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Keynote lecture: Systematic design of reinforcement and support schemes for excavations in jointed rock
Published in Ernesto Villaescusa, Christopher R. Windsor, Alan G. Thompson, Rock Support and Reinforcement Practice in Mining, 2018
The block shape and block size engines are used to conduct three simulations: Deterministic Simulation. This is conducted by driving the deterministic engines with mean values of the discontinuity characteristics to provide a measure of the mean of the outcome.Possibilistic Simulation. This is conducted by driving the deterministic engines with extreme values of the discontinuity characteristics to provide a measure of the possibility limits or extrema of the outcome.Probabilistic Simulation. This is conducted by driving the deterministic engines with distributions of values for the discontinuity characteristics to provide a measure of the relative probability of outcomes between the extrema.
Simulation
Published in Paul C. Etter, Underwater Acoustic Modeling and Simulation, 2017
Simulations are differentiated at three levels of system representations: static versus dynamic; deterministic versus stochastic; and continuous versus discrete. A static simulation represents a system state in which time is not a variable. Conversely, a dynamic simulation varies as a function of time. A deterministic simulation produces completely predictable values, whereas a stochastic simulation produces values that must be represented by statistical variables (e.g., means and variances). A continuous simulation produces state variables that change continuously with changes in time, whereas a discrete simulation produces values that change in a stepwise fashion as a function of time (Law and Kelton, 1991).
Modelling risk effect using Monte Carlo Technique
Published in Stephen O. Ogunlana, Prasanta Kumar Dey, Risk Management in Engineering and Construction, 2019
The deterministic simulation is for systems whose behaviour is completely predictable. An example of this system is the traditional planning of projects. A stochastic simulation is for a system whose behaviour cannot be completely predictable which fits well with the characteristics of innovation process information, as described above. The stochastic simulation refers to using mathematical models to study systems that are characterised by the occurrence of random events.
Accelerated Bayesian inference-based history matching of petroleum reservoirs using polynomial chaos expansions
Published in Inverse Problems in Science and Engineering, 2021
Sufia Khatoon, Jyoti Phirani, Supreet Singh Bahga
The phase behaviour properties for the simulation are taken from the standard problem [34]. The gas is injected at a constant rate of 100 mmscf/day in the injection well, and oil is produced at a maximum oil production rate (OPR) of 20,000 stb/day. Figure 2(a) shows the deterministic OPR () for a time period of 10 years. As the gas is injected at a constant rate of 100 mmscf/day into the injection well, the oil is displaced horizontally towards the production well. When the oil reaches the production well, the production of oil starts at the maximum rate of 20,000 stb/day for a time period of 52 months. As the oil production takes place from the reservoir, the pressure inside the reservoir continuously falls and reaches the minimum flowing bottom hole pressure of 1000 psia. Due to the fall in pressure inside the reservoir, the OPR starts decreasing from its maximum rate. Meanwhile, the injected gas also reaches the other end of the top layer, and due to continuous injection, it moves towards the bottom layer and reaches the production well at about t = 43 months. Figure 2(b) shows the gas production rate (GPR) for the deterministic simulation. Along with the production of oil, concurrently, gas is also produced from the production well. Initially, the gas that is dissolved in the reservoir oil is produced, but once the injected gas reaches the production well, the GPR increases suddenly at t = 43 months, as shown in Figure 2(b).
Gaussian process based optimization algorithms with input uncertainty
Published in IISE Transactions, 2020
Haowei Wang, Jun Yuan, Szu Hui Ng
Unlike deterministic simulation whose outputs are deterministic, stochastic simulation is driven by some random input process, and hence, its outputs have variability. In this article, we assume a parametric model for the input process and term as the input parameter. For example, in an inventory simulation, the customers’ random demand is the random input process. For stochastic simulation, we are interested in the expected simulation output which is a function of both the design parameter x and the input parameter . At any design point , given the input parameter is not directly observable, and can only be estimated from samples of stochastic simulation output . represents the output variability, whose distribution is assumed to be zero mean and finite variance. Given , the objective of stochastic simulation optimization is to minimize the expected simulation output . In most stochastic simulation optimization literature, the input parameter is assumed to be known, and thus, the dependence of simulation output on is usually omitted. Hence, the most common form of the stochastic simulation optimization problem is
Using system dynamics to understand long-term impact of new mobility services and sustainable mobility policies: an analysis pre- and post-COVID-19 pandemic in Rio de Janeiro, Brazil
Published in Transportation Letters, 2023
Wlisses B. Fontoura, Michael J. Radzicki, Glaydston M. Ribeiro
After the construction of all the sectors presented above, tests were carried out to ensure that the model reproduces behaviors that represent reality. Thus, tests of extreme conditions, evaluation of the structure, dimensional consistency, evaluation of parameters and sensitivity analysis were performed. Furthermore, we tested the model adding noise on some variables simultaneously and the results showed the same behavior. Therefore, it was chosen the deterministic simulation. The area of study, the data collection and the results of the simulation are presented below.