Search

An innovative optimal design framework is developed aiming at enhancing the crashworthiness while ensuring the lightweight design of a hybrid two-dimensional triaxial braided composite (2DTBC) tube, drawing insights from the mesostructure of the composite material. To achieve these goals, we first compile the essential mechanical properties of the 2DTBC using a concentric cylinder model (CCM) and an analytical laminate model. Subsequently, a kriging surrogate model to elucidate the intricate relationship between design variables and macroscopic crashworthiness is developed and validated. Finally, employing multi-objective evolutionary optimization, we identify Pareto optimal solutions, highlighting that reducing the total fiber volume and increasing the glass fiber content in the total fiber volume are crucial for optimal crashworthiness and the lightweight design of the hybrid 2DTBC tube. By integrating advanced predictive modeling techniques with multi-objective evolutionary optimization, the proposed approach not only sheds light on the fundamental principles governing the crashworthiness of hybrid 2DTBC but also provides valuable insights for the design of robust and lightweight composite structures. [ABSTRACT FROM AUTHOR]

Conventional methods of measuring total suspended sediments (TSS) and total particulate phosphorus (TPP) are typically low-resolution and miss critical processes that impact their exports in aquatic environments. To create high-resolution TSS and TPP estimates, echo intensity (EI), a biproduct of velocity measurements from acoustic devices, was utilized. An acoustic Doppler velocimeter (ADV) and an acoustic Doppler current profiler (ADCP) were deployed in three locations in the L-29 Canal in South Florida, USA, to obtain estimates near the canal bed and in the water column, respectively. Corrections for transmission losses from the ADCP proved unnecessary due to the low vertical variability in the measured EI. EI calibrations were performed using artificially created TSS obtained from bed sediments (ADV) and gravimetrically measured TSS from water samples that matched the depths and times of the ADCP deployments. The measured TSS values were then analyzed for total phosphorus and converted to TPP estimates. The results showed that high TSS and TPP were caused by the rapid discharge releases typical of managed canals. This work demonstrates that high-resolution estimates are imperative for assessing the effects of such swift hydrologic changes on the potential export of sediments and nutrients to delicate ecosystems downstream. [ABSTRACT FROM AUTHOR]

Bedload transport is recognized as a key process in the development of river channel forms; however, most rivers suffer from an absence of data. Performing bedload measurements to document bedload transport rates is a challenge, as the deployment of traditional bedload samplers is time consuming and risky in floods. Consequently, bedload measurements are rarely executed. Alternative techniques are being developed to complement the use of traditional bedload measurements and to provide continuous monitoring. Passive acoustic measurements are made with hydrophones, measuring the underwater sounds naturally generated by bedload impacts in rivers. This paper proposes an innovative deployment of hydrophones to record bedload sounds at the scale of a cross section. The measured acoustic signals are interpreted with bedload samplings and with hydraulic and river bed parameters. Field experiments were done in 14 different sites, exploring a diversity of rivers. Bedload flux was observed to be the most consistent variable explaining the monitored acoustic power. Based on 25 experiments on 14 rivers, the cross‐section‐averaged acoustic power was related to the specific bedload flux and showed a good agreement (60% of bedload flux estimated within a factor of 2). The robustness of the obtained calibration curve remains to be tested. However, the potential of passive acoustic profiles to provide a continuous measurement of bedload sounds that could be used in the development of bedload gauging stations is shown. Key Points: Using cross‐averaged values, the power of bedload self‐generated noise was related to sampled bedload flux at 14 different sites.The spectral analysis of bedload self‐generated noise shows that the acoustic measurements are affected by propagation effectsUncertainties on physical bedload measurements limit the understanding of indirect measurements of bedload transport [ABSTRACT FROM AUTHOR]

Abstract Surrogate methods for rapid calculation of femoral strain are limited by the scope of the training data. We compared a newly developed training-free method based on the superposition principle (Superposition Principle Method, SPM) and popular surrogate methods for calculating femoral strain during activity. Finite-element calculations of femoral strain, muscle, and joint forces for five different activity types were obtained previously. Multi-linear regression, multivariate adaptive regression splines, and Gaussian process were trained for 50, 100, 200, and 300 random samples generated using Latin Hypercube (LH) and Design of Experiment (DOE) sampling. The SPM method used weighted linear combinations of 173 activity-independent finite-element analyses accounting for each muscle and hip contact force. Across the surrogate methods, we found that 200 DOE samples consistently provided low error (RMSE < 100 µε), with model construction time ranging from 3.8 to 63.3 h and prediction time ranging from 6 to 1236 s per activity. The SPM method provided the lowest error (RMSE = 40 µε), the fastest model construction time (3.2 h) and the second fastest prediction time per activity (36 s) after Multi-linear Regression (6 s). The SPM method will enable large numerical studies of femoral strain and will narrow the gap between bone strain prediction and real-time clinical applications. [ABSTRACT FROM AUTHOR]

Multivariate adaptive regression spline (MARS) is a statistical modeling method used to represent a complex system. More recently, a version of MARS was modified to be piecewise linear. This paper presents a mixed integer linear program, called MARSOPT, that optimizes a non-convex piecewise linear MARS model subject to constraints that include both linear regression models and piecewise linear MARS models. MARSOPT is customized for an automotive crash safety system design problem for a major US automaker and solved using branch and bound. The solutions from MARSOPT are compared with those from customized genetic algorithms. [ABSTRACT FROM AUTHOR]

Crashworthiness optimization of aircraft and automotive structures has become one the main research targets for their respective leading industries. The following research proposes a new design of an aircraft’s vertical strut. The design consists of a hollow aluminum square tube with a glass-fiber reinforced polymer honeycomb-shaped inner structure. Size and shape surrogate-based optimization techniques are used, with the thicknesses of both materials, cell size and cell shape as design variables. The objective function chosen for the single-objective optimization is the specific energy absorption, while the metrics for the multi-objective optimization are the peak force, mass, absorbed energy and the specific energy absorption. An improvement of 22% of the specific energy absorption with low peak force values is obtained from the single-objective optimization by significantly changing all design variables. Two Pareto fronts have been obtained from the multi-objective optimization confronting, the specific energy absorption against the peak force and the mass against the energy absorbed. When compared to the baseline model, the optimized models show substantial improvement, increasing the specific energy absorption by 65% or reducing the peak force by over 55%. It has been observed an important effect of the cell shape on the model’s performance. [ABSTRACT FROM AUTHOR]

Fuel efficiency and occupant safety are two of the most important concerns in the automotive industry nowadays. Encouraged by the importance of this field of study, this research attempts an improvement in the crashworthiness of a vehicle crash absorber. This component consists in a square hollow steel tube filled with a honeycomb structure made of glass-fiber reinforced polyamide. Surrogate-based optimization techniques are used. The three objective functions chosen — mass, absorbed energy and peak load — are approximated by two different models: multivariate adaptive regression splines and Gaussian process (kriging). The thickness of both parts, the shape of the honeycomb and its height are selected as design variables. Two preliminary analyses of the specimen are performed: the computation of the interaction effect and a comparison of a hollow tube with the specimen. From the results of multi-objective crashworthiness optimization two Pareto frontiers are obtained, one for the absorbed energy and mass, and another one for the absorbed energy and peak load. The results achieved show great improvements on all objective functions compared to the original design. The peak load is reduced by 37% on a specimen with similar mass and absorbed energy, and the specific energy absorbed is increased by 39.5% for a specimen with a similar peak load to the one from the initial model. [ABSTRACT FROM AUTHOR]

This paper applies surrogate-based multi-objective optimization techniques to a crashworthiness problem in which the impact performance of a frontal crash absorber made of steel and a glass-fiber reinforced polyamide is optimized. Two well known crashworthiness indicators are considered as contrasting objective functions: the Specific Energy Absorption (SEA) and the Load Ratio (LR), whose responses are approximated by multiple types of surrogate models due to their computational cost and their noise levels. These models are quadratic and cubic polynomials, Gaussian process (kriging) and multivariate adaptive regression splines (MARS). The finite element model includes strain-rate sensitive properties, which is verified with experimental data from a drop-tower test. The thickness of the different parts, the geometry of the cross-section and the offsets of the reinforcement parts are chosen as design variables. Pareto solution is obtained after both models are verified. Results show improvements in both functions by almost 50% compared to the original design. [ABSTRACT FROM AUTHOR]

1. Halting biodiversity loss, a major environmental challenge, relies on the understanding of species richness patterns. The assessment of species richness is often hampered by limited taxonomic knowledge and the general dearth of trained systematists. Research has shown that we can predict the number of species in a community by the number of higher order taxonomic units present. Here, we test whether we need to know all the genera, families or orders in order to do so. Further, the number of common species in a region is a good predictor of total richness and we test if this predictability translates to using higher taxa. 2. We used data from 240 sites from the Natura 2000 network of protected areas in Greece, including 5148 plant species and subspecies, which are grouped in 1113 genera 174 families and 56 orders. We correlated species richness with the number of common genera, families or orders present. The analysis was repeated using the number of the most speciose higher orders instead of the most common. 3. We found that we do not need to know all higher order taxa present, in order to predict species richness. If we know how many out of the 30 most common orders are present, we can reliably predict the number of species. Similar results were obtained if we know how many of the 60 most common families or 200 most common genera are present. 4. Equally good results were obtained using the same numbers of the most speciose higher orders. 5. Synthesis and applications. Our analysis demonstrates that species richness can be predicted from the number of common or more speciose genera, families and orders present. These predictions hold without complete sampling of these higher taxa. The implication is that we need only limited systematic knowledge, resources and effort in order to predict species richness. Assuming these findings hold in other taxonomic groups and in other regions, we argue that the uncertainty introduced by limited knowledge of the systematics ... [ABSTRACT FROM AUTHOR]

Abstract: Nonlinearity tests have become an essential step in system analysis and modeling due to the computational demands and complexity of analysis involved in nonlinear modeling. Standard nonlinear measures are either too complicated to estimate accurately (such as Lyapunov exponents and correlation dimension), or not able to capture sufficient but not necessary conditions of nonlinearity (such as time asymmetry). Correntropy is a kernel-based similarity measure which contains the information of both statistical and temporal structure of the underlying dataset. The capability of preserving nonlinear characteristics makes correntropy a suitable candidate as a measure for determining nonlinear dynamics. Moreover, since correntropy makes use of kernel methods, its estimation is computationally efficient. Using correntropy as the test statistic, nonlinearity tests based on the null hypothesis that signals of interest are realizations of linear Gaussian stochastic processes are carried out via surrogate data methods. Experiments performed on linear Gaussian, linear non-Gaussian, and nonlinear systems with varying in-band noise levels, data lengths, and kernel sizes confirm that correntropy can be employed as a discriminative measure for detecting nonlinear characteristics in time series. Results of tests performed on data collected from natural systems are in agreement with findings in time series analysis literature. [Copyright &y& Elsevier]

A numerical investigation of 3-D fluid flow and heat transfer in a rectangular micro-channel has been carried out using water as a cooling fluid in a silicon substrate. Navier-Stokes and energy equations for laminar flow and conjugate heat transfer are solved using a finite volume solver. Solutions are first carefully validated with available analytical and experimental results; the shape of the micro-channel is then optimized using surrogate methods. Ratios of the width of the micro-channel to the depth and the width of the fin to the depth are selected as design variables. Design points are selected through a four-level full factorial design. A single objective function thermal resistance, formulated using pumping power as a constraint, is optimized. Mass flow rate is adjusted by the constant pumping power constraint. Response surface approximation, Kriging, and radial basis neural network methods are applied to construct surrogates and the optimum point is searched by sequential quadratic programming. [ABSTRACT FROM AUTHOR]