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This report provides detailed documentation of the algorithms that where developed and implemented in the Plato software over the course of the Optimization-based Design for Manufacturing LDRD project.
The development of control systems for jet engines requires a real-time computer simulation. The simulation provides an effective tool for evaluating control concepts and problem areas prior to actual engine testing. The development and use of a real-time simulation of the Pratt and Whitney F100-PW100 turbofan engine is described. The simulation was used in a multi-variable optimal controls research program using linear quadratic regulator theory. The simulation is used to generate linear engine models at selected operating points and evaluate the control algorithm. To reduce the complexity of the design, it is desirable to reduce the order of the linear model. A technique to reduce the order of the model; is discussed. Selected results between high and low order models are compared. The LQR control algorithms can be programmed on digital computer. This computer will control the engine simulation over the desired flight envelope.
A low pressure turbine (LPT) active clearance control (ACC) cooling system was developed to reduce the fuel consumption of current CF6-50 turbofan engines for wide bodied commercial aircraft. The program performance improvement goal of 0.3% delta sfc was determined to be achievable with an improved impingement cooling system. The technology enables the design of an optimized manifold and piping system which is capable of a performance gain of 0.45% delta sfc.
Results are presented from an experimental program aimed at optimizing the features of an advanced two-stage combustor that currently is being developed as part of the National Aeronautics and Space Administration/Pratt & Whitney Aircraft Energy Efficient Engine program. The combustor is designed to meet stringent goals for performance, emissions, durability and operational characteristics. An overview of the design selection process in light of these goals is discussed. Combustor rig test results, which highlight the techniques used to reduce emissions, development of the pilot and main power zone fuel injection systems, and methods of achieving efficient fuel staging, are presented.
An assessment is undertaken of the development status of technology, applicable to future civil air transport design, which is currently undergoing conceptual study or testing at NASA facilities. The NASA civil air transport effort emphasizes advanced aerodynamic computational capabilities, fuel-efficient engines, advanced turboprops, composite primary structure materials, advanced aerodynamic concepts in boundary layer laminarization and aircraft configuration, refined control, guidance and flight management systems, and the integration of all these design elements into optimal systems. Attention is given to such novel transport aircraft design concepts as forward swept wings, twin fuselages, sandwich composite structures, and swept blade propfans.
Silicon nitride is a high temperature material currently under consideration for heat engine and other applications. The objective is to improve the net shape fabrication technology of Si3N4 by injection molding. This is to be accomplished by optimizing the process through a series of statistically designed matrix experiments. To provide input to the matrix experiments, a wide range of alternate materials and processing parameters was investigated throughout the whole program. The improvement in the processing is to be demonstrated by a 20 percent increase in strength and a 100 percent increase in the Weibull modulus over that of the baseline material. A full characterization of the baseline process was completed. Material properties were found to be highly dependent on each step of the process. Several important parameters identified thus far are the starting raw materials, sinter/hot isostatic pressing cycle, powder bed, mixing methods, and sintering aid levels.
A systematic approach to the use of nondestructive evaluation (NDE) throughout the ceramic engine component development cycle is necessary to achieve the high reliability required for turbine engine applications. Specifically, NDE techniques must be developed and applied to component design, development, fabrication, and test to ensure adequate materials characterization. This approach has the potential of reducing ceramic component cost by optimizing the initial design and eliminating unacceptable components early in the fabrication cycle. Although research NDE techniques have been developed that have the potential to solve the problems of ceramic component inspection, systematic application of these NDE techniques for inspection and process control has not yet occurred. The application of NDE techniques for both material characterization and flaw detection is discussed as well as an approach for applying NDE throughout the fabrication process to achieve highly reliable ceramic components.
Spacecraft designs are driven by the payloads and mission requirements that they support. Many of the payload characteristics, such as mass, power requirements, communication requirements, moving parts, and so forth directly affect the choices for the spacecraft structural configuration and its subsystem design and component selection. The conceptual design process, which translates mission requirements into early spacecraft concepts, must be tolerant of frequent changes in the payload complement and resource requirements. A computer data base was designed and implemented for the purposes of containing the payload characteristics pertinent for spacecraft conceptual design, tracking the evolution of these payloads over time, and enabling the integration of the payload data with engineering analysis programs for improving the efficiency in producing spacecraft designs. In-house tools were used for constructing the data base and for performing the actual integration with an existing program for optimizing payload mass locations on the spacecraft.
Our scientific goal is to understand the process of human decision-making. Specifically, a model of human decision-making in piloting modern commercial aircraft which prescribes optimal behavior, and against which we can measure human sub-optimality is sought. This model should help us understand such diverse aspects of piloting as strategic decision-making, and the implicit decisions involved in attention allocation. Our engineering goal is to provide design specifications for (1) better computer-based decision-aids, and (2) better training programs for the human pilot (or human decision-maker, DM).
Sustained Hypersonic Flight capability is envisioned for several future military and civilian applications, such as long range immediate reaction reconnaissance, high speed interception of air targets, long range precision strike against hardened or time critical targets, and access to space. The inherent reduction in time-to-target and low vulnerability will permit new operational tactics. The symposium outlined mission needs and operational scenarios for hypersonic vehicles. Addressed were: Technological issues and challenges in external hypersonic aerodynamics and design, propulsion and engine/airframe integration, military ramjet applications, overall system design including structures and materials development needs, and test facilities. Ground based test facilities cannot provide full simulation at Mach numbers above 5. Therefore, the use of Computational Fluid Dynamics design tools is essential. For routine use of the computational tools in analysis, design and optimization, it is necessary to reduce the time for the entire computational process by two orders of magnitude. Hypersonic boundary layer transition remains a critical design issue because of the important impact on engine drag and on heating, which can affect the choice of materials and thermal protection systems. In addition, reliable prediction of scramjet net thrust is an absolute must in resolving hypersonic air breathing vehicle design issues. Due to current facility and computational shortfalls, the development of future hypersonic flight systems requires research flight tests in the technology areas of boundary layer transition and air-breathing propulsion engine performance. For sustained hypersonic flight beyond Mach 6, the supersonic combustion ramjet (scramjet) engine is the only choice for the near future. Only this air-breathing concept offers a significant promise of large reductions in required propellant fractions, increased payload fractions, and reduced size vehicles, together with a foreseeable technological feasibility. Airframe/engine integration, combustor design and thermal management are the predominant engineering tasks. Fuels, hydrogen or hydrocarbon, must be matched to the operational needs of military or civil use. Experience in existing ramjet propelled missiles capable of speeds up to Mach 4 can support the development effort. The potential mission and cost benefits of sustained hypersonic flight to both military and civil applications are tremendous. From the budget point of view, the possibility of sharing development costs between military and civil programs offers a specific advantage.
The Orbital Sciences Corporation X-34 vehicle demonstrates technologies and operations key to future reusable launch vehicles. The general flight performance goal of this unmanned rocket plane is Mach 8 flight at an altitude of 250,000 feet. The Main Propulsion System (MPS) supplies liquid propellants to the main engine, which provides the primary thrust for attaining mission goals. Major MPS design and operational goals are aircraft-like ground operations, quick turnaround between missions, and low initial/operational costs. Analyses related to optimal MPS subsystem design are reviewed in this paper. A pressurization system trade weighs maintenance/reliability concerns against those for safety in a comparison of designs using pressure regulators versus orifices to control pressurant flow. A propellant dump/feed system analysis weighs the issues of maximum allowable vehicle landing weight, trajectory, and MPS complexity to arrive at a final configuration for propellant dump/feed systems.
This paper extends and applies the strategies recently developed by the authors for handling constraints under uncertainty to robust design optimization. For the scope of this paper, robust optimization is a methodology aimed at problems for which some parameters are uncertain and are only known to belong to some uncertainty set. This set can be described by either a deterministic or a probabilistic model. In the methodology developed herein, optimization-based strategies are used to bound the constraint violation region using hyper-spheres and hyper-rectangles. By comparing the resulting bounding sets with any given uncertainty model, it can be determined whether the constraints are satisfied for all members of the uncertainty model (i.e., constraints are feasible) or not (i.e., constraints are infeasible). If constraints are infeasible and a probabilistic uncertainty model is available, upper bounds to the probability of constraint violation can be efficiently calculated. The tools developed enable approximating not only the set of designs that make the constraints feasible but also, when required, the set of designs for which the probability of constraint violation is below a prescribed admissible value. When constraint feasibility is possible, several design criteria can be used to shape the uncertainty model of performance metrics of interest. Worst-case, least-second-moment, and reliability-based design criteria are considered herein. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, these strategies are easily applicable to a broad range of engineering problems.
As a critical part of the NASA Constellation Program lunar transportation architecture, the Altair lunar lander will return humans to the moon and enable a sustained program of lunar exploration. The Altair is to deliver up to four crew to the surface of the moon and return them to low lunar orbit at the completion of their mission. Altair will also be used to deliver large cargo elements to the lunar surface, enabling the buildup of an outpost. The Altair Project initialized its design using a "minimum functionality" approach that identified critical functionality required to meet a minimum set of Altair requirements. The Altair team then performed several analysis cycles using risk-informed design to selectively add back components and functionality to increase the vehicle's safety and reliability. The analysis cycle results were captured in a reference Altair design. This design was reviewed at the Constellation Lunar Capabilities Concept Review, a Mission Concept Review, where key driving requirements were confirmed and the Altair Project was given authorization to began Phase A project formulation. A key objective of Phase A is to revisit the Altair vehicle configuration, to better optimize it to complete its broad range of crew and cargo delivery missions. Industry was invited to partner with NASA early in the design to provide their insights regarding Altair configuration and key engineering challenges. NASA intends to continue to seek industry involvement in project formulation activities. This paper will update the international coimmunity on the status of the Altair Project as it addresses the challenges of project formulation, including optinuzing a vehicle configuration based on the work of the NASA Altair Project team, industry inputs and the plans going forward in designing the Altair lunar lander.
An effort was undertaken to perform CFD analysis of fluid flow in Lean-Direct Injection (LDI) combustors with axial swirl-venturi elements for next-generation LDI-3 combustor design. The National Combustion Code (NCC) was used to perform non-reacting and two-phase reacting flow computations for a nineteen-element injector array arranged in a three-module, 7-5-7 element configuration. All computations were performed with a consistent approach of mesh-optimization, spray-modeling, ignition and kinetics-modeling with the NCC. Computational predictions of the aerodynamics of the injector were used to arrive at an optimal injector design that meets effective area and fuel-air mixing criteria. LDI-3 emissions (EINOx, EICO and UHC) were compared with the previous generation LDI-2 combustor experimental data at representative engine cycle conditions.
NASA’s future exploration missions mandate a significant paradigm change for mission planning, spacecraft design, human systems integration, and in-flight medical care due to constraints on mass, volume, power, resupply missions, and medical evacuation capabilities. These constraints require further development of the human health and performance system, which includes the medical, task performance, wellness, data, human and other systems necessary to keep the crew healthy and functioning optimally. The human health and performance system will be tightly integrated with mission and habitat design to provide a sufficient human health and performance infrastructure to enable mission success. A suite of systems engineering tools will aid in the decision making process for the development of such a human health and performance system. This Concept of Operations provides a vision for a tool suite to conduct evaluations of human health and performance system options, inform research prioritization, and provide trade study support, based on evidence, risks, and systems engineering principles. The integrated tool suite under development is IMPACT.
A trajectory design process is presented for a proposed National Launch System (NLS) vehicle configuration. A software trajectory planner was developed to generate near optimal pitch plane trajectories for variable mission plans, variable launch site selections, and last minute wind conditions at the time of launch. In addition, the trajectory planner performs in-flight near optimal trajectory redesign in the event of an engine-out or unexpected wind dispersions. The trajectory planner iteratively simulates trial trajectories defined by variables corresponding to a parameterized trajectory shape. The trajectory planner optimizes these trajectory parameters according to the fuel used to realize the trajectories. For prelaunch scenarios, the trajectory planner generated near optimal trajectories of consistent fuel performance irrespective of the winds encountered at the launch site. In-flight trajectory redesigns were also shown to be effective in fulfilling the mission plan and preserving the structural integrity of the vehicle. The in-flight redesign results indicate that closed-loop guidance in the early stages of ascent is highly effective in accommodating engine failures or wind dispersions. All trajectory designs were verified using a 6DOF simulation with flight controllers.
In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.
Topology optimization (TO) is commonly applied to design the unit cells of periodic structures. For example, metamaterials, lattice structures, phononic crystals (PhC), and photonic crystals (PC) have all been previously designed via TO. Unfortunately, the optimal structures for certain design objectives, e.g., bandgaps, are often impossible to manufacture as they have disconnected regions or “islands” of solid material (ISM) that are not self-supporting. Additionally, designs with enclosed void space (EVS) are problematic for additive manufacturing (AM) since support material or pre-sintered powder cannot be removed after manufacturing. We present a series of constraints that may be incorporated into any TO framework to ensure structures are self-supporting without enclosed voids. Additionally, we employ homogenization-based constraints that allow the designer to tune the elastic stiffness and isotropy of the optimized design. The proposed constraints are evaluated on example microstructures and utilized in a simple optimization test problem to highlight their abilities and limitations so that guidelines for appropriate combinations of constraints may be proposed. Effective constraint combinations are demonstrated on the design of 3D photonic crystals for maximum bandgap subject to manufacturing and stiffness constraints.