Flexible Simulation Research Articles

Personalized Vulnerability Assessment of Customized Low-Rise Wood-Frame Residential Structures under Hurricane Wind Loads: A Flexible Scenario-Based Simulation Approach

Hurricanes are one of the costliest and deadliest weather and climate disasters that impact the United States, largely affecting the residential sector. Residential structures are essential for the health and safety of the community during a hurricane event. However, homeowners typically have to rely on general information and instructions to understand their level of risks. The knowledge of structural vulnerability to hurricanes at the individual residential building level is often limited, which hinders personalized disaster preparation and mitigation decision-making. To this end, a vulnerability assessment method is proposed for individual homeowners to create customized finite-element models that quantify wind damage of low-rise wood-frame residential structures under various scenarios using ANSYS 2020 R1 parametric design language. A case study was conducted to test the proposed methodology and investigate prevailing failure modes under different structural characteristics and wind scenarios. Specifically, the impacts of roof framing features and previous exposure to environmental conditions on the potential damage of residential buildings under hurricane wind loads were studied. Analysis results, including the percentage of failed sheathing, nailed connections and framing members, are used to inform personalized mitigation recommendations. The proposed customized vulnerability assessment may increase homeowners’ situational awareness of disasters and promote rational retrofitting decisions.

Artificial nanophotonic neuron with internal memory for biologically inspired and reservoir network computing

Neurons with internal memory have been proposed for biological and bio-inspired neural networks, adding important functionality. We introduce an internal time-limited charge-based memory into a III–V nanowire (NW) based optoelectronic neural node circuit designed for handling optical signals in a neural network. The new circuit can receive inhibiting and exciting light signals, store them, perform a non-linear evaluation, and emit a light signal. Using experimental values from the performance of individual III–V NWs we create a realistic computational model of the complete artificial neural node circuit. We then create a flexible neural network simulation that uses these circuits as neuronal nodes and light for communication between the nodes. This model can simulate combinations of nodes with different hardware derived memory properties and variable interconnects. Using the full model, we simulate the hardware implementation for two types of neural networks. First, we show that intentional variations in the memory decay time of the nodes can significantly improve the performance of a reservoir network. Second, we simulate the implementation in an anatomically constrained functioning model of the central complex network of the insect brain and find that it resolves an important functionality of the network even with significant variations in the node performance. Our work demonstrates the advantages of an internal memory in a concrete, nanophotonic neural node. The use of variable memory time constants in neural nodes is a general hardware derived feature and could be used in a broad range of implementations.

(Invited) A Novel Computational Framework for Simulations of Bio-Field Effect Transistors

In this paper, we present the simulation results of a BioFET using a novel simulation methodology implemented in a computational framework called the Biomolecule-Oxide Simulator (BOxSim). Our novel simulation methodology is based on Gouy-Chapman-Stern and the modified Site-binding models [1][2]. Moreover, our “BOxSim” framework is the computational simulation framework that works on the hierarchical inclusion of interdependent functions to operate multiple models simultaneously. BOxSim is flexible simulation environment which considers the problems of reactive sites from oxide surfaces or non-specific bindings along with the noise present in the experimental signal to calibrate and extract the required device output characteristics [3]. It can help in generating the sensor response which can be experimentally calibrated to confirm the surface density of the specific and non-specific binding as a yield of the process while filtering the noise with the help of the developed methodology. BOxSim is designed in a way [shown in the attached block diagram] that can interact with the commercial tool (e.g., Synopsys TCAD) and our inhouse-simulator called Nano-electronic Simulation Software (NESS) to simulate the main device characteristics for designing either Ion-sensitive FET (ISFET) or Chem/Bio-FET based on the application. Industry-standard compact (BSIM) models can also be used to interact with the BOxSim which takes the generated surface potential from the BOxSim simulator and transfers it into a circuit simulator to evaluate various ISFET circuit designs.To show the capabilities of our BOxSim framework, as input we have considered several oxides, amino acids (AA) and short peptides which generate distinct signatures for arch AA or peptide. We have investigated the impact of various high-k dielectric materials [4][5] as the gate oxide on important Figures of Merit (FoM) such as current in the channel of the device, surface potential (Ψ o ), sensitivity and intrinsic buffer capacitance [6]. More importantly, the variation of differential capacitance (CT ) with the second gradient of drain current (d2 I o /dpH2 ) and surface potential (d2 Ψ o /dpH2 ) are used to uniquely identify the signatures of different amino acids or peptides. Fig. 1 shows the characteristics of different oxides with respect to the pH variation. The surface potential of oxides is dependent on the affinity constants and the zero-crossover point represents the zwitterion state. Similarly, we have simulated the detection process of different AA such as Glutamic Acid (E) and Lysine (K) immobilized with carboxyl (C-Imm.) or amine (N-Imm.) terminal [Fig. 2]. The affinity constants, isoelectric point, and density of amino acids can be extracted from the d2Ψo/dpH2 variation with the bulk pH. Other than amino acids, we have shown the distinct features of short a peptide Phenylalanine-Proline (FP) immobilized with carboxyl-terminal and the addition of Glutamic Acid to the sequence [Fig. 3]. The sensor output can be observed as the variation of drain current with the pH for the immobilized peptide sequence on the sensing surface. We have also confirmed the reliability of the designed model by calibrating the simulated results with the experimental data [7] for different physical conditions [Fig. 4]. The proposed method can be helpful in defining an efficient method for label-free protein sequencing.

Modelling hydrogen storage and filling systems: A dynamic and customizable toolkit

Hydrogen plays a vital role in decarbonizing the mobility sector. With the number of hydrogen vehicles expected to drastically increase, a network of refuelling stations needs to be built to keep up with the hydrogen demand. However, further research and development on hydrogen refuelling infrastructure, storage and standardization is required to overcome technical and economic barriers. Simulation tools can reduce time and costs during the design phase, but existing models do not fully support calculations of complete and arbitrary system layouts. Therefore, a flexible simulation toolbox for rapid investigations of hydrogen refuelling and extraction processes as well as development of refuelling infrastructure, vehicle tank systems and refuelling protocols for non-standardized applications was developed. Our model library, H2VPATT, comprises of typical components found in refuelling infrastructure. The key component is the hydrogen tank model. The simulation model was successfully validated with measurement data from refuelling tests of a 320 l type III tank.

Does Regular Practice with a "Flexible Bronchoscopy Simulator" Improve Fibreoptic Intubation Skills in Experts and Novices? A Randomized Controlled Study.

The appropriate management of a "difficult airway" remains a challenge for novices and experienced anaesthetists. With the current available airway technologies, e.g., video laryngoscopy, flexible bronchoscopy (fibreoptic intubation (FOI)) for endotracheal intubation is decreasing, likely diminishing caregiver skills. We investigated whether bronchoscopy simulator training improved FOI skills. 72 volunteers, consisting of anaesthetists, anaesthesia residents, and nurses, performed six exercises on a bronchoscopy simulator. At baseline and after 12 months, the six exercises included one serious game (to train agility), two basic airways, and three difficult airways. After a baseline assessment, subjects were randomly allocated to the intervention group (with) or control group without bronchoscopy simulator training every six weeks for 10 min using a preloaded serious game. The primary outcome was the difference in the time to reach the carina after 12 months, as measured objectively by the simulator. The level of stress and FOI confidence after 12 months were secondary outcomes. The control and intervention groups had a similar time to reach the carina in difficult airway cases and the reported stress levels, at baseline and 12 months, showed no difference. In contrast, the intervention group's self-reported confidence in FOI skills improved more. Although participants rated higher in confidence, practicing FOI skills on an airway simulator with an agility game did not increase their performance in simulated challenging airway instances.

Flexible simulation of traffic with microservices, agents & REST

Hypermedia Multi-Agent System (MAS) Simulation is a novel approach to building agent-based simulations in which the environment is modelled as a set of linked hypermedia resources that are implemented using microservices. This paper discusses the implementation of an prototypical simulation system based on this concept and the lessons learnt in the process.

A Unified Model of Coherent Direction-of-Arrival Simulation for Radar Target Simulators

In order to simulate highly realistic scenarios for modern radar sensors, like traffic scenarios, radar target simulators require the capability to generate targets in all four radar dimensions, which are range, radial velocity, azimuth and elevation angle. To generate targets with angular information, coherent direction-of-arrival (DoA) simulation allows a highly accurate and flexible angular simulation, at a minimum of required hardware. In this article a unifying general model for coherent DoA simulation with channel knowledge for radar target simulators is derived. It comprises previous models. We present methods which allow the simulation of arbitrary and adjustable DoAs, without any constraints on the radar array geometry or the implemented beamformer. The proposed models and methods are analytically derived, analyzed by simulations and finally verified by measurements. Lastly, a measurement of a 2D-DoA simulation for a radar with twelve channels is conducted, in order to show the general applicability of the concept.

SimCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data.

Single-cell chromatin accessibility sequencing (scCAS) technology provides an epigenomic perspective to characterize gene regulatory mechanisms at single-cell resolution. With an increasing number of computational methods proposed for analyzing scCAS data, a powerful simulation framework is desirable for evaluation and validation of these methods. However, existing simulators generate synthetic data by sampling reads from real data or mimicking existing cell states, which is inadequate to provide credible ground-truth labels for method evaluation. We present simCAS, an embedding-based simulator, for generating high-fidelity scCAS data from both cell- and peak-wise embeddings. We demonstrate simCAS outperforms existing simulators in resembling real data and show that simCAS can generate cells of different states with user-defined cell populations and differentiation trajectories. Additionally, simCAS can simulate data from different batches and encode user-specified interactions of chromatin regions in the synthetic data, which provides ground-truth labels more than cell states. We systematically demonstrate that simCAS facilitates the benchmarking of four core tasks in downstream analysis: cell clustering, trajectory inference, data integration, and cis-regulatory interaction inference. We anticipate simCAS will be a reliable and flexible simulator for evaluating the ongoing computational methods applied on scCAS data. simCAS is freely available at https://github.com/Chen-Li-17/simCAS.

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems.

Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques.

Analysis of the Influence of Downhole Drill String Vibration on Wellbore Stability

Most studies related to aspects of wellbore stability, such as wellbore breakage, block dropping, and wellbore expansion, revolve around the physicochemical interaction between drilling fluid and surrounding rock, but relevant studies show that drill string vibration during drilling also has a crucial and even decisive influence on wellbore stability. In order to thoroughly explore the influence mechanism of drill string vibration on wellbore stability, our research group established a finite element flexible simulation model of drill string dynamics and used a storage downhole vibration measurement device to collect downhole real drilling vibration data to verify the correctness of the simulation model. Then, based on the critical conditions of wellbore breakage, a wellbore stability evaluation method was established, and the wellbore stability under different drilling parameters and drilling tool combination conditions was evaluated and analyzed. The research results play an important role in revealing the influence mechanism of drill string vibration on wellbore stability and can provide theoretical guidance for engineering problems such as wellbore instability risk assessment.

Research on H7006C Angular Contact Ball Bearing Slipping Behavior under Operating Conditions

During operation, angular contact ball bearings exhibit slipping behavior, which accelerates bearing wear and reduces the bearing’s overall service life. This paper focuses on studying the degree of slippage and the variation in slippage rate in the H7006C angular contact ball bearing under constant inner ring speed and angular acceleration conditions. To achieve this objective, the study defines the contact between rolling elements, inner and outer rings, and the cage as flexible. A rigid–flexible coupling slippage dynamic simulation model is then developed, which utilizes cage slippage rate as the evaluation index. The analysis method of cage slippage rate change is established through the Gaussian function fitting method. By comparing simulation and testing results, the effectiveness of the analysis method of angular contact ball bearing slip behavior is validated. On this basis, the influence law of bearing speed and acceleration conditions on the slipping behavior of angular contact ball bearings is further studied. The results of the study show that as the axial load increases at different inner ring speeds, the cage speed increases until it reaches a stable state, while the cage slip rate continuously decreases until reaching a stable state. The higher the inner ring speed, the greater the axial load required for the bearing to enter the steady state. Under different angular accelerations, the cage speed increases with time; when the cage speed increases to stable and constant, the cage slip rate decreases to a stable state, and the higher the angular acceleration, the longer it takes for the bearing to enter the stable state.

Complexation of Bromelain, Ficin, and Papain with the Graft Copolymer of Carboxymethyl Cellulose Sodium Salt and N-Vinylimidazole Enhances Enzyme Proteolytic Activity.

This study investigates the features of interactions between cysteine proteases (bromelain, ficin, and papain) and a graft copolymer of carboxymethyl cellulose sodium salt with N-vinylimidazole. The objective is to understand the influence of this interactions on the proteolytic activity and stability of the enzymes. The enzymes were immobilized through complexation with the carrier. The interaction mechanism was examined using Fourier-transform infrared spectroscopy and flexible molecular docking simulations. The findings reveal that the enzymes interact with the functional groups of the carrier via amino acid residues, resulting in the formation of secondary structure elements and enzyme's active sites. These interactions induce modulation of active site of the enzymes, leading to an enhancement in their proteolytic activity. Furthermore, the immobilized enzymes demonstrate superior stability compared to their native counterparts. Notably, during a 21-day incubation period, no protein release from the conjugates was observed. These results suggest that the complexation of the enzymes with the graft copolymer has the potential to improve their performance as biocatalysts, with applications in various fields such as biomedicine, pharmaceutics, and biotechnology.

High-Frequency Oscillation Suppression Strategy for Flexible DC Transmission Systems Based on Additional Joint Damping

Flexible DC transmission systems experience negative damping characteristics in the high-frequency range due to the delay in the system control link. This can lead to interactions between the impedance of modular multilevel converters (MMCs) and AC transmission lines, resulting in high-frequency oscillation (HFO) issues. To resolve this problem, a simplified MMC control model was developed, taking into account the current inner loop control link. The Sobol method was employed to analyze the factors influencing the impedance characteristics of MMC. Additionally, a combined approach involving a virtual passive filtering method and an equivalent current sampling method was proposed to provide additional damping. The study compared and analyzed the impact of additional damping control on the impedance characteristics and system stability of MMCs. Lastly, a flexible direct current grid connection simulation model was constructed using PSCAD to validate the effectiveness of the proposed additional damping control strategy.

Modeling pesticide dissipation half-lives in plants with effects of fertilizer application: A flexible simulation tool

Fertilizer is commonly applied to plants alongside pesticides, which can significantly affect pesticide dissipation behavior in plants. Taking into account the fertilizer effect when modeling pesticide dissipation from plants is crucial for accurately predicting pesticide residue levels in crops, which is essential for ensuring agricultural food safety, conducting consumer exposure assessments, and protecting environmental health. However, mechanistic modeling approaches for estimating dissipation half-lives in plants while taking fertilizer application into account are currently lacking. To address this research gap, we simulate pesticide dissipation half-lives using mechanistic models, and the method can be tabulated in spreadsheets to help users perform modeling exercises by modifying fertilizer application conditions. In addition, a spreadsheet simulation tool with a step-by-step procedure is provided, allowing users to easily estimate pesticide dissipation half-lives in plants. The simulation results for the cucumber plant demonstrated that plant growth dynamics played a significant role in the overall elimination kinetics for the majority of pesticides, indicating that fertilizer application could significantly affect pesticide dissipation half-lives in plants. On the other hand, some moderately or highly lipophilic pesticides may reach their peak concentrations in plant tissues over a longer period of time following pesticide application, depending on their uptake kinetics and dissipation rates on plant surfaces or soil. Therefore, the first-order dissipation kinetic model, which generated pesticide dissipation half-lives in plant tissues, must be fine-tuned with respect to its initial concentrations. With chemical-, plant-, and growth-specific model inputs, the proposed spreadsheet-based operational tool can assist users in estimating pesticide dissipation half-lives in plants with fertilizer application effects. To enhance the effectiveness of our modeling approach, it is recommended that future research investigate rate constants for other types of plant growth dynamics, chemical degradation, horticultural methods, and environmental conditions (such as temperature). These processes can be characterized using first-order kinetic rate constants as model inputs in the operational tool, which can significantly improve the simulation results.

A Flexible Dynamic Reliability Simulation Approach for Predicting the Lifetime Consumption of Extravehicular Spacesuits during Uncertain Extravehicular Activities

The special use environment and uncertainty of extravehicular activities (EVAs) make it difficult to predict the lifetime consumption of extravehicular spacesuits in the traditional way. This paper presents a flexible reliability dynamic simulation model to predict the life loss of extravehicular spacesuits. Based on the images of traditional reliability change curves, new life assessment parameters, based on geometric analysis, are proposed as indicators of spacesuit life loss. Multiple influence factors are used to correct the spacesuit failure rate. The results of the study show that mission intensity is the main factor affecting the health status of the spacesuit, and the higher the mission intensity, the higher the failure rate. Additionally, the more frequently the spacesuit is used, the more times it is available, however, the overall service time will decrease. Concentrating on the mission at an early stage would lead to a significant and irreversible loss of life. Reliability is higher when more intense work is scheduled later in the EVA. Therefore, it is important to rationalize the mission duration, frequency, and work intensity of spacesuits. These reliability models predict the health status of the spacesuit and assist in optimizing the scheduling of EVA.

Integration of ligand and structure-based pharmacophore screening for the identification of novel natural leads against Euchromatic histone lysine methyltransferase 2 (EHMT2/G9a)

Herein, we report a blended ligand and structure-based pharmacophore screening approach to identify new natural leads against the Protein Lysine Methyltransferase 2 (EHMT2/G9a). The EHMT2/G9a has been associated with Cancer, Alzheimer’s, and aging and is considered an emerging drug target having no clinically passed inhibitor. Purposefully, we developed the ligand-based pharmacophore (Pharmacophore-L) based on the common features of known inhibitors and the structure-based pharmacophore (Pharmacophore-S) based on the interaction profile of available crystal structures. The Pharmacophore-L and Pharmacophore-S were subjected to multiple tiers of validations and utilized in combination for the screening of total 741543 compounds coming from multiple databases. Additional layers of stringency were applied in the screening process to test drug-likeness (using Lipinski’s rule, Veber’s rule, SMARTS and ADMET filtration), to rule out any toxicity (TOPKAT analysis). The interaction profiles, stabilities, and comparative analysis against the reference were carried out by flexible docking, MD simulation, and MM-GBSA analysis, which finally led to three leads as potential inhibitors of G9a. Communicated by Ramaswamy H. Sarma

MLPro-MPPS—A high-performance simulation framework for customizable production systems

Although the integration of machine learning into production systems has demonstrated significant potential, its real-life applications remain challenging. It is often necessary to rely on digital representations of the actual systems to design and test machine learning algorithms for controlling the systems because conducting such processes directly in real systems is expensive and risky. Hence, in this paper, we present a standardized, multi-purpose and flexible production systems simulator in Python for scientific and industrial activities, namely MLPro-MPPS, that is integrated with the MLPro package. Consequently, this allows the simulations by MLPro-MPPS to be compatible with machine learning tasks.

Vector–Logic Synthesis of Deductive Matrices for Fault Simulation

The main idea is to create vector-logic computing that uses only read-write transactions on address memory to process large data. The main task is to implement new simple and reliable models and methods of vector computing based on primitive read-write transactions in the technology of vector flexible interpretive simulation of digital system faults. Vector-logic computing is a computational process based on read-write transactions over bits of a binary vector of functionality, where the input data is the addresses of the bits. A vector method for the synthesis of deductive matrices for transporting input fault lists is proposed, which has a quadratic computational complexity. The method is a development of the deductive vector synthesis algorithm based on the truth table. The deductive matrix is intended for the synthesis and verification of tests using parallel simulation of faults, as addresses, based on a read-write transaction of deductive vector cells in memory.

DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation.

Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding the dependence structure over a collection of variables in both inference and simulation settings. However, while modern machine learning is applied to data of an increasingly complex nature, DAG-based simulation frameworks are still confined to settings with relatively simple variable types and functional forms. We here present DagSim, a Python-based framework for DAG-based data simulation without any constraints on variable types or functional relations. A succinct YAML format for defining the simulation model structure promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure simulation code modularization. We illustrate the capabilities of DagSim through use cases where metadata variables control shapes in an image and patterns in bio-sequences. DagSim is available as a Python package at PyPI. Source code and documentation are available at: https://github.com/uio-bmi/dagsim.

An evaluation framework for operational interventions on urban mass public transport during a pandemic

Decision making in a rapidly changing context, such as the development and progression of a pandemic, requires a dynamic assessment of multiple variable and competing factors. Seemingly beneficial courses of action can rapidly fail to deliver a positive outcome as the context changes. In this paper, we present a flexible data-driven agent-based simulation framework that considers multiple outcome criteria to increase opportunities for safe mobility and economic interactions on urban transit networks while reducing the potential for Covid-19 contagion in a dynamic setting. Using a case study of the Victoria line on the London Underground, we model a number of operational interventions with varied demand levels and social distancing constraints including: alterations to train headways, dwell times, signalling schemes, and train paths. Our model demonstrates that substantial performance gains ranging from 12.3–195.7% can be achieved in metro service provision when comparing the best performing operational scheme and headway with those realised on the Victoria line during the pandemic.