Rapid prediction of thrust force coupling scale-span model and revised ANN in drilling CFRPs

This study develops an integrated methodology to rapidly predict the thrust force with a tapered drill-reamer (TDR) by coupling a scale-span model and revised artificial neural networks (ANN) in drilling carbon fiber reinforced polymers (CFRPs). First, the optimum mesh size of the scale-span finite element (FE) model of CFRPs was optimized to enhance simulation efficiency on the premise of ensuring accuracy in drilling. Then, an order-driven FE computation approach was first proposed to improve computing efficiency for batch samples and maximize utilization of the available computing resources. Modeling and solving of the weight indices of material property parameters (MPPs) and machining parameters for the thrust force were first carried out entirely based on a feature selection model. A multi-layer revised ANN architecture model which considers the material properties of CFRPs and the corresponding initial weight indices was first designed for the thrust force prediction in Python software. Finally, drilling experiments involving T700S-12K/YP-H26 CFRPs specimens with different machining parameters were carried out, which more than 25 prediction results of the fresh samples showed that the established ANN prediction model with a 16-18-18-18-16-1 architecture is highly prediction precision, and the maximum absolute deviation is only 4.56% with the comparisons of experiments.

Improvement of a Nusselt-Based Simulation Model for Heat Transfer in Rotary Heat Exchangers

In the last 50 years, the technology of rotary heat exchangers has not changed considerably. A reliable simulation can help improve the design of this technology. In this work, a simulation for rotary heat exchangers was developed and validated with multiple experimental data. This simulation takes an innovative approach based on locally calculated heat transfer coefficients and considers the entry region effect. This approach proved to be accurate since the average difference between the experimental results and the proposed model with a constant heat boundary condition is 0.1% and the maximum absolute deviation 1%. Experimental, as well as simulation results, indicate that lower empty tube gas velocity (1 m/s) and higher rotational speed (12 rpm) improve thermal efficiency compared to commonly used operating conditions. Additionally, a new model for predicting the local internal Nusselt number for sine ducts in the rotor channels is proposed, which considers the entry region effect.

Using Parameter Elimination to Solve Discrete Linear Chebyshev Approximation Problems

We consider discrete linear Chebyshev approximation problems in which the unknown parameters of linear function are fitted by minimizing the least maximum absolute deviation of errors. Such problems find application in the solution of overdetermined systems of linear equations that appear in many practical contexts. The least maximum absolute deviation estimator is used in regression analysis in statistics when the distribution of errors has bounded support. To derive a direct solution of the problem, we propose an algebraic approach based on a parameter elimination technique. As a key component of the approach, an elimination lemma is proved to handle the problem by reducing it to a problem with one parameter eliminated, together with a box constraint imposed on this parameter. We demonstrate the application of the lemma to the direct solution of linear regression problems with one and two parameters. We develop a procedure to solve multidimensional approximation (multiple linear regression) problems in a finite number of steps. The procedure follows a method that comprises two phases: backward elimination and forward substitution of parameters. We describe the main components of the procedure and estimate its computational complexity. We implement symbolic computations in MATLAB to obtain exact solutions for two numerical examples.

P694 Validation of two commercial assays for therapeutic drug monitoring of adalimumab biosimilars

Background Therapeutic drug monitoring of adalimumab (ADM) is increasingly used to optimise the management of patients with inflammatory bowel disease. The recent settlements with Abbvie concerning Humira®, paved the way for biosimilar drugs to enter the European market. In this study, we aimed to validate two commercial assays, the RIDASCREEN® ADM Monitoring (ELISA; also known as the apDia Adalimumab ELISA) and the RIDA®QUICK ADM Monitoring (rapid assay), which were developed and validated using the originator drug, for the quantification of two ADM biosimilars, AMGEVITA® and Imraldi®. Methods To validate the RIDASCREEN® ADM Monitoring, accuracy and recovery were determined by diluting AMGEVITA® and Imraldi® to varying concentrations within the clinical measuring range and in comparison with Humira®. The specification of accuracy is met when the deviation of the measured ADM biosimilar value is within ±15% of the theoretical value. For the recovery, the deviation of the measured ADM biosimilar value has to be within ± 15% of the Humira® value. To validate the RIDA®QUICK ADM Monitoring for the quantification of AMGEVITA® and Imraldi®, the recovery and linearity was determined. The recovery was determined by spiking three samples containing a low concentration of ADM biosimilar with varying concentrations of ADM biosimilar. The rapid assay complies with the requirements of recovery, if the observed value of ADM biosimilar is within ± 20% of the expected value of ADM biosimilar. The linearity was performed on the basis of NCCLS-guideline EP6-A; a sample with high concentration of ADM biosimilar was diluted 1:1 to 1:38.4. All samples were measured in the ELISA and rapid assay following manufacturer’s instructions. Results In the RIDASCREEN® ADM Monitoring, the mean deviation of the measured AMGEVITA® and Imraldi® value vs. the theoretical value was −6.6% and 2.1%, respectively. Recovery of spiked AMGEVITA® and Imraldi® samples in serum revealed a maximum absolute deviation of 12.9% and 14.8% vs. Humira®. In the RIDA®QUICK ADM Monitoring, the mean recovery of three serum samples spiked with varying concentration of AMGEVITA® and Imraldi® ranged from 91% to 115%, and 95% to 101%, respectively. Linearity was shown for both AMGEVITA® and Imraldi®. Conclusion We successfully validated the biosimilars AMGEVITA® and Imraldi® in the RIDASCREEN® ADM Monitoring and RIDA®QUICK ADM Monitoring. These results encourage therapeutic drug monitoring of ADM biosimilars in routine clinical practice.

Density and Viscosity of Athabasca Bitumen Samples at Temperatures Up to 200°C and Pressures Up to 10 MPa

Summary This paper presents the measurements of bitumen thermophysical properties (density and viscosity) over a wide range of temperatures (ambient to 200°C) and pressures (atmospheric to 14 MPa). The measurements have been conducted on three Athabasca bitumen samples taken from different locations. A new method was proposed to correlate the density data as a function of temperature and pressure, with a maximum absolute deviation of 1.7 kg/m3. The viscosity data were also correlated with two correlations available in literature considering the effect of pressure and temperature on viscosity of bitumen, with an average absolute relative deviation of 9.2%. The measured data and correlations are applicable for the prediction and optimization of oil recovery in the solvent- and thermal-based bitumen-recovery processes such as expanding- solvent steam assisted gravity drainage (ES-SAGD) and heated vapor extraction (VAPEX).

Density functional theory for N–NO2 bond dissociation energies of N-nitroacylamide compounds in acetonitrile — Theoretical method assessment and prediction

The performance of various density functional theories (B3LYP, B3PW91, B3P86, B1LYP, BMK, MPWB1K, PBE0, and MPWB95) was examined for calculating N–NO2 bond dissociation energies (BDEs) of 10 N-nitroacylamide compounds. The CBS-4M method was also used. By comparing the calculated results with the experimental values, it was observed that B1LYP/6–31G** and B3LYP/6–31+G** provided accurate BDEs. Especially, B3LYP/6–31+G** was recommended because of its smaller maximum absolute deviation. Further, substituent effects based on the B3LYP/6–31+G** method were analyzed. The result shows that an electron-donating group increases the BDE of the parent C6H5–CON(CH3)NO2, while an electron-withdrawing group decreases the BDE of the parent C6H5–CON(CH3)NO2. Subsequently, the BDEs of the other N-nitroacylamindes were estimated.

Development of the Predictive Models for the Fabric Water Vapor Resistance

The water vapor transport properties of textile fabrics are of considerable importance in determining thermal comfort properties of clothing systems. There are different standard test methods available for measuring water vapor transport properties of fabrics. They are either time consuming or expensive methods. Objective of this work is to determine water vapor transmission resistance of the fabric using other properties of the fabric in such a manner that one can predict water vapor resistance. Both linear and nonlinear models were considered and different measures of model adequacy including residual sum of square, maximum absolute deviation and average absolute deviation were calculated. Using linear regression techniques, several statistically acceptable linear models were developed. The results revealed that several non-linear models can predict the water vapor resistance better than linear models.

Service Methane Number as a Means to Avoid Knock in Natural Gas Fuelled Spark Ignition Engines

Experimental investigations on the knock rating of gaseous fuels were carried out on a single cylinder SI engine of Lister-Petter make. The Service Methane Number (SMN) of different gas compositions is measured and then compared to the standard Methane Number (MN), calculated by the AVL software. Effects of engine parameters, by mean of the Methane Number Requirement (MNR) are also highlighted. A linear correlation, between the SMN and the MN, has been obtained with a maximum absolute deviation lower than 2 MN units. A prediction correlation giving the MNR from engine parameters has finally been deduced from experimental data with a good accuracy (mean absolute deviation of 0.5 MNR unit).