Multistage Simulation Research Articles

Multi-Stage Design Method for Complex Gas Supply and Exhaust System of Space Station

The supply and exhaust system of the experimental rack on the Chinese space station is a complex integrated system. In this paper, a multi-stage simulation, testing, and verification method is designed for a multi-team, multi-location, and multi-stage integrated gas system. This method is designed to solve the problem of missing input parameters between the gas supply system and the exhaust system. Preliminary tests and strategy verifications were carried out through theoretical simulation and semi-physical simulation, and good calculation results were obtained for the single-rack product. The external systems were tested using a simulation system, and a calculation method was designed to obtain relatively accurate parameters. In the early stage, the performance of the product was predicted using the parameter library of Flomaster and semi-physical simulation methods, but the error was large. In the middle and late stages of development, as some products became realistic, multi-stage testing was carried out using a vacuum simulator, simulated flow resistance, and other methods, achieving a performance prediction with an error of 12% before ground testing. The final ground test and on-orbit test showed that the design and calculation method of this paper is effective. The multi-stage design method proposed in this paper was successfully applied to the integrated gas system of the Chinese space station, which can provide a reference for the design of fluid components in long-term system engineering.

Dual Control Strategy for Non-Minimum Phase Behavior Mitigation in DC-DC Boost Converters Using Finite Control Set Model Predictive Control and Proportional–Integral Controllers

Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional–Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller’s behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method’s effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system’s robustness and potential for real-time applications in power electronics.

Modal analysis of taipei 101 building

In this paper, a multi-stage modeling and simulation approach incorporating different deformation conditions is used to analyze the structure of the Taipei 101 building. The modeling process mainly involves the formation of an initial simplified model based on the basic geometry, the determination of an accurate model based on the actual dimensions, and finally the introduction of a hollow structure design to mimic the density and mass distribution of the real building. After the model is established, mesh creation and mesh independence study are carried out. The simulation is carried out using Ansys software, which mainly performs the process of selecting boundary conditions, determining the equivalent density, dividing the mesh for modal analysis, and obtaining the modal vibration pattern as well as the corresponding intrinsic frequency. By analyzing the intrinsic frequencies and vibration modes, the structure of Taipei 101 Building is prone to resonance under certain circumstances, and this study provides some significance to the geology of local seismic activities.

Atomistic modeling of electric field poling of PMMA-based polymer material with guest quinoxaline chromophores

The effect of applied electric field strength on the chromophore alignment in composite PMMA-based material is studied by atomistic modeling. Polymer material of real density (1.07 g/cm3), established in the course of modeling using NPT protocol, is considered along with model materials with reduced density (0.6 and 0.9 g/cm3), applied fields ranging from 100 to 1000 V/μm. Glass transition temperature, at which materials simulation was performed, was estimated in the course of multistage simulation. Simulation was performed with OPLS3e force field. Both centric, 〈P2〉, and acentric, 〈cos3Θ〉, chromophores order parameters were estimated. For the polymer material with high density, ρ = 1.07 g/cm3, the value of the order parameter 〈cos3Θ〉 grows from 0.25 to 0.30 for field strength varying from 300 to 1000 V/μm; the values of 〈P2〉 for chromophores are in agreement with the results of measurements by UV–vis spectroscopy. The obtained values of order parameters (both 〈P2(ch)〉 and 〈cos3Θ〉 for model materials with decreased density (0.6 and 0.9 g/cm3) are quite close to each other and seem to be overestimated compared to the values for the material with high density; this observation is in agreement with calculated values of free volume in the composite. The applied approach may provide guidelines for the elaboration of optimal poling protocols used at the development of composite polymer materials with quadratic nonlinear optical properties, containing quinoxaline chromophores-guests possessing high dipole moments (more than 20 D).

New metric improving Bayesian calibration of a multistage approach studying hadron and inclusive jet suppression

We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution at high virtuality, and (linearized) Boltzmann transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high-pT charged hadrons, D mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient q̂. To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses. Published by the American Physical Society 2024

Impact of thermodynamics and kinetics on the carbon capture performance of the amine-based CO2 capture system

Solvent-based CO2 capture is a commonly employed post-combustion technique in processes involving absorber-stripper columns. This study focused on computer simulations with equilibrium- and rate-based modeling of CO2 capture using the amine solvents 2-amino-2-methyl-1-propanol (AMP), diethanolamine (DEA), and methyl diethanolamine (MDEA) and thermodynamic methods involving electrolyte NRTL models. The objective of this study was to understand the impacts of rate-based modeling, the type of amine, and thermodynamic methods on carbon capture. Within this study, the amine-based CO2 capture process from coal-power plant flue gas was studied using Aspen Plus modeling. Simulations were also conducted to determine the impact of thermodynamics and kinetics on the CO2 capture performance of the system. The results were analyzed on the basis of captured CO2 according to the solvents and models. The equilibrium approach was mostly invalid because of the oversimplified ideal stage assumptions through the column. The lowest carbon capture capacity was obtained with MDEA, while DEA yielded the best results. A sensitivity analysis with rate-based modeling showed the significant impact of the inlet CO2 composition. The amine-based CO2 capture process simulation included solution chemistry, electrolyte thermodynamics, rigorous transport property modeling, reaction kinetics, and rate-based multistage simulation, which could be applicable to different solvent systems.

Thermodynamic modelling of alkali-silica reactions in blended cements

Experiments and field observations have shown that supplementary cementitious materials (SCMs) mitigate the risk of alkali-silica reaction (ASR) in Portland cement blends, but this is difficult to describe thermodynamically due to the lack of appropriate thermodynamic database information, and challenges in defining the kinetic factors. This paper examines how four well-known SCMs (namely ground granulated blast furnace slag, silica fume, metakaolin, and fly ash) mitigate ASR, by using a multi-stage simulation and a newly extended thermodynamic database. Calcium hydroxide consumption by SCM hydration, dilution of available alkalis, and alkali binding in calcium-alkali-aluminosilicate hydrate solid solutions, play pivotal roles in understanding the influence of SCMs in ASRs. Blending aluminium-rich SCMs in ASR-prone systems appears to act by preventing the dissolution of reactive silica in aggregates rather than by suppressing the formation of ASR products. This paper provides new thermodynamic insights into the mechanisms by which SCMs enhance concrete durability against ASR.

Asymmetric Shape Control Ability and Mutual Influence of the S6-High Cold Rolling Mill

The control of the asymmetric shape of strips has always been an important and difficult part of the production of cold rolling strips. In this paper, the S6-High cold rolling mill is taken as the research object. A finite element model of this mill is constructed using ABAQUS 2022 software, and a multistage working condition simulation analysis is carried out. The independent effects of asymmetric Intermediate Roll Bending (IRB) and asymmetric Intermediate Roll Shifting (IRS) on the strip shape are investigated by constructing an asymmetric convexity evaluation index. The equivalent relationship between the asymmetric roll bending and the asymmetric roll shifting was determined by analysing the coupling effect of the benchmark bending and shifting rollers on their asymmetric shape control characteristics. The on-site application shows that optimizing the amount of preset asymmetric shape control can significantly improve the asymmetric situation of the shape, providing theoretical guidance for the asymmetric shape control of the S6-High cold rolling mill.

Multi-Stage and Multi-Parameter Influence Analysis of Deep Foundation Pit Excavation on Surrounding Environment

As urbanization accelerates, deep excavation projects have become increasingly vital in the construction of high-rise buildings and underground facilities. However, the potential risks to the surrounding environment and the inherent complexities involved necessitate thorough research to ensure the safety of those engineering projects with deep foundation pit excavation and to minimize their impact on adjacent structures. This study introduces a multi-stage and multi-parameter numerical simulation method to scrutinize the construction process of deep foundation pits. This approach not only investigates the influence of excavation activities on nearby buildings and roads but also enhances the fidelity of simulation models by establishing a three-dimensional finite element model integrated with on-site investigated geological information. Therefore, the proposed method can provide a more holistic and accurate analysis of the overall impacts of the pit excavation process. To examine the feasibility and effectiveness of the proposed method, this study adopts the multi-stage and multi-parameter influence analysis approach for a real practical engineering case to explore the impact of excavation on the foundation pit support structure, nearby buildings, and surrounding roads. The foundation pit support’s maximum displacement was 8.64 mm, well under the 25 mm standard limit. Anchor rod forces were about 10% below the standard limit. Building and road settlements were also minimal, at 10.33 mm and 16.44 mm, respectively, far below their respective limits of 200 mm and 300 mm. This study not only validates the feasibility of design and construction stability of deep foundation pits but also contributes theoretical and practical insights, serving as a valuable reference for future engineering projects of a similar scope.

Exploring the freeze-out hypersurface of relativistic nuclear collisions with a rapidity-dependent thermal model

Considering applications to relativistic heavy-ion collisions, we develop a rapidity-dependent thermal model that includes thermal smearing effect and longitudinal boost. We calibrate the model with thermal yields obtained from a multistage hydrodynamic simulation. Through Bayesian analysis, we find that our model extracts freeze-out thermodynamics with better precision than rapidity-independent models. The potential of such a model to constrain longitudinal flow from data is also demonstrated.

Multi-stage hydraulic fracturing simulation methodology at the wells of the gas condensate field X

The purpose of this work is to select the optimal method for multi-stage hydraulic fracturing simulation at the wells of a large gas condensate field X in conditions of low-permeability reservoirs and potential risk of condensate loss in the near bottomhole zone with a decrease in reservoir pressure. At the moment, there are many ways to simulate hydraulic fracturing, each of which is characterized by its advantages and disadvantages. For specific simulation conditions, it is necessary to choose the most optimal method of hydraulic fracturing simulation, which allows you to correctly simulate all the effects that are expected during development. Within the framework of the current work, the most common numerical methods of hydraulic fracturing simulation were described in abstract, their advantages and disadvantages for specific conditions of field X were reflected. Special attention was paid to the comparison of hydraulic fracturing simulation techniques using virtual perforations and local grid refinement, since these techniques are the most common ways to set hydraulic fracturing in existing hydrodynamic simulators. Scenario calculations were carried out, which made it possible to determine the limits of applicability of various methods, compare the correctness of reproducing downhole effects, quantify the deviation of the calculation results of different methods from the reference calculation. In addition, in the course of the work, various features of the application of the hydraulic fracturing simulation through local grid refinement were described, which were revealed experimentally. As a result, for multi-stage hydraulic fracturing simulation was used a method in which the cells with hydraulic fracturing are logarithmically chopped relative to the original grid. This method allows to simulate the actual rate of decline in well productivity in conditions of low reservoir permeability, and also allows to simulate in the hydrodynamic model the effect of condensate precipitation in the near bottomhole zone and the subsequent decrease in gas productivity.

Research on a Wire Rope Breakage Detection Device for High-Speed Operation Based on the Multistage Excitation Principle

Wire rope breakage, as damage easily produced during the service period of wire rope, is an important factor affecting the safe operation of elevators. Especially in the high-speed elevator operation process, the problem of magnetization unsaturation caused by speed effects can easily lead to deformation of the magnetic flux leakage detection signal, thereby affecting the accuracy and reliability of wire breakage quantitative detection. Therefore, this article focuses on the problem that existing wire rope detection methods cannot perform non-destructive testing on high-speed elevator wire ropes and conducts design and experimental research on a high-speed running wire rope breakage detection device based on the principle of multi-stage excitation. The main research content includes simulation research on the multistage excitation, structural design, and simulation optimization of open–close copper sheet magnetizers and the building of a detection device for wire rope breakage detection experimental research. The simulation and experimental results show that the multistage magnetization method can effectively solve the problem of magnetization unsaturation caused by the velocity effect. The multistage excitation device has a good wire breakage recognition effect for speeds less than or equal to 3 m/s. It can detect magnetic leakage signals with a minimum of four broken wires and has good detection accuracy. It is a new and effective wire breakage detection device for high-speed elevator wire rope, providing important technical support for the safe and reliable operation of high-speed elevators.

Molecular Dynamics Modeling of the Grade E Borosilicate Glass Structure Using a Crystal Structural Template

A new method for molecular dynamics (MD) modeling of the glass structure using a crystal structural template is proposed. The template is based on the unit cell of the crystalline phase, whose composition is qualitatively similar to the modeled glass. Using this approach and multistage MD simulation, the model of the spatial structure of grade E borosilicate glass, reproducing its physicochemical characteristics, is obtained. The proposed method enables to model the glass structure using classical MD methods with greater productivity and stability.

Numerical Modeling of Thermal Behavior during Lunar Soil Drilling

This paper presents a detailed thermal simulation analysis of the drilling process for icy soil in the lunar polar region. The aim is to investigate the temperature changes that occur in the debris removal area during the drilling process. We developed a multi-level particle size simulation model that includes a thermal sieve based on geometric constraints to evaluate the influence of specific heat capacity and thermal conductivity on particle temperature. Using the central composite design method, we carried out the simulation test design and analyzed the average temperature difference of particles within and outside the range of the thermal sieve. The parameters of the discrete element model were determined by comparing the temperature of the debris removal zone in the lunar environment with the temperature simulated by the discrete element method. The results show that the thermal conductivity of the sieve ranges from 100 to 400 W/m, and the average temperature inside the thermal sieve is negatively related to the specific heat capacity. The temperature deviation of the chip removal area is ±10 °C, which is consistent with the temperature deviation observed in the lunar environment and the lunar icy regolith drilling test. Furthermore, the addition of the thermal sieve to the multi-stage particle size simulation modeling significantly reduces the calculation time by 86%. This reduction in computational time may potentially increase the efficiency of drilling operations in the future. Our study provides insights into the thermal behavior of lunar icy regolith during drilling, and proposes a numerical model of heat transfer with a thermal sieve that can effectively reduce computational time while ensuring accurate temperature calculations.

A multi-stage coupling adaptive method for thermochemical nonequilibrium gas

A multi-stage coupling adaptive simulation method for thermochemical nonequilibrium gas is introduced. Based on the mathematic ideal of Lambert's local integration method, the method decomposes the time integration process of the thermochemical nonequilibrium steady flow simulation into multiple ladder-type advancing stages from simple to complex, and applies the progressive approximation strategy of gradually coupling characteristic equations of thermochemical effects to achieve the rapid flowfield simulation. Second, numerical comparative analysis are conducted on the arc-jet wind tunnel test flow conditions of the hemisphere model with radius of R = 50.8 mm. Finally, numerical evaluation of the aerothermal characteristics is carried out for the typical flow state of the OV102-like space shuttle model. The numerical studies show that the new method has excellent effect of accelerating the convergence. Compared with the commonly used uniform advancing implicit method of the tight coupling of flow/thermochemical reactions, the computational efficiency is improved by about 1/3 in the one-temperature 5-species chemical model, and nearly 1 time in the two-temperature 11-species thermochemical model. In addition, the method shows good computational reliability and numerical stability, and can satisfy the requirement of the simulation of the hypersonic flows over complex topological configurations under the large CFL number condition.

Monte Carlo based continuous aperture optimization for VMAT on conventional and MR-linacs: A proof of concept.

Currently, the commercial treatment planning systems for magnetic-resonance guided linear accelerators (MR-linacs) only support step-and-shoot intensity-modulated radiation therapy (IMRT). However, recent studies have shown the feasibility of delivering arc therapy on MR-linacs, which is expected to improve dose distributions and delivery speed. By accurately accounting for the electron return effect in the presence of a magnetic field, a Monte Carlo (MC) algorithm is ideally suited for the inverse treatment planning of thistechnique. We propose a novel MC-based continuous aperture optimization (MCCAO) algorithm for volumetric modulated arc therapy (VMAT), including applications to VMAT on MR-linacs and trajectory-based VMAT. A unique feature of MCCAO is that the continuous character of gantry rotation and multileaf collimator (MLC) motion is accounted for at every stage of theoptimization. The optimization process uses a multistage simulation of 4D dose distribution. A phase space is scored at the top surface of the MLC and the energy deposition of each particle history is mapped to its position in this phase space. A progressive sampling method is used, where both MLC leaf positions and monitor unit (MU) weights are randomly changed, while respecting the linac mechanical limits. Due to the continuous nature of the leaf motion, such changes affect not only a single control point, but propagate to the adjacent ones as well, and the corresponding dose distribution changes are accounted for. A dose-volume cost function is used, which includes the MC statisticaluncertainty. We applied our optimization technique to various treatment sites, using standard and flattening-filter-free (FFF) 6 MV beam models, with and without a 1.5 T magnetic field. MCCAO generates deliverable plans, whose dose distributions are in good agreement with measurements on ArcCHECK and stereotactic radiosurgery End-To-EndPhantom. We show that the novel MCCAO method generates VMAT plans that meet clinical objectives for both conventional andMR-linacs.

Transverse gradient undulator in a storage ring x-ray free electron laser oscillator

Modern electron storage rings produce bright x rays via spontaneous synchrotron emission, which is useful for a variety of scientific applications. The x-ray free-electron laser oscillator (XFELO) has the potential to amplify this output, both in terms of peak power and photon coherence. However, even current fourth generation storage rings (4GSRs) lack the requisite electron beam brightness to drive the XFELO due to its electron energy spread. The transverse gradient undulator (TGU) can overcome this issue, thus providing a practical means to couple the 4GSR to the XFELO. In this study, we first examine the theoretical basis of the TGU interaction by deriving the 3D TGU gain formula in the low gain approximation. Then, we perform an optimization study of the gain formula in order to determine optimal beam and machine parameters. Finally, we construct a hypothetical storage ring TGU-XFELO based on near-optimal parameters and report on its projected performance using multistage numerical simulation. We also discuss potential implementation challenges associated with the ring-FEL coupling.

Computer modeling of robotic systems made of composite materials, analysis of the stress-strain state under dynamic influences

In the present study, a robotic system is considered: a multi-stage semi-natural simulation stand. The stand is designed to simulate the flight characteristics of aircraft instruments in laboratory conditions. The use of such stands can significantly reduce the cost of developing and testing aircraft instruments and contains all the attributes of robotic systems, which allows the developed method of computer modeling and analysis of stands to be applied in the field of robotic systems. The problem is solved by the finite element method. As a result of the study, a technique has been developed for approximating parts that carry out movements: gear rims, bearings, gearboxes, motors in the finite element method. Comparison with the available experimental studies showed the effectiveness of the developed methodology. As the stand material, a composite material is used, which has a high specific strength, which makes it possible to change the physical and mechanical characteristics depending on the location of the composite base in the layers of the multilayer composite structure. A technique has been developed for arranging the base layers in a multilayer composite to obtain maximum strength and rigidity of the stand, which is one of the main factors in the efficiency of the stand. The analysis of the behavior and stress-strain state of the bench under dynamic impact has been carried out.

Towards lower CO2 emissions in iron and steel production: Life cycle energy demand-LEAP based multi-stage and multi-technique simulation

The energy-related CO2 emissions in all techniques used in the life cycle of iron and steel production (ISP) and mitigation measures need to be investigated. We combine life cycle energy demand and long-range energy alternatives planning to develop an integrated model. This model is firstly applied to measure the energy-related CO2 emissions in the life cycle of ISP including the stages of raw material acquisition, material processing, manufacturing and recycling in Jilin Province during 2020–2030. The model is also used to quantify the emission reduction potentials in three scenarios with different application levels of mitigation measures. Material processing induces over 80% of the life cycle CO2 emissions in all scenarios. A 13.90% reduction of CO2 emissions in 2030 is achieved in the low carbon scenario (LCS). 100% equipment large-scaling, application of hydrogen reduction ironmaking technology (HRIT) with a share of 30%, and increasing 15% of electric arc furnace (EAF) route contribute 29.67%, 19.55%, and 14.74% of total emission reduction in LCS, respectively. Achieving future low carbon development of ISP will depend heavily on the further increase in the proportions of HRIT, EAF route, and low carbon electricity. The findings are expected to provide government with references to formulating mitigation policies for ISP in China and other countries.

Machine learning derived dynamic operating reserve requirements in high-renewable power systems

Accurately forecasting wind and solar power output poses challenges for deeply decarbonized electricity systems. Grid operators must commit resources to provide reserves to ensure reliable operations in the face of forecast errors, a process which can increase fuel consumption and emissions. We apply neural network-based machine learning to expand the usefulness of median point forecast data by creating probabilistic distributions of short-term uncertainty in demand, wind, and solar forecasts that adapt to prevailing grid conditions. Machine learning derived estimates of forecast errors compare favorably to estimates based on incumbent methods. Reserves derived from machine learning are usually smaller than values derived using incumbent methods, which enables fuel savings during most hours. Machine learning reserves are generally larger than incumbent reserves during times of higher forecast error, potentially improving system reliability. Performance is tested using multistage production simulation modeling of the California Independent System Operator system. Machine learning reserves provide production cost and greenhouse gas (GHG) emission reductions of approximately 0.3% relative to historical 2019 requirements. Savings in the 2030 timeframe are highly dependent on battery storage capacity. At lower levels of battery capacity, savings of 0.4% from machine learning reserves are shown. Significant quantities of battery storage are expected to be added to meet California's resource adequacy needs and GHG reduction targets. The addition of these batteries saturates reserve needs and results in minimal within-hour balancing costs in 2030.