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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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Dynamic signatures of spin-lattice coupling in the layered ferrimagnet Mn 3 Si 2 Te 6
Magnetic van der Waals (vdW) materials exhibit a profound interconnectedness between their various degrees of freedom, pointing to a wealth of potential applications in low-power and high-speed spintronic devices. Recently, light-matter interactions have been leveraged as robust, dynamic pathways to gain control over the properties of vdW magnets through the use of ultrafast pulses of light. Here, we utilize ultrafast photoexcitation to drive coherent lattice oscillations in the layered ferrimagnetic crystal Mn 3 Si 2 Te 6 , which significantly stiffen below the magnetic ordering temperature. We suggest that this is due to an exchange-mediated contraction of the lattice, stemming from strong magneto-structural coupling in this material. Furthermore, simulations of the transient incoherent response uncover the critical role of the spin-mediated electronic relaxation pathways. These results underscore the importance of spin-lattice coupling in vdW magnets and demonstrate a promising strategy for their dynamic optical control via their entangled degrees of freedom.
Continuously-tuned tunneling behaviors of ferroelectric tunnel junctions based on BaTiO{sub 3}/La{sub 0.67}Sr{sub 0.33}MnO{sub 3} heterostructure
In this work, we fabricate BaTiO{sub 3}/La{sub 0.67}Sr{sub 0.33}MnO{sub 3} (BTO/LSMO) ferroelectric tunnel junction on (001) SrTiO{sub 3} substrate by pulsed laser deposition method. Combining piezoresponse force and conductive-tip atomic force microscopy, we demonstrate robust and reproducible polarization-controlled tunneling behaviors with the resulting tunneling electroresistance value reaching about 10{sup 2} in ultrathin BTO films (∼1.2 nm) at room temperature. Moreover, local poling areas with different conductivity are finally achieved by controlling the relative proportion of upward and downward domains, and different poling areas exhibit stable transport properties.
Spatially Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events
The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses output from global climate models to make simultaneous attribution statements about whether and how greenhouse gas emissions have contributed to extreme weather across the globe. However, in conducting a large number of simultaneous hypothesis tests, the WRAF is prone to identifying false “discoveries.” A common technique for addressing this multiple testing problem is to adjust the procedure in a way that controls the proportion of true null hypotheses that are incorrectly rejected, or the false discovery rate (FDR). Unfortunately, generic FDR procedures suffer from low power when the hypotheses are dependent, and techniques designed to account for dependence are sensitive to misspecification of the underlying statistical model. In this paper, we develop a Bayesian decision-theoretical approach for dependent multiple testing and a nonparametric hierarchical statistical model that flexibly controls false discovery and is robust to model misspecification. We illustrate the robustness of our procedure to model error with a simulation study, using a framework that accounts for generic spatial dependence and allows the practitioner to flexibly specify the decision criteria. Finally, we apply our procedure to several seasonal forecasts and discuss implementation for the WRAF workflow. Lastly, supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation
Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.
Multi-Megawatt-Scale Power-Hardware-in-the-Loop Interface for Testing Ancillary Grid Services by Converter-Coupled Generation
Power-hardware-in-the-loop (PHIL) is a simulation tool that can support electrical systems engineers in the development and experimental validation of novel, advanced control schemes that ensure the robustness and resiliency of electrical grids that have high penetrations of low-inertia variable renewable resources. With PHIL, the impact of the device under test on a generation or distribution system can be analyzed using a real-time simulator (RTS). PHIL allows for the interconnection of the RTS with a 7 megavolt ampere (MVA) power amplifier to test multi-megawatt renewable assets available at the National Wind Technology Center (NWTC). This paper addresses issues related to the development of a PHIL interface that allows testing hardware devices at actual scale. In particular, the novel PHIL interface algorithm and high-speed digital interface, which minimize the critical loop delay, are discussed.
FPGA-Based Optical Cavity Phase Stabilization for Coherent Pulse Stacking
Coherent pulse stacking (CPS) is a new time-domain coherent addition technique that stacks several optical pulses into a single output pulse, enabling high pulse energy from fiber lasers. We develop a robust, scalable, and distributed digital control system with firmware and software integration for algorithms, to support the CPS application. We model CPS as a digital filter in the Z domain and implement a pulse-pattern-based cavity phase detection algorithm on an field-programmable gate array (FPGA). A two-stage (2+1 cavities) 15-pulse stacking system achieves an 11.0 peak-power enhancement factor. Each optical cavity is fed back at 1.5kHz, and stabilized at an individually-prescribed round-trip phase with 0.7deg and 2.1deg rms phase errors for Stages 1 and 2, respectively. Optical cavity phase control with nanometer accuracy ensures 1.2% intensity stability of the stacked pulse over 12 h. The FPGA-based feedback control system can be scaled to large numbers of optical cavities.
Chiral Majorana fermion modes in a quantum anomalous Hall insulator–superconductor structure
Majorana fermion is a hypothetical particle that is its own antiparticle. We report transport measurements that suggest the existence of one-dimensional chiral Majorana fermion modes in the hybrid system of a quantum anomalous Hall insulator thin film coupled with a superconductor. As the external magnetic field is swept, half-integer quantized conductance plateaus are observed at the locations of magnetization reversals, giving a distinct signature of the Majorana fermion modes. This transport signature is reproducible over many magnetic field sweeps and appears at different temperatures. This finding may open up an avenue to control Majorana fermions for implementing robust topological quantum computing.
Lithographic crystallinity regulation in additive fabrication of thermoplastics (CRAFT)
For semicrystalline polyolefin thermoplastics, the balance between interconnected ordered crystalline and disordered amorphous regions is paramount to their performance and processability. However, contemporary manufacturing strategies, from injection molding to three-dimensional (3D) printing, result in monolithic objects, unable to spatially encode crystallinity. We develop a light-based approach for fabricating mechanically robust polyolefin thermoplastics with microscopic control over crystallinity in 3D space. Light dosage governs polymer stereochemistry giving access to a continuum of materials, from strong rigid plastics, such as high-density polyethylene, to more extensible materials akin to low-density polyethylene, all at the flick of a switch. Leveraging this finding in lithographic grayscale 3D printing enables rapid multimaterial fabrication with voxel-level control over optical and mechanical properties, opening avenues in information storage, soft robotics, and energy damping.
Automated Segmentation of Twin Boundaries in TRISO Silicon Carbide Using Deep Neural Networks
Coated particle fuels, such as the tristructural isotropic (TRISO) fuel particle, are essential for high-temperature gas reactor (HTGR) applications due to their efficiency and stability under normal and off-normal conditions. However, widespread commercialization and deployment of this technology for next-generation nuclear applications require robust quality assurance and quality control (QA/QC) methods linking fabrication, properties, and performance. Of the many important metrics for TRISO QA/QC, quantification of the silicon carbide (SiC) microstructure is critical because it correlates with fission product retention during irradiation. Previous work has shown extensive twinning of the SiC microstructure, which strongly affects microstructural metrics; however, twin grain boundaries are not expected play a significant role in fission product diffusion. This report summarizes the initial development, training, and testing of a machine learning image processing algorithm to detect twin grain boundaries in a backscattered electron image, which can be removed so that microstructural metrics can be recalculated for legacy data. Further development and deployment of this model will provide automated, scalable improvement of potential QA/QC methods for the SiC layer of TRISO particles.
Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods
In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.
Comparing Calibration Algorithms for the Rapid Characterization of Pretreated Corn Stover Using Near-Infrared Spectroscopy
Rapid characterization of biomass composition is a key enabling technology for biorefineries—the ability to measure the chemical composition of biomass materials entering the biorefinery as well as the composition of key process intermediate streams would allow real-time process control and the development of robust models to predict process performance. The utility of near-infrared (NIR) spectroscopy for rapid characterization requires multivariate algorithms for building calibration models. The most prevalent algorithm used for building calibration models using NIR spectra is the linear modeling algorithm Partial Least Squares Regression (PLS). Nonlinear regression algorithms (which are typically more computationally intensive than linear modeling approaches) have gained popularity in recent years due to their ability to solve a wide variety of classification and regression problems and the dramatic increase in available computational resources. In this work, we demonstrate that a calibration model can predict the composition of corn stover process intermediate samples pretreated with three different treatments—hot water (HW), dilute acid (DA), and deacetylation followed by dilute acid (DDA). We quantitatively compare three different algorithms for building prediction models based on near-infrared spectroscopy—partial least squares (PLS), support vector machines (SVM), and random forests (RF). We demonstrate the utility of improving model performance by accounting for instrument performance variability using repeated measurements of standard materials (e.g., the “repeatability file” strategy) and investigate its performance with nonlinear regression techniques, and we discuss methods for quantifying the uncertainties of specific predictions among the three methods.
Characteristics of the handling and positioning aid
The handling and positioning aid which (HPA) provides a wide range of adjustable work stations both inboard and outboard of the cargo bay is discussed. It can assist with berthing and docking, it is robust, stiff, has a simple control system, and is modular. An articulated arm version of HPA employed in a typical servicing mission is shown. Mounted on a base frame that spans the Orbiter cargo bay, the 6 m arm is long enough to hold the satellite being serviced and keep its solar array clear of the Orbiter radiators. By adjusting the length and angle of the support platform mast, and rotating the tip of the HPA arm, almost every item on the satellite can be reached. Spares and change-out units can be brought to and from the work site by the RMS, which is controlled from the aft flight deck. The fore and aft position of the base frame can be changed between Orbiter flights and this, together with the 5 degrees of freedom (DOF) of the long arm, allows work sites to be chosen that meet the clearance, reach and vision requirements of many missions. Flight article activities are shown are shown above the dashed horizontal line and Development Test Article (DTA) work below. Flight article requirements and concepts and the design of the DTA are developed.
On dynamics and control of multi-link flexible space manipulators
In this paper dynamics, inverse dynamics, and control problems for multi-link flexible space manipulators are presented. In deriving the flexible manipulator dynamics the following are assumed: flexible deformations are relatively small; angular rates of the links are much smaller than their fundamental frequencies; nonlinear terms (centrifugal and Coriolis forces) in the flexible manipulator model are the same as those in the rigid body model. These assumptions are reasonable for large space manipulators, such as the space crane. Flexible displacements are measured with respect to the rigid body configuration, for which a linear time-varying system is obtained. The inverse dynamics problem consists of determination of joint torques, given tip trajectory, such that joint angles in flexible configuration are equal to the angles in the rigid body configuration. The manipulator control system consists of the feedforward compensation and feedback control loops. Simulation results of a two-link space crane with large payload show that the performance of this linearized dynamics and control approach is reasonable and robust subject to parameter variations during slew operations.
Hybrid propulsion technology program: Phase 1, volume 1
A number of booster propulsion system concepts are being considered for the next generation of manned and unmanned space launch vehicles. The one propulsion system concept that has potential for reducing costs with increased safety, reliability, and performance is hybrid propulsion (HP). A HP system may be thought of as a liquid propulsion system with solid fuel or a solid propulsion system with a liquid oxidizer. The liquid propulsion features that are most attractive are the higher specific impulse, clean exhaust, separated propellants, and oxidizer loading just prior to launch. The most attractive solid propulsion features includes low life cycle costs, no rotating machinery, compact size, and a robust case. In addition, a HP system has a robust LO2 tank; provides thrust control for ignition, to alleviate flight loads, and for thrust termination; and uses an inert grain that is not sensitive to anomalies such as cracks, voids, and separations. The object is to develop the technology to enable the application of HP to manned and unmanned space launch vehicles. This program will identify the necessary technology, acquire that technology, and demonstrate that technology. This volume is the executive summary.
Optical correlators for automated rendezvous and capture
The paper begins with a description of optical correlation. In this process, the propagation physics of coherent light is used to process images and extract information. The processed image is operated on as an area, rather than as a collection of points. An essentially instantaneous convolution is performed on that image to provide the sensory data. In this process, an image is sensed and encoded onto a coherent wavefront, and the propagation is arranged to create a bright spot of the image to match a model of the desired object. The brightness of the spot provides an indication of the degree of resemblance of the viewed image to the mode, and the location of the bright spot provides pointing information. The process can be utilized for AR&C to achieve the capability to identify objects among known reference types, estimate the object's location and orientation, and interact with the control system. System characteristics (speed, robustness, accuracy, small form factors) are adequate to meet most requirements. The correlator exploits the fact that Bosons and Fermions pass through each other. Since the image source is input as an electronic data set, conventional imagers can be used. In systems where the image is input directly, the correlating element must be at the sensing location.
Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms.
Carbon cycling in high-latitude ecosystems
The carbon-rich soils and peatlands of high-latitude ecosystems could substantially influence atmospheric concentrations of CO2 and CH4 in a changing climate. Currently, cold, often waterlogged conditions retard decomposition, and release of carbon back to the atmosphere may be further slowed by physical protection of organic matter in permafrost. As a result, many northern ecosystems accumulate carbon over time (Billings et al., 1982; Poole and Miller, 1982), and although such rates of accumulation are low, thousands of years of development have left Arctic ecosystems with an extremely high soil carbon content; Schlesinger's (1984) average value of 20.4 kg C/m(sup 2) leads to a global estimate of 163 x 10(exp 15) g C. All GCM simulations of a doubled CO2 climate predict the greatest warming to occur in the polar regions (Dickinson, 1986; Mitchell, 1989). Given the extensive northern carbon pools and the strong sensitivity of decomposition processes to temperature, even a slight warming of the soil could dramatically alter the carbon balance of Arctic ecosystems. If warming accelerates rates of decomposition more than rates of primary production, a sizeable additional accumulation of CO2 in the atmosphere could occur. Furthermore, CH4 produced in anaerobic soils and peatlands of the Arctic already composes a good percentage of the global efflux (Cicerone and Oremlund, 1988); if northern soils become warmer and wetter as a whole, CH4 emissions could dramatically rise. A robust understanding of the primary controls of carbon fluxes in Arctic ecosystems is critical. As a framework for a systematic examination of these controls, we discussed a conceptual model of regional-scale Arctic carbon turnover, including CH4 production, and based upon the Century soil organic matter model.