Computational Speed Research Articles

Development and validation of a predictive combustion model for hydrogen-fuelled internal combustion engines

Internal combustion engines (ICEs) fuelled with hydrogen can play a major role in the short-term future transportation sector since they abate all criteria pollutants at engine-out reducing tailpipe CO2 emissions to near-zero levels. However, optimizing hydrogen ICEs is a challenging task that can be addressed through the development of a robust simulation tool capable to predict the H2 combustion process. In this study, a previously developed two-zone combustion model has been updated considering different laminar flame speed computations, both based on a detailed chemistry scheme: a polynomial correlation function and a tabulated approach. The predictive capabilities of the combustion model have been validated against experimental data coming from a 0.5L PFI single-cylinder engine under several operating conditions. The tabulated approach for laminar flame speed definition proved to be the best solution, leading to a combustion duration average error lower than 3 deg over a dataset containing more than 45 different operating conditions.

A long-term power system state calculation method using sequential power and holomorphic embedding

Traditional Power Flow (PF) calculations struggle to support the data analysis requirements as the power system develops. Therefore, this paper introduces a non-sequential and non-iterative PF calculation method to enhance the computational speed and analytical efficiency of power system state variables over long time scales. Firstly, a Power Sequential Division (PSD) method is proposed, representing the power curve using hierarchical energy with temporal characteristics. Next, based on the Fast and Flexible Holomorphic Embedding (FFHE) method, the paper explores the incremental properties of Voltage Power Series Coefficients (VPSC) and proposes an Incremental Holomorphic Embedding (IHE) method. Finally, by combining PSD and IHE, this study introduces a Power Sequential Flow (PSPF) method for rapid computation of power system state variables over long time scales. Unlike traditional PF methods, PSPF method shifts away from time-series calculations and instead analyzes the distribution of power system state variables over long time scales from an energy perspective. Various test cases are set up to analyze the performance characteristics of IHE and PSPF from multiple perspectives. The results indicate that the proposed methods significantly outperform traditional PF methods in terms of computational speed, adaptability to test cases, and analytical efficiency.

Bridge modal identification method based on adaptive VMD-HT algorithm

ABSTRACT Bridge engineering structures play a crucial role in transportation. Traditional methods of road and bridge closures for inspection and evaluation, while effective, increase bridge inspection costs and disrupt normal traffic flow. Therefore, a rapid detection and evaluation method for bridge structural modal parameters in the operational state is more suitable for practical engineering applications. However, under real operating conditions, bridge vibration signals typically contain a large amount of noise and interference, and modal identification must overcome issues of non-stationarity and non-linearity in the vibration signals caused by vehicle loads and environmental changes to ensure the accuracy of the identification. This study replaces the traditional Empirical Mode Decomposition algorithm in the Hilbert-Huang Transform with the Variational Mode Decomposition algorithm. It proposes a new method that combines the VMD algorithm with the Hilbert Transform, referred to as the Variational Mode Decomposition-Hilbert Transform method. Compared to the EMD algorithm, it offers significant improvements in suppressing mode mixing and endpoint effects. A vehicle-bridge coupling model was constructed, and the bridge’s natural modal information was successfully extracted from the vibration response signals. The research results indicate that the improved algorithm offers higher accuracy and faster computational speed compared to traditional identification algorithms.

Measuring exploration: evaluation of modelling to generate alternatives methods in capacity expansion models

Abstract As decarbonisation agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is modelling to generate alternatives (MGA), which systematically explores new objectives without explicit stakeholder elicitation. This paper provides comparative testing of four existing MGA methodologies and proposes a new Combination vector selection approach. We examine each existing method’s runtime, parallelizability, new solution discovery efficiency, and spatial exploration in lower dimensional (N ⩽ 100) spaces, as well as spatial exploration for all methods in a three-zone, 8760 h capacity expansion model case. To measure convex hull volume expansion, this paper formalizes a computationally tractable high-dimensional volume estimation algorithm. We find random vector provides the broadest exploration of the near-optimal feasible region and variable Min/Max provides the most extreme results, while the two tie on computational speed. The new Combination method provides an advantageous mix of the two. Additional analysis is provided on MGA variable selection, in which we demonstrate MGA problems formulated over generation variables fail to retain cost-optimal dispatch and are thus not reflective of real operations of equivalent hypothetical capacity choices. As such, we recommend future studies utilize a parallelized combined vector approach over the set of capacity variables for best results in computational speed and spatial exploration while retaining optimal dispatch.

Solving discrete network design problem using disjunctive constraints

AbstractThis paper introduces a deterministic algorithm to solve the discrete network design problem (DNDP) efficiently. This non‐convex bilevel optimization problem is well‐known as an non deterministic polynomial (NP)‐hard problem in strategic transportation planning. The proposed algorithm optimizes budget allocation for large‐scale network improvements deterministically and with computational efficiency. It integrates disjunctive programming with an improved partial linearized subgradient method to enhance performance without significantly affecting solution quality. We evaluated our algorithm on the mid‐scale Sioux Falls and large‐scale Chicago networks. We assess the proposed algorithm's accuracy by examining the objective function's value, specifically the total travel time within the network. When tested on the mid‐scale Sioux Falls network, the algorithm achieved an average 46% improvement in computational efficiency, compared to the best‐performing method discussed in this paper, albeit with a 4.17% higher total travel time than the most accurate one, as the value of the objective function. In the application to the large‐scale Chicago network, the efficiency improved by an average of 99.48% while the total travel time experienced a 4.34% increase. These findings indicate that the deterministic algorithm proposed in this research improves the computational speed while presenting a limited trade‐off with solution precision. This deterministic approach offers a structured, predictable, and repeatable method for solving DNDP, which can advance transportation planning, particularly for large‐scale network applications where computational efficiency is paramount.

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

An improved two-phase rate transient analysis method for multiple communicating multi-fractured horizontal wells in shale gas reservoirs: Field cases study

The reduction in well spacing for multi-fractured horizontal wells (MFHWs) and the increase in infill drilling exacerbate the risk of fracture communication. This presents substantial challenges for the rate transient analysis of the parent–child well system. This study proposes an improved two-phase straight-line analysis method to evaluate the linear flow parameters of multiple communicating MFHWs in shale gas reservoirs. Considering shale gas adsorption–desorption mechanisms, nonuniform length of fractures, stress-dependent effects, and matrix shrinkage effects, two-phase productivity equation is established within the dynamic drainage area (DDA). Subsequently, we develop a three-well square root of time plot based on DDA correction and propose a rigorous workflow for evaluating linear flow parameters in single-well, two-well, and three-well systems. The straight lines on the parent well's square root of the time plot exhibit varying degrees of jumps, depending on the frequency of fracture communication. After communication, it is necessary to adjust the cumulative production and production time of the parent well to accurately recalculate the average pressure within the drainage area. Numerical simulations are employed to generate a series of cases under different reservoir conditions to validate the accuracy of the proposed model. The results show that the model estimates fracture half-lengths with errors within 8%, meeting the precision requirements for field applications. Additionally, the method exceeds numerical simulations in computational speed. Two field case studies in the WY Basin, China, further illustrate the effectiveness and practicality of the proposed method, providing theoretical support for hydraulic fracturing construction design and development planning.

Extended King integral for modeling of parametric array loudspeakers with axisymmetric profiles.

Parametric array loudspeakers have been widely used in audio applications for generating directional audio beams. However, accurately calculating audio sound with a low computational load remains challenging, even for basic axisymmetric source profiles. This work addresses this challenge by extending the King integral in linear acoustics to incorporate both cumulative and local nonlinear effects, under the framework of the quasilinear solution without the paraxial approximation. The proposed method exploits the azimuthal symmetry in cylindrical coordinates to simplify modeling. To further improve computational efficacy, fast Hankel and Fourier transforms are employed for the radial and beam radiation directions, respectively. Numerical results with both uniform and focusing profiles demonstrate the advantages of the proposed approach over the traditional spherical wave expansion and direct integration methods, especially for larger aperture sizes. Specifically, for typical configurations with source aperture size of 0.2 m, we observe at least a 24-fold improvement in computational speed and a 227-fold reduction in memory requirements. These advancements allow us, for the first time, to present the sound field radiated by parametric array loudspeakers with a large aperture size of up to 0.5 m, without paraxial approximations. The implementation codes are available on https://github.com/ShaoZhe-LI/PAL_King.

Joint Physics and Data Driven Geometrical Factors of Array Laterolog in Layered Formations

Numerical geometrical factors (GFs) are developed for array laterolog measurements in one-dimensional (1D) cylindrically and planarly layered formations. The kernel of GFs assumes that laterolog responses can be expressed in terms of linear superposition of formation resistivities at different units. Two techniques, namely physics-driven modeling and data-driven approximation, have been employed to ensure both computational accuracy and efficiency of GFs. Physics-driven modeling utilizes a three-dimensional finite element algorithm to generate high-precision and fully labeled GFs data and library. Subsequently, these predetermined data are trained offline using classical neural network algorithm to establish an implicit relationship between formation parameters and GFs. The joint physics and data-driven GFs presents three main advantages. Firstly, it enables the direct representation of the spatial sensitivity of layered formations both laterally and horizontally in vertical and horizontal wells. Secondly, GFs can be used to rapidly calculate the array laterolog responses. The computation speed can be enhanced by thousands of times in comparison with traditional physic-driven modeling, allowing for real-time data processing. Another noteworthy aspect is that GFs forward modeling can handle formations with arbitrary layers, thereby resolving the fixed-layer modeling issue inherent in traditional data-driven methods. Thirdly, the GFs can be employed to approximate the derivative of logging response with respect to formation resistivity, significantly reducing the complexity of Jacobian matrix calculation for deterministic inversion. These numerical GFs not only serve as an excellent alternative simulator for array laterolog, but also will be helpful to accelerate the modeling and inversion of other logging measurement in layered structures.

Research on the Recognition and Characterization of Fractures in Coal Rock Based on CT Scanning

Abstract Coal rock fractures play a vital role as key channels for gas migration and storage in the exploration and development of coalbed methane. The characteristics of these fractures, such as porosity distribution, specific surface area, and connectivity, directly impact the adsorption, diffusion, and permeability behaviors of coal reservoirs. Therefore, a detailed analysis of coal rock fracture characteristics is vital in assessing the extractability of coalbed methane and during its exploration and development process. Currently, techniques like imaging logging, seismic detection, and micro-CT are employed for characterizing and studying fractures at various scales. Among these, high-resolution non-destructive CT scanning technology, by constructing three-dimensional data models of core samples, enables a quantitative analysis of fracture distribution features and size parameters. Traditional digital image processing methods, such as threshold segmentation and binary segmentation, though advantageous in computational speed, have limitations in the accuracy of fracture identification. With the advancement of artificial intelligence technology, deep learning-based image processing techniques have increasingly become significant tools for fracture detection due to their powerful feature extraction capabilities. This study conducted micrometer-level CT scanning experiments on coal rock samples, using CT image processing technology to construct a digitalized coal rock core model. Deep learning methods were applied for precise fracture identification, establishing an appropriate deep learning architecture and optimized parameters for coal rock fracture recognition. Furthermore, this paper provides a detailed quantitative characterization of the precisely identified fractures, offering robust technical support for the exploration and development of coalbed methane. The high-resolution non-destructive CT scanning technology and three-dimensional fracture network reconstruction method used in this study have been proven to be effective tools for investigating the micro-features of rocks, enabling the three-dimensional visualization and detailed characterization of rock microstructures.

Multi-view support vector machine classifier via [formula omitted] soft-margin loss with structural information

Multi-view learning seeks to leverage the advantages of various views to complement each other and make full use of the latent information in the data. Nevertheless, effectively exploring and utilizing common and complementary information across diverse views remains challenging. In this paper, we propose two multi-view classifiers: multi-view support vector machine via L0/1 soft-margin loss (MvL0/1-SVM) and structural MvL0/1-SVM (MvSL0/1-SVM). The key difference between them is that MvSL0/1-SVM additionally fuses structural information, which simultaneously satisfies the consensus and complementarity principles. Despite the discrete nature inherent in the L0/1 soft-margin loss, we successfully establish the optimality theory for MvSL0/1-SVM. This includes demonstrating the existence of optimal solutions and elucidating their relationships with P-stationary points. Drawing inspiration from the P-stationary point optimality condition, we design and integrate a working set strategy into the proximal alternating direction method of multipliers. This integration significantly enhances the overall computational speed and diminishes the number of support vectors. Last but not least, numerical experiments show that our suggested models perform exceptionally well and have faster computational speed, affirming the rationality and effectiveness of our methods.

KLASIFIKASI DAN VOLUME KENDARAAN BINA MARGA DARI REKAMAN VIDEO LALULINTAS DENGAN METODE ARTIFICIAL INTELLIGENCE - YOLO V8 PADA JALAN TOL JAKARTA-BOGOR DAN JAKARTA-TANGERANG

The software helps solve things faster, e.g., Epanet software, Pipe Flow Expert, WaterCAD, SAP2000 planning software, etc. Likewise, in the field of calculating traffic volume with the development of artificial intelligence technology, using video recording, volume, and speed calculations can be done easily. At this time, the classification of traffic counting is only up to 3 (three) types of vehicles classified by YOLO V8, so in this research, the classification is added to be 6 (six) types of vehicles, namely mini cars, mobile passengers, small buses, big buses, small trucks, and large trucks, according to vehicle classification established in Indonesia. The research method is done by recording the traffic on the road and then video recording with the help of the software developed with the Python program so that the volume of the vehicle according to its classification is obtained. The result obtained from this research is compared to the manual traffic counting. The comparison between vehicle counting by the software and manual counting is mini-car 97.02%, passenger car 99.49%, small bus 96.94%, large bus 97.34%, small truck 93.21%, and large truck 97.18%.

NPTS-PK: A new point kernel code for fast calculation of 3D gamma radiation field

The point kernel integration method is commonly utilized for the analytical calculation of gamma radiation fields in the field of radiation protection and shielding design. This study introduces NPTS-PK, a program developed for rapid calculation of 3D gamma radiation fields based on the NPTS program and the point kernel integration method. Based on the geometric construction and input method of the NPTS program, NPTS-PK supports point kernel calculations for radiation sources and shielding structures of various complex shapes and materials. By calculating material attenuation coefficients using continuous energy cross-section parameters, combined with a more precise buildup factor calculation method, the accuracy of calculation results has been enhanced. Improvements in ray tracing processes and the implementation of the probability neighbor list method have accelerated geometric processing. To address the challenge of excessive computation time associated with large-scale grid counting in point kernel programs, NPTS-PK integrates several efficient acceleration techniques, elevating the speed of radiation field calculation by an order of magnitude. Tests on typical model and engineering scenario demonstrate that the deviation between NPTS-PK results and the reference values is within a few tens of percent, and the computational efficiency and accuracy are improved compared to the standard point kernel programs.

Effect of orthodontic space closure on dental pulp sensitivity. Prospective clinical trial.

Orthodontic tooth movement (OTM) is a biological process that can influence the function of the pulp, including its innervation. The excitability of the nerve fibres of the pulp may be altered by forces exerted on the nerve fibres or by reduced blood flow to the pulp. The aim of this clinical study was to evaluate the sensitivity of the dental pulp during levelling and during the phase of space closure, to assess the role of certain controlled risk factors. Twenty-two adolescent participants requiring orthodontic space closure in transcanine sector were enrolled in a prospective clinical study. Patients were observed before OTM, after levelling and 1 month during active space closure. The sensitivity threshold of the pulp was measured using the electric pulp test (EPT). Dental models were obtained using an intraoral scanner, allowing measurement of interdental distances and calculation of OTM speed. The teeth were categorized according to position and tooth type. The EPT values increased significantly during orthodontic treatment (one-way RM-ANOVA, P = .014). There was a significant difference in EPT values between the tooth categories. Teeth with a single root adjacent to the residual space had the highest EPT thresholds (two-way RM-ANOVA, P < .001; Holm-Sidak, P < .05). OTM reduced pulpal sensitivity. Pulpal sensitivity during active space closure was similar to sensitivity during the levelling phase. The pulpal sensitivity of molars was less affected by OTM than that of single-rooted teeth, while teeth closer to the gap had a significantly higher pulpal sensitivity threshold during active OTM.

Coupling algorithm of cavity expansion theory and finite element for penetrating reinforced concrete

Aiming at the dynamic problem of projectile penetrating reinforced concrete, this paper creatively proposes a coupling algorithm combining cavity expansion theory and the finite element method. This approach effectively resolves the problem of low efficiency in finite element simulations while ensuring computational accuracy. In this study, based on the cavity expansion theory, we have redeveloped the display dynamics software. Radial stress in the concrete is applied to the warhead surface in the normal direction, and the interaction between the projectile and the steel bar is simulated using the finite element contact algorithm. A coupling algorithm has been developed that can stably and quickly simulate the penetration of reinforced concrete by a projectile. The study indicates that the implementation of the coupling algorithm primarily includes four steps: model establishment and meshing, stress load program design, load application, and initial condition setting. Through verification and analysis, the coupling algorithm presented in this paper is demonstrated to effectively compute the dynamic response of a projectile during penetration. It exhibits superior computational stability and speed compared to the finite element method, and shows minimal sensitivity to grid size. When applied to numerical models with small meshes, it achieves both high precision and rapid computation. The coupling algorithm program design proposed in this paper can be implemented in any display dynamics software, offering a robust approach for engineers and researchers to predict projectile penetration effects. Furthermore, it provides a rapid assessment method for the anti-penetration performance of reinforced concrete structures.

Accelerating DNA Sequence Alignment using Altera DE2-115

DNA sequence alignment is a technique for discovering information between two base sequences which the Smith-Waterman algorithm is the accurate method that provides a precise result for alignment compared to others. However, the performance was influence by size of dataset and a long DNA base sequence which resulted the time required for the alignment process is much longer in relation to the number of DNA sequence samples. There are many ways to accelerate DNA sequence alignment, and Field Programmable Gate Array (FPGA) is a good choice due to its parallel processing and cost efficiency. Although FPGA acceleration approaches are not new, this work investigates a purely software-based FPGA acceleration using the Altera Cyclone IV EP4CE115F29C7N FPGA as the target device. The SW algorithm was developed using the C language in Quartus II version 18.1 and the Nios II software build tools for Eclipse. The development starts with setting up the Qsys architecture before developing the code in Eclipse to determine the computational performance. The result shows the computational timing and speed of the implementation, with the highest speed achieved being 198.76 cells per millisecond. To summarise, the computational performance ultimately depends on the maximum matrix size of the FPGA, which is also influenced by the DNA-based pair length and able to complete using low-cost FPGA.

Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network

This paper proposes a novel intelligent method for defining and solving the reservoir performance prediction problem within a manifold space, fully considering geological uncertainty and the characteristics of reservoirs performance under time-varying well control conditions, creating a surrogate model for reservoir performance prediction based on Conditional Evolutionary Generative Adversarial Networks (CE-GAN). The CE-GAN leverages conditional evolution in the feature space to direct the evolution of the generative network in previously uncontrollable directions, and transforms the problem of reservoir performance prediction into an image evolution problem based on permeability distribution, initial reservoir performance and time-varying well control, thereby enabling fast and accurate reservoir performance prediction under time-varying well control conditions. The experimental results in basic (egg model) and actual water-flooding reservoirs show that the model predictions align well with numerical simulations. In the basic reservoir model validation, the median relative residuals for pressure and oil saturation are 0.5% and 9.0%, respectively. In the actual reservoir model validation, the median relative residuals for both pressure and oil saturation are 4.0%. Regarding time efficiency, the surrogate model after training achieves approximately 160-fold and 280-fold increases in computational speed for the basic and actual reservoir models, respectively, compared with traditional numerical simulations. The reservoir performance prediction surrogate model based on the CE-GAN can effectively enhance the efficiency of production optimization.

Automatic Extraction of VLF Constant‐Frequency Electromagnetic Wave Frequency Based on an Improved Vgg16‐Unet

AbstractConstant Frequency Electromagnetic Waves (CFEWs) refer to electromagnetic waves with a constant frequency. Man‐made CFEWs are mainly used in wireless communication, scientific research, global navigation and positioning systems, and military radar. CFEWs exhibit horizontal line characteristics higher than the background on spectrograms. In this study, we focus on Very Low Frequency (VLF) waveform data and power spectral data collected by the China Seismo‐Electromagnetic Satellite (CSES) Electromagnetic Field Detector (EFD). We utilize deep learning techniques to construct an improved Vgg16‐Unet model for automatically detecting horizontal lines on time‐frequency spectrogram and extracting their frequencies. First, we transform waveform data into time‐frequency spectrogram with a duration of 2 s using Short‐Time Fourier Transform. Then, we manually label horizontal lines on the time‐frequency spectrogram using the Labelme tool to establish the dataset. Next, we establish and improve the Vgg16‐Unet deep learning model. Finally, we train and test the model using the dataset. Statistical experimental results show that the error rate of line detection is 0, indicating high reliability of the model, with fewer parameters and fast computation speed suitable for practical applications. Not only do we detect lines through the model, but we also obtain their frequencies. Additionally, in batch‐generated power spectrogram of CFEWs, we discover some unstable phenomena such as frequency shifts and fluctuations, which contribute to understanding the propagation mechanism of CFEWs in the ionosphere and improving the accuracy of related systems.

A new simulation approach of air source heat pump adopting the holistic process distributed parameter method and hybrid PID-bisection control algorithm

Air source heat pump (ASHP) faces problems of performance deterioration when operating at low ambient temperature due to the low compression ratio, high discharge temperature, frost accumulation, etc., and may even become nonfunctional at sub-zero-centigrade ambient temperature, requiring attention and tools for studying. This paper proposed a kind of holistic process distributed parameter simulation approach of ASHP system adopting the hybrid PID-bisection (PID: proportional-integral-derivative) control algorithm. Main components of the ASHP are modeled with the distributed parameter method. An adiabatic compression model of two-phase fluid based on thermodynamics is proposed. The PID-bisection control algorithm and variable speed integral PID & bisection control algorithm are proposed and applied to iterative computation of the model. A single/two-stage compression ASHP with an intercooler is simulated by this approach. The average deviation of simulation results of the model from experimental data is not more than 7 %, and the maximum deviation is not more than 18 %. For simulation of single-stage compression mode, the maximum error is not more than 4%. For simulation of two-stage compression mode under low-evaporating-temperature operating conditions, the maximum error is not more than 4 %. Computational speed of the ASHP model is significantly improved by using the PID-bisection control algorithm, and could be further accelerated by using the variable speed integral PID & bisection control algorithm. The proposed simulation approach is not only effective in sophisticated simulation of performance of single-stage and two-stage compression ASHP, but also potential for research on the optimization of the ASHP in cold regions.

An optimal penalty method for the joint stiffening in beam models of additively manufactured lattice structures

Additive manufacturing is revolutionizing structural design, with lattice structures becoming increasingly prominent due to their superior mechanical properties. However, simulating these structures quickly and accurately using the finite element method (FEM) remains challenging. Recent research has highlighted beam element simulation within FEM as a more efficient alternative to traditional solid FE simulations, achieving similar accuracy with reduced computational resources. However, a significant challenge is managing the lack of rigidity at nodes and the prevalence of low aspect ratio beams. While various methodologies have been proposed to address these issues, there is still a gap in the comprehensive evaluation of their limitations. An optimal node penalization methodology is required to expand the limited range of accurately represented lattice behavior. A preliminary study investigates lattice geometries through comparative analysis of solid and beam FE simulations. Built on this, we developed a methodology suitable to linear, dynamics and nonlinear beam FE simulations, contributing to enhanced computational speed and accuracy. Several lattice structures were printed using material jetting and quasi-static compressive tests were conducted to validate the methodology’s accuracy. The numerical results reveal a good accuracy between the proposed beam FE methodology and the experimental data, offering a better alternative to conventional FEM for energy absorption in terms of computing time.