Model-based System Research Articles

A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme.

In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration.

Co-evolutionary digital twins: A multidimensional dynamic approach to digital engineering

Digital Engineering (DE) must be able to maximize efficiency and update dynamically in order to keep up with the wave of global interconnection. The evolution of the Digital Twin (DT) plays a significant role in DE as it enables autonomous optimization and an iterative cycle. However, the intricacy of real-world settings renders conventional evolutionary investigations of a solitary DT inadequate in tackling intricate systems at a comprehensive lifecycle magnitude. To address this, this paper presents the Co-evolutionary DTs (CoEDT), which has several key characteristics. (1) CoEDT study the evolutionary behavior of interconnected multi-DT. (2) CoEDT use a co-evolutionary distributed system architecture and is a DT technology that integrate MBSE. (3) CoEDT offers detailed dynamic models for the complex interactions and co-evolution among multi-DT throughout the lifecycle. It also supports real time measurement of multi-DT behavior to detect system anomalies. The significance of CoEDT lies in providing a more comprehensive insight for future product development by constructing a parallel world that simulates the lifecycle. In the CoEDT, we tackle the behavioral identification and structure of these evolving multi-DT throughout product lifecycle through the following approaches. Initially, drawing inspiration from biological cytology, we have conceived a concept of Collective DTs (CollDTs) to observing a collective behavioral of multi-DT. Subsequently, we further developed CoEDT to depict co-evolutionary behavior patterns and established a co-evolution architecture for all DT throughout the lifecycle by fusing Model-Based Systems Engineering (MBSE). Then, dynamic expressions and algorithm that can measure the co-evolution of every DT are inferred using information theory. Finally, the viability of the proposed CoEDT framework is demonstrated through the development of a solid rocket engine, which promotes the application of DT in the DE.

Research and Application of Large Model-Based Intelligent Customer Service System

With the rapid development of artificial intelligence technology, intelligent customer service systems have been widely used. This paper addresses the limitations of traditional intelligent customer service systems, such as limited language understanding ability, narrow knowledge coverage, and insufficient personalized service. It proposes an intelligent customer service system design scheme based on the RAG model. The scheme leverages the powerful language understanding and generation capabilities of large models, combined with dialogue management and knowledge base retrieval enhancement techniques, to build an efficient and intelligent customer service system. This paper introduces the overall architecture of the system, the design and implementation of each module, and comprehensively evaluates the system through experiments. The experimental results show that the system can provide accurate and fluent customer service, significantly improving customer satisfaction. The research in this paper provides new ideas and references for the development of intelligent customer service systems.

Assessment of an auto-routing method for topology generation of aircraft power supply systems

During early phases of aircraft development, overall systems design is performed to estimate relevant system design parameters such as mass, power consumption, and compliance with architectural requirements (redundancy). The GeneSys software framework has been developed for parametric modeling of aircraft on-board systems for overall systems design. To this end, model-based architecture definition, topology generation (components positioning and connections routing), and system sizing are conducted. The topology generation is currently performed using design templates containing knowledge-based heuristics derived from systems topologies of existing aircraft. However, using such design templates limits their generic application to novel aircraft models because exceptions for geometric differences between these models need to be implemented. This especially applies to the topology generation of power supply systems (electric, hydraulic) due to their high number of interfaces to consumer systems. To overcome these limitations, an automated routing method is presented in this paper as a generic approach for topology generation. With this method, the system topology is generated based on a defined routing network, which comprises areas of the aircraft for allowed components positioning and connections routing. Using Dijkstra’s algorithm, the shortest path connections are found within the established routing network. Furthermore, boundary conditions for routing, for instance adding a cost function for power segregation, can be defined. An assessment of the automated routing method applied on the electric power supply system is performed using qualitative parameters such as the capability to adapt the routing network. Quantitative parameters like cable length and system mass are assessed as well.

Virtual design on the heat pump refrigeration cycle: challenges and approaches

Virtual product design (VPD) of the refrigeration cycle has long been a major subject of many engineers and manufacturers. Issues should be the question of how simulation gets close to the real world in virtual ways. It is especially true when we consider variable refrigerant flow (VRF) heat pumps, having various types of indoor unit combinations, long and complicated piping configurations, and simultaneous cooling and heating functions under extreme weather conditions. In this regard, model-based systems engineering (MBSE) or model-based design (MBD) guides a methodology to make a virtual model easily. Now, there are plenty of engineering platforms and tools to make a strong simulation model, which provides interfacing technology among multiple physics, different dimensions, and codes in different languages. In the study, the authors share their experience in the virtual design for a VRF heat pump system including the architecture, multiple physics, and the connection with the control SW. Modelica, an acausal and object-oriented modeling language, constructs the backbone of the model to describe the dynamic behavior of refrigerants and to combine external components in the form of functional mock-up units (FMUs). The developed virtual model is validated against measured data, showing it can reproduce all the essential physics of interest both in qualitative and quantitative ways.

Model-based closed-loop control of thalamic deep brain stimulation.

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study.

This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of lateral cervical lymph nodes (LNs) on original ultrasound images of papillary thyroid carcinoma (PTC) patients. A retrospective data set of 1801 cervical LN ultrasound images from 1675 patients with PTC and a prospective test set including 185 images from 160 patients were collected. Four different deep leaning models were trained and validated in the retrospective data set. The best model was selected for CAD system development and compared with three sonographers in the retrospective and prospective test sets. The Deformable Detection Transformer (DETR) model showed the highest diagnostic efficacy, with a mean average precision score of 86.3% in the retrospective test set, and was therefore used in constructing the CAD system. The detection performance of the CAD system was superior to the junior sonographer and intermediate sonographer with accuracies of 86.3% and 92.4% in the retrospective and prospective test sets, respectively. The classification performance of the CAD system was better than all sonographers with the areas under the curve (AUCs) of 94.4% and 95.2% in the retrospective and prospective test sets, respectively. This study developed a Deformable DETR model-based CAD system for automatically detecting and classifying lateral cervical LNs on original ultrasound images, which showed excellent diagnostic efficacy and clinical utility. It can be an important tool for assisting sonographers in the diagnosis process.

Time Transfer Link fusion algorithm based on wavelet multi-resolution analysis

To raise the precision and stability of time transfer links, a wavelet-based multi-resolution data fusion technique is offered. This approach fixes the “Day-Boundary discontinuities” in global positioning system (GPS) precise point positioning (PPP) link and lessens the diurnal in Two-way satellite time and frequency transfer (TWSTFT) link by integrating the model-based dynamic system analysis method with the statistically based multi-scale signal transformation method. It can also handle data from GPS PPP links and TWSTFT links at different sampling rates. More specifically, after applying wavelet transformation to the raw data and Kalman filtering estimation on various scales, this method uses the Mallat fast reconstruction algorithm of wavelet transform to achieve the fused results. The National Time Service Center (NTSC) and the Physikalisch-Technische Bundesanstalt (PTB) handle time transfer results using this method. Experiments show that this strategy greatly improves the link's short-term stability and can manage atypical interruptions while also boosting the link's robustness and reliability, which has a wide range of possible applications.

Axial Rotation Analysis in Total Ankle Arthroplasty Using Weight-Bearing Computer Tomography and Three-Dimensional Modeling

Introduction/Purpose: Post-operative alignment is the most critical indicator for a successful total ankle arthroplasty (TAA). Total ankle malrotation is associated with an increased risk for polyethylene wear and medial gutter pain. Currently, there is no consensus on the correct way to measure the alignment of the tibial and talar component rotations in the axial plane. In the current study, the post-operative analysis system was assessed using weight-bearing computer tomography and a three- dimensional (3D) model. The purpose of the study was to assess the inter-observer and intra-observer agreement of this system. Methods: Four angles were measured by two raters independently in two separate readings: posterior tibial component rotation angle (PTIRA), posterior talar component rotation angle (PTARA), tibia talar component axial angle (TTAM), and tibial component to the second metatarsal angle (TMRA). Agreement analysis was quantified according to the interclass coefficient. Results: Sixty TAAs across 60 patients were evaluated. A good inter-observer agreement and intra-observer agreement when measuring the PTIRA, PTARA, and TTAM angles was observed along with an excellent inter-observer agreement and intra- observer agreement when measuring the TMRA angle. Conclusion: In conclusion, the current 3D model-based measurement system demonstrates good to excellent inter and intra- agreement. According to these results, 3D modelling can be reliably used to measure and assess the axial rotation of TAA components. Figure 3: Example case of how to measure the angles for assessing axial rotation of TAA and forefoot. 3 A) Posterior TIbial component Rotational Angle (PTIRA). The chosen slice is the one with the straightest posterior wall component of the tibia. The angle is measured on the following steps. First a reference is put on the posterior wall of the tibial component (blue horizontal line). Then the angle is measured starting from the center of the tibial axis (red dot explained on fig. 2), going to the fibular axis (medial part of the red line at the right of the draw), and finally ending 90° to the posterior reference line. 3 B) Posterior TAlar component Rotational Angle (PTARA). The chosen slice is the one with the straightest posterior wall component of the talus. For the angle measurement. The same steps as figure 3 A are followed with the talar component. 3 C) Talar component to 2nd Metatarsal Rotational Angle (TMRA): Defined as an intersection between a line parallel to the lateral border of the tibia component and a line perpendicular to the anatomical axis of the second metatarsal. This was measured using a 3D rendering tool

An overall evaluation toolchain for assessing helicopter ship deck landings

AbstractA compact toolchain is implemented for the control system design for helicopter ship deck landings in a simulated environment which provides a cost-efficient and safe environment to test different scenarios. The evaluation of the ship deck landing manoeuvres is mostly performed for Ship Helicopter Operation Limits (SHOL) evaluation and performance of the helicopter. In most of the assessments, a subjective assessment is performed using different pilot rating scales which is not sufficient and the objective assessment of the simulation data for the mission success itself is seldom performed. The developed toolchain evaluates the successful operation of ship-based rotorcraft by considering both ship and helicopter dynamics, environmental conditions, and the pilot’s responses. The toolchain evaluates various touchdown conditions like helicopter position, velocity and attitude relative to the ship deck. It also displays additional evaluation parameters for the entire approach. A preliminary pilot study was performed in a simulator within a complete maritime scenario design. The maritime simulation environment includes a nonlinear helicopter flight model with a model-based flight control system, a ship dynamic model with the simulated turbulent air wake and realistic wave and water effects. For a subjective assessment of the ship deck landings, subjective pilot ratings using different rating scales were recorded during the pilot study. Simulation results show that the toolchain is convenient and well suited for assessing helicopter ship deck landing performance in a simulation environment as it provides a complete objective and subjective evaluation.

Investigation into optimal feedforward control for remote handling of tokamak breeding blankets

Remote handling of breeding blankets poses an unprecedented challenge in future tokamaks like EU-DEMO, where individual blanket segments can weigh upwards of 80 tonnes and extend beyond 10 m in length. The unparalleled scale of these components, coupled with extremely tight positional tolerances, demands careful consideration of structural flexibility during manoeuvres. This underscores the need for model-based control systems capable of mitigating oscillations and static deflections induced by gravity and disturbances.In this paper, we present recent experiments towards this objective, focussed on mitigating the effects of gravity. We applied model-based, optimal feedforward control to the motion of a planar slender payload, chosen to represent the flexible part of a candidate breeding blanket segment. We combined a predictive path planning algorithm with input shaping techniques to manoeuvre the payload effectively accounting for the influence of gravity, while also reducing post-manoeuvre oscillations. Our results highlight the advantages of model-based control and the limitations associated with the use of feedforward control.

Neural correlates of model-based behavior ininternet gaming disorder and alcohol usedisorder.

An imbalance between model-based and model-free decision-making systems is a commonfeature in addictive disorders. However, little is known about whether similar decision-making deficits appear in internet gaming disorder (IGD). This study compared neurocognitive featuresassociated with model-based and model-free systems in IGD and alcohol use disorder (AUD). Participants diagnosed with IGD (n = 22) and AUD (n = 22), and healthy controls (n = 30) performed the two-stage task inside the functional magnetic resonance imaging (fMRI) scanner. We used computational modeling and hierarchical Bayesian analysis to provide a mechanistic account of their choice behavior. Then, we performed a model-based fMRI analysis and functional connectivity analysis to identify neural correlates of the decision-making processes in each group. The computational modeling results showed similar levels of model-based behavior in the IGD and AUD groups. However, we observed distinct neural correlates of the model-based reward prediction error (RPE) between the two groups. The IGD group exhibited insula-specific activation associated with model-based RPE, while the AUD group showed prefrontal activation, particularly in the orbitofrontal cortex and superior frontal gyrus. Furthermore, individuals with IGD demonstrated hyper-connectivity between the insula and brain regions in the salience network in the context of model-based RPE. The findings suggest potential differences in the neurobiological mechanisms underlying model-based behavior in IGD and AUD, albeit shared cognitive features observed in computational modeling analysis. As the first neuroimaging study to compare IGD and AUD in terms of the model-based system, this study provides novel insights into distinct decision-making processes in IGD.

ACCESS: Assurance Case Centric Engineering of Safety–critical Systems

Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. Historically, assurance cases have been manually created documents, which are evaluated by system stakeholders through lengthy and complicated processes. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. This becomes increasingly important, as systems becomes more complex, it is a challenge to manage their development life-cycles, including coordination of development, verification and validation activities, and change impact analysis in inter-connected system assurance artifacts. Moreover, there is a need for assurance cases that support evolution during the operational life of the system, to enable continuous assurance in the face of an uncertain environment, as Robotics and Autonomous Systems (RAS) are adopted into society. In this paper, we contribute ACCESS — Assurance Case Centric Engineering of Safety–critical Systems, an engineering methodology, together with its tool support, for the development of safety–critical systems around evolving model-based assurance cases. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts (e.g. system architectural models, system safety analysis, system behaviour models, etc.), and how formal methods can be integrated during the development process. We demonstrate how assurance cases can be automatically evaluated both at development and runtime. We apply our approach to a case study based on an Autonomous Underwater Vehicle (AUV).

Online neural sliding mode control with guaranteed stability

Model-based control systems allow engineers to leverage known system information in controllers, often resulting in improved control accuracy or robustness. However, these system models can change over time, be difficult to obtain for complex systems, or fail to capture higher-order effects in the system. In this work, we use a highly robust model-based control methodology, sliding mode control, without the need for an a priori model. We use a neural network to learn an approximate system model fully online, with no pretraining. The neural network term is found to increase control accuracy while alleviating chatter, the primary drawback of conventional sliding mode control. The controller is verified by simulating a non-planar 3 degree-of-freedom robotic arm picking up a load, and the proposed controller is shown to outperform conventional model-based sliding mode control, especially when the built-in load margin on the end effector in the conventional model-based controller is exceeded.

Research on design requirements for passive residual heat removal system of lead cooled fast reactor via model-based system engineering

Traditional text-based system engineering, which has been used in the design and application of passive residual heat removal system (PRHRS) for lead-cooled fast reactors, is prone to several problems such as low development efficiency, long iteration cycles, and model ambiguity. This study aims to effectively address the above-mentioned problems by adopting a model-based system engineering (MBSE) method, which has been preliminarily applied to meet the design requirements of a PRHRS. The design process has been implemented based on the preliminary design of the system architecture and consists of three stages: top-level requirement analysis, functional requirements analysis, and design requirements synthesis. The results of the top-level requirements analysis and the corresponding use case diagram can determine the requirements, top-level use cases, and scenario flow of the system. During the functional requirements analysis, the sequence, activity, and state machine diagrams are used to develop the system function model and provide early confirmation. By comparing these sequence diagrams, the requirements for omissions and inconsistencies can be effectively checked. In the design requirements synthesis stage, the Analytic Hierarchy Process is used to conduct preliminary trade-off calculations on the system architecture, after which a white box model is established during the system architecture design. Through these two steps, the analysis and design of the system architecture are ultimately achieved. The resulting system architecture ensures the consistency of the design requirements. Ultimately, a functional hazard analysis was conducted for a specific incident to validate case requirements and further refine the system architecture. Future research can further reduce the design risk, improve the design efficiency, and provide a practical reference for the design and optimization of PRHRS in digital lead-cooled fast reactors.

Model predictive control of nonlinear processes using transfer learning-based recurrent neural networks

Artificial neural networks (ANNs), one of the deep learning techniques that has sparked a lot of attention recently for its exceptional modeling capabilities of nonlinear systems, are an essential candidate for model-based control systems. In particular, recurrent neural networks (RNN) have shown remarkable capacity to forecast the dynamic behavior of nonlinear processes using time-series data. In order to regulate the rate at which chemical species are produced in intricate processes, RNNs have successfully served as the predictive model in model-based controllers. However, the necessity for massive amounts of data to capture complex processes is a drawback of typical RNN models. With the goal of efficient utilization of the available data, a different, transfer learning-based training approach for RNNs is presented in this work. The transfer learning strategy is taken into consideration to overcome the challenges stemming from lack of data for the construction of RNN models. In particular, weight-sharing RNNs are developed using a priori physical knowledge. Next, a comprehensive analysis of a complex chemical process on a large scale is conducted to showcase the benefits of the suggested approach across various data sizes and its effectiveness when combined with MPC in contrast to conventional RNNs.

Effective Model-Based Systems Engineering framework for academic nanosatellite project management and design

Effective Model-Based Systems Engineering framework for academic nanosatellite project management and design

Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine. The latest research demonstrates that Subspace-based Improved Model Predictive Control (SIMPC) can overcome the difficulty in solving the predictive model in MPC/NMPC applications. However, applying constant design parameters cannot maintain consistent control effects in all states. Meanwhile, the designed system relies too much on sensor-measured data, and thus it is difficult to thoroughly validate the safety of the system because of its high complexity. This means that any potential hardware/software faults will endanger the engine. Therefore, this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle (PLA) and real-time relative tracking error. Thus, without introducing additional design parameters, an Adaptive Tracking Weight-based SIMPC (ATW-SIMPC) controller is designed to improve the control performance in all operating states effectively. Then, a Primary/Backup Hybrid Control (PBHC) strategy with the ATW-SIMPC controller as the primary system and the traditional speed (Nf) controller as the backup system is proposed to ensure safety. The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems, so as to avoid bump transition. The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults; otherwise, the ATW-SIMPC controller keeps acting on the engine. The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship, compared with the existing SIMPC controller, uplifts the dynamic control performance by 32% and reduces overshoots to an allowable limit, resulting in a better control effect in full state. The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults, such as sensor/onboard adaptive model faults. The approach proposed is applicable to the design of a model-based engine intelligent control system.

Early Validation and Verification of System Behaviour in Model-based Systems Engineering: A Systematic Literature Review

In the Systems Engineering (SE) domain there has been a paradigm shift from document-based to model-based system development artefacts; in fact, new methodologies are emerging to meet the increasing complexity of current systems and the corresponding growing need of digital workflows. In this regard, Model-Based Systems Engineering (MBSE) is considered as a key enabler by many central players of the SE community. MBSE has reached an adequate level of maturity, and there exist documented success stories in its adoption in industry. In particular, one significant benefit of utilising MBSE when compared to the traditional manual and document-centric workflows is that models are available from early phases of systems development; these enable a multitude of analyses prior any implementation effort together with other relevant capabilities, like the automation of development tasks. Nonetheless, it is noticeable there is a lack of a common understanding for how formal analyses for the verification and validation (V&V) of systems behaviour, specifically in the early phases of development, could be placed in an MBSE setting. In this article, we report on the planning, execution, and results of a systematic literature review regarding the early V&V of systems behaviour in the context of model-based systems engineering. The review aims to provide a structured representation of the state of the art with respect to motivations, proposed solutions, and limitations. From an initial set of potentially relevant 701 peer-reviewed publications we selected 149 primary studies, which we analysed according to a rigorous data extraction, analysis, and synthesis process. Based on our results, early V&V has usually the goal of checking the quality of a system design to avoid discovering flaws when parts are being concretely realised; SysML is a de facto standard for describing the system under study, while the solutions for the analyses tend to be varied; also V&V analyses tend to target varied properties with a slight predominance of functional concerns, and following the variation mentioned so far the proposed solutions are largely context specific; the proposed approaches are usually presented without explicit limitations, while when limitations are discussed, readiness of the solutions, handling of analyses simplifications/assumptions, and languages/tools integration are among the most frequently mentioned issues. Based on the survey results and the standard SE practices, we discuss how the current state-of-the-art MBSE supports early V&V of systems behaviour with a special focus on industrial adoption and identify relevant challenges to be researched further.

Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex Systems

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is, of course, exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology (FRAM) which did allow the analyst insights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative approach. In the last 10 years, this original approach, developed to model systems as sets of interactive, interdependent “functions” (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component-focussed system engineering “models”, and can pull them all together into dynamic models (as opposed to the static, fixed System Theoretic Process Accimaps) from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.