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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.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Improved Creep Measurements for Ultra-High Temperature Materials

Our team has developed a novel approach to measuring creep at extremely high temperatures using electrostatic levitation (ESL). This method has been demonstrated on niobium up to 2300 C, while ESL has melted tungsten (3400 C). This method has been extended to lower temperatures and higher stresses and applied to new materials, including a niobium-based superalloy, MASC. High-precision machined spheres of the sample are levitated in the NASA MSFC ESL, a national user facility and heated with a laser. The samples are rotated with an induction motor at up to 30,000 revolutions per second. The rapid rotation loads the sample through centripetal acceleration, producing a shear stress of about 60 MPa at the center, causing the sample to deform. The deformation of the sample is captured on high-speed video, which is analyzed by machine-vision software from the University of Massachusetts. The deformations are compared to finite element models to determine the constitutive constants in the creep relation. Furthermore, the non-contact method exploits stress gradients within the sample to determine the stress exponent in a single test.

Hyers, Robert W.↗
NASA NTRS1977

Method of forming shrink-fit compression seal

A method for making a glass-to-metal seal is described. A domed metal enclosure having a machined seal ring is fitted to a glass post machined to a slight taper and to a desired surface finish. The metal part is then heated by induction in a vacuum. As the metal part heats and expands relative to the glass post, the metal seal ring, possessing a higher coefficient of expansion than the glass post, slides down the tapered post. Upon cooling, the seal ring crushes against the glass post forming the seal. The method results in a glass-to-metal seal possessing extremely good leak resistance, while the parts are kept clean and free of the contaminants.

Podgorski, T. J.↗
NASA NTRS2014

Launch Vehicle Manual Steering with Adaptive Augmenting Control In-flight Evaluations Using a Piloted Aircraft

An adaptive augmenting control algorithm for the Space Launch System has been developed at the Marshall Space Flight Center as part of the launch vehicles baseline flight control system. A prototype version of the SLS flight control software was hosted on a piloted aircraft at the Armstrong Flight Research Center to demonstrate the adaptive controller on a full-scale realistic application in a relevant flight environment. Concerns regarding adverse interactions between the adaptive controller and a proposed manual steering mode were investigated by giving the pilot trajectory deviation cues and pitch rate command authority.

pilot ratings↗
NASA NTRS1985

Issues in rule identification and logical induction

The relationship between language and empirical fitting of data is discussed. The production system is presented as an appropriate description of human behavior in Man-Machine systems. Issues arising in the identification of rules from data are examined. Rules identified through logical generalization are shown to be equivocal. Difficulties arising from the use of logic-based procedures with human performance data containing errors are explored. Problems relating to rule sets which are not disjoint are discussed and a solution presented. Significant testing issues are raised for rule identification and a procedure based on controlling contrivedness is presented. A synthesis of data and knowledge-based approaches is suggested as a remedy to many of the difficulties discussed.

Lewis, C. M.↗
NASA NTRS1997

Voltage Controller

Power Efficiency Corporation, specifically formed to manufacture and develop products from NASA technology, has a license to a three-phase power factor controller originally developed by Frank Nola, an engineer at Marshall Space Flight Center. Power Efficiency and two major distributors, Performance Control and Edison Power Technologies, use the electronic control boards to assemble three different motor controllers: Power Commander, Performance Controller, and Energy Master. The company Power Factor Controller reduces excessive energy waste in AC induction motors. It is used in industries and applications where motors operate under variable loads, including elevators and escalators, machine tools, intake and exhaust fans, oil wells, conveyors, pumps, die casting, and compressors. Customer lists include companies such as May Department Stores, Caesars Atlantic City, Ford Motors, and American Axle.

Source record↗
DOE OSTI2024

Anomaly detection in collider physics via factorized observables

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called : factorized observables for regressing conditional expectations. Our approach is based on the inductive bias of factorization, which is the idea that the physics governing different energy scales can be treated as approximately independent. Assuming factorization holds separately for signal and background processes, the appearance of nontrivial correlations between low- and high-energy observables is a robust indicator of new physics. Under the most restrictive form of factorization, a machine-learned model trained to identify such correlations will in fact converge to the optimal new physics classifier. We test on a benchmark anomaly detection task for the Large Hadron Collider involving collimated sprays of particles called jets. By teasing out correlations between the kinematics and substructure of jets, our method can reliably extract percent-level signal fractions. This strategy for uncovering new physics adds to the growing toolbox of anomaly detection methods for collider physics with a complementary set of assumptions. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2026

Enabling integrated AI control on DIII-D: a control system design with state-of-the-art experiments

We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learning (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode (TM) free scenarios, ELM-free scenarios and stable advanced tokamak conditions. The architecture presented here was deployed on DIII-D to facilitate the end-to-end implementation of advanced control experiments, from diagnostic processing to final actuation commands. This paper describes the detailed design of the algorithm and explains the motivation behind each design point. We also describe several successful ML control experiments in DIII-D using this algorithm, including a reinforcement learning controller targeting advanced non-inductive plasmas, a wide-pedestal quiescent H-mode ELM predictor, an Alfvén Eigenmode controller, a Model Predictive Control plasma profile controller and a state-machine TM predictor-controller. There is also discussion on guiding principles for real-time ML controller design and implementation.

machine learning↗
NASA NTRS1992

Automating sky object classification in astronomical survey images

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged data.

Fayyad, Usama M.↗
NASA NTRS1945

A New Apparatus for Measuring the Temperature at Machine Parts Rotating at High Speeds

After a brief survey of the available methods for measuring the temperatures of machine parts at high speed, in particular turbine blades and rotors, an apparatus is described which is constructed on the principle of induction. Transmission of the measuring current by sliding contacts therefore is avoided. Up-to-date experiments show that it is possible to give the apparatus a high degree of sensitivity and accuracy. In comparison with sliding contact types, the present apparatus shows the important advantage that it operates for any length of time without wear, and that the contact difficulties, particularly occurring at high sliding speeds,are avoided.

Gnam, E.↗
DOE OSTI2025

WEST L-mode record long pulses guided by predictions using Integrated Modeling

A new record was set on the WEST Tokamak, designed to operate long duration plasmas in a tungsten (W) environment, with an injected energy of 1.15 GJ and a plasma duration 364s. Scenario development was supported by integrated modeling using the High Fidelity Plasma Simulator (HFPS), the European IMAS-coupled version of JETTO/JINTRAC, which integrates physics-driven modules into a unified framework. In particular, a reduced model for Lower-Hybrid heating and Current-Drive (LHCD) and the quasi-linear turbulent transport model TGLF are crucial for long pulses predictions up to the Last Closed Flux Surface (LCFS). Using this workflow, a 100 s reference discharge was modeled and plasma kinetic profiles and loop voltage were quantitatively well matched. In preparation for the recent long duration experiments, non-inductive current-drive actuators (I P , n e , P LHCD ) were varied to determine the operational domain going towards fully non-inductive discharges. In particular, decreasing the plasma current is shown to ease the access to such conditions, with a careful monitoring of (n e , P LHCD ) to avoid machine limitations. In addition, post-prediction experiments conducted within the investigated parameter range validated the predicted dependencies and were shown to be in quantitative agreement. Exploratory work on the use of ECCD for MHD stability purpose is also introduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Optimal Hairpin Winding Configuration for EV Traction Motors to Enhance Active CMV Cancellation Capability of NPL.X Inverter

Active common-mode voltage (CMV) cancellation in neutral-point-less (NPL.X) inverter is an effective approach for reducing common-mode electromagnetic interference and alleviating voltage stress associated with high-speed switching in EV traction drives. However, the existing hairpin winding configuration often exhibits impedance imbalance due to slot leakage inductance and manufacturing constraints, limiting the effectiveness of active CMV cancellation. This paper investigates symmetric hairpin winding configurations for a dual-three-phase EV traction machine to improve impedance symmetry. Considering the manufacturing constraints of six-layer hairpin windings, four feasible symmetric winding configurations are analyzed. Two-dimensional finite-element analysis is performed to extract the impedance parameters of each configuration. The extracted impedance parameters are used to quantify the impedance mismatch between complementary phase pairs and evaluate its influence on active CMV cancellation. The results show that the fully interleaved winding configuration achieves perfectly balanced phase impedances with 0 % mismatch, whereas the mismatch in the other configurations ranges from approximately 2.4 % to 0.8 %. Consequently, the resulting total CMV overshoot is significantly suppressed to near zero voltage under high-voltage and high switching frequency operating conditions, demonstrating the importance of winding symmetry for maximizing the active CMV cancellation capability of the NPL.X inverter.

Lee, Kangbeen [Purdue Univ., West Lafayette, IN (U↗
DOE OSTI2026

Compositional Reasoning for Hierarchical State Machines

Harel statecharts and its derivatives are popular graphical languages for specifying discrete control systems via hierarchical state machines. Separately, there has been a long line of work on specifying concurrent systems with process calculi which come equipped with an algebraic theory, the ability reason compositionally about various temporal properties, and strong type systems. While these two approaches to modeling systems are tantalizingly similar, the integrated reasoning principles that exist for process calculi have not been demonstrated in hierarchical state machines. A key issue is that operational theories for process calculi do not behave like control systems, and thus, there is virtually no tool support for modeling control systems with such languages. For a control system designer, bringing the integrated, more scalable reasoning from the process calculi to state-machine languages would enable the specification of more complex systems and a more modular systems development process. Our insight is that we can recover many important results from the process calculi in hierarchical state machines with local scope. We employ a structural operational semantics, which is ubiquitous in process and 𝜆-calculi but uncommon in hierarchical statemachine formalizations, to enable inductive reasoning about behavior. Taking inspiration from the structure of process calculi metatheories, we define a calculus of refinement and equivalence that we prove sound with respect to local notion of (bi)simulation. Furthermore, we prove that the calculus preserves the behavioral properties of reactivity, observational determinism, traces, and linear temporal properties. Our results are mechanized in the Rocq proof assistant.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

Control strategy for a variable-speed wind energy conversion system

A control concept for a variable-speed wind energy conversion system is proposed, for which a self-exited asynchronous cage generator is used along with a system of thyristor converters. The control loops are the following: (1) regulation of the entrainment speed as function of available mechanical energy by acting on the resistance couple of the asynchronous generator; (2) control of electric power delivered to the asynchronous machine, functioning as a motor, for start-up of the vertical axis wind converter; and (3) limitation of the slip value, and by consequence, of the induction currents in the presence of sudden variations of input parameters.

Jacob, A.↗
NASA NTRSDate not supplied

Development of a Composite Material Aerodynamic Demise Model for the Object Reentry Survival Analysis Tool (ORSAT)

The reentry demise of fiber-reinforced polymer (FRP) composites is an increasing concern for modern spacecraft at end of life. Unlike traditional materials such as metals, shredding of the material by aerodynamic forces appears to be a major component of the reentry demise mechanism. This paper will describe a new mechanical, strength-based, material demise model for the Object Reentry Survival Analysis Tool (ORSAT) Version 7.1. The model is based on laboratory in-situ and residual strength tests of several FRP materials performed during the NASA Orbital Debris Program Office’s Phase II Composite Material Demise test campaign. To test the residual strength of partially charred FRP materials, the authors tested 107 rectangular shaped samples of different thicknesses that were previously exposed to high-enthalpy flow at the University of Texas at Austin’s Inductively Coupled Plasma Torch facility. Tests were performed under normal atmospheric conditions at NASA Johnson Space Center’s Experimental Impact Laboratory using a Chatillon TCD1000 tensile test machine configured with a three-point bending jig. Mean fracture load, residual strength, delamination, and flexural modulus were measured for each test. An engineering model of aeromechanical demise for each material was developed by correlating the measured residual strength of the samples with the duration and magnitude of the applied heat flux and the mass loss and char progression. This model has been implemented in ORSAT 7.1 for the built-in, charring carbon fiber/epoxy and charring glass fiber/epoxy material models. The new model was verified by calculating the demise of hundreds of FRP composite fragments of various shapes and sizes and checking the calculations for consistency, with specific focus on material characterization in one-dimension. Further tests are planned in hypersonic flow facilities to validate the assumptions used in the model.

Priscilla A. Mendoza↗
NASA NTRSDate not supplied

Development of a Composite Material Aerodynamic Demise Model for the Object Reentry Survival Analysis Tool (ORSAT)

The reentry demise of fiber-reinforced polymer (FRP) composites is an increasing concern for modern spacecraft at end of life. Unlike traditional materials such as metals, shredding of the material by aerodynamic forces appears to be a major component of the reentry demise mechanism. This paper will describe a new mechanical, strength-based, material demise model for the Object Reentry Survival Analysis Tool (ORSAT) Version 7.1. The model is based on laboratory in-situ and residual strength tests of several FRP materials performed during the NASA Orbital Debris Program Office’s Phase II Composite Material Demise test campaign. To test the residual strength of partially charred FRP materials, the authors tested 107 rectangular shaped samples of different thicknesses that were previously exposed to high-enthalpy flow at the University of Texas at Austin’s Inductively Coupled Plasma Torch facility. Tests were performed under normal atmospheric conditions at NASA Johnson Space Center’s Experimental Impact Laboratory using a Chatillon TCD1000 tensile test machine configured with a three-point bending jig. Mean fracture load, residual strength, delamination, and flexural modulus were measured for each test. An engineering model of aeromechanical demise for each material was developed by correlating the measured residual strength of the samples with the duration and magnitude of the applied heat flux and the mass loss and char progression. This model has been implemented in ORSAT 7.1 for the built-in, charring carbon fiber/epoxy and charring glass fiber/epoxy material models. The new model was verified by calculating the demise of hundreds of FRP composite fragments of various shapes and sizes and checking the calculations for consistency, with specific focus on material characterization in one-dimension. Further tests are planned in hypersonic flow facilities to validate the assumptions used in the model.

Priscilla A. Mendoza↗
DOE OSTI2025

Synergistic learning with multi-task DeepONet for efficient PDE problem solving

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Finally, our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.

42 ENGINEERING↗
NASA NTRS1994

Learning time series for intelligent monitoring

We address the problem of classifying time series according to their morphological features in the time domain. In a supervised machine-learning framework, we induce a classification procedure from a set of preclassified examples. For each class, we infer a model that captures its morphological features using Bayesian model induction and the minimum message length approach to assign priors. In the performance task, we classify a time series in one of the learned classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. We report results from experiments in a monitoring domain of interest to NASA.

Manganaris, Stefanos↗
NASA NTRS1990

Induction motor control

Electromechanical actuators developed to date have commonly utilized permanent magnet (PM) synchronous motors. More recently switched reluctance (SR) motors have been advocated due to their robust characteristics. Implications of work which utilizes induction motors and advanced control techniques are discussed. When induction motors are operated from an energy source capable of controlling voltages and frequencies independently, drive characteristics are obtained which are superior to either PM or SR motors. By synthesizing the machine frequency from a high frequency carrier (nominally 20 kHz), high efficiencies, low distortion, and rapid torque response are available. At this time multiple horsepower machine drives were demonstrated, and work is on-going to develop a 20 hp average, 40 hp peak class of aerospace actuators. This effort is based upon high frequency power distribution and management techniques developed by NASA for Space Station Freedom.

Hansen, Irving G.↗