has informed a more accurate semi-empirical model for predicting the SNB condition.

Dimensional change and cook loss during heating of fish: Problem formulation and semi-empirical modeling approach

Shrinkage of cod filets and loins during heating is associated with unwanted changes in the product appearance and eating properties, including liquid loss, loss of juiciness and increased hardness. In this paper, shrinkage of minced cod filets exposed to 45–100 °C for up to 1800 s in a convection oven with 100% relative humidity was investigated and related to degree of mass loss. The result was compared to data for cut (non-minced) samples of the same dimension. A kinetic prediction for the mass loss was developed, and it was found that the volume of shrinkage could be approximated by the volume of mass loss. The kinetic prediction was coupled with a model for heat transfer to investigate the effect of shrinkage on the temperature prediction. Further work should include investigation of the effect of the fish macrostructure orientation on shrinkage, as well as optimization of the prediction and validation for large samples such as whole loins.

Snow Property Controls on Modeled Ku-Band Altimeter Estimates of First-Year Sea Ice Thickness: Case Studies From the Canadian and Norwegian Arctic

Uncertainty in snow properties impacts the accuracy of Arctic sea ice thickness estimates from radar altimetry. On first-year sea ice (FYI), spatiotemporal variations in snow properties can cause the Ku-band main radar scattering horizon to appear above the snow/sea ice interface. This can increase the estimated sea ice freeboard by several centimeters, leading to FYI thickness overestimations. This article examines the expected changes in Ku-band main scattering horizon and its impact on FYI thickness estimates, with variations in snow temperature, salinity, and density derived from ten naturally occurring Arctic FYI Cases encompassing saline/nonsaline, warm/cold, simple/complexly layered snow (4–45 cm) overlying FYI (48–170 cm). Using a semi-empirical modeling approach, snow properties from these Cases are used to derive layer-wise brine volume and dielectric constant estimates, to simulate the Ku-band main scattering horizon and delays in radar propagation speed. Differences between modeled and observed FYI thickness are calculated to assess sources of error. Under both cold and warm conditions, saline snow covers are shown to shift the main scattering horizon above from the snow/sea ice interface, causing thickness retrieval errors. Overestimates in FYI thicknesses of up to 65% are found for warm, saline snow overlaying thin sea ice. Our simulations exhibited a distinct shift in the main scattering horizon when the snow layer densities became greater than 440 kg/m3, especially under warmer snow conditions. Our simulations suggest a mean Ku-band propagation delay for snow of 39%, which is higher than 25%, suggested in previous studies.

Performance characterization of small variable-capacity reciprocating compressors using a minimal dataset

This paper presents a semi-empirical modelling approach for predicting the performance characteristics of variable-capacity crankshaft and linear reciprocating compressors for household and light commercial refrigeration applications. The model is built on thermodynamic grounds, and its key parameters are calibrated using experimental data. A total of 33 crankshaft compressors were used for validation purposes, being 26 single-speed and 7 variable-speed, with strokes ranging from 2 to 20 cm³. For conventional single-peed compressors, approximately 97% of the mass flow rate and 93% of the power consumption data presented errors within the 10% thresholds. For variable-speed compressors, more than 95% of the datapoints presented absolute errors lower than 10% for both power consumption and mass flow rate. In addition, the proposed approach was employed to predict the performance of a linear oil-free compressor with variable-displacement capacity control, showing errors for performance characteristics within the range of the experimental uncertainties.

Predicting trends in structural and physical properties of a model polymer with embedded natural fibers: Viability of molecular dynamics studies for a bottom up design

ABSTRACTThe effects of relatively high‐ and low‐shear processing on the macroscopic and microscopic porosities of a standard packaging polymer—linear low density polyethylene (LLDPE) and its composites with pine flour are studied. The macroscopic porosities, represented by deviations of observed density from the theoretical density, primarily drive the trends in mechanical and solvent uptake properties. The microscopic porosities of the system, however, are independent of the processing method and pine flour concentration. Furthermore, by using molecular dynamics (MD), molecules of LLDPE and pine flour were constructed. The material properties of the molecules, specifically microporosity and elastic moduli, were compared to experiment. A semiempirical modeling approach was used to fit the simulated data to the experimental composite moduli. The model of Halpin and Tsai was found to be the most efficacious for this system, and the benefits of the combined MD‐semiempirical modeling for bottom up design are discussed. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 48189.

A semi-empirical modeling approach for predicting the deformational capacity of axially-compressed cylindrical shells based on a novel material stress-strain characterization method

Adoption of cylindrical shell structures for various load-resistance applications has enjoyed wide-spread acceptance in the field of civil, mechanical and aerospace engineering, mainly due to the exceptional structural efficiency of cylindrical shells to withstand significant longitudinal and circumferential in-plane loading without bending. However, where such in-plane loading conditions are compressive, cylindrical shells are likely to exhibit an unstable response characterized by localized out-of-plane deformation. Computerized numerical simulation is often required for accurate and efficient estimation of the strains and resultant stresses in cylindrical shells under loading, especially where the thickness of the shell is sufficient to evoke an inelastic buckling response in the structure. The buckling behavior of thin-walled cylindrical shells subjected to uniform axial compression has been studied in this paper using the finite element (FE) simulation method to assign respective material, geometric, loading and boundary properties to computer-generated cylindrical shell specimens. Extensive parametric analysis, consisting of approximately 720 FE runs, was then conducted based on a full-factorial empirical design, applying ample variations of the relevant parameters that influence the buckling response of axially-compressed cylindrical-shell structures. Nonlinear multiple regression techniques were then employed to derive the coefficients of nonlinear mathematical expressions, each developed as an arithmetic product of appropriate variable functions related to the respective functional sensitivities of the investigated parameters. Strain-hardening properties were incorporated into the mathematical expressions based on the shape constants of the Ndubuaku stress-strain model; which has proven to be remarkably useful for accurate parameterization of the stress-strain behavior over the full range of strains for a wide range of metallic materials, including materials with a well-defined yield plateau. Excellent predictions of FEA-derived values for the critical limit strain limit were obtained, and a simple statistical approach was presented to increase the conservativeness of the semi-empirical model as required.

POLINSAR BASED SCATTERING INFORMATION RETRIEVAL FOR FOREST ABOVEGROUND BIOMASS ESTIMATION

Abstract. Forests play a crucial role in storing carbon and are of paramount importance in maintaining global carbon cycle. Assessment of forest biomass at regional and global level is vital for understanding and monitoring health of both tree species and entire cover. Changes in forest biomass are caused by human activities, natural factors and variations in climate. Forest biomass measurement is necessary for gauging the changes in forest ecosystems. Remote sensing is indispensable for mapping forest biophysical parameters. Microwaves are capable of collecting data even in case of cloud cover as the microwaves are of long wavelength. Microwaves help in retrieving scattering information of target. The goal of this research was to map aboveground biomass (AGB) over Barkot forest range in Dehradun, India. The current work focuses on the retrieval of PolInSAR based scattering information for the estimation of aboveground biomass. Radarsat-2 fully Polarimetric C-band data was used for the estimation of AGB in Barkot forest area. A semi-empirical model, which is Extended Water Cloud Model (EWCM) was utilized for AGB estimation. EWCM considers ground-stem interactions. Due to overestimation of volume scattering, polarization orientation angle shift correction was implemented on the PolInSAR pair. Field biomass data was utilized for accuracy assessment. The results show that coefficient of determination (R2) value of 0.47, Root Mean Square Error (RMSE) of 56.18 (t ha−1) and accuracy of 72% was obtained between modelled biomass against field measured biomass. Hence, it can be inferred from the obtained results that PolInSAR technique, in combination with semi-empirical modelling approach, can be implemented for estimating forest biomass.