Frequency: Quarterly E- ISSN: 2277-6273 P- ISSN: Awaited Abstracted/ Indexed in: Ulrich's International Periodical Directory, Google Scholar, SCIRUS, getCITED, Genamics JournalSeek, EBSCO Information Services
"Inventi Impact: Modeling & Simulation" is a quarterly journal providing global forum for researchers, scholars and engineers in the fields of modeling and simulation. It publishes high quality refereed research and review papers disseminating latest theoretical results and their practical applications.
Traditional analysis of embedded earth-retaining walls relies on simplistic lateral earth pressure theory methods, which do not allow for direct computation of wall displacements. Contemporary numerical models rely on the Mohr–Coulomb model, which generally falls short of accurate wall displacement prediction. The advanced constitutive small-strain hardening soil model (SS-HSM), effectively captures complex nonlinear soil behavior. However, its application is currently limited, as SS-HSM requires multiple input parameters, rendering numerical modeling a challenging and timeconsuming task. This study presents an extensive numerical investigation, where wall displacements from numerical models are compared to empirical findings from a large and reliable database. A novel automated computational scheme is created for model generation and advanced data analysis is undertaken for this objective. The main findings indicate that the SS-HSM can provide realistic predictions of wall displacements. Ultimately, a range of input parameters for the utilization of SS-HSM in earth-retaining wall analysis is established, providing a good starting point for engineers and researchers seeking to model more complex scenarios of embedded walls with the SS-HSM....
In model-based system engineering (MBSE), reuse of existing models in the development of a new system can be advantageous.\nAutomatic assignment of existing models to each design task within a design task set has been proven to be feasible. However,\nwhile several studies have discussed the significance of models in MBSE and methodologies for models reuse, solving the model\nreusability problem through a model assignment method has not been discussed. Additionally, a significant challenge in model\nassignment is to address the conflict between the maximization of the model value summations, which are yielded by assigning the\nmodels to a design task set, and the minimization of the execution cycle of the task set. This study (a) proposes a design-taskoriented\nmodel assignment method that establishes a multiobjective model, based on a model assignment integration framework,\nand (b) designs a differential-evolution-combined adaptive nondominated sorting genetic algorithm-II to provide an optimal\ntradeoff between maximizing the total model values and minimizing the execution cycle of the task set. By comparing the\nperformance of the algorithm in resolving the assignment of models to a design task set with those of two conventional algorithms\nin a phased-array radar development project, the algorithmâ??s performance and promotion of system development are verified to\nbe superior.The new method can be applied for developing model scheduling software for MBSE-compliant product development\nprojects to improve using effects of the models and development cycle....
A dynamic stiffness element for flexural vibration analysis of delaminated multilayer beams is developed and subsequently used to\r\ninvestigate the natural frequencies and modes of two-layer beam configurations. Using the Euler-Bernoulli bending beam theory,\r\nthe governing differential equations are exploited and representative, frequency-dependent, field variables are chosen based on the\r\nclosed form solution to these equations. The boundary conditions are then imposed to formulate the dynamic stiffness matrix\r\n(DSM), which relates harmonically varying loads to harmonically varying displacements at the beam ends. The bending vibration\r\nof an illustrative example problem, characterized by delamination zone of variable length, is investigated. Two computer codes,\r\nbased on the conventional Finite ElementMethod (FEM) and the analytical solutions reported in the literature, are also developed\r\nand used for comparison. The intact and defective beam natural frequencies and modes obtained from the proposed DSM method\r\nare presented along with the FEM and analytical results and those available in the literature....
Methanol synthesis from CO2 is a key strategy for carbon capture and utilization, offering a viable solution to mitigate climate change. The direct synthesis of methanol not only reduces greenhouse gases but also produces valuable chemicals for industrial applications. The aim of this study is to model and optimize the methanol synthesis process from CO2, focusing on maximizing methanol yield while minimizing CO2 content in the product stream. In this work, a detailed methanol synthesis process simulation was developed using the Soave–Redlich–Kwong equation of state in the Aspen Plus V11 commercial software environment. Pure CO2 streams, which are produced from the postcombustion carbon capture process, and renewable hydrogen streams were used. The results are compared with open literature sources. In addition, a sensitivity analysis was employed to evaluate the effects of the pressure, temperature, and recirculation fraction on process efficiency. The results showed that the highest methanol yield of 76,838 kg/h was obtained at 80 bar, 276 ◦C, and a recirculation fraction of 0.9. The lowest CO2 content in the final product (73 kg/h) occurred at 80 bar, 220 ◦C, and a recirculation fraction of 0.6. These findings demonstrate the trade-off between maximizing methanol output and reducing unreacted CO2. In conclusion, optimal operating conditions for both the high yield and low CO2 content were identified, providing a foundation for further process refinement. Future work will involve developing a more complex multi-reactor model and conducting economic assessments for large-scale industrial implementation....
The following paper offers a modern REE 1.0 computer application designed to model the behavior of REE ions in adsorptive materials and membranes. The current version of the application is based on several models, such as the Lagergren pseudo-first order, pseudo-second-order and Elovich kinetic models, and the intraparticle diffusion model, the diffusion-chemisorption model, and the Boyd model. The application has been verified on a sample of four different types of adsorptive materials and membranes. The proposed application allowed the analysis of kinetics, but also the mechanisms of the adsorption process, especially those responsible for the rate-determining steps. It was found that Lagergren pseudo-second-order kinetic model was the best-fit model to describe the adsorption behavior of REE ions onto the novel materials and membranes. Other models determined the process of chemisorption was in force for the analyzed cases, and the mechanisms controlling the adsorption processes are diffusion-chemisorption and adsorption is mostly controlled by film diffusion. Additionally, characteristic parameters, such as qe designated from two different models, showed very similar values, which indicates the correctness of the analysis....
The literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is,\nwhen multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion. In this study, a modified\nnew two-parameter estimator based on prior information for the vector of parameters is proposed to circumvent the problem of\nmulticollinearity. This new estimator includes the special cases of the ordinary least squares estimator (OLSE), the ridge estimator\n(RRE), the Liu estimator (LE), the modified ridge estimator (MRE), and the modified Liu estimator (MLE). Furthermore, the\nsuperiority of the new estimator over OLSE, RRE, LE, MRE, MLE, and the two-parameter estimator proposed by Ozkale and\nKaciranlar (2007) was obtained by using the mean squared error matrix criterion. In conclusion, a numerical example and a\nsimulation study were conducted to illustrate the theoretical results....
This paper presents a modified grey model GMC(1,...
Developing a suitable nonlinear model is the most challenging problem in the application of nonlinear model based controllers\nto distillation column. Hammerstein model consists of a nonlinear static element described by wavenet based nonlinear function,\nfollowed by a linear dynamic element described by the Output Error(OE) model was used in this study to represent the nonlinear\ndynamics of the distillation column. The model parameters were identified using iterative prediction-error minimization method.\nThe model validation results proved that the Hammerstein model was capable of capturing the nonlinear dynamics of distillation\ncolumn....
A mathematical model was developed to correlate the four heat penetration parameters of 57 Stumbo�s tables (18,513 datasets) in\r\ncanned food: g (the difference between the retort and the coldest point temperatures in the canned food at the end of the heating\r\nprocess), fh/u (the ratio of the heating rate index to the sterilizing value), z (the temperature change required for the thermal\r\ndestruction curve to traverse one log cycle), and Jcc, (the cooling lag factor). The quantities g, z, and Jcc, are input variables for\r\npredicting fh/U, while z, Jcc and fh/U are input variables for predicting the value of g, which is necessary to calculate the heating\r\nprocess time B, at constant retort temperature, using Ball�s formula. The process time calculated using the g value obtained from\r\nthemathematical model closely followed the time calculated fromthe tabulated g values (rootmean square of absolute errors RMS\r\n= 0.567 min, average absolute error = 0.421 min with a standard deviation SD = 0.380 min). Because the mathematical model can\r\nbe used to predict the intermediate values of any combination of inputs, avoiding the storage requirements and the interpolation of\r\n57 Stumbo�s tables, it allows a quick and easy automation of thermal process calculations and to perform these calculations using a\r\nspreadsheet....
Aiming at the shortcomings of traditional broadcast transmitter noise test methods, such as low efficiency, inconvenient data\nstorage, and high requirements for testers, a dynamic online test method for transmitter noise is proposed. The principle of system\ncomposition and test method is given. The transmitter noise is real-time changing. The Voice Active Detection (VAD) noise\nestimation algorithm cannot track the transmitter noise change in real time. This paper proposes a combined noise estimation\nalgorithm for VAD and dynamic estimation. By setting the threshold of the double-threshold VAD detection to be low, it can\naccurately detect the silent segment. The silent segment is used as a noise signal for noise estimation. For the nonsilent segment\ndetected by the VAD, a minimum value search dynamic spectrum estimation algorithm based on the existence probability of the\nspeech (IMCRA) is used for noise estimation. Transmitter noise is measured by calculating the noise figure (NF).The test method\ncollects the input and output data of the transmitter in real time, which has better accuracy and real-time performance, and the\nfeasibility of the method is verified by experimental simulation....
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