Orthogonal-Array based Design Methodology for Complex, Coupled Space Systems

The process of designing a complex system, formed by many elements and sub-elements interacting between each other, is usually completed at a system level and in the preliminary phases in two major steps: design-space exploration and optimization. In a classical approach, especially in a company environment, the two steps are usually performed together, by experts of the field inferring on major phenomena, making assumptions and doing some trial-and-error runs on the available mathematical models. To support designers and decision makers during the design phases of this kind of complex systems, and to enable early discovery of emergent behaviours arising from interactions between the various elements being designed, the authors implemented a parametric methodology for the design-space exploration and optimization. The parametric technique is based on the utilization of a particular type of matrix design of experiments, the orthogonal arrays. Through successive design iterations with orthogonal arrays, the optimal solution is reached with a reduced effort if compared to more computationally-intense techniques, providing sensitivity and robustness information. The paper describes the design methodology in detail providing an application example that is the design of a human mission to support a lunar base

MHACO : a multi-objective hypervolume-based ant colony optimizer for apace trajectory optimization

In this paper, we combine the concepts of hypervolume, ant colony optimization and nondominated sorting to develop a novel multi-objective ant colony optimizer for global space trajectory optimization. In particular, this algorithm is first tested on three space trajectory bi-objective test problems: an Earth-Mars transfer, an Earth-Venus transfer and a bi-objective version of the Jupiter Icy Moons Explorer mission (the first large-class mission of the European Space Agency’s Cosmic Vision 2015-2025 programme). Finally, the algorithm is applied to a four-objectives low-thrust problem that describes the journey of a solar sail towards a polar orbit around the Sun. The results on both the test cases and the more complex problem are reported by comparing the novel algorithm performances with those of two popular multi-objective optimizers (i.e., a nondominated sorting genetic algorithm and a multi-objective evolutionary algorithm with decomposition) in terms of hypervolume metric. The numerical results of this study show that the multi-objective hypervolume-based ant colony optimization algorithm is not only competitive with the standard multi-objective algorithms when applied to the space trajectory test cases, but it can also provide better Pareto fronts in terms of hypervolume values when applied to the complex solar sailing mission

STA, the Space Trajectory Analysis Project

This article describes the objectives of the Space Trajectory Analysis (STA) project. The article also details the birth of STA, and its present configuration. STA is a project to develop an open source astrodynamics software suite involving university science departments and space research institutions. It was initiated by ESA as internal activity in 2005 and now it involves 16 partners. The article explains the partnership into the STA Steering Board. The main purpose of the STA is to allow advanced simulation for the analysis of space trajectories in an open and free environment under the premises of innovation and reliability.Further, the article explains that the STA development is open source and is based on the state of the art astrodynamics routines that are grouped into modules. Finally, the article concludes about the benefits of the STA initiative: the STA project allows a strong link among applied mathematics, space engineering, and informatics disciplines by reinforcing the academic community with requirements and needs coming from real missions

Virology Experts in the Boundary Zone Between Science, Policy and the Public: A Biographical Analysis

This article aims to open up the biographical black box of three experts working in the boundary zone between science, policy and public debate. A biographical-narrative approach is used to analyse the roles played by the virologists Albert Osterhaus, Roel Coutinho and Jaap Goudsmit in policy and public debate. These figures were among the few leading virologists visibly active in the Netherlands during the revival of infectious diseases in the 1980s. Osterhaus and Coutinho in particular are still the key figures today, as demonstrated during the outbreak of novel influenza A (H1N1). This article studies the various political and communicative challenges and dilemmas encountered by these three virologists, and discusses the way in which, strategically or not, they handled those challenges and dilemmas during the various stages of the field’s recent history. Important in this respect is their pursuit of a public role that is both effective and credible. We will conclude with a reflection on the H1N1 pandemic, and the historical and biographical ties between emerging governance arrangements and the experts involved in the development of such arrangements

Poxvirus MVA Expressing SARS-CoV-2 S Protein Induces Robust Immunity and Protects Rhesus Macaques From SARS-CoV-2

Novel safe, immunogenic, and effective vaccines are needed to control the COVID-19 pandemic, caused by SARS-CoV-2. Here, we describe the safety, robust immunogenicity, and potent efficacy elicited in rhesus macaques by a modified vaccinia virus Ankara (MVA) vector expressing a full-length SARS-CoV-2 spike (S) protein (MVA-S). MVA-S vaccination was well tolerated and induced S and receptor-binding domain (RBD)-binding IgG antibodies and neutralizing antibodies against SARS-CoV-2 and several variants of concern. S-specific IFNγ, but not IL-4, -producing cells were also elicited. After SARS-CoV-2 challenge, vaccinated animals showed a significant strong reduction of virus loads in bronchoalveolar lavages (BAL) and decreased levels in throat and nasal mucosa. Remarkably, MVA-S also protected macaques from fever and infection-induced cytokine storm. Computed tomography and histological examination of the lungs showed reduced lung pathology in MVA-S-vaccinated animals. These findings favor the use of MVA-S as a potential vaccine for SARS-CoV-2 in clinical trials.This research was supported by Fondo COVID-19 grant COV20/00151 (Spanish Health Ministry, Instituto de Salud Carlos III (ISCIII)), Fondo Supera COVID-19 grant (Crue Universidades-Banco Santander), and Spanish Research Council (CSIC) grant 202120E079 (to JG-A); CSIC grant 2020E84, la Caixa Banking Foundation grant CF01-00008, Ferrovial, and MAPFRE donations (to ME); a Spanish Ministry of Science and Innovation (MCIN)/Spanish Research Agency (AEI)/10.13039/501100011033 grant (PID2020-114481RB-I00; to JG-A and ME); and internal funding from the BPRC. This research work was also funded by the European Commission-NextGenerationEU, through CSIC’s Global Health Platform (PTI Salud Global) (to JG-A and ME). RD received grants from the European Commission Horizon 2020 Framework Programme (Project VIRUSCAN FETPROACT-2016: 731868 and Project EPIC-CROWN-2: 101046084), and Fundación Caixa-Health Research HR18-00469 (Project StopEbola).Peer reviewe

GUIDANCE FOR AUTONOMOUS RENDEZVOUS AND DOCKING WITH ENVISAT USING HARDWARE-IN-THE-LOOP SIMULATIONS

In this paper a convex guidance and control algorithm is developed to enable Rendezvous and docking with the non-operational, rotating satellite Envisat. The algorithm employs the concept of model predictive control to allow for an unconstrained time until the docking has to be achieved, while still maintaining a guidance algorithm that is computationally efficient enough to be applied in realtime. Tests are performed to evaluate the performance of the MPC, with and without feedback control. These test cases are evaluated using a functional simulator. This is complemented by hardware-in-the-loop tests using the flat-floor test facility, TEAMS. Both the functional and the real-time simulations results show that the developed algorithms enable a performance with sufficient accuracy to complete the operations. It is also demonstrated that a higher accuracy and propellantefficiency is obtained when model predictive control, without feedback control, is employed

Real Time Adaptive Feedforward Guidance for Entry Vehicles

One of the most powerful analysis tools to deal with entry guidance problems is the possibility to formulate them as optimal control problems (OCPs). Environmental constraints, actuator limits, and strict requirements on the final conditions can be efficiently transcribed, resulting in a discrete, finite-dimension non-linear programmming (NLP) problem. However, NLP problems require a computational power, which often exceeds the vehicle’s onboard capabilities. Moreover, it is important to ensure that the nominal optimal solution can be adapted to the actual flight conditions, which can significantly differ from the nominal scenario. This paper proposes an approach based on multivariate interpolation to generate entry guidance solutions. The real-time capability is ensured in virtue of the lower CPU efforts required to execute the interpolation operation. The approach is here proposed for initial-conditions variations, but can in principle be applied to every mission parameter, which allows to find a corresponding optimal solution. Results have been generated for SHEFEX-3, an entry demonstrator vehicle scheduled to be launched in 2016