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NASA NTRSDate not supplied

Progress in Magnetic Gearing for Aeronautics and Space Applications

Magnetic gears harness the magnetic force between non-contacting sets of permanent magnets. They achieve a gear ratio through the geometry and constrained motion of the magnets or the transformation of the magnetic fields by carefully shaped soft magnetic material. Their non-contact nature eliminates the need for gear lubrication and the wear mechanisms associated with mechanical contact. Magnetic gears have the potential to reduce the strong, tonal vibration and noise that is inherent to mechanical gears. Magnetic gears are also easily integrated into electric machines because they rely on the same physics and utilize the same materials and many of the same components. For these reasons and the invention of higher performing magnetic gear configurations over the past 20 years, magnetic gears have received growing attention for aerospace applications. This presentation summarizes the motivation for studying magnetic gears and the progress made in developing the technology to meet the demanding requirements of aerospace applications. The focus is on the development completed and ongoing at NASA, but SBIR projects in the field are summarized. The discussion focuses on the application of magnetic gears and magnetically-geared motors in electrified aircraft propulsion systems and actuation systems for space mechanisms. The NASA work includes the design, optimization, and testing of magnetic gears and magnetically-geared motors for electrified vertical takeoff and landing aircraft and the design and ambient environment testing of a magnetically-geared actuator for use in extremely cold and dusty space environments. The presentation concludes with thoughts on the technical barriers to adoption and potentially fruitful topics for future research.

Justin J. Scheidler↗

NASA NTRSDate not supplied

Adaptive Learning for Reliability Analysis using Support Vector Machines

A novel algorithm is presented for adaptive learning of an unknown function that separates two regions of a domain.In the context of reliability analysis these two regions represent the failure domain, where a set of constraints or requirements are violated, and a safe domain where they are satisfied. The Limit State Function (LSF) separates these two regions. Evaluating the constraints for a given parameter point requires the evaluation of a computational model that may well be expensive. For this reason we wish to construct a meta-model that can estimate the LSFas accurately as possible, using only a limited amount of training data. This work presents an adaptive strategy employing a Support Vector Machine (SVM) as a meta-model to provide a semi-algebraic approximation of the LSF.We describe an optimization process that is used to select informative parameter points to add to training data at each iteration to improve the accuracy of this approximation. A formulation is introduced for bounding the predictions of the meta-model; in this way we seek to incorporate this aspect of Gaussian Process Models (GPMs) within anSVM meta-model. Finally, we apply our algorithm to two benchmark test cases, demonstrating performance that is comparable with, if not superior, to a standard technique for reliability analysis that employs GPMs

Adaptive learning↗

NASA NTRS2003

Modeling of Unsteady Three-dimensional Flows in Multistage Machines

Despite many years of development, the accurate and reliable prediction of unsteady aerodynamic forces acting on turbomachinery blades remains less than satisfactory, especially when viewed next to the great success investigators have had in predicting steady flows. Hall and Silkowski (1997) have proposed that one of the main reasons for the discrepancy between theory and experiment and/or industrial experience is that many of the current unsteady aerodynamic theories model a single blade row in an infinitely long duct, ignoring potentially important multistage effects. However, unsteady flows are made up of acoustic, vortical, and entropic waves. These waves provide a mechanism for the rotors and stators of multistage machines to communicate with one another. In other words, wave behavior makes unsteady flows fundamentally a multistage (and three-dimensional) phenomenon. In this research program, we have has as goals (1) the development of computationally efficient computer models of the unsteady aerodynamic response of blade rows embedded in a multistage machine (these models will ultimately be capable of analyzing three-dimensional viscous transonic flows), and (2) the use of these computer codes to study a number of important multistage phenomena.

Hall, Kenneth C.↗

NASA NTRS1993

The coming technological singularity: How to survive in the post-human era

The acceleration of technological progress has been the central feature of this century. I argue in this paper that we are on the edge of change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater than human intelligence. There are several means by which science may achieve this breakthrough (and this is another reason for having confidence that the event will occur): (1) the development of computers that are 'awake' and superhumanly intelligent (to date, most controversy in the area of AI relates to whether we can create human equivalence in a machine. But if the answer is 'yes, we can', then there is little doubt that beings more intelligent can be constructed shortly thereafter); (2) large computer networks (and their associated users) may 'wake up' as a superhumanly intelligent entity; (3) computer/human interfaces may become so intimate that users may reasonably be considered superhumanly intelligent; and (4) biological science may find ways to improve upon the natural human intellect. The first three possibilities depend in large part on improvements in computer hardware. Progress in computer hardware has followed an amazingly steady curve in the last few decades. Based largely on this trend, I believe that the creation of greater than human intelligence will occur during the next thirty years.

Vinge, Vernor↗

NASA NTRS1993

Emerging technologies in microguidance and control

Employing recent advances in microfabrication, the Charles Stark Draper Laboratory has developed inertial guidance instruments of very small size and low cost. Microfabrication employs the batch processing techniques of solid state electronics, such as photolithography, diffusion, and etching, to carve mechanical parts. Within a few years, microfabricated gyroscopes should perform in the 10 to 100 deg/h range. Microfabricated accelerometers have demonstrated performance in the 50 to 500 microgravity range. These instruments will result in not only the redesign of conventional military products, but also new applications that could not exist without small, inexpensive sensors and computing. Draper's microfabricated accelerometers and gyroscopes will be described and test results summarized. Associated electronics and control issues will also be addressed. Gimballed, vibrating gyroscopes and force rebalance accelerometers constructed from bulk silicon, polysilicon surface-machined tuning fork gyroscopes, and quartz resonant accelerometers and gyroscopes are examined. Draper is pursuing several types of devices for the following reasons: to address wide ranges of performance, to realize construction in a flat pack, and to lessen the risks associated with emerging technologies.

Weinberg, Marc S.↗

NASA NTRS1995

Defining the IEEE-854 floating-point standard in PVS

A significant portion of the ANSI/IEEE-854 Standard for Radix-Independent Floating-Point Arithmetic is defined in PVS (Prototype Verification System). Since IEEE-854 is a generalization of the ANSI/IEEE-754 Standard for Binary Floating-Point Arithmetic, the definition of IEEE-854 in PVS also formally defines much of IEEE-754. This collection of PVS theories provides a basis for machine checked verification of floating-point systems. This formal definition illustrates that formal specification techniques are sufficiently advanced that is is reasonable to consider their use in the development of future standards.

Miner, Paul S.↗

NASA NTRS2015

Assessment of New Load Schedules for the Machine Calibration of a Force Balance

New load schedules for the machine calibration of a six-component force balance are currently being developed and evaluated at the NASA Ames Balance Calibration Laboratory. One of the proposed load schedules is discussed in the paper. It has a total of 2082 points that are distributed across 16 load series. Several criteria were applied to define the load schedule. It was decided, for example, to specify the calibration load set in force balance format as this approach greatly simplifies the definition of the lower and upper bounds of the load schedule. In addition, all loads are assumed to be applied in a calibration machine by using the one-factor-at-a-time approach. At first, all single-component loads are applied in six load series. Then, three two-component load series are applied. They consist of the load pairs (N1, N2), (S1, S2), and (RM, AF). Afterwards, four three-component load series are applied. They consist of the combinations (N1, N2, AF), (S1, S2, AF), (N1, N2, RM), and (S1, S2, RM). In the next step, one four-component load series is applied. It is the load combination (N1, N2, S1, S2). Finally, two five-component load series are applied. They are the load combination (N1, N2, S1, S2, AF) and (N1, N2, S1, S2, RM). The maximum difference between loads of two subsequent data points of the load schedule is limited to 33 % of capacity. This constraint helps avoid unwanted load "jumps" in the load schedule that can have a negative impact on the performance of a calibration machine. Only loadings of the single- and two-component load series are loaded to 100 % of capacity. This approach was selected because it keeps the total number of calibration points to a reasonable limit while still allowing for the application of some of the more complex load combinations. Data from two of NASA's force balances is used to illustrate important characteristics of the proposed 2082-point calibration load schedule.

wind tunnel testing↗

NASA NTRS1987

Research on knowledge representation, machine learning, and knowledge acquisition

Research in knowledge representation, machine learning, and knowledge acquisition performed at Knowledge Systems Lab. is summarized. The major goal of the research was to develop flexible, effective methods for representing the qualitative knowledge necessary for solving large problems that require symbolic reasoning as well as numerical computation. The research focused on integrating different representation methods to describe different kinds of knowledge more effectively than any one method can alone. In particular, emphasis was placed on representing and using spatial information about three dimensional objects and constraints on the arrangement of these objects in space. Another major theme is the development of robust machine learning programs that can be integrated with a variety of intelligent systems. To achieve this goal, learning methods were designed, implemented and experimented within several different problem solving environments.

Buchanan, Bruce G.↗

NASA NTRS2004

Durability Assessment of Gamma Tial

Gamma TiAl was evaluated as a candidate alloy for low-pressure turbine blades in aeroengines. The durability of g-TiAl was studied by examining the effects of impact or fretting on its fatigue strength. Cast-to-size Ti-48Al-2Cr-2Nb was studied in impact testing with different size projectiles at various impact energies as the reference alloy and subsequently fatigue tested. Impacting degraded the residual fatigue life. However, under the ballistic impact conditions studied, it was concluded that the impacts expected in an aeroengine would not result in catastrophic damage, nor would the damage be severe enough to result in a fatigue failure under the anticipated design loads. In addition, other gamma alloys were investigated including another cast-to-size alloy, several cast and machined specimens, and a forged alloy. Within this Ti-48-2-2 family of alloys aluminum content was also varied. The cracking patterns as a result of impacting were documented and correlated with impact variables. The cracking type and severity was reasonably predicted using finite element models. Mean stress affects were also studied on impact-damaged fatigue samples. The fatigue strength was accurately predicted based on the flaw size using a threshold-based, fracture mechanics approach. To study the effects of wear due to potential applications in a blade-disk dovetail arrangement, the machined Ti-47-2-2 alloy was fretted against In-718 using pin-on-disk experiments. Wear mechanisms were documented and compared to those of Ti-6Al-4V. A few fatigue samples were also fretted and subsequently fatigue tested. It was found that under the conditions studied, the fretting was not severe enough to affect the fatigue strength of g-TiAl.

Draper, Susan L.↗

NASA NTRS2017

The Challenges of Human-Autonomy Teaming

Machine intelligence is improving rapidly based on advances in big data analytics, deep learning algorithms, networked operations, and continuing exponential growth in computing power (Moores Law). This growth in the power and applicability of increasingly intelligent systems will change the roles humans, shifting them to tasks where adaptive problem solving, reasoning and decision-making is required. This talk will address the challenges involved in engineering autonomous systems that function effectively with humans in aeronautics domains.

artificial intelligence↗

NASA NTRS1977

Evaluation of a low aspect ratio small axial compressor stage, volume 1

A program was conducted to evaluate the effects of scaling, tip clearance, and IGV reset on the performance of a low aspect ratio compressor stage. Stage design was obtained by scaling an existing single stage compressor by a linear factor of 0.304. The design objective was to maintain the meanline velocity field of the base machine in the smaller size. Adjustments were made to account for predicted blockage differences and to chord lengths and airfoil edge radii to obtain reasonable blade geometries. Meanline velocity diagrams of the base stage were not maintained at the scaled size. At design speed and flowrate the scaled stage achieved a pressure ratio of 1.423, adiabatic efficiency of 0.822, and surge margin of 18.5%. The corresponding performance parameters for the base stage were 1.480, 0.872, and 25.2%, respectively. The base stage demonstrated a peak efficiency at design speed of 0.872; the scaled stage achieved a level of 0.838. When the scaled stage rotor and stator tip clearances were doubled, the stage achieved a pressure ratio of 1.413, efficiency of 0.799, and surge margin of 16.0% at the design flowrate. The peak stage efficiency at design speed was 0.825 with the increased clearance. Increased prewhirl lowered the stage pressure ratio as expected. Stage efficiency was maintained with ten degrees of increased prewhirl and then decreased substantially with ten additional degrees of reset.

Sawyer, C. W., III↗

NASA NTRS1992

NASA/USRA advanced design program activity, 1991-1992

The School of Textile and Fiber Engineering continued to pursue design projects with the Mechanical Engineering School giving the students an outstanding opportunity to interact with students from another discipline. Four problems were defined which had aspects which would be reasonably assigned to an interdisciplinary team. The design problems are described. The projects included lunar preform manufacturing, dust control for Enabler, an industrial sewing machine variable speed controllor, Enabler operation station, and design for producing fiberglass fabric in a lunar environment.

Dorrity, J. Lewis↗

NASA NTRS1992

Manifold traversing as a model for learning control of autonomous robots

This paper describes a recipe for the construction of control systems that support complex machines such as multi-limbed/multi-fingered robots. The robot has to execute a task under varying environmental conditions and it has to react reasonably when previously unknown conditions are encountered. Its behavior should be learned and/or trained as opposed to being programmed. The paper describes one possible method for organizing the data that the robot has learned by various means. This framework can accept useful operator input even if it does not fully specify what to do, and can combine knowledge from autonomous, operator assisted and programmed experiences.

Szakaly, Zoltan F.↗

NASA NTRS1993

Exercise countermeasure protocol management expert system

Exercise will be used primarily to countermeasure against deconditioning on extended space flight. In this paper we describe the development and evaluation of an expert system for exercise countermeasure protocol management. Currently, the system includes two major subsystems: baseline prescription and prescription adjustment. The baseline prescription subsystem is designed to provide initial exercise prescriptions while prescription adjustment subsystem is designed to modify the initial prescription based on the exercised progress. The system runs under three different environments: PC, SUN workstation, and Symbolic machine. The inference engine, baseline prescription module, prescription adjustment module and explanation module are developed under the Symbolic environment by using the ART (Automated Reasoning Tool) software. The Sun environment handles database management features and interfaces with PC environment to obtain physical and physiological data from exercise units on-board during the flight. Eight subjects' data have been used to evaluate the system performance by comparing the prescription of nine experienced exercise physiologists and the one prescribed by the expert system. The results of the validation test indicated that the performance of the expert system was acceptable.

NASA Discipline Data Analysis↗

NASA NTRS2017

The Challenges of Human-Autonomy Teaming

Human-Autonomy teaming↗

NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗

NASA NTRS1986

Fatigue Criterion for System Design

Report discusses principles of structural-life prediction. Generalized methodology developed for structural life prediction, design, and reliability, based upon fatigue criterion. Approach incorporates computed life of elemental stress volumes of complex machine elements to predict system life. Results of coupon fatigue testing incorporated into analysis, allowing for life prediction and component or structural renewal rates, with reasonable statistical certainty.

Zeretsky, E. V.↗

NASA NTRS1989

Measurements of the stress supported by the crush zone in open hole composite laminates loaded in compression

Measurements of the stress supported by the crush zone in open hole specimens loaded in compression were carried out on two composite laminates, AS4/PEEK and IM6/HST-7, containing circular holes of three different diameters. Compression tests were conducted in a specially designed high-axial-alignment material test system machine. Results indicated that the local stress supported in the crush zone is much less than the stress required to initiate the crush, providing the reason for the finding of Guynn et al. (1987) that the Dugdale model does not accurately predict the load-damage size relationship of open hole composite specimens loaded in compression.

Guynn, E. Gail↗