Design Of Aerospace Systems Research Articles

Aviation-BERT-NER: Named Entity Recognition for Aviation Safety Reports

This work introduces Aviation-BERT-NER, a Named Entity Recognition (NER) system tailored for aviation safety reports, building on the Aviation-BERT base model developed at the Georgia Institute of Technology’s Aerospace Systems Design Laboratory. This system integrates aviation domain-specific data, including aircraft types, manufacturers, quantities, and aviation terminology, to identify named entities critical for aviation safety analysis. A key innovation of Aviation-BERT-NER is its template-based approach to fine-tuning, which utilizes structured datasets to generate synthetic training data that mirror the complexity of real-world aviation safety reports. This method significantly improves the model’s generalizability and adaptability, enabling rapid updates and customization to meet evolving domain-specific requirements. The development process involved careful data preparation, including the synthesis of entity types and the generation of labeled datasets through template filling. Testing on real-world narratives from the National Transportation Safety Board (NTSB) database highlighted Aviation-BERT-NER’s robustness, with a precision of 95.34%, recall of 94.62%, and F1 score of 94.78% when evaluated over 50 manually annotated (BIO tagged) paragraphs. This work addresses a critical gap in English language NER models for aviation safety, promising substantial improvements in the analysis and understanding of aviation safety reports.

Multistability of segmented rings by programming natural curvature

Multistable structures have widespread applications in the design of deployable aerospace systems, mechanical metamaterials, flexible electronics, and multimodal soft robotics due to their capability of shape reconfiguration between multiple stable states. Recently, the snap-folding of rings, often in the form of circles or polygons, has shown the capability of inducing diverse stable configurations. The natural curvature of the rod segment (curvature in its stress-free state) plays an important role in the elastic stability of these rings, determining the number and form of their stable configurations during folding. Here, we develop a general theoretical framework for the elastic stability analysis of segmented rings (e.g., polygons) based on an energy variational approach. Combining this framework with finite element simulations, we map out all planar stable configurations of various segmented rings and determine the natural curvature ranges of their multistable states. The theoretical and numerical results are validated through experiments, which demonstrate that a segmented ring with a rectangular cross-section can show up to six distinct planar stable states. The results also reveal that, by rationally designing the segment number and natural curvature of the segmented ring, its one- or multiloop configuration can store more strain energy than a circular ring of the same total length. We envision that the proposed strategy for achieving multistability in the current work will aid in the design of multifunctional, reconfigurable, and deployable structures.

Epistemic modeling uncertainty of rapid neural network ensembles for adaptive learning

Emulator embedded neural networks, which are closely related to physics informed neural networks, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizations of the neural network model are trained with different random initializations. The ensemble of model realizations is used to assess epistemic modeling uncertainty caused by a lack of training samples. This uncertainty estimation is crucial information for successful goal-oriented adaptive learning in an aerospace system design exploration. However, the costs of training the ensemble models often become prohibitive and pose a computational challenge, especially when the models are not trained in parallel during adaptive learning. In this work, a new type of emulator embedded neural network is presented using the rapid neural network paradigm. Unlike the conventional neural network training that optimizes the weights and biases of all the network layers by using gradient-based backpropagation, rapid neural network training adjusts only the last layer connection weights by applying a linear regression technique. It is found that the proposed emulator embedded neural network trains near-instantaneously, typically without loss of prediction accuracy. The proposed method is demonstrated on multiple analytical examples, as well as an aerospace flight parameter study of a generic hypersonic vehicle.

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

The design of aerospace systems is recognized as a complex interdisciplinary process. Many studies have shown that the exchange of information among multiple disciplines often results in strong coupling and nonlinearity characteristics in system optimization. Meanwhile, inevitable multi-source uncertainty factors continuously accumulate during the optimization process, greatly compromising the system’s robustness and reliability. In this context, uncertainty-based multidisciplinary design optimization (UMDO) has emerged and has been preliminarily applied in aerospace practices. However, it still encounters major challenges, including the complexity of multidisciplinary analysis modeling, and organizational and computational complexities of uncertainty analysis and optimization. Extensive research has been conducted recently to address these issues, particularly uncertainty analysis and artificial intelligence strategies. The former further enriches the UMDO technique, while the latter makes outstanding contributions to addressing the computational complexity of UMDO. With the aim of providing an overview of currently available methods, this paper summarizes existing state-of-the art UMDO technologies, with a special focus on relevant intelligent optimization strategies.

Uncertainty design and optimization of a hybrid rocket motor with mixed random-interval uncertainties

Uncertainties, which exist widely in the design of aerospace systems, have a great impact on system response and may lead to design failures. Both the probability and non-probability uncertainties exist in real engineering cases. Thus, it is imperative to incorporate the mixed uncertainties into the aerospace systems design and optimization. Present study proposed an efficient design optimization method considering the random and interval uncertainties to conduct the uncertainty analysis and optimize the design of a hybrid rocket motor with mixed uncertainties. A polynomial chaos expansion model was selected to deal with the random uncertainties, and an interval analysis method based on the first order Taylor expansion was adopted to calculate the response bounds of interval uncertainties. A simple structure was firstly used to verify the efficiency and accuracy of the proposed method. The random-interval analysis method was very efficient in searching the optimal results, showing considerable computational cost savings. Subsequently, a design model of hybrid rocket motor was introduced and verified by a long-time firing test. In the optimization of the upper-stage hybrid rocket motor, the comparison between uncertainty analysis and traditional methods showed that the design optimization considering the uncertainty in design variables was able to achieve more robust results. Compared with the deterministic scheme, the uncertainty scheme increased the total engine mass by nearly 18%, but the probabilities of all constraints considering parameter fluctuations remained above 0.9. Capable of solving complex problems with mixed uncertainties, this method shows its potential in aerospace engineering applications.

Performance Degradation Based on Importance Change and Application in Dissimilar Redundancy Actuation System

The importance measure is a crucial method to identify and evaluate the system weak link. It is widely used in the optimization design and maintenance decision of aviation, aerospace, nuclear energy and other systems. The dissimilar redundancy actuation system (DRAS) is a key aircraft control subsystem which performs aircraft attitude and flight trajectory control. Its performance and reliability directly affect the aircraft flight quality and flight safety. This paper considers the influence of the Birnbaum importance measure (BIM) and integrated importance measure (IIM) on the reliability changes of key components in DRAS. The differences of physical fault characteristics of different components due to performance degradation and power mismatch, are first considered. The reliability of each component in the system is then estimated by assuming that the stochastic degradation process of the DRAS components follows an inverse Gaussian (IG) process. Finally, the weak links of the system are identified using BIM and IIM, so that the resources can be reasonably allocated to the weak links during the maintenance period. The proposed method can provide a technical support for personnel maintenance, in order to improve the system reliability with a minimal lifecycle cost.

Efficient subset simulation for rare-event integrating point-evolution kernel density and adaptive polynomial chaos kriging

Rare-event probability estimation has a wide range of applications, including the design and manufacture of precision equipment, aerospace systems, and critical industrial and civil structures. However, traditional simulation-based reliability calculation methods, such as brute Monte Carlo simulation (MCS) and subset simulation (SS), face challenges in efficiently evaluating small-failure probabilities due to the need for a large number of simulations, especially for non-linear and complex scenarios. Thus, to efficiently assess the probability of rare failure events in structural engineering, this paper develops a novel method for assessing the small-failure probability by integrating the point-evolution kernel density (PKDE), SS, and polynomial chaos kriging (PCK). The proposed PKDE-Adaptive PCK-based SS (PAPS) method aims to reduce the implementation of the original performance function by PCK and enrich the training set using an adaptive strategy. Moreover, the initial cumulative distribution function (CDF) of the performance function estimated by PKDE is modified gradually to facilitate the estimation of small-failure probability. Four numerical examples of small-failure probability estimation involving classical analytical cases, time-variant cases, and non-linear stochastic structures are used to illustrate the accuracy and efficiency of the proposed method. The computational results show that the proposed method can provide accurate computational results with a smaller computational burden than traditional methods (e.g., MCS, SS, LHS-PCK-SS).

Adaptive control for linear parameter-varying systems with application to a VTOL aircraft

As a systematic method for gain-scheduling, the linear parameter-varying (LPV) system framework has been widely used for the design of aerospace control systems with large operating envelopes. This paper presents design and analysis of an adaptive control architecture for LPV systems subject to time-varying parametric uncertainties and external disturbances, with both transient and steady-state performance guarantees. We introduce new tools relying on parameter-dependent Lyapunov functions and linear matrix inequality techniques for stability and performance analysis. These tools – limited to linear systems – potentially yield less conservative results than the approach employed for this architecture in previous adaptive control literature based on the small-gain theorem. The transient and steady-state performance of the adaptive closed-loop system, in terms of input and output signals, is quantified with respect to a non-adaptive reference system that depends on the true values of the uncertainties and represents the best achievable performance. It is shown that the transient performance bounds can be made arbitrarily small in the case of zero initialization error for the state predictor and will include additional exponentially decaying terms in the presence of non-zero initialization error. The approach can be used to design an adaptive augmentation of a baseline LPV control system. A simulation example of the longitudinal dynamics of a conceptual Urban Air Mobility aircraft is used to illustrate the efficacy of the proposed framework.

Errors and uncertainties in simulation of steady, viscous flow past a circular cylinder at Re = 20 using a systematic approach

The use of Computational Fluid Dynamics as a tool for design and analysis of aerospace systems is well established. Since the results generated by a CFD solver are numerical approximations, the solution is inherently produced with errors and uncertainties. In this paper, a simple fluid flow problem of laminar, incompressible flow past a circular cylinder at Reynolds number of 20 is allowed to be solved by the well-known finite-volume solver ANSYS Fluent. The effect of variations in mesh resolution, domain boundary location and residual criteria settings is investigated. For all the cases, finite, structured meshes of acceptable quality are used. The influence of variables on the cylinder’s drag results is analyzed and discussed. An interesting pattern in results has been observed. The study on the variation in mesh resolution showed no presence of mesh independent solution. The study on the variation of the domain distance showed that it is necessary to increase the diameter of the circle several thousand times to obtain a domain independent solution.

Bayesian optimization using deep Gaussian processes with applications to aerospace system design

Bayesian Optimization using Gaussian Processes is a popular approach to deal with optimization involving expensive black-box functions. However, because of the assumption on the stationarity of the covariance function defined in classic Gaussian Processes, this method may not be adapted for non-stationary functions involved in the optimization problem. To overcome this issue, Deep Gaussian Processes can be used as surrogate models instead of classic Gaussian Processes. This modeling technique increases the power of representation to capture the non-stationarity by considering a functional composition of stationary Gaussian Processes, providing a multiple layer structure. This paper investigates the application of Deep Gaussian Processes within Bayesian Optimization context. The specificities of this optimization method are discussed and highlighted with academic test cases. The performance of Bayesian Optimization with Deep Gaussian Processes is assessed on analytical test cases and aerospace design optimization problems and compared to the state-of-the-art stationary and non-stationary Bayesian Optimization approaches.

Quantum light sources based on deterministic microlenses structures with (111) In(Ga)As and AlInAs QDs.

The results of the development and implementation of a single photon source based on a bottom semiconductor Bragg reflector, top deterministic GaAs microlens structures and a single (111) In(Ga)As QD are presented. The structure of the microcavity ensures effective pumping of a single (111) In(Ga)As QD and high emission output efficiency, a clear single – photon emission was detected with a second – order correlation function at zero delay g(2)(0) = 0.07. A system of QD’s on the basis of AlXIn1-XAs/AlYGa1-YAs solid solutions has been studied. The usage of broadband AlXIn1-XAs solid solutions as the basis of quantum dots makes it possible to expand considerably the spectral emission range into the short-wave region, including the wavelength region near 770 nm being of interest for the design of aerospace systems of quantum cryptography. The optical characteristics of single AlXIn1-XAs quantum dots grown according to the Stranski–Krastanov mechanism are studied by the cryogenic microphotoluminescence method.

Experimental investigation of a hydrofoil designed via hydrostructural optimization

In the last decade, there has been an increased interest in the use of multidisciplinary optimization techniques for the design of aerospace, maritime, and wind engineering systems. However, validation of numerically optimized results using experimental measurements has been scarce. In this paper, numerical predictions are compared with experimental measurements of the hydrodynamic forces, deformations, and cavitation performance for a baseline NACA 0009 hydrofoil and an optimized hydrofoil. Both hydrofoils are made of solid aluminum, and are cantilevered at the root. One of the hydrofoils is optimized using a high-fidelity hydrostructural solver combined with a gradient-based optimizer, as detailed by Garg et al. (2017). The numerical predictions agree well with experimental measurements for both the baseline NACA 0009 and the optimized hydrofoils. For the optimized hydrofoil, the mean differences between the predicted and measured values for mean lift, drag coefficient, and moment coefficients, are 2.9%, 5.1%, and 3.0%, respectively. For the non-dimensional tip bending deflection, the mean difference is 3.4%. Although the optimized hydrofoil is significantly thicker to withstand higher loads than the baseline, it yields an overall measured increase in the lift-to-drag ratio of 29% for lift coefficients ranging from −0.15 to 0.75 and exhibits significantly delayed cavitation inception compared to the baseline. The improvement in hydroelastic and cavitation performance is attributed to the changes in the distribution of camber, twist, thickness, and the leading edge radius of the optimized hydrofoil. The results validate the analysis and optimization of the high-fidelity hydrostructural design optimization approach, and opens up new possibilities for the design of high-performance hydrofoils, marine propellers, and turbines.

Light-weighting in aerospace component and system design

Light-weighting involves the use of advanced materials and engineering methods to enable structural elements to deliver the same, or enhanced, technical performance while using less material. The concept has been extensively explored and utilised in many industries from automotive applications to fashion and packaging and offers significant potential in the aviation sector. Typical implementations of light-weighting have involved use of high performance materials such as composites and optimisation of structures using computational aided engineering approaches with production enabled by advanced manufacturing methods such as additive manufacture, foam metals and hot forming. This paper reviews the principal approaches used in light-weighting, along with the scope for application of light-weighting in aviation applications from power-plants to airframe components. A particular area identified as warranting attention and amenable to the use of light-weighting approaches is the design of solar powered aircraft wings. The high aspect ratio typically used for these can be associated with insufficient stiffness, giving rise to non-linear deformation, aileron reversal, flutter and rigid-elastic coupling. Additional applications considered include ultralight aviation components and sub-systems, UAVs, and rockets. Advanced optimisation approaches can be applied to optimise the layout of structural elements, as well as geometrical parameters in order to maximise structural stiffness, minimise mass and enable incorporation of energy storage features. The use of additive manufacturing technologies, some capable of producing composite or multi-material components is an enabler for light-weighting, as features formally associated with one principal function can be designed to fulfil multiple functionalities.

System identification of flexible aircraft in frequency domain

PurposeThe purpose of this paper is to identify the flexible aircraft model accurately from the frequency responses.Design/methodology/approachThe frequency domain output error method is used to estimate the aerodynamic (rigid body and elastic body) derivatives, and mode shape parameters in the process of identification of flexible aircraft model. The accurate identification of lightly damped low frequency rigid-body response modes requires a careful selection of the frequency sweep length and the fast Fourier transform (FFT) window size, as the FFT window length cannot be longer than any individual sweep records. To address this issue, an effort is made to derive the FFT window length for the application of frequency domain estimation approach.FindingsThe investigations are initially made to select a suitable FFT window size for the accurate identification of the lightly damped low frequency rigid-body response modes of the flexible aircraft. Subsequently, frequency domain estimation approach is applied to simulated data of flexible aircraft. Besides the stability and control derivatives, the structural modes of the flexible aircraft are also estimated as part of state space model identification, and it is shown that all the model parameter estimates are accurate. Identification of such flexible aircraft aerodynamic (rigid body and elastic body) derivatives and structural mode shape parameters will lead to mathematical models of flexible aircraft that are accurate over a wide frequency range. The identified models are validated using the time response of frequency sweep data.Research limitations/implicationsAircraft system identification is an integral part of aerospace system design and life cycle process. This becomes a complex process when the aircraft has significant effects of flexibility on the flight dynamics, especially as the frequencies of the elastic modes become lower and approach those of the rigid body modes. Thus, an integrated mathematical model of flexible aircraft is required to develop, and it should be valid for a wide frequency range and relevant for the design of flight control system.Originality/valueThis paper focuses on the application of frequency domain approach to identify the valid model of flexible aircraft by estimating the aerodynamic (rigid body and elastic body) derivatives and structural mode shape parameters of flexible aircraft. The unknown frequencies of structural modes are also able to identify accurately in frequency domain. This gives more value addition to analyze the flight data of flexible aircraft, as it is challenging problem in parameter estimation of flexible aircraft.

Scope of inextensible frame hypothesis in local action analysis of spherical reservoirs

Spherical reservoirs, as objects perfect with respect to their weight, are used in spacecrafts, where thin-walled elements are joined by frames into multifunction structures. The junctions are local, which results in origination of stress concentration regions and the corresponding rigidity problems. The thin-walled elements are reinforced by frame to decrease the stresses in them. To simplify the analysis of the mathematical model of common deformation of the shell (which is a mathematical idealization of the reservoir) and the frame, the assumption that the frame axial line is inextensible is used widely (in particular, in the manual literature). The unjustified use of this assumption significantly distorts the concept of the stress-strain state. In this paper, an example of a lens-shaped structure formed as two spherical shell segments connected by a frame of square profile is used to carry out a numerical comparative analysis of the solutions with and without the inextensible frame hypothesis taken into account. The scope of the hypothesis is shown depending on the structure geometric parameters and the load location degree. The obtained results can be used to determine the stress-strain state of the thin-walled structure with an a priori prescribed error, for example, in research and experimental design of aerospace systems.

AlInAs quantum dots

A system of quantum dots on the basis of AlxIn1-xAs/AlyGa1-y As solid solutions has been studied. The usage of broadband AlxIn1-x solid solutions as the basis of quantum dots makes it possible to expand considerably the spectral emission range into the short-wave region, including the wavelength region near 770 nm being of interest for the design of aerospace systems of quantum cryptography. The optical characteristics of single AlxIn1-xAs quantum dots grown according to the Stranski–Krastanov mechanism are studied by the cryogenic microphotoluminescence method. The fine structure of exciton states of quantum dots is studied in the wavelength region near 770 nm. It is shown that the splitting of exciton states is comparable with the natural width of exciton lines, which is of great interest for the design of emitters of pairs of entangled photons on the basis of AlxAs1-x quantum dots.

ESA multibody simulator for spacecrafts’ ascent and landing in a microgravity environment

The investigation of planets, moons and small bodies, including comets and asteroids can contribute substantially to our understanding of the formation and history of the solar system. In situ observations by landers play an important role in this field: for example, the Rosetta Lander Philae has been the first spacecraft to accomplish a soft touchdown on a comet. Since the last decade, the urgency to anticipate hardware performance and correlate testing results with mathematical models is generating the trend for more extensively applying multibody approach in supporting the design and verification of complex aerospace systems, from the early phases of the project. Some of the essential multibody technologies assisting the analysis of this class of problems consist of a reliable attitude control algorithm, a variable-step and variable-order numerical integrator and a consistent contact-friction formulation. This paper illustrates the particular implementation of these crucial features in the European Space Agency’s multibody software DCAP and shows a direct application to a feasibility case study.

Incorporating Multicriteria Decision Analysis Techniques in Aircraft Conceptual Design Process

In aerospace systems design, conflicting disciplines and technologies are always involved in the design process. There are often subjective decisions made in the conceptual design phase, and these subjective decisions have significant impacts on the performance of the final design. Multicriteria decision analysis techniques can help designers to effectively deal with such situations and make wise design decisions. The objective of this paper is to explore the feasibility and added values of applying multicriteria decision analysis techniques in aircraft design. In the first part of the paper, we establish a new optimization framework incorporating multicriteria decision analysis techniques in aircraft conceptual design process. Then, we propose an improved multicriteria decision analysis method to aggregate the multiple design criteria into one composite figure of merit for the optimization. The improved multicriteria decision analysis method is able to maintain ranking consistency for the top-ranked alternative. In the second part of the paper, we assess the subjective preferences from different designers in aircraft design process. We solve the specification problem of weighting factors by using Latin hypercube sampling (LHS) with Dirichlet distribution. Finally, we develop surrogate models for the multiple design criteria in terms of weighting factors to perform the uncertainty assessment efficiently.

Intelligent Multicriteria Decision Support System for Systems Design

It is challenging to assess new technology in complex, interdisciplinary integrated systems, such as air transportation systems. Conflicting disciplines and technologies are always involved in aerospace systems design processes. Multicriteria Decision Analysis techniques can help decision makers to effectively deal with such situations and make wise design decisions. There is a variety of existing methods; thus, selection of the most appropriate method is critical because the use of inappropriate methods is often the cause of misleading design decisions. In this paper, an appropriateness index is used to quantify the goodness of a method for solving the problem under consideration. This method selection approach is implemented and an intelligent knowledge-based system is developed consisting of a multicriteria decision analysis library storing widely used decision analysis methods and a knowledge base providing the information required for the method selection process. Furthermore, a new approach for uncertainty assessment in the decision analysis process is proposed in this study. This novel approach for uncertainty assessment can be used to aggregate input data from tools with different fidelity levels and is capable of propagating uncertainties in an assessment chain. A business aircraft evaluation problem is conducted as a proof of implementation.

Frequency and Time Domain Recursive Parameter Estimation For a Flexible Aircraft

Aerodynamic parameter estimation is an integral part of aerospace system design and life cycle process. Recent advances in computational power have allowed the use of online parameter estimation techniques in various applications. In this paper, two online parameter estimation methods are considered for the identification of aerodynamic derivatives of a flexible aircraft: Equation Error Method and Kalman Filtering Method. The performance of the two algorithms is compared in terms of accuracy, computational efficiency and convergence. Simulation results indicate that Kalman filtering method has good convergence property and yields parameter estimates with better accuracy compared to the equation error method.