Rapid Performance Evaluation Research Articles

Evaluation of the performance and effect of steel fiber reinforced cellular concrete for underwater blast protection under contact explosion loading

The reinforced concrete structure is more likely to be destroyed under the action of underwater explosion load, which makes the overall structure have the risk of instability. The purpose of this paper is to study the underwater anti-explosion protection performance of steel fiber reinforced cellular concrete protective layer. The FEM-SPH coupling method was used to study the underwater damage process, dynamic response, failure mode and protection effect of the protected structure strengthened by eight different proportions of protective layers. The results show that the addition of steel fiber reinforced cellular concrete protective layer can effectively improve the anti-explosion performance of the protected structure, and its anti-explosion performance is directly related to the volume fraction of steel fiber in the protective layer. Under different ratio protection schemes, the total energy absorption and protection rate of the protective layer increase first and then decrease with the increase of the volume fraction of steel fiber in the protective layer, while the peak pressure of the protected structure at the measuring point and the failure volume rate of the whole structure decrease first and then increase. Among them, the underwater wave attenuation and energy consumption effect of SAP10S15 protective layer is the most prominent, and its total energy absorption is 20 % higher than that of other protective layers. Under this ratio protection scheme, the shock wave pressure of the protected structure is greatly attenuated, and the peak pressure of each measuring point is reduced by about 35.8 %, 37.9 %, 23.7 %, 27.9 % and 35.1 % respectively compared with the unprotected scheme. The damage degree of the protected structure under the SAP10S15 ratio protection scheme is significantly reduced. The failure volume rate and damage index are 8.03 % and 0.21, respectively, which are 37.7 % and 42.5 % lower than those of the unprotected scheme. The damage level is reduced from serious damage to moderate damage. In addition, the damage grade prediction curve of the protected structure is constructed, and the predicted value of the curve is in good agreement with the simulation results, which can provide a reference for the rapid evaluation of the underwater explosion-proof performance of the steel fiber reinforced cellular concrete protective layer.

Polarization-Accelerated Seawater Splash Simulation for Rapid Evaluation of Protection Performance of an Epoxy Coating on Carbon Steel.

The application of organic coatings is the most cost-effective and common method for metallic equipment toward corrosion, whose anti-corrosion property needs to be improved and evaluated in a short time. To rapidly and rationally assess the anti-corrosion property of organic coatings in the ocean splash zone, a new accelerated test was proposed. In the study, the corrosion protection property of the coating samples was measured by an improved AC-DC-AC test in a simulated seawater of 3.5 wt.% NaCl solution, a simulated ocean splash zone test and a new accelerated test combining the above two tests. The results showed that the corrosion rate of the coating samples was high in the improved AC-DC-AC test, which lost its anti-corrosion property after 24 cycles equal to 96 h. The main rapid failure reason was that the time of the water and corrosive media arriving at the carbon steel substrate under the alternating cathodic and anodic polarization with symmetrical positive and negative electric charges was shortened. The entire impedance of the coating samples was improved by about 1.6 times more than that in the initial early time in the simulated ocean splash zone test, which was caused by the damage effect from the salt spraying, drying, humidifying, salt immersion, high temperature and UVA irradiation being weaker than the enhancement effect from the post-curing process by the UVA irradiation. In the new accelerated test, the samples lost their corrosion resistance after 12 cycles equal to 288 h with the fastest failure rate. On account of the coupling process of the salt spraying, drying, humidifying, salt immersion, high temperature combined with the cathodic and anodic polarization and the UVA irradiation, the penetration and transmission rate of water and corrosive media in the coating were further accelerated, the corrosion rate on the carbon steel substrate was reinforced even larger and the destruction of the top polymer molecules was more serious. The new accelerated test showed the strongest damage-acceleration effect than that in the other two tests.

Study on the Active Wave Absorption Methods in Lattice Boltzmann Numerical Wave Tank

The active wave absorption method has been widely employed in numerical wave tanks. The wave absorption performance of active wave absorption methods is investigated within a numerical wave tank based on a lattice Boltzmann method. Specifically, two active wave absorption methods—the classical shallow water method and the extended range method—are compared. By analyzing the contributions of free and bound components in the harmonics of the reflected wave to the reflection coefficient, we found that the extended-range method is more effective than the shallow-water method in absorbing the reflection of the primary harmonic. Moreover, a wave absorption performance index is proposed to carry out rapid evaluation of active wave absorption method performance without resorting to numerical simulations. Our findings indicate that the performance index ratio of two active wave absorption methods closely mirrored their reflection coefficient ratio. Notably, the extended-range method significantly reduces the performance index in both shallow and deep waters, exhibiting superior active absorption performance within the lattice Boltzmann method-based numerical wave tank context compared to the shallow-water method.

An automatic completion method for design domain knowledge graph using surrogate model, for rapid performance evaluation

The data and information generated during the design process, such as part parameters, affect the efficiency and quality of product design. Knowledge graph (KG) is often used to express and reuse the above knowledge. However, due to the decentralised and complex nature of the KG, it lacks the quantitative knowledge required for part selection, e.g. the relationship between part type and performance. To complete the knowledge and meet the requirements for rapid and accurate performance evaluation, this paper proposes an automatic KGC method based on a surrogate model. Firstly, the schema layer is established based on the design knowledge ontology. For the sampling process, the query statement is applied to extract knowledge such as design parameters, which define the range of sample points. Secondly, the design of experiments (DOE) method can obtain the data set of the target performance through simulation. Surrogate models are built based on multiple machine learning algorithms. Meanwhile, the hyperparameter optimisation method can further improve the prediction accuracy of the model. Finally, the performance of the design scheme is predicted, and the results are used to complete the KG through knowledge extraction. The method is validated by the selection and evaluation of bogie parts.

A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings

Traditional methods of evaluating the performance of journal bearings, for example thermal-elastic-hydrodynamic- lubrication theory, are limited to simplified conditions that often fail to accurately model real-world components. Numerical models that include additional phenomena such as cavitation and fully coupled effects like deformation, temperature, pressure and viscosity can be more accurate but require a large amount of computational overhead, making analysis slower and more costly. To address this limitation, a novel machine learning-driven approach is developed to predict the 2D distribution of surface deformation, film thickness, temperature, and pressure across the bearing surface as a function of design variables such as load and speed. The training dataset, generated using a fully coupled Reynolds’ Equation solver implemented in OpenFOAM, contains a significantly extended range of conditions than in previous studies with approximately 39000000 points encompassing 4925 different test cases. Modelled bearing speeds range from 2000 to 10000rpm, while load values are varied between 1 and 30kN. Predicting surface deformation, film thickness, temperature and pressure across the bearing surface results in a mean absolute percentage error below 0.4% or better. The work also demonstrates that the trained models have a strong ability to generalise the prediction beyond the original training data range with only a 1% error at up to 200% of the highest trained speed. This work also demonstrates that machine learning-based processes are a practical alternative to physics-based numerical modelling, especially in cases where rapid performance evaluation is desired as real-time calculation is possible with significantly reduced computational cost. This has the potential to enable development of rapid design optimisation tools and real-time performance monitoring at high resolution and with low latency. Using consumer hardware, it is found that the neural network-based approach is faster than the existing numerical modelling technique by a factor of over 10000, enabling real-time predictions of lubrication systems.

An integrated climate-based daylight performance evaluation framework for indoor arenas' roof system

Daylight environment plays a crucial role in indoor arenas' comfort and energy performance, characterized by their large spans and large spaces. Design parameters of roof systems in these arenas are essential for enhancing the daylight within competition halls. However, due to the complexity of spatial archetypes in these halls and the unique characteristics of freeform surfaces, more research is needed on the interplay between roof system design parameters and daylight performance. Rapid evaluation of daylight performance in competition halls during the design phase remains a challenge. This study proposes a climate-based evaluation framework for assessing daylight performance in indoor arenas and integrates an efficient practical and research workflow using Rhino + Grasshopper in conjunction with Python.Further, based on this framework and workflow, controlled variable simulation experiments were conducted to explore the relationships between external design conditions, such as climate (represented by three typical climatic zones) and seating capacity, and roof system design parameters, including roof form, redundant height, orientation, skylight form, distribution, and skylight-to-floor ratio (SFR), with time-series, climate-based daylight performance indicators. To integrate the critical factors affecting indoor lighting environments in arenas - illuminance potential, glare evaluation, and uniformity assessment - this study selected Continuous Daylight Autonomy (cDA), Useful Daylight Illuminance (UDI), and Glare Autonomy (GA) as performance indicators, supplemented by two time-series climate indicators specifically for evaluating illuminance uniformity in indoor arenas: “Uniformity Autonomy (UA) “and “Uniformity Variance Autonomy (UVA) ". Statistical analysis of five performance indicators across 860 experimental models in seven groups shows that different design conditions and parameters affect the optimal range of SFR. The significance of the impact of various design parameters varies across different performance indicators. There is a strong correlation between cDA, UDI, and GA, while UA and UVA effectively supplement the evaluation of the first three indicators.

Rapid and flexible lithium-ion battery performance evaluation using random charging curve based on deep learning

Accurate performance evaluation of lithium-ion battery is crucial for its detection, screening and echelon utilization. However, existing evaluation methods rely on specific or complex tests, leading to limited flexibility and high time costs. To address these challenges, a novel two-stage approach based on random charging curve is proposed to evaluate battery performance rapidly and flexibly. Firstly, a deep residual convolutional neural network (Res-CNN) is developed, requiring only a small portion of the charging curve as input to preliminarily estimate battery performance and screen. Secondly, multi-parameters are extracted from the batteries of the same level, and the comprehensive performance is evaluated based on an improved grey TOPSIS algorithm. Finally, two hundred and thirty LiFePO4 batteries are used to verify the effectiveness of this method. The results show that 94.55% accuracy at any initial voltage is achieved to quickly classify batteries into four levels according to battery performance. Furthermore, the consistency of the regrouped batteries has been greatly improved after comprehensive evaluation by comparative test. Obviously, this research has important guiding significance for residual value evaluation and rapid reorganization of battery echelon utilization in different scenarios.

Joint Use of Circuit Models and Complexor Diagrams in Generator Design.

Impedance models have been routinely used in generator design. Despite their many limitations, they are indispensable for rapid performance evaluation, which makes them also suitable for use in conjunction with design optimisation algorithms. This paper discusses the combined use of impedance models and complexor diagrams in a.c. generator design. The approach is demonstrated by applying it to the design of a permanent magnet synchronous generator with surface mounted magnets on the rotor.

Rapid detection of non-normal teeth on dental X-ray images using improved Mask R-CNN with attention mechanism.

Dental health has been getting increased attention. Timely detection of non-normal teeth (caries, residual root, retainer, teeth filling, etc.) is of great importance for people's health, well-being, and quality of life. This work proposes a rapid detection of non-normal teeth based on improved Mask R-CNN, aiming to achieve comprehensive screening of non-normal teeth on dental X-ray images. An improved Mask R-CNN based on attention mechanism was used to develop a non-normal teeth detection method trained on a high-quality annotated dataset, which can segment the whole mask of each non-normal tooth on the dental X-ray image immediately. The average precision (AP) of the proposed non-normal teeth detection was 0.795 with an intersection-over-union of 0.5 and max detections (maxDets) of 32, which was higher than that of the typical Mask R-CNN method (AP = 0.750). In addition, validation experiments showed that the evaluation metrics (AP, recall, precision-recall (P-R) curve) of the proposed method were superior to those of the Mask R-CNN method. Furthermore, the experimental results indicated that proposed method exhibited a high sensitivity (95.65%) in detecting secondary caries. The proposed method took about 0.12s to segment non-normal teeth on one dental X-ray image using the laptop (8G memory, NVIDIA RTX 3060 graphics processing unit), which was much faster than conventional manual methods. The proposed method enhances the accuracy and efficiency of abnormal tooth diagnosis for practitioners, while also facilitating early detection and treatment of dental caries to substantially lower patient costs. Additionally, it can enable rapid and objective evaluation of student performance in dental examinations.

A product performance rapid simulation approach driven by digital twin data: Part 2. For variable operating conditions

As mentioned in Part 1, for product iterative design, the digital simulation method, as the product performance analysis method, has the problems of time cost and computing resources. To address this issue, a product performance rapid simulation approach driven by digital twin (DT) data for variable product structures in Part 1 was proposed, which could replace the digital simulation method. Meanwhile, for performance analysis under variable operating conditions during the product design and operation phase, digital simulation also has the above problems. Therefore, based on the method for variable product structures proposed in Part 1, a product performance rapid simulation approach driven by DT data for variable operating conditions is proposed in this paper. Firstly, the framework for variable operating conditions is designed based on the makeTwin reference architecture, including twin source data acquisition for the product under different operating conditions, twin data-driven product performance rapid simulation model (RSM) construction for variable operating conditions, and rapid evaluation of product critical performance based on RSM. Then, the implementation of the framework is introduced in detail. At last, a case study for critical loosening load evaluation of bolted joint is carried out. The result indicates that the proposed method is feasible, as well as costs shorter simulation time than the traditional digital simulation method.

Performance Evaluation of Damaged Blades Using Numerical Simulation and Machine Learning

The formulation of maintenance strategies for gas turbines requires an accurate and rapid assessment of damaged blade performance. Variety and uncertainty in the three-dimensional geometric deformation of gas turbine vanes and blades increase the difficulty of degradation analysis. In this paper, the effects on the aerodynamic performance of partial damage to turbine nozzle guide vanes are investigated experimentally and numerically. A methodology for constructing a deformation–degradation model for high-pressure gas turbine vanes/blades is proposed based on machine learning. A database consisting of 670 numerical simulation results with various deformation patterns is constructed for the vane cascade. A mapping is constructed between the matrices defining geometric deformations and the total pressure loss coefficient of the cascades based on a convolutional neural network model. The accuracy of this deformation–degradation model is verified against experimental results. We find that deformation on the suction side of the blade has the most significant influence on aerodynamic performance. The deformation–degradation model constructed for a specific cascade provides a rapid evaluation of aerodynamic performance based on rough geometrical measurements without additional experiments or numerical simulations, providing a potential tool in the precise repair of gas turbines.

Calculating of the tunnel face deformations reinforced by longitudinal fiberglass dowels: From analytical method to artificial intelligence

Deformation calculation of the reinforced tunnel is always the key and difficult problem in tunnel support design. To achieve simple and rapid tunnel performance evaluation, this paper attempts to establish a novel calculation system based on the artificial intelligence, which is transitioned from an analytical method named the Convergence-Confinement Method (CCM) based on the spherical symmetry hypothesis. 200 simulations were completed by using an analytical method to describe the reinforced tunnel behavior and seven parameters were considered to calculate the tunnel face deformation. The Boruta algorithm with a random forest (RF) model was utilized to eliminate unnecessary parameters in the analytical method to build high-precision prediction model. The performance evaluation results indicated that the prediction model based the artificial intelligence has obtained excellent accuracy for estimating the deformation of the tunnel face reinforced by the longitudinal fiberglass dowels. Furthermore, the Geological Strength Index (GSI) is the most important parameter for predicting the deformations of tunnel face in the weak rock masses. Interestingly, the tentative parameter, rock type (mi), cannot be ignored when using the proposed artificial intelligence model to predict the deformations of a tunnel face reinforced by longitudinal fiberglass dowels. In general, this successful transition helps popularize the application range of analytical methods and improve the prediction accuracy for the deformations of reinforced tunnel.

Mechanism and performance evaluation of transient and selective laser processing of glass based on optical monitoring.

Femtosecond laser processing has been widely applied in glass processing owing to its ability to fabricate microscale components. To improve processing efficiency, a transient and selective laser (TSL) processing technique was previously developed, in which electron excitation was induced inside a transparent medium by a single pulse of femtosecond (fs) laser, and a single pulse of microsecond (µs) laser can be selectively absorbed in this excited region to heat and remove the material. However, because of its high speed removal process, the unclear mechanism and inefficient evaluation of its processing performance limit its further application. This study analyzes the transient spatiotemporal evolution of the induced plasma and the related material removal mechanism of the TSL processing using a side high-speed monitoring method. To achieve a rapid performance evaluation, a quantitative analysis of the optical plasma signals (on a microsecond timescale) generated in TSL processing was performed by employing a developed coaxial high-speed monitoring method using a photodetector. The variations in the shapes, intensity distribution, and dimensions of the plasma were quantitatively investigated. In addition, the relation between the plasma signal and drilling performance under different laser parameters, including hole depth, hole types, and cracks, was explored and quantitatively analyzed. The revealed mechanism is expected to contribute to the broadening of the application of TSL processing in microfabrication. Furthermore, the developed high-speed and precision monitoring technology can be utilized for high-speed evaluation and precision control of machining quality in real time during ultrahigh-speed laser machining, without time-consuming camera observations.

Designing impact-resistant bio-inspired low-porosity structures using neural networks

Biological structural designs in nature, like hoof walls, horns, and antlers, can be used as inspiration for generating structures with excellent mechanical properties. A common theme in these designs is the small percent porosity in the structure, ranging from 1 to 5%. In this work, the sheep horn was used as an inspiration due to its higher toughness when loaded in the radial direction compared to the longitudinal direction. Under dynamic transverse compression, we investigated the structure-property relations in low porosity structures characterized by their two-dimensional (2D) cross-sections. A diverse design space was created by combining polygonal tubules with different numbers of sides placed on a grid with varying numbers of rows and columns. The volume fraction and the orientation angle of the tubules were also varied. The finite element (FE) method was used with a rate-dependent elastoplastic material model to generate the stress-strain curves under plane-strain conditions. A gated recurrent unit (GRU) model was trained to predict the structures’ stress-strain response and energy absorption under different strain rates and applied strains. The parameter-based model uses eight discrete parameters to characterize the design space and as inputs to the model. The trained GRU model can efficiently predict the response of a new design in as little as 0.16 ms and allows rapid performance evaluation of 128,000 designs in the design space. The GRU predictions identified high-performance structures, and four design trends that affect the specific energy absorption were extracted and discussed.

High-throughput screening to evaluate optimum coagulation conditions via colloidal stability analysis

Current methods of optimizing the coagulant dosage in wastewater treatment processes typically rely on the use of labor- and material-intensive jar testers, which are inadequate when coagulation processes require frequent adjustments due to variations in properties of the incoming feed. Analytical centrifuges (ACs) employ an integrated optics system that simultaneously monitors the position of the boundary between two separating phases in multiple samples of fairly low volumes (∼2 mL) – thus it was expected that ACs would be ideally suited to study the stability and settling kinetics of coagulation treatment processes. In this study, wastewater samples from a biogas generation facility (known as centrate) were collected in February 2022 (Batch A) and July 2022 (Batch B). A comprehensive screening of the treatment performance for Batch B was conducted at three pHs (5, 6, and 7) and nine concentrations of ferric chloride (0–500 mg-Fe3+/L) – it was found that the front-tracking profiles measured by the integrated optics system could be used to identify the minimal coagulation conditions needed to transition from slow to rapid settling. While the settling velocity was found to be well correlated with the instability index, a dimensionless number between 0 and 1 (where values closer to 1 indicate better separation), it was determined that the percentage of COD removal from the centrate samples increased up to an instability index of approximately 0.5 and then plateaued. Finally, it was found that the front-tracking profiles could be used to estimate the volume of sludge produced at various coagulation conditions. Thus, the results from this study establish ACs as an important screening tool for rapid evaluation of treatment performance while consuming minimal material and time – in this study, a total of 132 screening experiments were conducted using approximately ∼11 L of centrate and ∼6 hours of operator time.

Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations

For the development of low-temperature power systems in aviation, the transport synergistic carrier optimization of lithium-ions and electrons is conducted to improve the low-temperature adaptability of lithium-ion batteries. In this paper, an improved robust multi-time scale singular filtering-Gaussian process regression-long short-term memory (SF-GPR-LSTM) modeling method is proposed for the remaining capacity estimation. The optimized multi-task training strategy is constructed for the rapid battery performance evaluation, realizing the refined mathematical dynamic characterization for the mapping relationship of the physical carrier transports to obtain the simultaneous improvement of multi-dimensional physical features and a spiral-up iterative optimization scheme. The adaptability of the model is verified using datasets from the whole-life-cycle test conducted on two batteries, and evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared metrics. Even when only using 55% of the datasets to estimate the remaining capacity, the estimation has good effects, with RMSE of 2.3484%, MAE of 0.82526%, MAPE of 0.90716%, and R-squared value of 92.457%. The proposed SF-GPR-LSTM model enables the carrier transport synergistic optimization effectively, laying a theoretical foundation for the whole-life-cycle battery remaining capacity estimation at extremely low temperatures.

TransCFD: A transformer-based decoder for flow field prediction

The computational fluid dynamics (CFD) method is computationally intensive and costly, and evaluating aerodynamic performance through CFD is time-consuming and labor-intensive. For the design and optimization of aerodynamic shapes, it is essential to obtain aerodynamic performance efficiently and accurately. This paper proposed TransCFD, a Transformer-based decoding architecture for flow field prediction. The aerodynamic shape is parameterized and used as input to the decoder, which learns an end-to-end mapping between the shape and the flow fields. Compared with the CFD method, the TransCFD was evaluated to have a mean absolute error (MAE) of less than 1%, increase the speed by three orders of magnitude, and perform very well in generalization capability. The method simplifies the input requirements compared to most existing methods. Although the object of this work is a two-dimensional airfoil, the setup of this scheme is very general. TransCFD is promising for rapid aerodynamic performance evaluation, with potential applications in accelerating the aerodynamic design.

Electrothermally Driven Reconfiguration of Microrobotic Beam Structures for the ChipSail System

Solar sailing enables efficient propellant-free attitude adjustment and orbital maneuvers of solar sail spacecraft with high area-to-mass ratios. However, the heavy supporting mass for large solar sails inevitably leads to low area-to-mass ratios. Inspired by chip-scale satellites, a chip-scale solar sail system named ChipSail, consisting of microrobotic solar sails and a chip-scale satellite, was proposed in this work. The structural design and reconfigurable mechanisms of an electrothermally driven microrobotic solar sail made of Al\Ni50Ti50 bilayer beams were introduced, and the theoretical model of its electro-thermo-mechanical behaviors was established. The analytical solutions to the out-of-plane deformation of the solar sail structure appeared to be in good agreement with the finite element analysis (FEA) results. A representative prototype of such solar sail structures was fabricated on silicon wafers using surface and bulk microfabrication, followed by an in-situ experiment of its reconfigurable property under controlled electrothermal actuation. The experimental results demonstrated significant electro-thermo-mechanical deformation of such microrobotic bilayer solar sails, showing great potential in the development of the ChipSail system. Analytical solutions to the electro-thermo-mechanical model, as well as the fabrication process and characterization techniques, provided a rapid performance evaluation and optimization of such microrobotic bilayer solar sails for the ChipSail.

Failure mode-based calculation method for bearing capacities of corroded RC columns under eccentric compression

This paper proposed a failure mode-based calculation method for bearing capacities of corroded RC columns under eccentric compressive loads, which considered corrosion-induced mechanical property degradation of steel bars, cross-sectional reduction of steel bars and cross-sectional reduction of concrete. Through analyzing the stress and strain distributions over the normal cross-section, four balanced failures and six failure modes were identified for corroded RC columns under eccentric compressive loads. Balanced corrosion degrees were defined and methods to calculate them were proposed. Based on corrosion degree and balanced corrosion degrees, the failure mode of an eccentrically compressed corroded RC column could be predetermined in advance. Then, the methods to calculate the eccentric compressive bearing capacity under each failure mode were proposed. Subsequently, a dataset of eccentric compressive tests on 118 corroded and 27 non-corroded RC columns was established. The eccentric compressive bearing capacities calculated by the proposed failure mode-based method were found in good agreement with the test results, which demonstrated the validity of the proposed method. The failure mode-based calculation method will contribute to not only rapid and accurate performance evaluation of existing corroded RC columns, but also fast and efficient time-dependent reliability analysis for planned RC columns.

Rapid seismic performance evaluation of existing frame structures using equivalent SDOF modeling and prior dynamic testing

Rapid seismic performance evaluation of existing frame structures using equivalent SDOF modeling and prior dynamic testing