Semi-empirical Approach Research Articles

Improvement of Theoretical Algorithm for Prediction of Carbon Nanotubes Catalytical Activity, Based on Quantum Chemical Semi-Empirical Approaches

Improvement of Theoretical Algorithm for Prediction of Carbon Nanotubes Catalytical Activity, Based on Quantum Chemical Semi-Empirical Approaches

Calculated Stark widths of the triply charged argon ion Ar iv

ABSTRACT In this work we studied the Stark widths of Ar iv ion which are important for modelling atmosphere of some particular stars. Using semiclassical perturbation and modified semi-empirical approach, we present in this work Stark widths of triply ionized argon Ar iv spectral lines which will be implemented in the STARK-B data base. Energy levels and oscillator strengths needed as input for Stark width calculations were obtained from the atomic structure data base TOPbase. If we need to interpolate these Stark widths at different temperatures for the studied transitions of Ar iv, we use a simple and accurate fitting formula based on a least-squares method. The calculated values were compared to the existing experimental and calculated Stark width values.

Structure-Dependent Mechanism of Organophosphate Release from Albumin and Butyrylcholinesterase Adducts When Exposed to Fluoride Ion: A Comprehensive In Silico Study.

The most favorable targets for retrospectively determining human exposure to organophosphorus pesticides, insecticides, retardants, and other industrial organophosphates (OPs) are adducts of OPs with blood plasma butyrylcholinesterase (BChE) and human serum albumin (HSA). One of the methods for determining OP exposure is the reactivation of modified BChE using a concentrated solution of KF in an acidic medium. It is known that under the action of fluoride ion, OPs or their fluoroanhydrides can be released not only from BChE adducts but also from the adducts with albumin; however, the contribution of albumin to the total pool of released OPs after plasma treatment with KF has not yet been studied. The efficiency of OP release can be affected by many factors associated with the experimental technique, but first, the structure of the adduct must be taken into account. We report a comparative analysis of the structure and conformation of organophosphorus adducts on HSA and BChE using molecular modeling methods and the mechanism of OP release after fluoride ion exposure. The conformational analysis of the organophosphorus adducts on HSA and BChE was performed, and the interaction of fluoride ions with modified proteins was studied by molecular dynamics simulation. The geometric and energy characteristics of the studied adducts and their complexes with fluoride ion were calculated using molecular mechanics and semiempirical approaches. The structural features of modified HSA and BChE that can affect the efficiency of OP release after fluoride ion exposure were revealed. Using the proposed approach, the expediency of using KF for establishing exposure to different OPs, depending on their structure, can be assessed.

Uniform space-charge-limited current for a two-dimensional planar emitter with nonzero monoenergetic initial velocity

While characterizing space-charge-limited current (SCLC) is important for numerous applications, no analytical solutions for SCLC with monoenergetic initial velocity exist for two-dimensional (2D) geometries. Here, we derive approximate closed-form solutions for uniform SCLC with monoenergetic emission of electrons in a 2D planar diode, where emission is restricted to a long patch of width W for electrodes separated by a distance D. We also derive a semiempirical approach for estimating the SCLC for these cases by treating the geometric and velocity correction factors as multiplicative corrections to the SCLC for a one-dimensional vacuum diode given by the Child–Langmuir (CL) law. We show that the SCLC for a finite patch with nonzero velocity can exceed the CL law by three orders of magnitude. The theoretically calculated SCLCs for various emission widths and initial velocities in the 2D diode agree well with particle-in-cell simulations using the over-injection method in XOOPIC; they agree with the semiempirical relationship for lower initial velocities. In the limit of high initial velocity, the geometry and velocity corrections to the CL law cannot be decoupled, invalidating the assumption of the semiempirical approach and causing it to diverge from the theoretical solution and XOOPIC simulations. These results provide valuable estimates for determining the onset of virtual cathode formation for photocathodes and thermionic cathodes, which operate in the over-injection regime to avoid beam quality degradation.

Semi-empirical evaluation of isospin mixing from forbidden E1 transitions

ΔT = 0 electric dipole transitions in self conjugate nuclei, forbidden by isospin selection rules, are readily seen in nature. This implies that the states participating in these transitions may not be of pure isospin T. This work identifies some of these transitions in a few upper-sd shell self-conjugate nuclei and attempts to calculate the degree of isospin mixing in these nuclei by using the standard two-level mixing formalism in a semi-empirical approach. The physically observed E1 transitions are broken down into two theoretically allowed ΔT = 1 channels. These allowed components have been calculated within the shell model. Our results are consistent with both theoretical and experimental values found in the literature.

Semi-empirical Haken–Strobl model for molecular spin qubits

Understanding the physical processes that determine the relaxation T 1 and dephasing T 2 times of molecular spin qubits is critical for envisioned applications in quantum metrology and information processing. Recent spin-echo measurements of solid-state molecular spin qubits have stimulated the development of quantum mechanical models for predicting intrinsic qubit timescales using first-principles electronic structure methods. We develop an alternative semi-empirical approach to construct Redfield quantum master equations for molecular spin qubits using a stochastic Haken–Strobl theory for a central spin with fluctuating gyromagnetic tensor due to spin-lattice interaction and fluctuating local magnetic field due to interactions with lattice spins. Using two vanadium-based spin qubits as case studies, we compute qubit population and decoherence times as a function of temperature and magnetic field, using a bath spectral density parametrized with a small number of T 1 measurements. The theory quantitatively agrees with experimental data over a range of conditions beyond those used to parameterize the model, demonstrating the generalization potential of the method. The ability of the model to describe the temperature dependence of the ratio is discussed and possible applications for designing novel molecule-based quantum magnetometers are suggested.

Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

AbstractOver half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.

Application of a Semi-Empirical Approach to Map Maximum Urban Heat Island Intensity in Singapore

Differences in land surface characteristics across a city produce great spatial and temporal variability in air temperature. This fact is particularly pronounced between urban and surrounding rural areas giving rise to the canopy-layer urban heat island (CL-UHI) phenomenon. In the present study, we apply the dimensional analysis technique to develop a simple semi-empirical equation to map daily maximum CL-UHI (UHImax) intensities during nighttime over the city of Singapore for specific weather conditions. By adopting the methodology proposed by Theeuwes et al., but selecting meteorological and morphological parameters that affect UHImax intensity most for Singapore, evaluation of the developed equation shows good agreement with observations (RMSE = 1.13 K and IOA = 0.76). Model performance depends strongly on wind conditions and is best during weak winds when ‘ideal’ conditions for UHI development are approached (RMSE = 0.65 K and IOA = 0.85). Results using the simple equation developed to map UHImax intensities in Singapore under dry weather conditions are comparable to those obtained from more sophisticated numerical models, which demand significant computational resources, and the complex parameterizations involved require expertise to carry out the simulations. The resulting maps of the present study can be used to investigate less favorable thermal conditions and assess population vulnerability to a certain temperature excess, as well as provide insights for urban planning strategies of mitigation measures according to the land cover and morphology of a location.

Machine learning prediction of wave characteristics: Comparison between semi-empirical approaches and DT model

Prediction of wave characteristics plays a crucial role in design and performance assessment of various coastal projects. The computational complexity and time-consuming procedures have limited the applications of numerical models for wave predictions. This study develops a new model for wave prediction using the capabilities of the M5p Decision Tree (DT) algorithm. The wind speed was employed as the model input parameter, and satellite measured altimeter data was used to train and test the developed models. The proposed DT-based model is compared with the existing semi-empirical wave prediction methods recommended by Coastal Engineering Manual (CEM). For a comprehensive assessment of the model's performance, four scenarios with different input data and modelling approaches are investigated. It was shown that the locally calibrated CEM formula can provide the best performance amongst the modified semi-empirical formulations. Comparison results show that M5p DT models' prediction are far more accurate and closely match the satellite measured altimeter data than the semi-empirical models. Furthermore, the ‘Short-term’ M5p tree model is shown to have the best predictive results, and the obtained results highlight that the proposed M5p model can provide a robust alternative for wave prediction across large spatial and temporal scales.

An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters

Abstract. Satellite remote sensing of PM2.5 (fine particulate matter) mass concentration has become one of the most popular atmospheric research aspects, resulting in the development of different models. Among them, the semi-empirical physical approach constructs the transformation relationship between the aerosol optical depth (AOD) and PM2.5 based on the optical properties of particles, which has strong physical significance. Also, it performs the PM2.5 retrieval independently of the ground stations. However, due to the complex physical relationship, the physical parameters in the semi-empirical approach are difficult to calculate accurately, resulting in relatively limited accuracy. To achieve the optimization effect, this study proposes a method of embedding machine learning into a semi-physical empirical model (RF-PMRS). Specifically, based on the theory of the physical PM2.5 remote sensing (PMRS) approach, the complex parameter (VEf, a columnar volume-to-extinction ratio of fine particles) is simulated by the random forest (RF) model. Also, a fine-mode fraction product with higher quality is applied to make up for the insufficient coverage of satellite products. Experiments in North China (35∘–45∘N, 110∘–120∘E) show that the surface PM2.5 concentration derived by RF-PMRS has an average annual value of 57.92 µg m−3 vs. the ground value of 60.23 µg m−3. Compared with the original method, RMSE decreases by 39.95 µg m−3, and the relative deviation is reduced by 44.87 %. Moreover, validation at two Aerosol Robotic Network (AERONET) sites presents a time series change closer to the true values, with an R of about 0.80. This study is also a preliminary attempt to combine model-driven and data-driven models, laying the foundation for further atmospheric research on optimization methods.

Investigation of the effect of nose shape and geometry at supersonic speeds for missile performance optimisation

This paper investigated and analysed the influence of the missile nose shapes and geometry on the drag force coefficients at supersonic Mach numbers ranging from 2 to 5. The nose shape investigation includes a pointed cone, pointed ogive, and blunt cone with a constant length and diameter of cylindrical afterbody. Subsequently, the nose length was manipulated to investigate how the nose geometry with different fineness ratios affected each drag component. The drag force coefficient and its components were determined using a program called MATLAB_Aerody, in which a semi-empirical approach was implemented for the calculations. The program was validated against the semi-empirical results with existing theoretical and experimental results. The comparison showed a maximum error of 3.6%. The results of the nose shape analysis show that a sharper and more slender nose with a pointy tip produces a minimal drag. Nose bluntness, however, creates considerable additional drag force due to a higher wave drag coefficient by forming detached or bow shock waves. At supersonic speeds, increasing the fineness ratio significantly reduces the drag force by drastically decreasing the wave drag coefficient with a marginal increase in the skin friction coefficients. However, the results show an insignificant additional gain for the skin friction drag and a reduction in the wave drag coefficient when the nose has a fineness ratio beyond 4.5. Furthermore, the results from both analyses show that the base drag coefficient is invariant with the nose shape and the fineness ratio. Therefore, a better missile performance can be achieved with a sharper and more slender nose shape with a higher fineness ratio.

Proposing a one-way shear design model for FRP-RC members: Evaluation and reliability calibration

Existing shear design models for reinforced concrete are based on semi-empirical approaches and were calibrated with different experimental databases. The existing FRP-RC shear design models vary significantly in accuracy and conservatism. In particular, various studies indicated that the ACI 440-15 model is less accurate and highly conservative compared to other design models. The recently published ACI 440.11-22 adopts a similar procedure to ACI 440.1R-15 but incorporates the size effect and minimum shear strength. This study presents a detailed evaluation of different design codes based on a worldwide surveyed database spanning 344 beams. A modified shear design model consistent with the current ACI 318-19 model is proposed. This newly proposed model, which accounts for FRP axial stiffness, has demonstrated higher accuracy and hence was calibrated to achieve a reliability index of 4.0 to balance safety and conservatism. The calibrated proposed model for concrete resistance is)Vc = 0.4 (nc ρfl)1/3 λs √f’c bw d(, where nc is the elastic modular ratio of longitudinal FRP bars to concrete. Finally, the applicability of generalizing and extending the proposed model to steel-RC concrete beams has been tested on a published evaluation shear database. The proposed model outperformed the ACI 318-19 model in different statistical measures.

A semi-empirical approach to calibrate simulation models for semiconductor devices

Semiconductor device optimization using computer-based prototyping techniques like simulation or machine learning digital twins can be time and resource efficient compared to the conventional strategy of iterating over device design variations by fabricating the actual device. Ideally, simulation models require perfect calibration of material parameters for the model to represent a particular semiconductor device. This calibration process itself can require characterization information of the device and its precursors and extensive expert knowledge of non characterizable parameters and their tuning. We propose a hybrid method to calibrate multiple simulation models for a device using minimal characterization data and machine learning-based prediction models. A photovoltaic device is chosen as the example for this technique where optical and electrical simulation models of an industrially manufactured silicon solar cell are calibrated and the simulated device performance is compared with the measurement data from the physical device.

An improved approach to model compressors, condenser/gas cooler and their control system in refrigeration systems with R404A and R744 based on physical principles

This study introduces an improved methodology to evaluate with accuracy the performance of a conventional system with R404A and a Booster system with R744. Reciprocating and scroll compressors are modeled with improved semi-empirical approaches, and their operation is ideally controlled to maintain the required refrigerant mass flow rate with an On/Off and variable speed control system. The R744 gas cooler is modeled with a four-zones model and the R404A condenser with a three-zones model. The R744 condenser is modeled with the superheated zone discretized into eight zones to avoid unrealistic results due to the variation of its thermophysical properties and the two-phase and subcooled zones with one zone respectively. In line with the design, the Booster condenser enters into the pseudo-critical zone at an ambient temperature of 23 °C, while the gas cooler is activated after 31 °C. The compressor models present errors lower than 10%, while the Booster model presents mean deviations lower than 2.3%. The Booster system presents better COPs than the conventional at ambient temperatures lower than 15ºC. At higher ambient temperatures, the COPs of both systems are similar: the conventional system overperforms the Booster up to 6%, being in disagree with the literature results where these differences increase up to 30%. The proposed methodology allows to reduce the overall model error by 3% with respect to a thermodynamic cycle-based model; allowing to improve the comparison between the Booster system with R744 and the conventional one with R404A, which leads to more transparent results than the reported common comparisons.

Non-parametric characterization of blast loads

Mathematical analysis of blast pressures has typically involved the empirical fitting of parametric models, which assumes a specific function shape. In reality, the true shape of the blast pressure is unknown and may lack a parametric form, particularly in the negative phase following arrival of the secondary shock. In this work, we develop a non-parametric (NP) representation that makes few assumptions and relies on the observed experimental data to fit a unique and previously unknown model. This differs from traditional approaches by not arbitrarily selecting a single, restrictive class of functions and estimating a minimal set of parameters, but rather estimating the underlying function class for which the blast pressure is generated; learning the model directly from the observed data. The method was applied to experimental blast measurements and the NP estimates matched the experimental data with a high degree of accuracy, both qualitatively and quantitatively. The NP approach was shown to significantly outperform other commonly used approaches, near-perfectly track the entire pressure and specific impulse history and predicting experimental peak specific impulse to within ±0.5% in all cases (compared to ±5.0% for a trained artificial neural network (ANN) and ±7.5% for the UFC semi-empirical approach). The NP approach predicts experimental net specific impulses (positive and negative phases combined) with a maximum variation of 2.7%, compared to maximum variations of −116% and 55% for the UFC and ANN approaches, respectively. Since the framework is probabilistic in nature, it can naturally account for random noise in sensor measurements, which are typically more pronounced in blast experiments than many other machine learning applications.

Blast response of laminated glass: A gas-gun experimental investigation of the fracture pattern and yield lines observed in full-scale blast tests

The applications of architectural and automotive laminated glass continue to grow. This is largely due to the enhanced safety in extreme events, such as blast. However, the complex interaction between the glass fragments and the polymer interlayer after glass fracture is still only partially understood, with existing analysis methods adopting a semi-empirical approach for the post-fracture response. These include a plastic yield-line analysis based on a failure pattern repeatedly observed during blast tests. In recent research it was demonstrated that yield lines can develop in fractured laminated glass through the composite bending action of the glass fragments, working in compression, together with the interlayer working in tension. This paper investigates the influence of the inertia loading associated with blast response, which has not been explicitly studied previously, with the aim of understanding the pattern of yield lines observed in blast tests. Impact tests are performed on laminated glass specimens using foam projectiles launched from a gas gun, which simulate the loading from a blast pulse. These tests demonstrate that the yield line pattern formed under short-duration dynamic loading depends on the loading intensity, thereby providing further evidence of a dynamic, plastic collapse mechanism in which inertia plays a significant role.

Experimentally-verified thermal-electrochemical simulations of a cylindrical battery using physics-based, simplified and generalised lumped models

The parameterisation for fully physics-based, simplified, and semi-empirical electrochemical-thermal coupled models for prediction of the core temperature of a cylindrical 21,700 cell are addressed in this paper for the first time. When compared to fully physics-based models, the semi-empirical approaches require fewer input parameters overall, simplifying the necessary experimental values and reducing the burden on the battery management system. There are roughly 40 and 30 parameters needed for a Doyle-Fuller-Newman (DFN) and a single particle, respectively. The generalised lumped electrochemical-thermal model, in contrast, requires ten input and three fitting parameters. The present work, aimed at thermal management challenges, couples these electrochemical models with a 2D-axisymmetric heat transfer model to compare qualities such as computational time, accuracy, generalisation, high fidelity, and experimental parameterisations required. The core temperature and voltage predictions for a 21,700 NMC/Si-Graphite cell has been made using coupled thermal-electrochemical models that are accurate (voltage and core temperature standard deviations: 38-130 mV and 0.24–0.79 K) and have a quick response (computation time: 18–262 s).The electrochemical and thermal parameterisation for the models were conducted using the experimental measurements at 1C, 0.3, and 0.7C-rates. Measurements are the core temperature made with a fiber optic sensing system and voltage curves using a battery cycler. For the single particle and P2D models, electrode diffusivities (both negative and positive) are adjusted to calibrate the models. For the single particle model, the results revealed that the core temperature and voltage standard deviations were 0.6 K and 0.13 V, respectively. Depending on the number of mesh, computational times were between 16 and 262 s. For the P2D model, the core temperature and cell voltage standard deviations were found to be 0.038 V and 0.7 K, respectively. Each simulation takes between 27 and 52 s. This is an increase of 189 % over the time required to compute a previously developed lumped model, for which the voltage and core temperature had 0.12 V and 0.8 K standard deviations. P2D simulations in the literature take between 21 and 43 times longer than simulations in the current P2D model. For the generalised lumped model, only the 1C-rate discharge results are used in the calibration, and a generalisation test has been conducted by using a full discharge-charge curve for the first time. The results showed that the change in standard deviations of core temperature (within 21 %) and voltage (within 31 %) are acceptable when a discharge-calibrated model used for full-cycle validations.

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.

Quantifying Volume Change in Porous Electrodes via the Multi-Species, Multi-Reaction Model

Automotive manufacturers are working to improve individual cell and overall pack design by increasing their performance, durability, and range, while reducing cost; and active material volume change is one of the more complex aspects that needs to be considered during this process. As the time from initial design to manufacture of electric vehicles is decreased, design work that used to rely solely on testing needs to be supplemented or replaced by virtual methods. As electrochemical engineers drive battery and system design using model-based methods, the need for coupled electrochemical/mechanical models that take into account the active material change utilizing physics based or semi-empirical approaches is necessary. In this study, we illustrated the applicability of a mechano-electrochemical coupled modeling method considering the multi-species, multi-reaction model as popularized by Verbrugge and Baker. To do this, validation tests were conducted using a computer-controlled press apparatus that can control the press displacement and press force with precision. The coupled MSMR volume change model was developed and its applicability to graphite and NMC cells was illustrated. The increased accuracy of the model considering the coupled MSMR volume change approach shows in the importance of accounting for individual gallery volume change behavior on cell level predictions.

Multiyear variations of time-correlated mesoscale OH temperature perturbations near the mesopause at Maymaga, Tory and Zvenigorod

Ground-based observations of the natural hydroxyl (OH) nightglow at altitudes of 85–90 km are used for deriving the rotational temperature of excited OH, which is close to the neutral atmospheric temperature. For filtering of mesoscale perturbations, we use differences between pairs of measured values of OH rotational temperature separated with fixed time intervals in the range of 0.5–2 h. The filtering is applied for studying mesoscale variations of temperature near the mesopause according to the data of spectral OH nightglow measurements at observatories of Zvenigorod (56°N, 37°E.) in the years 2004–2016, Tory (52°N, 103°E) in 2012–2017 and Maymaga (63°N, 130°E) in 2000–2015. Monthly-mean values and variances of temperature disturbances with periods 0.7–8.2 h are determined. Semiempirical and statistical approaches are used to estimate and subtract variances of the instrumental dark current noise and uncorrelated in time fluctuations. Seasonal and interannual variations in standard deviations of correlated in time mesoscale perturbations of the OH rotational temperature at the considered observational sites are studied. They can give information about multiyear changes in the activity of atmospheric acoustic-gravity waves propagating through the OH layer near the mesopause.