Search Results (134)

A new technique is proposed to increase the prediction accuracy of artificial neural networks (ANNs). This technique applies a stepwise regression (SR) procedure to the input data variables, which adds nonlinear terms into the input data in a way that maximizes the regression between the output and the input data. In this study, the SR procedure adds quadratic terms and products of the input variables on pairs. Afterwards, the ANN is trained based on the enhanced input data obtained by SR. After testing the proposed SR-ANN algorithm in four benchmark function approximation problems found in the literature, six examples of multivariate training data are considered, of two different sizes (big and small) often encountered in engineering applications and of three different distributions in which the diversity and correlation of the data are varied, and the testing performance of the ANN for varying sizes of its hidden layer is investigated. It is shown that the proposed SR-ANN algorithm can reduce the prediction error by a factor of up to 26 and increase the regression coefficient between predicted and actual data in all cases compared to ANNs trained with ordinary algorithms. Full article

Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy consumption of the vehicle, including the air conditioning (A/C) system. The loads within the A/C system are mainly contributed by the compressor, condenser fan, and underhood aerodynamic drag, which are the components targeted for overall vehicle energy use reduction in this paper. This paper explores a new avenue for A/C system control by considering the power consumption due to vehicle drag (regulated by the condenser fan and active grille shutters (AGS)) to reduce the energy consumption of the A/C system and improve the overall vehicle fuel economy. The control approach used in this paper is model predictive control (MPC). The controller is designed in Simulink, where the compressor clutch signal, condenser fan speed, and AGS open-fraction are inputs. The controller is connected to a high-fidelity vehicle model in Gamma Technologies (GT)-Suite (which is treated as the real physical vehicle) to form a software-in-the-loop simulation environment, where the controller sends actuator inputs to GT-Suite and the vehicle response is sent back to the controller in Simulink. Quadratic programming is used to solve the MPC optimization problem and determine the optimal input trajectory at each time step. The results indicate that using MPC to control the compressor clutch, condenser fan, and AGS can provide a 37.6% reduction in the overall A/C system energy consumption and a 32.7% reduction in the error for the air temperature reference tracking compared to the conventional baseline proportional integral derivative control present in the GT-Suite model. Full article

The automotive market is currently shifting away from traditional vehicles reliant on internal combustion engines, favoring battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). The widespread acceptance of these vehicles, especially without government subsidies, hinges on market dynamics, particularly customers opting for vehicles with the lowest overall cost of ownership. This paper aims to model the total cost of ownership for various powertrains, encompassing conventional vehicles, HEVs, PHEVs, and BEVs, focusing on both sedans and sports utility vehicles. The modeling uses vehicle dynamics to approximate the fuel and electricity consumption rates for each powertrain. Following this, the analysis estimates the purchase cost and the lifetime operational cost for each vehicle type, factoring in average daily mileage. As drivers consider vehicle replacements, their choice tends to lean towards the most economical option, especially when performance metrics (e.g., range, acceleration, and payload) are comparable across the choices. The analysis seeks to determine the percentage of drivers likely to choose each vehicle type based on their specific driving habits. Advances in battery technology will reduce the battery weight and cost; further, the cost of electricity will decrease as more renewable energy sources will be integrated into the grid. In turn, the total cost of ownership will decrease for the electrified vehicles. By following battery trends, this study is able to model the makeup of the automotive market over time as it transitions from fossil-fuel based vehicles to fully electric vehicles. The model finds until the cost of batteries and electricity is significantly reduced, the composition of the vehicle market is a mixture of all vehicle types. Full article

The remarkable growth of process mining applications in care pathway monitoring is undeniable. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes constitute an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. Therefore, we mitigate the issue by introducing cognitive process mining through the DIAG approach, which consists of a meta-model and an algorithm. This approach enables reasoning and diagnosing in process mining through an ontology-driven framework. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of our approach. Full article

This paper thoroughly examines the application of the Finite Element Method (FEM) to the numerical modal analysis of Herøysund Bridge, focusing on the theoretical backdrop, the construction process, and FEM techniques. This work examines the specific applied FEM approaches and their advantages and disadvantages. This Herøysund Bridge analysis employs a two-pronged strategy consisting of a 3D–solid model and a shell model. To forecast the physical behavior of a structure, assumptions, modeling methodologies, and the incorporation of specific components such as pillars are applied to both approaches. This research also emphasizes the importance of boundary conditions, examining the structural effects of standard Earth gravity, a post-tensioned load, and a railing and asphalt load. The Results section thoroughly explores the mode shapes and frequencies of the 3D–solid and shell models. The conclusion of this work includes findings obtained from the study, implications for Herøysund Bridge, and a comparison of both modeling strategies. It also incorporates ideas for future research and guides employing FEM 3D–solid and shell methods to design and construct more efficient, resilient, and durable bridge structures. Full article

In 2020, 37% of global CO₂eq. emissions were attributed to the construction sector. The major effort to reduce this share of emissions has been focused on reducing the operational carbon of buildings. Recently, awareness has also been raised on the role of embodied carbon: emissions from materials and construction processes must be urgently addressed to ensure sustainable buildings. To assess the embodied carbon of a building, a life-cycle assessment (LCA) can be performed; this is a science-based and standardized methodology for quantifying the environmental impacts of a building during its life. This paper presents the comparative results of a “cradle-to-cradle” building LCA of an office building located in Luxembourg with 50 years of service life. Three equivalent structural systems are compared: a steel–concrete composite frame, a prefabricated reinforced concrete frame, and a timber frame. A life-cycle inventory (LCI) was performed using environmental product declarations (EPDs) according to EN 15804. For the considered office building, the steel–concrete composite solution outperforms the prefabricated concrete frame in terms of global warming potential (GWP). Additionally, it provides a lower GWP than the timber-frame solution when a landfill end-of-life (EOL) scenario for wood is considered. Finally, the steel–concrete composite and timber solutions show equivalent GWPs when the wood EOL is assumed to be 100% incinerated with energy recovery. Full article

Nowadays, engineers possess a wealth of numerical packages in order to design civil engineering structures. The finite element method offers a variety of sophisticated element types, nonlinear materials, and solution algorithms, which enable engineers to confront complicated design problems. However, one of the difficult tasks is the verification of the produced numerical results. The present paper deals with the in-depth verification of a basic problem, referring to the axisymmetric loading by edge forces/moments of a spherical dome, truncated at various roll-down angles, ${\mathsf{\phi }}_{\mathrm{o}}$. Two formulations of analytical solutions are derived by the bibliography; their results are compared with those produced by the implementation of the finite element method. Modelling details, such as the finite element type, orientation of joints, application of loading, boundary conditions, and results’ interpretation, are presented thoroughly. Four different ratios of the radius of curvature, $\mathrm{r}$ and shell’s thickness, and $\mathrm{t}$ are examined in order to investigate the compatibility between the implementation of the finite element method to the “first-order” shell theory. The discussion refers to the differences not only between the numerical and analytical results, but also between the two analytical approaches. Furthermore, it emphasizes the necessity of contacting even linear elastic preliminary verification numerical tests as a basis for the construction of more elaborated and sophisticated models. Full article

The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°. Full article

Manufacturing defects, such as porosity and inclusions, can significantly compromise the structural integrity and performance of additively manufactured parts by acting as stress concentrators and potential initiation sites for failure. This paper investigates the effects of pore system morphology (number of pores, total volume, volume fraction, and standard deviation of size of pores) on the material response of additively manufactured Ti6Al4V specimens under a shear–compression stress state. An automatic approach for finite element simulations, using the J2 plasticity model, was utilized on a shear–compression specimen with artificial pores of varying characteristics to generate the dataset. An artificial neural network (ANN) surrogate model was developed to predict peak force and failure displacement of specimens with different pore attributes. The ANN demonstrated effective prediction capabilities, offering insights into the importance of individual input variables on mechanical performance of additively manufactured parts. Additionally, a sensitivity analysis using the Garson equation was performed to identify the most influential parameters affecting the material’s behaviour. It was observed that materials with more uniform pore sizes exhibit better mechanical properties than those with a wider size distribution. Overall, the study contributes to a better understanding of the interplay between pore characteristics and material response, providing better defect-aware design and property–porosity linkage in additive manufacturing processes. Full article

The prediction of the underwater-radiated noise for a vessel is classically performed at a model scale and extrapolated by semi-empirical laws. The accuracy of such a method depends on many parameters. Among them, the acoustic propagation model used to estimate the noise measured at a model scale is important. The present study focuses on the impact of the presence of a source in the transverse plane. The scattering effect, often neglected in many studies, is here investigated. Applying different methods for computation, we perform several simulations of the acoustic pressure field to show the influence of the scattered field. We finally discuss the results and draw some conclusions about the scattering effect in our experimental configuration. Full article

Perforated steel members (PSMs) are now frequently used in building construction due to their beneficial features, including their proven blast-resistance abilities. To safeguard against structural failures from explosions and terrorist threats, perforated steel beams (PSBs) and perforated steel columns (PSCs) offer a viable alternative to traditional steel members. This is attributed to their impressive energy absorption potential, a result of their combined high strength and ductile behavior. In this study, numerical examinations of damage assessment under the combined effects of gravity and blast loads are carried out to mimic real-world scenarios of external explosions close to steel structures. The damage assessment for PSBs and PSCs considers not just the initial deformation from the blast, but also takes into account the residual capacities to formulate dependable damage metrics post-explosion. Comprehensive explicit finite element (FE) analyses are performed with the LSDYNA software. The FE model, when compared against test results, aligns well across all resistance phases, from bending and softening to tension membrane regions. This validated numerical model offers a viable alternative to laboratory experiments for predicting the dynamic resistance of PSBs and PSCs. Moreover, it is advisable to use fully integrated solid elements, featuring eight integration points on the element surface, in the FE models for accurate predictions of PSBs’ and PSCs’ behavior under blast loading. A parametric study is presented to investigate the effect of web-opening shapes, retrofitting, and different blast scenarios. The results obtained from the analytical FE approaches are used to obtain the ductile responses of PSMs, and are considered an important key in comparisons among the studied cases. Full article

This article focuses on the interval estimation of the generalized Rayleigh distribution with scale and shape parameters. The generalized fiducial method is used to construct the fiducial point estimators as well as the fiducial confidence intervals, and then their performance is compared with other methods such as the maximum likelihood estimation, Bayesian estimation and parametric bootstrap method. Monte Carlo simulation studies are carried out to examine the efficiency of the methods in terms of the mean square error, coverage probability and average length. Finally, two real data sets are presented to demonstrate the applicability of the proposed method. Full article

Small bodies in the Solar System are appealing targets for scientific and technological space missions, owing to their diversity in intrinsic and extrinsic properties, besides orbit and other factors. Missions to small bodies pass through the critical onboard object detection phase, where the body’s light becomes visible to the spacecraft camera. The relative line-of-sight to the object is acquired and processed to feed relative guidance and navigation algorithms, therefore steering the spacecraft trajectory towards the target. This work assesses the distance of detection for each small body in the Solar System considering the target radiometric properties, three typical spacecraft camera setups, and the relative observation geometry by virtue of a radiometric model. Several uncertainties and noises are considered in the modelling of the detection process. The detection distances for each known small body are determined for small-, medium-, and large-class spacecraft. This proves useful for early mission design phases, where a waypoint for detection needs to be determined, allowing the shift from an absolute to a relative guidance and navigation phase. The work produces an extensive dataset that is freely accessible and useful for teams working on the design phases of space missions. Full article

Social media significantly influences business, politics, and society. Easy access and interaction among users allow information to spread rapidly across social networks. Understanding how information is disseminated through these new publishing methods is crucial for political and marketing purposes. However, modeling and predicting information diffusion is challenging due to the complex interactions between network users. This study proposes an analytical approach based on diffusion models to predict the number of social media users engaging in discussions on a topic. We develop a modified version of the susceptible–infected (SI) model that considers the heterogeneity of interactions between users in complex networks. Our model considers the network structure, abandons the assumption of homogeneous mixing, and focuses on information diffusion in scale-free networks. We provide explicit algorithms for modeling information propagation on different types of random graphs and real network structures. We compare our model with alternative approaches, both those considering network structure and those that do not. The accuracy of our model in predicting the number of informed nodes in simulated information diffusion networks demonstrates its effectiveness in describing and predicting information dissemination in social networks. This study highlights the potential of graph-based epidemic models in analyzing online discussion topics and understanding other phenomena spreading on social networks. Full article

We consider the problem of system-level balanced scheduling in a pediatric hospital setting. A hospital clinic has a queue for patients needing care. After being seen in clinic, many require follow-up surgery, for which they also wait in a queue. The rate-limiting factor is physician availability for both clinic visits and surgical cases. Although much existing work has been done to optimize clinic appointments, as well as to optimize surgical appointments, this novel approach models the entire patient journey at the system level, through both clinic and surgery, to optimize the total patient experience. A discrete-event simulation model of the system was built based on historic patient encounter data and validated. The system model was then optimized to determine the best allocation of physician resources across the system to minimize total patient wait time using machine learning. The results were then compared to baseline. Full article