Simulation Engine Research Articles

Development of an ammonia/diesel combustion mechanism for high ammonia energy ratio: Validation of the mechanisms in kinetic simulation and RCCI optical engine simulation

Ammonia blending in diesel is an efficient combustion strategy that overcomes the combustion resistance of ammonia while maintaining high engine adaptability. A smaller and accurate chemical kinetic mechanism is crucial for exploring the application of ammonia/diesel in engine. This study, based on the stepwise validation and optimization mechanisms, developed a novel ammonia/diesel mechanism comprising 130 species and 772 reactions. The mechanism was validated through kinetic verification for ammonia, n-dodecane, diesel and ammonia/diesel fuel. For profound understanding of the low-temperature autoignition chemistry of ammonia/diesel, applying this mechanism construct precise CFD models in RCCI optical engine. The results indicate that this mechanism can accurately capture ignition under high AER, wide working conditions, and low-temperature conditions. In the kinetic analysis, the C–N interaction reaction, particularly C2H4+NH2=C2H3+NH3, plays a crucial role in predicting the IDT of ammonia/diesel. In addition, NH2 undergoes deoxygenation reaction with HO2, converting inactive radicals HO2 into OH active radicals, enhances the reactivity of ammonia/diesel under low-temperature conditions. Applying the mechanism to CFD models, the model accurately predicts the pressure and heat release rate in RCCI optical engine, capturing the phenomenon of the high-temperature flame rapidly spreading towards the low-temperature regions in the cylinder. The research on this mechanism can construct accurate CFD model for optimizing efficient and clean combustion simulations of ammonia/diesel.

Design and Additive Manufacturing of Medical Face Shield for Healthcare Workers Battling Coronavirus (COVID-19).

During the coronavirus disease-19 pandemic, the demand for specific medical equipment such as personal protective equipment has rapidly exceeded the available supply around the world. Specifically, simple medical equipment such as medical gloves, aprons, goggles, surgery masks, and medical face shields have become highly in demand in the health-care sector in the face of this rapidly developing pandemic. This difficult period strengthens the social solidarity to an extent parallel to the escalation of this pandemic. Education and government institutions, commercial and noncommercial organizations and individual homemakers have produced specific medical equipment by means of additive manufacturing (AM) technology, which is the fastest way to create a product, providing their support for urgent demands within the health-care services. Medical face shields have become a popular item to produce, and many design variations and prototypes have been forthcoming. Although AM technology can be used to produce several types of noncommercial equipment, this rapid manufacturing approach is limited by its longer production time as compared to conventional serial/mass production and the high demand. However, most of the individual designer/maker-based face shields are designed with little appreciation of clinical needs and nonergonomic. They also lack of professional product design and are not designed according to AM (Design for AM [DfAM]) principles. Consequently, the production time of up to 4 – 5 h for some products of these designs is needed. Therefore, a lighter, more ergonomic, single frame medical face shield without extra components to assemble would be useful, especially for individual designers/makers and noncommercial producers to increase productivity in a shorter timeframe. In this study, a medical face shield that is competitively lighter, relatively more ergonomic, easy to use, and can be assembled without extra components (such as elastic bands, softening materials, and clips) was designed. The face shield was produced by AM with a relatively shorter production time. Subsequently, finite element analysis-based structural design verification was performed, and a three-dimensional (3D) prototype was produced by an original equipment manufacturer 3D printer (Fused Deposition Modeling). This study demonstrated that an original face shield design with <10 g material usage per single frame was produced in under 45 min of fabrication time. This research also provides a useful product DfAM of simple medical equipment such as face shields through advanced engineering design, simulation, and AM applications as an essential approach to battling coronavirus-like viral pandemics.

A parallel discrete event simulation engine for the low-earth-orbit satellite constellation networks

A parallel discrete event simulation engine for the low-earth-orbit satellite constellation networks

Исследование процессов тепломассопереноса в нефтяной скважине с~призабойным нагревателем различной длины

This paper considers a three-dimensional mathematical model of heat and mass transfer in an oil well with a bottom-hole heater. The vertical section of an oil well with an electric bottom-hole heater device for wells with highly viscous and paraffinic oil was investigated. The bottomhole heater is a monolithic cylinder located in the lower part of the tubing. The tubing contains perforations through which the oil fluid enters the tubing. The use of a local heater in the near-wellbore area makes it possible to increase the temperature of the fluid flowing through it, thereby reducing the viscosity of the oil and the load on the electric centrifugal pump, for which the critical value of the viscosity of the pumped medium is determined. The implementation of this model was carried out by the finite volume method using the ANSYS engineering simulation software. The geometry of finite elements and the finite volume mesh generated, an analogue of a continuous computational domain, were constructed by the MESH preprocessor. The finite volume mesh consists of polyhedral elements. Using the model, the fields of temperature, velocity and viscosity were obtained throughout the entire volume of the area under study. The curves showing the dependence of the average temperature of the cross section on the longitudinal coordinate of the tubing and the dependence of the average temperature at the inlet to an electric centrifugal pump on the heater power are presented. The viscosity value at the maximum permissible temperature of the heater, as well as the value of sufficient heater power to ensure uninterrupted pumping of petroleum liquid, were determined. The results obtained can be used to significantly improve the operating efficiency of an electric centrifugal pump, to increase the turnaround time and to reduce material costs during field development.

PhoenixMR: A GPU-based MRI simulation framework with runtime-dynamic code execution.

Simulations of physical processes and behavior can provide unique insights and understanding of real-world problems. Magnetic Resonance Imaging (MRI) is an imaging technique with several components of complexity. Several of these components have been characterized and simulated in the past. However, several computational challenges prevent simulations from being simultaneously fast, flexible, and accurate. The simulation of MRI experiments is underutilized by medical physicists and researchers using currently available simulators due to reasons including speed, accuracy, and extensibility constraints. This paper introduces an innovative MRI simulation engine and framework that aims to overcome these issues making available realistic and fast MRIsimulation. Using the CUDA C/C++ programing language, an MRI simulation engine (PhoenixMR), incorporating a Turing-complete virtual machine (VM) to simulate abstract spatiotemporal complexities, was developed. This engine solves a set of time-discrete Bloch equationsusing the symmetric operator splitting technique. An extensible front-end framework package (written in Python) aids the use of PhoenixMR to simplify simulationdevelopment. The PhoenixMR library and front-end codes have been developed and tested. A set of example simulations were performed to demonstrate the ease of use and flexibility of simulation components such as geometrical setup, pulse sequence design, phantom design, and so forth. Initial validation of PhoenixMR is performed by comparing its accuracy and performance against a widely used MRI simulator using identical simulation parameters. Validation results show PhoenixMR simulations are three orders of magnitude faster. There is also strong agreement betweenmodels. A novel MRI simulation platform called PhoenixMR has been introduced. This research tool is designed to be usable by physicists and engineers interested in performing MRI simulations. Examples are shown demonstrating the accuracy, flexibility, and usability of PhoenixMR in several key areas of MRIsimulation.

Non-stationary modeling and simulation of strong winds

Wind velocity is usually assumed to obey a stationary stochastic process in wind engineering, and this may cause significant bias in describing extremely severe strong wind such as typhoons and thunderstorms. To take into account the non-stationary characteristics of extreme wind, a novel evolutionary power spectral density (EPSD) model is proposed, and the spectral representation method (SRM) is introduced to simulate the whole process of strong winds. Firstly, the wavelet transform (WT) method is adopted to capture the three-dimensional time-varying properties of the low-frequency mean winds, and the associated turbulence features, including turbulent intensity, gust factor, probability density function, and power spectrum, are analyzed in depth. Secondly, the measured horizontal EPSD of strong winds are estimated. Thirdly, the performance of the proposed EPSD model is validated. Finally, the whole process of non-stationary strong winds are simulated and discussed. The results show that the proposed EPSD models are in good agreement with the measured EPSD, and the time-frequency features of the power spectrum of the simulated winds are well reproduced, which provides a powerful tool for large eddy simulation and wind engineering studies under non-stationary extreme wind climate.

Turbocharger turbine map scaling for virtual engine use

Emissions requirements, performance expectations, and expanding range of fuels for off-road engines are broadening engine air system requirements. As part of reducing engine development cost and risk, virtual engine simulation is used to select architecture and components capable of covering a broad range of requirements through low risk changes to the turbocharger turbine. A method is presented for predicting turbine performance changes due to turbine housing area change, the most effective of these low risk changes, from available turbine map data. The method, developed from a mean line model, learns four simplified geometry parameters, three loss coefficients, and two maximum efficiency rotor inlet incidence coefficients from a training turbine map data set, then predicts a scaled turbine map for changes to a simplified geometry parameter associated with housing area. Reduced mass flow RMS errors less than 4.3% and efficiency RMS errors less than 5.2% are expected when generating scaled turbine maps with this method, which is additional 2% error for flow and 3% error for efficiency over that expected when modeling actual turbine map data.

Prediction of Soot in an RQL Burner Using a Semi-Detailed Jeta-1 Chemistry

Abstract The present work proposes a methodology to include accurate kinetics for soot modeling taking into account real fuel complexity in Large Eddy Simulation of aeronautical engines at a reasonable computational cost. The methodology is based on the construction of an analytically reduced kinetic mechanism describing both combustion and gaseous soot precursors growth with sufficient accuracy on selected target properties. This is achieved in several steps, starting from the selection of the detailed kinetic model for combustion and soot precursors growth, followed by the determination of a fuel surrogate model describing the complex real fuel blend. Finally the selected kinetic model is analytically reduced with the code ARCANE while controlling the error on flame properties and soot prediction for the considered fuel surrogate. To perform all evaluation and reduction tests on canonical sooting flames, a Discrete Sectional Model for soot has been implemented in Cantera. The resulting code (Cantera-soot) is now available for the fast calculation of soot production in laminar flames for any fuel. The obtained reduced kinetic scheme is finally validated in a Rich-Quench-Lean burner of the literature in terms of soot prediction capabilities by comparison of LES coupled to the Lagrangian Soot Tracking model with measurements. Results show a significant improvement of the soot level prediction when using the reduced more realistic kinetics, which also allows a more detailed analysis of the soot emission mechanisms. This demonstrates the gain in accuracy obtained with improved reduced kinetics, and validates the methodology to build such schemes.

Patient Flow Analysis with a Custom Simulation Engine

Patient flow simulation and analysis is one of the oldest IT -based methods used to optimize patient care processes and hospital management. During the pandemic, interest in this domain suddenly increased due to the various constraints and recommendations to reduce the likelihood of further infections in the hospital. Suddenly, metrics such as the number of patients waiting in the same area, the maximum time a patient could stay in a single room, and the minimum distance between patients became important issues to monitor and optimize. Using data and modelling concepts from various hospitals, our team developed a simulation tool that used bpmn models to define an emergency department. We then modified a single day's usual patient flow with various real-world inspired edge cases to evaluate how the simulated flow would change and which stations would become bottlenecks, where the quality of patient care would deteriorate and rooms would become overcrowded. To execute the models, we developed our own tool based on the open-source Camunda modeling tool and the Business Process Model Notation (BPMN) file format. To execute the generated models, we use our own Python-based execution environment based on the SpiffWorkflow library, which permits extensive logging and extensive customization of the attributes analysed. In addition, the modelling toolkit of Camunda was narrowed down and compiled so that it could be easily used by researchers who are not programmers. In the paper, we present both the modeling process and the scenario design process, as well as the results obtained through the runs, including the maximum waiting times during the model runs and the maximum number of patients waiting at once, which allowed us to validate the effectiveness of the framework.

Seismic performance of frame with middle partially encased composite brace and steel-hollow core partially encased composite spliced frame beam

Steel-hollow core partially encased composite spliced frame beam (SHSFB) and middle partially encased composite brace (MPECB) demonstrate the potential to economize on steel consumption while exhibiting superior performance. To investigate the seismic performance of frames with SHSFBs and MPECBs, horizontal low-cyclic loading tests were conducted on four frame specimens, involving two two-story frames with no brace and two multi-story X-braced frames. Numerical simulations were performed using the “Open System for Earthquake Engineering Simulation” (OpenSees). The results indicate that the novel composite frames exhibit commendable seismic performance and energy dissipation capacity. The unbraced frame with SHSFBs has deformation capacity and mechanical properties comparable to bare steel frame. In contrast to the frame specimen equipped with bare steel braces featuring a 6 mm flange thickness, the specimen utilizing MPECBs with 4 mm flange thickness exhibits only a slight reduction of 3.60 % in maximum bearing capacity and 2.68 % in initial lateral stiffness. Significantly, each SHSFB and MPECB component reduces steel consumption by 16.71 % and 24.79 %, respectively, compared to bare steel components. Therefore, while ensuring adequate mechanical properties, frame structures equipped with SHSFBs and MPECBs will require less steel. Based on both experimental and simulation results, the formulas for predicting the ultimate bearing capacity and initial lateral stiffness of the novel composite frame were proposed, and the structural design for the multi-story X-braced frame was analyzed, offering valuable insights for practical engineering applications.

Seeking Efficiency for the Accurate Draping of Digital Garments in Production.

Digital garments are set to revolutionize the apparel industry in the way we design, produce, market, sell and try-on real garments. But for digital garments to play a central role, from designer to consumer, they must be a faithful digital replica of their real counterpart: a digital twin. Yet, most industry-grade tools used in the apparel industry do not focus on accuracy, but rather on producing fast and plausible drapes for interactive editing and quick feedback, thus limiting the value and the potential of digital garments. The key to accuracy lies in using the proper underlying simulation technology, well documented in the academic literature but historically sidelined in the apparel industry in favor of simulation speed. In this paper, we describe our industry-grade cloth simulation engine, built with a strong focus on accuracy rather than sheer speed. Using a global integration scheme and adopting state of the art simulation practices from the Computer Graphics field, we evaluate a wide range of algorithms to improve its convergence and overall performance. We provide qualitative and quantitative insights on the cost and capabilities of each of these features, with the aim of giving valuable feedback and useful guidelines to practitioners seeking to implement an accurate and robust draping simulator.

Embedded System Based on Task Interval Data Interaction Model

In the Internet of Things era, more intelligent systems can communicate with each other. Embedded system combined with network communication applications has become the basis for Internet of Things research. The programmable logic unit designed by ARM architecture has great advantages in running speed, power control and so on. In the paper, to solve the problems of common embedded MCU control resources occupying a large amount of memory and the slow speed of building engineering simulation model, it is necessary to use the queuing connection algorithm model to directly input the timing physical characteristics of the code stream in the embedded system to calculate and match the timing physical characteristics of the output code stream. The optimization algorithm of DTF-MARTE to detect the probability of timing deviation is used in the paper. It is to detect the problem of inaccurate timing information in the demand. We compare the expected physical characteristics of the timing sequence and obtain the timing deviation probability of the output data stream. The model developed in this paper has the characteristic of dynamic reconfiguration of the task interval. The design of monotonically decreasing data tasks can be realized, and the reconfigured task modules are used for interacting the data buffer area and dynamically reconstructing the instruction overhead and transmission. We analyze the performance comparison between the proposed model and the traditional communication connection model. It proves that the proposed model can further improve the priority queue and guide the data flow. According to that method, the problem of asynchronous spatial data interaction by controlling and combining different communication modes in a large scene can be solved. Data interaction can be triggered at a fixed time, and mutual interference of randomly triggered wireless communication and data acquisition modules can be avoided. It can solve the problem of insufficient computing power when future embedded devices need massive data encryption in the Internet of Things era, and provide a new way of thinking for fast, safe and efficient implementation.

CARING: Cannula for Alleviation of Retinal Injury Caused by Needle Fluidic Gashing.

Infusion-related iatrogenic retinal breaks (IRBs) are a significant complication in vitrectomies, particularly when smaller-gauge cannulas are used during fluid infusion. Using two-dimensional finite element analysis (FEA), we analyzed forces exerted on the retina from different cannulas: traditional 25-gauge, 20-gauge, 23-gauge, and 27-gauge, then investigated four alternative new cannula designs: (A) oblique orifices, (B) external obstruction, (C) side ports, and (D) perpendicular orifices. The analysis revealed that the standard 25-gauge cannula had a force of 0.546 milli-Newtons (mN). Optimized cannulas demonstrated decreased forces: 0.072 mN (A), 0.266 mN (B), 0.417 mN (C), and 0.117 mN (D). While all the designs decrease fluid jet force, each has unique challenges: Design A may complicate manufacturing, B requires unique attachment techniques, C could misdirect fluid toward the lens and peripheral retina, and D requires a sealed trocar/cannula design to prevent unwanted fluid ejection. These four innovative cannula designs, identified with detailed engineering simulations, provide promising strategies to reduce the risk of IRBs during vitrectomy, bridging the gap between engineering insights and clinical application.

STORMM: Structure and topology replica molecular mechanics for chemical simulations.

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle-mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license.

Model-Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing.

Precision dosing, the tailoring of drug doses to optimize therapeutic benefits andminimize risks in each patient, is essential for drugs with a narrow therapeutic window and severe adverse effects. Adaptive dosing strategies extend the precision dosing concept to time-varying treatments which require sequential dose adjustments based on evolving patient conditions. Reinforcement learning (RL) naturally fits this paradigm: it perfectly mimics the sequential decision-making process where clinicians adapt dose administration based on patient response and evolution monitoring. This paper aims to investigate the potentiality of coupling RL with population PK/PD models to develop precision dosing algorithms, reviewing the most relevant works in the field. Case studies in which PK/PD models were integrated within RL algorithms as simulation engine to predict consequences of any dosing action have been considered and discussed. They mainly concern propofol-induced anesthesia, anticoagulant therapy with warfarin and a variety of anticancer treatments differing for administered agents and/or monitored biomarkers. The resulted picture highlights a certain heterogeneity in terms of precision dosing approaches, applied methodologies, and degree of adherence to the clinical domain. In addition, a tutorial on how a precision dosing problem should be formulated in terms of the key elements composing the RL framework (i.e., system state, agent actions and reward function), and on how PK/PD models could enhance RL approaches is proposed for readers interested in delving in this field. Overall, the integration of PK/PD models into a RL-framework holds great promise for precision dosing, but further investigations and advancements are still needed to address current limitations and extend the applicability of this methodology to drugs requiring adaptive dosing strategies.

Investigation on drag reduction on rotating blade surfaces with microtextures.

To enhance the aerodynamic performance of aero engine blades, simulations and experiments regarding microtextures to reduce the flow loss on the blade surfaces were carried out. First, based on the axisymmetric characteristics of the impeller, a new simulation method was proposed to determine the aerodynamic parameters of the blade model through the comparison of flow field characteristics and simulation results. Second, the placement position and geometrical parameters (height, width, and spacing) of microtextures with lower energy loss were determined by our simulation of microtextures on the blade surface, and the drag reduction mechanism was analyzed. Triangular ribs with a height of 0.2 mm, a width of 0.3 mm, and a spacing of 0.2 mm exhibited the best drag reduction, reducing the energy loss coefficient and drag by 1.45% and 1.31% for a single blade, respectively. Finally, the blades with the optimal microtexture parameters were tested in the wind tunnel. The experimental results showed that the microtexture decreased energy loss by 3.7% for a single blade under 57° angle of attack and 136.24 m/s, which was favorable regarding the drag reduction performance of the impeller with 45 blades.

A Review of Training Procedures for Simulated Engine Failure after Take-Off Exercises with Twin-Engine Aircraft under 5700 kg

Engine failure after take-off (or one engine being inoperative) is an exercise conducted as part of multi-engine flight training and on-going competency checking. To prepare pilots to manage a real in-flight emergency, this exercise has traditionally been conducted immediately after take-off. This has led to increased risks of fatal accidents due to the reduced height at which these exercises are typically conducted. Yet, there is variation in the heights stipulated in training procedures published by different stakeholders worldwide. Additionally, the conduct of the exercise has resulted in fatal accidents worldwide. This paper aims to review the previous literature on aviation training and aviation occurrence data to determine what empirical data exists to support the method of conducting simulated engine failures. Peer-reviewed academic publications on aviation training, aviation occurrence databases such as aviation investigation reports, and guidance materials published by aviation authorities on simulated training exercises will be included in this paper. It was found that the previous research on these exercises has focused on the transfer of motion cues or pilot responses to abnormal situations, but did not include specific data comparing pilot performance at different heights above ground level. A review of aviation occurrences found that actual engine failures occurred at higher heights that those used in simulated engine failures. A comparison of the guidance published by aviation authorities identified variations in the minimum altitude published and differing justifications for the minimum height chosen. Future research is needed to compare pilot performance during simulated engine failures to determine the ideal height to conduct the exercise to be representative of an actual engine failure while maintaining safety margins.

Development of Prediction Model for Oil Formation Volume Factor for Sudanese Crude Oil

Understanding Oil Formation Volume Factor βo is crucial for effective oil field development, impacting well performance analysis, reservoir simulation, and production engineering calculations. Traditionally, βo is determined through costly and time-consuming laboratory tests, prompting the need for accurate mathematical correlations. Existing correlations such as Vasquez-Beggs, Standing-Glaso, and others have been widely used but show varying degrees of accuracy across different operating conditions. In this study, these correlations were evaluated against 95 datasets of experimental βo data for Sudanese crude oils. Statistical analysis revealed that Vasquez-Beggs and Standing- Glaso models performed best, with average absolute errors of 3.4219 and 3.4477, and correlation coefficients of 0.7563 and 0.7213 respectively. Motivated by the limitations of existing correlations, a new ap- proach using Polynomial Neural Networks (PNN) was developed. This model utilized reservoir temperature, gas gravity, gas oil ratio, and API as input parameters, trained on 70% of the dataset and tested on the remaining 30%. The PNN model exhibited superior predictive performance with a relative average absolute error of 2.8607 and a correlation coefficient of 0.9080. This study contributes a robust predictive tool for estimating βo in Sudanese oil fields, offering enhanced accuracy over traditional correlations and facilitating more reliable reservoir management decisions.

On languaging a simulation engine: Rapid modeling of nanoporous media sorption by hierarchical language model

Language model intelligence is revolutionizing the way we program materials simulations. However, the diversity of simulation scenarios renders it challenging to precisely transform human language into a tailored simulator. Here, using three types of expert language model, we propose a language-to-simulation (Lang2Sim) framework that enables interactive navigation on languaging a simulation engine, by taking a scenario instance of water sorption in porous matrices. Unlike line-by-line coding of a target simulator, the language models interpret each simulator as an assembly of invariant tool function and its variant input–output pair. Lang2Sim enables the precise transform of textual description by functionalizing and sequentializing the language models of, respectively, rationalizing the tool categorization, customizing its input–output combinations, and distilling the simulator input into executable format. Importantly, depending on its expert type, each language model features a distinct processing of chat history to best balance its memory limit and information completeness, thus leveraging the model intelligence to unstructured nature of human request. Overall, this work establishes language model as an intelligent platform to unlock the era of languaging a simulation engine.

Modeling of glubam roof truss, parameter identification and updating based on parallel genetic algorithm

This research introduces an innovative approach to the design and simulation of bio-based laminated structures, specifically focusing on glue-laminated bamboo (glubam) used in roof trusses. Our study fills a critical gap by investigating the mechanical behaviors of bolted connections in bamboo-based structures, which have not been comprehensively studied before. We employ a dual-phase methodology: initially, cyclic tests on bolted steel to glubam joints assess their hysteretic behavior, followed by tests on glubam planar roof trusses to evaluate structural responses under practical conditions. Our novel contribution is the development of a simplified mechanical-based hysteretic model, incorporating connector and spring elements in series or parallel within the ABAQUS software. This model significantly improves on existing models by allowing for initial calibration through a parallel genetic algorithm (PGA), enhancing both accuracy and efficiency. Subsequent incorporation of this model into the simulation of truss joints enabled the creation of an advanced hybrid roof truss model within ABAQUS. The final stage of our research demonstrates the application of a PGA-based model-updating framework, which substantially increases the model’s predictive accuracy. This work not only advances the understanding of structural behavior in bio-based construction materials but also introduces a robust framework for model updating that can be applied to other engineering simulations, contributing to more sustainable and resource-efficient construction practices.