Experimental Test Data Research Articles

Experimental Evaluation on Blast Resistance of Reinforced Concrete Structures under Partially Confined Explosion

As the risk of accidental explosions at ammunition storage or hydrogen charging station increases in populated area, it is needed to design the facilities against blast loading, particularly subjected to partially confined explosion. However, the partially confined explosion lacks experimental test data to efficiently design the facilities subjected to the potential threat, when compared to unconfined or confined explosion cases. As a fundamental study on partially confined explosion, two partially buried tunnel-type structures with normal- and high-strength concretes were tested under a weight charge of 5.9 kg TNT explosion. The major parameters were concrete compressive strength and reinforcement ratio. The test result showed that the damage of the concrete structure with normal-strength concrete was extremely severe, whereas that of the specimen with high-strength was relatively mild. Further, finite element (FE) analyses were performed to investigate the confinement effect of blast load. The FE analysis result showed that under partially confined explosion, the reduction of the maximum displacement by using higher concrete strength was significant, while preventing the failure of the structure. This study provides fundamental data for designing the facilities with explosives subjected to the partially confined explosion.

Modeling and analysis of plastic abrasive jet flow characteristics

In this study, the particle velocity and spatial distribution of a plastic abrasive jet were modeled for the removal of surface coatings from a new generation of ship hulls by plastic abrasive jet machining. The particle velocity in the jet has been accurately tested through high-speed photography. It was found that the particle velocity of plastic abrasives with the same particle size was larger than conventional abrasives. Downstream from the nozzle exit axis, the divergence profile of the abrasive jet is approximately linear at distances up to 6.2dN; at distances higher than 6.2dN, the divergence profile line of the abrasive jet is approximately “flared”. The particle velocity of the plastic abrasive increased with increasing jet pressure, and no significant decrease of particle velocity was found in the limited measurement range. The instantaneous velocity distribution of the particles has been discussed in terms of axial and radial directions along the jet. For the radial velocity distribution of the abrasive jet, a relatively steady state is observed in the cross-section of the jet near the nozzle outlet. The results of the model calculations are in accordance with the experimental test data in terms of both tendency and margin of change.

Optimizing Insulated-Gate Bipolar Transistors’ Lifetime Estimation: A Critical Evaluation of Lifetime Model Adjustments Based on Power Cycling Tests

This paper presents a detailed refinement and validation of two well-known lifetime prediction models for IGBTs, namely CIPS08 and SKiM63, using experimental power cycling test data. This study focuses on adapting these models to reflect the operational conditions and degradation patterns to more accurately fit different IGBT types and applications. Key modifications include recalibrating the scale factor and temperature coefficients in the SKiM63 model and refining the CIPS08 model coefficients (β1 = −2.910, β2 = 1083.714, β3 = −4.521) based on the impact of temperature fluctuations, bond wire diameter, and electrical stresses observed during power cycling tests. These adjustments provide a significant shift from traditional values, with the recalibrated models offering a better fit, as evidenced by a reasonable coefficient of determination (R2) and root mean square error (RMSE). Utilizing Monte Carlo simulations with a 5% uncertainty, the study calculates the B10 lifetimes of PV inverters, demonstrating a substantial reduction from 43 years in the unmodified model to 13 years in the modified model. This emphasizes the critical need for ongoing modification and validation of predictive models based on the actual operational data to enhance the reliability and efficiency of IGBTs in power electronic systems.

Stress localization investigation of additively manufactured GRCop-42 thin-wall structure

A full-field crystal plasticity (CP) framework is presented for the GRCop-42 alloy to study microscopic mechanical behavior and local stress heterogeneities. The microstructures of additively manufactured (AM) materials are often unique relative to conventionally processed materials, and the local thermal histories drive these differences during the build process. These thermal histories depend on the process parameters (laser power, scan speed, and scan strategy) and the part geometry. Prior research has shown that the mechanical properties of thin-walled structures can vary significantly with wall thickness due to changes in the thermal boundary conditions during manufacturing. It is, therefore, desirable to perform CP simulations based on the phenomenological constitutive model to predict the local mechanical responses induced by microstructural heterogeneities. This work generates representative microstructures based on experimentally collected grain information (i.e., texture) for grain scale stress analysis, and the material constitutive parameters are calibrated using the experimental mechanical testing data. We specifically investigated the effect of crystallographic texture and grain morphologies on the size-dependent mechanical properties of AM GRCop-42. The selection of appropriate material properties for implementing an effective free surface boundary condition and the influence of adjacent buffer layers are also discussed. Analysis of local field results reveals a strong correlation between stress localization and the initial grain orientation. However, no significant relationship between the misorientation of the individual adjacent grains and the average misorientation is observed.

Mix design strategy and optimization considering characteristic evaluation of geopolymer concrete

In this study, a unique strategy for mix design of geopolymer concrete (GPC) was suggested by integrating fly ash, silica fume, and GGBFS as precursors with varied alkali activator molarities in both ambient and oven curing environments. GPC's crystalline and microscopic qualities were assessed by X-ray diffraction and scanning electron microscopy tests; while its' mechanical properties were assessed using compressive strength test (CST). The experimental results showed a rising trend between the alkali activator (NaOH) concentration and the compressive strength of the GPC. GGBFS precursor based GPC had the highest compressive strength of 87.53 MPa at 14 molarity of NaOH after oven curing at 60°C. Furthermore, Artificial Neural Network (ANN) and Response Surface Method (RSM) were used to evaluate significant correlation and interaction between different parameters considered in mix design. By incorporating six input variables from the experimental test data, namely total precursor content used, secondary precursor content used, curing temperature, number of curing days, total aggregate content, and molarity of NaOH used, and compressive strength as output/response variable. The R-value for ANN found to be 0.99, while the R2 value for the RSM model was 0.98, indicating a satisfactory fit having a good interaction and optimization of GPC parameters. The optimize values of different independent parameters evaluated can obtain maximum CST value of 111.104 MPa.

Machine learned environment-dependent corrections for a spds∗ empirical tight-binding basis

Abstract Empirical tight-binding (ETB) methods have become a common choice to simulate electronic and transport properties for systems composed of thousands of atoms. However, their performance is profoundly dependent on the way the empirical parameters were fitted, and the found parametrizations often exhibit poor transferability. In order to mitigate some of the the criticalities of this method, we introduce a novel Δ-learning scheme, called MLΔTB. After being trained on a custom data set composed of ab-initio band structures, the framework is able to correlate the local atomistic environment to a correction on the on-site ETB parameters, for each atom in the system. The converged algorithm is applied to simulate the electronic properties of random GaAsSb alloys, and displays remarkable agreement both with experimental and ab-initio test data. Some noteworthy characteristics of MLΔTB include the ability to be trained on few instances, to be applied on 3D supercells of arbitrary size, to be rotationally invariant, and to predict physical properties that are not exhibited by the training set.

Improvement of a hypoplastic model for sand under undrained loading conditions

An important drawback of the hypoplastic model is the inaccurate prediction of the sand behavior under undrained monotonic loading conditions. The model is not able to reproduce the limited liquefaction type response widely observed in undrained tests on loose sand, and it often underestimates the initial stiffness and hardening rate of sand during the shearing. To address these issues, three novel modifications are introduced into a basic hypoplastic model to enhance its undrained predictive capability. Firstly, a new factor is added to the nonlinear term of the model, allowing the simulation of a purely elastic response at the beginning of loading. By doing so, the model can accurately capture the initial stiffness and undrained effective stress path of sand. Secondly, the characterized void ratios are related to an evolving state variable, enabling the model to reasonably reproduce the limited flow response and quasi-steady state. Furthermore, a new term is incorporated into the deviatoric part of the strain rate to adjust the hardening rate of the model. The model performance for undrained loading is significantly improved through the above modifications, as evidenced by the good agreement between simulation results and experimental data for tests with varying densities and confining pressures.

A simulation-driven prediction model for state of charge estimation of electric vehicle lithium battery

Accurately predicting the state of charge (SOC) of lithium-ion batteries in electric vehicles is crucial for ensuring their stable operation. However, the component values related to SOC in the circuit typically require estimation through parameter identification. This paper proposes a three-stage method for estimating the SOC of lithium batteries in electric vehicles. Firstly, the parameters of the constructed second-order RC circuit are identified using the Forgetting Factor Recursive Least Squares (FFRLS) method. Secondly, an innovative approach is employed to construct a battery simulation model using modal-data fusion method. Finally, the predicted values of the simulation model are corrected using the unscented Kalman filter (UKF). Validation through datasets demonstrates the high precision of this method in parameter identification. Moreover, in the comparison of SOC prediction corrections with Particle Filter (PF), Extended Kalman Filter (EKF), and the proposed UKF on simulated prediction data and experimental test data. The proposed method achieves the lowest root mean square error (RMSE) of 0.0025 for simulation prediction data and 0.0186 for experimental test data. It also maintained its error within 5 % on actual data.

Numerical modelling of different airbag folding patterns and their influence on occupant responses in frontal vehicle impact

This paper presented a procedure for airbag folding for the application in occupant safety studies and analysed the influence of different airbag folding patterns on the occupant severity in frontal impact. Airbags were folded in two patterns: zig-zag and top-roll, using two folding techniques: Initial Metric Method and Explicit Folding. The explicit folding was found to be more expensive in terms of preparation time. However, this approach provided more control over the whole folding process. The Initial Metric Method was more robust, however, it’s hard to apply for complex folding patterns. Deployments of airbags were validated against experimental data of pendulum tests. Those airbag models were applied to 2006 Ford F250 pickup truck. This vehicle was used for simulations of frontal vehicle impact where the 50th male Hybrid III crash test dummy was an occupant. Results of this simulation were compared with an actual test with a similar vehicle, under the same impact conditions. Results showed the head and chest accelerations were lower for top-roll folded airbag cases, however neck normal forces were higher compared to zig-zag folded airbag. The internal pressure in the early stage of deployment was 33% higher for the top-roll folded airbags.

Using Fluid Curtains to Improve Sealing Performance in Turbomachinery Applications

Abstract The results from an investigation into the physics of how fluid curtains can be applied to improve the aerodynamic performance of conventional turbomachinery shaft and rotor seals are described in this paper. Computational fluid dynamics and testing on two experimental facilities are used in the study. In the first part of the work, computational fluid dynamics simulations validated against experimental test data demonstrate the fundamental mechanism by which the presence of the curtain can act to reduce leakage flow through conventional seals. These results are consolidated into a single performance carpet map, showing how the leakage reduction performance and the curtain supply pressure needed to achieve it vary with changes in values of key geometrical parameters. In the second part of the work the effect of swirl in the seal inlet flow, as is often encountered in turbomachinery applications, on the performance of the fluid curtain is investigated experimentally. Test results show that if the swirl momentum in the inlet flow is greater than the momentum of the curtain flow, the performance benefit from applying the curtain is greatly diminished. Overall, the results provide some fundamental design rules for applying fluid curtains to enhance turbomachinery sealing performance for the general type of leakage path geometry (cylindrical channel, 45-deg jet angle, curtain upstream of a conventional seal) and working fluid type and conditions (air, ambient temperature, subsonic leakage channel flow) used in the study.

Experimental determination of hypoplastic parameters and cyclic numerical analysis for railway bridge backfills

AbstractLong integral bridges experience an enhanced cyclic soil structure interaction with their granular backfills, especially due to seasonal thermal loading. For numerical modelling of this interaction behaviour under cyclic loading, it is important to employ a suitable constitutive model and calibrate it thoroughly. However, up to the present, experimental data and calibrated soil models for this purpose with focus on typical well-graded coarse-grained bridge backfill materials are rarely available in the literature. Therefore, one aim of this paper is to present results of a comprehensive cyclic laboratory testing programme on highly compacted gravel backfill material. Based on this, a hypoplastic constitutive model with intergranular strain extension for small strain and cyclic behaviour is calibrated and evaluated against the experimental test data. The soil model’s abilities and limitations are discussed at element test level. In addition, cyclic FE analyses of an integral bridge are conducted with several hypoplastic parameter sets from the literature and compared to the calibrated gravel backfill material. The investigation highlights that poorly-graded sands show significantly smaller cyclic earth pressures compared to well-graded gravels intended for the backfilling of a bridge. The soil structure interaction behaviour is clearly governed by the general soil model stiffness, including the small strain stiffness.

Cyclic flexural fatigue of porous Ti6Al4V constructs for use in mandibular reconstruction

Porous Ti6Al4V constructs have been developed to replace mandibular bone, however the flexural fatigue properties of these constructs must first be assessed. In this study, porous constructs were built by selective laser melting (SLM) and subjected to cyclic flexural loading using a three-point bending setup and a servo-hydraulic Instron machine. Maximum flexural stress was plotted against the number of cycles. Also, numerical models were developed to predict the fatigue strength of porous constructs. These models were validated using experimental test data and extended to account for bone ingrowth. Scanning electron microscopy was used to study the internal and surface structures of dynamically loaded constructs. We found that numerical models of the SLM-built constructs accurately predicted flexural fatigue strength within ≈10 % deviation. Numerical fatigue models and experimental tests demonstrated that fatigue strength of constructs were ≈30 % of their flexural yield strengths. Numerical models with bone ingrowth revealed that fatigue strength was doubled when compared to models without bone ingrowth. This may have been due to a damping effect of bone, resulting in reduction of fatigue failure. Overall, our study demonstrates the effectiveness of using numerical modeling in estimating the fatigue strength of SLM-built constructs intended for mandibular implant designs.

Enhanced mechanical properties of magnesium alloy reinforced by layered Ti3AlC2 MAX phase fabricated by SPS at a near solidus temperature

Powder metallurgy stands out as a highly promising and versatile method for manufacturing particle-reinforced metal matrix composite (MMCs) as compared to other fabrication processes. This method ensures excellent binding between the matrix and reinforcement, uniform microstructure, and effectively prevents the formation of undesirable phases. In this study, magnesium alloy (AZ91) metal matrix composites (MMCs) reinforced with 0–30 vol% of Ti3AlC2 MAX phase were prepared by Spark Plasma Sintering (SPS) from powders at temperature close to the solidus. Ti3AlC2 is a member of the MAX phases which are known for their layered structure, machinability and being ternary carbides. The latter was synthetized from pure components (Ti, TiC and Al) using insulated SPS technology at high temperature. Sintered composites reached quasi-full density, as measured by Archimedes’ method. Their microstructure has been assessed by SEM and X-ray diffraction (XRD) respectively. Mechanical properties in relationship with the microstructure of all composites were carefully investigated. The experimental compressive tests data were extracted to obtain true strain-true stress curves. Vickers hardness (HV1), ultimate compressive strength (UCS) and conventional yield strength (0.2%YCS) increase with an increasing fraction of Ti3AlC2 in the composites and compare favorably with literature. SEM observations of fracture surfaces after compression tests seem to show that cracks initiate in both Ti3AlC2 clusters and α-Mg/MAX interfaces.

A modelling approach for laser welding of busbars to lithium-ion prismatic cell terminals to enhance failure prediction

Laser welding is increasingly being used to connect automotive battery packs and weld busbars to prismatic cell terminals due to its ability to weld a wide variety of materials at high speed. The choice of material for the connecting busbars is primarily based on weldability, weight, electrical/thermal conductivity and cost. Aluminum (Al) is increasingly being chosen as busbar material to meet these criteria. This paper investigates laser overlap welding of Al for producing busbar-to-terminal interconnects for prismatic cell assembly. To understand the strength of these laser-welded joints in the early stages of design, the joints must be modelled. In addition, the structural modelling of battery module connections is critical to understanding the impact resistance (crash safety) of electric vehicles. Fusion-based modelling of laser welding is computationally inefficient and difficult to integrate into large structural models e.g., full vehicle crash models. Instead, it is more computationally efficient to generate a structural model of the weld, tuned to experimental test data, which can be easily integrated into larger models. LS-DYNA was employed to model the mechanical behavior of 1 mm to 1 mm AA1050 laser welds. Welding was performed using a wobble head integrated with a 1 kW CW fiber laser system. The simulation is based on laser weld with a rectangular pattern and was developed from experimental data from lap shear, T-peel, cross-tension and torsion tests. This modelling approach was extended to obtain joint strength for laser welding a 1.5 mm 1060 Al busbar to a 4 mm 1060 Al prismatic cell terminal. The predicted load–displacement curves closely matched the test data.

Study on Electrical Characteristics and Formation Mechanism of Micro-Ion Capacitor in Shale Pore and Fracture Structure

Due to the micro/nanoscale and intricate pore structure, poor connectivity, complex pathways, the presence of microfractures, and the coexistence of organic and inorganic pores, shale and other tight reservoirs exhibit increasingly complex conductive characteristics. This paper mainly studied the electrical properties of shale based on experimental test data, including scanning electron microscope (SEM) thin-section and microtomography CT (μ-CT) images, and analyzed the shale pore type, pore structure characteristics, and development of fracture. Then, the distribution of pyrite, content, and graphitization of organics and their influence on the electrical properties are discussed. Furthermore, the double electrical layer and zeta potential in the shale zone were discussed in depth. The results revealed that, within the content of pyrite, organics, and its graphitization, the vitrinite maturity are inversely proportional to shale resistivity. It was also found that in the presence of an external electromagnetic field, the fluid in shale pores is subjected to the combined strength of pore pressure and external field potential difference. Thus, its response equation should be an improved Navier-Stokes equation, which considers pore pressure, zeta potential, and Coulomb force. When shale is subjected to an external electromagnetic field, due to the complex pores structure and organic and inorganic minerals, it will represent more of a dielectric-like property than electricity. So, it will form special microscopic ionic capacitors, which are different from common plate capacitors. There are three special kinds of microscopic ionic capacitors, they are (I) the intergranular pore microscopic ionic capacitor model, (II) the particle with isolated pore microscopic ionic capacitor model, and (III) the pyrite or graphite or other organics microscopic ionic capacitor model. Finally, the characteristics of microscopic ion capacitors are summarized: irregular polar area and varying distance between poles, varying charges with time, and salinity of the formation water.

Modified plastic damage model for steel fiber reinforced concrete

AbstractSteel fiber reinforced concrete (SFRC) structures have been widely adopted and attracted great research attention due to their excellent performance in resisting tension and flexure bending. However, the existing analytical and numerical analyses of SFRC structures rely mainly on the experimental data of material tests, thereby being suitable for a case‐by‐case basis. This is due to the lack of a general and reliable constitutive material model for SFRC, which analytically considers the fiber‐dependent parameters such as fiber geometry, fiber stiffness, and interface properties of fibers and concrete matrix. This study presents an approach to modify the concrete plastic damage model to represent the SFRC material constitutive relations for simulating the structural behavior of SFRC. In this approach, the general procedure to integrate the bridging effect of fibers through the pull‐out mechanism into the constitutive relation of SFRC was proposed. The comparison between the numerical and experimental results was conducted to verify the reliability of the proposed model. The results demonstrated the proposed model could well represent the material performance of SFRC and the numerical simulations could capture reasonably the effect of the volume fraction, geometry, and properties of fibers on the structural response of SFRC.

Wear life prediction of piston ring and cylinder liner based on integration of experiment and bench test data

The wear data of piston ring-cylinder liner (PRCL) of high-power diesel engines faces the problems of small samples, randomness, and underutilization, which leads to the wear life prediction of PRCL has been a difficult task in the engineering field. A wear life prediction method of PRCL based on the integration of experimental data and bench test data is proposed. First, a large amount of wear data is rapidly obtained through accelerated wear tests on specimens, and the distribution type of wear depth of PRCL is determined. Secondly, based on Rhee's wear equation, the nonlinear relationship between time, load, temperature, and wear depth is considered, and the acceleration models of temperature and load were introduced as the Arrhenius model and the inverse power law model, respectively. The wear life prediction model (WLPM) for specimens and parts with stochasticity is developed. The eigenvalues of WLPM’s parameters of specimens and parts were utilized to construct the mapping relationship from the specimen to the part. Finally, the Bayesian statistical method is used to merge the wear data of specimens and the parts, and the WLPM of specimens and the mapping relationship are taken as the a priori information, the bench data are taken as the new sample information, and the MCMC-Gibbs sampling method is used to estimate the mapping-based WLPM of parts. The results show that through the validation test, the method predicts the wear depth of PRCL with 100 % falling into the 95 % confidence interval better than the WLPM prediction results of a single specimen or part, and the errors of predicting the mean wear life of PRCL are all within 10 %. The method can provide a reference for the quality evaluation and engineering application of PRCL.

Deep neural network for damage detection in Infante Dom Henrique bridge using multi-sensor data

This paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto. The main contribution of this work lies in exploiting the combination of raw measurements from local (inclinations and stresses) and global (eigenfrequencies) variables in a full-scale structural health monitoring application. We exhaustively analyze and compare the advantages and drawbacks of employing each variable type and explore the potential of combining them. An autoencoder-based deep neural network is employed to properly reconstruct measurements under healthy conditions of the structure, which are influenced by environmental and operational variability. The damage-sensitive feature for outlier detection is the reconstruction error that measures the discrepancy between current and estimated measurements. Three autoencoder architectures are designed according to the input: local variables, global variables, and their combination. To test the performance of the methodology in detecting the presence of damage, we employ a finite element model to calculate the relative change in the structural response induced by damage at four locations. These relative variations between the healthy and damaged responses are employed to affect the experimental testing data, thus producing realistic time-domain damaged measurements. We analyze the receiver operating characteristic curves and investigate the latent feature representation of the data provided by the autoencoder in the presence of damage. Results reveal the existence of synergies between the different variable types, producing almost perfect classifiers throughout the performed tests when combining the two available data sources. When damage occurs far from the instrumented sections, the area under the curve in the combined approach increases [Formula: see text] compared to using local variables only. The classificatoin metrics also demonstrate the enhancement of combining both sources of data in the damage detection task, reaching close to [Formula: see text] precision values for the four considered test damage scenarios. Finally, we also investigate the capability of local variables to localize the damage, demonstrating the potential of including these variables in the damage detection task.

Optimum design of HNBR and PCR-based isolation system to protect a sensitive article stored in a naval vessel container subjected to shock load

In naval engineering, protecting sensitive articles from shock loads is a major concern, as it may lead to adverse consequences. This paper focuses on the design of a novel Hydrogenated Nitrile Butadiene Rubber (HNBR) and Polychloroprene Rubber (PCR)-based shock isolation system to protect a sensitive article stored in a hollow cylindrical naval vessel container (NVC). Firstly, NVC is modeled as a two-degree-of-freedom lumped parameter model (LPM), wherein isolators made of HNBR and PCR materials are modeled with 5-order polynomial nonlinear stiffness and linear damping. The optimal values of the isolator parameters are obtained using a multi-objective genetic algorithm, and corresponding responses to a shock load are found using MATLAB. Furthermore, a three-dimensional (3D) finite element model (FEM) of the NVC is generated in ABAQUS and subjected to shock loading in a transverse direction to conduct a comprehensive dynamic analysis of the isolation system. In 3D simulation, HNBR and PCR materials are modeled using a hyper-viscoelastic model developed based on experimental test data. The 3D simulation results of the NVC for the optimized design of HNBR and PCR isolators agree with the optimized results of the LPM. Moreover, the proposed HNBR and PCR isolators are very effective in shock reduction.

Validation of a New Method for Continuous Flare Combustion Efficiency Monitoring

A new method is described for calculating flare combustion efficiency (CE) and destruction and removal efficiency (DRE) using a numerical parametric model. The method combines key variables that affect flare performance including the flare vent gas net heating value (NHV), flare design, flow rate, exit velocity, and inert gas composition, alongside the environmental influence of crosswind speed. Each effect is characterized using a parametric model derived from experimental testing data and computational fluid dynamics (CFD). The inclusion of CFD allows the model to be extended into the high-wind conditions that cannot be adequately controlled for in empirical testing yet represent some of the most challenging conditions in which to maintain good combustion. This new parametric model method (PMM) is coupled with ultrasonic flowmeters from which the molecular weight and net heating value of the flare gas can be derived using the vent gas speed of sound measurement. In doing so, this method provides a reliable continuous flare combustion efficiency measure that can be deployed at scale with minimum hardware updates. The system was verified using an extractive sampling method with tests conducted on three full-scale industrial flares including non-assisted, single-arm pressure-assisted, and multi-arm pressure-assisted flare designs. A total of seventy valid test points were carried out with varying flow rate and flare gas heating value, covering a CE range from 46–100%. The uncertainty of the method was assessed using both traditional error propagation and Monte Carlo methodology. The results from the new method agree with the extractive method to within 0.8% in the ≥98% DRE region where flares are expected to operate to limit the impacts of flaring as a source of methane as a greenhouse gas. Uncertainty analysis revealed that the larger DRE discrepancy for DRE ≤ 98% correlates to the measurement uncertainties for both methods.