Search

Metal diborides are a class of ceramic materials with crystal structures consisting of hexagonal sheets of boron atoms alternating with planes of metal atoms held together with mixed character ionic/covalent bonds. Many of the metal diborides are ultrahigh-temperature ceramics such as HfB2, TaB2, and ZrB2, which have melting points above 3000 °C, high mechanical hardness and strength at high temperatures, and high chemical resistance, while MgB2 is a superconductor with a transition temperature of 39 K. Here, we demonstrate that this diverse family of non-van der Waals (vdW) materials can be processed into stable dispersions of quasi-two-dimensional (2D) nanosheets using ultrasonication-assisted exfoliation. We generate quasi-2D nanosheets of the metal diborides AlB2, CrB2, HfB2, MgB2, NbB2, TaB2, TiB2, and ZrB2 and use electron and scanning probe microscopy techniques to characterize their structures, morphologies, and compositions. The exfoliated layers have a distribution of lateral dimensions from tens of nanometers up to several micrometers and a distribution of thicknesses from as low as 2–3 nm up to tens of nanometers, all while retaining their hexagonal atomic structure and chemical composition. We exploit the convenient solution-phase dispersions of exfoliated CrB2 nanosheets to incorporate them directly into polymer composites. In contrast to the hard and brittle bulk CrB2, we find that CrB2 nanocomposites remain very flexible and simultaneously provide increases in the elastic modulus and the ultimate tensile strength of the polymer. The successful liquid-phase production of quasi-2D metal diborides enables their processing using scalable low-temperature solution-phase methods, extending their use to previously unexplored applications, and reveals a new family of non-vdW materials that can be efficiently exfoliated into quasi-2D forms.

This article introduces multiangular imagery into physics-based bathymetry in order to compensate for the shortage of bathymetric spectral bands caused by the low spectral resolution of current high-spatial-resolution satellite multispectral imagery. The focus is to propose a selective bathymetric retrieval method to eliminate the negative effect of nonoptimal image data on depth retrieval in multiangular imagery-based bathymetry. The elimination of the negative effect is implemented by excluding nonoptimal pixels in every individual image from bathymetric retrieval. An empirical criterion is designed for the determination of nonoptimal pixels. The proposed method can use multiangular image data selectively, avoiding situations where bathymetric retrieval results from the whole multiangular imagery are poorer than that from a part of the individual images. The method was tested in two typical areas within the Xisha (Paracel) Islands of the South China Sea using two-angle WorldView-2 multispectral images. The test showed that the derived depths of the method (i.e., depths derived from the selective image data) provided a better fit to the validation depths than those from the entirety of both images. The underestimation of depths derived from the entirety of both images was also improved to some extent.

The log-ratio method (LRM) proposed by Stumpf et al. has been widely used to map bathymetry from multispectral imagery for oligotrophic waters, while the selection criteria of bands for the LRM have been subject to tradeoffs between maximum detectable depth and sensitivity. In this article, we first applied a method for global sensitivity analysis to a semianalytical forward model of optically shallow waters with the WorldView-2 band-set. The results show that the sensitive wavelength band in water-leaving reflectance for water depth varies from the longer wavelength band to the shorter wavelength band with increasing water depth. Then, we developed an adaptive blended algorithm approach (ABAA) to seamlessly map bathymetry from the shallower region to the deeper region. The LRM with different band combinations was selected for the sub-algorithms of the ABAA. The subalgorithms and depth range used for each subalgorithm of the ABAA were automatically determined by the proposed applicable depth range analysis that considers logarithmic regression for the LRM. The ABAA was applied to WorldView-2 and Landsat-8 imagery of the Xisha Qundao. When the in situ bathymetry data are available, compared with the LRM with the blue and green bands, the ABAA significantly improves the accuracy of the estimated depth, especially for waters shallower than 6 m (root–mean- square error (RMSE) = 0.31 to 0.94 m for WorldView-2 data, RMSE= 0.25 to 1.42 m for Landsat-8 data). When the in situ bathymetry data are absent, the ABAA performs better than the LRM with a single band ratio and an optimization-based method overall.

Based on a DrivAer model with notchback, the characteristics of convective and acoustic pressure fluctuations on the side window, as well as their contributions to interior noise were studied. Firstly, a full-size DrivAer clay model was produced with a real glass set on the front left window, and the rest parts with thick clay. In this way, the side glass becomes the exclusive transmission path for the exterior convective and acoustic pressures into acoustic cabin inside. In this study, the acoustic pressure fluctuation on the side window surface was calculated by solving the acoustic perturbation equation (APE) based on the calculation results of convective pressure fluctuation with the incompressible Detached Eddy Simulation (DES). Furthermore, with the convective and acoustic pressure fluctuations as power inputs, the interior noise was calculated with Statistical Energy Analysis (SEA). The calculated interior noise level shows good agreement with the tested results in the wind tunnel, which indirectly validates the reliability of the calculated acoustic pressures with APE method. The contributions of the convective and acoustic pressure fluctuations to the interior noise show that the acoustic pressure fluctuation takes much higher transmission efficiency than the convective one, especially at the high frequency range above the coincidence frequency of the glass, the contribution of acoustic pressure fluctuation is absolutely dominant.

This article develops a tight integrity risk bound for residual-based (RB) advanced receiver autonomous integrity monitoring (ARAIM). ARAIM measurement models include nominal biases accounting for unknown but bounded errors, and faults of unbounded magnitude. In RB methods, upper bounding the integrity risk requires that one finds the worst-case directions of both the multisatellite fault vector and of the all-in-view nominal bias vector. Previous methods only account for the worst-case fault direction assuming zero nominal bias. To address this issue, in this article, we derive a new bounding method in parity space. The method establishes a direct relationship between mean estimation error and RB test statistic noncentrality parameter, which accounts for both faults and nominal errors. ARAIM performance is evaluated to quantify the improvement provided by the proposed method over previous approaches.

A new method for online multiple-aircraft collision avoidance using an autonomous dependent surveillance-broadcast system is proposed. The key objective is to take advantage of satellite-based navi...

With advanced manufacturing processes, many modern structural designs need to include microscale uncertainties of material properties and microstructures that can affect overall performances, such as microelectromechanical systems, micro–opto–electro–mechanical systems, and micro-optical electronics systems. Topology design optimization at this scale must consider uncertainties from material microstructures as the scale of the structure and material microstructure is comparable. Very few studies consider microstructure uncertainties in topology design due to their scales are much different in classical mechanical/civil engineering. A novel framework of reliability-based topology optimization is proposed to specifically address this gap for the design. The microscale uncertainties of heterogeneous materials are quantified by the explicit mixture random field model. Then, the material distribution is optimized by a heuristic updating scheme, and sensitivities of reliability constraints are calculated using the adjoint design-point-based importance sampling method. The proposed methodology simultaneously considers microscale hierarchical uncertainties in microstructures and material property variations for each phase. The feasibility of the framework is demonstrated by several numerical examples with different multi-phase materials. Compared with deterministic topology optimization and classical topology optimization with first-order reliability method approximations, the optimal design obtained from the proposed method can achieve accurate target reliability with minimized limit state function evaluations.

Effective control of a fly ash/polymer interface is often hindered by poor compatibility. Advanced surface modification of fly ash by using corrosive microorganisms is proposed in this study. Microbial adhesion and catabolism of corrosive microorganisms resulted in a lipophilic surface and a cellular structure of fly ash, which contribute to the compatibility between fly ash and polymers. The mechanisms of corrosive microorganisms modifying the fly ash were revealed by X-ray photoelectron spectroscopy. Owing to the favorable interface bonding between modified fly ash and rubber, the fluororubber compounds achieved a favorable tensile strength exceeding 10.0 MPa. The reinforcement effect, which was almost identical to that of medium thermal black, indicates that microbial modification can be used to treat fly ash and other similar inorganic particles.

A probabilistic methodology for separation loss probability assessments is proposed in this paper. The key focus is on the effect of uncertainties from multiple Automatic Dependent Surveillance-Broadcast (ADS-B) systems on the separation loss probability assessment. First, a brief review of the ADS-B system and its associated uncertainty quantification metrics is discussed. It is found that most existing studies focus on the individual ADS-B uncertainty quantification for a single aircraft, which is not sufficient for separation loss probability assessment when two or more aircraft are involved. Next, a probabilistic positioning model with multiple aircraft is proposed and various types of uncertainties are included in the proposed model. Numerical simulations show that a navigation satellite fault can significantly affect separation error when individual aircraft see different satellite sets. Following this, several demonstration examples are illustrated to show the bounds for separation loss probability estimation. Finally, several conclusions and suggestions are discussed based on this study. One major finding is that the separation risk significantly increases when two nearby aircraft use different satellite sets to navigate. Real-time assessment of this risk should be performed.

The fast and accurate prediction of flight trajectories is crucial for the real-time prognostics of the air transportation system. However, the computation costs of simulating aircraft dynamics can be expensive or even prohibitive especially for a large number of aircrafts in the airspace system. This study presents a novel physics-based learning method as a model order reduction (MOR) method for the simulation of aircraft dynamics. The idea of physics-based learning method is to integrate the underlying physics of aircraft dynamical systems into machine learning models to reduce training costs and enhance simulation performances. A recently proposed recurrent neural network (RNN) known as the deep residual RNN (DR-RNN) is used as a tool of physics-based learning. The application of the physics-based learning method is demonstrated on simulating the dynamics of a Boeing 747-100 aircraft. The results show that the DR-RNN can accurately predict aircraft responses and shows excellent extrapolation performances. Furthermore, a purely data-driven approach using the long short-term memory (LSTM) network is also used for the simulation. The comparison demonstrates that incorporating the physics of aircraft dynamics into the learning model can significantly improve prediction performances and effectively reduce training costs compared with using purely data-driven methods. Finally, it is found that the physics-based learning method exhibits superior computation efficiency compared with a classical numerical method since the physics-based learning method can use large time step sizes that violate the numerical stability condition while being explicit in time.

Polysaccharides purified from natural herbs possess immunoregulatory functions, while the efficacy of natural polysaccharides on cancer treatment remains unreliable, likely due to their low prescribed doses and fast clearances in clinical settings. In this study, gold nanocomposites containing Ganoderma lucidum polysaccharide (GLP-Au) efficiently induced dendritic cell (DC) activation, evident by the increase of CD80/CD86/CD40/MHCII, decrease of phagocytic ability and acid phosphatase activity, and increased cytokine transcription. GLP-Au significantly promoted the proliferation of CD4+ and CD8+ T cells in splenocytes. DC/T cell co-culture study proved that GLP-Au activation on DC directly resulted in T cell proliferation. GLP-Au exhibited strong inhibitory effects on 4T1 tumor growth and pulmonary metastasis when combined with doxorubicin. GLP-Au recovered body weight loss by doxorubicin and increased the percentage of CD4+/CD44+ memory T cells. This work suggests that polysaccharides from natural herbs can be incorporated into nanocomposites with immunoregulatory characteristics for enhanced efficacy on tumor therapy.

Carbamates weaken the luminol-H$_{2}$O$_{2}$ chemiluminescence (CL) catalyzed by sodium copper chlorophyll (SCC). The capacity to inhibit the CL of three carbamates is in the order of carbaryl (CBL) \textgreater carbofuran (CBF) \textgreater metolcarb (MTC). Mechanisms of carbamates inhibiting SCC-luminol-H$_{2}$O$_{2}$ CL are investigated using fluorescence quenching and quantum chemistry simulations for the first time in this work. Carbamate-SCC interactions studied using fluorescence spectroscopy show that with the increasing concentration of the SCC, the fluorescence of carbamate is quenched regularly, and the quenching mechanism is a static quenching process. Binding constants ($K_{B})$ of the three carbamates with SCC are CBL (4.39 x 10$^{5})$ > CBF (1.46 x 10$^{4})$ > MTC (2.16 x 10$^{3}$ L/mol), which is completely harmonious with the capacity to inhibit CL of the carbamates. Furthermore, the carbamate-SCC interaction energies from quantum chemistry simulations are CBL-porphyrin copper (PPCu) (-30.1), CBF-PPCu (-21.0), and MTC-PPCu (-19.9 kJ/mol), which is also identical to their inhibiting capacity. This provides further evidence that the formation of carbamate-SCC complexes reduces the SCC catalytic activity and the CL intensity decreases. In addition, a novel flow injection chemiluminescence method for the determination of carbamate was established based on carbamate inhibiting CL of the SCC-luminol-H$_{2}$O$_{2}$ CL system under alkaline conditions. This work may contribute to the study of the mechanism of CL inhibition using fluorescence quenching and quantum chemistry calculation methods.

Bathymetry is important to shallow coral reef management. Remote sensing is one of the most important techniques for mapping bathymetry. For instance, multispectral remote sensing images have been widely used to map bathymetry of coral reefs. These physically based methods, which are able to retrieve bathymetry, water column inherent optical properties (IOPs), and benthic reflectance simultaneously, generally can achieve good bathymetry results. However, there are strong limitations when facing the scenarios of mixing benthic reflectance for bathymetry with multispectral images. In this study, aiming at handling the mixing phenomenon on the use of multispectral remote sensing images for mapping bathymetry, we propose a new unmixing-based method, namely unmixing-based multispectral optimization process exemplar method (UMOPE). The new method incorporates endmember variability with a linear combination of three fixed endmembers and the relaxation of the sum-to-one constrained. UMOPE was validated with two existing methods with in situ data and a WorldView-2 image in the South China Sea. Results from in situ data show that the proposed method performs the best, with the smallest absolute root mean square error ${\rm{(RMSE)}}$ (2.26 m) and the best agreement ( $R^{2}= {\text{0.91}}$ ) between the measured and estimated water depth. Moreover, results from WV-2 imagery demonstrate the superior performance for the UMOPE at depth range from about 9 to 26 m. Furthermore, since the errors from IOPs can propagate to bathymetry, we further take this issue into account by analyzing the rule of influence of bottom on IOPs. Finally, the result of benthic classification map from UMOPE is shown.

Aiming at the problem that the traditional reliability models of mechanical products are used to predict the reliability of hydraulic automatic transmission and the expected result is relatively large, firstly, the empirical distribution model line is used to statistically analyze the failure distribution law of the hydraulic automatic transmission; then, the Fourier transform is used to perform frequency domain analysis on experience distribution; on this basis, comprehensively consider the characteristics of experience distribution and frequency domain characteristics of experience distribution, constructs the reliability model of exponential decay oscillation distribution and the corresponding reliability, failure efficiency and average life calculation model; meanwhile, studies the influence of attenuation coefficient, oscillation amplitude, oscillation angle frequency, and other parameters on the probability distribution characteristics. On this basis, the established probability distribution models are adopted to fit the failure time data of hydraulic automatic gearbox carried by a forklift, and the fitting results are compared with exponential distribution models, three-parameter Weibull models, and “bathtub curve” models. The comparing results show that the established exponential decayed oscillation distribution model can better describe the probability distribution characteristics of the fault-free working time of automatic transmission, and the use of this model can obtain a smaller root mean square error. Simultaneously, the research conclusions of this paper can provide meaningful guidance and reference for the analysis of the life distribution model of mechanical products with exponentially attenuated oscillation probability density change law.

The workload of air traffic controllers (ATCs) is increasing due to the growing air traffic. Early alarms of loss of separation (LoS) events between aircraft are critical for ATCs to coordinate intensive traffic safely. The authors studied the time series of traffic densities and numbers of turning aircraft in a given sky section as early indicators of pending LoS. Simulator experiment produced data for comparing the prediction accuracies of the logistic regression models generated from the time series of traffic densities and numbers of turning aircraft, and combinations of these two. We studied different sections of the time series to examine the possibility of early detection and found that 1) the regression model based on the traffic density time series is more accurate than the model using the numbers of turning aircraft; 2) properly combining sections of the time series could produce models that achieve earlier predictions without losing accuracy.

This paper reports an experimental and theoretical investigation on the fatigue performance enhancement of damaged steel structures by bonded carbon fiber reinforced polymer (CFRP) systems considering the temperature effects. First, thermomechanical properties of the epoxy adhesive and CFRPs were examined via dynamic mechanical analysis (DMA) and tensile tests. The appropriate curing procedure for the structural adhesive was obtained. Next, the fatigue behavior of notched steel plates strengthened by bonded CFRPs was investigated at different temperatures. During the fatigue tests, beach marking and back-face strain techniques were used to monitor the fatigue crack growth on steel components and the damage evolution within the bonding interface. The results show that the high-temperature resistance of the epoxy adhesive can be effectively improved by increasing the curing temperature and duration. The properly cured bonded CFRP patches can effectively enhance the fatigue performance of damaged steel structures. However, and the elevated temperatures significantly degrade the fatigue behavior of the CFRP-strengthened steel components.

Life prediction in energy infrastructure such as gas pipelines is important to maintain the integrity of such systems. This paper explores a life prediction model for polyethylene materials in natural gas distribution pipelines under creep damage. The model uses a power law equation to describe the crack growth rate and an asymptotic solution for the stress intensity factor (SIF) calculation considering local geometry variations. The SIF solution considers the effect of stress concentration introduced by common damages in pipes such as rock impingement and slit. An effective initial crack size model is proposed for the life prediction of plastic pipes considering the intrinsic initial defect. Large loading-induced plastic deformation is included by a correction factor in the crack growth model. The model is calibrated and validated using experimental data on Aldyl-A pipes with different types of damage. Due to the stochastic nature of the crack growth process, uncertainty quantification is performed, and Monte Carlo (MC) simulation is used to estimate the failure probability. The predicted probabilistic life distributions under different loading conditions are compared with the experimental data. Some conclusions and future work are drawn based on the proposed study and experimental validation.

A probabilistic fatigue life prediction framework for concrete bridges is proposed in this study that considers the stress history from the construction stage to the operation stage. The proposed fatigue analysis framework combines the fatigue crack growth-based material life prediction model and a nonlinear structural analysis method. A reliability analysis is proposed using the developed probabilistic model to consider various uncertainties associated with the fatigue damage. A Bayesian network is established to predict the fatigue life of a concrete bridge according to the proposed framework. The proposed methodology is demonstrated using an experimental example for fatigue life prediction of a concrete box-girder. Comparison with experimental data of fatigue life shows a satisfactory accuracy using the proposed methodology, and the ratio of the posterior predicted mean (updating time n = 8) to the test value decreases to 33%–1% in the current investigation.

A novel damage imaging of engineering structures and materials methodology using the k-space method with adjoint method is proposed in this paper. The proposed methodology consists of two parts: the wave propagation solution algorithms for elastic solids in k-space and adjoint method-based optimization algorithms for damage imaging. One major advantage of the proposed method is the superior computational efficiency compared to the classical finite element method-based wave propagation and gradient-based optimization algorithms. The k-space formulation for wave propagation in 2D and 3D solids are discussed first. Adjoint method and a modified conjugate gradient method is presented for the inverse problem of damage imaging. Following this, parametric studies for measurement duration, number of sensors, noise effect, and computational efficiency are discussed in detail. Next, demonstration examples for concrete structures with and without reinforcement are presented. Several conclusions and future work are given based on the proposed method.

Comprehending how ground incidents and accidents arise and propagate during air traffic control is vital to both the efficiency and safety of air transportation systems. Historical data about airport operational events capture some processes about how improper collaboration among air traffic controllers and pilots result in incidents, accidents, and delays, but could hardly cover all possible air traffic control failures in various environmental conditions. The computational simulation could use historical data to identify repetitive aircraft movement behaviors and air traffic control processes shared in multiple air traffic control sessions and create stochastic models that represent the probabilities of the occurrence of certain aircrafts movements and control events under various contexts. This paper synthesizes four scenarios of aircraft operations around ramp areas during air traffic peak time for supporting the development of a quantitative spatiotemporal simulation that can help predict air traffic control risks based on historical data.

A probabilistic finite element model is implemented to estimate microstructurally small fatigue crack growth in bridge steel welds. Simulations are based on a microstructure-sensitive crystal plasticity model to quantify fatigue indicator parameters (FIPs) at the slip system level and a fatigue model that relates FIPs to fatigue lives of individual grains. Microstructures from three weld zones, namely, fusion zone (FZ), heat affected zone (HAZ), and base metal (BM), are constructed based on their microstructural attributes such as grain morphology, size, and orientation. Statistical volume elements (SVEs) are generated and meshed independently for the three welding zones. Each grain within the SVEs is divided into several slip bands parallel to crystallographic planes. During the loading process, cracks nucleate at the slip bands (SBs) with the largest FIP next to the free surface. The crack extension path is assumed to be transgranular along SBs and the number of cycles required to crack the neighbor grain is calculated by the corresponding FIP-based crack growth rate equation. The simulation process is carried out using ABAQUS with a user defined subroutine UMAT for crystal plasticity. After the calibration of the constitutive model and irreversibility parameters, numerical simulations for small crack growth in three zones are presented. The crack length vs. the predicted fatigue resistance shows significant differences in the mean values and variability among the three weld zones.

A novel approach for reliability assessment of aging gas pipeline systems based on a Bayesian network methodology is proposed in this paper with a focus on the improvement of the pipeline strength prediction. A multimodal diagnosis is performed by assessing the variation in the mechanical property (e.g., yield strength) within the pipe in terms of material property measurements, such as microstructure, composition, and hardness. The multimodality measurements are then integrated with the Bayesian network information fusion model. Prototype testing is carried out for model verification, validation and demonstration. The model updating scheme employs a Markov Chain Monte Carlo algorithm to infer the posterior distribution of the pipe strength using the multimodality measurements, whereas, the priors are derived from the literature knowledge of such systems. Moreover, through-thickness studies of pipe cross-sections are performed to demonstrate the mechanical property variation from the surface to bulk. Finally, data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. Discussions on the observations and future work are provided.

A critical plane-based mixed-mode delamination growth model for composite materials under fatigue loadings is proposed in this study. A brief review for mixed-mode delamination is given and the special focus in the study is on a critical plane-based fracture criterion since it can automatically adapt for different local failure modes. An equivalent energy release rate range suitable for fatigue analysis is proposed for the delamination growth rate prediction under general proportional and non-proportional multiaxial loadings. The proposed methodology is validated with extensive experimental data available in the open literature. A general good agreement is observed between model predictions and experimental observations. Finally, some discussion and conclusions are drawn based on the proposed model.

Submersibles and submarines are subject to cyclic compressive sea loading during their service life, which is quite different from the ships and platforms under tensile and bending loading. It is important to study the fatigue crack propagation under cyclic compression in order to assess the fatigue life of the submersible and submarine. An extended McEvily model is proposed for the fatigue crack growth prediction under cyclic compression-compression loading. First, Finite Element Method (FEM) simulation and a simplified stress estimation method are proposed for efficient fatigue crack growth analysis. In the Finite Element Method simulation, the crack opening loads of different crack lengths are calculated individually instead of plastic wake calculation behind crack tip with node releasing technique, which will save a lot of calculating time. Afterwards the fatigue life under cyclic compression is predicted based on new fatigue crack growth rate curve model for cyclic compression. Meanwhile the analytical method is an engineering estimation for fatigue crack growth under constant amplitude compression. In this method, the crack opening load is estimated based on the symmetry of compression and tension. Then the fatigue life under cyclic compression is also predicted based on a new model for cyclic compression. Finally, fatigue life prediction of a double edged specimen under cyclic compressive loading is taken for example to illustrate the analysis procedure of two simplified methods. By comparing the predicted results with the test data, it is found that the a-N curve and final crack length by two methods are in good agreement with the test data, so the Finite Element Method and analytical method are reasonable and feasible for fatigue crack growth prediction of deepwater structure under cyclic compression.

A new fatigue crack growth model at the subcycle scale is proposed for both positive and negative stress ratios. The term “subcycle” refers to the crack growth modeling within a cyclic loading duration rather than the classical cycle averaged crack growth rate (i.e., da/dN). The proposed model includes two major components: a subcycle crack growth rate function and a crack opening stress estimation algorithm for tension–tension and tension–compression cyclic loading. The subcycle crack growth rate function is proposed based on existing in situ scanning electron microscopy testing results. Following this, separation of far field crack contact and near tip crack contact are discussed for both positive and negative stress ratios. A simple linearized model is proposed to estimate the crack opening stress. The developed simplified crack opening stress estimation model is compared with several other models available from open literatures. Next, the proposed subcycle fatigue crack growth methodology is validated with several sets experimental data for various metallic materials under different positive and negative stress ratios. Some discussions and conclusions are drawn based on the proposed model.

A 3D finite element model (FEM) with realistic field measurements of temperature distributions is proposed to investigate the thermal stress variation in the steel–concrete composite bridge deck system. First, a brief literature review indicates that traditional thermal stress calculation in suspension bridges is based on the 2D plane structure with simplified temperature profiles on bridges. Thus, a 3D FEM is proposed for accurate stress analysis. The focus is on the incorporation of full field arbitrary temperature profile for the stress analysis. Following this, the effect of realistic temperature distribution on the structure is investigated in detail and an example using field measurements of Aizhai Bridge is integrated with the proposed 3D FEM model. Parametric studies are used to illustrate the effect of different parameters on the thermal stress distribution in the bridge structure. Next, the discussion and comparison of the proposed methodology and simplified calculation method in the standard is given. The calculation difference and their potential impact on the structure are shown in detail. Finally, some conclusions and recommendations for future bridge analysis and design are given based on the proposed study.

Foams offer extremely large surface area per unit mass, making them competitive material for heat exchangers and energy storage systems. Understanding the influence of foam characteristics, i.e., size, distribution and concentration of pores, ligament defects as well as foam architecture, on thermal transport is important when designing the foam-based devices. In this article, we present the effective thermal conductivity of open-cell polyurethane (PU) foam (20 PPI) with ~10 μm thick nickel coating measured by transient plane source (TPS) method. A calibration methodology for TPS method is developed to obtain accurate measurements. A finite element model and thermal resistance model are developed for the heat transfer in metal coated foams occurring near room temperature. For precisely modeling the foam architecture topology, an X-Ray tomography is employed. The developed models are used to investigate how the Ni coating thickness affects the effective thermal conductivity. Lastly, we discuss how the model assumptions are related to the discrepancy between the model predictions and measurements for the polymer-metal foams.

Denitrification in electroactive constructed wetland (EW) systems is constrained by the carbon source and the carbon/nitrogen (C/N) ratio (the COD/TN ratio). In this study, wastewater with a high C/N from a brewery was added to wastewater with a low C/N (dairy wastewater) in an EW system, and the pollutant removal, bioelectricity generation, transformations of dissolved organic matter, and microbial community structures were evaluated. The results showed that the average removal rates of ammonium nitrogen, total nitrogen, and chemical oxygen demand from the wastewater mixture were 6.40%, 46.44%, and 23.85% higher than those from the wastewater with a low C/N, respectively. Dissimilatory nitrate reduction to ammonium was effectively inhibited, and the NH4+-N removal was 25.52% higher, when the wastewater mixture was used instead of the high C/N wastewater. Similarly, the output voltage was significantly increased, and the internal resistance of the device was reduced, for the wastewater mixture. The structure of the microbial community improved, the relative abundance of electrochemically active bacteria was higher, and the protein-like and humic-like components were lower, in the mixture treatment than in the individual treatment. The results show that the nitrogen removal and biopower generation improved in an EW system when high C/N wastewater was used as the carbon source.

A novel Bayesian-Entropy Gaussian Process (BEGP) is proposed for constrained metamodeling. Gaussian Process (GP) regression is a flexible and robust tool for surrogate modeling using observation data. For many engineering problems, available information other than observations may be known, such as physical constraints, boundary conditions, and empirical knowledge. Based on the Bayesian-Entropy (BE) principle, this paper introduces a novel framework for encoding extra information in addition to point data in constructing a GP regression model. The extra information is treated as constraints on the mean prediction of GP. The BE method can rigorously incorporate extra information as constraints into classical Bayesian framework. The constraint term is added into the posterior distribution of the hyperparameters when training the GP model. BEGP serves as an information fusion tool to enhance the extrapolation behavior of the GP model by incorporating additional knowledge about the problem. The proposed methodology is demonstrated on a numerical toy example and a structural analysis example highlighting the ability to smoothly connect two local GPs and incorporate boundary conditions as extra constraints. The BEGP shows the ability of incorporating physics constraints to enhance prediction and extrapolation behaviors. Finally, conclusions and future work are drawn based on the proposed study.

An experimental study is proposed to investigate the high-cycle fatigue behavior of prestressed concrete box-girders. Special focus is on the shear diagonal cracks in the webs of girders, which is rarely investigated in the open literature. Laboratory fatigue tests were designed and conducted to study the propagation of shear diagonal crack of PC box-girder specimens under cyclic loadings. The development of stirrup strain, propagation characteristics of diagonal crack and the related factors are discussed in detail. A finite element (FE) model for concrete damage of PC box girder under high-cycle fatigue loading is established to investigate the crack propagation behavior. Following this, a model for the calculation of diagonal crack widths of PC box-girder under fatigue loading is proposed based on the experimental observations. The calculation results are compared with experimental data for validation. It is observed that the maximum stirrup strain in crack zone increases 67.9% based on this investigation. The ratio of displacement during the last cycle and that during initial loading can research 40.3% in the current investigation. Finally, several conclusions are drawn based on the proposed study.

To assess fatigue damage accumulation for civil infrastructure, Miner’s rule or linear elastic fracture mechanics (LEFM) are usually used in many codes and specifications. However, large uncertainties are found when the short cracks are initiated. Currently, most physics-based models for crack initiation are built for constant amplitude loads (CA). Since the environmental or service load-induced stresses at local details for crack initiation are time-variant, these physics-based models could not be directly applied for real civil infrastructure. In this paper, a two-fold nonlinear grain-based fatigue damage model is proposed to process the variable amplitude loads (VA) induced fatigue damages by combining Persistent Slip Band (PSB)-based short fatigue crack model and grain-based Miner’s rule. In the sub-grain regime, linear damage accumulation is adopted. For the repeated stress blocks with variable amplitude, the stress block is rain-flow counted and the damage accumulation is modeled as short crack extension. Therefore, the crack associated damage will be accumulated grain by grain until it reaches characteristic length of a long crack. The damage accumulation from a stress block is added for each grain. Effect from subcritical loads is also considered in this damage accumulative model. Finally, a numerical demonstration is performed on statistical representative elements under variable stress histories from a long span bridge.

Eliminating accidents while maintaining the integrity of the National Airspace System is one of the central objectives of the Next Generation Air Transportation System. This paper presents a Bayesian framework for accurate trajectory and accident prediction in National Airspace System using a high-fidelity trajectory simulation platform. Various uncertainties in aircraft trajectory prediction due to pilot behavior and weather effects are included as random variables in the simulations. Bayesian-Entropy method fuses available observation data (e.g., positioning system) with existing physical constraints (e.g. runway location) to update these simulation parameters. The posterior distributions of parameters are used to predict the probability of an adverse incident and time-remaining to incident. The proposed Bayesian updating scheme offers a flexible and rigorous way for adverse incident diagnostics and prognostics in current and future Air Traffic Management. Two realistic examples are given to show that it is possible to derive advance warning using the proposed methodology. The approach integrates data from a simulation model, with real-time traffic data streams and available physical constraints, using the Bayesian-Entropy information fusion methodology. This advance warning will allow the pilots/controllers to take actions to mitigate adverse incident in the National Airspace System.

Transmission characteristics and mechanism of the exterior wall pressure fluctuations (WPFs) on the side window of a DrivAer model to the interior wind noise were studied. After separation of hydrodynamic and acoustic pressure components with compressible CFD simulation and wavenumber decomposition (WND), an improved modal analytical approach was employed to calculate cabin interior noise with a simplified plate-cavity equivalent model. The simulation result was then validated through comparison with wind tunnel test. With the help of the analytical model, transmission loss (TL) of the exterior WPF to the interior noise was calculated with two sub-processes to study transmission efficiency and corresponding mechanism of the two pressure components. The result shows that hydrodynamic pressure loses much more energy than that of the acoustic pressure. The difference of transmission efficiency of the two pressure components is influenced by three main factors: “modal preference effect”, resonance effect and the coupling characteristic between modes of the side window and the cabin, especially the first two factors make the side window acting as a “low-wavenumber filter” for exterior WPF and form the primary causes for the transmission difference. Additionally, the generation of the coincidence effect was discussed from the perspective of modal analysis, indicating that it is essentially a particular resonance phenomenon, with the coincident wavenumber of excitation and structure.

Traditionally, the fatigue damage assessment of steel bridges is conducted using cycle-based methods. Cycle-based methods require stress time history be transformed to cycle history first before fatigue analysis can be performed. However, well-defined cycle history does not exist for the traffic-induced stress time history of bridges as it contains numerous large and small cycles with random stress ranges embedded together. To address this inherent difficulty, a time-based fatigue crack growth (FCG) model is proposed for fatigue damage prognosis of steel bridges under traffic loading. The time-based FCG method describes the crack growth in the time domain through a crack growth kinetics function derived based on the physical mechanism of fatigue crack propagation. To demonstrate the proposed method, numerical simulations are first performed using full-scale coupled vehicle-bridge interaction framework to generate stress time histories of a typical steel bridge under traffic loading. Then, FCG analysis is performed using the time-based method and the effects of road surface roughness and vehicle weight on the FCG are studied. Finally, a case study on the I-10 Twin Span Bridge is presented to demonstrate the time-based FCG method for fatigue damage prognosis using in situ monitoring data. The results show that the proposed method can be used to directly predict the FCG of steel bridges in the time domain using stress time histories obtained either from stress analysis or in situ monitoring.

A Probabilistic Physics-guided Neural Network (PPgNN) is proposed in this paper for probabilistic fatigue S-N curve estimation. The proposed model overcomes the limitations in existing parametric regression models and classical machine learning models for fatigue data analysis. Compared with explicit regression-type models (such as power law fitting), the PPgNN is flexible and does not impose restrictions on function types at different stress levels, mean stresses, or other factors. One unique benefit is that the proposed method includes the known physics/knowledge constraints in the machine learning model; the method can produce both accurate and physically consistent results compared with the classical machine learning model, such as neural network models. In addition, the PPgNN uses both failure and runout data in the training process, which encodes the runout data using a new proposed loss function, and is beneficial when compared with some existing models using only numerical point value data. A mathematical formulation is derived to include different types of physics constraints, which can deal with mean value, variance, and derivative/curvature constraints. Several data sets from open literature for fatigue S-N curve testing are used for model demonstration and model validation. Next, the proposed network architecture is extended to include multi-factor (e.g., mean stress, corrosion, frequency effect, etc.) fatigue data analysis. It is shown that the proposed PPgNN can serve as a flexible and robust model for general fitting and uncertainty quantification of fatigue data. This paper provides a feasible way to incorporate known physics/knowledge in neural network-based machine learning. This is achieved by properly designing the network topology and constraining the neural network’s biases and weights. The benefits for the proposed physics-guided learning for fatigue data analysis are illustrated by comparing results from neural network models with and without physics guidance. The neural network model, without physics guidance, produces results contradictory to the common knowledge, such as a monotonic decrease of S-N curve slope and a monotonic increase of fatigue life variance as the stress level decreases. This problem can be avoided using the physics-guided learning model with encoded prior physics knowledge.

A time-based subcycle fatigue crack growth (FCG) formulation and validation are proposed to calculate the fatigue crack growth under general random variable amplitude loadings. The intrinsic difficulties of the classical cycle-based formulation for general random variable loadings are discussed first. Several typical spectrums that are not appropriate for cycle-based FCG are illustrated, such as the “Christmas tree” spectrums. A time-based subcycle formulation is then proposed to address this difficulty. The proposed model includes three major component: (1) a time-based crack growth kinetics function at the subcycle (time) scale; (2) an efficient crack tip opening displacement (CTOD) estimation method; (3) a crack tip plasticity zone tracking algorithms for crack opening level determination of a growing crack. Detailed derivation and calculation procedures are given. Following this, several numerical examples are illustrated for the proposed model under different loading spectrums for the crack growth and CTOD calculation. Randomly generated loading spectrums are used to illustrate the capability of the proposed method under arbitrary loadings. Next, in-house testing for “Christmas tree spectrum” and literature data on several representative variable loading spectrums are used for model validation. Finally, some conclusions and future work are drawn based on the proposed study.

An experimental testing procedure is proposed for generating creep-dominant creep-fatigue interaction in Alloy 617 at 950 °C. Most experimental studies on Alloy 617 at elevated temperatures employ a purely strain-controlled loading waveform with a tensile hold period. Strain-controlled loading can only generate interaction in the fatigue-dominant regime of the damage interaction diagram, due to the saturation effect of increasing hold time on cycle life. This saturation is caused by the rapid stress relaxation of Alloy 617 at high temperatures. Design codes for components undergoing creep-fatigue interaction at high temperatures require creep-fatigue testing data in the creep-dominant regime. This study investigates multiple creep-fatigue loading waveforms with the objective of producing creep-dominant interaction. The custom loading waveforms are implemented on a servo-hydraulic load frame with a furnace. A hybrid-control scheme with strain-controlled ramps and load-controlled hold periods is proposed due to its flexibility in generating varying proportions of creep and fatigue damage. Experimental data is presented and different loading schemes are compared. Special emphasis is on the creep and fatigue damage contributions by the newly proposed testing method. Several conclusions are discussed and an extension of this current work is suggested to improve the damage interaction diagram for design purposes.

Bismuth compounds have been used for treatment of bacterial infection, and recently bismuth nanoparticles (BiNP) were synthesized for imaging and diagnostic purpose, while safety concern of bismuth cannot be ignored. Here, we prepared ultrasmall BiNP and showed an enhanced tumor imaging, but BiNP revealed a differentiated cytotoxicity in human embryonic kidney 293 cells (HEK293) compared to other cell types. For the first time, we found that BiNP can induce autophagy, shown as the increase of monodansylcadaverine fluorescence staining and the amount of LC3II that can be inhibited by 3-MA. BiNP were capable of entering cells in a dose and time dependent manner by fluorescence and element detection methods BiNP were found to be localized in the cytoplasm observed by transmission electron microscopy and intracellular bismuth element confirmed by energy dispersive X-ray analysis. Using endocytic inhibitors, BiNP were found to require ATP and endosomal trafficking pathways for their cellular uptake. Internalized BiNP did not co-localize with EEA1, but co-localized with Lysotracker/LAMP1/LAMP2 at late time points, indicating BiNP may be retained in the non-early endosomal vacuoles and late endosomes. With our novel finding of bismuth induced autophagy and endocytic mechanisms, potential approaches may be applied to reduce the toxicity by bismuth.

A new critical plane-energy model is proposed in this paper for multiaxial fatigue life prediction of metals. A brief review of existing methods, especially on the critical plane-based and energy-based methods, is given first. Special focus is on the Liu–Mahadevan critical plane approach, which has been shown to work for both brittle and ductile metals. One potential drawback of the Liu–Mahadevan model is that it needs an empirical calibration parameter for non-proportional multiaxial loadings because only the strain terms are used and the out-of-phase hardening cannot be explicitly considered. An energy-based model using the Liu–Mahadevan concept is proposed with the help of the Mroz–Garud plasticity model. Thus, the empirical calibration for non-proportional loading is not needed because the out-of-phase hardening is naturally included in the stress calculation. The model predictions are compared with experimental data from open literature, and the proposed model is shown to work for both proportional and non-proportional multiaxial loadings without the empirical calibration.

A novel hybrid Anderson-type polyoxometalate, {[(CH 3 ) 3 N(CH 2 ) 2 OH] 2 }[H 0.2 K 0.2 Na 2.6 (H 2 O) 6 ][IMo 6 O 24 ] (Abbreviated as (ChO) 2 K 0.2 Na 2.6 IMo 6 ) was synthesized in a deep eutectic solvent and characterized by XRD, FT-IR, thermal analysis, electrochemical measurements and inductively-coupled plasma, atomic emission spectrometer (ICP-AES). This hybrid polyoxometalate was used as a catalyst in ionic liquids (ILs) for desulfurization of model diesel. The catalyst (ChO) 2 K 0.2 Na 2.6 IMo 6 in the extraction and catalytic oxidative desulfurization (ECODS) system, containing H 2 O 2 and 1-octyl-3-methylimidazolium tetrafluoroborate (OmimBF 4 ), displayed so high activity that the removal of DBT can reach 100% at 60 °C in 60 min. Sulfur removal for S-compounds was in the following order: DBT > 4,6-DMDBT > BT. The desulfurization system shows so good stability that it can be recycled 20 times without noticeable decrease in activity.

An experimental test is performed to investigate the flexural behavior of bonded post-tensioned concrete beams under strand corrosion. Eight beams are designed and subjected to accelerated method to different corrosion levels. The initial stiffness of beams is observed by cyclic loading-unloading test during the corrosion procedure. Corrosion effects on concrete cracking, post-cracking stiffness, ultimate strength, failure mode and ductility are then clarified by the flexural test. And, a coefficient is introduced to quantify the incompatible strain between corroded strand and concrete. Results show that the prestress force loss of strand has almost the linear relation with corrosion loss. Strand corrosion affects slightly the initial stiffness of beam before flexural cracking, but degrades significantly the post-cracking stiffness of beam as the corrosion loss exceeds 27.0%. Slight corrosion of strand has little effects on beams flexural behavior. The severe corrosion, however, decreases the number of crack, changes the failure mode form the concrete crushing to strand rupture, degrades the ductility and the ultimate strength of beams, and leads to the incompatible strain between strand and concrete. In the present test, the incompatible strain decreases about 20% of the flexural strength as the corrosion loss exceeds 27.0%.

An analysis model is proposed in the present study to predict the failure of corroded prestressed concrete (PC) beams under flexural load. The corrosion induced strand section loss, material deterioration and bond degradation are considered in the proposed model. The bond degradation is treated as a function of corrosion loss. The slip region caused by bond deterioration is determined by comparing the effective bond force and tension force of strand. And then, a strain-incompatibility analysis method is developed to calculate the flexural strength of beams. The proposed model is verified by experimental tests on corroded post-tensioned concrete beams. Results show that the bond degradation induced incompatible strain is important and should be considered into the flexural strength prediction. The proposed model provides an effective way to measure the incompatible strain and a reasonable prediction for flexural strength of beams. The incompatible strain depends on the corrosion loss and applied load levels. Corrosion loss

Strand corrosion within the insufficient grouted duct has become a serious problem affecting the durability of bridge structures. The residual flexural behavior of the locally ungrouted post-tensioned concrete beams after strand corrosion is investigated in the present study. Two experiments are performed: corroded strand test for the constitutive law development and beam test for the flexural behavior investigation. An analytical model is proposed to predict the ultimate strength, the deformation, and the strain response of corroded beams under external loadings. The proposed model considers the strain compatibility between strand and concrete, material mechanics deterioration of strand, and the asymmetric deflection due to local corrosion in ungrouted duct. The accuracy of the model is verified by the experimental observations. Results show that corrosion loss has less effect on the yield strength and elastic modulus than that on the ultimate strain of strand. It is also observed that light corrosion has little effect on the beam cracking and the load-deflection behavior before beam cracking. Severe corrosion degrades significantly the flexural capacity and the local post-cracking stiffness of beams, which leads to the asymmetric deformation and crack formation.

Alloy 617 is a primary candidate material to be used in the next generation of nuclear power plants. Structural materials for these plants are expected to undergo creep and fatigue at temperatures as high as 950 °C. This study uses a hybrid-control creep-fatigue loading profile, as opposed to the traditional strain-controlled loading, to generate creep dominated failure. Qualitative and quantitative image analysis through SEM, EDS, and EBSD, is used to show that hybrid control testing is capable of producing creep dominated failure and that time fraction approach is not a valid indicator of creep or fatigue dominated damage. The focus of image analysis is on surface fatigue cracks and internal creep voids. A creep-fatigue damage interaction diagram based on these micro-scale features is plotted. It is shown that the classical time fraction approach suggested by the ASME code does not agree with the experimental findings and has a poor correlation with observed microscale damage features. A new definition of creep damage fraction based on an effective hold time is found to correlate well with the micro-scale image analysis.

A new fatigue reliability assessment function which takes into account the vehicle and temperature loadings has been developed in this study. The vehicle and temperature loadings are important parameters as they can cause fatigue failure of the welds in a steel box girder. The temperature affects the traffic loading effect by changing the elastic modulus of asphalt pavement. The effect of the temperature difference has been considered based on the measured data and the finite element analysis. Linear regression equations between the equivalent stress and the temperature for different vehicle types have been developed. Using the thermal stress analysis and the rain-flow counting method, the temperature difference fatigue stress spectrum has been determined. Further, a limit equation for the fatigue reliability assessment, which takes into account both the vehicle and temperature loadings, has been developed. Finally, the effects of the temperature and the traffic growth rate on the fatigue reliability of two welding types of Nan-xi Yangtze River Suspension Bridge have been assessed.

Accurate analysis of prostate cancer specific biomarkers plays an important role in the early diagnosis of prostate cancer. Traditional colorimetric lateral flow assay (LFA) has the limitations of low detection sensitivity and qualitative or semiquantitative detection. In this study, we developed a novel surface-enhanced Raman scattering (SERS)-based competitive LFA for the rapid and highly sensitive quantitative evaluation of prostate cancer antigen 3 (PCA3) mimic DNA. Herein, the competitive hybridization interaction with capture DNA between target PCA3 mimic DNA and reporter DNA-labeled SERS nanotags results in a change in the amount of SERS nanotags on the test line. The quantitative analysis of target PCA3 mimic DNA was realized by monitoring the Raman peak intensity of SERS nanotags on the test line. The limit of detection of PCA3 mimic DNA was estimated to be 3 fM, which is about three orders of magnitude more sensitive than that of a commercially available kit. By combining the outstanding characteristics of the well-established SERS-based competitive strategy and LFA platform, our design has strong potential for the early diagnosis of prostate cancer and other diseases.

Chromophoric dissolved organic matter (CDOM) retrieval remains to be a challenging task in water color remote sensing research due to its highly spatial and temporal variability. In this paper, we present a novel CDOM retrieval algorithm that takes advantage of the sparse learning, which can simultaneously perform feature selection and parameter estimation. More specifically, by incorporating the band interaction terms into the original spectral matrix and let it be the basis matrix, then the inversion task can be converted to a classical sparse regression problem, namely LASSO, which can be efficiently solved by the coordinate descend algorithm. Experimental results conducted on both simulated and in-situ datasets have demonstrated the efficiency and superiority of the proposed method over some conventional empirical algorithms.

Cardiac troponin I (cTnI) was considered as the “gold standard” for acute myocardial infarction (AMI) diagnosis owing to its superior cardiac specificity for cardiac damage and showing little or no changes in patients with a skeletal muscle disease or trauma. Herein, a new signal amplification surface-enhanced Raman scattering (SERS) platform was developed for recognition and detection of cTnI by using gold nanoparticles (AuNPs), graphene oxide (GO) and magnetic beads (MB). Here, antibody/Raman reporter labeled AuNP–functionalized GO were employed as both SERS nanotags and signal amplification carriers. Monoclonal antibody modified MB were applied as the capture probe and separation agents. In the presence of cTnI, sandwich type immunocomplexes, “capture probe/target/SERS nanotags”, were formed through antibody–antigen–antibody interactions. Due to the strong SERS enhancement ability of the designed GO/AuNP complexes and a high binding chance between cTnI and the GO/AuNP complexes, the proposed SERS-based immunoassay could selectively detect cTnI with a high sensitivity (detection limit of 5 pg mL−1) and a good linearity was obtained in a range of 0.01–1000 ng mL−1. In addition, this method was also successfully applied for detecting cTnI in serum substitute media with a similar linear range. Furthermore, this strategy can be constructed with different kinds of antibodies and Raman reporters, and thus can be easily used for simultaneous detection of multiple biomarkers. Therefore, this proposed SERS-based signal amplification technique shows strong potential for the clinical diagnosis of AMI disease.

Imbalanced data widely exist in many high-impact applications. An example is in air traffic control, where among all three types of accident causes, historical accident reports with ‘personnel issues’ are much more than the other two types (‘aircraft issues’ and ‘environmental issues’) combined. Thus, the resulting data set of accident reports is highly imbalanced. On the other hand, this data set can be naturally modeled as a network, with each node representing an accident report, and each edge indicating the similarity of a pair of accident reports. Up until now, most existing work on imbalanced data analysis focused on the classification setting, and very little is devoted to learning the node representations for imbalanced networks. To bridge this gap, in this paper, we first propose Vertex-Diminished Random Walk (VDRW) for imbalanced network analysis. It is significantly different from the existing Vertex Reinforced Random Walk by discouraging the random particle to return to the nodes that have already been visited. This design is particularly suitable for imbalanced networks as the random particle is more likely to visit the nodes from the same class, which is a desired property for learning node representations. Furthermore, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, where context sampling uses VDRW and the limited label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs from different classes. Experimental results demonstrate that ImVerde based on VDRW outperforms state-of-the-art algorithms for learning network representations from imbalanced data.