Iterative Simulation Research Articles

An optimization method for deformable mirror configuration in multi-conjugate adaptive optics systems

Multi-conjugate adaptive optics (MCAO) is a crucial technology for achieving high-resolution imaging over a wide field of view with modern ground-based optical telescopes. The configuration of deformable mirrors (DMs) is a key component in the analysis and optimization of MCAO performance. Currently, the search for the optimal DM configuration often relies on iterative and time-consuming Monte Carlo simulations. This issue arises from the lack of an appropriate optimization method for DM configurations. The primary objective of this paper is to establish an optimization method for DM configurations in MCAO systems. We established a quantitative criterion for evaluating DM configurations by analyzing their correction capabilities for turbulence aberrations at different altitudes. Then, we optimized the DM configurations based on this criterion. This method provides a new theoretical foundation and practical tool for the design and performance optimization of MCAO systems. Based on the pupil phase structure function, we established a DM configuration evaluation criterion, namely the non-conjugate correction index (NCCI). Using NCCI as the optimal criterion, combined with the particle swarm optimization algorithm, we searched for the optimal solution across different DM configuration spaces. We conducted simulations based on the turbulence profiles of typical telescope sites. We validated our proposed theoretical model against Monte Carlo simulation models and find that the NCCI error ranges from 0.05 to 0.1. For optimizing DM conjugate heights, the results of our optimization algorithm differ by less than 1 km from those obtained via Monte Carlo simulations. Regarding the performance of the DM optimization algorithm, the average convergence accuracy error is less than 0.1 km, and the average convergence speed is approximately ten iterations. Additionally, our optimization method runs in just a few minutes; Monte Carlo simulations, in comparison, require several dozen hours.

Optimizing Wireless Power Transfer Systems: A Simulation-Based Approach Using CST Studio Suite

In the rapidly evolving field of wireless power transfer (WPT), achieving systems that combine efficiency with reliability is crucial. This paper presents a simulation-based approach using CST Studio Suite to optimize WPT systems for wireless battery chargers in electric bikes. The study fixes the size of both the transmitter and receiver coils, the spacing between turns, and the number of coil turns, with a set distance of 20mm between the transmitter and receiver coils. CST Studio Suite is employed to determine the parameters of resonance capacitors and the optimal switching frequency to enhance WPT efficiency. This powerful simulation tool allows researchers and engineers to design resonance compensation circuits more effectively, optimizing power transfer efficiency. The simulation conducted with CST Studio Suite confirms its effectiveness in designing a WPT system, achieving an impressive peak efficiency of 99.61% at the resonant frequency. The paper demonstrates CST Studio Suite's capability to accurately predict WPT system performance by developing detailed electromagnetic models and fine-tuning simulation parameters to reflect real operating conditions. Iterative simulations resulted in optimized practices, validated by select results that highlight the method's effectiveness and showcase an optimal balance between system complexity and efficiency. These findings provide a strategic framework for future WPT system development, offering practical guidance for researchers and industry professionals aiming to advance WPT technology.

Non-linear MHD modelling of transients in tokamaks: a review of recent advances with the JOREK code

Abstract Transient MHD events like edge localized modes (ELMs) or disruptions are a concern for magnetic confinement fusion power plants. Research with the MHD code JOREK towards understanding control of such instabilities is reviewed here. Experimental validation for unmitigated vertical displacement events (VDEs) progressed. The mechanism of vertical force mitigation by impurity injection was identified. Two-way eddy current coupling to CARIDDI was completed. Shattered pellet injection (SPI) was simulated in JET, KSTAR and ASDEX Upgrade (AUG) and ITER. Benign runaway electron (RE) beam termination in JET and ITER was studied. Coupling of kinetic REs to the MHD is ongoing and a virtual RE synchrotron radiation diagnostic was developed. Regarding pedestal physics, regimes devoid of large ELMs in AUG were simulated and predictive JT60-SA simulations are ongoing. For ELM suppression by resonant magnetic perturbations (RMPs), AUG, ITER and EAST simulations were performed. A free boundary RMP model was validated against experiments. Evidence for penetrated magnetic islands at the pedestal top based on AUG experiments and simulations was found. Simulations of the naturally ELM-free quiescent H-mode (QH) in AUG and HL-3 show external kink mode formation prevents pedestal build-up towards an ELM within windows of the edge safety factor. With kinetic neutral particles, high field side high density (HFSHD) formation in ITER was simulated and with kinetic impurities, tungsten transport in AUG RMP plasmas was studied. To capture turbulent transport, electro-static full-f PiC models for ion temperature gradient (ITG) and trapped electron modes (TEM) were established and benchmarked. Application to RMP plasmas shows enhanced turbulence in comparison to unperturbed states. Energetic particle (EP) interactions with MHD were studied. Flux-pumping that prevents the safety factor on axis from dropping below unity was simulated. First non-linear stellarator applications include current relaxation in l=2 stellarators, while verification for advanced stellarators progresses.

Machine learning method predicting thermal performance of conformal cooling systems

The incorporation of conformal cooling systems has significantly enhanced the efficiency and quality of injection molding process. While several automated methods have been developed for creating conformal cooling channels in injection molds, the current optimization process for conformal cooling design parameters is hindered by labor-intensive iterative thermal simulation processes and the substantial reliance on empirical human knowledge. This paper presents an innovative machine learning method to assess the thermal performance of conformal cooling systems by employing a combination of a non-linear regression model and a neural network. By employing a logarithmic regression model describing the temperature graph and a neural network predicting the coefficients of the logarithmic regression model, the thermal performance of specified conformal cooling systems can be assessed and predicted precisely. This methodology empowers designers to evaluate the thermal efficiency of conformal cooling systems efficiently and effectively to further optimize the conformal cooling design parameters without relying on tedious manual thermal and fluid simulation processes.

A Fast Numerical Approach for Investigating Adhesion Strength in Fibrillar Structures: Impact of Buckling and Roughness

This study presents a numerical investigation into the adhesion strength of micro fibrillar structures, incorporating statistical analysis and the effects of excessive pre–load leading to fibril buckling. Fibrils are modeled as soft cylinders using the Euler–Bernoulli beam theory, with buckling conditions described across three distinct states, each affecting the adhesive properties of the fibrils. Iterative simulations analyze how adhesion strength varies with pre–load, roughness, number of fibrils, and the work of adhesion. Roughness is modeled both in fibril heights and in the texture of a rigid counter surface, following a normal distribution with a single variance parameter. Results indicate that roughness and pre–load significantly influence adhesion strength, with excessive pre–load causing substantial buckling and a dramatic reduction in adhesion. This study also finds that adhesion strength decreases exponentially with increasing roughness, in line with theoretical expectations. The findings highlight the importance of buckling and roughness parameters in determining adhesion strength. This study offers valuable insights into the complex adhesive interactions of fibrillar structures, offering a scalable solution for rapid assessment of adhesion in various rough surface and loading scenarios.

A novel iterative double auction design and simulation platform for packetized energy trading of prosumers in a residential microgrid

AbstractPacketized energy encapsulates energy into modulated, routable, and trackable energy packets, enhancing the flexibility of managing distributed energy resources and expediting prosumers’ participation in transactive energy markets. In the context of packetized energy trading (PET), energy prosumers are naturally deemed as self‐interested agents seeking to obtain their own benefits. To align with prosumers’ demand, supply, quality of service (QoS), and system‐level social welfare, it is necessary to explore the design of prosumers’ bidding strategies and the market clearing methods, considering prosumers’ utility and the best demand response to markets. This study addresses challenges arising from prosumers’ selfishness and asymmetric preferences by proposing a PET‐oriented iterative double auction (IDA‐PET) design, where prosumers are allowed to iteratively change the bids before the auctioneer clears the market. Moreover, IDA‐PET accommodates system capacity constraints, energy balance, and economic constraints, providing cooperative strategies for both prosumers and the auctioneer. To validate the effectiveness of IDA‐PET, a novel and dedicated co‐simulation platform based on the hierarchical engine for large‐scale infrastructure co‐simulation platform is developed and case studies are conducted within a residential microgrid. The simulation results demonstrate that IDA‐PET can efficiently enhance the revenue of the auction market while meeting prosumers’ QoS requirements.

Machine learning-assisted investigation of anisotropic elasticity in metallic alloys

This study aims to develop a machine learning (ML)-based method for predicting the elasticity tensor of anisotropic metallic alloys with 21 independent constants. In this respect, the elastic moduli of a batch of additive-manufactured specimens with various orientations, determined by Tait–Bryan angles, are utilized as input data, while the anisotropic elastic tensor are targeted for prediction. Our primary findings indicate the efficacy of the radial basis function neural network (RBFNN) as a robust ML model for elasticity tensor prediction. Notably, the model demonstrates slightly higher efficiency in predicting elasticity tensor components aligned with principal axes compared to off-diagonal components. Moreover, with increasing anisotropy, the model assigns greater contribution to shear and orthotropic components, optimizing prediction performance. Additionally, a case study using Inconel 718 further illustrates the model's effectiveness in capturing the elasticity tensor. Importantly, it is suggested that the proposed ML model offers advantages over experimental methods, such as impulse excitation-based procedures, and iterative numerical simulations, by reducing sample preparation requirements and saving time.

Time-scale invariant contingency yields one-shot reinforcement learning despite extremely long delays to reinforcement

Reinforcement learning inspires much theorizing in neuroscience, cognitive science, machine learning, and AI. A central question concerns the conditions that produce the perception of a contingency between an action and reinforcement-the assignment-of-credit problem. Contemporary models of associative and reinforcement learning do not leverage the temporal metrics (measured intervals). Our information-theoretic approach formalizes contingency by time-scale invariant temporal mutual information. It predicts that learning may proceed rapidly even with extremely long action-reinforcer delays. We show that rats can learn an action after a single reinforcement, even with a 16-min delay between the action and reinforcement (15-fold longer than any delay previously shown to support such learning). By leveraging metric temporal information, our solution obviates the need for windows of associability, exponentially decaying eligibility traces, microstimuli, or distributions over Bayesian belief states. Its three equations have no free parameters; they predict one-shot learning without iterative simulation.

Process design and global sensitivity analysis of hydrogen fuel station with dual-operating modes for a sustainable H2 city: Iterative optimization-based simulation using generalized disjunctive programming

This study proposes a novel process involving dual-mode operation for an on-site hydrogen (H2) refueling station. In particular, two distinct H2 production technologies, steam methane reforming (SMR), and autothermal reforming (ATR), are simultaneously considered, which enables a flexible operational mode that responds to operational variability. By synergistically combining the technical advantages of these two technologies (i.e., the low cost of SMR and the short start-up and shut-down times of ATR), the proposed fuel station can reduce a H2 storage tank cost, which is needed to respond to demand fluctuations. To achieve our goal, we developed iterative optimization-based simulation (IOS) framework. The proposed framework determines the optimal design configuration (e.g., the storage facilities and the capacity of production) and the dual-mode operational strategy (e.g., on/off timing) under demand fluctuations and given CO2 emission targets. The case study showed that lower storage costs and offset the increased capital cost of the production facility, thereby leading to a low H2 supply cost of 3.60 USD/kg and a production capacity of 1.2 tons/day. We then performed a global sensitivity analysis to provide a comprehensive strategy and practices for the deployment of the proposed system over demand levels, regional demand fluctuation, and CO2 emission targets.

Design and Analysis of a Dual-Band Microstrip Patch Antenna for Sub-6 GHz Applications

This paper explores the design, simulation, and analysis of a dual-band Microstrip patch antenna designed to operate efficiently at frequency ranges of 3.32 GHz - 3.62 GHz and 4.72 GHz - 6.83 GHz, utilizing Ansys High-Frequency Structure Simulator (HFSS) software. The primary objective is to enhance performance metrics such as bandwidth, gain, and radiation efficiency to meet the requirements of applications necessitating concurrent operation at both frequency bands. The study involves a comprehensive design, iterative simulation, and detailed analysis using Ansys HFSS, with customized design considerations aimed at optimizing performance for modern wireless communication systems. The research was conducted in a simulated environment over a period that included iterative design processes, multiple simulation runs, and thorough analysis phases. Methodologically, the study focused on iterative design refinement to improve key performance parameters such as reflection coefficient, gain, radiation efficiency, Voltage Standing Wave Ratio (VSWR), and relative permittivity at the specified frequency bands. Results indicate that at 3.32 GHz - 3.62 GHz, the antenna achieves a reflection coefficient of -5 dB, a gain of 2.77 dB, a radiation efficiency of 0.7429, and a VSWR of 1.9299. At the higher frequency range of 4.72 GHz - 6.83 GHz, the antenna exhibits a reflection coefficient of -20.5213 dB, a gain of 3.34 dB, a radiation efficiency of 0.7, and a VSWR of 1.2203. These findings underscore the antenna's capability to deliver improved performance metrics across both frequency bands. In conclusion, the dual-band Microstrip patch antenna designed and analyzed through Ansys HFSS demonstrates significant potential for use in contemporary wireless communication systems, offering enhanced bandwidth, gain, and radiation efficiency suitable for multi-frequency applications.

Effect of Heat Exchanger and Capillary Geometry on the performance of Joule- Thomson Refrigerators operating with different mixtures

Abstract Joule-Thomson (J-T) refrigerators or J-T cryocoolers are extensively used in many low-temperature applications. J-T refrigerators operating with nitrogen-hydrocarbon (N2-HC) refrigerant mixtures offer several advantages, such as low operating pressures (<20 bar), high exergy efficiency, no moving parts in the cold section, and low cost. The cooling power or cooling capacity of the J-T refrigerator depends on the hardware used as well as the refrigerant composition. The proposed work focuses on estimating the cooling capacity of a mixed refrigerant J-T (MRJ-T) refrigerator of the given hardware and specified refrigerant. An iterative steady-state full-cycle simulation procedure has been presented in this work to simulate the complete system and estimate the cooling capacity, taking into account the possibility of choking of the expansion capillary. Some of the results have been validated against experimental results of an MRJT refrigerator available in the open literature. The details of the simulation model and the results of our studies on the prediction of stable operating range, maximum cooling capacity, the effect of heat exchanger geometry, expansion capillary geometry, mixture composition, and choking of the refrigerant mixture on the performance of an MRJ-T refrigerator are presented in this paper

Optimizing Camera Setup for In-Home First-Person Rendering Mixed-Reality Gaming System

With the advent of 3D humanoid reconstruction techniques, using a realistic 3D human avatar in a serious game has become popular. This realistic representation in the virtual environment could be achieved using a single low-cost RGB-D camera such as Kinect. However, properly setting up such a camera system for high-quality rendering can be challenging due to the relatively restricted in-home environment. In this paper, we address the challenge of finding optimized camera setup guidelines for an in-home first-person perspective mixed reality (MR) gaming system. We use an MR system with personalized humanoids to simulate the texture reconstruction for a user under a specific camera configuration. Then, a derivative-free optimization is leveraged as a black box approach to search for the optimized camera setup through iterative simulation. We also introduce a novel skeleton-based calibration to address the effects of physically varying the camera's position. For evaluation, two experiments are carried out to evaluate the correctness and effectiveness of the proposed calibration. Furthermore, we conduct a case study using simulation-based optimization for reconstructing lower limb amputees. This work can potentially help locate a proper camera setup for an MR system within the constraints of an in-home environment.

A systematic approach to adaptive sequential design for clinical trials: using simulations to select a design with desired operating characteristics

ABSTRACT The failure rates of phase 3 trials are high. Incorrect sample size due to uncertainty of effect size could be a critical contributing factor. Adaptive sequential design (ASD), which may include one or more sample size re-estimations (SSR), has been a popular approach for dealing with such uncertainties. The operating characteristics (OCs) of ASD, including the unconditional power and mean sample size, can be substantially affected by many factors, including the planned sample size, the interim analysis schedule and choice of critical boundaries and rules for interim analysis. We propose a systematic, comprehensive strategy which uses iterative simulations to investigate the operating characteristics of adaptive designs and help achieve adequate unconditional power and cost-effective mean sample size if the effect size is in a pre-identified range.

Algorithm for steady contaminant distribution in ventilation systems with air recirculation: Contaminant release in ventilation ducts

When contaminants are released naturally or deliberately in ventilation ducts, they can contaminate the supply air and rooms through the return air recirculation of the ventilation and air conditioning system. Few studies have considered the impact of contaminant transport through air recirculation on the non-uniform indoor contaminant distribution. In this study, a method for calculating the steady-state contaminant distribution in a ventilation system with air recirculation when there is a contaminant release from the ventilation duct is presented. The ventilation ducts are considered as a virtual room and modelled separately from the rooms, both using linear superposition expressions based on accessibility indicators to obtain a relationship between contaminant concentrations at the outlet and inlet. A total of four sub-methods are proposed considering different treatments for the return and supply air ducts connected to each air handling unit (AHU). The proposed methods (Methods 1–3) considering the non-uniform characteristics of the ducts are shown to have an accuracy comparable to that of CFD (computational fluid dynamics) simulations, with an average prediction deviation of less than 5%, whereas the use of well-mixed assumption in the ducts (Method 4) results in an average prediction deviation of up to 45% in one of the rooms when the contaminant source is close to the diversion position of the supply air duct. The proposed method avoids the need for holistic modelling and iterative simulation of complex ventilation and air-conditioning system and multiple rooms, and allows for rapid prediction of contaminant distribution.

PASS4SWAT: Orchestration of containerized SWAT for facilitating computational reproducibility of model calibration and uncertainty analysis

When performing modeling routines with iterative simulations, a research gap exists in developing the parallelizing frameworks that are adaptable to the increasing complexities of the hydrologic models. This study addresses this gap by proposing a parallel simulation stack for SWAT (PASS4SWAT) that leverages the power of cloud-native infrastructure. Cloud-native applications are designed to be scalable, agile, and resilient, making them ideal for computationally intensive tasks such as hydrologic modeling. To achieve this goal, PASS4SWAT was created by integrating a message broker, a container runtime and orchestration tool, and certain components from SWAT-CUP. We evaluated PASS4SWAT using the Jinjiang watershed model and a synthetic model with high input/output demands to demonstrate its effectiveness. The results show that PASS4SWAT can consistently replicate the results obtained with SWAT-CUP and significantly reduce the runtime, achieving 91.5% and 93.2% reductions in single-node and multinode Kubernetes clusters, respectively. Therefore, in this paper, we conclude that PASS4SWAT can effectively address the high computational demands of the SWAT model by scaling out parallel tasks on a cluster, and can adapt flexibly to diverse environments, including single-node clusters, multinode clusters, and potential cloud platforms.

A probabilistic framework to evaluate seismic resilience of substations based on three-stage uncertainty

Electrical substation systems exhibit vulnerability after an earthquake. It results in significant economic losses because of the uncertainty of the functional state and recovery process. To quantitatively assess the uncertainty of substations, this paper proposes a probability-based seismic resilience assessment framework. This framework considers uncertainty in three stages: functional state, functional recovery, and resource constraints. Based on the functional characteristics of substation systems, a directed acyclic graph was constructed, and the Monte Carlo algorithm was used to obtain the functional state matrix, thereby establishing a probability-based functional state model. Moreover, a dynamic post-earthquake functional recovery analysis framework was built based on recovery efficiency metrics, and functional recovery paths were obtained through iterative simulations. A stepped functional recovery curve was developed based on resource constraints. Three-stage uncertainty parameters were transformed into resilience features for quantitative assessment. By analysing seismic resilience of a typical 220 kV step-down substation, probability distributions of functional recovery curves and confidence intervals for seismic resilience metrics were obtained. Notably, mutual constraint relationships among resource conditions were identified.

Fast and accurate Zero-defect manufacturing using collaborative real-time photogrammetry

Abstract. For the machining of industrial components, their accurate alignment on operative machines is a crucial step with significant impacts on the whole process, especially when dealing with large-scale elements. To achieve this alignment, a trained operator is needed along with expensive equipment, such as laser trackers, whose correct use requires the measurements of many significant points and is time-consuming. The lack of a precise alignment comes with possible incorrect parts machining, resulting in quality concerns and potentially expensive rework. To face this problem, collaborative real-time photogrammetry is an effective solution for guiding untrained users in correctly positioning various measured and codified components to generate the best solution for machining high-volume parts. The solution proposed in this contribution is part of the TACCO project, co-financed by EIT Manufacturing (co-funded by the EU). It takes advantage of the TACCO system, where auxiliary components (scale bars, dome-shaped components, coded targets and a cross-reference system) are placed on the object to be measured for their correct positioning in order to obtain an optimal photogrammetric reconstruction. This allows the reduction of the error ellipsoids on several uncoded targets, which are calculated through an iterative simulation carried out in a MATLAB environment. Unskilled operators are then guided through an Augmented Reality headset for their correct placement on the real-world object and for defining the positions and orientations from which to acquire images. This methodology will lead to an accurate measurement of the target points to be machined and, thus, a precise alignment process for the subsequent machining operations.

A computational enzymatic optimization for fixing carbon dioxide to starch

Carbon dioxide is fixed and processed into starch by the plants photosynthesis through complicated molecular pathways. While planting and cultivating crops are the major ways to harvest starch, an artificial anabolic pathway has recently been realized in China. Traditional crop production demands extended harvest periods, extensive land, and substantial water use. In contrast, the artificial pathway enhances starch synthesis efficiently, using fewer resources for a more sustainable approach. In 2021, Chinese researchers reported the anabolic starch artificial pathway (ASAP) to synthesize starch in vitro. Although the previous research established a milestone, steps need to be optimized. In this work, enzymatic starch synthesis is chosen to be further engineered, building mutants with similar catalytic functions. Computational tools are used to build an iterative docking-mutating simulation (IDMS). It can automatically finish the cycle of protein mutations and docking. Autodock and Rosetta are used in the coding. 445 different protein mutants are generated and analyzed in silico, among which the best five were chosen for experimental investigation. In the experimental analysis, mutant E shows nearly the same catalytic efficiency as the wild-type in the first hour, with a 2.5-fold expression rate.

Performance Optimization Design Study of Box-Type Substations Subjected to the Combined Effects of Wind, Snow, and Seismic Loads

As a pivotal node in both urban and rural power grids, the box-type substation not only serves the functions of power conversion and distribution but also need to provide structural support and environmental adaptability. However, deficiencies in strength, stiffness, or vibration characteristics may lead to vibration and noise issues, and extreme environmental changes can pose risks of structural damage. This study aims to verify and optimize the seismic resistance and environmental adaptability of box-type substations through finite element simulation methods. Using SOLIDWORKS, a three-dimensional model of the box-type substation was constructed, and static and dynamic analyses were conducted using Ansys Workbench to comprehensively evaluate the dynamic response of the box-type substation under wind, snow loads, and seismic action. Through iterative simulations and a comparison of multiple design solutions, the structural optimization of the substation was achieved. The optimized structure balances strength and stiffness, significantly reducing the weight of the substation body, with the wall thickness reduced by 60%. Additionally, the phenomenon of stress concentration on the side walls was eliminated, ensuring that the equivalent stress is below the material yield strength. This research provides methods and empirical results for enhancing the performance and reliability of box-type substations under seismic conditions, confirming the feasibility of a lightweight design, while ensuring structural safety.

Myocardial biomechanical effects of fetal aortic valvuloplasty

Fetal critical aortic stenosis with evolving hypoplastic left heart syndrome (CAS-eHLHS) can progress to a univentricular (UV) birth malformation. Catheter-based fetal aortic valvuloplasty (FAV) can resolve stenosis and reduce the likelihood of malformation progression. However, we have limited understanding of the biomechanical impact of FAV and subsequent LV responses. Therefore, we performed image-based finite element (FE) modeling of 4 CAS-eHLHS fetal hearts, by performing iterative simulations to match image-based characteristics and then back-computing physiological parameters. We used pre-FAV simulations to conduct virtual FAV (vFAV) and compared pre-FAV and post-FAV simulations. vFAV simulations generally enabled partial restoration of several physiological features toward healthy levels, including increased stroke volume and myocardial strains, reduced aortic valve (AV) and mitral valve regurgitation (MVr) velocities, reduced LV and LA pressures, and reduced peak myofiber stress. FAV often leads to aortic valve regurgitation (AVr). Our simulations showed that AVr could compromise LV and LA depressurization but it could also significantly increase stroke volume and myocardial deformational stimuli. Post-FAV scans and simulations showed FAV enabled only partial reduction of the AV dissipative coefficient. Furthermore, LV contractility and peripheral vascular resistance could change in response to FAV, preventing decreases in AV velocity and LV pressure, compared with what would be anticipated from stenosis relief. This suggested that case-specific post-FAV modeling is required to fully capture cardiac functionality. Overall, image-based FE modeling could provide mechanistic details of the effects of FAV, but computational prediction of acute outcomes was difficult due to a patient-dependent physiological response to FAV.