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The ESS-EDS and EDS-Sigma interfaces within the standalone engine simulator are described. The operation of these interfaces, including the definition and use of special function signals and data flow paths within them during data transfers, is presented along with detailed schematics and circuit layouts of the described equipment.
The Systems Engineering Simulator has addressed the major issues in providing visual data to its real-time man-in-the-loop simulations. Out-the-window views and CCTV views are provided by three scene systems to give the astronauts their real-world views. To expand the window coverage for the Space Station Freedom workstation a rotating optics system is used to provide the widest field of view possible. To provide video signals to as many viewpoints as possible, windows and CCTVs, with a limited amount of hardware, a video distribution system has been developed to time-share the video channels among viewpoints at the selection of the simulation users. These solutions have provided the visual simulation facility for real-time man-in-the-loop simulations for the NASA space program.
The Systems Engineering Simulator (SES) houses a variety of real-time computer generated visual systems. The earliest machine dates from the mid-1960's and is one of the first real-time graphics systems in the world. The latest acquisition is the state-of-the-art Evans and Sutherland CT6. Between the span of time from the mid-1960's to the late 1980's, tremendous strides have been made in the real-time graphics world. These strides include advances in both software and hardware engineering. The purpose is to explore the history of the development of these real-time computer generated image systems from the first machine to the present. Hardware advances as well as software algorithm changes are presented. This history is not only quite interesting but also provides us with a perspective with which we can look backward and forward.
Apollo engineering simulation activity, particularly analog-digital real time hybrid simulation for guidance and control
The Simulation Engineer's Handbook is a guide for new engineers assigned to Experiment Simulation and a reference for engineers previously assigned. The experiment simulation process, development of experiment simulator requirements, development of experiment simulator hardware and software, and the verification of experiment simulators are discussed. The training required for experiment simulation is extensive and is only referenced in the handbook.
As a vessel operates in the open seas, a marine diesel engine simulator whose engine rotation is controlled to transmit through propeller shaft is a new methodology for the self propulsion tests to track the fuel saving in a real time. Considering the circumstance, this paper presents the real time of marine diesel engine simulator system to track the real performance of a ship through a computer-simulated model. A mathematical model of marine diesel engine and the propeller are used in the simulation to estimate fuel rate, engine rotating speed, thrust and torque of the propeller thus achieve the target vessel’s speed. The input and output are a real time control system of fuel saving rate and propeller rotating speed representing the marine diesel engine characteristics. The self-propulsion tests in calm waters were conducted using a vessel model to validate the marine diesel engine simulator. The simulator then was used to evaluate the fuel saving by employing a new mathematical model of turbochargers for the marine diesel engine simulator. The control system developed will be beneficial for users as to analyze different condition of vessel’s speed to obtain better characteristics and hence optimize the fuel saving rate.
Expert systems were applied in a number of ways to the field of simulation. One of these is the application of an expert system to drive a simulation, by making run-time decisions which effect the simulation. This approach was successful for a number of specific simulation models. The simulation engine extends this capability by supporting this type of interaction in a general purpose setting. A general purpose simulation language is provided for building models, and an inference engine is provided for knowledge processing. This combination results in a mechanism which allows general purpose models to be simulated in concert with interactive knowledge bases.
A digital computer simulation of a mixed flow, twin spool turbofan engine was assembled to evaluate and improve the dynamic characteristics of the engine simulation to disturbance frequencies of at least 100 Hz. One dimensional forms of the dynamic mass, momentum and energy equations were used to model the engine. A TF30 engine was simulated so that dynamic characteristics could be evaluated against results obtained from testing of the TF30 engine at the NASA Lewis Research Center. Dynamic characteristics of the engine simulation were improved by modifying the compression system model. Modifications to the compression system model were established by investigating the influence of size and number of finite dynamic elements. Based on the results of this program, high frequency engine simulations using finite dynamic elements can be assembled so that the engine dynamic configuration is optimum with respect to dynamic characteristics and computer execution time. Resizing of the compression systems finite elements improved the dynamic characteristics of the engine simulation but showed that additional refinements are required to obtain close agreement simulation and actual engine dynamic characteristics.
3D CFD spark-ignition IC engine simulations are extremely complex for the regular user. Truly-predictive CFD simulations for the turbulent flame combustion that solve fully coupled transport/chemistry equations may require large computational capabilities unavailable to regular CFD users. A solution is to use a simpler phenomenological model such as the G-equation that decouples transport/chemistry result. Such simulation can still provide acceptable and faster results at the expense of predictive capabilities. While the G-equation is well understood within the experienced modeling community, the goal of this paper is to document some of them for a novice or less experienced CFD user who may not be aware that phenomenological models of turbulent flame combustion usually require heavy tuning and calibration from the user to mimic experimental observations. This study used ANSYS® Forte, Version 17.2, and the built-in G-equation model, to investigate two tuning constants that influence flame propagation in 3D CFD SI engine simulations: the stretch factor coefficient, Cms and the flame development coefficient, Cm2. After identifying several Cm2-Cms pairs that matched experimental data at one operating conditions, simulation results showed that engine models that used different Cm2-Cms sets predicted similar combustion performance, when the spark timing, engine load, and engine speed were changed from the operating condition used to validate the CFD simulation. A dramatic shift was observed when engine speed was doubled, which suggested that the flame stretch coefficient, Cms, had a much larger influence at higher engine speeds compared to the flame development coefficient, Cm2. Therefore, the Cm2-Cms sets that predicted a higher turbulent flame under higher in-cylinder pressure and temperature increased the peak pressure and efficiency. This suggest that the choice of the Cm2-Cms will affect the G-equation-based simulation accuracy when engine speed increases from the one used to validate the model. As a result, for the less-experienced CFD user and in the absence of enough experimental data that would help retune the tuning parameters at various operating conditions, the purpose of a good G-equation-based 3D engine simulation is to guide and/or complement experimental investigations, not the other way around. Only a truly-predictive simulation that fully couples the turbulence/chemistry equations can help reduce the amount of experimental work.
This work evaluates different optimization algorithms for computational fluid dynamics (CFD) simulations of engine combustion. Due to the computational expense of CFD simulations, emulators built with machine learning algorithms were used as surrogates for the optimizers. Two types of emulators were used: a Gaussian process (GP) and a weighted variety of machine learning methods called SuperLearner (SL). The emulators were trained using a dataset of 2048 CFD simulations that were run concurrently on a supercomputer. The design of experiments (DOE) for the CFD runs was obtained by perturbing nine input parameters using a Monte-Carlo method. The CFD simulations were of a heavy duty engine running with a low octane gasoline-like fuel at a partially premixed compression ignition mode. Ten optimization algorithms were tested, including types typically used in research applications. Each optimizer was allowed 800 function evaluations and was randomly tested 100 times. The optimizers were evaluated for the median, minimum, and maximum merits obtained in the 100 attempts. Some optimizers required more sequential evaluations, thereby resulting in longer wall clock times to reach an optimum. The best performing optimization methods were particle swarm optimization (PSO), differential evolution (DE), GENOUD (an evolutionary algorithm), and micro-genetic algorithm (GA). These methods found a high median optimum as well as a reasonable minimum optimum of the 100 trials. Moreover, all of these methods were able to operate with less than 100 successive iterations, which reduced the wall clock time required in practice. Two methods were found to be effective but required a much larger number of successive iterations: the DIRECT and MALSCHAINS algorithms. A random search method that completed in a single iteration performed poorly in finding optimum designs but was included to illustrate the limitation of highly concurrent search methods. The last three methods, Nelder–Mead, bound optimization by quadratic approximation (BOBYQA), and constrained optimization by linear approximation (COBYLA), did not perform as well.
An object-oriented gas turbine engine simulation program was developed. This program is a prototype for a more complete, commercial grade engine performance program now being proposed as part of the Numerical Propulsion System Simulator (NPSS). This report discusses architectural issues of this complex software system and the lessons learned from developing the prototype code. The prototype code is a fully functional, general purpose engine simulation program, however, only the component models necessary to model a transient compressor test rig have been written. The production system will be capable of steady state and transient modeling of almost any turbine engine configuration. Chief among the architectural considerations for this code was the framework in which the various software modules will interact. These modules include the equation solver, simulation code, data model, event handler, and user interface. Also documented in this report is the component based design of the simulation module and the inter-component communication paradigm. Object class hierarchies for some of the code modules are given.
With the resurgence of interest in high supersonic and hypersonic flight there is a need to simulate airbreathing engines which may be used in this flight regime. To meet this requirement the RAMSCRAM code was developed. The code calculates 1-D flow properties at each component interface and the overall performance of the engine. It uses equilibrium thermodynamics which accounts for dissociation and allows for any fuel or combination of fuels. The program can simulate ramjet, scramjet, rocket, and ducted rocket engines.
Spacecraft Engineering Simulation II (SES II) is a C-language computer program for simulating diverse aspects of operation of a spacecraft characterized by either three or six degrees of freedom. A functional model in SES can include a trajectory flight plan; a submodel of a flight computer running navigational and flight-control software; and submodels of the environment, the dynamics of the spacecraft, and sensor inputs and outputs. SES II features a modular, object-oriented programming style. SES II supports event-based simulations, which, in turn, create an easily adaptable simulation environment in which many different types of trajectories can be simulated by use of the same software. The simulation output consists largely of flight data. SES II can be used to perform optimization and Monte Carlo dispersion simulations. It can also be used to perform simulations for multiple spacecraft. In addition to its generic simulation capabilities, SES offers special capabilities for space-shuttle simulations: for this purpose, it incorporates submodels of the space-shuttle dynamics and a C-language version of the guidance, navigation, and control components of the space-shuttle flight software.
The Apollo Program experience from early 1962 to July 1969 with respect to the engineering-simulation support and the problems encountered is summarized in this report. Engineering simulation in support of the Apollo guidance and control system is discussed in terms of design analysis and verification, certification of hardware in closed-loop operation, verification of hardware/software compatibility, and verification of both software and procedures for each mission. The magnitude, time, and cost of the engineering simulations are described with respect to hardware availability, NASA and contractor facilities (for verification of the command module, the lunar module, and the primary guidance, navigation, and control system), and scheduling and planning considerations. Recommendations are made regarding implementation of similar, large-scale simulations for future programs.
As part of the NPSS (Numerical Propulsion Simulation System) project, NASA Lewis has a goal of developing an U.S. industry standard for an axisymmetric engine simulation environment. In this program, AlliedSignal Engines (AE) contributed to this goal by evaluating the ENG20 software and developing support tools. ENG20 is a NASA developed axisymmetric engine simulation tool. The project was divided into six subtasks which are summarized below: Evaluate the capabilities of the ENG20 code using an existing test case to see how this procedure can capture the component interactions for a full engine. Link AE's compressor and turbine axisymmetric streamline curvature codes (UD0300M and TAPS) with ENG20, which will provide the necessary boundary conditions for an ENG20 engine simulation. Evaluate GE's Global Data System (GDS), attempt to use GDS to do the linking of codes described in Subtask 2 above. Use a turbofan engine test case to evaluate various aspects of the system, including the linkage of UD0300M and TAPS with ENG20 and the GE data storage system. Also, compare the solution results with cycle deck results, axisymmetric solutions (UD0300M and TAPS), and test data to determine the accuracy of the solution. Evaluate the order of accuracy and the convergence time for the solution. Provide a monthly status report and a final formal report documenting AE's evaluation of ENG20. Provide the developed interfaces that link UD0300M and TAPS with ENG20, to NASA. The interface that links UD0300M with ENG20 will be compatible with the industr,, version of UD0300M.
Heat loss through wall boundaries play a dominant role in the overall performance and efficiency of internal combustion engines. Typical engine simulations use constant temperature wall boundary conditions. These boundary conditions cannot be estimated accurately from experiments due to the complexities involved with engine combustion. As a result they introduce a large uncertainty in engine simulations and serve as a tuning parameter. Modeling the process of heat transfer through the solid walls in an unsteady engine computational fluid dynamics (CFD) simulation can lead to the development of higher fidelity engine calculations. These models can be used to study the impact of heat loss on engine efficiency and explore new design methodologies that can reduce heat losses. In this work, a single cylinder diesel engine is modeled along with the solid piston coupled to the fluid domain. Conjugate heat transfer (CHT) modeling techniques were implemented to model heat losses for a full cycle of a Navistar diesel engine. This CFD model is then validated against experimental data available from thermocouples embedded inside the piston surface. The overall predictions from the model match closely with the experimental observations. The validated model is further used to explore the benefits of thermal barrier coatings (TBC) on piston bowls. The effect of TBC coatings were modeled as a thermal resistance in the heat transfer models. Full cycle 3D engine simulations provide quantitative insights into heat loss and thus calculate the efficiency gain by the use of TBC coatings. The work establishes a validated modeling framework for CHT modeling in reciprocating engine simulations.
The stiffness of large chemistry mechanisms has been proved to be a major hurdle toward predictive engine simulations. As a result, detailed chemistry mechanisms with a few thousand species need to be reduced based on target conditions so that they can be accommodated within the available computational resources. The computational cost of simulations typically increases super-linearly with the number of species and reactions. This work aims to bring detailed chemistry mechanisms within the realm of engine simulations by coupling the framework of unsteady flamelets and fast chemistry solvers. A previously developed tabulated flamelet model (TFM) framework for nonpremixed combustion was used in this study. The flamelet solver consists of the traditional operator-splitting scheme with variable coefficient ordinary differential equation (ODE) solver (VODE) and a numerical Jacobian for solving the chemistry. In order to use detailed mechanisms with thousands of species, a new framework with the Livermore solver for ODEs in sparse form (LSODES) chemistry solver and an analytical Jacobian was implemented in this work. Results from 1D simulations show that with the new framework, the computational cost is linearly proportional to the number of species in a given chemistry mechanism. As a result, the new framework is 2-3 orders of magnitude faster than the conventional variable coefficient ODE (VODE) solver for large chemistry mechanisms. This new framework was used to generate unsteady flamelet libraries for ndodecane using a detailed chemistry mechanism with 2755 species and 11,173 reactions. The engine combustion network (ECN) spray A experiments, which consist of an igniting n-dodecane spray in turbulent, high-pressure engine conditions are simulated using large eddy simulations (LES) coupled with detailed mechanisms. A grid with 0.06 mm minimum cell size and 22 x 10(6) peak cell count was implemented. The framework is validated across a range of ambient temperatures against ignition delay and liftoff lengths (LOLs). Qualitative results from the simulations were compared against experimental OH and CH2O planar laser-induced fluorescence (PLIF) data. The models are able to capture the spatial and temporal trends in species compared to those observed in the experiments. Quantitative and qualitative comparisons between the predictions of the reduced and detailed mechanisms are presented in detail. The main goal of this study is to demonstrate that detailed reaction mechanisms (similar to 1000 species) can now be used in engine simulations with a linear increase in computation cost with number of species during the tabulation process and a small increase in the 3D simulation cost.