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The purpose of this study is to re-evaluation fuselage design when the main wing’s has the ability to fulfill stability requirements without the need for a tailplane. The aerodynamic requirements of the fuselage usually involve a trade-off between reducing drag and providing enough length for positioning the empennage to ensure stability. However, if the main wing can fulfill the stability requirements without the need for a tailplane, then the fuselage design requirements can be re-evaluated. The optimisation of the fuselage can then include reducing drag and also providing a component of lift amongst other potential new requirements.

A careful investigation of parameterisation and trade-off optimisation methods to create such fuselage shapes was performed. The A320 Neo aircraft is optimised using a parameterised 3D fuselage model constructed with a modified PARSEC method and the SHERPA optimisation strategy, which was validated through three case studies. The geometry adjustments in relation to the specific flow phenomena are considered for the three optimal designs to investigate the influencing factors that should be considered for further optimisation.

The top three aerodynamic designs show a distinctive characteristic in the low aspect ratio thick wing-like aftbody that has pressure drag penalties, and the aftbody camber increased surface area notably improved the fuselage’s lift characteristics.

This work contributes to the development of a novel set of design requirements for a fuselage, free from the constraints imposed by stability requirements. By gaining insights into the flow phenomena that influence geometric designs when a lift requirement is introduced to the fuselage, we can understand how the fuselage configuration was optimised. This research lays the groundwork for identifying innovative design criteria that could extend into the integration of propulsion of the aftbody.

Reynolds-averaged Navier–Stokes (RANS) models often perform poorly in shock/turbulence interaction regions, resulting in excessive wall heat load and incorrect representation of the separation length in shockwave/turbulent boundary layer interactions. The authors suggest that this can be traced back to inadequate numerical treatment of the inviscid fluxes. The purpose of this study is an extension to the well-known Harten, Lax, van Leer, Einfeldt (HLLE) Riemann solver to overcome this issue.

It explicitly takes into account the broadening of waves due to the averaging procedure, which adds numerical dissipation and reduces excessive turbulence production across shocks. The scheme is derived based on the HLLE equations, and it is tested against three numerical experiments.

Sod’s shock tube case shows that the scheme succeeds in reducing turbulence amplification across shocks. A shock-free turbulent flat plate boundary layer indicates that smooth flow at moderate turbulence intensity is largely unaffected by the scheme. A shock/turbulent boundary layer interaction case with higher turbulence intensity shows that the added numerical dissipation can, however, impair the wall heat flux distribution.

The proposed scheme is motivated by implicit large eddy simulations that use numerical dissipation as subgrid-scale model. Introducing physical aspects of turbulence into the numerical treatment for RANS simulations is a novel approach.

The purpose of this paper is to present a procedure to design an experimental setup meant to validate an innovative approach for simulating, via computational fluid dynamics, a high-pressure gas release from a rupture (e.g. on an offshore oil and gas platform). The design is based on a series of scaling exercises, some of which are anything but trivial.

The experimental setup is composed of a wind tunnel, the instrumented scaled (1:10) mock-up of an offshore platform and a gas release system. A correct scaling approach is necessary to define the reference speed in the wind tunnel and the conditions of the gas release to maintain similarity with respect to the real-size phenomena. The scaling of the wind velocity and the scaling of the gas release were inspired by the approach proposed by Hall et al. (1997): a dimensionless group was chosen to link release parameters, wind velocity and geometric scaling factor.

The theoretical scaling approaches for each different part of the setup were applied to the design of the experiment and some criticalities were identified, such as the existence of a set of case studies with some release parameters laying outside the applicability range of the developed scaling methodology, which will be further discussed.

The resulting procedure is one of a kind because it involves a multi-scaling approach because of the different aspects of the design. Literature supports for the different scaling theories but, to the best of the authors’ knowledge, fails to provide an integrated approach that considers the combined effects of scaling.

The purpose of this paper is to reshape a fast-jet electronics pod’s external geometry to ensure compliance with aircraft pylon load limits across its carriage envelope while adhering to onboard system constraints and fitment specifications.

Initial geometric layout determination used empirical methods. Performance approximation on the aircraft with added fairings and stabilising fin configurations was conducted using a panel code. Verification of loads was done using a full steady Reynolds-averaged Navier–Stokes solver, validated against published wind tunnel test data. Acceptable load envelope for the aircraft pylon was defined using two already-certified stores with known flight envelopes.

Re-lofting the pod’s geometry enabled meeting all geometric and pylon load constraints. However, due to the pod's large size, re-lofting alone was not adequate to respect aircraft/pylon load limitations. A flight restriction was imposed on the aircraft’s roll rate to reduce yaw and roll moments within allowable limits.

The geometry of an electronics pod was redesigned to maximise the permissible flight envelope on its carriage aircraft while respecting the safe carriage load limits determined for its store pylon. Aircraft carriage load constraints must be determined upfront when considering the design of fast-jet electronic pods.

A process for determining the unknown load constraints of a carriage aircraft by analogy is presented, along with the process of tailoring the geometry of an electronics pod to respect aerodynamic load and geometric constraints.

This paper aims to analyze the pressure distribution of rectangular aerostatic thrust bearing with a single air supply inlet using the complex potential theory and conformal mapping.

The Möbius transform is used to map the interior of a rectangle onto the interior of a unit circle, from which the pressure distribution and load carrying capacity are obtained. The calculation results are verified by finite difference method.

The constructed Möbius formula is very effective for the performance characteristics researches for the rectangular thrust bearing with a single air supply inlet. In addition, it is also noted that to obtain the optimized load carrying capacity, the square thrust bearing can be adopted.

The Möbius transform is found suitable to describe the pressure distribution of the rectangular thrust bearing with a single air supply inlet.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.