Fidelity Simulation Model Research Articles

Modelling and identification methods for simulation of cable-suspended dual-arm robotic systems

This paper proposes rigid-body modelling and identification procedures for long-reach dual-arm manipulators in a cable-suspended pendulum configuration. The proposed model relies on a virtually constrained open kinematic chain and lends itself to be simulated through the most commonly used robotic simulators without explicitly account for the cables constraints and flexibility. Moreover, a dynamic parameters identification procedure is devised to improve the simulation model fidelity and reduce the sim-to-real gap for controllers deployment. We show the capability of our model to handle different cable configurations and suspension mechanisms by customising it for two representative cable-suspended dual-arm manipulation systems: the LiCAS arms suspended by a drone and the CRANEbot system, featuring two Pilz arms suspended by a crane. The identified dynamic models are validated by comparing their evolution with data acquired from the real systems showing a high (between 91.3% to 99.4%) correlation of the response signals. In a comparison performed with baseline pendulum models, our model increases the simulation accuracy from 64.4% to 85.9%. The simulation environment and the related controllers are released as open-source code.

Multi-Stage and Multi-Parameter Influence Analysis of Deep Foundation Pit Excavation on Surrounding Environment

As urbanization accelerates, deep excavation projects have become increasingly vital in the construction of high-rise buildings and underground facilities. However, the potential risks to the surrounding environment and the inherent complexities involved necessitate thorough research to ensure the safety of those engineering projects with deep foundation pit excavation and to minimize their impact on adjacent structures. This study introduces a multi-stage and multi-parameter numerical simulation method to scrutinize the construction process of deep foundation pits. This approach not only investigates the influence of excavation activities on nearby buildings and roads but also enhances the fidelity of simulation models by establishing a three-dimensional finite element model integrated with on-site investigated geological information. Therefore, the proposed method can provide a more holistic and accurate analysis of the overall impacts of the pit excavation process. To examine the feasibility and effectiveness of the proposed method, this study adopts the multi-stage and multi-parameter influence analysis approach for a real practical engineering case to explore the impact of excavation on the foundation pit support structure, nearby buildings, and surrounding roads. The foundation pit support’s maximum displacement was 8.64 mm, well under the 25 mm standard limit. Anchor rod forces were about 10% below the standard limit. Building and road settlements were also minimal, at 10.33 mm and 16.44 mm, respectively, far below their respective limits of 200 mm and 300 mm. This study not only validates the feasibility of design and construction stability of deep foundation pits but also contributes theoretical and practical insights, serving as a valuable reference for future engineering projects of a similar scope.

Advances and Modern Applications of Frequency-Domain Aircraft and Rotorcraft System Identification

This paper provides an overview of advances and applications of aircraft and rotorcraft flight dynamics system identification at the U.S. Army Combat Capabilities Development Command Aviation & Missile Center over the past 20 years. An overarching topic is how frequency-domain system identification has evolved to aid modeling work of modern aircraft and rotorcraft configurations. The paper starts with a brief overview of the frequency-response method for system identification. Subsequently, the paper highlights and demonstrates several new advances using flight-test data from a conventional helicopter, fixed-wing aircraft, advanced coaxial compound rotorcraft, and a small multirotor unmanned aerial vehicle. The first advancement is the use of identification results to improve simulation model fidelity. The next advancement is the Joint Input-Output Method for identification of aircraft with highly correlated inputs. Next, the paper discusses the advancement of flexible constraints, which provide the ability to directly determine physical parameters from flight-test data. The paper then discusses a methodology to identify lower-order linearized dynamic inflow models from higher-order free wake aerodynamic models. Finally, the paper discusses model stitching, which is the technique of combining or stitching together individual linear models and trim data for discrete flight conditions to produce a continuous, full flight-envelope real-time stitched simulation model.

Platform for investigating continuum manipulator behavior in orthopedics.

The use of robotic continuum manipulators has been proposed to facilitate less-invasive orthopedic surgical procedures. While tools and strategies have been developed, critical challenges such as system control and intra-operative guidance are under-addressed. Simulation tools can help solve these challenges, but several gaps limit their utility for orthopedic surgical systems, particularly those with continuum manipulators. Herein, a simulation platform which addresses these gaps is presented as a tool to better understand and solve challenges for minimally invasive orthopedic procedures. An open-source surgical simulation software package was developed in which a continuum manipulator can interact with any volume model such as to drill bone volumes segmented from a 3D computed tomography (CT) image. Paired simulated X-ray images of the scene can also be generated. As compared to previous works, tool-anatomy interactions use a physics-based approach which leads to more stable behavior and wider procedure applicability. A new method for representing low-level volumetric drilling behavior is also introduced to capture material variability within bone as well as patient-specific properties from a CT. Similar interaction between a continuum manipulator and phantom bone was also demonstrated between a simulated manipulator and volumetric obstacle models. High-level material- and tool-driven behavior was shown to emerge directly from the improved low-level interactions, rather than by need of manual programming. This platform is a promising tool for developing and investigating control algorithms for tasks such as curved drilling. The generation of simulated X-ray images that correspond to the scene is useful for developing and validating image guidance models. The improvements to volumetric drilling offer users the ability to better tune behavior for specific tools and procedures and enable research to improve surgical simulation model fidelity. This platform will be used to develop and test control algorithms for image-guided curved drilling procedures in the femur.

Knowledge-based expedited parameter tuning of microwave passives by means of design requirement management and variable-resolution EM simulations

The importance of numerical optimization techniques has been continually growing in the design of microwave components over the recent years. Although reasonable initial designs can be obtained using circuit theory tools, precise parameter tuning is still necessary to account for effects such as electromagnetic (EM) cross coupling or radiation losses. EM-driven design closure is most often realized using gradient-based procedures, which are generally reliable as long as the initial design is sufficiently close to the optimum one. Otherwise, the search process may end up in a local optimum that is of insufficient quality. Furthermore, simulation-based optimization incurs considerable computational expenses, which are often impractically high. This paper proposes a novel parameter tuning procedure, combining a recently reported design specification management scheme, and variable-resolution EM models. The former allows for iteration-based automated modification of the design goals to make them accessible in every step of the search process, thereby improving its immunity to poor starting points. The knowledge-based procedure for the adjustment of the simulation model fidelity is based on the convergence status of the algorithm and discrepancy between the current and the original performance specifications. Due to using lower-resolution EM simulations in early phase of the optimization run, considerable CPU savings can be achieved, which are up to 60 percent over the gradient-based search employing design specifications management and numerical derivatives. Meanwhile, as demonstrated using three microstrip circuits, the computational speedup is obtained without design quality degradation.

Improved-Efficacy EM-Based Antenna Miniaturization by Multi-Fidelity Simulations and Objective Function Adaptation

The growing demand for the integration of surface mount design (SMD) antennas into miniaturized electronic devices has imposed increasing limitations on the structure dimensions. Examples include embedded antennas in applications such as on-board devices, picosatellites, 5G communications, or implantable and wearable devices. The demands for size reduction while ensuring a satisfactory level of electrical and field performance can be managed through constrained numerical optimization. The reliability of optimization-based size reduction requires utilization of full-wave electromagnetic (EM) analysis, which entails significant computational costs. This can be alleviated by incorporating surrogate modeling techniques, adjoint sensitivities, or the employment of sparse sensitivity updates. An alternative is the incorporation of multi-fidelity simulation models, normally limited to two levels, low and high resolution. This paper proposes a novel algorithm for accelerated antenna miniaturization, featuring a continuous adjustment of the simulation model fidelity in the course of the optimization process. The model resolution is determined by factors related to violation of the design constraints as well as the convergence status of the algorithm. The algorithm utilizes the lowest-fidelity model for the early stages of the optimization process; it is gradually refined towards the highest-fidelity model upon approaching convergence, and the constraint violations improve towards the preset tolerance threshold. At the same time, a penalty function approach with adaptively adjusted coefficients is applied to enable the precise control of constraints, and to increase the achievable miniaturization rates. The presented procedure has been validated using five microstrip antennas, including three broadband, and two circularly polarized structures. The obtained results corroborate the relevance of the implemented mechanisms from the point of view of improving the average computational efficiency of the optimization process by 43% as compared to the single-fidelity adaptive penalty function approach. Furthermore, the presented methodology demonstrates a performance that is equivalent or even superior to its single-fidelity counterpart in terms of an average constraint violation of 0.01 dB (compared to 0.03 dB for the reference), and an average size reduction of 25% as compared to 25.6%.

Assessment of a Novel Urology Resident Simulation-Based Curriculum.

Interactive simulation training during residency is a rapidly growing initiative due to work-hour restrictions, improved simulation model fidelity and an evolving technological field. We designed and implemented a simulation curriculum for urology residents. The objective of this study was to assess its impact on trainee confidence and knowledge. This was a prospective nonblinded case series analyzing the use of simulation education with entrance and exit surveys. We developed and implemented monthly workshops featuring fundamental urological procedures and skills. Residents from a single academic urology residency program received didactic instruction as well as supervised simulation training from an expert faculty member. The majority of residents highly rated all topics and affirmed that they would repeat the simulation topic in the future. Overall, junior residents averaged a 27% gain in base knowledge (p <0.001), and senior residents reported a 15.5% gain in base knowledge (p <0.001). Didactic and mentored simulation portions of the sessions were considered equally valuable (p >0.5). Of the residents 84% felt the sessions better prepared them to be in the operating room and 94% reported that they would recommend the simulation sessions to a peer (p <0.001). Simulation is a valuable tool that prepares residents for operating room experiences. We have developed a robust simulation curriculum that serves as a feasible and valuable model for other training programs, and has led to increased resident confidence and knowledge in urological procedures and skills.

Hybrid Evolutionary Algorithm with an Optimal Sample Allocation Strategy for Multifidelity Simulation Optimization Problems

Simulation has been applied to evaluate system performance even when the target system does not exist in practice. Dealing with model fidelity is required to apply simulation to practice. A high-fidelity (HF) simulation model is generally more accurate and requires more computational resources than a low-fidelity (LF) one. A low-fidelity model may have less accuracy than a HF one, but it can rapidly evaluate a design alternative. Consequently, the performance accuracy of the constructed simulation model and its computational cost involves a tradeoff. In this research, the simulation optimization problem under a large design space, where a LF model may not be able to evaluate all design alternatives in the limited computational resource, is studied. We extended multifidelity (MF) optimization with ordinal transformation and optimal sampling (MO2TOS), which enables the use of LF models to search for a HF one efficiently, and proposed a combination of the genetic algorithm and MO2TOS. A novel optimal sample allocation strategy called MO2TOSAS was proposed to improve search efficiency. We applied the proposed methods to two experiments on MF function optimization and a simultaneous scheduling problem of machine and vehicles (SSPMV) in flexible manufacturing systems. In SSPMV, we developed three fidelity simulation models that capture important characteristics, including the preventive deadlock situation of vehicles and alternative machines. Simulation results show that the combination of more than one fidelity level of simulation models can improve search efficiency and reduce computational costs.

Impact of simulation model fidelity and simulation method on ship operational performance evaluation in sea passage scenarios

In this paper, we present and investigate important factors that influence a vessels fuel consumption during operation and what model fidelity that is required to adequately capture these factors in fuel consumption estimations. Our study focuses on evaluating effect of model fidelity and the methodology used to assess the ship behaviour in operational conditions. Operational performance is affecting cost of operation and estimations of operational performance is used in design development, operational research and as basis for emission analysis, affecting decision making at an operational and technical level. A comprehensive case study is presented where we compare fuel consumption and engine operational profile for time domain and discrete-event simulations, and a static statistical model. Of the factors that have been included in the study, variation in propeller loading and consequently propulsion efficiency is the most prominent physical factor for estimation of required power and fuel consumption. Further, the ability to replicate realistic scenarios using simulators has a significant effect on our understanding of how operational and environmental factors affect operational performance.

An algorithmic framework for the optimization of computationally expensive bi-fidelity black-box problems

We introduce an algorithm for the optimization of problems whose objective functions are evaluated by computationally expensive black-box simulations and for which an analytic description of the objective and its derivatives are not available. We consider the case where two levels of simulation model fidelity are available, namely a high fidelity model that is computationally very expensive to evaluate, and a low fidelity model that is less accurate and computationally cheaper but still time consuming. The computational effort is alleviated by using computationally cheap surrogate models that approximate the simulations at both fidelity levels. The local correlation between both fidelity surrogate models determines when the low fidelity model can be trusted for making sampling decisions for the high fidelity model. In the numerical experiments we investigate how well our algorithm responds to problems whose objective function fidelity levels are well correlated and badly correlated. We study how different initial design strategies and parameter settings impact the performance of the algorithm. The results show that our algorithm actively learns from the local correlation computations how well suited the low fidelity model is for making sampling decisions and it ignores the low fidelity model if the correlation is too low.

Teaching video-assisted thoracic surgery lobectomy-using an ex vivo simulation model.

Video-assisted thoracic surgery (VATS) is gradually replacing conventional open thoracotomy as the standard approach of many lung resections and thoracic surgical trainees and experienced surgeons must learn and master this technique. Simulation based training may be a good option to acquire or improve these advanced skills, however realistic and inexpensive simulators are not accessible or commercially available. We developed a low cost and hi fidelity simulation model using porcine heart-lung blocks.

The Effect of Model Fidelity on Learning Outcomes of a Simulation-Based Education Program for Central Venous Catheter Insertion.

Simulation-based education for central venous catheter (CVC) insertion has been repeatedly documented to improve performance, but the impact of simulation model fidelity has not been described. The aim of this study was to examine the impact of the physical fidelity of the simulation model on learning outcomes for a simulation-based education program for CVC insertion. Forty consecutive residents rotating through the medical intensive care unit of an academic medical center completed a simulation-based education program for CVC insertion. The curriculum was designed in accordance with the principles of deliberate practice and mastery learning. Each resident underwent baseline skills testing and was then randomized to training on a commercially available CVC model with high physical fidelity (High-Fi group) or a simply constructed model with low physical fidelity (Low-Fi group) in a noninferiority trial. Upon completion of their medical intensive care unit rotation 4 weeks later, residents returned for repeat skills testing on the high-fidelity model using a 26-item checklist. The mean (SD) posttraining score on the 26-item checklist for the Low-Fi group was 23.8 (2.2) (91.5%) and was not inferior to the mean (SD) score for the High-Fi group of 22.5 (2.6) (86.5%) (P < 0.0001). Residents in both groups judged the training program to be highly useful despite perceiving a lesser degree of physical realism in the low-fidelity model compared with the high-fidelity model (P = 0.05). Simulation-based education using equipment with low physical fidelity can achieve learning outcomes comparable with those with high-fidelity equipment, as long as other aspects of fidelity are maintained and robust educational principles are applied during the design of the curriculum.

Performance Analysis of Simulation of Pedestrian Traffic inside the Hub

Transport hub belongs to the people highly gathering places,the pedestrian traffic within the hub is a nonlinear stochastic dynamic problem with uncertain cluster density. The computer dynamic simulation is one of the effective methods to investigate the dynamic characteristics of pedestrian traffic flow at present within the hub.The article studies the error between pedestrian traffic simulation of the commuter simulation software and the actual pedestrian traffic,and explains the simulation error due to the discrete space, fidelity are introduced to evaluate the accuracy of the simulation model.By comparing the Hohhot underground tunnel experimental data and simulation results and the analysis of pedestrian traffic characteristics, it is concluded that the pedestrian traffic simulation performance is closely related to the environment in the hub and the importance of the fidelity of simulation model.

On fidelity and model selection for discrete event simulation

In simulation, perhaps the most common use of the term ‘fidelity’ refers to the faithfulness with which model behavior reflects modeled system behavior. While there have been studies of fidelity seeking absolute and quantitative measures, there is not yet a consensus on a workable fidelity metric. We propose a formal modeling framework for comparing discrete event system simulation models in terms of fidelity, using a relative fidelity indicator. Based on the framework, we consider the possibility that the higher fidelity simulation models can also be more productive, even though they are more expensive to develop and use, since they can be used to achieve multiple objectives. First, we propose a formal simulation modeling framework within which the fidelity of simulation models can be discussed. With this framework and a simple example, we then define a relative fidelity indicator that provides a systematic way of comparing the fidelity of two simulation models. The relative fidelity indicator focuses on the most important characteristics in simulation studies: the input and output interfaces and the variables used for specifying a real-world system and simulation models. It does not require any special modeling formalism for model comparison. Based on the relative fidelity indicator and simulation modeling framework, we state the optimum simulation model selection problem to achieve given simulation objectives. Under a practical assumption, we analyze the simulation model selection problem and derive properties related to simulation modeling and the fidelity of simulation models.

Validation of a Colonoscopy Simulation Model for Skills Assessment

The purpose is to provide initial validation of a novel simulation model's fidelity and ability to assess competence in colonoscopy skills. In a prospective, cross-sectional design, each of 39 endoscopists (13 staff, 13 second year fellows, and 13 novices) performed a colonoscopy on a novel bovine simulation model. Staff endoscopists also completed a survey examining different aspects of the model's realism as compared to human colonoscopy. The groups' simulation performances were compared. Additionally, individual performances were correlated to patient-based performance data. Median model realism evaluation scores were favorable for nearly all parameters evaluated with mucosa appearance, endoscopic view, and paradoxical motion parameters receiving the highest scores. During simulation procedures, each group outperformed the less experienced groups in all parameters evaluated. Specifically, median cecal intubation times were: staff 226 s (IQR [interquartile range] 179-273), fellows 340 s (282-568), and novices 1,027 s (970-1,122) (P < 0.05). Median total procedure times on the model were: staff 468 s (416-501), fellows 527 s (459-824), and novices 1,350 s (1,318-1,428) (P < 0.05). Finally, individual cecal intubation times on the simulation model had a very high correlation to their respective patient-based times (r = 0.764). Overall, this model possesses a favorable degree of realism and is able to easily differentiate users based on their level of colonoscopy experience. More impressive, however, is the strong correlation between individual's simulated intubation times and actual patient-based colonoscopy data. In light of these findings, we speculate that this model has potential to be an effective tool for assessment of colonoscopic competence.

Optimization design for scramjet and analysis of its operation performance

Flow field numerical simulation methods of scramjet introduced in this paper, include using CFD method to analyze the flow phenomenon in each component of scramjet, i.e. forebody/inlet, combustor and afterbody/nozzle. Design and optimization of a generic scramjet model are investigated based on the bilevel integrated system synthesis method. Analysis and design procedures of each component are treated as a black box in scramjet system design. Components of scramjet are designed and optimized at subsystem level in the first step, and then a system level optimization is performed for the whole scramjet. Multi-fidelity simulation models are developed and applied to analyze and design the scramjet model. According to the requirement of design, appropriate fidelity simulation models are adopted in design cycle under the limitation of time and budget. A design and optimization example is presented in which some parameters are chosen as scramjet system level variables by sensitivity analysis of all possible design parameters; other parameters become component level variables of forebody/inlet, combustor and afterbody/nozzle. The design of experiment method and genetic algorithm arc applied to explore design space and to obtain optimal or near optimal solution. Operation performance of scramjet is also analyzed in this paper, considering the interaction between scramjet and vehicle airframe.

Determination of efficient simulation model fidelity for flexible manufacturing systems

This paper presents a framework for the determination of an efficient level of simulation model fidelity for flexible manufacturing systems, which will achieve acceptable output accuracy with minimum resources and thereby reduce model building effort and computation time. To this end, we first formally define different levels of model fidelity using building blocks available in object-oriented (O-O) modelling, where an operation at a higher level is either decomposed into more detailed operations or subjected to more constraints at a lower level. In this paper, five models with different fidelities are defined. Then, simulation models that conform to these O-O models are constructed. Using these simulation models, intensive experiments are conducted to examine how the factors that characterize an FMS contribute to the relative errors of outputs from different models. Since no actual systems are considered, the results generated from the most detailed simulation model are used as references. The experimental results are then summarized by regression-based meta-models. In the proposed framework, the most efficient model for a new FMS is identified so that the relative error of a model estimated from the meta-model is closest to the threshold value provided by users. This framework is tested by two sample FMSs, and the initial results look quite promising.

Simulation model-building procedure for dynamic systems integration

A procedure for building simulation models that are useful in aircraft dynamic systems integration is described. The objective of the procedure is increased simulation model fidelity while reducing the time required to develop and modify these models. The equations of motion for an elastic aircraft and their impact on the procedure are discussed in broad terms. A software tool that automatically generates FORTRAN code to perform tabular data lookups, the language used to develop a simulation model, and the requirements for passing information into a simulation are described. A simulation-variable nomenclature is presented. The procedure has been applied to build an open-loop F/A-18 simulation model. This example model is used to illustrate model-reduction issues.