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This study explores what it means to be a mission-driven arts organisation (MDAO) in the UK. Drawing on literature relating to artistic risk and rupture, mission and vision, and arts participation, the purpose of this paper is to shed light on how Slung Low, a theatre organisation with a core staff of five, creates large and complex initiatives and seeks to make a difference to its local community.

Using a case study approach, this interpretive study makes use of qualitative data to offer context-specific knowledge about how MDAOs create new initiatives including: interviews with members of the Slung Low team; attendance at company meetings; analysis of internal organisational documents, company website and artistic director’s blog; and articles about Slung Low from the local, national and theatre industry press. Data was gathered through a research collaboration with Slung Low which is supported by Paul Hamlyn Foundation.

The results offer fresh insight into how MDAOs take a positive approach to rupture and rapid change. The study finds that by embracing risk and committing to an ambitious and provocative mission, small-scale arts organisations can achieve artistic, cultural and social objectives which far exceed their size.

This paper offers an organisational perspective on the research questions and so participants were not interviewed on this occasion. However, the participant view will be the subject of further research with Slung Low.

This research paper provides insight into one of the UK’s most innovative theatre companies during a period of monumental change, and advances knowledge on mission-driven organisations by offering reflections on what it means to be an arts organisation which places rupture, risk and usefulness at the heart of its mission.

The purpose of this study is to examine state-of-the-art in hybrid-electric propulsion system modeling and suggest new methodologies for sizing such advanced concepts. Many entities are involved in the modelling and design of hybrid electric aircraft; however, the highly multidisciplinary nature of the problem means that most tools focus heavily on one discipline and over simplify others to keep the analysis reasonable in scope. Correctly sizing a hybrid-electric system requires knowledge of aircraft and engine performance along with a working knowledge of electrical and energy storage systems. The difficulty is compounded by the multi-timescale dynamic nature of the problem. Furthermore, the choice of energy management in a hybrid electric system presents multiple degrees of freedom, which means the aircraft sizing problem now becomes not just a root-finding exercise, but also a constrained optimization problem.

The hybrid electric vehicle sizing problem can be sub-divided into three areas: modelling methods/fidelity, energy management and optimization technique. The literature is reviewed to find desirable characteristics and features of each area. Subsequently, a new process for sizing a new hybrid electric aircraft is proposed by synthesizing techniques from model predictive control and detailed conceptual design modelling. Elements from model predictive control and concurrent optimization are combined to formulate a new structure for the optimization of the sizing and energy management of future aircraft.

While the example optimization formulation provided is specific to a hybrid electric concept, the proposed structure is general enough to be adapted to any vehicle concept which contains multiple degrees of control freedom that can be optimized continuously throughout a mission.

The proposed technique is novel in its application of model predictive control to the conceptual design phase.

The purpose of this paper is to characterise the effects of mission and performance parameters on the design space of low observable subsonic unmanned combat aerial vehicles (UCAVs) operating in typical Hi-Lo-Hi ground strike missions.

Conceptual design methodologies appropriate to low observable, tailless UCAVs have been integrated into a multidisciplinary aircraft design environment, GENUS, developed at Cranfield University’s aircraft design group. A basic Hi-Lo-Hi mission is designed and a baseline configuration is established through the GENUS framework. Subsequently, an evolutionary optimiser and a robust gradient-based optimiser are used to obtain convergent design solutions for various leading edge sweep angles, mission ranges, cruise Mach numbers and other operational constraints.

The results indicate that performance constraints, specifically in the form of specific excess power (SEP), have a large influence on the overall sizing of subsonic tailless UCAVs. This requirement drives the engine sizing, which represents a considerable proportion of the empty and gross mass of the vehicle. Cruise Mach number studies show that no significant advantages exist for operating at low speeds while maintaining performance requirements consistent with combat missions. There is a drastic increase in the vehicle’s mass and thrust requirements for flight speeds above Mach 0.8, with low sweep configurations showing a more pronounced effect. Increases in the range are not overly dependent on the leading edge sweep angle. Top-level radar cross section (RCS) results also favour configurations with higher leading edge sweep angles, especially from the nose-on aspect. Finally, research and development costs are shown to be directly linked to engine size.

This research shows the use of an integrated aircraft design environment that incorporates aerodynamics, performance, packaging and low observability aspects into the optimisation loop. Through this methodology, this study supports the efforts towards characterising and establishing alternate visions of the future of aerial warfare through the use of low cost, survivable unmanned platforms in network-centric cooperative tasks.

This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level.

Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate.

Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day.

The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

Computational efficiency is always the major concern in aircraft design. The purpose of this research is to investigate an efficient jig-shape optimization design method. A new jig-shape optimization method is presented in the current study and its application on the high aspect ratio wing is discussed.

First, the effects of bending and torsion on aerodynamic distribution were discussed. The effect of bending deformation was equivalent to the change of attack angle through a new equivalent method. The equivalent attack angle showed a linear dependence on the quadratic function of bending. Then, a new jig-shape optimization method taking integrated structural deformation into account was proposed. The method was realized by four substeps: object decomposition, optimization design, inversion and evaluation.

After the new jig-shape optimization design, both aerodynamic distribution and structural configuration have satisfactory results. Meanwhile, the method takes both bending and torsion deformation into account.

The new jig-shape optimization method can be well used for the high aspect ratio wing.

The new method is an innovation based on the traditional single parameter design method. It is suitable for engineering application.

A collaborative design environment is needed for multidisciplinary design optimization (MDO) process, based on all the modules those for different design/analysis disciplines, and a systematic coupling should be made to carry out aerodynamic shape optimization (ASO), which is an important part of MDO.

Computerized environment for aircraft synthesis and integrated optimization methods (CEASIOM)-ASO is developed based on loosely coupling all the existing modules of CEASIOM by MATLAB scripts. The optimization problem is broken down into small sub-problems, which is called “sequential design approach”, allowing the engineer in the loop.

CEASIOM-ASO shows excellent design abilities on the test case of designing a blended wing body flying in transonic speed, with around 45 per cent drag reduction and all the constraints fulfilled.

Authors built a complete and systematic technique for aerodynamic wing shape optimization based on the existing computational design framework CEASIOM, from geometry parametrization, meshing to optimization.

CEASIOM-ASO provides an optimization technique with loosely coupled modules in CEASIOM design framework, allowing engineer in the loop to follow the “sequential approach” of the design, which is less “myopic” than sticking to gradient-based optimization for the whole process. Meanwhile, it is easily to be parallelized.

The purpose of this paper is to describe the development of an integrated framework suitable for preliminary airframe design, called PyPAD (Python module for Preliminary Aircraft Design), providing the capability to define models to compute loads and to perform the structural sizing.

The modules developed until now allow for the definition of multi-fidelity aero-structural models starting from a Common Parametric Aircraft Configuration Schema (CPACS) input file and to compute static loads (trim) and flutter margin with minimum user effort. PyPAD take advantages of Abaqus-CAE, and the main functions are developed in Python, to take advantages of the simplicity in terms of software development and maintenance, but the core routines are developed in Fortran, taking advantages of parallel programming to get the best performances.

A complete test case, starting from the CPACS input and ending with the definition of structural, aerodynamic and aero-elastic models, with the computation of different design loads, is reported. An example will show that the framework developed is able to handle different problematics of the preliminary projects using quite complex global models.

All the tools developed in the framework, and the ones currently under development, could be a valid help during the preliminary design of a new aircraft, speeding up the iterative process and improving the design solution.

PyPAD is the first framework developed around Abaqus-CAE for the preliminary aircraft design and is one of the few tools looking at the different problematics involved in a preliminary airframe design: design, loads and aero-elasticity, sizing and multi-disciplinary optimization.

The preliminary aircraft design process comprises multiple disciplines. During performance analysis, parameters of the design mission have to be optimized. Mission performance optimization is often challenging, especially for complex mission profiles (e.g. for unmanned aerial vehicles [UAVs]) or hybrid-electric propulsion. Therefore, the purpose of this study is to find a methodology that supports aircraft performance analysis and that is applicable to complex profiles and to novel designs.

As its core element, the developed method uses a computationally efficient C++ software “Aircraft Performance Program” (APP), which performs a segment-based mission computation. APP performs a time integration of the equations of motion of a point mass in the vertical plane. APP is called via a command line interface from a flexible scripting language (Python). On top of APP’s internal radius of action optimization, state-of-the-art optimization packages (SciPy) are used.

The application of the method to a conventional climb schedule shows that the definition of the top of climb has a significant influence on the resulting optimum. Application of the method to a complex UAV mission optimization, which included maximizing the radius of action, was successful. Low computation time enables to perform large parametric studies. This greatly improves the interpretation of the results.

The scope of the paper is limited to the methodology that allows for advanced performance analysis at the conceptual and preliminary design stages with an emphasis on novel propulsion concepts. The methodology is developed using existing, validated methods, and therefore, this paper does not contain comprehensive validation. Other disciplines, such as cost analysis, life-cycle assessment or market analysis, are not considered.

With the proposed method, it is possible to obtain not only the desired optimum mission performance but also off-design performance of the investigated design. A thorough analysis of the mission performance provides insight into the design’s capabilities and shortcomings, ultimately aiding in obtaining a more efficient design.

Recent developments in the area of hybrid or hybrid-electric propulsion systems have shown the need for performance computation tools aiding the related design process. The presented method is especially valuable when novel design concepts with complex mission profiles are investigated.