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This paper attempts to assess the benefits of a unique distributed propulsion concept, known as the Multi-Gas Generator Fan (MGGF) system, over conventional turbofan engines on civilian vertical takeoff and landing (VTOL) applications. The MGGF-based system has shown the potential to address the fundamental technical challenge in designing a VTOL aircraft: the significant mismatch between the power requirements at lift-off/hover and cruise. Vehicle-level performance and sizing studies were implemented using the Grumman Design 698 tilt-nacelle V/STOL aircraft as a notional personal air vehicle (PAV), subjected to hypothetical single engine failure (SEF) emergency landing requirements and PAV mission requirements.

Heterogeneous physical systems can often be considered as highly complex, consisting of a large number of subsystems and components, along with the associated interactions and hierarchies amongst them. The simulation of a large-scale, complex system can be computationally expensive and the dynamic interactions may be highly nonlinear. One approach to address these challenges is to increase the computing power or resort to a distributed computing environment. An alternative to improve the simulation computational performance and efficiency is to reduce CPU required time through the application of surrogate models. Surrogate modeling techniques for dynamic simulation models can be developed based on Recurrent Neural Networks (RNN).This study will present a method to improve the overall speed of a multi-physics time-domain simulation of a complex naval system using a surrogate modeling technique.

The quantification of aircraft survivability in modern battlefield environments is a complex mathematical problem. In general, consideration must be given to the quantification of aircraft vulnerability to individual weapon systems, single encounter aircraft survivability, and the mathematical mapping of single encounter aircraft survivability into mission attrition. A methodology for quantifying the impacts of electronic warfare (EW) upon aircraft survivability is realized by the General Effectiveness Methodology (GEM) which is based upon a hierarchy of computer models. This paper describes this hierarchy of computer simulation tools which extensively employs probability theory to estimate the various engagement events such as aircraft detection, acquisition, missile launch, missile intercept, and probability of aircraft kill.

A mean value based sizing and simulation model has been developed for use in the conceptual design and sizing of hydrogen fueled spark-ignition internal combustion engines (HICE) in the aerospace industry, here ‘mean value’ includes mean effective pressure (MEP), mean piston speed, mean specific power, etc. This model is developed since there is currently no such model readily available for this purpose. When sizing the HICE, statistical data and common practice for gasoline internal combustion engines (GICE) are used to obtain preliminary sizes of the HICE, such as total cylinder volume, bore and stroke; to capture the effect of low volumetric efficiency, the preliminary results are adjusted by a volumetric correction factor until the cycle parameters of HICE are reasonable. A non-dimensional combustion model with hydrogen as fuel is incorporated with existing GICE methods. With this combustion model, the high combustion temperature and high combustion pressure are captured.

An integrated approach for modeling the ice accretion and shedding of ice on helicopter rotors is presented. A modular framework is used that includes state of the art computational fluid dynamics, computational structural dynamics, rotor trim, ice accretion, and shedding tools. Results are presented for performance degradation due to icing, collection efficiency, surface temperature and water film properties associated with runback-refreeze phenomena, and shedding. Comparisons with other published simulations and test data are given.

The recent development of electric vehicles creates a new area of interest regarding their potential impacts on natural resource and energy networks. Water consumption is of particular interest, as water scarcity becomes a growing problem in many regions of the world. Water usage can be traced to the production of gasoline, as well as electricity, for regular operation of these vehicles. This paper focuses on the development of a framework to analyze the amount of water consumed in the operation of both conventional and electric vehicles. Using the Systems Modeling Language, a model was developed based on the water consumed directly in energy generation and processing as well as water consumed in obtaining and processing a vehicle's fuels. This model and framework will use the above water consumption breakdown to examine conventional and electric vehicles in metropolitan Atlanta to assess their impacts on that and other urban networks.

Spray processes, such as primary breakup, play an important role for subsequent combustion processes and emissions formation. Accurate modeling of these spray physics is therefore key to ensure faithful representation of both the global and local characteristics of the spray. However, the governing physical mechanisms underlying primary breakup in fuel sprays are still not known. Several theories have been proposed and incorporated into different engineering models for the primary breakup of fuel sprays, with the most widely employed models following an approach based on aerodynamically-induced breakup, or more recently, based on liquid turbulence-induced breakup. However, a complete validation of these breakup models and theories is lacking since no existing measurements have yielded the joint liquid mass and drop size distribution needed to fully define the spray, especially in the near-nozzle region.

Several approaches to robust design have been proposed in the past. Only few acknowledged the paradigm shift from performance based design to design for cost. The incorporation of economics in the design process, however, makes a probabilistic approach to design necessary, due to the inherent ambiguity of assumptions and requirements as well as the operating environment of future aircraft. The approach previously proposed by the authors, linking Response Surface Methodology with Monte Carlo Simulations, has revealed itself to be cumbersome and at times impractical for multi-constraint, multi-objective problems. In addition, prediction accuracy problems were observed for certain scenarios that could not easily be resolved. Hence, this paper proposes an alternate approach to probabilistic design, which is based on a Fast Probability Integration technique.

In early phases of conceptual design stages for developing a new car in the modern automobile industry, the lack of systematic methodology to efficiently converge to an agreement between the aesthetics and aerodynamic performance tremendously increases budget and time. During these procedures, one of the most important tasks is to create geometric information which is versatilely morphable upon the demands of both of stylists and engineers. In this perspective, this paper proposes a Spline-based Modeling Algorithm (SMA) to implement into performing aerodynamic design optimization research based on CFD analysis. Once a 3-perspective schematic of a car is given, SMA regresses the backbone boundary lines by using optimum polynomial interpolation methods with the best goodness of fit, eventually reconstructing the 3D shape by linearly interpolating from the extracted boundaries minimizing loss of important geometric features.

One the most critical barriers to the advancement of Personal Air Vehicles in today's market environment is that the technological capabilities can never seem to outweigh the risks associated with financing such an endeavor. To address such a need, a system dynamics approach with the capability to model the uncertainties in the supply chain is presented in this paper. The overall modeling framework is first presented and the modeling process of the various relevant elements, such as demand prediction and manufacturer analysis, is then described. The aim of this research is ultimately to assess the viability of a next-generation aircraft program beyond the static confines of a net present value approach, through the inclusion of dynamic events and uncertainties that can occur throughout the life-cycle of the aircraft.

A technique is proposed for examining complex behaviors in the “pilot - vehicle - operational conditions” system using an autonomous situational model of flight. The goal is to identify potentially critical flight situations in the system behavior early in the design process. An exhaustive set of flight scenarios can be constructed and modeled on a computer by the designer in accordance with test certification requirements or other inputs. Distinguishing features of the technique include the autonomy of experimentation (the pilot and a flight simulator are not involved) and easy planning and quick modeling of complex multi-factor flight cases. An example of mapping airworthiness requirements into formal scenarios is presented. Simulation results for various flight situations and aircraft types are also demonstrated.

Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice.

This paper looks at a method for decomposing a manufactured product into what is called an “activity space.” The method uses an activity based costing scheme to structure the model and organize the information. It is discussed how the activity space is used to perform sustainability assessments of a manufactured product and the manufacturing process from different viewpoints and perspectives. The way in which the activity space is used to perform an assessment from several viewpoints is discussed.

The interest in flying cars comes with the question of characterizing aerodynamic loads on shapes that go beyond traditional aircraft shapes. When carried as slung loads under aircraft, vehicles can encounter severe aerodynamic loads, which may also cause them to go into divergent oscillations that can threaten the vehicle and aircraft. Slung loads can encounter the wind at arbitrary attitudes. Flight test certification for every vehicle-aircraft combination is prohibitive. Characterizing the aerodynamic loads with sufficient resolution for use in dynamic simulation, has in the past been extremely arduous. Sharp changes that drive instabilities arise over small ranges of yaw and pitch. With the Continuous Rotation technique developed by our group, aerodynamic load characterization is viable and efficient. With two well-chosen attitude sweeps and appropriate transformations, the entire 6-DOF load map can be obtained, for several rates.

This paper brings together three special aspects of bluff-body aeromechanics. Experiments using our Continuous Rotation method have developed a knowledge base on the 6-degree-of-freedom aerodynamic loads on over 50 different configurations including parametric variations of canonical shapes, and several practical shapes of interest. Models are mounted on a rod attached to a stepper motor placed on a 6-DOF load cell in a low speed wind tunnel. The aerodynamic loads are ensemble-averaged as phase-resolved azimuthal variations. The load component variations are obtained as discrete Fourier series for each load component versus azimuth about each of 3 primary axes. This capability has enabled aeromechanical simulation of the dynamics of roadable vehicles slung below rotorcraft. In this paper, we explore the genesis of the loads on a CONEX model, as well as models of a short and long container, using the “ROTCFD” family of unstructured Navier-Stokes solvers.

Hybrid-electric powertrains for passenger vehicles and light trucks are generally being designed with two different configurations described as follows: The Toyota Hybrid System, THS-II, implemented in the 2004 Prius, the Lexus 400-H, and the Ford Hybrid Escape, is a power-split approach involving two electric machines and an internal combustion engine (ICE) mechanically coupled by a three-shaft planetary gear train. The second leading approach is a parallel hybrid-electric powertrain that generally includes a single electric machine and an ICE with a mating multi-ratio transmission. These parallel configurations are further divided as weak parallel and strong parallel. Honda uses a weak parallel powertrain in their Insight and Hybrid Civic. At Georgia Tech a strong (full), split-parallel hybrid powertrain has been implemented in a Ford Explorer. The vehicle is referred to as the Model GT.

An affordable technique is proposed for fast quantitative analysis of aerobatics and other complex flight domains of highly maneuverable aircraft. A generalized autonomous situational model of the “pilot (automaton) – vehicle – operational environment” system is employed as a “virtual test article”. Using this technique, a systematic knowledge of the system behavior in aerobatic flight can be generated on a computer, much faster than real time. This information can be analyzed via a set of knowledge mapping formats using a 3-D graphics visualization tool. Piloting and programming skills are not required in this process. Possible applications include: aircraft design and education, applied aerodynamics, flight control systems design, planning and rehearsal of flight test and display programs, investigation of aerobatics-related flight accidents and incidents, physics-based pilot training, research into new maneuvers, autonomous flight, and onboard AI.

Battery modeling is of major concern for Hybrid Electric Vehicle (HEV) and Electric vehicle (EV) modeling. The major issue lies in characterizing the battery power output in relation to battery's State of Charge (SOC) in various application conditions. In particular, the challenge is associated with the difficulty that the characteristic parameters of the battery, i.e. the accurate data on the open circuit voltage and the internal resistance are hardly obtainable in practical conditions. In this paper, a battery capacity representation and a practical way of battery modeling is introduced for simulation model development based on the experimental data. A realistic way of battery SOC representation is generated from the battery output data. Empirical formulation is derived from the data to correlate the battery current, voltage output with the battery SOC.

Coaxial rotors are finding use in advanced rotorcraft concepts. Combined with lift offset rotor technology, they offer a solution to the problems of dynamic stall and reverse flow that often limit single rotor forward flight speeds. In addition, coaxial rotorcraft systems do not need a tail rotor, a major boon during operation in confined areas. However, the operation of two counter-rotating rotors in close proximity generates many possible aerodynamic interactions between rotor blades, blades and vortices, and between vortices. With two rotors, the parameter design space is very large, and requires efficient computations as well as basic experiments to explore aerodynamics of a coaxial rotor and the effects on performance, loads, and acoustics.

This work contributes to the understanding of physical mechanisms that control flashback, or more appropriately combustion recession, in diesel sprays. A large dataset, comprising many fuels, injection pressures, ambient temperatures, ambient oxygen concentrations, ambient densities, and nozzle diameters is used to explore experimental trends for the behavior of combustion recession. Then, a reduced-order model, capable of modeling non-reacting and reacting conditions, is used to help interpret the experimental trends. Finally, the reduced-order model is used to predict how a controlled ramp-down rate-of-injection can enhance the likelihood of combustion recession for conditions that would not normally exhibit combustion recession. In general, fuel, ambient conditions, and the end-of-injection transient determine the success or failure of combustion recession.