Helicopter Turboshaft Engine Database as a Conceptual Design Tool

Before a new structure can be built, it must be designed. This design phase is a very important step in the building process. The total cost of the structure and its structural performance are largely dependent on the structural design process. The impact of decisions on the design process is initially high and declines as the design matures. However, few computational tools are available for the conceptual design phase; thus, an opportunity exists to create such tools. In the conventional workflow, the architect uses geometric modeling tools and the engineer uses structural analysis tools in sequential steps. Parametric modeling tools represent an improvement to this workflow, as structural analysis plug-ins are available. This allows the architect or engineer to receive structural feedback at an earlier stage, but still as a sequential step to the geometric modeling. The present work aims to improve this workflow by integrating structural feedback with geometric modeling.The user interfaces of conceptual design tools should be interactive and agile enough to follow the designer’s iterative workflow. Direct manipulation involves human-computer interaction, which enables an interactive user interface. In this user interface style, users can directly manipulate on-screen objects using real-world metaphors, which engages the users with their task and encourages further explorations. This is achieved by reducing the perceptual and cognitive resources required to understand and use the interface. New technologies have opened up the possibility of creating new design tools that make use of very direct manipulation. This possibility is further explored in this thesis through the development of two such applications. The first application makes use of multi-touch tablets. The multi-touch interface has literally closed the gap between humans and computers, enabling very direct manipulation interactions with two-dimensional user interfaces. The developed application is an interactive conceptual design tool with real-time structural feedback that allows the user to quickly input and modify structural models through the use of gestures. The second application extends these concepts and ideas into a three-dimensional user interface using an input device named the Leap Motion Controller.

This article proposes a mathematical model for protecting helicopter turboshaft engines from surges, starting with fuel metering supply and maintaining stable compressor operation. The model includes several stages: first, fuel is supplied according to a specified program; second, an unstable compressor operation signal is determined based on the gas temperature in front of the compressor turbine and the gas generator rotor speed derivatives ratio; at the third stage, when the ratios’ threshold value is exceeded, fuel supply is stopped, and the ignition system is turned on. Then, the fuel supply is restored with reduced consumption, and the rotor speed is corrected, followed by a return to regular operation. The neural network model implementing this method consists of several layers, including derivatives calculation, comparison with the threshold, and correction of fuel consumption and rotor speed. The input data for the neural network are the gas temperature in front of the compressor turbine and the rotor speed. A compressor instability signal is generated if the temperature and rotor speed derivatives ratio exceed the threshold value, which leads to fuel consumption adjustment and rotor speed regulation by 28…32%. The backpropagation algorithm with hyperparameter optimization via Bayesian optimization was used to train the network. The computational experiments result with the TV3-117 turboshaft engine on a semi-naturalistic simulation stand showed that the proposed model effectively prevents compressor surge by stabilizing pressure, vibration, and gas temperature and reduces rotor speed by 29.7% under start-up conditions. Neural network quality metrics such as accuracy (0.995), precision (0.989), recall (1.0), and F1-score (0.995) indicate high efficiency of the proposed method.

In this paper we discuss how tools for conceptual design in civil engineering can be developed using graph transformation specifications. These tools consist of three parts: (a) for elaborating specific conceptual knowledge (knowledge engineer), (b) for working out conceptual design results (architect), and (c) automatic consistency analyses which guarantee that design results are consistent with the underlying specific conceptual knowledge. For the realization of such tools we use a machinery based on graph transformations.In a traditional PROGRES tool specification the conceptual knowledge for a class of buildings is hard-wired within the specification. This is not appropriate for the experimentation platform approach we present in this paper, as objects and relations for conceptual knowledge are due to many changes, implied by evaluation of their use and corresponding improvements.Therefore, we introduce a parametric specification method with the following characteristics: (1) The underlying specific knowledge for a class of buildings is not fixed. Instead, it is built up as a data base by using the knowledge tools. (2) The specification for the architect tools also does not incorporate specific conceptual knowledge. (3) An incremental checker guarantees whether a design result is consistent with the current state of the underlying conceptual knowledge (data base).KeywordsConceptual DesignCivil EngineeringConceptual KnowledgeDesign ResultGraph TransformationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Many current systems rely for their performance on achieving a balance between electronics, software and mechanical systems and the transfer of functionality between those domains. The design of such mechatronic systems therefore relies on the ability of the individual domain specialists to transfer knowledge about their domain within the overall design process. Enhancing the ability of non-specialists to understand the relationships between the various system elements and to communicate with the domain specialists will serve to enhance and support the design process. The paper therefore considers a tool based around the use of case-based reasoning which is intended to provide such support by allowing a non-specialist to access information from a range of domains in a way which is easy to use and understand and which will establish and define the links between the various areas of technology.

Broadband noise reduction by circular multi-cavity mufflers operating in multimodal propagation conditions

This research substantiates the necessity for developing and implementing structural reconfiguration methods for automatic control systems in the event of a parametric sensor failure to enhance the helicopter turboshaft engine’s overall reliability and safety. The research aim is the substantiation of the helicopter turboshaft engine’s mathematically reconfigured automatic control system in the event of the failure of a standard sensor, which will ensure the helicopter turboshaft engine’s stable operation under failure conditions, minimizing the impact on engine control and performance. A theorem was developed and proven concerning the reconfiguration of the helicopter turboshaft engine’s automatic control system structure, defining the system’s new mathematical form using nonlinear thermogas-dynamic parameters. A method was proposed to determine the values of these parameters that keep the reconfigured control system stable. This method uses numerical optimization to find the best thermogas-dynamic parameters to ensure system stability. Experimental results showed that for slow changes, using parameters from the previous step works best, while for fast changes, restarting is more effective due to significant differences in the system states. The accuracy of the proposed mathematical model for the reconfigured control system was confirmed through mean square error analysis (within 0.4% and 0.77% under white noise), regression analysis (with a determination coefficient of 0.986), and cross-validation (with a metric deviation from the maximum mean square error of 3.88%).

This article presents a vibration signal analysis method to diagnose helicopter turboshaft engine defects such as bearing imbalance and wear. The scientific novelty of the article lies in the development of a comprehensive approach to diagnosing helicopter turboshaft engine defects based on the vibration signals amplitude and frequency characteristics integral analysis combined with a neural network for probabilistic defect detection. Unlike existing methods, the proposed approach uses the energy criterion for the vibration characteristics. It averages the assessment of unique signal processing algorithms, which ensures reliable defect classification under flight vibration conditions. The method is based on representing vibration signals as a sum of harmonic oscillations supplemented by noise components, which helps to identify deviations from typical values. The developed method includes a state function in which the amplitudes and frequency characteristics from nominal parameters estimate deviations. When the critical threshold is exceeded, the function signals possible malfunctions. A multilayer neural network is used to classify defect types, providing high classification accuracy (from 0.985 to 0.994). Computer experiments on the developed seminaturalistic modeling stand confirm that the method can detect increased vibration levels, which is the potential failure indicator. Comparative analysis shows the proposed method’s accuracy and noise resistance superiority, emphasizing the importance of introducing modern technologies to improve aircraft operation reliability and safety.

A model-based method has been developed for the performance simulation and conceptual design of rocket-type pulse detonation engines (PDEs). A reduced-order model (ROM) has been generated based on the high order singular value decomposition of a data tensor obtained from CFD computations. This ROM could be used to solve the direct (performance) and inverse (design) problems in the context of the early phases of pulse detonation engine design. Output performance parameters are predicted from prescribed input operation/geometry parameters in the direct problem, and vice versa in the inverse problem. The focus of this method is industrial application in situations where large parametric searches are to be performed with a reasonable level of fidelity at a low computational cost. It was found that the performance and conceptual design tool thus developed provides results that deviate, on average, by less than 10% from the CFD results. Regarding practical implementation, the method allows for shifting the heavier computational load off-line. In this way, when working on-line, the user can obtain results in less than a second for every single case. The main contribution of this study is showing that a model-based approach that combines CFD and tensor decomposition has the potential to extract a maximum of information from a given computational effort. This characteristic makes the method of interest for early design phases in the aerospace industry.

An integrated approach for selecting design parameters in smart product-service system conceptual design

Development efforts to reduce pollutants such as NOx and CO produced by fossil-fuelled systems such as gas turbine engines have become a necessity. The realisation of these efforts in conceptual and detailed design phases before production is very important in terms of reducing the time and costs of the projects. In spite of rapid computer technology development, it is still a difficult and time-consuming process to calculate the minor chemical species such as NOx and CO which are released during the combustion process. In this study, a methodology is proposed to predict the emissions of a reverse flow combustor of a 1000 horsepower turbo-shaft helicopter engine in the design process. In this methodology, the temperature and mass flow values are obtained by computational fluid dynamics (CFD) simulations and the CRN model is constructed by using these values. A MATLAB based tool was developed for chemical reactor network (CRN) modelling. And obtained results were compared with empirical correlations.

A case is made that introducing CAD-based geometry prematurely in the aircraft design process actually hinders multidisciplinary design. Consequently, a design process which allows a designer to quickly build and analyze aerospace configurations without CAD or computational meshing specialists is proposed. This allows higher fidelity analyses to be applied at conceptual design and decreases the turnaround time for trade studies and optimizations. The basic premise of conceptual geometry modeling is presented and discussed. Key features and capabilities which are essential for a successful geometry modeler are identified. Desktop Aeronautics' conceptual geometry modeler, RAGE, is described, with emphasis on recent progress and advances in the tool. A preliminary graphical-user-interface is presented along with an application programming interface. Extensibility of the RAGE modeler is discussed along with the importance of this feature. The applicability of RAGE to simple trade studies is demonstrated in an example problem. The precision of the RAGE modeler is also demonstrated on a complex aircraft design. Future plans for the geometry modeler are also discussed. I. Background In the conceptual phase of aircraft design, there is often a wide range of geometric configurations that the designer wishes to consider, analyze, and assess. A designer’s intuition and creativity are crucial to a successful conceptual design, yet are easily inhibited by the modeling and analysis process itself, particularly if high-fidelity analysis is warranted. If the time between inception and assessment of a candidate design is relatively long, a designer may be forced to limit the number of concepts considered. New data revealed in a design iteration may suggest or require significant changes to the geometry configuration. Since the majority of considered design configurations will eventually be discarded, an ideal conceptual design process should involve a highly efficient and easy to use geometry modeler. Significant changes should be allowed without tedious re-work of the geometry model. Exporting the model for analysis should require little effort and have the capability to be automated. Therefore, any modeling application used in this process should be flexible and not restrict the designer’s choice of user interface, analysis method, and design optimization environment. Most current design processes in the aerospace industry rely heavily on computer-aided-design (CAD) applications. As a general design tool, CAD is a powerful geometry modeler capable of precisely representing practically any shape. Unfortunately, this generality is realized at the cost of requiring specialized training to efficiently and effectively use the software. Also, a CAD model is often not easily modified, especially if significant re-work of the geometry is necessary. This can lead to the premature freeze of an aircraft design due to employing excessively high-fidelity geometry modeling too early in the design process. Parametric CAD modeling could address this issue, but creating a well-designed parametric CAD model is difficult. Not only does it require an extremely skilled operator, but also the designer must have some insight into what parameterization may ultimately be desired. Modifying a parametric CAD model is often more difficult than modifying a conventional CAD model. Significant changes, such as adding or removing cross-sections, may invalidate the parameterization in some cases. In most organizations, CAD modeling (whether it be parametric or conventional) is often assigned to a dedicated CAD specialist. Since the conceptual designer is usually not a CAD specialist, the latter must communicate his design intent to the former, often using sketches, dimensions, and word of mouth. When the CAD specialist interprets the sketches, any part or detail not precisely defined usually requires judgment and artistic license. This interpretation may result in loss of design intent and indeterminate iteration between the CAD specialist and designer to obtain a satisfactory result. Because this process is iterative, CAD operators often must recreate similar geometry

This work conducts a validation study for the XH-59A helicopter using a rigid coaxial rotor system in order to establish the techniques of the conceptual design and performance analysis for the lift-offset compound rotorcraft. As a tool for conceptual design and performance analysis, NDARC (NASA Design and Analysis of Rotorcraft) is used for the present study. An assumed mission profile is considered for the conceptual design of the XH-59A. As a validation result of the design, the dimensions and weight of the XH-59A are appropriately designed when compared to the target values since the relative error is less than 0.5%. Then, performance analyses are conducted for the designed XH-59A model with and without auxiliary propulsion in hover and forward flight conditions. The present analyses show good validity since the prediction results compare well with both the flight test and previous analyses. Therefore, the techniques for the conceptual design and performance analysis of the lift-offset compound helicopter are overall considered to be appropriately established. In addition, this study investigates the influence of the lift-offset on the rotor effective lift-to-drag ratio of the XH-59A helicopter with auxiliary propulsion. As a result, the improvement of the rotor effective lift-to-drag ratio can be obtained by appropriately increasing the lift-offset in high-speed flight.

Modular equipment design in optimization-based process synthesis

In a typical engineering design, there are often many design parameters to consider. Also, there are multiple competing requirements and objectives to meet. Manual approach of adjusting the parameters to achieve specific objectives is not optimal especially as the design becomes complex. In the quest for optimizing complex engineering systems, the exploration of the design space becomes imperative, especially when dealing with multi-objective systems characterized by an array of independent variables. This paper presents a comprehensive study on the design space mapping of complex engineering systems, utilizing a turboshaft engine as a case study. The initial phase of our methodology employs a physics-based model to generate synthetic dataset, reflecting the intricate interplay of various system parameters underpinning the engine's operation. This synthesized data serves as a foundation for the subsequent development of a Machine Learning or Deep Learning based surrogate model. The surrogate AI model, will be crafted to encapsulate multiple inputs and outputs inherent in the turboshaft engine's functioning, thereby facilitating an efficient and accurate exploration of the design space. Through this investigation, we will evaluate the efficacy of combining physics-based models with AI-driven techniques in mapping the design space of multi-objective systems. The core of our investigation revolves around the utilization of the AI surrogate model for achieving multi-objective optimization. This optimization process is not only focused on enhancing specific performance metrics but is also geared towards identifying a comprehensive family of feasible design solutions. Such an approach enables the delineation of the entire design space, offering invaluable insights into the trade-offs and synergies among different design objectives. Through this methodology, our goal is to uncover a wide spectrum of viable design alternatives, thereby providing a robust framework for decision-making in the engineering design process.

Conceptual design is the seminal phase in the design process. This phase determines the level of product innovation, the efficiency of the product, and the effectiveness of the down-stream stages of the design process. It has tremendous leverage on the final product performance, cost, and time-to-market. To improve the efficiency of the conceptual design process, the paper combines the parameter analysis methodology with a fundamental insight from robust design. Parameter analysis is a generic design methodology that aids in systematically developing an idea into a viable design. It is particularly useful in creating innovative conceptual designs. Robust design is, often, used after finalizing the conceptual design. Robust design improves the product quality by first reducing the variability in product performance and then, tuning the low variability performance onto the target. The paper presents guidelines for executing the parameter analysis methodology which in turn provides consistent or low variability performance by considering robustness in the very early phases of the design process. The paper illustrates this process with two case studies. The case studies also show how to qualitatively optimize a conceptual design by developing the overall concept before details.