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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

An Overview of Recent Developments in Computational Aeroelasticity

The motivation for Computational Aeroelasticity (CA) and the elements of one type of the analysis or simulation process are briefly reviewed. The need for streamlining and improving the overall process to reduce elapsed time and improve overall accuracy is discussed. Further effort is needed to establish the credibility of the methodology, obtain experience, and to incorporate the experience base to simplify the method for future use. Experience with the application of a variety of Computational Aeroelasticity programs is summarized for the transonic flutter of two wings, the AGARD 445.6 wing and a typical business jet wing. There is a compelling need for a broad range of additional flutter test cases for further comparisons. Some existing data sets that may offer CA challenges are presented.

Bennett, Robert M.↗
NASA NTRSDate not supplied

Launch Availability Analysis for the Artemis Program

On March 26, 2019, Vice President Pence stated that the policy of the Trump administration and the United States of America is to return American astronauts to the Moon within the next five years i.e., by 2024. Since that time, NASA has begun the process of developing concepts of operations and launch campaign options to achieve that goal as well as to provide a sustainable human presence on the Moon. Whereas the Apollo program utilized one Saturn V rocket to carry out a single lunar landing mission of short duration, NASA’s preliminary plans for the Artemis Program call for a combination of medium lift class rockets along with the heavy lift Space Launch System (SLS)to achieve a lunar landing by 2024 as well as subsequent missions. This paper describes how discrete event simulation is used to model the launch campaigns and provide metrics on launch availability and mission duration for each element being launched. Possible methods for improving launch availability are presented.

Grant Cates↗
NASA NTRS2006

Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.

Rai, Man Mohan↗
NASA NTRS2002

Numerical Simulation of One- And Two-Phase Flows In Propulsion Systems

In this report, we present some results of problems investigated during joint research between the Hampton University (HU) Fluid Mechanics and Acoustics Laboratory (FM&AL), NASA Glenn Research Center (GRC) and the Hyper-X Program of the NASA Langley Research Center (LaRC). This work is supported by joint research between the NASA GRC/HU FM&AL and the Institute of Mechanics at Moscow State University (IM/MSU) in Russia under a Civilian Research and Development Foundation (CRDF) grant, #RE1-2068. The main areas of current scientific interest of the FM&AL include an investigation of the proposed and patented advanced methods for aircraft engine thrust and noise benefits. These methods are based on nontraditional 3D (three dimensional) corrugated and composite nozzle, inlet, propeller and screw designs such as the Bluebell and Telescope nozzles, Mobius-shaped screws, etc. These are the main subject of our other projects, of which one is the NASA MURED's (Minority University Research and Education Division) FAR (Faculty Awards for Research) Award, #NAG-3-2249. Working jointly with this project team, our team also analyzes additional methods for exhaust jet noise reduction. These methods are without essential thrust loss and even with thrust augmentation. The research is focused on a wide regime of problems in the propulsion field as well as in experimental testing and theoretical and numerical simulation analyses for advanced aircraft and rocket engines. The FM&AL Team uses analytical methods, numerical simulations and experimental tests at the Hampton University campus, NASA and IM/MSU. The main results obtained by FM&AL team were published in the papers and patents.

Gilinsky, Mikhail M.↗
NASA NTRS1987

Boundary layer simulation improvement

The current Boundary Layer Integral Matrix Procedure - Version J (BLIMPJ) is advanced with regard to the previously modeled calculation methods accounting for surface roughness, relaminarization, condensed phase, and thick boundary layer effects. Current analytical limitations will be relaxed to permit more realistic wall and flow conditions and to use appropriate turbulence models with specific emphasis for the projected Orbit Transfer Vehicle (OTV) engine application. The updates will be incorporated into the BLIMPJ computer program, and the modified program will be made operational on the MSFC CRAY computer system. Experimental feasibility studies were conducted to find out how to obtain quality test data with advanced instrumentation for concept verification purposes.

Praharaj, Sarat C.↗
NASA NTRS1983

Optimization methods applied to hybrid vehicle design

The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.

Donoghue, J. F.↗
NASA NTRS1995

Model checking

Automatic formal verification methods for finite-state systems, also known as model-checking, successfully reduce labor costs since they are mostly automatic. Model checkers explicitly or implicitly enumerate the reachable state space of a system, whose behavior is described implicitly, perhaps by a program or a collection of finite automata. Simple properties, such as mutual exclusion or absence of deadlock, can be checked by inspecting individual states. More complex properties, such as lack of starvation, require search for cycles in the state graph with particular properties. Specifications to be checked may consist of built-in properties, such as deadlock or 'unspecified receptions' of messages, another program or implicit description, to be compared with a simulation, bisimulation, or language inclusion relation, or an assertion in one of several temporal logics. Finite-state verification tools are beginning to have a significant impact in commercial designs. There are many success stories of verification tools finding bugs in protocols or hardware controllers. In some cases, these tools have been incorporated into design methodology. Research in finite-state verification has been advancing rapidly, and is showing no signs of slowing down. Recent results include probabilistic algorithms for verification, exploitation of symmetry and independent events, and the use symbolic representations for Boolean functions and systems of linear inequalities. One of the most exciting areas for further research is the combination of model-checking with theorem-proving methods.

Dill, David L.↗
NASA NTRS2004

Scripting Scenarios for the Human Patient Simulator

The Human Patient Simulator (HPS) is particularly useful in providing scenario-based learning which can be tailored to fit specific scenarios and which can be modified in realtime to enhance the teaching environment. Scripting these scenarios so as to maximize learning requires certain skills, in order to ensure that a change in student performance, understanding, critical thinking, and/or communication skills results. Methods: A "good" scenario can be defined in terms of applicability, learning opportunities, student interest, and clearly associated metrics. Obstacles to such a scenario include a lack of understanding of the applicable environment by the scenario author(s), a desire (common among novices) to cover too many topics, failure to define learning objectives, mutually exclusive or confusing learning objectives, unskilled instructors, poor preparation , disorganized approach, or an inappropriate teaching philosophy (such as "trial by fire" or education through humiliation). Results: Descriptions of several successful teaching programs, used in the military, civilian, and NASA medical environments , will be provided, along with sample scenarios. Discussion: Simulator-based lessons have proven to be a time- and cost-efficient manner by which to educate medical personnel. Particularly when training for medical care in austere environments (pre-hospital, aeromedical transport, International Space Station, military operations), the HPS can enhance the learning experience.

Bacal, Kira↗
NASA NTRS1978

A new look at performance capabilities of arc-driven shock tubes

High shock-tube velocities simulating Jovian entry have been achieved with arc-driver operation using a method of tailoring electrical circuit impedance to arc-plasma impedance. The concept of impedance matching also prescribes a requirement for minimum electrical capacitance. The technique of developing the strongest shock-generating capability for an arc-driver system with the least total energy is discussed. Several programs using the shock tube for developmental research in energy and industrial applications are outlined.

Dannenberg, R. E.↗
NASA NTRS1978

Identification of cost effective energy conservation measures

In addition to a successful program of readily implemented conservation actions for reducing building energy consumption at Kennedy Space Center, recent detailed analyses have identified further substantial savings for buildings representative of technical facilities designed when energy costs were low. The techniques employed for determination of these energy savings consisted of facility configuration analysis, power and lighting measurements, detailed computer simulations and simulation verifications. Use of these methods resulted in identification of projected energy savings as large as $330,000 a year (approximately two year break-even period) in a single building. Application of these techniques to other commercial buildings is discussed

Bierenbaum, H. S.↗
NASA NTRS1994

Space proton transport in one dimension

An approximate evaluation procedure is derived for a second-order theory of coupled nucleon transport in one dimension. An analytical solution with a simplified interaction model is used to determine quadrature parameters to minimize truncation error. Effects of the improved method on transport solutions with the BRYNTRN data base are evaluated. Comparisons with Monte Carlo benchmarks are given. Using different shield materials, the computational procedure is used to study the physics of space protons. A transition effect occurs in tissue near the shield interface and is most important in shields of high atomic number.

NASA Discipline Number 45-10↗
NASA NTRS2005

A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

Alexandrov, Natalia↗
NASA NTRS2005

Intelligent Pilot Aids for Flight Re-Planning in Emergencies

Effective and safe control of an aircraft may be difficult or nearly impossible for a pilot following an unexpected system failure. Without prior training, the pilot must ascertain on the fly those changes in both manual control technique and procedures that will lead to a safe landing of the aircraft. Sophisticated techniques for determining the required control techniques are now available. Likewise, a body of literature on pilot decision making provides formalisms for examining how pilots approach discrete decisions framed as the selection between options. However, other aspects of behavior, such as the task of route planning and guidance, are not as well studied. Not only is the pilot faced with possible performance changes to the aircraft dynamics, but he or she is also tasked to create a plan of actions that will effectively take the aircraft down to a safe landing. In this plan, the many actions that the pilot can perform are closely intertwined with the trajectory of the aircraft, making it difficult to accurately predict the final outcome. Coupled with the vast number of potential actions to be taken, this problem may seem intractable. This is reflected in the lack of a pre-specified procedure capable of giving pilots the ability to find a resolution for this task. This report summarizes a multi-year effort to examine methods to aid pilots in planning an approach and arrival to an airport following an aircraft systems failure. Ultimately, we hypothesize that automatic assistance to pilots can be provided in real-time in the form of improving pilot control of a damaged aircraft and providing pilots with procedural directives suitable for critical flight conditions; such systems may also benefit pilot training and procedure design. To achieve this result, a systematic, comprehensive research program was followed, building on prior research. This approach included a pencil-and-paper study with airline pilots examining methods of representing a flight route in an immediately understandable manner, and in a manner that would allow the pilot to modify an automatically-generated route and/or detect any inappropriate elements in an automatically-generated route. Likewise, a flight simulator study examined different cockpit systems for the relative merits of providing pilots with any of a variety of automated functions for emergency flight planning. The results provide specific guidance for the design of such systems.

Pritchett, Amy R.↗
NASA NTRS2013

Small Launch Vehicle Trade Space Definition: Development of a Zero Level Mass Estimation Tool with Trajectory Validation

Recent high level interest in the capability of small launch vehicles has placed significant demand on determining the trade space these vehicles occupy. This has led to the development of a zero level analysis tool that can quickly determine the minimum expected vehicle gross liftoff weight (GLOW) in terms of vehicle stage specific impulse (Isp) and propellant mass fraction (pmf) for any given payload value. Utilizing an extensive background in Earth to orbit trajectory experience a total necessary delta v the vehicle must achieve can be estimated including relevant loss terms. This foresight into expected losses allows for more specific assumptions relating to the initial estimates of thrust to weight values for each stage. This tool was further validated against a trajectory model, in this case the Program to Optimize Simulated Trajectories (POST), to determine if the initial sizing delta v was adequate to meet payload expectations. Presented here is a description of how the tool is setup and the approach the analyst must take when using the tool. Also, expected outputs which are dependent on the type of small launch vehicle being sized will be displayed. The method of validation will be discussed as well as where the sizing tool fits into the vehicle design process.

Waters, Eric D.↗
NASA NTRS1988

Spur gears: Optimal geometry, methods for generation and Tooth Contact Analysis (TCA) program

The contents of this report include the following: (1) development of optimal geometry for crowned spur gears; (2) methods for their generation; and (3) tooth contact analysis (TCA) computer programs for the analysis of meshing and bearing contact on the crowned spur gears. The method developed for synthesis is used for the determination of the optimal geometry for crowned pinion surface and is directed to reduce the sensitivity of the gears to misalignment, localize the bearing contact, and guarantee the favorable shape and low level of the transmission errors. A new method for the generation of the crowned pinion surface has been proposed. This method is based on application of the tool with a surface of revolution that slightly deviates from a regular cone surface. The tool can be used as a grinding wheel or as a shaver. The crowned pinion surface can also be generated by a generating plane whose motion is provided by an automatic grinding machine controlled by a computer. The TCA program simulates the meshing and bearing contact of the misaligned gears. The transmission errors are also determined.

Litvin, Faydor L.↗
NASA NTRS1990

Neural networks as a control methodology

While conventional computers must be programmed in a logical fashion by a person who thoroughly understands the task to be performed, the motivation behind neural networks is to develop machines which can train themselves to perform tasks, using available information about desired system behavior and learning from experience. There are three goals of this fellowship program: (1) to evaluate various neural net methods and generate computer software to implement those deemed most promising on a personal computer equipped with Matlab; (2) to evaluate methods currently in the professional literature for system control using neural nets to choose those most applicable to control of flexible structures; and (3) to apply the control strategies chosen in (2) to a computer simulation of a test article, the Control Structures Interaction Suitcase Demonstrator, which is a portable system consisting of a small flexible beam driven by a torque motor and mounted on springs tuned to the first flexible mode of the beam. Results of each are discussed.

Mccullough, Claire L.↗
NASA NTRS2003

Kalman Filtering with Inequality Constraints for Turbofan Engine Health Estimation

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops two analytic methods of incorporating state variable inequality constraints in the Kalman filter. The first method is a general technique of using hard constraints to enforce inequalities on the state variable estimates. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate state variables that are known to vary slowly with time. (Soft constraints are constraints that are required to be approximately satisfied rather than exactly satisfied.) The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results. The use of the algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate health parameters. The turbofan engine model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

Simon, Dan↗
NASA NTRS2005

Adaptive Optics: Arroyo Simulation Tool and Deformable Mirror Actuation Using Golay Cells

The Arroyo C++ libraries, written by Caltech post-doc student Matthew Britton, have the ability to simulate optical systems and atmospheric signal interference. This program was chosen for use in an end-to-end simulation model of a laser communication system because it is freely distributed and has the ability to be controlled by a remote system or "smart agent." Proposed operation of this program by a smart agent has been demonstrated, and the results show it to be a suitable simulation tool. Deformable mirrors, as a part of modern adaptive optics systems, may contain thousands of tiny, independently controlled actuators used to modify the shape of the mirror. Each actuator is connected to two wires, creating a cumbersome and expensive device. Recently, an alternative actuation method that uses gas-filled tubes known as Golay cells has been explored. Golay cells, operated by infrared lasers instead of electricity, would replace the actuator system thereby creating a more compact deformable mirror. The operation of Golay cells and their ability to move a deformable mirror in excess of the required 20 microns has been demonstrated. Experimentation has shown them to be extremely sensitive to pressure and temperature, making them ideal for use in a controlled environment.

Lint, Adam S.↗