General Simulation Algorithm Research Articles

РАЗРАБОТКА ИМИТАЦИОННОЙ МОДЕЛИ СИСТЕМЫ АВТОМАТИЧЕСКОГО УПРАВЛЕНИЯ УРОВНЕМ ЗЕЛЁНОГО ЩЁЛОКА В БАКЕ-РАСТВОРИТЕЛЕ ПЛАВА СОДОРЕГЕНЕРАЦИОННОГО КОТЛОАГРЕГАТА

The use of existing or development of new simulation models of the green liquor recovery process in the recovery boiler (RB) remains relevant due to the ever-increasing requirements for process safety and product quality, therefore a general algorithm for simulation of the automatic control system of the green liquor level in the dissolver tank of the RB is proposed. Application of the developed analytical model in practice will allow users to increase the accuracy of green liquor level control in the dissolver tank and reduce the influence of disturbing actions on the quality of the technological process

Bridge Resistance Updating Based on the General Particle Simulation Algorithms of Complex Bayesian Formulas

The existing bridges are subjected to time-variant loading and resistance degradation processes. How to update resistance probability distribution functions with resistance degradation model and proof load effects has become one of the research hotspots in bridge engineering field. To solve with the above issue, this paper proposed the general particle simulation algorithms of complex Bayesian formulas for bridge resistance updating. Firstly, the complex Bayesian formulas for updating resistance probability model are built. For overcoming the difficulty for the analytic calculation of complex Bayesian formulas, the general particle simulation methods are provided to obtain the particles of complex Bayesian formulas; then, with the improved expectation maximization optimization algorithm obtained with the combination of K-MEANS algorithm and Expectation Maximization (EM) algorithm, the above simulated particles can be used to estimate the posteriori probability density functions of resistance probability model; finally, a numerical example is provided to illustrate the feasibility and application of the proposed algorithms.

Application of sequential quadratic programming based on active set method in cleaner production

On the platform of general chemical process simulation software (it was named Optimization Engineer, OPEN), a general optimization algorithm for chemical process simulation is developed using C + + code. The algorithm is based on sequential quadratic programming (SQP). We adopt the activity set algorithm and the rotation axis algorithm to generate the activity set to solve the quadratic programming sub-problem. The active set method can simplify the number of constraints and speed up the calculation. At the same time, we used limited memory BFGS algorithm (L-BFGS) to simplify the solution of second derivative matrix. The special matrix storage mode of L-BFGS algorithm can save the storage space and speed up the computing efficiency. We use exact penalty function and traditional step-size rule in the algorithm. These two methods can ensure the convergence of the algorithm, a more correct search direction and suitable search step. The example shows that the advanced optimization function can meet the requirements of General Chemical Process Calculation. The number of iterations can reduce by about 6.0%. The computation time can reduce by about 6.5%. We combined this algorithm with chemical simulation technology to develop the optimization function of chemical engineering simulation. This optimization function can play an important role in the process optimization calculation aiming at energy saving and green production.

Gutzwiller wave function on a digital quantum computer

The determination of the ground state of quantum many-body systems via digital quantum computers rests upon the initialization of a sufficiently educated guess. This requirement becomes more stringent the greater the system. Preparing physically-motivated ans\"{a}tze on quantum hardware is therefore important to achieve quantum advantage in the simulation of correlated electrons. In this spirit, we introduce the Gutzwiller Wave Function (GWF) within the context of the digital quantum simulation of the Fermi-Hubbard model. We present a quantum routine to initialize the GWF that comprises two parts. In the first, the noninteracting state associated with the $U = 0$ limit of the model is prepared. In the second, the non-unitary Gutzwiller projection that selectively removes states with doubly-occupied sites from the wave function is performed by adding to every lattice site an ancilla qubit, the measurement of which in the $|0\rangle$ state confirms the projection was made. Due to its non-deterministic nature, we estimate the success rate of the algorithm in generating the GWF as a function of the lattice size and the interaction strength $U/t$. The scaling of the quantum circuit metrics and its integration in general quantum simulation algorithms are also discussed.

Push-forward method for piecewise deterministic biochemical simulations

A biochemical network can be simulated by a set of ordinary differential equations (ODE) under well-stirred reactor conditions, for large numbers of molecules, and frequent reactions. This is no longer a robust representation when some molecular species are in small numbers and reactions changing them are infrequent. In this case, discrete stochastic events trigger changes of the smooth deterministic dynamics of the biochemical network. Piecewise-deterministic Markov processes (PDMP) are well adapted for describing such situations. Although PDMP models are now well established in biology, these models remain computationally challenging. Previously we have introduced the push-forward method to compute how the probability measure is spread by the deterministic ODE flow of PDMPs, through the use of analytic expressions of the corresponding semigroup. In this paper we provide a more general simulation algorithm that works also for non-integrable systems. The method can be used for biochemical simulations with applications in fundamental biology, biotechnology and biocomputing. This work is an extended version of the work presented at the conference CMSB2019.

Relaxing the assumption of constant transition rates in a multi-state model in hospital epidemiology

BackgroundMulti-state models are being increasingly used to capture complex disease pathways. The convenient formula of the exponential multi-state model can facilitate a quick and accessible understanding of the data. However, assuming time constant transition rates is not always plausible. On the other hand, obtaining predictions from a fitted model with time-dependent transitions can be challenging. One proposed solution is to utilise a general simulation algorithm to calculate predictions from a fitted multi-state model.MethodsPredictions obtained from an exponential multi-state model were compared to those obtained from two different parametric models and to non-parametric Aalen-Johansen estimates. The first comparative approach fitted a multi-state model with transition-specific distributions, chosen separately based on the Akaike Information Criterion. The second approach was a Royston-Parmar multi-state model with 4 degrees of freedom, which was chosen as a reference model flexible enough to capture complex hazard shapes. All quantities were obtained analytically for the exponential and Aalen-Johansen approaches. The transition rates for the two comparative approaches were also obtained analytically, while all other quantities were obtained from the fitted models via a general simulation algorithm. Metrics investigated were: transition probabilities, attributable mortality (AM), population attributable fraction (PAF) and expected length of stay. This work was performed on previously analysed hospital acquired infection (HAI) data. By definition, a HAI takes three days to develop and therefore selected metrics were also predicted from time 3 (delayed entry).ResultsDespite clear deviations from the constant transition rates assumption, the empirical estimates of the transition probabilities were approximated reasonably well by the exponential model. However, functions of the transition probabilities, e.g. AM and PAF, were not well approximated and the comparative models offered considerable improvements for these metrics. They also provided consistent predictions with the empirical estimates in the case of delayed entry time, unlike the exponential model.ConclusionWe conclude that methods and software are readily available for obtaining predictions from multi-state models that do not assume constant transition rates. The multistate package in Stata facilitates a range of predictions with confidence intervals, which can provide a more comprehensive understanding of the data. User-friendly code is provided.

Abstract 5510: Mass-action law based general pharmacodynamics theory and algorithm for quantitative computer simulation of drug-evaluations in cells, in animals and in clinical trials

Abstract The Median-Effect Equation (MEE) for single drug, and the Combination Index Equation (CIE) for multiple drug combinations, are basis for general Pharmacodynamics (PD), and Biodynamics (BD). Both, MEE and CIE are derived from system analysis of the Mass-Action Law (MAL). The MAL-PD/BD theory, equations and algorithms allow automated computer simulations for diagnostic plots and quantitative, digital/indexed conclusions. To date, the MAL dynamic theory is cited over 15,000 times, in over 1,248 biomedical journals. The publications include studies in vitro, in cell, in animal and in clinical trial levels, as indicated in Google Scholar Citations -Ting-Chao Chou, or in Pharmacol. Rev. 58: 621-681, 2006 (cited 3,119 times in 914 journals). MEE indicates dose and effect are interchangeable, and all PD Dose-Effect curves (DEC) can be linearized by the Median-Effect Plot (MEP), x= Log (dose) vs y= Log {(fraction affected (fa)/(fraction unaffected (fu)}, where fa+fu=1, and when fa=fu, gives the Median-Effect Dose (Dm). The slope gives “m” value (dynamic order, signifying the shape of DEC), and the Antilog of x-intercept gives the “Dm” value, (signifying the potency of half-affected, such as IC50, ED50, or LD50). The MEE, is the unified theory of the Michaelis-Menten Eq. for enzyme half-saturation, Handerson-Hasselbalch Eq. for pH half-ionization, Hill Eq. for ligand half-occupancy, and the Scatchard Eq, for receptor half-bond and half-free. Since MEP linearizes the DEC, it requires only a minimum of two data pints to simulate entire DEC. Thus, the Two-Data-Point Theory is extremely cost-effective for animal studies and clinical trial protocol designs, as only small number data points is required to achieve efficient computer simulations, with digital/index conclusions. In CIE, CI<1, =1, and >1, quantitatively defines Synergism, Additive Effect, and Antagonism, respectively, for 2 to n drug combs, using CompuSyn. PD requires multiple doses (3-4 doses in animals; 2-3 doses in clinical trials) since single dose yields a data Point, but “a point” which has “no shape”, thus, it is Not a PD in Vivo. For 2-drug-combos using the CI method in vivo for drug A, B, and A+B, require only 10 data points, (3+3+3 plus one control). Earlier examples with CI method in vivo are: 1. Anti-HIV Combos (AZT+IFN, using only 36 AIDS patients), and 2. Anti-cancer Combos (Taxotere+T607, against HCT-116 colon carcinoma xenograft in nude mice), both used only 10 data points and both achieved quantitative determination of Synergism. This report reaffirm Example 2 with the same drug combos against MX-1 mammary carcinoma xenograft in nude mice, using only 10 data points, showed similar synergy at ED75 to ED97, CI= 0.82-0.79 for MX-1, and CI= 0.98-0.64 for HCT-116. Citation Format: Ting-Chao Chou. Mass-action law based general pharmacodynamics theory and algorithm for quantitative computer simulation of drug-evaluations in cells, in animals and in clinical trials [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5510.

Monte Carlo Simulation Approach to Reliability Analysis of Complex Systems

Abstract The article presents new results concerned with general procedures and algorithms to assess the reliability of complex systems with various reliability structures. The analytical method and based on it the simulation method were used to estimate the reliability characteristics of the port grain transportation system. Finally, the general simulation algorithm was developed to evaluate the reliability characteristics of ageing complex systems. In this case, the systems operating processes were described by any distributions of sojourn times in operation states and the reliability functions of their components were modified in such a way that these components are not characterized by a “lack of memory”. The application of this algorithm has been illustrated by the results for exemplary complex two-state systems.

Uncertain random quadratic bottleneck assignment problem

Uncertain random expected value simulation is one of the most important techniques to solve uncertain random optimization problems where random variables coexist with uncertain variables. A general simulation algorithm is designed to estimate the uncertain random expected value. Furthermore, an uncertain random quadratic bottleneck assignment problem is proposed and an uncertain random expected value model is presented. An algorithm is designed to find a lower bound of the uncertain random quadratic bottleneck assignment problem.

General simulation algorithm for autocorrelated binary processes.

The apparent ubiquity of binary random processes in physics and many other fields has attracted considerable attention from the modeling community. However, generation of binary sequences with prescribed autocorrelation is a challenging task owing to the discrete nature of the marginal distributions, which makes the application of classical spectral techniques problematic. We show that such methods can effectively be used if we focus on the parent continuous process of beta distributed transition probabilities rather than on the target binary process. This change of paradigm results in a simulation procedure effectively embedding a spectrum-based iterative amplitude-adjusted Fourier transform method devised for continuous processes. The proposed algorithm is fully general, requires minimal assumptions, and can easily simulate binary signals with power-law and exponentially decaying autocorrelation functions corresponding, for instance, to Hurst-Kolmogorov and Markov processes. An application to rainfall intermittency shows that the proposed algorithm can also simulate surrogate data preserving the empirical autocorrelation.

ADAMS-MATLAB Co-Simulation of A Serial Manipulator

This paper presents the dynamic modelling and simulation of a now redundant robot, Mitsubishi RM-501, and proposes a general algorithm for experimental simulation in kinematics, dynamics and control analysis to any such robot. Through reverse engineering, a model as accurate as the real robot was developed in SolidWorks.The simulations of the same were performed in ADAMS (dynamicmodeling software offered by MSC Software Corp)along with MATLAB for motion studies and control dynamics. Finally, with a user-input path the accuracy and precision of the simulator was verified.

Hybrid transient simulation platform for interconnected transmission and distribution system based on powerfactory and PSASP

With the development of active distribution system, the interactions between transmission and distribution (T&D) system become more closely and complicated. To study coupled dynamics of the interconnected T&D systems, hybrid simulation is highly required. This study presents a general hybrid simulation algorithm for the interconnected T&D system. A hybrid transient simulation platform is developed based on the proposed algorithm and two commercial software, i.e. PowerFactory and power system analysis software package (PSASP). The distribution system is modelled and analysed in PowerFactory using electromagnetic transient simulation, while the large-scale transmission system executes transient stability simulation in PSASP. The proposed algorithm and the developed platform are verified through a small test case, and are applied to analyse a typical distribution system in Beijing. Results show good coincidence with the fault recording waveform, which validates the availability of the hybrid simulation platform.

Reliability for systems with self-healing effect under shock models

ABSTRACTIn the paper, the concept of self-healing effects, permanent or limited duration on system health, is introduced by building a class of cumulative damage shock models in terms of the integral for counting processes including Poisson process, Hawkes process and Affine process, and so on. Based on the established shock models, the property on lifetime and the bounds of system reliability are discussed, especially for the simplest case. Then the general simulation algorithm is given for the general situation under the given cumulative damage shock models, because it is impossible to present the general analytical results on the reliability of systems. Some numerical examples are given to illustrate the algorithm and bounds of reliability. Finally, the whole works in the paper are concluded.

Linear optimization of forest management for dynamic recursive model

Our study presents development of a forest management model based on LP which can be easily integrated into a large-scale dynamic recursive model and contain the instruments providing future consideration for harvesting plans under recursive limitations. We introduce a general structure and simulation algorithm of the model. The forest management algorithm was tested applying historical data of Ukrainian forests. Obtained modeling results demonstrate a correct age class transition. They prove as well adequacy of utilizing benefit losses and delay costs as regulative mechanisms for temporal allocation of forest harvesting. As far as statistic data concerning age structure of Ukrainian forests are not publicly available, FesT was validated by comparing projected forest age structure with results of Global Forest Model (G4M).The comparison shows that the projections of both models are fairly close to each other. The divergence between the results can be explained by difference in forest management modelling approach.

Lessons from the Congested Clique applied to MapReduce

The main result of this paper is a simulation algorithm which, under quite general constraints, transforms algorithms running in the Congested Clique model into algorithms running in the MapReduce model. As a case study of applying this simulation algorithm, we first present a distributed O(Δ)-coloring algorithm running on the Congested Clique in O(1) rounds, if Δ≥log2⁡n, and O(log⁡log⁡log⁡n) rounds otherwise. Applying the simulation theorem to this Congested Clique O(Δ)-coloring algorithm yields an O(1)-round O(Δ)-coloring algorithm in the MapReduce model. We apply our simulation algorithm to other Congested Clique algorithms including the 2-ruling set and metric facility location algorithms of Berns et al. (ICALP 2012).Our simulation algorithm illustrates a natural correspondence between per-node bandwidth in the Congested Clique model and memory per machine in the MapReduce model. In the Congested Clique (and more generally, any network in the CONGEST model), the major impediment to constructing fast algorithms is the O(log⁡n) restriction on message sizes. Similarly, in the MapReduce model, the combined restrictions on memory per machine and total system memory have a dominant effect on algorithm design. In showing a fairly general simulation algorithm, we highlight the similarities and differences between these models.

Exponentially Fitted Two-Derivative Runge-Kutta Methods for Simulation of Oscillatory Genetic Regulatory Systems.

Oscillation is one of the most important phenomena in the chemical reaction systems in living cells. The general purpose simulation algorithms fail to take into account this special character and produce unsatisfying results. In order to enhance the accuracy of the integrator, the second-order derivative is incorporated in the scheme. The oscillatory feature of the solution is captured by the integrators with an exponential fitting property. Three practical exponentially fitted TDRK (EFTDRK) methods are derived. To test the effectiveness of the new EFTDRK methods, the two-gene system with cross-regulation and the circadian oscillation of the period protein in Drosophila are simulated. Each EFTDRK method has the best fitting frequency which minimizes the global error. The numerical results show that the new EFTDRK methods are more accurate and more efficient than their prototype TDRK methods or RK methods of the same order and the traditional exponentially fitted RK method in the literature.

Strategies for efficient and fast wave optics simulation of coded-aperture and other x-ray phase-contrast imaging methods

We derive a Fourier formulation of coded-aperture x-ray phase-contrast imaging, based on the wave theory of optics in the Fresnel approximation. We use this model to develop a flexible, efficient, and general simulation algorithm that can be easily adapted to other implementations of x-ray phase contrast imaging. Likewise, the algorithm enables a simple extension to 2D aperture designs, different acquisition schemes, etc. Problems related to numerical implementation of the algorithm are analyzed in detail, and simple rules are derived that enable us to avoid or at least mitigate them. Finally, comparisons with experimental data and data obtained with a different simulation algorithm are presented to validate the model and demonstrate its advantages in practical implementations. This also enabled us to demonstrate an increase in computational speed of more than one order of magnitude over a previous algorithm.

Time-ordered product expansions for computational stochastic system biology

The time-ordered product framework of quantum field theory can also be used to understand salient phenomena in stochastic biochemical networks. It is used here to derive Gillespie’s stochastic simulation algorithm (SSA) for chemical reaction networks; consequently, the SSA can be interpreted in terms of Feynman diagrams. It is also used here to derive other, more general simulation and parameter-learning algorithms including simulation algorithms for networks of stochastic reaction-like processes operating on parameterized objects, and also hybrid stochastic reaction/differential equation models in which systems of ordinary differential equations evolve the parameters of objects that can also undergo stochastic reactions. Thus, the time-ordered product expansion can be used systematically to derive simulation and parameter-fitting algorithms for stochastic systems.

Modeling pedestrian queuing using micro-simulation

The system considered is a cinema ticketing booth system. A general simulation algorithm is presented as well as the system’s operating characteristics. The results of the experiment were verified by comparing them with video observation data and theoretical values. Finally, with comparative analysis of experiment data, the developed simulation model was able to replicate the situation in which pedestrians find an available booth to occupy while waiting in a queue. The model can facilitate the availability of various pedestrian flows and a range of operating times. With some efforts of computer programming, the situations where multiple booths are available were simulated to identify pedestrian movement. The developed simulation model captures important details, such as travel time, wait time, queue length and the number of waiting pedestrians with the different number of pedestrian flows and booths. The paper presents a means to designing the pedestrian operation and plan on the basis of the estimated number of people.

Improving SAMC using smoothing methods: Theory and applications to Bayesian model selection problems

Stochastic approximation Monte Carlo (SAMC) has recently been proposed by Liang, Liu and Carroll [J. Amer. Statist. Assoc. 102 (2007) 305–320] as a general simulation and optimization algorithm. In this paper, we propose to improve its convergence using smoothing methods and discuss the application of the new algorithm to Bayesian model selection problems. The new algorithm is tested through a change-point identification example. The numerical results indicate that the new algorithm can outperform SAMC and reversible jump MCMC significantly for the model selection problems. The new algorithm represents a general form of the stochastic approximation Markov chain Monte Carlo algorithm. It allows multiple samples to be generated at each iteration, and a bias term to be included in the parameter updating step. A rigorous proof for the convergence of the general algorithm is established under verifiable conditions. This paper also provides a framework on how to improve efficiency of Monte Carlo simulations by incorporating some nonparametric techniques.