International Journal of Modeling, Simulation, and Scientific Computing

This paper presents the state of piloted flight simulation fidelity with a focus on the missing link needed to complete the flight simulation experience, namely the simulated ATC environment (SATCE).

To date, there has been a great deal of effort invested in providing the highest level of flight realism possible. However, little investment has gone into systems which are used to improve communication skills with ATC while in a populated active airspace.

It is important to note that the relatively few SATCEs is not due to the lack of technology, since such products have been available for about a decade. The primary reason for its absence is the inability and unwillingness for operators to justify the investment in such a training tool.

In the meantime, the aviation industry has recognized that pilots need to have better communication skills while operating in various conditions. Consequently ICAO, with help from ARINC Industry Activities/FSEMC, has already taken steps to recommend the inclusion of SATCE characteristics in flight simulation devices.

The aviation and research communities need to assist efforts by producing the necessary studies and metrics which can be used to evaluate and validate SATCEs used in the flight training.

A simple analytical real-time capable model to account for fuselage-induced velocities at rotor blade elements is described at the example of the Bo105 fuselage. Data of the fuselage-induced flow fields in the volume of rotor operation above the fuselage are first computed by a panel method in the range of angle of attack and sideslip of $\pm 90^{\circ }$. The model parameters are then estimated based on these data. The usefulness of the model in combinations of angle of attack and sideslip is demonstrated.

Modeling and simulation are used to support a wide range of applications including design, analysis, operations research, test and evaluation, training, etc. Each application has different fidelity requirements and computational limitations. Consequently, a variety of models are required to support a single aircraft type throughout its life cycle. Validation and configuration management of this array of models is costly. FLIGHTLAB${}^{\text{TM}}$is a software tool that supports selective fidelity modeling and simulation to insure trace-ability and commonality between models, ranging from the comprehensive models required for design to the real time models required for training and hardware-in-the-loop testing. Providing a single tool with this range of modeling options greatly enhances the configuration management capability. Also once the highest fidelity, comprehensive, model is validated, it can be used to provide reference data for validation of simpler models, thereby expediting the validation process for all levels of modeling. This paper describes the real-time simulation capability of FLIGHTLAB${}^{\text{TM}}$models and their trace-ability to higher level FLIGHTLAB${}^{\text{TM}}$models.

When tuning motion platforms, subjective opinion is usually regarded as sufficient for most applications, as it should provide verification that no false cueing occurs. However, often systems are not configured to fully utilize the available motion for the desired purpose. This paper presents a new method to objectively tune simulation motion platforms. Enhanced usage of the available platform motion space is shown through an example of its application to a specific mission task. The difference in motion filter settings for two simulator platform geometries is shown. This displays the suitability of the platforms for completion of the two-axis lateral reposition task.

This study presents the development of computationally efficient coupling of Navier–Stokes Computational Fluid Dynamics (CFD) with a helicopter flight dynamics model with the ultimate goal of real-time simulation of airwake effects in the helicopter/ship Dynamic Interface (DI). The flight dynamics model is free to move within a computational domain, where the main rotor forces are converted to source terms in the momentum equations of the CFD solution using an actuator disk model. Simultaneously, the CFD solver calculates induced velocities that are fed back to the simulation and affect the aerodynamic loads in the flight dynamics. The CFD solver models the inflow, ground effect and interactional aerodynamics in the flight dynamics simulation, and these calculations can be coupled with the solution of the external flow (e.g., ship airwake effects). The simulation framework for fully-coupled pilot-in-the-loop (PIL) flight dynamics/CFD is demonstrated for a simplified shedding wake. Initial tests were performed with 0.38 million structured grid cells running on 352 processors and showed near-real-time performance. Improvements to the coupling interface are described that allow the simulation run at near-real-time execution speeds on currently available computing platforms. Improvements in computing hardware are expected to allow real-time simulations.

Mechanical response and simulation for constitutive equation with distributed order derivatives were considered. We investigated the creep compliance, creep recovery, relaxation modulus, stress–strain behavior under harmonic deformation for each case of two constitutive equations. We express these responses and results as easily computable forms and simulate them by using MATHEMATICA 8. The results involve the exponential integral function, convergent improper integrals on the infinite interval $(0,+\infty )$ and the numerical integral method for the convolution integral. For both equations, stress responses to harmonic deformation display hysteresis phenomena and energy dissipation. The two constitutive equations characterize viscoelastic models of fluid-like and solid-like, respectively.

The multigrid method has been widely used in computational fluid dynamics (CFD) numerical calculations because of its strong convergence. To achieve real-time simulation of a fluid in computer graphics (CG), the operation efficiency is also a significant factor to consider except for operational accuracy. For this problem, we introduced two multigrid cycling schemes, V-Cycle and full multigrid (FMG). Moreover, we have proposed a simple geometric multigrid method (GMG), and compared with the existing wide application of algebraic multigrid (AMG). All the calculations are the solution of parallel computing of GPU in this paper. The results showed that our approaches have improved the algorithm’s computational speed and convergence time, which prominently enhanced the efficiency of the fluid simulation.

Railway capacity has been extensively investigated for the purpose of utilizing rail infrastructure in a possible efficient strategy. Nevertheless, the estimation of high speed rail (HSR), which is different from common railways in many aspects, is still a challenge work needed to be solved. Consequently, this work focuses on the capacity estimation of HSR corridor. The objectives and constraints are developed for the HSR with the consideration of passenger service level and buffer time uncertainty, and a two-stage optimization model is proposed. The first stage determines the optimal number of trains in terms of the passenger origin–destination demand, and the second stage is a multi-objective mixed integer programming (MO-MIP) aiming to estimate the optimal capacity usage. The branch-and-bound algorithm is extended and applied to the proposed model, and a case study is performed to Beijing–Shanghai HSR line. The optimal solution is obtained, and the sensitivity of the two objectives is analyzed.

The investigation of the new generation of a photovoltaic cell for the optimized parameters is mainly carried out using modeling and simulation, because the fabrication of quantum structures is still a challenge. Hence, new models to simulate realistic devices are developed. In the past, the Drift Diffusion equation has been exploited to simulate quantum well solar cells by introducing various assumptions and new parameters. Non Equilibrium Green Function’s Formalism has been widely used to study the transport in the nanostructures of the photovoltaic devices. We have developed a model of simulating quantum dot (QD) $p$-$i$-$n$ junction solar cell by hybridizing the Drift Diffusion Equation and the Transfer Hamiltonian Approach. The developed model is applied to a QD $p$-$i$-$n$ junction solar cell to determine the optimized position of the QD within the intrinsic region. Our results show that when the QD is placed closer to the edges of the $i$-region, the highest possible efficiency is observed.

Multiple-load carriers are widely introduced for material delivery in manufacturing systems. The real-time scheduling of multiple-load carriers is so complex that it deserves attention to pursue higher productivity and better system performance. In this paper, a support vector machine (SVM)-based real-time scheduling mechanism was proposed to tackle the scheduling problem of parts replenishment with multiple-load carriers in automobile assembly plants under dynamic environment. The SVM-based scheduling mechanism was trained first and then used to make the optimal real-time decisions between “wait” and “deliver” on the basis of real-time system states. An objective function considering throughput and delivery distances was established to evaluate the system performance. Moreover, a simulation model in eM-Plant software was developed to validate and compare the proposed SVM-based scheduling mechanism with the classic minimum batch size (MBS) heuristic. It simulated both the steady and dynamic environments which are characterized by the uncertainty of demands or scheduling criteria. The simulation results demonstrated that the SVM-based scheduling mechanism could dynamically make optimal real-time decisions for multiple-load carriers and outperform the MBS heuristic as well.

With the aim of solving the detection problems for current complex event detection models in detecting a related event for a complex event from the high proportion disordered RFID event stream due to its big uncertainty arrival, an efficient complex event detection model based on Extended Nondeterministic Finite Automaton (ENFA) is proposed in this paper. The achievement of the paper rests on the fact that an efficient complex event detection model based on ENFA is presented to successfully realize the detection of a related event for a complex event from the high proportion disordered RFID event stream. Specially, in our model, we successfully use a new ENFA-based complex event detection model instead of an NFA-based complex event detection model to realize the detection of the related events for a complex event from the high proportion disordered RFID event stream by expanding the traditional NFA-based detection model, which can effectively address the problems above. The experimental results show that the proposed model in this paper outperforms some general models in saving detection time, memory consumption, detection latency and improving detection throughput for detecting a related event of a complex event from the high proportion out-of-order RFID event stream.

Cloud-resolving atmospheric general circulation models using large-scale supercomputers reproduce realistic behavior of 3-dimensional atmospheric field on a global scale. To understand the simulation result for scientists, conventional visualization methods based on 2-dimensional cloud classification are not enough for understanding individual clouds and their physical characteristics. In this study, we propose a new 3-dimensional extraction and classification method of simulated clouds based on their 3-dimensional shape and physical properties. Our proposed method extracts individual clouds by cloud water and cloud ice, and classifies them into six types by their altitude and upward flow. We applied the method to time-varying atmospheric simulation data, and attempted to visualize atmospheric phenomena on the tropics such as developing cumulonimbus and tropical cyclone. Two case studies clearly visualize the behavior of individual cloud type and clarify that some cloud types have a relationship with rainfall during active weather phenomena. The proposed method has the potential to analyze such phenomena that develop in the vertical direction as well as the horizontal direction.

Value function approximation plays an important role in reinforcement learning (RL) with continuous state space, which is widely used to build decision models in practice. Many traditional approaches require experienced designers to manually specify the formulization of the approximating function, leading to the rigid, non-adaptive representation of the value function. To address this problem, a novel Q-value function approximation method named ‘Hierarchical fuzzy Adaptive Resonance Theory’ (HiART) is proposed in this paper. HiART is based on the Fuzzy ART method and is an adaptive classification network that learns to segment the state space by classifying the training input automatically. HiART begins with a highly generalized structure where the number of the category nodes is limited, which is beneficial to speed up the learning process at the early stage. Then, the network is refined gradually by creating the attached sub-networks, and a layered network structure is formed during this process. Based on this adaptive structure, HiART alleviates the dependence on expert experience to design the network parameter. The effectiveness and adaptivity of HiART are demonstrated in the Mountain Car benchmark problem with both fast learning speed and low computation time. Finally, a simulation application example of the one versus one air combat decision problem illustrates the applicability of HiART.

Hybrid systems such as Cyber Physical System (CPS) are becoming increasingly popular, mainly due to the involvement of information technology in different aspects of life. For analysis and verification of hybrid system models, simulation is used extensively. As parts of a common hybrid system may belong to different domains of study, it is sometimes beneficial to use specialized simulation packages (SPs) for each domain. In this case, parts of a system are simulated in different SPs. The idea may seem simple, but coupling more than one simulation component presents challenges related to numerical stability. The presented article suggests an implicit solver coupling technique enhanced to facilitate simulation of hybrid models using multiple simulation components. The technique is developed using two of the most popular simulation interoperability standards, namely, the High Level Architecture and the Functional Mock-up Interface. By using these standards, the developed algorithm will be useful for a large number of practitioners and researchers. The developed algorithm is described using a generic distributed computation model, which makes it reproducible even without using the standards. For the verification of results, the algorithm is tested on two case studies. The results are compared to a monolithic simulator and the proximity of results initiates the validity of the developed algorithm.

Personalization is the adaptation of the services to fit the user’s interests, characteristics and needs. The key to effective personalization is user profiling. Apart from traditional collaborative and content-based approaches, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, they are not able to achieve accurate user profiles. In this paper, we present a new clustering algorithm, namely Multi-Dimensional Clustering (MDC), to determine user profiling. The MDC is a version of the Instance-Based Learner (IBL) algorithm that assigns weights to feature values and considers these weights for the clustering. Three feature weight methods are proposed for the MDC and, all three, have been tested and evaluated. Simulations were conducted with using two sets of user profile datasets, which are the training (includes 10,000 instances) and test (includes 1000 instances) datasets. These datasets reflect each user’s personal information, preferences and interests. Additional simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed algorithm. Experimental results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other algorithms. This work is based on the doctoral thesis of the corresponding author.