Wenfei Wang

Can computer simulators accurately represent the pathophysiology of individual COPD patients

BackgroundComputer simulation models could play a key role in developing novel therapeutic strategies for patients with chronic obstructive pulmonary disease (COPD) if they can be shown to accurately represent the pathophysiological characteristics of individual patients.MethodsWe evaluated the capability of a computational simulator to reproduce the heterogeneous effects of COPD on alveolar mechanics as captured in a number of different patient datasets.ResultsOur results show that accurately representing the pathophysiology of individual COPD patients necessitates the use of simulation models with large numbers (up to 200) of compartments for gas exchange. The tuning of such complex simulation models ‘by hand’ to match patient data is not feasible, and thus we present an automated approach based on the use of global optimization algorithms and high-performance computing. Using this approach, we are able to achieve extremely close matches between the simulator and a range of patient data including PaO2, PaCO2, pulmonary deadspace fraction, pulmonary shunt fraction, and ventilation/perfusion (V̇/Q) curves. Using the simulator, we computed combinations of ventilator settings that optimally manage the trade-off between ensuring adequate gas exchange and minimizing the risk of ventilator-associated lung injury for an individual COPD patient.ConclusionsOur results significantly strengthen the credibility of computer simulation models as research tools for the development of novel management protocols in COPD and other pulmonary disease states.

Veriflcation and Validation of Attitude and Orbit Control Systems for Flexible Satellites

In this paper, an optimisation-based approach is proposed for the veriflcation and validation (V & V) of an attitude and orbit control system (AOCS) for ∞exible satellites. Several optimisation methods, including local gradient-based algorithms, global evolutionary algorithms, and hybrid local/global algorithms are applied to the problem of analysing the robustness of a full-authority multivariable controller with respect to several frequencydomain performance criteria, for a 6 degree of freedom simulation model of a satellite with large sun shields. The results of our study reveal the advantages of optimisation-based worst-case analysis over traditional Monte-Carlo simulations for systems with ∞exible dynamics. In particular, it is shown that hybrid local/global optimisation algorithms can produce more reliable estimates of worst-case performance, while also reducing the associated computational overheads. The proposed approach appears to have signiflcant potential for improving the industrial V & V process for next-generation high-performance satellite control systems.

Development of an integrated model of cardiovascular and pulmonary physiology for the evaluation of mechanical ventilation strategies.

We describe the development of an integrated cardiovascular and pulmonary model for use in the investigation of novel mechanical ventilation strategies in the intensive care unit. The cardiac model includes the cardiac chambers, the pulmonary circulation and the systemic circulation. The modeling of complex mechanisms for vascular segments, time varying elastance functions of cardiovascular components and the effect of vascular resistances, in health and disease under the influence of mechanical ventilation is investigated. The resulting biomedical simulator can aid in understanding the underlying pathophysiology of critically-ill patients and facilitate the development of more effective therapeutic strategies for evaluation in clinical trials.

Worst-case Analysis of Autonomous Rendezvous Systems

Abstract This paper describes the development and application of optimization-based worst-case analysis methods to the problem of verifying correct functionality of autonomous GNC systems in the terminal rendezvous phase. A number of different hybrid global/local optimisation algorithms based on evolutionary (Genetic Algorithms, Differential Evolution) and deterministic (DIviding RECTantles) approaches are employed to analyze the robustness of a GNC system in the presence of realistic levels of parametric uncertainty in the spacecraft simulation model with respect to a number of specified safety criteria. Parametric sensitivity analyses about the worst-case point in the uncertain parameter space provide additional important insights into the robustness of the autonomous GNC system with respect to the defined uncertainties. The study presents a rigorous comparison of the computational overheads associated with the different optimisation-based methods compared with traditional Monte-Carlo simulation approaches. The results of our study indicate potentially significant advantages for the proposed optimization-based approach when compared with traditional Monte Carlo simulations, in terms of both improved reliability and efficiency.

Creating virtual ARDS patients

This paper presents the methodology used in patient-specific calibration of a novel highly integrated model of the cardiovascular and pulmonary pathophysiology associated with Acute Respiratory Distress Syndrome (ARDS). We focus on data from previously published clinical trials on the static and dynamic cardio-pulmonary responses of three ARDS patients to changes in ventilator settings. From this data, the parameters of the integrated model were identified using an optimization-based methodology in multiple stages. Computational simulations confirm that the resulting model outputs accurately reproduce the available clinical data. Our results open up the possibility of creating in silico a biobank of virtual ARDS patients that could be used to evaluate current, and investigate novel, therapeutic strategies.

An Integrated Analytical/Numerical Framework for Verication and Validation of Attitude Control Systems for Flexible Satellites

Modern geostationary telecommunication satellites typically employ large antenna and solar arrays which can generate flexible modes close to the bandwidth of the attitude control system. Combined with uncertainties in mass and inertias and effects such as fuel sloshing in large tanks without membranes, such dynamics represent a particular problem for accurate attitude control. This paper describes a framework for assessing the effects of these uncertain dynamics on the stability and performance of attitude control systems, using a combination of analytical and numerical tools. We develop detailed Linear Fractional Transformation (LFT)-based models of the uncertainties present in a modern telecom satellite and apply -analysis to these models in order to generate robustness guarantees. We validate these models and results by cross-checking them against worst-case analysis results produced by global optimisation algorithms applied to the original system model. The proposed integrated analytical/numerical framework is shown to provide more reliable results, and to be significantly more efficient, than standard Monte-Carlo simulations. I. Introduction A key challenge in the design of modern telecom satellites is the assessment of the effects of flexible modes generated by large appendages on the satellite’s attitude control system. Both the frequency and damping of these flexible modes are very difficult to predict accurately, and are typically only specified to lie within certain minimum and maximum bounds. The problem is compounded by the possibility of interactions with other sources of uncertainty in the system, in particular the effects of fuel sloshing in large tanks that are required in order to reach and maintain geostationary orbits. Attitude control systems for telecom satellites are generally designed using simplified models which do not include all sources of uncertainty and variation present in the system. This necessitates a formal process of verification and validation which assess the stability and performance properties of the controller when implemented on a high-fidelity simulation model. The standard approach to this problem adopted by satellite manufacturers is to combine tests on configurations that (based on engineering judgement) are considered likely to be problematic, with extensive Monte Carlo simulation campaigns in order to accumulate statistical confidence in the robustness properties of the controller. Such campaigns can have significant computational and cost overheads for manufacturers, since very large numbers of simulations are required in order to provide strong statistical guarantees of robustness. 1 In addition, there is now accumulating evidence that such campaigns can fail to reliably assess worst-case behaviour, especially for systems which are subject to flexible dynamics. 2–5

Verification and Validation of Autonomous Rendezvous Systems in the Terminal Phase

This paper describes the development and application of worst-case analysis methods to the problem of verifying correct functionality of autonomous GNC systems in the terminal rendezvous phase. Hybrid global/local optimization algorithms are employed to analyze the robustness of a GNC system in the presence of a large number of parametric uncertainties in the spacecraft simulation model with respect to the defined safety criteria for the capture specification. A framework for integrating information from analytical tools from robust control theory into the optimization-based analysis is proposed and applied to the rendezvous problem. The results from the proposed approaches are compared with those obtained via traditional Monte-Carlo simulation, and indicate potentially significant advantages in terms of both improved reliability and efficiency.

VVAF - Worst case & safety analysis tools for autonomous rendezvous system

The traditional verification and validation (V&V) process can produce sufficiently safe and reliable systems in the demanding aerospace field. But this comes at great cost and effort, with the necessary simulation of all possible operational conditions for increasingly complex control systems involving substantial autonomy. It is of interest to go beyond the statistical confidence of traditional Monte Carlo methods.

Inhaled sGC Modulator Can Lower PH in Patients With COPD Without Deteriorating Oxygenation

This study uses a highly fidelity computational simulator of pulmonary physiology to evaluate the impact of a soluble guanylate cyclase (sGC) modulator on gas exchange in patients with chronic obstructive pulmonary disease (COPD) and pulmonary hypertension (PH) as a complication. Three virtual patients with COPD were configured in the simulator based on clinical data. In agreement with previous clinical studies, modeling systemic application of an sGC modulator results in reduced partial pressure of oxygen (PaO2) and increased partial pressure of carbon dioxide (PaCO2) in arterial blood, if a drug‐induced reduction of pulmonary vascular resistance (PVR) equal to that observed experimentally is assumed. In contrast, for administration via dry powder inhalation (DPI), our simulations suggest that the treatment results in no deterioration in oxygenation. For patients under exercise, DPI administration lowers PH, whereas oxygenation is improved with respect to baseline values.

Verification and Validation Framework for Autonomous Rendezvous Systems in Terminal Phase

T HIS Note reports results from a study carried out for the ESA with the objective of improving the safety of future autonomous rendezvous guidance, navigation, and control (GNC) systems during the terminal rendezvous mission phase, recognized as a key capability for Mars sample return (MSR). The robustness of the critical terminal phase of the mission must be rigorously investigated under different conditions [1,2]. Availability of reliable verification and validation (VV) techniques, which can estimate the worst-case behavior of the system to provide guarantees of correct functionality with a desired safety level under different mission scenarios with a large number of uncertain conditions [3], is key to the success of the mission. In the case of collision avoidance, for example, the distance between chaser and target must be proven to always be greater than a specified minimum value even under worst-case conditions during the approach phase [1,4]. The most widely used VV technique in the space industry is still Monte Carlo (MC) simulation campaigns, which randomly explore the uncertain parameter space using highperformance simulators. Although easy to implement, key drawbacks are computational complexity and lack of guarantee to assess the trueworst-case (rare event) behavior of the system; see the results in [5] for an example of this phenomenon in the context of reusable launch vehicles. The contribution of the Note is an integrated approach combining the analytical μ analysis and the simulation-and-optimization-based method [5], which includes the global optimization algorithms such as Differential Evolution (DE) and Dividing Rectangles (DIRECT), as well as local optimization algorithm Nealder–Mead simplex [6]. The worst-case behavior of an autonomous rendezvous system, based on the industry standard high-integrity autonomous rendezvous and docking (HARVD) andGNC system [7] during the terminal rendezvous phase of a realistic MSR mission scenario, obtained by the proposed approach reveals the significant potential of the methodology when compared with traditional Monte Carlo simulations, in terms of both reliability and efficiency.