Simulation Of Process Research Articles

Generalizing feature preservation in iso-surface extraction from triple dexel models

We present a method to resolve visual artifacts of a state-of-the-art iso-surface extraction algorithm by generating feature-preserving surface patches for isolated arbitrarily complex, single voxels without the need for further adaptive subdivision. In the literature, iso-surface extraction from a 3D voxel grid is limited to a single sharp feature per minimal unit, even for algorithms such as Cubical Marching Squares that produce feature-preserving surface reconstructions. In practice though, multiple sharp features can meet in a single voxel. This is reflected in the triple dexel model, which is used in simulation of CNC manufacturing processes. Our approach generalizes the use of normal information to perfectly preserve multiple sharp features for a single voxel, thus avoiding visual artifacts caused by state-of-the-art procedures.

Integrated Solar Production of Fertilisers and Fuels

Solar fertiliser production including the internal utilisation of the side-product oxygen is analysed as one promising example of an innovative process utilising renewable energy resources and addressing the aspect of integrated production – here in the case of nitrogen containing fertilisers. Alternative methods to supply solar energy into the energy intensive generation of precursors needed for nitrate-based fertiliser via the Haber Bosch and Ostwald processes are introduced. Those methods are assessed in a comparative way through process design and simulation with a focus on oxygen injection inside the NOx combustion of the Ostwald process and through calculation of its economic impacts.

Finite element method based modeling of cutting forces and cutting temperature in micro-milling LF21

The demands for aluminum alloy LF21 micro precision parts are increasing in the fields of aerospace, high-tech electronic products, and the other fields. Micro-milling is an effective technology for machining small LF21 precision parts. Cutting forces and temperature are crucial factors in micro-milling process, directly affect tool vibration, tool wear, and surface quality of the workpiece, and even result in large deformation of the tool and workpiece. Direct measurement of cutting forces during micro-milling requires high-precision and expensive instruments. Moreover, due to the small cutting area in micro-milling, it is challenging to achieve accurate measurements of cutting area temperature. Therefore, accurate prediction of cutting forces and temperature in micro-milling is urgent and challenging. Nowadays, there are few studies on prediction of cutting forces in micro-milling LF21. The study on prediction of temperature in micro-milling LF21 is still blank. To solve the above problems, this paper proposes a finite element method based on modeling for prediction of cutting forces and temperature in micro-milling LF21. ABAQUS software is adopted. First, the geometry models of the micro-milling tool and workpiece are established. Then, the assembly and mesh division of the established models are completed. Johnson-Cook constitutive model and Johnson-Cook damage criteria are used to describe the material constitutive relationship and chip separation criteria, respectively. The suitable tool-workpiece friction models are determined. Finally, the simulation of the micro-milling LF21 process is achieved. Experiments of micro-milling LF21 are conducted and the cutting forces are measured using the dynamometer. The validity of the built process simulation model and the correctness of the cutting force prediction results are verified by the comparison of experiment and simulation cutting forces. Then, the prediction of temperature is achieved based on the verified process simulation model of micro-milling LF21.

Hydrogen–Water Isotope Catalytic Exchange Process Analysis by Simulation

The hydrogen–water isotope catalytic exchange process has been widely applied in the tritium-containing water treatment process. It can be compared and analyzed conveniently with process simulation software. In this study, the catalytic exchange process was simulated by Aspen Plus software (V11). According to the simulation results, the main reaction process was that HDO in the liquid phase converts into HD in the gas phase, and the reaction mainly occurred at the bottom of the column, exhibiting a two-orders-of-magnitude-higher reaction amount compared to that observed in the top section. Different side reactions occur at distinct positions along the column, exhibiting a reaction amount that is lower by one to two orders of magnitude compared to the main reaction and aligning in the same direction as the main reaction. The optimum operating temperature is 60~80 °C, with the best performance observed at 70 °C, because of the large reaction equilibrium constant and the suitable ratio of vapor to hydrogen (1:4~1:1.5) in the gas phase. The influence of the residence time was investigated by introducing reaction kinetic equations. The residence time should be more than 1 s to ensure an adequate reaction. The influence of operating conditions on the hydrogen–water isotope catalytic exchange process can be deeply investigated by process simulation, and more mass transfer process quantities can be obtained. It plays a promoting role in guiding the process design and condition optimization.

Production of Sustainable Methanol from Industrial Exhaust Gases

AbstractCarbon capture and utilization (CCU) is part of the European Union (EU)’s strategy to become climate‐neutral by 2050. CO2 is needed as a feedstock for the production of climate‐friendly base chemicals and sustainable fuels. Although CO2 capture from industrial point sources is technically feasible and requires less energy than direct air capture, its integration into the circular economy is challenging due to only the temporary recognition of fossil CO2 as a source for the production of climate‐friendly renewable fuels. In this article, CO2 sources such as waste incineration plants, lime production, and steel production are analyzed based on process simulation in terms of their energy demand for CCU and compliance with current EU legislation on renewable fuels over the service life of a CCU plant.

Weight Factor as a Parameter for Optimal Part Orientation in the L-PBF Printing Process Using Numerical Simulation.

The L-PBF process belongs to the most modern methods of manufacturing complex-shaped parts. It is used especially in the automotive, aviation industries, and in the consumer products industry as well. Numerical simulation in the powder sintering process is a means of optimizing time efficiency, accuracy and predicting future errors. It is one of the means to optimize the L-PBF process, which makes it possible to investigate the influence of individual parameters on additive manufacturing. This research makes it possible to predict the correct orientation of a part based on selected criteria, which are assigned a weighting factor in the form of parameters with which the simulation software Simufact Additive can work. Based on these, three possible orientations of the part were analysed with respect to the area of the supporting material, the volume of the supporting material, the number of voxels, and the building risk. Finally, the results of a simulation and the results of the tensile test were compared. From the results of the static tensile test, as well as from the results of the numerical simulation, it was found that better characteristics were achieved for the orientation of part no. 1 compared to orientation of part No. 3.

Biodiesel production using various methanol sources and catalytic routes: A techno-environmental analysis

The primary objective of this research is to investigate the biodiesel production via the transesterification reaction between triolein and methanol in the presence of a basic and acidic catalysts, namely sodium hydroxide and sulfuric acid, using CHEMCAD simulation software as a process modelling and simulation tool. Methanol, one of the raw-materials used in biodiesel manufacturing, was synthesized using reforming technology applied to: i) coke oven gas and carbon dioxide, and ii) glycerol that represents an important by-product of the biodiesel synthesis. Four different cases were developed and compared, with detailed operating parameters and equipment designs for each of the investigated scenarios. The production capacity was set to 1000.00 kg/h of biodiesel and a purity higher than 98 % was registered in all investigated cases. Based on the material and energy balances derived from modelling and simulation section, an environmental analysis using the Life Cycle Assessment methodology was performed with the aid of the GaBi software. Ten environmental impact categories (i.e., Global Warming Potential, Fossil Depletion Potential, Human Toxicity, Water Depletion, etc.) are generated and relevant discussions about the sub-processes that exhibit the highest influence are given. The environmental evaluation shows that the most environmentally friendly scenario consists of the methanol production using coke oven gas and carbon dioxide, and its subsequent utilization for the biodiesel production following the alkali catalysis. A scenario analysis regarding several electric and thermal energy sources (i.e., natural gas, biogas, biomass, grid mix) was performed. According to this scenario analysis, while evaluating the synthesis of biodiesel through the alkali catalysis pathway, using biomass as a renewable source improves the environmental performances across certain impact categories.

Design of a sustainable biorefinery for the valorization of waste from the baker’s yeast industry through process simulation: A case study for the production of animal feed, fertilizers, and biofuels from vinasse

The present work deals with the biotechnological valorization of the vinasse through a sustainable biorefinery. The aim was to design and analyze, through process simulation, a biorefinery for the valorization of vinasse in products such as organo-mineral fertilizers, supplements for animal feed, and biogas. For this purpose, the SuperPro Designer software was used, and a modified hierarchical decomposition method was applied. One base case and three scenarios were techno-economically assessed, and the net present value was used as a selection criterion. The technological configuration with the best techno-economic criterion was analyzed socially and environmentally. The results suggest that the best configuration of the biorefinery corresponded to scenario 3, with a net present value of USD$73,364,000, a number of direct employees of 38, and a blue water footprint of 16.16 m3/h. These findings highlight the potential of the design of biorefineries to address the valorization of vinasses in Colombia and worldwide.

Assessment of urban inundation and prediction of combined flood disaster in the middle reaches of Yellow river basin under extreme precipitation

Watershed Cities often face the dual flood risks of upstream floods and urban waterlogging. This compound disaster is caused by the interaction of multiple factors (topography, storms, upstream water and urban drainage, etc.), and is particularly affected by extreme rainfall events and the large and small drainage carrying capacity. To explore the combined effects of extreme rainfall and drainage systems, this study applied the Chicago rainfall hyetograph method and constructed design under different return periods. A comprehensive model combining the verified SWMM and LISFLOOD hydraulic models was utilised to build one-dimensional and two-dimensional coupled watershed urban flood models, simulating inundation depth and duration. By considering urban rainfall, runoff, topography, and drainage system characteristics, the flood processes under different extreme rainfall scenarios were simulated, and the node overflow and inundation depth were obtained. A flood risk assessment of watershed cities was conducted by combining methods such as the HR threshold method, evaluation level, and threshold division. In this study, Huyi District of Xi’an City, China, was used as the research area. The main research findings show: (1) the precipitation design values for different return periods were calculated based on the proposed method, with amount for a 100-year event being 87.196 mm, suitable for composite flood simulation; (2) applying the SWMM-LISFLOOD coupled model constructed for midstream urban areas ensured the accurate simulation of composite flood processes. Under different extreme rainfall return period scenarios, the percentage of flood inundation depth exceeding 1 m caused by multiple disaster factors increased from 0.73 % to 5.19 %; (3) extreme rainfall leads to different levels of internal flooding risk. When the return period of extreme rainfall is 5 years, the area inundated by composite floods in the research area is 67.21 km2. In comparison, when the return period of extreme rainfall is 50 years, the area inundated by composite floods in the research area significantly increases to a total inundation area of 286.85 km2. This study is expected to provide scientific references for flood prevention planning, composite disaster risk management, and urban resilience building in watershed urban areas while significantly impacting modern economic growth, social stability, and the safety of watershed cities.

Numerical investigation of the sensible heat storage in novel electrostatic precipitators with liquid-filled collection electrodes

Heat recovery from flue gas holds significant potential for energy conservation. A strategy is proposed for recovering and storing heat in electrostatic precipitators by utilizing liquid-filled collection electrodes. In the prototype, the hollow collection electrode is connected to the heat storage tank. Convective heat transfer occurs between the gas stream and the liquid contained in the collection electrode. The heat storage mechanism is revealed by numerical simulation of corona discharge, flow and heat transfer processes in a simplified electrostatic precipitator. The results indicate that both the gas-side and liquid-side heat transfer coefficients reach steady values within approximately 100 s. The buoyancy-driven liquid flow results in a heat transfer coefficient that is 28 times higher than that for gas-side forced convection. Increasing the gas inlet temperature leads to a greater buoyancy force and, consequently, a higher heat transfer coefficient on the liquid side. Both ionic wind and inlet velocity affect the gas-side heat transfer. The ionic wind disturbs the thermal boundary layer, leading to a 27.9 % increase in the gas-side heat transfer coefficient at an inlet velocity of 0.5 m/s. Furthermore, this enhancement becomes more pronounced as the inlet velocity decreases. The flow pattern of liquid in the collection electrode and heat storage tank is crucial to the heat storage process. Additionally, installing the heat storage tank above the electrostatic precipitator provides added benefits. During the process of heat storage, the largest heat resistance is found on the gas side.

Comparative assessment of simulation-based and surrogate-based approaches to flowsheet optimization using dimensionality reduction

This work proposes a framework for simulation-based and surrogate-based reduced space Bayesian optimization of process flowsheets. The framework uses global sensitivity analysis for dimensionality reduction via the identification of critical process variables that contribute significantly to the variability of the objective function (e.g. productivity and operating costs). Both simulation- and surrogate-based algorithms are applied to a biopharmaceutical and a chemical process simulator for the production of plasmid DNA and dimethyl ether (DME), respectively. Their capabilities are assessed in terms of the trade-off between computational effectiveness and solution accuracy. Results indicate that simulation-based Bayesian optimization achieves better objective function values, while surrogate-based Bayesian optimization is more computationally effective.

Automated hybrid distillation sequence synthesis framework of mixed azeotropes and nonazeotropes

Distillation technology is a crucial, energy-intensive component of energy-supply systems, thereby motivating the improved design of distillation systems. This study investigated an automated framework for synthesizing hybrid distillation sequences, providing a systematic approach for the design and optimization of distillation sequences involving azeotropes. The proposed method separated mixed azeotropes and nonazeotropes by replacing the simple columns in the superstructure of a conventional distillation sequence with extractive distillation columns. The automated calculation of the superstructures was implemented by calling the external interface of the process simulator. The results of these calculations were used to formulate a mixed-integer linear programming problem that was optimized with the objective function of the total annualized cost (TAC). Finally, the optimal solution and all feasible alternatives were obtained. Moreover, the reliability and efficiency of the proposed framework were demonstrated through two case studies. The results show that the separation position of the azeotrope in the sequence and the presence or absence of heat integration significantly impact the TAC of the system. However, using an existing component versus an external component as an entrainer makes very little difference in the TAC for the optimal sequences.

Simulation of photon migration process in the biological environment

Simulation of photon migration process in the biological environment

Simulation-based data reduction and data processing for sheet metal forming in the hybrid twin framework

In sheet metal forming, the interaction between virtual models and the real world remains challenging. Process simulations can exhibit significant errors, and reliable measurements are often scarce during early production stages. This study presents a hybrid twin framework that systematically unifies computer-aided design, simulation, and measurement data in an adaptive manner. Central to this framework is a reverse engineering algorithm that reconstructs and transforms the geometry of deep-drawn components from optical scan data into B-spline surfaces. The algorithm demonstrated high precision, indicating its suitability for process control and geometric analysis. The hybrid twin framework integrates virtual data from simulations and real-world data, as evidenced by a sensor concept for inline surface measurement. The framework ensures robust and redundant measurement concepts by estimating complete geometries from a few systematically preselected measuring points. This adaptive approach permits continuous updates and extensions to the database, accommodating both sparse inline signals and offline inspection data. This framework provides a conceptual model for integrating direct feedback interactions between virtual and physical environments, thereby enhancing the precision of analytical and predictive models in sheet metal forming processes.

Pulse repetition-rate effect on the critical power for self-focusing of femtosecond laser in air

The femtosecond laser filamentation is of significant interest due to its remarkable characteristics, and determining the critical power of self-focusing is essential for the process of filamentation. In this work, the critical power for self-focusing of intense femtosecond laser pulses at different repetition rates is experimentally measured according to the focus-shift method. A bimodal fitting method is proposed to more accurately determine the self-focusing critical power. It is found that the self-focusing critical power decreases as the laser repetition rate increases. A numerical simulation of the filamentation process based on the modified nonlinear Schrödinger equation effectively explains the experimental results obtained. This work provides valuable insights for the generation and application of high repetition rate femtosecond laser filamentation.

Optimization Strategy for Process Design in Rubber Injection Molding: A Simulation-Based Approach Allowing for the Prediction of Mechanical Properties of Vulcanizates.

Selecting the optimal settings for the production of rubber goods can be a very time-consuming and resource-intensive process. A promising method for optimizing rubber processing in a short period of time is the use of simulation routines. However, process simulations have only recently enabled meaningful predictions of not only the part's state of cure but also its mechanical characteristics. As a first approach, second-order polynomials were considered suitable for describing the properties of compression-molded parts. However, more precision is required for injection molding due to the narrower distribution of mechanical characteristics of parts produced at different vulcanization temperatures. This became evident when the approximation of mechanical data with second order models partly revealed significant failures of part behavior prediction. To tackle this issue, a combined approach for approximation is proposed in this contribution by means of logistic growth function in addition to second order polynomials. To feed the model, an experimental plan was designed for producing injection-molded parts from an SBR compound at various temperatures and to different degrees of cure. The parts obtained were then characterized mechanically, and the results were opposed to varying degrees of cure and extents of reaction to calculate the model coefficients. Once available, a simulation-based calculation of the mechanical part quality is possible. The comparison of test results from the simulation and the real process has shown a reliable prediction, as simulation results were found within the natural deviation of the real measurements.

Finite element modeling and simulation of IN738 laser powder bed fusion process assisted via machine learning

Finite element modeling and simulation are considered one of the effective methods for optimizing additive manufacturing process windows, which can better capture metallurgical features under the interaction of multiple physical fields. However, correctly selecting or obtaining heat source parameters, boundary conditions, and thermophysical parameters is the biggest challenge of finite element modeling. In this work, the laser semi-ellipsoid heat source parameters and powder thermophysical parameters of the laser powder bed fusion process was effectively determined by machine learning method, and an accurate laser powder bed fusion process model of IN738 alloy was established. The simulation error of the size of single-track molten pool under different process conditions is less than 2.0 %. The simulation results of stress field by multi-layer and multi-track under different process conditions are consistent with the micro crack and density test results of deposited samples. The relevant research results provide a new idea for correctly establishing the model of additive manufacturing process and realizing reliable finite element simulation.

A Numerical Study of Fault Reactivation Mechanisms in CO2 Storage.

Due to extensive industrialization and ongoing fossil fuel consumption, CO2 emissions have significantly contributed to climate change and rising greenhouse gas levels. The collection and storage of CO2 in subsurface geological formations have been proposed as a feasible alternative. The well-documented In Salah storage site is the subject of the chosen case study. For the numerical analysis, a two-dimensional finite element simulation of fault reactivation processes was conducted in the context of the CO2 storage. The goal of this research is to conduct a mechanistic numerical analysis of a typical CO2 storage condition. The selected analysis domain is 4 km (width) × 2 km (depth) and includes all important domains and formations (overburden, main caprock, lower caprock, and underburden) of the In Salah site. The simulation results indicate that the influence of fault reactivation under 32 MPa of base injection pressure results in peak vertical deformation of 0.044 m in the caprock and a vertical displacement magnitude of 0.015-0.020 m at the surface level (Z = 0 m). The derived vertical deformation findings at the surface level are in agreement with the data obtained from the in situ InSAR monitoring system in 2009. The effects of the changes in the fault dip angle, key caprock mechanical parameters, and in situ stress ratio on the displacement profile are evaluated within the parametric study. In comparison to the benchmark numerical run, the scenario ratio of k = 0.5 led to a significant reduction in the displacement. The simulation in which the fault dip angle was 30° produced a more pessimistic result with a larger displacement field. This could be an indication of heightened fault reactivation risks associated with low-angle faults in storage sites with strong horizontal stress regimes due to the combined effect of increased shear stress and reduced inherent frictional resistance on the fault plane. Considering that a vertical fault dip angle (90°) and an additional three 20 m long vertical fractures above the reservoir produced similar vertical displacement observed with the fault dipping at a 60° angle, this indicated that the vertical faults in the vicinity of the storage site pose limited safety risks to the integrity of the sealing rock.

A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration

AbstractCalibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.

Fuel reactor simulation for coal chemical looping process: Effects of ash on flow behavior

Chemical looping gasification (CLG) is an environmentally friendly strategy for using coal that has significant promise for application in reality. Nevertheless, the process of generating and accumulating ash from coal presents obstacles to the advancement of this technology. To fully comprehend the effects of ash from coal on the internal flow properties of a fuel reactor (FR). The Computational Particle Fluid Dynamics (CPFD) approach was applied to predict the CLG with coal at the FR, uncovering distinctive flow properties of gas-solid reactions. This study investigated the effects of ash particles content, polydispersity and sphericity on the flow behavior within the bed. The simulation accurately predicted the motion of the bubbles within the FR, and the gas phase composition distribution at the outlet is closer to the experimental data. Introducing coal ash creates a reduction in the concentration of ilmenite, resulting in a drop in the rates of the related reactions. The narrow particles diameter distribution among coal ash facilitates the particles arrangement in the bed and improves the overall reaction. The addition of moderately irregular-shaped coal ash particles has a certain positive effect on improving the gas-solid contact.