Simulation of Human Limb Movement

*† ‡ We develop an approach for estimating model parameters which result in the “best distribution fit” between experimental and simulation data. Best distribution fit means matching moments of experimental data to those of a simulation (and possibly matching a full probability distribution). This approach extends typical nonlinear least squares methods which identify parameters maximizing agreement between experimental points and computational simulation results. Several analytic formulations for the distribution matching problem are provided, along with results for solving test problems and comparisons of this parameter estimation technique with a deterministic least squares approach. I. Introduction Nonlinear models are frequently used to model physical phenomena, including engineering applications. In this paper, we refer to a nonlinear model very broadly: the output of the model is a nonlinear function of the parameters [Draper98]. Thus, nonlinear models can include systems of partial differential equations (PDEs). Some examples include CFD (computational fluid dynamics), groundwater flow, heat transport, etc. Nonlinear models also include functional approximations of uncertain data via regression or response surface models. In most cases, we have some type of simulation model which is a nonlinear model, so we use the term nonlinear model and simulation model interchangeably in this paper. In addition to a simulation model, we assume that we have experimental data which may be used to calibrate the model. The calibration often requires the solution of an optimization problem to determine the optimal parameter settings for the simulation model. In this paper, we are concerned with identifying model parameters which result in a “best fit” between experimental data and simulation results in a nondeterministic context. That is, instead of matching point estimates we are concerned with matching moments (e.g., mean or variance) between experimental and simulation data where the variability in the model output is due to parametric uncertainty. We develop an approach extending the typical nonlinear least squares formulation to allow for this distribution matching. Note that “parameters” may be parameters in an approximation model such as a regression model, or physics modeling parameters which are used in physical simulation models such as PDEs. We distinguish data from parameters: data are physical data which are input either to a regression or physical simulation. For example, in groundwater flow modeling, hydraulic conductivity is a parameter and data may include measured flow rates from well tests. In this paper, we denote parameters that will be calibrated as θ, and the independent input data (e.g., state variables, configuration data, boundary conditions) as x. We also assume that there are uncertain variables, denoted by u, that represent inherent variability or lack of exact knowledge influencing the simulation, but which we cannot observe. The effect of the uncertain variables is reflected in both the output variability of the nonlinear model and the experimental data, but we can only explicitly account for these uncertain variables in the simulation model. Thus, our simulation model, f,

Simulation modeling provides powerful decision support capabilities for production planning in manufacturing systems, yet widespread adoption remains limited by significant barriers in model development and maintenance. Traditional simulation approaches require substantial expertise, time investment, and financial resources, creating accessibility challenges particularly for small and medium enterprises. Current research on automatic simulation generation overlooks the significant potential that generative AI can offer in bridging the gap between natural language expression and executable model creation. This paper presents a novel methodology integrating large language models (LLMs) with automatic simulation model generation (ASMG) to enable direct generation of executable simulation models from natural language conversations. Unlike existing ASMG approaches that require formal specifications or simulation knowledge, our framework reduces programming requirements by allowing users to create functional models through conversational interaction. Our approach leverages natural language processing capabilities to transform user requests into executable simulation models without requiring specialized programming knowledge, enhancing human–machine interaction in production planning environments. The framework implements a four-step process: user intent expression through natural language interfaces, knowledge extraction using structured templates, simulation model construction via object-oriented data-driven components, and simulation execution with comprehensive result analysis for resource utilization, bottleneck identification, and production scheduling optimization. Performance testing demonstrates successful model generation across 18 supported action types with response times ranging from 8 s for simple operations to 5 min for complete manufacturing system creation. By enabling direct conversion of natural language requests into functional simulation models, this LLM-enhanced ASMG approach represents a step toward expanding access to simulation technology for non-expert users while maintaining the analytical power needed for complex manufacturing decision support and production control.

This paper presents a method for assortment–quantity production scheduling in a printing company. The company uses specialized machinery to make prints on clothing. The method is based on a study of the company’s practical operations and the production technologies used. It involves the construction of simulation and optimization models of the process. The simulation models reflect the technical aspects of the production process and the business requirements. Optimization models provide solutions that balance product sales revenue with appropriate production schedules. On this basis, managers can make resource-balanced decisions on the implementation of selected production plans, taking into account the current economic conditions of the company. The experiments used the FlexSim simulation program (by FlexSim Software Products, Inc., Orem, UT 84097 USA; v. 20.1.3.1) and the OptQuest optimization package (embedded in FlexSim), resulting in a cost-effective solution in a short time. The proposed method, thanks to the optimization of the production program, provides savings in the use of materials for production, as well as water and energy savings in the production process. Thanks to the possibility of analyzing the process without interfering with it, provided by simulation modelling, the method practically eliminates the costs and time needed to prepare the execution of new production orders. This contributes to the sustainable development of the company and provides an opportunity to assess the impact of potential business decisions in the company prior to their implementation. The method has been directly applied in a company to improve its performance. The method is scalable and can be applied to problems of varying complexity and production systems of different types and sizes. This is especially true for small- and medium-sized companies that use discrete manufacturing in the textile, metal, and furniture industries.

The objective of this study was to construct a finite element model of a golf ball and an impact simulation model between the ball and a club, and to devise a method for the construction of simulation models by investigating the factors affecting the behaviour of the ball during and after impact. A ball model with hyperelasticity and viscoelasticity was constructed using solid elements, and the relationship between the material properties and the ball behaviour at impact was revealed by varying several material constants of the ball model. A finite element model of a club with elasticity was created from a computer-aided design surface model of a commercially available driver. The club head and shaft were modelled using solid and shell elements, respectively. The effects of initial conditions at impact on the rebound behaviour of the ball, that is, the velocity, angle and spin rate, were investigated from impact simulations by varying the impact points of the ball and the postures of the club. Then, methods for the construction of simulation models were proposed based on these relationships. Impact experiments were also conducted to obtain the ball behaviour. The results simulated using the models, constructed by the proposed methods, closely matched the experimental results.

SAXS Ensemble Refinement of ESCRT-III CHMP3 Conformational Transitions

Real-time imaging products using Spaceborne Trinodal Pendulum synthetic aperture radar formation can provide valuable information in certain applications. The onboard orbit determination data of the spaceborne SAR platform is essential for the SAR imaging procedure. For real-time SAR imaging, the onboard orbit determination data is relatively low in accuracy compared with the orbit data obtained by off-line processing for the focusing of SAR data. The influence of errors in onboard real-time orbit determination data on SAR image quality should be considered. This paper proposes a Monte Carlo simulation model for inspecting the influence of onboard orbit determination data on imaging quality. This simulation model and its result may be helpful for the development of SAR real-time imaging focusing on providing terrain change information in a short time.

With the rapid advancement of phased array radar, the guidance and anti-jamming capabilities of phased array radar seekers have been further enhanced. Traditional radar jamming decision-making methods are no longer applicable in electronic warfare. Thus, radar jamming decision-making methods based on reinforcement learning have emerged, which can effectively address the issues of poor performance and low efficiency of traditional jamming approaches. Given the extremely high cost and poor repeatability of physical experiments, the construction of simulation models is frequently adopted for simulation experiments. Unlike most other studies on radar jamming decision-making that employ functional-level simulation modeling, this paper adopts signal-level simulation of radar jamming decision-making, presenting a more realistic and intuitive reflection of the entire process of interference equipment interfering with the missile terminal guidance. Through the signal-level simulation of phased array radar terminal guidance and the introduction of reinforcement learning algorithms, this study investigates how the interference equipment should act to maximize the interference benefit. Through simulation experiments, on the one hand, it is demonstrated that the reinforcement learning algorithm can improve the interference effect. On the other hand, the accuracy of the signal-level simulation model is verified.

The primary objectives of this study are to design a gas injection pilot in the Eagle Ford and to estimate the benefits of gas injection under different operational scenarios. This pilot design study entails the construction of multiple reservoir simulation models to understand the hydraulic fracturing and flow dynamics of multiple wells and gas injection operations in the Eagle Ford. Two DSUs with multiple hydraulically fractured wells were studied to achieve the proposed objectives. One of the DSUs was identified as the main study area to design a huff-and-puff gas injection pilot. Having an existing gas injection operation, the other DSU was selected to improve our understanding of the physics associated with gas injection. A dual porosity numerical reservoir simulation model coupled with geo-mechanics was built to replicate the historical well performances of the pilot area using a sophisticated numerical reservoir simulator. Another dual porosity simulation model was constructed to assimilate the existing huff-and-puff performance of the second DSU in which data was only publically available. The methodology used in this study integrates the hydraulic fracturing process, multi-phase flow, geo-mechanics, and proppant transport within the reservoir simulation. The simulation model was calibrated to match the historical hydraulic fracture treatment, fluid flow back and post-stimulation production. The proppant entrapment and migration from child well to the parent well was captured. The calibrated simulation model was then utilized to design a huff-and-puff gas injection pilot. Learnings and observations obtained from modeling of the existing gas injection operation in the second DSU were integrated into the pilot model. Additional sensitivity runs were performed to examine the potential benefits of gas injection under different operational scenarios. The calibration results indicated that the stimulated rock volume geometries of pilot study wells vary based on their completion practices. The historically observed well interference and frac hits between parent and child wells were captured by establishing a proper connectivity between wells during calibration. Proppant entrapment and movement of the proppant impacted the well performance. The results showed that significant amount of depletion leads to considerable matrix permeability reduction around wells. The most important knowledge gained from the calibration of the second DSU with huff-and-puff data is the identification of reservoir model characteristics that have the largest impact on the huff-and-puff performance. This study allows us to identify opportunities to design and improve huff-and-puff operation as well as estimating benefits of gas injection under different operational scenarios. The utilized technology in this study is unique and novel as it solves the geomechanics and flow in a single process. Proppant flow and entrapment was captured successfully. The multi-well calibration of the simulation model provides physics-based explanations for the historical well performances in the Eagle Ford.

Obtaining single cells: analysis and evaluation of an experimental protocol by means of a simulation model

A reservoir simulator has been developed to solve gas-water reservoir problems where the gas may be treated as a two component miscible mixture. It is specifically designed to study gas-storage problems where an inert gas such as nitrogen, may be used as a displacing fluid during cycling operations. The model includes both the convective transport of components within the gas phase plus the diffusion or dispersion of components. The solution to the multicomponent gas problem is accomplished using the method of characteristics, so that numerical dispersion is eliminated or minimized. A description of the model is included along with several example problems. The example problems include comparisons of calculated results with analytical solutions and experimental laboratory data in one and two dimensional models. The experimental data were measured on both liquid-liquid and gas-gas miscible systems.

In this work, simulation modeling of mass service network is investigated. The relevance of researching the topic lies in its potential to enhance efficiency and effectiveness across various service industries. In today's increasingly interconnected and technologically driven world, understanding how to optimize service delivery processes is paramount. By employing simulation modeling techniques, researchers and practitioners can simulate real-world scenarios, identify bottlenecks, test different strategies, and ultimately improve the overall performance of mass service networks. This research can have practical applications in areas such as transportation, healthcare, telecommunications, and customer service, where efficient management of large-scale service networks is essential for meeting demand, reducing costs, and enhancing customer satisfaction. Moreover, the insights gained from such research can inform policy-making decisions aimed at improving service accessibility, equity, and resilience in the face of disruptions, thus contributing to the advancement of both academic knowledge and practical applications in the field of service operations management. It is noted that simulation modeling can be considered as a methodology and not a theory of problem solving. One of the features of simulation models is the practically significant assumption that they can serve as a tool for analyzing the behavior of the system under the conditions determined by the experimenter. The elements of a mass service network are the input flow, the mass service system, and communication. The input flow is characterized by the interval of requests to the network, which can be a random or deterministic value, as well as the number of the mass service system to which the request is received. The state of the incoming stream is determined by the moment in time when the next request arrives in the mass service network. In the case when the time interval for the arrival of requirements is a random variable with a known distribution law, a random number generator is used to calculate it.

In order to study the temperature characteristics of magnetorheological fluid dynamometer, the torque model and working principle of magnetorheological fluid dynamometer were analyzed, the temperature experiment of magnetorheological fluid dynamometer was conducted, and the experimental data were analyzed and processed in this paper. The fitting curves between the speed of magnetorheological fluid dynamometer and the experimental temperature, the speed and the torque, the current and the torque as well as the current and the temperature were obtained. Then, the temperature field of magnetorheological fluid was simulated by FLUENT based on the experimental data. The temperature field nephograms and temperature values of test points under different working conditions were obtained. Comparing the experimental values with the simulation values, the relative error was less than 7.8 %, which was within a reasonable range. Finally, from the aspect of temperature reduction, the simulation model was optimized. Based on the principle of heat convection, the simulation model was reconstructed. The maximum temperature drop was 10℃.

This research is about performance investigation of a multiple-tank integrated-solar collector storage heater. A prototype of the heater having a capacity of 300 Liter was constructed and experimentally tested outdoors to observe the variation of water temperature in the storage tanks. A Fluent program is used to predict the storage water temperature. The experimental data was verified with the results from the simulation model. In vice versa, the simulation model was validated using the experimental data. Two cases have been studied, namely with and without flow rate. The results show that the maximum water temperature exited from the storage tanks during February month of 2010 was 48°C. The results illustrated that the present integrated solar water heater was a success in providing hot water suitable for day time use by households during the winter in Iraq.

Summary In this paper we describe using a commercial transient multiphase-flow simulator to develop a new operational procedure for liquid-assisted gas lift (LAGL) unloading. The simulation model is used in our study to perform sensitivity analysis on the controlling parameters for the LAGL unloading operation. This simulation model is validated with experimental data from field-scale test data presented by Coutinho et al. (2018). From the simulation results and experimental data, it is possible to demonstrate how the injection of a gas/liquid mixture can significantly decrease the injection pressure for unloading operations. Different combinations of injection gas/liquid ratio are numerically tested to evaluate the effect of gas/liquid ratio on the injection pressure during the complete unloading operation. The validated model was used with a newly developed procedure for the complete unloading operation. The modeling results show that using the LAGL technique enabled us to reduce the injection pressure from 1,200 psig, when using single-phase gas in a single-point injection system, to approximately 700 psig, when injecting gas/liquid mixtures in a single-point injection system. Analyses on the effect of gas lift valve-orifice size, also presented here, show that using large orifice sizes might reduce the effect of flow friction through the gas lift valve, which directly affects the efficiency of the LAGL unloading operations.

This paper describes the simulation technique and results of 3D Simulation of Laser Additive Manufacturing [LAM] process, and its correlation with experimental data. The simulation models laser-powder-substrate interaction, using a combined analytical-numerical approach, such that it is transparent to the user. The shape and thermal history through multiple passes for the build height is tracked accurately. The near field, or laser spot region, calculation is linked to a far field solution to create a complete picture of LAM behavior as a function of its process parameters. The simulation makes use of commercially available pre- and post processors, and the simulation time is comparable to present-day casting simulations.This paper describes the simulation technique and results of 3D Simulation of Laser Additive Manufacturing [LAM] process, and its correlation with experimental data. The simulation models laser-powder-substrate interaction, using a combined analytical-numerical approach, such that it is transparent to the user. The shape and thermal history through multiple passes for the build height is tracked accurately. The near field, or laser spot region, calculation is linked to a far field solution to create a complete picture of LAM behavior as a function of its process parameters. The simulation makes use of commercially available pre- and post processors, and the simulation time is comparable to present-day casting simulations.