Multielectron Redox and Insulator-to-Metal Transition upon Lithium Insertion in the Fast-Charging, Wadsley-Roth Phase PNb[subscript 9]O[subscript 25]
Abstract not provided
Preefer, Molleigh B.↗
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Results for “tuning technique”
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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Flat band carrier confinement in magic-angle twisted bilayer graphene
Abstract Magic-angle twisted bilayer graphene has emerged as a powerful platform for studying strongly correlated electron physics, owing to its almost dispersionless low-energy bands and the ability to tune the band filling by electrostatic gating. Techniques to control the twist angle between graphene layers have led to rapid experimental progress but improving sample quality is essential for separating the delicate correlated electron physics from disorder effects. Owing to the 2D nature of the system and the relatively low carrier density, the samples are highly susceptible to small doping inhomogeneity which can drastically modify the local potential landscape. This potential disorder is distinct from the twist angle variation which has been studied elsewhere. Here, by using low temperature scanning tunneling spectroscopy and planar tunneling junction measurements, we demonstrate that flat bands in twisted bilayer graphene can amplify small doping inhomogeneity that surprisingly leads to carrier confinement, which in graphene could previously only be realized in the presence of a strong magnetic field.
36 MATERIALS SCIENCE↗
A machine-learning inverse model framework for rapid forecasting and history matching in unconventional reservoirs
Model-based optimization for real-time forecasting in unconventional reser-voirs requires novel methods and work?ows since the strategies and work?ows used in conventional reservoirs are either inapplicable, or prohibitively expen-sive and time-consuming. Insu?cient site data and computational expense of high-?delity simulations mean that work?ows with high-?delity simulations are not ideal for usage in comprehensive uncertainty quanti?cation stud-ies that require 1000s of forward model runs. We present an alternative, novel work?ow for unconventional reservoirs, based on the interplay between reduced-order models and machine-learning. Our physics-informed machine-learning (PIML) work?ow addresses the challenges to real-time reservoir management in uncoventionals, namely lack of data (the time-frame for which the wells have been producing), and computational expense of high-?delity modeling. We use the machine-learning paradigm of transfer-learning to bind together fast but less accurate reduced-order models with slow, but accurate high-?delity models and circumvent the di?culties inherent in the current state-of-the-art for unconventionals. Such a PIML work?ow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir. The signi?cance of our approach is that while it is developed for a particu-lar well and site in the Marcelus Shale gas reservoir of the Appalachian basin (MSEEL), it is not wedded to it. We expect the same work?ow can be ap-plied to other shale formations (e.g., Woodford, Barnett, Utica, EagleFord) should site-data become available, using the same set of machine-learning techniques from transfer learning. Some ?ne-tuning (or minimal retraining of the neural networks) will be required to transfer knowledge across shale gas sites/formations but it is a clearly superior alternative to developing a new machine-learning model altogether when considering a di?erent site.
Srinivasan, Shriram↗
Automatic Qubit Characterization and Gate Optimization with QubiC
As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC, which is an open source FPGA (field-programmable gate array) based control and measurement system for superconducting quantum information processors. We propose multi-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. Finally, the single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of 4.9(1.1) × 10 -4 and 1.4(3) × 10 -2 , respectively.
97 MATHEMATICS AND COMPUTING↗
Fabrication of ultra-low-noise amplifier
Three construction techniques reduce noise temperature of microwave parametric amplifier. Techniques include electroformed idler cavity, screw-tuned idler, and low-loss matching section.
Kraemer, E.↗
Nylon screws make inexpensive coil forms
Standard nylon screws act as coil form copper wire laid down in spiral thread. Completed coil may be bonded to printed-circuit board. However, it is impossible to tune coil by adjusting spacing between windings, technique sometimes used with air-core coils.
Aucoin, G.↗
The design of hydraulic pressure regulators that are stable without the use of sensing line restrictors or frictional dampers
A direct-acting hydraulic pressure regulator design which incorporates stability margin, response and droop margin is developed. The pressure regulator system does not involve a nonlinear sensing line restrictor (which may degrade transient response) or linear damping (which is sensitive to clearance and viscosity). The direct-acting hydraulic pressure regulator makes use of the technique of lead network stabilization (i.e., the tuned stabilizer concept). An analytically derived circuit pressure regulator is tested to study the stability limit under a parallel capacitive plus resistive load and the stabilizing effect of the tuned stabilizer.
Gold, H.↗
Aerodynamic detuning for aeroelastic control of stability and forced response of supersonic rotors
An unsteady aerodynamic model is developed to analyze flutter and aerodynamically forced response of aerodynamically detuned supersonic axial flow rotors. Alternate blade aerodynamic detuning is considered, accomplished by alternating the circumferential spacing of adjacent blades as small solidity variations which do not have a dominant effect on the steady performance of a rotor. The unsteady aerodynamics are determined by developing an influence coefficient technique which is appropriate for both aerodynamically tuned and detuned rotor configurations. Torsion mode rotor stability and aerodynamically forced response are then analyzed with this unsteady aerodynamic model by combining it with a single-degree-of-freedom structural model. The effects of this detuning on the flutter and forced response characteristics of supersonic axial flow rotors is then demonstrated by considering baseline twelve bladed rotors.
Spara, Karen M.↗
Acoustic Benefits of Stator Sweep and Lean for a High Tip Speed Fan
A model high-speed fan stage was acoustically tested in the NASA Glenn 9- by 15-Foot Low Speed Wind Tunnel at takeoff/approach flight conditions. The fan was designed for a corrected rotor tip speed of 442 m/s (1450 ft/s), and had a powered core, or booster stage, giving the model a nominal bypass ratio of 5. The model also had a simulated engine pylon and nozzle bifurcation contained within the bypass duct. The fan was tested with three stator sets to evaluate acoustic benefits associated with a swept and leaned stator and with a swept integral vane/frame stator which incorporated some of the swept and leaned features as well as eliminated some of the downstream support structure. The baseline fan with the wide chord rotor and baseline stator approximated a current GEAE CF6 engine. A flyover effective perceived noise level (EPNL) code was used to generate relative EPNL values for the various configurations. Flyover effective perceived noise levels (EPNL) were computed from the model data to help project noise benefits. A tone removal study was also performed. The swept and leaned stator showed a 3 EPNdB reduction at lower fan speeds relative to the baseline stator; while the swept integral vane/frame stator showed lowest noise levels at intermediate fan speeds. Removal of the bypass blade passage frequency rotor tone (BPF) showed a 4 EPNdB reduction for the baseline and swept and leaned stators, and a 6 EPNdB reduction for the swept integral vane/ frame stator. Therefore, selective tone removal techniques such as active noise control and/or tuned liner could be particularly effective in reducing noise levels for certain fan speeds.
Woodward, Richard P.↗
Developing Natural Language Processing and Supervised Learning Techniques to Classify Mars Tasks
As NASA's Human Research Program (HRP) prepares for long-duration Mars missions, understanding astronaut tasks is crucial. This study, conducted at NASA Glenn Research Center (GRC), employed Natural Language Processing (NLP) and machine learning techniques to analyze and classify Mars tasks. A list of 1,058 Mars tasks was provided by HRP experts including binary labeling of 18 Human System Task Categories (HSTCs). We developed an NLP model using Google's BERT language model to capture the semantic and syntactic nuances of these tasks. Supervised training was initially applied to a subset of the NLP-analyzed tasks to assess the model's effectiveness in classifying the remaining tasks. Incorporating HSTC descriptions significantly enhanced the classification accuracy for 9 out of the 18 HSTCs and reduced training time. To address the issue of severe class imbalance in the HSTC data, we introduced innovative weighting and sampling techniques for data augmentation. We then fine-tune BERT to implement a pairwise relatedness scoring method, allowing us to cluster tasks based on their relatedness and similarity, getting a step closer to labeling the tasks without supervision. In this presentation we guide you through data preprocessing, deciphering key syntax components using BERT, and performing supervised classification of the Mars tasks. This work showcases the potential use of advanced NLP techniques to analyze Mars missions to be incorporated into various crew health and performance analyses.
GenAI↗
Developing Humanoid Robots for Real-World Environments
Humanoids are steadily improving in appearance and functionality demonstrated in controlled environments. To address the challenges of operation in the real-world, researchers have proposed the use of brain-inspired architectures for robot control, and the use of robot learning techniques that enable the robot to acquire and tune skills and behaviours. In the first part of the paper we introduce new concepts and results in these two areas. First, we present a cerebellum-inspired model that demonstrated efficiency in the sensory-motor control of anthropomorphic arms, and in gait control of dynamic walkers. Then, we present a set of new ideas related to robot learning, emphasizing the importance of developing teaching techniques that support learning. In the second part of the paper we propose the use in robotics of the iterative and incremental development methodologies, in the context of practical task-oriented applications. These methodologies promise to rapidly reach system-level integration, and to early identify system-level weaknesses to focus on. We apply this methodology in a task targeting the automated assembly of a modular structure using HOAP-2. We confirm this approach led to rapid development of a end-to-end capability, and offered guidance on which technologies to focus on for gradual improvement of a complete functional system. It is believed that providing Grand Challenge type milestones in practical task-oriented applications accelerates development. As a meaningful target in short-mid term we propose the 'IKEA Challenge', aimed at the demonstration of autonomous assembly of various pieces of furniture, from the box, following included written/drawn instructions.
Stoica, Adrian↗
Linewidth characteristics of Raman-shifted dye laser output at 720 and 940 nm
A compact and simple simultaneous multi-wavelength dye laser cavity was developed for a differential absorption technique. Dielectric multilayer interference filters were inserted inside the cavities as tuning elements, and two types of a DIAL system were constucted by using the dye laser tuned with dielectric multilayer filters to measure NO2 concentration. The usefulness of this dye laser was clarified for the differential absoroption technique in outdoor experiments. Some basic designs of the laser cavity with these filters to get simultaneously multi-wavelength output are summarized.
Grossmann, B. E.↗
Telerobotic control of a mobile coordinated robotic server, executive summary
This interim report continues with the research effort on advanced adaptive controls for space robotics systems. In particular, previous results developed by the principle investigator and his research team centered around fuzzy logic control (FLC) in which the lack of knowledge of the robotic system as well as the uncertainties of the environment are compensated for by a rule base structure which interacts with varying degrees of belief of control action using system measurements. An on-line adaptive algorithm was developed using a single parameter tuning scheme. In the effort presented, the methodology is further developed to include on-line scaling factor tuning and self-learning control as well as extended to the multi-input, multi-output (MIMO) case. Classical fuzzy logic control requires tuning input scale factors off-line through trial and error techniques. This is time-consuming and cannot adapt to new changes in the process. The new adaptive FLC includes a self-tuning scheme for choosing the scaling factors on-line. Further the rule base in classical FLC is usually produced by soliciting knowledge from human operators as to what is good control action for given circumstances. This usually requires full knowledge and experience of the process and operating conditions, which limits applicability. A self-learning scheme is developed which adaptively forms the rule base with very limited knowledge of the process. Finally, a MIMO method is presented employing optimization techniques. This is required for application to space robotics in which several degrees-of-freedom links are commonly used. Simulation examples are presented for terminal control - typical of robotic problems in which a desired terminal point is to be reached for each link. Future activities will be to implement the MIMO adaptive FLC on an INTEL microcontroller-based circuit and to test the algorithm on a robotic system at the Mars Mission Research Center at North Carolina State University.
Lee, Gordon↗
Suppression of hybridization by Cd doping in CeCoIn 5
The interaction between superconductivity and magnetism in heavy fermion compounds has remained a challenge ever since the discovery of unconventional superconductivity in such systems. The heavy fermion Ce T In 5 ( T = Co,Rh,Ir) family offers a good platform to study the relationship between superconductivity and magnetism, whose ground state can be easily tuned by chemical doping, making the ambient-pressure experimental techniques like angle-resolved photoemission spectroscopy possible to address this old but engaging question. By Cd doping, the superconducting state of CeCoIn 5 can be smoothly tuned to an antiferromagnetic ground state, which can be reversed by applying pressure. In this study, we present an electronic structure study of CeCo ( In 0.85 Cd 0.15 ) 5 with an antiferromagnetic ground state and the results are compared with CeCoIn 5 . We found that the hybridization strength between the f electrons and conduction electrons in CeCoIn 5 has been suppressed by Cd doping, but still the Fermi surface includes a significant contribution from the Ce f electrons. Our results find that the electronic tuning effect by Cd doping arises from the change in the hybridization strength between the Ce f electrons and conduction electrons caused by the Cd dopant, and the subtle interplay between itinerancy and localization of the f electrons determines the ground state.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
A Self-Tuning Kalman Filter for Autonomous Spacecraft Navigation
Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman Filter and Global Positioning System (GPS) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. Current techniques of Kalman filtering, however, still rely on manual tuning from analysts, and cannot help in optimizing autonomy without compromising accuracy and performance. This paper presents an approach to produce a high accuracy autonomous navigation system fully integrated with the flight system. The resulting system performs real-time state estimation by using an Extended Kalman Filter (EKF) implemented with high-fidelity state dynamics model, as does the GPS Enhanced Orbit Determination Experiment (GEODE) system developed by the NASA Goddard Space Flight Center. Augmented to the EKF is a sophisticated neural-fuzzy system, which combines the explicit knowledge representation of fuzzy logic with the learning power of neural networks. The fuzzy-neural system performs most of the self-tuning capability and helps the navigation system recover from estimation errors. The core requirement is a method of state estimation that handles uncertainties robustly, capable of identifying estimation problems, flexible enough to make decisions and adjustments to recover from these problems, and compact enough to run on flight hardware. The resulting system can be extended to support geosynchronous spacecraft and high-eccentricity orbits. Mathematical methodology, systems and operations concepts, and implementation of a system prototype are presented in this paper. Results from the use of the prototype to evaluate optimal control algorithms implemented are discussed. Test data and major control issues (e.g., how to define specific roles for fuzzy logic to support the self-learning capability) are also discussed. In addition, architecture of a complete end-to-end candidate flight system that provides navigation with highly autonomous control using data from GPS is presented.
Truong, Son H.↗
Autonomous Performance Monitoring System: Monitoring and Self-Tuning (MAST)
Maintaining the long-term performance of software onboard a spacecraft can be a major factor in the cost of operations. In particular, the task of controlling and maintaining a future mission of distributed spacecraft will undoubtedly pose a great challenge, since the complexity of multiple spacecraft flying in formation grows rapidly as the number of spacecraft in the formation increases. Eventually, new approaches will be required in developing viable control systems that can handle the complexity of the data and that are flexible, reliable and efficient. In this paper we propose a methodology that aims to maintain the accuracy of flight software, while reducing the computational complexity of software tuning tasks. The proposed Monitoring and Self-Tuning (MAST) method consists of two parts: a flight software monitoring algorithm and a tuning algorithm. The dependency on the software being monitored is mostly contained in the monitoring process, while the tuning process is a generic algorithm independent of the detailed knowledge on the software. This architecture will enable MAST to be applicable to different onboard software controlling various dynamics of the spacecraft, such as attitude self-calibration, and formation control. An advantage of MAST over conventional techniques such as filter or batch least square is that the tuning algorithm uses machine learning approach to handle uncertainty in the problem domain, resulting in reducing over all computational complexity. The underlying concept of this technique is a reinforcement learning scheme based on cumulative probability generated by the historical performance of the system. The success of MAST will depend heavily on the reinforcement scheme used in the tuning algorithm, which guarantees the tuning solutions exist.
Peterson, Chariya↗
A novel technique for minimizing energy functional using neural networks
An energy functional describes the equilibrium state of a system. In this work, we present a novel technique, Functional Optimization using Neural Networks (FONN), for minimizing the system’s energy. FONN utilizes neural networks to process information at discrete grid points, considering their interactions with neighboring grid points, to update the state of the system. The training process involves formulating a loss function based on the system’s energy, and with the help of multiple fine-tuning steps, the method employs a progressive energy reduction technique that decreases the energy in multiple steps. FONN’s effectiveness is demonstrated across various problems, including the minimization of the heat and Lyapunov energy. Furthermore, the paper explores the minimization of the elastic bending energy with an area constraint.
97 MATHEMATICS AND COMPUTING↗
Leveraging prior mean models for faster Bayesian optimization of particle accelerators
Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.
43 PARTICLE ACCELERATORS↗