System architecting is a crucial early stage of the engineering design process: it establishes the high-level layout of a system. However, traditional architecting approaches often only consider a limited set of alternatives. This paper investigates strategies to integrate architectural decisions more tightly with downstream design optimization, enabling a thorough exploration of the hierarchical design space. Three optimization strategies are evaluated: global exploration, nested optimization, and decision chain optimization. These are tested on problems of varying complexity, from airfoil shape optimization to multi-objective benchmark problems to an industry aileron design study. The global exploration strategy optimizes all variables in a single loop and generally demonstrates the best performance. However, the nested strategy, which partitions variables into separate optimization loops, can be advantageous when a complete problem formulation is unavailable. Although the decision chain strategy using reinforcement learning provides interesting perspectives, it exhibits high complexity in implementation without significant performance gains. The main recommendation is to employ the global exploration strategy when possible, with nested optimization as an alternative if the complete design space is unknown. This work provides insights into optimization strategies connecting system architecting and concept development in engineering design.@en

During the early design stages of airframe components, many possible design architectures and production methods need to be traded to find the best configuration. Evaluating different production methods can be challenging as different production methods put different requirements on the product to be designed. This paper presents a new methodology that enables the inclusion of manufacturing and assembly in the design process. By extending the architectural design space model with components of the production system, the design choices regarding production are made explicit. Through the modeling of product and production requirements and assigning them a verification method, a dynamic MDAO workflow is formulated. Within a dynamic workflow, the design variables, analysis tools, and constraints change depending on the current design vector. The methodology has been applied to the design and manufacturing of a wing rib in which two manufacturing options were traded: metal machining and composite stamp forming. The dynamic MDAO workflow successfully found the Pareto front for both manufacturing methods. The main benefit is that only one workflow needed to be formulated and executed, whereas previously a separate MDAO workflow needed to be created for each combination of product design and production method. Overall, the newly presented methodology enables the optimization and trade-off between different production methods while ensuring the design complies with the production-specific requirements.@en

Including production considerations in the early design stages of aircraft structures is challenging. Production information is mostly known by experts and rarely formally documented such that it can be effectively used during the design process. Producibility is mostly considered after completing the design, resulting in increased cost and development time due to the late discovery of production issues. This paper presents a new model, called the Manufacturing Information Model (MIM), which supports the automatic inclusion of production considerations into the design process. The MIM provides a single source of truth and a generic structure to capture and organize production-related information in a product system. Furthermore, it provides compatibility analyses to automatically warn for or exclude infeasible designs. Analysis tools use the information stored within the MIM to calculate the mass, costs, and production rate of the product. To show the functionalities of the MIM, it has been applied to the conceptual design of a wing box at a Tier 1 company. This use case shows how the MIM supports trade-off decisions, as it allows for the identification of trends and the ranking of different manufacturing concepts. Overall, the MIM provides a structured and formal approach to include production information in the conceptual design, improving the decision-making process.@en

This paper compares optimum control surface layouts designed and sized to obtain the same Flying Qualities (FQs) performance with different Control Allocation (CA) methods, and proposes novel layouts for staggered box-wing aircraft aimed at transonic commercial flight. Box-wings allow the installation of redundant control surfaces for which no explicit role can be defined a priori, but present challenges related to aerodynamic interaction and interference effects. To evaluate the impact of different CA methods on top-level layout parameters, the cumulative control surface span and the properties of the Attainable Moment Set (AMS) corresponding to each control surface layout are used. A physics-based multi-disciplinary optimization framework is developed to size the control surface layout. FQs are evaluated through non-linear flight dynamics simulation, using a variable-architecture flight control system that allows their assessment as a function of different CA methods. The most traditional Mechanical Gearing and Ganging (MGG) approach, the Constrained Pseudo-Inverse (CPI) method and the Direct Control Allocation (DCA) method are compared. Results show that different optimum layouts exist with comparable cumulative span, for a given CA method and same FQs requirements. The traditional MGG approach requires the largest cumulative control surface span, but retains the best ability to generate coupled roll-pitch moments. DCA requires the smallest cumulative control surface span, with the largest AMS volume. By using this method, a novel layout featuring a mid-wing rear elevon has been discovered, which reduces the total required control surface span by about 13%, results in a 3.7% increase of span available for flaps on the front wing, and avoids detrimental aerodynamic interaction effects near the wing-tail intersection region.@en

During the aircraft conceptual design phase, many different design options need to be explored and compared in a short time frame. To speed up this process, efforts have been made in the past decades to digitalize parts of the design process, with a focus on the automation of the repetitive and non-creative tasks inherent to the iterative design process. Whilst many of the newly developed methodologies focus on specific parts of the design process, a holistic model-based design framework, incorporating the latest design technology developments, is lacking. To fill this gap, this paper presents the latest version of the Design and Engineering Engine (DEE) framework, originally proposed in the early 2000s and progressively matured through the experience of several international research collaborations. The DEE enables the setup and execution of Multidisciplinary Design Analysis and Optimization (MDAO) problems for aircraft (sub)systems, leveraging the automated, rule-based modeling capabilities offered by Knowledge-Based Engineering (KBE) and recent developments in the automatic formulation and integration of MDAO workflows. While the traditional MDAO process focuses on a given product architecture, the DEE allows also architectural design studies and makes use of Model-Based Systems Engineering (MBSE) principles to address the whole design process, from requirements modeling up to the automatic verification of the requirements. In practice, the DEE provides a single conceptual framework or template from which specific design framework instances can be formulated and executed, according to the user's needs. This paper describes the DEE architecture and its implementation concepts. Furthermore, it demonstrates the application of the DEE template to four different scenarios, ranging from a simple requirement verification study, up to the simultaneous synthesis and optimization of an aircraft system and its production process, including multiple system architecture options.@en

This article proposes a novel approach to support Knowledge Based Engineering (KBE) application development based on Model-Based Systems Engineering (MBSE). In this methodology, the related knowledge is captured in a well-structured Systems Modeling Language (SysML) model, instead of (static) documents. The knowledge model is then automatically translated to application (skeleton) code using a model-to-code tool developed in this research. The proposed methodology is applied to a use case at GKN Fokker Elmo for the development of a KBE application to design Electrical Wiring Interconnection Systems (EWIS) architectures for aircraft. The results show that the proposed MBSE approach improves the knowledge acquisition process, reduces the time needed for developing new KBE applications (initial knowledge model and code skeleton) by almost 50%, and enables traceability of requirements within the KBE application and knowledge model. These benefits allow effective project-to-project knowledge transfer while mitigating the black-box effect often experienced by KBE application users. In the next phase of this research, reverse engineering capabilities will also be incorporated to enable code-to-model translation, so as to guarantee the application code and knowledge model synchronization throughout the application's lifetime. @en

This paper discusses the approach for architecture design space optimization of aeronautical systems investigated in the DEFAINE project. In system architecture optimization problems, hierarchical relations between design variables may exist. This means that the quantity and type of some variables are dependent on the value of other variables. To address this challenge, a nested optimization strategy is proposed, where an outer loop deals with the independent design variables and an inner loop with the dependent ones. As the characteristics of the dependent variables may become known only during workflow execution, a dynamic workflow formulation approach is developed to introduce these variables into the MDAO workflow, automatically, during execution. The proposed methodology is implemented using a suite of technologies provided by partners of the DEFAINE consortium to enable formulation and execution of collaborative MDAO problems. This implementation is then applied to the design and optimization of the structural layout of a UAV aileron. The goal is to minimize the aileron mass, subjected to failure criteria constraints. In this use case, hierarchical relations between variables are present. The aileron skin is discretised into material zones, each one defined by a (dependent) design variable. The number of zones, hence the number of design variables, is not known a priori as it depends on the number and position of ribs and spars(both independent variables). A preliminary implementation of the proposed approach proved effective in dealing with the hierarchical mixed-integer design space. Trade off studies comparing different aileron architectures could be efficiently set up and executed. Some limitations in the implemented workflow management technology, however prevent performing a full nested architecture optimization. Therefore, to demonstrate the effectiveness of the proposed nested approach, a parallel study was conducted on the same aileron, using a less generic and manual implementation of the nested MDAO workflow. This second study successfully minimized the mass and cost of the aileron structure, thereby proving the merit of the proposed nested approach to address system architecture optimization. @en

According to a study performed by the Project Management Institute, around 47% of unsuccessful projects do not meet their goals and objectives due to poor requirements management. Taking requirements into account during the aircraft design process and ensuring requirement compliance during all design phases is important to obtain good and feasible aircraft designs. However, a typical aircraft design process is very complex and many requirements need to be taken into account. This paper proposes a new framework that implements requirements in the design process by establishing a direct link between Model-Based Systems Engineering and Multidisciplinary Design Analysis and Optimization (MDAO). Model-based requirements are directly implemented in the optimization problem and based on the requirement verification methods the MDAO workflows are formulated. When requirements or verification methods change, the workflow is automatically updated accordingly. This way, requirement compliance can either be automatically enforced or checked based on the optimization or analysis results. Automatically generated requirement reports provide information on the requirement compliance results. The framework has been implemented in a software prototype, which was applied to the design of a wing box, showing the functionalities of the framework. With the framework, the traceability from requirements to product design is improved, as all stakeholders can see how the design process was formulated and how requirement compliance has been achieved. Furthermore, optimized designs can be obtained that satisfy all the stakeholders' needs.@en

Growing interest in unconventional aircraft designs coupled with miniaturization of electronics and advancements in manufacturing techniques have revived the interest in the use of Sub-scale Flight Testing (SFT) to study the flight behaviour of full-scale aircraft in the early stages of design process by means of free-flying sub-scale models. SFT is particularly useful in the study of unconventional aircraft configurations as their behaviour cannot be reliably predicted based on legacy aircraft designs. In this paper, we survey the evolution of various design approaches (from 1848 to 2021) used to ensure similitude between a sub-scale model and its full-scale counterpart, which is an essential requirement to effectively perform SFT. Next, we present an exhaustive list of existing sub-scale models used in SFT and analyse the key trends in their design approaches, test-objectives, and applications. From this review, we conclude that the state-of-the-art sub-scale model design methods available in literature have not been used extensively in practice. Furthermore, we argue that one sub-scale model is not sufficient to predict the complete flight behaviour of a full-scale aircraft, but a catalog of tailored sub-scale models is needed to predict full-scale behaviour. An introduction to the development of such a catalog is presented in this paper, but the development of a formal methodology remains an open challenge. Establishing an approach to develop and use a SFT catalog of models to predict full-scale aircraft behaviour will help engineers enhance confidence on their designs and make SFT a viable and attractive testing method in the early stages of design.

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View Video Presentation: https://doi-org.tudelft.idm.oclc.org/10.2514/6.2021-3078.vid

Decisions regarding the system architecture are important and taken early in the design process, however suffer from large design spaces and expert bias. Systematic design space exploration techniques, like optimization, can be applied to system architecting. Realistic engineering benchmark problems are needed to enable development of optimization algorithms that can successfully solve these black-box, hierarchical, mixed-discrete, multi-objective architecture optimization problems. Such benchmark problems support the development of more capable optimization algorithms, more suitable methods for modeling system architecture design space, and educating engineers and other stakeholders on system architecture optimization in general. In this paper, an engine architecting benchmark problem is presented that exhibits all this behavior and is based on the open-source simulation tools pyCycle and OpenMDAO. Next to thermodynamic cycle analysis, the proposed benchmark problem includes modules for the estimation of engine weight, length, diameter, noise and NOx emissions. The problem is defined using modular interfaces, allowing to tune the complexity of the problem, by varying the number of design variables, objectives and constraints. The benchmark problem is validated by comparing to pyCycle example cases and existing engine performance data, and demonstrated using both a simple and a realistic problem formulation, solved using the multi-objective NSGA-II algorithm. It is shown that realistic results can be obtained, even though the design space is subject to hidden constraints due to the engine evaluation not converging for all design points.@en

After almost three decades of evolution, it is not yet possible to apply MDO in collaborative projects within large, heterogeneous and distributed teams of experts, whilst nowadays necessary for the development of any complex product. The H2020 project AGILE took the challenge of devising a novel paradigm to swiftly set up and deploy large distributed MDO systems, that are easy to (re)configure and monitor during the whole process, from requirements definition to data post-processing. The main outcome is an advanced set of tools and methods contributing to a 3rd generation MDO environment, specifically tailored to the aerospace industry. The AGILE paradigm is built on top of two main pillars, the so-called knowledge architecture and the collaborative architecture. The former, which is the main focus of this paper, provides a structured approach and the related workbench to formulate and inspect any automated design process, including fully formalized MDO systems. The latter includes the tools and methods to translate these formulations into executable workflows and deploy them across distributed networks. Although AGILE aims specifically at aircraft MDO, the proposed knowledge architecture provides a general conceptual framework that is suitable for the development of any complex product. The knowledge architecture has a multi-level hierarchical structure, consisting of four layers: development process, automated design, design competences and data schemas. Interfaces between the various layers are defined to achieve a fully.interconnected development process. This paper provides first a description of the knowledge architecture as a generalized paradigm to formulate collaborative and distributed MDO systems. Then, the specific implementation of such a paradigm within the AGILE project is illustrated: four knowledge architecture applications and two data schemas are described in detail. Finally, the whole approach is demonstrated by means of a realistic aircraft design case. This implementation proved successful in multiple aspects. First of all, in allowing heterogeneous teams of experts to generate complete and correct MDO system formulations involving large amount of distributed disciplinary tools, while maintaining full control and systematic overview of the complete system archi- tecture. Second, in offering the necessary agility to adjust and reconfigure the formulated MDO systems, such to support the iterative and evolutionary nature of their development process. Finally, by dramatically accelerating the setup time of the MDO system, thanks to the automation of the complex, lengthy and repetitive operations involved in the partitioning and coordination process, and to the effective support in inspecting and resolving the eventual inconsistencies in the data flow, arising every time tools are added or modified, or different solution strategies are implemented.

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Routing design of aircraft Electrical Wiring Interconnection System (EWIS) is time-consuming and error-prone. A solution, which automatically routes the EWIS inside the aircraft Digital MockUp (DMU), has been proposed and presented in the previous publications. The DMU, however, includes over-detailed features, which hardly influence the routing results but significantly increase the geometry-involved computational time thus hampering any automated routing. These features cannot be easily and efficiently suppressed. Therefore, a quick 3 D geometry simplification method, named Alpha-SIM, is proposed to enable a quick simplification of the airframe components included in the DMU and improve the benefit of the aforementioned automatic EWIS routing approach. The method is inspired by Descriptive Geometry techniques and the 3 D modelling approach using 2 D sketches, and aims at removing very detailed and/or internal features while preserving the intuitive notional shape of the given CAD model. The intuitive notional shape is represented by a 3 D point cloud of the model outer boundary and their 2 D projections on user-defined planes. These 2 D projections are then processed such to generate a set of 2 D profiles, called Alpha-Shapes, which are used, eventually, to re-build the 3 D model of the DMU components in a simplified/de-featured manner. By controlling the density of the 3 D points and the Alpha value to generate the 2 D profiles from the point projections, various geometric approximation levels can be achieved. The results of the test cases demonstrate the efficiency and effectiveness of the proposed method on the geometry simplification for automatic EWIS routing.

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The research and innovation AGILE project has developed an approach, the so-called AGILE Paradigm, focusing on the acceleration of the deployment and operation of collaborative Multidisciplinary Design Analysis Optimization systems, which in turns can be exploited to accelerate the development of complex products, such as novel aerospace systems. Although the technologies developed for the implementation of the paradigm, have proved to reduce the deployment and operational time to more than 40% with respect to conventional MDAO approaches, the AGILE Paradigm has not been formalized and model by digital design engineering practices. This work introduces a novel approach leveraging MBSE principles to streamline the development of agile MDAO design systems, and establishing a bridge between MBSE and MDAO. Major outcomes here presented are the MBSE-driven models of the so-called AGILE MDAO system, representing the architecture, the requirements, as well as the organizational aspects, and all the interactions and activities implemented during the life-cycle stages of the MDAO system. The MBSE Architectural Framework, which defines the underlying ontological concepts and perspectives driving the development of the AGILE MDAO system model, are modeled and presented as well. The paper introduces for the first time the overall approach, as well as the high-level elements of the models developed, here represented by making use of SysML standard. The described approach is at the core of the recently launched project AGILE4.0, in which its scope will be expanded to cover the entire life-cycle of the development of complex aeronautical systems.

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Sub-scale Flight Testing (SFT) is potentially useful in predicting aircraft flight behaviour, especially in the case of unconventional designs for which legacy information is unavailable and wind tunnel tests are unable to predict aircraft dynamics. A necessary condition for SFT is the design of properly scaled models. However, even in case of perfect scaling, the sub-scale model needs adequate flight performance and handling qualities to enable the execution of flight tests. Thus, the (static and dynamic) stability and control (S&C) and handling qualities (HQ) of sub-scale designs should be evaluated accurately as well as quickly, to allow conceptual design iterations. To this purpose, we propose the use of a 3D panel method (3DPM) for the generation of the non-linear aerodynamic database, in combination with a non-linear flight dynamics analysis. Two main challenges affect the proposed approach. The first concerns the validity of the low-fidelity 3DPM data for the assessment of the sub-scale design S&C and HQ. The second is about the time consuming and error-prone pre/post-processing activity demanded by the hundreds of analysis cases for the aerodynamic database generation. The first issue is investigated by predicting the longitudinal S&C performance and HQ of a sub-scale design using 3DPM analysis and comparing them with the prediction from wind-tunnel test (static) data supplemented by (dynamic) data from 3DPM. Both models appear trimmable and stable and the difference in their HQ are quantified, thus verifying the suitability of 3DPM analysis for sub-scale design assessment. The pre/post-processing challenge is tackled by the development of a knowledge-based engineering application to automate the aerodynamics database generation, reducing the time needed for geometry modeling, discretization and postprocessing of hundreds of cases from weeks to hours. The proposed methodology and its flexibility are demonstrated in this paper, where a commercial 3DPM code and an in-house developed non-linear flight dynamics analysis tool have been used to assess two sub-scale designs, one conventional and one based on the box-wing configuration. @en

The research and innovation AGILE project developed the next generation of aircraft Multidisciplinary Design and Optimization processes, which target significant reductions in aircraft development costs and time to market, leading to more cost-effective and greener aircraft solutions. The high level objective is the reduction of the lead time of 40% with respect to the current state-of-the-art. 19 industry, research and academia partners from Europe, Canada and Russia developed solutions to cope with the challenges of collaborative design and optimization of complex products. In order to accelerate the deployment of large-scale, collaborative multidisciplinary design and optimization (MDO), a novel methodology, the so-called AGILE Paradigm, has been developed. Furthermore, the AGILE project has developed and released a set of open technologies enabling the implementation of the AGILE Paradigm approach. The collection of all the technologies constitutes AGILE Framework, which has been deployed for the design and the optimization of multiple aircraft configurations. This paper focuses on the application of the AGILE Paradigm on seven novel aircraft configurations, proving the achievement of the project’s objectives.

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This paper proposes a novel methodology and its software implementation, called KADMOS (Knowledge- and graph-based Agile Design for Multidisciplinary Optimization System), to increase the agility of design teams in collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Agility here refers to the ease and flexibility to assemble, adjust and reconfigure MDAO computational systems. This is a necessary feature to comply with the complex and iterative nature of the (aircraft) design process. KADMOS has been developed on the notion that a formal specification of an MDAO system is required before proceeding with integration of the executable workflow. A thorough formulation of the system becomes essential when such system is built on the many contributions of large, heterogeneous design teams. KADMOS can automate the generation of such formulations through a graph-based methodological approach. The graph syntax and manipulation algorithms form the core content of this paper. First, a simple MDAO benchmark problem is used to illustrate KADMOS's working principles. Second, a wing aerostructural design case is discussed to demonstrate KADMOS's capabilities to enable collaborative MDAO on large problems of industry-representative complexity. Next to its graph-theoretic foundation, KADMOS makes use of two data schemas: one containing the parametric representation of the product being designed and a second to store the achieved formulation of the MDAO system. The latter enables the interchangeable use of different process integration and design optimization platforms to automatically integrate the generated MDAO system formulation as an executable workflow. The proposed approach has been estimated to be capable of halving the time typically required to set up and iteratively reconfigure a complex MDAO system, while allowing discipline experts and system architects to maintain constant oversight and control of the overall system and its components by means of human-readable dynamic visualizations.

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Sub-scaled Physical Models (SPM) are often employed in wind-tunnel tests or in free-flight tests (physical tests) to predict flight behavior of aircraft Full-scale Design (FD). However, a quality prediction of both the static and dynamic behavior is to date an open challenge. In this research, a methodology for designing SPMs is proposed for those cases where dynamic similarity between SPM and FD cannot be achieved and legacy information to compare subscale flight results to FD is unavailable. Instead of attempting to use just one SPM to achieve complete similarity with full scale design, this methodology enables the design and comparison of multiple SPMs to determine the Sub-scale Design (SD) best suited the estimation of specific aspect the FD flight behavior. To this purpose, a metric called Degree of Similitude (DoS) is defined, to quantify the similarity of FD and SPM based on the aerodynamic coefficients that are relevant for a given test. The DoS estimation first requires the evaluation of relevant aerodynamic coefficients, by means of Computational Fluid Dynamics (CFD). CFD analysis, requires complex geometry generation, adequate grid generation, expensive calculation and laborious post processing. To this purpose, a Knowledge Based Engineering (KBE) tool called Multi-model Generator (MMG) is developed, to automate all the labor intensive tasks in the evaluation of the DoS including the integration of CFD tool. Validation of results produced by MMG-VSAERO tool-chain is performed by means of a wind-tunnel test campaign using a 8.8% aerodynamically scaled SPM of the Cessna Citation II 550 (citation). The results of this test are compared with flight test data of full-scale aircraft (which is co-owned and operated by Delft University of Technology). Furthermore, this SPM was compared with three other Sub-scale Designs (SD) to estimate their DoS with the full-scale aircraft for two different eigenmodes, namely short period mode and phugoid mode. Of the four SDs compared, it was found that the geometrically scaled SD showed highest DoS for short period motion and one of the aerodynamically scaled SD had highest DoS for phugoid motion. From the cases studied, it can already be inferred that geometrically scaled SDs are not always preferred and in many cases, aerodynamically scaled SDs can be much more similar to FD. This case study proved the convenience of the proposed coefficient DoS which, in the next phase of the research, will be used as objective function to design optimum SPMs for a given test. @en

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A new system is presented that enables the visualization of large multidisciplinary design optimization (MDO) problems and their solution strategy. It was developed within the scope of the European project AGILE. In AGILE, collaborative MDO is performed in large, heterogeneous teams of experts by solving MDO problems using a collection of design and analysis tools. This paper focuses on the visualizations required to support the formulation phase of an MDO project. The Knowledge and graph-based AGILE Design for Multidisciplinary Optimization System (KADMOS), an open-source MDO support system developed by Delft University of Technology, uses graph-based analysis to formulate an MDO problem and its solution strategy, based on the disciplinary analyses available in a repository. The results of KADMOS are stored in the standardized format CMDOWS (Common MDO Workflow Schema), which comprises the entire information on an MDO system. Although, based on Extensible Markup Language, the readability of the CMDOWS file is quite poor also for MDO experts, especially for large MDO systems involving thousands of variables. Providing visualization capabilities to thoroughly inspect the outcome of the different MDO formulation steps becomes a key factor to enable the specification of large MDO systems in a heterogeneous team. Therefore, VISTOMS (VISualization TOol for MDO Systems), a dynamic visualization package, was developed by RWTH Aachen University to enable the visualization and inspection of the different MDO system specification steps, thereby removing one of the main hurdles for using MDO as a development process. The developed visualization capabilities are demonstrated by means of an aerostructural wing design optimization project.

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This paper presents a critical discussion on the automated problem formulation and workflow creation approach developed within the European project AGILE to support and accelerate the setup of aircraft MDO workflows in a large, heterogeneous team of experts. The developed framework is based on a methodological approach, called the AGILE paradigm, where a complete MDO system is formulated and executed in five main steps. In Step I the requirements are collected, in Step II a repository of disciplinary tools is established, in Step III the design optimization problem is formulated and structured according to a selected MDO architecture, in Step IV an executable workflow is assembled and finally operated in Step V. All steps have been streamlined and highly automated through the development of a novel set of MDO support applications and data standards, addressed as the AGILE MDO framework. This framework was tested through a series of design campaigns culminating with four design tasks, where a variety of unconventional aircraft configurations is collaboratively designed using MDO. After a brief introduction on the AGILE paradigm and the four design tasks, this paper will focus on a set of AGILE framework core components enabling the automated process to formulate and execute collaborative MDO systems. The strengths and current limitations of these components are discussed, based on the extensive feedback from the heterogeneous set of specialists involved in the four design tasks. Although drastic reductions in the setup time of an MDO system (up to 40 percent) appear to be already achievable, recommendations are provided to improve the flexibility, usability and scalability of the framework.

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