Improved multilevel optimization approach for the design of complex engineering systems
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Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.
A preliminary aircraft engine design methodology is being developed that utilizes a cascade optimization strategy together with neural network and regression approximation methods. The cascade strategy employs different optimization algorithms in a specified sequence. The neural network and regression methods are used to approximate solutions obtained from the NASA Engine Performance Program (NEPP), which implements engine thermodynamic cycle and performance analysis models. The new methodology is proving to be more robust and computationally efficient than the conventional optimization approach of using a single optimization algorithm with direct reanalysis. The methodology has been demonstrated on a preliminary design problem for a novel subsonic turbofan engine concept that incorporates a wave rotor as a cycle-topping device. Computations of maximum thrust were obtained for a specific design point in the engine mission profile. The results (depicted in the figure) show a significant improvement in the maximum thrust obtained using the new methodology in comparison to benchmark solutions obtained using NEPP in a manual design mode.
Optimization research at NASA Glenn Research Center has addressed the design of structures, aircraft and airbreathing propulsion engines. The accumulated multidisciplinary design activity is collected under a testbed entitled COMETBOARDS. Several issues were encountered during the solution of the problems. Four issues and the strategies adapted for their resolution are discussed. This is followed by a discussion on analytical methods that is limited to structural design application. An optimization process can lead to an inefficient local solution. This deficiency was encountered during design of an engine component. The limitation was overcome through an augmentation of animation into optimization. Optimum solutions obtained were infeasible for aircraft and airbreathing propulsion engine problems. Alleviation of this deficiency required a cascading of multiple algorithms. Profile optimization of a beam produced an irregular shape. Engineering intuition restored the regular shape for the beam. The solution obtained for a cylindrical shell by a subproblem strategy converged to a design that can be difficult to manufacture. Resolution of this issue remains a challenge. The issues and resolutions are illustrated through a set of problems: Design of an engine component, Synthesis of a subsonic aircraft, Operation optimization of a supersonic engine, Design of a wave-rotor-topping device, Profile optimization of a cantilever beam, and Design of a cylindrical shell. This chapter provides a cursory account of the issues. Cited references provide detailed discussion on the topics. Design of a structure can also be generated by traditional method and the stochastic design concept. Merits and limitations of the three methods (traditional method, optimization method and stochastic concept) are illustrated. In the traditional method, the constraints are manipulated to obtain the design and weight is back calculated. In design optimization, the weight of a structure becomes the merit function with constraints imposed on failure modes and an optimization algorithm is used to generate the solution. Stochastic design concept accounts for uncertainties in loads, material properties, and other parameters and solution is obtained by solving a design optimization problem for a specified reliability. Acceptable solutions can be produced by all the three methods. The variation in the weight calculated by the methods was found to be modest. Some variation was noticed in designs calculated by the methods. The variation may be attributed to structural indeterminacy. It is prudent to develop design by all three methods prior to its fabrication. The traditional design method can be improved when the simplified sensitivities of the behavior constraint is used. Such sensitivity can reduce design calculations and may have a potential to unify the traditional and optimization methods. Weight versus reliability traced out an inverted-S-shaped graph. The center of the graph corresponded to mean valued design. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure. Weight can be reduced to a small value for a most failure-prone design. Probabilistic modeling of load and material properties remained a challenge.
A procedure for automatically designing a liquid propellant rocket engine combustion chamber in an optimal fashion is outlined. The procedure is contained in a digital computer code, AUTOCOM. The code is applied to an existing engine, and design modifications are generated which provide a substantial potential payload improvement over the existing design. Computer time requirements for this payload improvement were small, approximately four minutes in the CDC 6600 computer.
This computer program manual describes in two parts the automated combustor design optimization code AUTOCOM. The program code is written in the FORTRAN 4 language. The input data setup and the program outputs are described, and a sample engine case is discussed. The program structure and programming techniques are also described, along with AUTOCOM program analysis.
The quality engineering methods of Dr. Genichi Taguchi, employing design of experiments, are important statistical tools for designing high quality systems at reduced cost. The Taguchi method was utilized to study several simultaneous parameter level variations of a lunar aerobrake structure to arrive at the lightest weight configuration. Finite element analysis was used to analyze the unique experimental aerobrake configurations selected by Taguchi method. Important design parameters affecting weight and global buckling were identified and the lowest weight design configuration was selected.
Link analysis is a system engineering process in the design, development, and operation of communication systems and networks. Link models that are mathematical abstractions representing the useful signal power and the undesirable noise and attenuation effects (including weather effects if the signal path transverses through the atmosphere) that are integrated into the link budget calculation that provides the estimates of signal power and noise power at the receiver. Then the link margin is applied which attempts to counteract the fluctuations of the signal and noise power to ensure reliable data delivery from transmitter to receiver. (Link margin is dictated by the link margin policy or requirements.) A simple link budgeting approach assumes link parameters to be deterministic values typically adopted a rule-of-thumb policy of 3 dB link margin. This policy works for most S- and X-band links due to their insensitivity to weather effects. But for higher frequency links like Ka-band, Ku-band, and optical communication links, it is unclear if a 3 dB link margin would guarantee link closure. Statistical link analysis that adopted the 2-sigma or 3-sigma link margin incorporates link uncertainties in the sigma calculation. (The Deep Space Network (DSN) link margin policies are 2-sigma for downlink and 3-sigma for uplink.) The link reliability can therefore be quantified statistically even for higher frequency links. However in the current statistical link analysis approach, link reliability is only expressed as the likelihood of exceeding the signal-to-noise ratio (SNR) threshold that corresponds to a given bit-error-rate (BER) or frame-error-rate (FER) requirement. The method does not provide the true BER or FER estimate of the link with margin, or the required signalto-noise ratio (SNR) that would meet the BER or FER requirement in the statistical sense. In this paper, we perform in-depth analysis on the relationship between BER/FER requirement, operating SNR, and coding performance curve, in the case when the channel coherence time of link fluctuation is comparable or larger than the time duration of a codeword. We compute the "true" SNR design point that would meet the BER/FER requirement by taking into account the fluctuation of signal power and noise power at the receiver, and the shape of the coding performance curve. This analysis yields a number of valuable insights on the design choices of coding scheme and link margin for the reliable data delivery of a communication system - space and ground. We illustrate the aforementioned analysis using a number of standard NASA error-correcting codes.
NASA's Advanced Engineering Environment (AEE) is a research and development program that will improve collaboration among design engineers for launch vehicle conceptual design and provide the infrastructure (methods and framework) necessary to enable that environment. In this paper, three major technical challenges facing the AEE program are identified, and three specific design problems are selected to demonstrate how advanced methods can improve current design activities. References are made to studies that demonstrate these design problems and methods, and these studies will provide the detailed information and check cases to support incorporation of these methods into the AEE. This paper provides background and terminology for discussing the launch vehicle conceptual design problem so that the diverse AEE user community can participate in prioritizing the AEE development effort.
The performance seeking control algorithm is designed to continuously optimize the performance of propulsion systems. The performance seeking control algorithm uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated engine deviation parameters may not reflect the engine's actual off-nominal condition. This factor has a necessary impact on the overall performance seeking control scheme exacerbated by the open-loop character of the algorithm. The effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the performance seeking control algorithm to an F100 engine. An equivalence relation between the biases and engine deviation parameters stems from an observability study; therefore, it is undecided whether the estimated engine deviation parameters represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.
Additive manufacturing (AM) is advancing many applications of component design for liquid rocket engines. The ability to rapidly design and manufacture components has provided significant opportunities for development and flight programs across the propulsion industry. AM has demonstrated significant advantages that include cost and schedule savings in addition to performance improvements through new design opportunities. While these performance advantages can be in the characteristics of complex design features, they can also be in the form of new materials. AM has been demonstrated in these various component applications using a variety of metal alloys, many of which are traditional alloys for extreme environments. Additional developments were completed using AM to provide new alloys and maturing these material uses for high performance applications. Almost all of the prior developments across component applications have focused on single-alloys in these AM processes. NASA and industry partners have focused in recent years to advance processing to create bimetallic and multicomponent AM processes and materials. The role of multi-alloy AM offers advantages since it can further optimize weight, optimize reliability and performance by increasing the strength to weight ratio of a component, and can optimize materials for various engineering requirements. This is particularly important in liquid rocket engine combustion devices that must reject heat in high heat flux environments yet maintain adequate structural margins under high operational pressure. NASA has been exploring several AM processes, materials, and applications for combustion devices, specifically combustion chambers, injectors, nozzles, and ignition systems. These components require fine geometric features for internal flow or cooling functionality. They experience high thermal gradients across thin-walls and must survive high pressures and temperatures from propellants and the combustion process. A copper-based alloy is normally used to provide high thermal conductivity, but at the detriment of increased weight if used as a single alloy in an AM chamber. Various AM processes were demonstrated on these components using a copper-based alloy/superalloy bimetallic solution. The AM processes being explored individually and in combination for bimetallic applications include Laser Powder Bed Fusion (L-PBF), Laser Powder Directed Energy Deposition (LP-DED), and cold spray. The combination of bimetallic material combinations explored in this research include copper-based material primarily and superalloys, Inconel 625 or NASA HR-1. The various aspects of the additive manufacturing processes and challenges, materials characterization, and the testing of bimetallic components in a relevant environment will be discussed.
Additive manufacturing (AM) is advancing many applications of component design for liquid rocket engines. The ability to rapidly design and manufacture components has provided significant opportunities for development and flight programs across the propulsion industry. AM has demonstrated significant advantages that include cost and schedule savings in addition to performance improvements through new design opportunities. While these performance advantages can be in the characteristics of complex design features, they can also be in the form of new materials. AM has been demonstrated in these various component applications using a variety of metal alloys, many of which are traditional alloys for extreme environments. Additional developments were completed using AM to provide new alloys and maturing these material uses for high performance applications. Almost all of the prior developments across component applications have focused on single-alloys in these AM processes. NASA and industry partners have focused in recent years to advance processing to create bimetallic and multicomponent AM processes and materials. The role of multi-alloy AM offers advantages since it can further optimize weight, optimize reliability and performance by increasing the strength to weight ratio of a component, and can optimize materials for various engineering requirements. This is particularly important in liquid rocket engine combustion devices that must reject heat in high heat flux environments yet maintain adequate structural margins under high operational pressure. NASA has been exploring several AM processes, materials, and applications for combustion devices, specifically combustion chambers, injectors, nozzles, and ignition systems. These components require fine geometric features for internal flow or cooling functionality. They experience high thermal gradients across thin-walls and must survive high pressures and temperatures from propellants and the combustion process. A copper-based alloy is normally used to provide high thermal conductivity, but at the detriment of increased weight if used as a single alloy in an AM chamber. Various AM processes were demonstrated on these components using a copper-based alloy/superalloy bimetallic solution. The AM processes being explored individually and in combination for bimetallic applications include Laser Powder Bed Fusion (L-PBF), Laser Powder Directed Energy Deposition (LP-DED), and cold spray. The combination of bimetallic material combinations explored in this research include copper-based material primarily and superalloys, Inconel 625 or NASA HR-1. The various aspects of the additive manufacturing processes and challenges, materials characterization, and the testing of bimetallic components in a relevant environment will be discussed.
Assuming Space Shuttle technology to be state-of-the-art, projected technological advances to improve the capabilities of single-stage-to-orbit (SSTO) derivatives are examined. An increase of about 30% in payload performance can be expected from upgrading the present Shuttle system through weight and drag reductions and improvements in the propellants and engines. The ODINEX (Optimal Design Integration Executive Computer Program) program has been used to explore design options. An advanced technology SSTO baseline system derived from ODINEX analysis has a conventional wing-body configuration using LOX/LH engines, three with two-position nozzles with expansion ratios of 40 and 200 and four with fixed nozzles with an expansion ratio of 40. Two assisted-takeoff approaches are under consideration in addition to a concept in which the orbital vehicle takes off empty using airbreathing propulsion and carries out a rendezvous with two large cryogenic tankers carrying propellant at an altitude of 6100 m. Further approaches under examination for propulsion, aerothermodynamic design, and design integration are described.
An advanced, small 2.27 kb/sec (5 lbs/sec), high temperature, variable area radial turbine was studied for a rotor craft application. Variable capacity cycles including single-shaft and free-turbine engine configurations were analyzed to define an optimum engine design configuration. Parametric optimizations were made on cooled and uncooled rotor configurations. A detailed structural and heat transfer analysis was conducted to provide a 4000-hour life HP turbine with material properties of the 1988 time frame. A pivoted vane and a moveable sidewall geometry were analyzed. Cooling and variable geometry penalties were included in the cycle analysis. A variable geometry free-turbine engine configuration with a design 1477K (2200 F) inlet temperature and a compressor pressure ratio of 16:1 was selected. An uncooled HP radial turbine rotor with a moveable sidewall nozzle showed the highest performance potential for a time weighted duty cycle.
This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.
The design characteristics of the baseline engine configuration of the advanced expander cycle engine are described. Several aspects of engine optimization are considered which directly impact the design of the baseline thrust chamber. Four major areas of the power cycle optimization are emphasized: main turbine arrangement; cycle engine source; high pressure pump design; and boost pump drive.
FEM studies are presented of the nozzle and the low-pressure fuel-pump inducer designs for the Space Shuttle Main Engine (SSME) to analyze the effects of structural vibrations. FEM preprocessing software based on a CAD system is employed to develop a model of the component's sophisticated geometry. The nozzle geometry is also defined by means of the preprocessing technique and subsequently analyzed with respect to time-transient loading. The analysis is conducted with a Cray supercomputer using the SPAR/EAL FEM program. The investigation of the nozzle demonstrates the advantageous use of symmetry in the determination of nozzle response to SSME start-up transients. Plots of time vs strain are developed for gages on the nozzle wall and steerhorn tubing. The results of the inducer modeling are found to be adequate for investigating the component's principle modes, and the nozzle results indicate the suitability of the FEM techniques for optimizing the design of engine components.
The performance seeking control (PSC) algorithm is designed to continuously optimize the performance of propulsion systems. The PSC algorithm uses a nominal propulsion system model and estimates, in flight, the engine deviation parameters (EDPs) characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated EDPs may not reflect the engine's actual off-nominal condition. This factor has a direct impact on the PSC scheme exacerbated by the open-loop character of the algorithm. In this paper, the effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the PSC algorithm to an F100 engine. An equivalence relation between the biases and EDPs stems from the analysis; therefore, it is undecided whether the estimated EDPs represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.