SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Based on partial differential equations of motion the closed form solution for the optimal estimation of a spatially continuous state vector is derived, using a continuously distributed sensor. Local control is shown to be the feedback that minimizes a quadratic performance index of sensor and process disturbances. A detailed example of the control of a string in tension is presented.
There are innumerable two-component systems in which two very different liquid phases co-exist in equilibrium over a range of temperature and composition, e.g., oil and water, salt fluxes and solders, aluminum and lead. Often it is of practical concern to fabricate a solid component consisting of a uniform dispersion of one phase in the other. Unfortunately, uniform microstructural development during solidification of two immiscible liquids is hampered by inherent, often large, density differences between the phases that lead to severe segregation. Uniformity is also compromised by preferential wetting and coalescence phenomena. It is, however, well known that ultrasonic energy can initiate and maintain a fine liquid-liquid dispersion. The work presented here extends that observation by application of ultrasonic energy to promote uniform phase incorporation during controlled directional solidification. To this end experiments with the transparent organic, immiscible, succinonitrile-glycerol system were conducted and the numerous processing parameters associated with this technique were evaluated in view of optimizing dispersion uniformity. In view of the initial experimental results a model that predicts the dispersed liquid droplet size as a function of material properties, sample geometry, and applied energy has been developed. In the mathematical model we consider the ultrasonic field in an experimental ampoule of length L and diameter D induced by a probe having a vibration frequency of f=2OKhz (circular frequency omega = 2 pi f). The amplitude is adjustable from A=65 to 13Omicrons. The probe tip diameter is d, the liquid has a density of p, in which the speed of sound and surface tension are, respectively, c and sigma. The mathematical model and numerical investigation for the experiments [1] is done using the following assumptions: (i) The droplet size is small in comparison to the sound wave length; (ii) The forces between droplets are neglected (relative concentration is small); (iii) The droplet is stable if the kinetic energy, E(sub K), of the liquid motion due to ultrasonic field influence is less then the binding energy, E(sub S), due to the surface tension (it is easy to show that the surface energy of two droplets resulting from one is larger by about a factor of two.); (iv) The stability limit is characterized by E(sub S) to approx. E(sub K).
An extremely innovative approach has been presented, which is to have the surgeon operate through a simulator running in real-time enhanced with an intelligent controller component to enhance the safety and efficiency of a remotely conducted operation. The use of a simulator enables the surgeon to operate in a virtual environment free from the impediments of telecommunication delay. The simulator functions as a predictor and periodically the simulator state is corrected with truth data. Three major research areas must be explored in order to ensure achieving the objectives. They are: simulator as predictor, image processing, and intelligent control. Each is equally necessary for success of the project and each of these involves a significant intelligent component in it. These are diverse, interdisciplinary areas of investigation, thereby requiring a highly coordinated effort by all the members of our team, to ensure an integrated system. The following is a brief discussion of those areas. Simulator as a predictor: The delays encountered in remote robotic surgery will be greater than any encountered in human-machine systems analysis, with the possible exception of remote operations in space. Therefore, novel compensation techniques will be developed. Included will be the development of the real-time simulator, which is at the heart of our approach. The simulator will present real-time, stereoscopic images and artificial haptic stimuli to the surgeon. Image processing: Because of the delay and the possibility of insufficient bandwidth a high level of novel image processing is necessary. This image processing will include several innovative aspects, including image interpretation, video to graphical conversion, texture extraction, geometric processing, image compression and image generation at the surgeon station. Intelligent control: Since the approach we propose is in a sense predictor based, albeit a very sophisticated predictor, a controller, which not only optimizes end effector trajectory but also avoids error, is essential. We propose to investigate two different approaches to the controller design. One approach employs an optimal controller based on modern control theory; the other one involves soft computing techniques, i.e. fuzzy logic, neural networks, genetic algorithms and hybrids of these.
Shape Change Technologies has developed a process to manufacture net-shaped TiNi foam torque tubes that demonstrate the shape memory effect. The torque tubes dramatically reduce response time by a factor of 10. This Phase II project matured the actuator technology by rigorously characterizing the process to optimize the quality of the TiNi and developing a set of metrics to provide ISO 9002 quality assurance. A laboratory virtual instrument engineering workbench (LabVIEW'TM')-based, real-time control of the torsional actuators was developed. These actuators were developed with The Boeing Company for aerospace applications.
Proposed is a method, implemented in software, for estimating fault state of an apparatus outfitted with sensors. At each execution period the method processes sensor data from the apparatus to obtain a set of parity parameters, which are further used for estimating fault state. The estimation method formulates a convex optimization problem for each fault hypothesis and employs a convex solver to compute fault parameter estimates and fault likelihoods for each fault hypothesis. The highest likelihoods and corresponding parameter estimates are transmitted to a display device or an automated decision and control system. The obtained accurate estimate of fault state can be used to improve safety, performance, or maintenance processes for the apparatus.
We compared two possible implementations of local linear models for control: one approach is based on a self-organizing map (SOM) to cluster the dynamics followed by a set of linear models operating at each cluster. Therefore the gating function is hard (a single local model will represent the regional dynamics). This simplifies the controller design since there is a one to one mapping between controllers and local models. The second approach uses a soft gate using a probabilistic framework based on a Gaussian Mixture Model (also called a dynamic mixture of experts). In this approach several models may be active at a given time, we can expect a smaller number of models, but the controller design is more involved, with potentially better noise rejection characteristics. Our experiments showed that the SOM provides overall best performance in high SNRs, but the performance degrades faster than with the GMM for the same noise conditions. The SOM approach required about an order of magnitude more models than the GMM, so in terms of implementation cost, the GMM is preferable. The design of the SOM is straight forward, while the design of the GMM controllers, although still reasonable, is more involved and needs more care in the selection of the parameters. Either one of these locally linear approaches outperform global nonlinear controllers based on neural networks, such as the time delay neural network (TDNN). Therefore, in essence the local model approach warrants practical implementations. In order to call the attention of the control community for this design methodology we extended successfully the multiple model approach to PID controllers (still today the most widely used control scheme in the industry), and wrote a paper on this subject. The echo state network (ESN) is a recurrent neural network with the special characteristics that only the output parameters are trained. The recurrent connections are preset according to the problem domain and are fixed. In a nutshell, the states of the reservoir of recurrent processing elements implement a projection space, where the desired response is optimally projected. This architecture trades training efficiency by a large increase in the dimension of the recurrent layer. However, the power of the recurrent neural networks can be brought to bear on practical difficult problems. Our goal was to implement an adaptive critic architecture implementing Bellman s approach to optimal control. However, we could only characterize the ESN performance as a critic in value function evaluation, which is just one of the pieces of the overall adaptive critic controller. The results were very convincing, and the simplicity of the implementation was unparalleled.
This paper presents a methodology for automated model order reduction (MOR) of flexible aircrafts to construct linear parameter-varying (LPV) reduced order models (ROM) for aeroservoelasticity (ASE) analysis and control synthesis in broad flight parameter space. The novelty includes utilization of genetic algorithms (GAs) to automatically determine the states for reduction while minimizing the trial-and-error process and heuristics requirement to perform MOR; balanced truncation for unstable systems to achieve locally optimal realization of the full model; congruence transformation for "weak" fulfillment of state consistency across the entire flight parameter space; and ROM interpolation based on adaptive grid refinement to generate a globally functional LPV ASE ROM. The methodology is applied to the X-56A MUTT model currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that X-56A ROM with less than one-seventh the number of states relative to the original model is able to accurately predict system response among all input-output channels for pitch, roll, and ASE control at various flight conditions. The GA-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The adaptive refinement allows selective addition of the grid points in the parameter space where flight dynamics varies dramatically to enhance interpolation accuracy without over-burdening controller synthesis and onboard memory efforts downstream. The present MOR framework can be used by control engineers for robust ASE controller synthesis and novel vehicle design.
One of the ongoing challenges of the aerospace industry is to develop more efficient turbine engines. Greater efficiency entails reduced specific strength and larger temperature gradients, the latter of which means higher operating temperatures and increased thermal conductivity. Continued development of nickel-based superalloys has provided steady increases in engine efficiency and the limits of superalloys have probably not been realized. However, other material systems are under intense investigation for possible use in high temperature engines. Ceramic, intermetallic, and various composite systems are being explored in an effort to exploit the much higher melting temperatures of these systems. NiAl is considered a potential alternative to conventional superalloys due to its excellent oxidation resistance, low density, and high melting temperature. The fact that NiAl is the most common coating for current superalloy turbine blades is a tribute to its oxidation resistance. Its density is one-third that of typical superalloys and in most temperature ranges its thermal conductivity is twice that of common superalloys. Despite these many advantages, NiAl requires more investigation before it is ready to be used in engines. Binary NiAl in general has poor high-temperature strength and low-temperature ductility. On-going research in alloy design continues to make improvements in the high-temperature strength of NiAl. The factors controlling low temperature ductility have been identified in the last few years. Small, but reproducible ductility can now be achieved at room temperature through careful control of chemical purity and processing. But the mechanisms controlling the transition from brittle to ductile behavior are not fully understood. Research in the area of fatigue deformation can aid the development of the NiAl system in two ways. Fatigue properties must be documented and optimized before NiAl can be applied to engineering systems. More importantly though, probing the deformation mechanisms operating in fatigue will lead to a better understanding of NiAl's unique characteristics. Low cycle fatigue properties have been reported on binary NiAl in the past year, yet those studies were limited to two temperature ranges: room temperature and near 1000 K. Eventually, fatigue property data will be needed for a wide range of temperatures and compositions. The intermediate temperature range near the brittle-to-ductile transition was chosen for this study to ascertain whether the sharp change occurring in monotonic behavior also occurs under cyclic conditions. An effort was made to characterize the dislocation structures which evolved during fatigue testing and comment on their role in the deformation process.
Modern space flight mission operations and associated ground data systems are increasingly dependent upon reliable, quality software. Critical functions such as command load preparation, health and status monitoring, communications link scheduling and conflict resolution, and transparent gateway protocol conversion are routinely performed by software. Given budget constraints and the ever increasing capabilities of processor technology, the next generation of control centers and data systems will be even more dependent upon software across all aspects of performance. A key challenge now is to implement improved engineering, management, and assurance processes for the development and maintenance of that software; processes that cost less, yield higher quality products, and that self-correct for continual improvement evolution. The NASA Goddard Space Flight Center has a unique experience base that can be readily tapped to help solve the software challenge. Over the past eighteen years, the Software Engineering Laboratory within the code 500 Flight Dynamics Division has evolved a software development and maintenance methodology that accommodates the unique characteristics of an organization while optimizing and continually improving the organization's software capabilities. This methodology relies upon measurement, analysis, and feedback much analogous to that of control loop systems. It is an approach with a time-tested track record proven through repeated applications across a broad range of operational software development and maintenance projects. This paper describes the software improvement methodology employed by the Software Engineering Laboratory, and how it has been exploited within the Flight Dynamics Division with GSFC Code 500. Examples of specific improvement in the software itself and its processes are presented to illustrate the effectiveness of the methodology. Finally, the initial findings are given when this methodology was applied across the mission operations and ground data systems software domains throughout Code 500.
The Payload Conceptual Sizing Code (PAYCOS), a new multidisciplinary computer program for use in the conceptual development phase of hypersonic lifting vehicles (HV's), is described. The program allows engineers to rapidly determine the feasibility of an HV concept and then improve upon the concept by means of optimization theory. The code contains analysis modules for aerodynamics, thermodynamics, mass properties, flight stability, controls, loads, structures, and packaging. Motivation for the code lies with the increased complexity of HV's over their body-of-revolution ballistic predecessors. With these new shapes, the need to rapidly screen out poor concepts and actively develop new and better concepts is an even more crucial part of the early design process. Preliminary results are given which demonstrate the optimization capabilities of the code.
The structure of the program, the five priority levels, the drive routines, the stepwise drive plan, the figure routines, meander X and y, the range of measurement table, the optimization of figure drive, the figure drive plan, dialogue routines, stack processing, the drive for the main terminal, the protocol routines, the drive for the microterminal, the drive for the experiment computer, and the main program are discussed.
A uniform derivation of minimum-variance reduced-order (MVRO) filter-smoother algorithms from Pontrygin's Minimum Principle is presented. An appropriate performance index for a general class of reduced order estimation problem is formulated herein to yield optimal results over the entire time interval of estimation. These results provide quantitative criteria for measuring the performance of certain classes of heuristically designed, suboptimal reduced-order estimators as well as explicit guidance to the suboptimal filter design process with both continuous and discrete filter-smoother algorithms being considered. By the duality principle, the algorithms of reduced-order estimation can be easily extended to the deterministic problems of optimal control (i.e., the regulator and linear tracking problem).
The adoption of robust numerical optimization techniques in trajectory simulation programs has resulted in powerful design and analysis tools. These trajectory simulation/optimization programs are widely used, and a representative list includes the GTS system, the POST program, and newer collocation methods such as OTIS and FONPAC. All of these programs rely on optimization algorithms which require objective function and constraint gradient data during the iteration process. However, most trajectory optimization problems lack simple analytical expressions for these derivatives. In the general case a function evaluation involves integrating aerodynamic, propulsive, and gravity forces over multiple trajectory phases with complex control models. With the newer collocation methods, the integration is replaced by defect constraints and cubic approximations for the state. While analytic gradient expressions can sometimes be derived for trajectory optimization problems, the derivation is cumbersome, time consuming, and prone to mistakes. Fortunately, an alternate method exists for the gradient evaluation, namely finite difference approximations. In this paper some finite difference gradient techniques developed for use with the GTS system are presented. These techniques include methods for computing first and second partial derivatives of single and multiple sets of functions. A key feature of these methods is an error control mechanism which automatically adjusts the perturbation size to obtain accurate derivative values.
High aspect ratio silicon carbide (SiC) microstructures are needed for microengines and other harsh environment micro-electro-mechanical systems (MEMS). Previously, deep reactive ion etching (DRIE) of low aspect ratio (AR less than or = 1) deep (greater than 100 micron) trenches in SiC has been reported. However, existing DRIE processes for SiC are not well-suited for definition of high aspect ratio features because such simple etch-only processes provide insufficient control over sidewall roughness and slope. Therefore, we have investigated the use of a time-multiplexed etch-passivate (TMEP) process, which alternates etching with polymer passivation of the etch sidewalls. An optimized TMEP process was used to etch high aspect ratio (AR greater than 5) deep (less than 100 micron) trenches in 6H-SiC. Power MEMS structures (micro turbine blades) in 6H-SiC were also fabricated.
The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions.
The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions
During conceptual design speed and accuracy are often at odds. Specifically in the realm of launch vehicles, optimizing the ascent trajectory requires a larger pool of analytical power and expertise. Experienced analysts working on familiar vehicles can produce optimal trajectories in a short time frame, however whenever either "experienced" or "familiar " is not applicable the optimization process can become quite lengthy. In order to construct a vehicle agnostic method an established global optimization algorithm is needed. In this work the authors develop an "artificial" error term to map arbitrary control vectors to non-zero error by which a global method can operate. Two global methods are compared alongside Design of Experiments and random sampling and are shown to produce comparable results to analysis done by a human expert.
A process for adjusting perturbation magnitude for accurate difference-quotient estimation of derivatives is described. The process is intended to be carried out sequentially, alternating with iterations of a parameter-optimization algorithm. A more complex and computationally-expensive scheme for occasional auxiliary use is also described.