High-fidelity Model Research Articles

How optimal is the minimum-time manoeuvre of an artificial race driver?

Minimum-lap-time optimal control problems (MLT-OCPs) are a popular tool to assess the best lap time of a vehicle on a racetrack. However, MLT-OCPs with high-fidelity dynamic vehicle models are computationally expensive, which limits them to offline use. When using autonomous agents in place of an MLT-OCP for online trajectory planning and control, the question arises of how far the resulting manoeuvre is from the maximum performance. In this paper, we improve a recently proposed artificial race driver (ARD) for online trajectory planning and control, and we compare it with a benchmark MLT-OCP. The novel challenge of our study is that ARD controls the same high-fidelity vehicle model used by the benchmark MLT-OCP, which enables a direct comparison of ARD and MLT-OCP. Leveraging its physics-driven structure and a new formulation of the g-g-v performance constraint, ARD achieves lap times comparable to the offline MLT-OCP (few milliseconds difference). We analyse the different trajectories resulting from the ARD and MLT-OCP solutions, to understand how ARD minimises the effect of local execution errors in search of the minimum-lap-time. Finally, we show that ARD consistently maintains its performance when tested on unseen circuits, even with unmodelled changes in the vehicle's mass.

A parametric approach for creating finite element models from point clouds for steel superstructure components in steel girder bridges

Finite element (FE) analysis is widely used to evaluate the conditions of existing bridges. However, the creation of high-fidelity FE models (FEM) for existing bridges is often a challenging task. In view of the ability of 3D laser scanning to capture detailed geometrical information, this study proposes a direct scan-to-FEM strategy specifically for steel bridge superstructure and hence to facilitate the creation of high-fidelity FE models for existing steel girder bridges. Parametric representations are proposed to address partial occlusions in the point clouds as well as to reinforce the connectivity and alignment between the superstructure elements. Mesh generation algorithms are developed to automatically convert the parametric representations into all-hexahedron FE mesh assembly that can be used directly for FE analysis without any mesh verification or editing. Experimental validations are carried out to evaluate the effectiveness of proposed method in term of both mesh quality and analysis accuracy.

Automatic modeling and optimization of tapered laminates with ply drops

Ply-drop (PD) is the termination of specific plies for laminated composite structures to obtain continuous thickness changes. It brings flexibility to the design of tapered composite laminates. However, as a structural defect, ply drops could have an impact on performance. Considering the impact of ply drop during stacking sequence design can provide more accurate performance analysis, but this will bring challenges in modeling and optimization. To consider the PD impact and achieve convenience in optimization, this paper proposes a high-fidelity finite element automatic modelling method of tapered laminates and corresponding optimization framework. By parameterizing the PD information and defining the basic elements and nodes of start stacking surface of the structure, the entire finite element model is layer-wisely constructed and controllable. Subsequently, based on the genetic algorithm framework, a repair strategy and its genetic operations are proposed to ensure that the design variables satisfy the ply-drop design guidelines. And a detailed optimization process is provided. Finally, the strength and deflection performance optimization problem of a tapered laminate with PD from 28 layers to 16 layers under three-point bending test is introduced for illustration of the proposed automatic modeling and optimization method. Comparisons between simulation results and experimental data of the obtained optimization solution verify the effectiveness of the proposed modeling and optimization method.

IMRA/SRS Delphi consensus on international standards for common core components of robotic surgical training design

Robotic surgery has expanded internationally at pace. There are multiple local robotic training pathways but there is inconsistency in standardisation of core common components for curricula internationally. A framework is required to define key objectives that can be implemented across robotic training ecosystems. This Delphi consensus aimed to provide recommendations for core considerations in robotic training design across diverse training environments internationally. A literature search was performed and an international steering committee (AG, KL, JW, HM, TC) proposed key components for contemporary robotic training design and a modified Delphi approach was used to gather stakeholder opinion. The outcomes were then discussed at a face-to-face international expert consensus at the IMRA educational session at the Society of Robotic Surgery (SRS) meeting and final voting was conducted on outstanding items. Stakeholders included robotic surgeons, proctors, trainees and robotic surgical training providers. There was consensus achieved in 139 statements organised into 15 themes. There was 100% agreement that standardised themes in robotic curricula may improve patient safety. Key take-home messages include—training curricula should be multiplatform, non-technical skills are an important component of a robotic curriculum as well as console and bedside skills, clinically relevant performance metrics should be used for assessment where available, the reliance on cadaveric and live animal models should be reduced as high-fidelity synthetic models emerge, and stepwise component training is useful for advanced procedural training. These consensus recommendations are intended to guide design of fit for purpose contemporary robotic surgical curricula. Integration of these components into robotic training pathways internationally is recommended.

Data-Driven Modeling of Tire-Soil Interaction with POD-Based Model Order Reduction

Abstract A data-driven model capable of predicting time-domain solutions of a high-fidelity tire-soil interaction model is developed to enable quick prediction of mobility capabilities on deformable terrain. The adaptive model order reduction based on the proper orthogonal decomposition (POD), for which the high-dimensional equations are projected onto the reduced subspace, is utilized as the basis for predicting the time-domain tire-soil interaction behavior. The projection-based model order reduction, however, requires many online matrix operations due to the successive updates of the nonlinear functions and Jacobians at every time step, thereby hindering the computational improvement. Therefore, a data-driven approach using a Long Short Term Memory (LSTM) neural network is introduced to predict the reduced order coordinates without the projection and time integration processes for computational speedup. With this model, a hybrid data-driven/physics-based off-road mobility model is proposed, where four separate LSTM-POD data-driven tire-soil interaction models are integrated into the physics-based multibody dynamics (MBD) vehicle model through a force-displacement coupling algorithm. By doing so, the individual data-driven tire-soil interaction model can be constructed efficiently, and the MBD and LSTM models are assembled as a single off-road mobility model and analyzed with existing off-road mobility solvers. The predictive ability and computational benefit of the proposed data-driven tire-soil interaction model with the POD-based model order reduction are examined with several numerical examples.

Validation evidence with experimental and clinical data to establish credibility of TAVI patient-specific simulations

PurposeThe objective of this study is to validate a novel workflow for implementing patient-specific finite element (FE) simulations to virtually replicate the Transcatheter Aortic Valve Implantation (TAVI) procedure. MethodsSeven patients undergoing TAVI were enrolled. Patient-specific anatomical models were reconstructed from pre-operative computed tomography (CT) scans and subsequentially discretized, considering the native aortic leaflets and calcifications. Moreover, high-fidelity models of CoreValve Evolut R and Acurate Neo2 valves were built. To determine the most suitable material properties for the two stents, an accurate calibration process was undertaken. This involved conducting crimping simulations and fine-tuning Nitinol parameters to fit experimental force-diameter curves. Subsequently, FE simulations of TAVI procedures were conducted. To validate the reliability of the implemented implantation simulations, qualitative and quantitative comparisons with post-operative clinical data, such as angiographies and CT scans, were performed. ResultsFor both devices, the simulation curves closely matched the experimental data, indicating successful validation of the valves mechanical behaviour. An accurate qualitative superimposition with both angiographies and CTs was evident, proving the reliability of the simulated implantation. Furthermore, a mean percentage difference of 1,79 ± 0,93 % and 3,67 ± 2,73 % between the simulated and segmented final configurations of the stents was calculated in terms of orifice area and eccentricity, respectively. ConclusionThis study shows the successful validation of TAVI simulations in patient-specific anatomies, offering a valuable tool to optimize patients care through personalized pre-operative planning. A systematic approach for the validation is presented, laying the groundwork for enhanced predictive modeling in clinical practice.

Modeling and Identification of Electromechanical Actuators for the ILR-33 AMBER Rocket

The aim of the work was to develop a method for modeling, testing, and identifying electromechanical actuators for rocket applications. Works were performed using a prototype solution designed for the ILR-33 AMBER suborbital rocket, developed by Łukasiewicz Research Network – Institute of Aviation. A set of physical relationships was used to create system’s mathematical model, including Kirchhoff’s laws, Newton’s laws, and nonlinear friction models. System’s tests were then performed. A new method of results analysis was applied herein to gather unknown parameters for the model and to confirm an elevated level of convergence between both model and experiment in all cases analyzed. The identification approach proved itself to be effective and useful. A complex approach concerning modeling, testing and identification of such actuators was explained for the first time in this paper. The methods presented herein can be applied in other disciplines, wherever electromechanical actuator systems are used, and where their proper identification is necessary to ensure system reliability and safety. Presented solutions are simple to implement, and the test stands do not require expensive measurement equipment. The results obtained permit to create a high fidelity model at a reasonably low computational cost.

Numerical modeling of electromagnetic field spatiotemporal evolution to evaluate the effects on calcium carbonate crystallization

Calcium carbonate (CaCO3) scaling is a significant impediment to water systems. Electromagnetic field (EMF) treatment is a promising approach to control scaling owing to its simplicity and low or no energy requirements. However, the underlying mechanisms by which EMF impacts CaCO3 crystallization remain unclear due to the challenges in measuring the EMFs in feed solutions and the lack of a fundamental understanding of the applied EMFs and the observed physicochemical phenomena. To fill this knowledge gap, a high-fidelity COMSOL model was first developed to simulate EMFs in bulk solutions for three alternating current-induced EMF devices with different configurations and properties. These were then integrated with experimental data to unveil the underlying mechanism by which applied EMFs alter the physicochemical processes. The study revealed that even low-strength EMFs (e.g., electric fields <0.15 V/m and magnetic fields <0.03 mT) promoted CaCO3 precipitation in bulk solutions. The electric fields created by these EMF devices resulted in higher Lorentz force compared to their induced magnetic fields. The methodology of this study offers the capability to predict the effectiveness of different EMF devices in facilitating crystallization processes, and these mechanistic insights lay the foundation for the smart design of EMF devices for diverse water treatment applications.

CORR Insights®: Can Repetition-based Training in a High-fidelity Model Enhance Critical Trauma Surgical Skills Among Trainees and Attending Surgeons Equally?

CORR Insights®: Can Repetition-based Training in a High-fidelity Model Enhance Critical Trauma Surgical Skills Among Trainees and Attending Surgeons Equally?

Progressive bearing failure analysis and strength prediction method for the initial assembly and tensile process of composite bolted joints

Composite bolted joints have extensive application in aerospace and marine equipment. Failure analysis and strength prediction are crucial steps in the design of composite bolted joints. This study proposes a method that can accurately analyze the progressive bearing failure and static strength of composite bolted joints under different initial assembly parameters. This method first obtains an accurate initial assembly state based on the high-fidelity finite element model with detailed thread structure. Then it uses this as an input to analyze the progressive bearing failure process and the bearing strength value. Using the above method, the formation and evolution mechanism of the tightening yielding phenomenon during the assembly process are analyzed. And the influence of the two key assembly parameters, washer type and initial bolt tension, on the bearing strength of composite bolted joints is analyzed. The experimental results verify the effectiveness and accuracy of the simulation methods and results.

Unprecedented mechanical wave energy absorption observed in multifunctional bioinspired architected metamaterials

In practical engineering, noise and impact hazards are pervasive, indicating the pressing demand for materials that can absorb both sound and stress wave energy simultaneously. However, the rational design of such multifunctional materials remains a challenge. Herein, inspired by cuttlebone, we present bioinspired architected metamaterials with unprecedented sound-absorbing and mechanical properties engineered via a weakly-coupled design. The acoustic elements feature heterogeneous multilayered resonators, whereas the mechanical responses are based on asymmetric cambered cell walls. These metamaterials experimentally demonstrated an average absorption coefficient of 0.80 from 1.0 to 6.0 kHz, with 77% of the data points exceeding the desired 0.75 threshold, all with a compact 21 mm thickness. An absorptance-thickness map is devised for assessing the sound-absorption efficiency. The high-fidelity microstructure-based model reveals the air friction damping mechanism, with broadband behavior attributed to multimodal hybrid resonance. Empowered by the cambered design of cell walls, metamaterials shift catastrophic failure toward a progressive deformation mode characterized by stable stress plateaus and ultrahigh specific energy absorption of 50.7 J/g—a 558.4% increase over the straight-wall design. After the deformation mechanisms are elucidated, a comprehensive research framework for burgeoning acousto-mechanical metamaterials is proposed. Overall, our study broadens the horizon for multifunctional material design.

Improved a posteriori error bounds for reduced port-Hamiltonian systems

Projection-based model order reduction of dynamical systems usually introduces an error between the high-fidelity model and its counterpart of lower dimension. This unknown error can be bounded by residual-based methods, which are typically known to be highly pessimistic in the sense of largely overestimating the true error. This work applies two improved error bounding techniques, namely (a) a hierarchical error bound and (b) an error bound based on an auxiliary linear problem, to the case of port-Hamiltonian systems. The approaches rely on a secondary approximation of (a) the dynamical system and (b) the error system. In this paper, these methods are adapted to port-Hamiltonian systems. The mathematical relationship between the two methods is discussed both theoretically and numerically. The effectiveness of the described methods is demonstrated using a challenging three-dimensional port-Hamiltonian model of a classical guitar with fluid–structure interaction.

Vibration analysis of rotating variable-angle-tow composite cylindrical structures via high-fidelity shell models

The paper focuses on rotordynamics analyses of various rotating variable-angle-tow (VAT) composite cylindrical structures using both low- and high-fidelity structural models. To model the spatially varying composite shell structures, the Carrera Unified Formulation (CUF) is employed. The CUF provides a hierarchical and automated approach for developing shell finite element models. Various shell models are created using different kinematics models based on Taylor or Lagrange expansion functions. The linearized equations of motion include the Coriolis and initial stress contributions. Several VAT composite curved panels and thin and thick cylinders have been analyzed to validate the presented approach and provide benchmark solutions. The results are discussed and compared with commercial software solutions. The results demonstrate the good accuracy and reliability of the proposed numerical methodology.

Multidisciplinary optimization methodology for truss-braced wing aircraft using high-fidelity structure sizing

In this research, a method is developed to optimize the truss-braced wing aircraft configuration in a multidisciplinary design framework. Physics-based high-fidelity methods, that can capture the nature of the configuration changes, are employed for the disciplines where the existing classical methods are not reliable. High-fidelity geometry modeling, structure loading, structure optimization, and aeroelastic sizing methods are integrated into the aircraft multidisciplinary design and optimization. The developed algorithm is applied for the multi-objective optimization of a regional jet aircraft to minimize the cost and weight. The results demonstrate that the cost-optimum solution converges to a higher aspect ratio wing equipped with a higher bypass ratio engine, and a 7.94% reduction in the direct operating cost can be achieved. On the other hand, the weight-optimum wing planform tends to a slightly lower aspect ratio wing with a lower bypass ratio engine, while a 6.18% reduction in take-off weight is achieved. In addition to that, the findings of this study highlight the considerable effect that the engine technology has on the optimum layout, which suggests that the engine technology and its performance should also be a part of the design optimization process. The developed modular framework offers further optimization potential for the truss-braced wing aircraft, as more detailed models can be integrated.

SP8.12 - Evaluating the pig tissue model for acute proctology simulation at General Surgery ST3 Bootcamp

Abstract Evaluating the pig tissue model for acute proctology simulation at General Surgery ST3 Bootcamp Background Acute proctology is an important skill for any general surgical trainee; however it is a skill which often is overlooked when it comes to training. Most, if not all teaching happens during patient procedures. Simulation in this area is underdeveloped and there is a lack of validated models for simulation. These models are also usually expensive and difficult to obtain. The aim of this study was to assess face validity of a low-cost model for teaching acute proctology during the ST3 general surgical bootcamp. Methods We created a model using a pig rectum and perineum. This provided two uses. Firstly, we were able to teach relevant clinical anatomy. Secondly, we were able to simulate EUA, rigid sigmoidoscopy, decompression of volvulus and haemorrhoidal banding. The models allowed for repeated use. Results Twenty-five trainees took part in this simulation. Overall, the trainees felt the rectal examination was realistic (76%), the sigmoidoscope moved realistically in the model (72%) as did the proctoscope (88%). In terms of ability, trainees felt the simulation improved their confidence in EUA (68%), rectal examination (72%) and banding (80%). A total of 80% of trainees thought the model was overall “Good or Excellent”. Conclusions Our study demonstrates that this low-cost, high-fidelity simulation model has a high face validity and may be explored for the use for training new general surgical registrars.

Accelerating segmentation of fossil CT scans through Deep Learning

Recent developments in Deep Learning have opened the possibility for automated segmentation of large and highly detailed CT scan datasets of fossil material. However, previous methodologies have required large amounts of training data to reliably extract complex skeletal structures. Here we present a method for automated Deep Learning segmentation to obtain high-fidelity 3D models of fossils digitally extracted from the surrounding rock, training the model with less than 1%-2% of the total CT dataset. This workflow has the capacity to revolutionise the use of Deep Learning to significantly reduce the processing time of such data and boost the availability of segmented CT-scanned fossil material for future research outputs. Our final Unet segmentation model achieved a validation Dice similarity of 0.96.

Comparing Large-Eddy Simulation and Gaussian Plume Model to Sensor Measurements of an Urban Smoke Plume

The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM2.5. The emission was significantly greater than a ‘normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.

Exploring mouse necropsy through augmented reality: developing a web application for enhanced learning and visualization

Necropsy, the examination of animal carcasses to determine the cause of death, is an essential skill for many professionals. Traditional training methods, however, are costly and time-consuming. The article suggests that Web-based Augmented Reality (WebAR) can offer an immersive and cost-effective training experience for laboratory animal necropsy. It describes using photogrammetry techniques to create a virtual necropsy environment consisting of 10 necropsy steps. A questionnaire was used to evaluate the usability, educational value, and drawbacks of the designed application by students who tested it. The paper outlines best practices for developing WebAR simulations, including high-fidelity 3D models and interactive elements. Additionally, it presents methods for creating new WebAR applications using specific programs or scripts. This paper highlights the potential benefits of WebAR for laboratory animal necropsy training, emphasizing its accessibility, cost-effectiveness, and scalability.

Adaptive-Quadratic-Neural-Network-Based Multifidelity Modeling Approach for Buckling of Stiffened Panels

This paper presents a method for predicting the buckling load of stiffened panels using multifidelity modeling based on quadratic neural networks (QNNs) with adaptive activation functions. The effectiveness of the proposed approach is demonstrated through a series of simulations on a range of stiffened panel configurations, and the results are compared to those obtained from traditional multifidelity and high-fidelity models in terms of accuracy and computational efficiency. Numerical experiments demonstrate that the model can accurately and efficiently predict the buckling load of stiffened panels while significantly reducing the computational cost of evaluating the surrogate model. In particular, the proposed adaptive quadratic neural networks (AQNNs) model achieves convergence approximately three times faster and four times less trainable parameters compared to traditional artificial neural networks while maintaining the same validation loss. This approach can significantly improve the design and optimization of aerospace structures by easily and quickly exploring various design configurations and finding stable and efficient configurations. This study highlights the potential of a new multifidelity modeling framework for predicting the buckling load and collapse load of aerospace structures by enhancing convergence speed and prediction accuracy while reducing the computational complexity of neural networks.

Analytic optimal control for multi-satellite assembly using linearized twistor-based model

This paper presents Guidance and Control (G&C) systems for multi-satellite assembly in proximity operations. The systems utilize the twistor model, which is linearized through Taylor’s series. Decentralized control laws, designed using Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC), are employed to track an energy-optimal trajectory generated using the Hamiltonian approach. Data exchange between satellites and their neighbors is represented using graph theory. The decentralized MPC framework is implemented using the CasADi package. To ensure collision avoidance between the satellites, a repulsive control law is designed, considering symmetric input saturation in the actuators. The proposed G&C systems are tested using a high-fidelity nonlinear satellite relative motion model that incorporates orbital perturbations. Numerical simulations are performed in a MATLAB® environment, and the results are visualized using STK®. Furthermore, a comparative study is conducted to evaluate tracking performance and fuel consumption between the two control methods. The results demonstrate that the use of an optimal trajectory reduces fuel consumption for both control algorithms.