A Forward Approach for Helicopter Track and Balance

Dynamic Systems and Control ◽

10.1115/imece2002-33453 ◽

2002 ◽

Author(s):

Shengda Wang ◽

Kourosh Danai

Keyword(s):

Simulation Model ◽

Forward Model ◽

Estimation Accuracy ◽

Vibration Measurements ◽

Interval Model ◽

Stochastic Nature ◽

Sensitivity Coefficients ◽

Helicopter Vibration

A method of helicopter track and balance is introduced that uses a forward-model to search for the appropriate blade modifications. This method uses an interval model to represent the ranges of effects of blade modifications on helicopter vibration, instead of exact values, in order to cope with the stochastic nature of aircraft vibration. The coefficients of the interval model are initially defined according to sensitivity coefficients between the blade modifications and helicopter vibration, but they are subsequently updated after each tuning iteration to improve the model’s estimation accuracy. The effectiveness of the proposed method is demonstrated through a simulation model that represents experimental vibration measurements of Black Hawk helicopters.

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Adaptive Method of Helicopter Track and Balance

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.1913683 ◽

2004 ◽

Vol 127 (2) ◽

pp. 275-282 ◽

Cited By ~ 8

Author(s):

Shengda Wang ◽

Kourosh Danai ◽

Mark Wilson

Keyword(s):

Neural Networks ◽

A Priori ◽

Adaptive Method ◽

Estimation Accuracy ◽

A Priori Knowledge ◽

Interval Model ◽

Vibration Data ◽

Stochastic Nature ◽

Flight Regimes ◽

Helicopter Vibration

An adaptive method of helicopter track and balance is introduced to improve the search for the required blade adjustments. In this method, an interval model is used to represent the range of effect of blade adjustments on helicopter vibration, instead of exact values, to cope with the nonlinear and stochastic nature of aircraft vibration. The coefficients of the model are initially defined according to sensitivity coefficients between the blade adjustments and helicopter vibration, to include the ‘a priori’ knowledge of the process. The model coefficients are subsequently transformed into intervals and updated after each tuning iteration to improve the model’s estimation accuracy. The search for the required blade adjustments is performed according to this model by considering the vibration estimates of all of the flight regimes to provide a comprehensive solution for track and balance. The effectiveness of the proposed method is evaluated in simulation using a series of neural networks trained with actual vibration data. The results indicate that the proposed method improves performance according to several criteria representing various aspects of track and balance.

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Robust Life-Cycle Production Optimization With a Support-Vector-Regression Proxy

SPE Journal ◽

10.2118/191378-pa ◽

2018 ◽

Vol 23 (06) ◽

pp. 2409-2427 ◽

Cited By ~ 21

Author(s):

Zhenyu Guo ◽

Albert C. Reynolds

Keyword(s):

Life Cycle ◽

Simulation Model ◽

Support Vector Regression ◽

Reservoir Simulation ◽

Production Optimization ◽

Forward Model ◽

Support Vector ◽

Well Control ◽

Steepest Ascent ◽

Reservoir Simulation Model

Summary We design a new and general work flow for efficient estimation of the optimal well controls for the robust production-optimization problem using support-vector regression (SVR), where the cost function is the net present value (NPV). Given a set of simulation results, an SVR model is built as a proxy to approximate a reservoir-simulation model, and then the estimated optimal controls are found by maximizing NPV using the SVR proxy as the forward model. The gradient of the SVR model can be computed analytically so the steepest-ascent algorithm can easily and efficiently be applied to maximize NPV. Then, the well-control optimization is performed using an SVR model as the forward model with a steepest-ascent algorithm. To the best of our knowledge, this is the first SVR application to the optimal well-control problem. We provide insight and information on proper training of the SVR proxy for life-cycle production optimization. In particular, we develop and implement a new iterative-sampling-refinement algorithm that is designed specifically to promote the accuracy of the SVR model for robust production optimization. One key observation that is important for reservoir optimization is that SVR produces a high-fidelity model near an optimal point, but at points far away, we only need SVR to produce reasonable approximations of the predicting output from the reservoir-simulation model. Because running an SVR model is computationally more efficient than running a full-scale reservoir-simulation model, the large computational cost spent on multiple forward-reservoir-simulation runs for robust optimization is significantly reduced by applying the proposed method. We compare the performance of the proposed method using the SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir-simulations runs for three synthetic examples, including one field-scale example. We also compare the optimization performance of our proposed method with that obtained from a linear-response-surface model and multiple SVR proxies that are built for each of the geological models.

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Simulation model of situational control of transshipment machinery fleet

E3S Web of Conferences ◽

10.1051/e3sconf/201913502028 ◽

2019 ◽

Vol 135 ◽

pp. 02028

Author(s):

Igor Zub ◽

Yuriy Ezhov ◽

Viktor Shchemelev

Keyword(s):

Simulation Model ◽

Petri Nets ◽

Service Time ◽

Statistical Data ◽

Stochastic Nature ◽

Conflict Situations ◽

Short Period ◽

Situational Management ◽

And Storage

The seaport is a place for cargo transshipment and storage. The speed of vehicle processing is one of the indicators of port’s performance. The time of vehicles being under processing depends on the performance of a fleet of transshipment machinery. The stochastic nature of vehicles’ arriving leads to the intense operation of handling facilities and formation of wait lists for vehicles to be loaded or unloaded, or to the downtime of transshipment machinery when vehicles are absent. Depending on the operational situation, transshipment machinery is brought out for maintenance operations that can be performed by unit, which provides the increase in the technical use coefficient. For maintenance of individual units, the decomposition of handling facility is performed; statistical data on the time of maintenance conduction for all units is collected. Statistical data are entered into the database. When transshipment machinery is brought out of operation for a short period, the unit is selected, the service time of which coincides with the downtime of a brought out facility. To visualize the process of situational management of maintenance and define conflict situations, a simulation model was developed with the use of the apparatus of Petri nets. The analysis of the model helped to reveal conflict situations that can be solved both during situational management of transshipment machinery fleet and by means of models nested into conflict places.

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Travel time estimation in urban networks using limited probes data

Canadian Journal of Civil Engineering ◽

10.1139/l11-001 ◽

2011 ◽

Vol 38 (3) ◽

pp. 305-318 ◽

Cited By ~ 10

Author(s):

Mohamed El Esawey ◽

Tarek Sayed

Keyword(s):

Simulation Model ◽

Travel Time ◽

Network Performance ◽

Time Estimation ◽

Performance Measure ◽

Estimation Accuracy ◽

Travel Time Estimation ◽

Travel Times ◽

Time Data ◽

Travel Time Data

Travel time is a simple and robust network performance measure that is well understood by the public. However, travel time data collection can be costly especially if the analysis area is large. This research proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. Within a homogeneous road network, nearby links of similar character are exposed to comparable traffic conditions, and therefore, their travel times are likely to be positively correlated. This correlation can be useful in developing travel time relationships between nearby links so that if data becomes available on a subset of these links, travel times of their neighbours can be estimated. A methodology is proposed to estimate link travel times using available data from neighbouring links. To test the proposed methodology, a case study was undertaken using a VISSIM micro-simulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. Neighbour links travel time estimation accuracy was assessed using different error measurements and the results were satisfactory. Overall, the results of this research demonstrate the feasibility of using neighbour links data as an additional source of information to estimate travel time, especially in case of limited coverage.

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A simple island biodiversity model is robust to trait dependence in diversification and colonization rates

10.1101/2022.01.01.474685 ◽

2022 ◽

Author(s):

Shu Xie ◽

Luis Valente ◽

Rampal Etienne

Keyword(s):

Simulation Model ◽

Phylogenetic Trees ◽

Size Variation ◽

Simulated Data ◽

Estimation Accuracy ◽

Rate Variation ◽

State Dependent ◽

Island Biodiversity ◽

Speciation Rates ◽

Diversity Dynamics

The application of state-dependent speciation and extinction (SSE) models to phylogenetic trees has revealed an important role for traits in diversification. However, this role remains comparatively unexplored on islands, which can include multiple independent clades resulting from different colonization events. Here, we perform a robustness study to identify how trait-dependence in rates of island colonization, extinction and speciation (CES rates) affects the estimation accuracy of a phylogenetic model that assumes no rate variation between trait states. We extend the DAISIE (Dynamic Assembly of Islands through Speciation, Immigration and Extinction) simulation model to include state-dependent rates, and evaluate the robustness of the DAISIE inference model using simulated data. Our results show that when the CES rate differences between trait states are moderate, DAISIE shows negligible error for a variety of island diversity metrics. However, for large differences in speciation rates, we find large errors when reconstructing clade size variation and non-endemic species diversity through time. We conclude that for many biologically realistic scenarios with trait-dependent speciation and colonization, island diversity dynamics can be accurately estimated without the need to explicitly model trait dynamics. Nonetheless, our new simulation model may provide a useful tool for studying patterns of trait variation.

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Research on Remaining Driving Range Estimation of Electric Vehicle Based on Dynamic Working Condition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.945-949.509 ◽

2014 ◽

Vol 945-949 ◽

pp. 509-515

Author(s):

Chang Qing Du ◽

Gang Du ◽

Ke Cheng Tan ◽

Yong Shan Liu

Keyword(s):

Simulation Model ◽

Electric Vehicle ◽

Working Condition ◽

Estimation Accuracy ◽

Estimation Model ◽

Range Estimation ◽

Optimization Scheme ◽

Optimized Model ◽

Driving Range ◽

Braking Energy Recovery

Aimed at estimating the remaining driving range of electric vehicle (EV) on dynamic working condition and exploring the influences on remaining driving range estimation, combined GT-Drive and Matlab, EV simulation model and remaining driving range estimation model have established. Based on UDDS, compared simulation results with estimation results of remaining driving range, the influences on remaining driving range estimation have been discovered, then a optimization scheme is put forward. With the method of corrected parameter, the error of initial estimation value is reduced; when braking energy recovery module is added, EV simulation model is optimized. The results of test indicates that the optimized model has realized to accurately estimate the remaining driving range, meets the requirements of the expected estimation accuracy.

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Model of multimodal transport node functioning

Archives of Transport ◽

10.5604/08669546.1185202 ◽

2015 ◽

Vol 36 (4) ◽

pp. 43-54 ◽

Cited By ~ 5

Author(s):

Vitalii Naumov ◽

Ievhen Nagornyi ◽

Yana Litvinova

Keyword(s):

Simulation Model ◽

Object Oriented ◽

Object Oriented Programming ◽

Optimal Management ◽

Basic Tool ◽

Class Library ◽

Stochastic Nature ◽

Multimodal Transport ◽

Probabilistic Nature ◽

Production Resources

To increase the efficiency of transport nodes functioning taking into account the logistics management principles it is necessary to optimize the structure and capacity of transport nodes' production resources, and to develop such a method for calculating of joint schedules for vehicles and freight hubs of the transport node, which takes into account stochastic nature of the parameters of material and informational flows. For solving the problems of optimal management of transport nodes functioning processes it is proposed to use the specific efficiency indicator, which is determined as a ratio of total costs of clients servicing in transport node to the costs of production resources used while servicing. According to the used approach for formalization of the transport node internal processes, development of the simulation model was implemented on the base of object-oriented programming principles. TransportNode.dll class library has been used as basic tool for simulations. The model implemented on the base of the library allows to take into account stochastic nature of demand and probabilistic nature of technological processes in transport nodes. Some results of numeric simulations for the loading area “Amur-Gavan” of Dnipropetrovsk River Port have been described in the paper.

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An Inverse Stratigraphic Simulation Model – Is Stratigraphic Inversion Possible?

Energy Exploration & Exploitation ◽

10.1177/014459879601400606 ◽

1996 ◽

Vol 14 (6) ◽

pp. 627-637 ◽

Cited By ~ 7

Author(s):

Margaret A. Lessenger ◽

Timothy A. Cross

Keyword(s):

Simulation Model ◽

Process Parameters ◽

Flexural Rigidity ◽

Inverse Model ◽

Sediment Supply ◽

Forward Model ◽

Gradient Descent Method ◽

Data Set ◽

Stratigraphic Model ◽

Stratigraphic Record

Inversion is a systematic method of determining values of process parameters of a forward model that allow a match between observed and modeled data. Historically, geologists have considered the stratigraphic record to be nonunique. That is, geologists have assumed that it is impossible to determine values for and separate stratigraphic process variables such as eustasy, tectonics and sediment supply that operated to form the stratigraphic record. If stratigraphic data are nonunique, then inversion of stratigraphic data is impossible. In an influential paper. Burton et al. (1987) argued that inversion of stratigraphic data using a stratigraphic forward model is not possible. The purpose of this study was to determine if inversion of stratigraphic data using a stratigraphic forward model is theoretically possible. In this study, we designed a stratigraphic inverse simulation model using a forward stratigraphic model capable of simulating realistic temporal and spatial distributions of fades tracts and stratigraphic surfaces. For numerical optimization, we used a gradient descent method that minimizes errors in the least squares sense. We tested this inverse model on synthetic stratigraphic data which act as a proxy for real-world stratigraphic data, to test multiple aspects of the inverse model. In these experiments, we inverted synthetic stratigraphic data for eustasy, sediment supply, tectonic subsidence, lithosphere flexural rigidity, and initial basin topography. Results from these inversion experiments establish that inversion of stratigraphic data is theoretically possible. We determined limits of convergence, degrees of parameter separatability, nonuniqueness of data, and types of data necessary for inversion. Results suggest that using distributions of facies tracts and stratigraphic surfaces within a genetic sequence stratigraphic framework is necessary for inversion. Results from inverse model experiments also suggest that nonuniqueness of these data types with respect to stratigraphic process parameters such as eustasy, tectonics, sediment supply and depositional topography is bounded. Moreover, the bounds of nonuniqueness are quite small. The next phase of our research is to first test an inverse algorithm that is more appropriate for stratigraphic inversion, and then to test an inverse stratigraphic model using a real stratigraphic data set.

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