Joshua J. Yi, David J. Lilja · 2004
Due to cost, time, and flexibility constraints, simulators are needed to explore the design space when developing a new processor architecture as well as when evaluating the performance of new compiler-based and microarchitectural mechanisms. However, improperly choosing the processor parameters, such as the cache size, the associativity, the number of functional units, and so forth, can significantly affect the simulation results, independent of any new mechanism being evaluated. As a first step in developing a methodology to appropriately choose the processor parameters to be used in a simulation, we quantify the effect of a select group of ten key parameters on the total execution time (measured in simulated cycles) using the statistical analysis of variance (ANOVA) technique. Our results show that, for the commonly used SimpleScalar simulator, the number of reorder buffer entries, the L1 D-Cache associativity and size, and the memory latency account for approximately 75 % of the total observed variation in the execution time. Conversely, the branch predictor has surprisingly little effect, only about 1%. In addition, we find that interactions between parameters also have little effect on the total execution time. This work clearly demonstrates that poor choices of simulated processor parameter values, or even a single poor choice, can significantly affect the simulation results and thereby lead to erroneous conclusions about the processor or mechanism being simulated. 1
Cited by 6
Boyabatlı, Onur · Bilkent University Institutional Repository (Bilkent University) · 2016
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Ognjen Arandjelović · 2013
In contrast to most scientific disciplines, sports science research has been characterized by comparatively little effort investment in the development of relevant phenomenological models. Scarcer yet is the application of said models in practice. We present a framework which allows resistance training practitioners to employ a recently proposed neuromuscular model in actual training program design. The first novelty concerns the monitoring aspect of coaching. A method for extracting training performance characteristics from loosely constrained video sequences, effortlessly and with minimal human input, using computer vision is described. The extracted data is subsequently used to fit the underlying neuromuscular model. This is achieved by solving an inverse dynamics problem corresponding to a particular exercise. Lastly, a computer simulation of hypothetical training bouts, using athlete-specific capability parameters, is used to predict the effected adaptation and changes in performance. The software described here allows the practitioner to manipulate hypothetical training parameters and immediately see their effect on predicted adaptation for a specific athlete. Thus, this work presents a holistic view of the monitoring-assessment-adjustment loop.
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Tianyue Xu, Hao Fu, Min Liu et al. · SSRN Electronic Journal · 2023
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Peter A. Henning · UPT. Syiah Kuala University Library (Syiah Kuala University) · 2006
Systems biology is an emerging field of study that seeks to provide systems-level understanding of biological systems through the integration of high-throughput biological data into predictive computational models. The integrative nature of this field is in sharp contrast as compared to the Reductionist methods that have been employed since the advent of molecular biology. Systems biology investigates not only the individual components of the biological system, such as metabolic pathways, organelles, and signaling cascades, but also considers the relationships and interactions between the components in the hope that an understandable model of the entire system can eventually be developed. This field of study is being hailed by experts as a potential vital technology in revolutionizing the pharmaceutical development process in the post-genomic era. This work not only provides a systems biology investigation into principles governing de novo sphingolipid metabolism but also the various computational obstacles that are present in converting high-throughput data into an insightful model.
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Rose, Cheryl A., Song, Kyongchan, Davila, Carlos G. · NASA STI Repository (National Aeronautics and Space Administration)
Turon s methodology for determining optimal analysis parameters for the simulation of progressive delamination is reviewed. Recommended procedures for determining analysis parameters for efficient delamination growth predictions using the Abaqus/Standard cohesive element and relatively coarse meshes are provided for single and mixed-mode loading. The Abaqus cohesive element, COH3D8, and a user-defined cohesive element are used to develop finite element models of the double cantilever beam specimen, the end-notched flexure specimen, and the mixed-mode bending specimen to simulate progressive delamination growth in Mode I, Mode II, and mixed-mode fracture, respectively. The predicted responses are compared with their analytical solutions. The results show that for single-mode fracture, the predicted responses obtained with the Abaqus cohesive element correlate well with the analytical solutions. For mixed-mode fracture, it was found that the response predicted using COH3D8 elements depends on the damage evolution criterion that is used. The energy-based criterion overpredicts the peak loads and load-deflection response. The results predicted using a tabulated form of the BK criterion correlate well with the analytical solution and with the results predicted with the user-written element.
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Mingyi Hong, Mónica F. Bugallo, Petar M. Djurić · IEEE Journal of Selected Topics in Signal Processing · 2010
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, we study the problem of joint model selection and parameter estimation under the Bayesian framework. We propose to use the Population Monte Carlo (PMC) methodology in carrying out Bayesian computations. The PMC methodology has recently been proposed as an efficient sampling technique and an alternative to Markov Chain Monte Carlo (MCMC) sampling. Its flexibility in constructing transition kernels allows for joint sampling of parameter spaces that belong to different models. The proposed method is able to estimate the desired <emphasis emphasistype="boldital">a posteriori</emphasis> distributions accurately. In comparison to the Reversible Jump MCMC (RJMCMC) algorithm, which is popular in solving the same problem, the PMC algorithm does not require burn-in period, it produces approximately uncorrelated samples, and it can be implemented in a parallel fashion. We demonstrate our approach on two examples: sinusoids in white Gaussian noise and direction of arrival (DOA) estimation in colored Gaussian noise, where in both cases the number of signals in the data is <emphasis emphasistype="boldital">a priori</emphasis> unknown. Both simulations show the effectiveness of our proposed algorithm. </para>
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Minrui Guo, Cheng Lei · MATEC Web of Conferences · 2018
The battery pack is one of the core components of pure electric vehicle, dynamic performance of the whole vehicle is closely related to the matching design of the battery, and is affected by the air resistance coefficient and the windward area of the whole vehicle. The dynamic indicators include maximum driving speed, 0-100km/h acceleration time and climbing grade, the battery parameters are designed and matched before the vehicle design, mainly analyze influence on the vehicle dynamic performance of the types of batteries, air resistance coefficient, windward area by the simulation software ADVISOR, and optimize the combination of these parameters. The results show that the dynamic performance of the vehicle reaches the initial design index and the dynamic performance of the vehicle is improved significantly.
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Huadong Yang, Qu Yannuo · 2019
Aiming at the problem of sea surface target selection, the multi-dimensional characteristics of group targets in sea background are summarized by the analysis of single target and group target feature parameters. The multi-dimensional characteristics of target indication information are normalized by relative situation calculation based on penetration direction. On the basis of this, the comprehensive matching of group target situation and multi-dimensional feature parameters is carried out, and the general principles and steps of group target parameter identification are given. In order to verify the effectiveness of the method, target selection parameters are selected for typical formation targets. The simulation calculation is identified, and the simulation examples and calculation process are given. The simulation results show that the proposed method can effectively improve the selection probability of formation targets.
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J.E. Pierce, Douglas E. Smith · Polymer Composites · 2025
ABSTRACT The orientation of fibers in short discontinuous fiber reinforced polymer composites is a critical factor in determining final mechanical and thermal properties. Tensor‐based models of fiber orientation are common but require difficult‐to‐obtain model parameters and are limited by assumptions. Direct fiber simulations (DFS) models individually simulate fibers and their interactions with each other and the surrounding fluid. The motion of fibers simulated in DFS is determined by interaction models which require proper calibration to accurately simulate a given fiber material system. An automated generalized method of parameter selection for DFS using surrogate model‐based optimization is developed for short fiber polymer composites processing. A DFS model is presented and subjected to the calibration for fibers with a 15.64 aspect ratio at a volume fraction of 8% in simple shear flow. To demonstrate the process, this system is calibrated using Jeffery's Orbit, a theoretical pARD tensor model, and an experimentally obtained orientation distribution function. The calibrated DFS model results are shown to agree well with targeted fiber behavior and provide unique insight into the contact mechanics of fiber interaction. Further, a DFS model calibrated to experimental results is used to obtain orientation tensor model parameters from simple physical experiments and demonstrate enhanced insight available from the data‐rich simulations. Results appearing here illustrate the advantages realized through the use of commercially available discrete element software for simulating short fiber composite suspensions.
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Fatemeh Khadivi (21647946), Bahram Hosseinzadeh Samani (21647949), Sajad Rostami (21647952) et al. · Figshare · 2025
Flowchart of parameter selection, simulation, and optimization for single-orifice oscillatory reactor.
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Jianjun Shen, Zheng Tang, Feng Jia et al. · Journal of Terramechanics · 2024
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Zalewski, Samuel Robert · QRU Quaderns de Recerca en Urbanisme · 2025
El filtre de Kalman i les seves variants no lineals, el filtre de Kalman estès (EKF) i el filtre de Kalman Unscented (UKF), són eines fonamentals per estimar sistemes dinàmics sorollosos. Tot i que s’ha desenvolupat molt treball teòric per innovar en les seves tècniques i directrius estadístiques, el complex procés de triar i parametritzar un filtre encara està lluny de ser senzill. La mesura en què les condicions d’aplicació, la selecció del model i l’afinació dels paràmetres afecten el rendiment del filtre encara requereix un estudi sistemàtic més ampli amb avaluació empírica. En els estudis de simulació posteriors, investiguem fins a quin punt les funcions de mesura no lineals, el soroll del procés i el soroll de mesura influeixen en el rendiment relatiu entre l’EKF i l’UKF. A més, s’exploren els paràmetres del model α, β, κ, Q i R per mesurar el seu impacte respectiu en la precisió del model. Aquests estudis revelen la capacitat de l’EKF per superar l’UKF en condicions de baix soroll de procés i alt soroll de mesura, alhora que demostren que la no linealitat de la funció de mesura afecta significativament la selecció del filtre, excepte en aquestes condi- cions de soroll específiques. També demostrem l’impacte extremadament limitat que els paràmetres d’afinació del punt sigma poden tenir en el rendiment de l’UKF, en contrast amb la importància d’escalar correctament els paràmetres de la seva matriu de covariància. A partir d’aquestes troballes, establim un marc clar per a les priori- tats d’implementació del filtre que s’han de tenir en compte durant tot el procés de selecció i ajustament.
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Stephen Thompson, Shijia Duan · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · 1991
This paper describes the procedure involved in developing a simulation model of a single-cylinder four-cycle engine, suitable for fuel injection control system studies. In particular, the modelling procedures and methods of parameter selection, including supporting experimental work, are indicated. Responses obtained from the simulation model are then compared with those obtained experimentally from the engine. Finally, future improvements to the model are suggested.
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Neda Boroumand, David W. Eaton · Geophysics · 2015
ABSTRACT We have investigated the use of an energy-based numerical algorithm and its ability to simulate growth of a single planar hydraulic fracture by matching modeled dimensions to those inferred from a microseismic map. The energy-based microseismic fracture model accounts for the various physical processes expressed as a balance between the work expended (input/injected energy) and work done (output/lost energy) during growth of a vertical, 3D, laterally symmetric, planar, ellipsoidal, and tensile (mode I) fracture. These canonical fracture models provide a simple but useful proxy for more complex fracture networks that occur in reality. The tensile fracture is positioned in a three-layer geologic medium defined by elastic, material, and stress properties. Fracture half-length, half-width, half-height, and effective crack-opening pressure were computed using a time-stepping algorithm that solved energy balance equations using a Lagrangian formulation. Two parameters are adjusted to calibrate the fracture model to microseismic data; observed upward growth is fit by adjusting stress-barrier contrasts, and fracture length is fit by altering an empirical parameter, fracture toughness. In the case of a symmetric model with equivalent stress and material properties above and below the fracture, an increase in fracture toughness results in a corresponding increase in modeled net pressure and fracture width profile. In the case of a model with different stress states in the layers above and below the injection level, fracture height growth is enhanced in the layer with lower in situ stress. In both cases, net work is minimized in response to trade-offs between creation of new fracture surface area and fracture volume. Our primary objective was to incorporate microseismic observations into a geomechanical simulation of hydraulic fracture growth. Our approach is novel; it explains hydraulic-fracture behavior from an energy-balance perspective using the spatial-temporal evolution of microseismicity to constrain and validate the model.
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Tianyue Xu, Ruxin Zhang, Fengwu Zhu et al. · Processes · 2022
To build a DEM model of Cyperus esculentus seed particles, the shape and size of the Cyperus esculentus seed particles were measured and analyzed. The results showed that the dispersity in size had a normal distribution. Additionally, a certain functional relationship between the primary dimension and secondary dimensions was determined. The width of the seed was the primary dimension, and the other secondary dimensions (length and thickness) were calculated based on their relationships with the primary dimension. On this basis, an approach for modeling Cyperus esculentus seed particles based on the multi-sphere (MS) method was proposed. The discrete element analysis models of three varieties of Cyperus esculentus seeds were established with different numbers of filing spheres. Moreover, to obtain more accurate simulation parameters, first, a range of values of the simulation parameters was obtained by the experimental method. Second, the Plackett–Burman (PB) test and the path of steepest ascent method were both adopted to correct and calibrate the simulation parameters, which were difficult to obtain through experiments, and simulation of the direct shear test was used for calibration. All of the methods guaranteed that the selected parameters were reasonable. The test results showed that the static friction coefficient of seed–seed had a significant effect on the simulation results. Finally, piling tests and the bulk density test were used for modeling verification. By comparing the simulated results and experimental results in the piling tests and bulk density test, when the number of filing spheres increased, the simulated results were close to those obtained experimentally. Therefore, the feasibility and validity of the modeling method for Cyperus esculentus seed particles that we proposed and the simulation parameters that were obtained were verified.
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Tianyue Xu, Hao Fu, Min Liu et al. · Materials Today Communications · 2023
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Dasha Shieff, Tobias Recker, Annika Raatz · Procedia CIRP · 2025
This work develops on the concept for an aerodynamic feeding system with higher flexibility (regarding the workpiece geometry) and drastically reduced retooling times compared to conventional feeding. The proposed design uses multiple, individually controllable air nozzles to re-orient workpieces. However, appropriate nozzle and feeder parameters would need to be selected before a physical prototype can be built. Design of Experiments is used to identify relevant parameters of the feeding process and minimize the number of simulations. Ideally the parameter selection would maintain high flexibility and low retooling times ensuring that aerodynamic part feeding can maintain personalization at minimal costs.
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Riccardo Pellegrini, Andrea Serani, Cecilia Leotardi et al. · Applied Soft Computing · 2017
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Chuxin Zhang, Zhen Kong, Zhiren Fu et al. · Computing in cardiology · 2024
Cardiac computational modeling has been extensivelyemployed to investigate the mechanisms underlying various cardiac arrhythmias.A three-dimensional (3D) tissue model (4 cm 4 cm 0.04 cm) was created using a tetrahedral mesh to study tissue-scale properties.The single-cell model published by Tomek et al. was integrated into the 3D tissue model, and simulations were conducted using openCARP.The parameter selection included two discretization methods (uniform and adaptive tetrahedral meshes), timesteps ranging from 5 to 20 s, and three initialization state variable generation methods: default model states (default), pacing the singlecell model 10 times with a basic cycle length of 600 ms (sv_init), and pre-pacing the 3D tissue model for 10 cycles with default model states (pre-pace).The results indicate that reentry characteristics in the 3D slab simulation were significantly influenced by different parameter selections.Careful selection of appropriate parameters in cardiac modeling is crucial for accurately interpreting the mechanisms of arrhythmia.
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