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Operation trims conductor to width of 0.004 in. and consistently leaves 0.035 in. spaces between adjacent conductors, adapting cable for termination with standard plug.
Optimal power flow (OPF) problems, which dispatch power targets to controllable generating units across a network, must generally account for non-convex constraints on power flow. Furthermore, adapting those problems so as to make them solvable with convex optimization techniques is an area of much academic and operational interest. In this paper, we present a method for solving OPF as a quadratic program by iteratively refining and re-initializing a linearized model of power flow based on the outputs of an associated nonlinear solver. The linear model on which we demonstrate this method is an adapted version of an approximation designed for use with unbalanced distribution networks. As an important benefit, the model allows for the explicit inclusion of nodal voltage phasor values in both the OPF problem's objective and its constraints, which opens the door to the idea of phasor-based control (PBC) design. We show in simulations on the IEEE 13-node test feeder that our method quickly converges to a set of phasor targets that are sufficiently precise for use in operations at the distribution level.
We construct a family of embedded pairs for optimal explicit strong stability preserving Runge–Kutta methods of order 2 ≤ p ≤ 4 to be used to obtain numerical solution of spatially discretized hyperbolic PDEs. In this construction, the goals include non-defective property, large stability region, and small error values as defined in Dekker and Verwer (1984) and Kennedy et al. (2000). The new family of embedded pairs offer the ability for strong stability preserving (SSP) methods to adapt by varying the step-size. Through several numerical experiments, we assess the overall effectiveness in terms of work versus precision while also taking into consideration accuracy and stability.
The classical process to recover uranium (U) and plutonium (Pu) from used nuclear fuel using tributyl phosphate (TBP), namely the Plutonium Uranium Redox EXtraction (PUREX) process, is complicated by the persistent presence of neptunium (Np) and thus requires extra purification steps. The concept of Adapted PUREX seeks to achieve Np recovery by adjusting the valence of the metal more effectively, thereby controlling its behavior more precisely. This study introduces the use of an aqueous hydroxypyridinone chelator, 3,4,3-LI(1,2-HOPO) (abbreviated as HOPO), to dictate the behavior of Np for recovery and meanwhile simplify cumbersome reprocessing steps. The interactions between Np and HOPO were probed mechanistically by way of absorption spectrophotometry, in conjunction with cyclic voltammetry. UV–Vis-NIR spectra illustrated the reduction of NpO 2 2+ to Np 4+ , with a fast reaction rate. Cyclic voltammetry revealed quasi-reversible processes between the oxidized and reduced forms of the ligand and its Np complexes. The corresponding heterogeneous rate constants (k 0 ) were estimated from the peak-to-peak separation potentials (ΔE p ), at ~ 4 – 35 μm/s for both HOPO and NpHOPO, with scan rates of 0.01 – 0.4 V/s. Meanwhile, the electromotive force (E MF ) as well as the change of Gibbs free energy (ΔG) were assessed from the half-wave potential (E 1/2 ), demonstrating the completeness of NpO 2 2+ reduction to Np 4+ by HOPO. The cumulative formation constant of the resulting NpHOPO complex (logβ 101 ) was determined by metal competition titration to be 42.0 ± 0.6, corroborating the extraordinarily high affinity of HOPO to tetravalent metal ions. Here, the prowess of valence control by HOPO and the high stability of the formed complex resulted in enhanced separations of Np from U and of Pu from U, with a maximum separation factor of ~7000 for both, nearly 90- and 10300-fold higher, respectively, than the values obtained using conventional PUREX formulae.
Atmospheric weather and climate models must perform simulations very quickly to be useful. Therefore, modelers have traditionally focused on reducing computations as much as possible. However, in our new era of increasingly compute-capable hardware, data movement is now the prohibiting expense. This study examines the computational benefits of a new algorithmic approach to modeling atmospheric dynamics on scales relevant to weather and climate simulation. Rather than minimizing computations, this new approach considers the larger problem more holistically, including spatial accuracy, temporal accuracy, robustness (i.e., oscillations), on-node efficiency, and internode data transfers together at once. Numerical experiments demonstrate how computations can be strategically increased to simultaneously address each of these constraints while reducing data movement to adapt to modern accelerated hardware. The new algorithm can achieve at times up to 80% peak floating point throughput in single precision on the Nvidia Tesla V100 GPU, where the traditional approach is shown to only achieve single-digit floating point efficiency. Further, the new algorithm is twice as fast as a standard Runge--Kutta time integrator, and high-order accuracy with Weighted Essentially Non-Oscillatory (WENO) limiting came at less than 30% additional runtime cost on a GPU, thus increasing the accuracy per degree of freedom.
This research presents an innovative approach to improving theSignalSignal-to -Noise Ratio (SNR) in superconducting quantumcomputing readout signals, utilizing adaptive filtering on thecontrol board. Our solution FPGA FPGA-based aims to bolsterquantum computations' reliability by providing a more accurateand precise way to obtain our readout traces and achieve ahigher discernability between excitation states in real real-time.
A four-element array conformed to a singly curved conducting surface has been demonstrated to provide 2 dB axial ratio of 14 percent, while maintaining VSWR (voltage standing wave ratio) of 2:1 and gain of 13 dBiC. The array is digitally controlled and can be scanned with the LMS Adaptive Algorithm using the power spectrum as the objective, as well as the Direction of Arrival (DoA) of the beam to set the amplitude of the power spectrum. The total height of the array above the conducting surface is 1.5 inches (3.8 cm). A uniquely configured microstrip-coupled aperture over a conducting surface produced supergain characteristics, achieving 12.5 dBiC across the 2-to-2.13- GHz and 2.2-to-2.3-GHz frequency bands. This design is optimized to retain VSWR and axial ratio across the band as well. The four elements are uniquely configured with respect to one another for performance enhancement, and the appropriate phase excitation to each element for scan can be found either by analytical beam synthesis using the genetic algorithm with the measured or simulated far field radiation pattern, or an adaptive algorithm implemented with the digitized signal. The commercially available tuners and field-programmable gate array (FPGA) boards utilized required precise phase coherent configuration control, and with custom code developed by Nokomis, Inc., were shown to be fully functional in a two-channel configuration controlled by FPGA boards. A four-channel tuner configuration and oscilloscope configuration were also demonstrated although algorithm post-processing was required.
Context. The Cassini-Huygens mission has revealed the importance of particle precipitation in the atmosphere of Titan thanks to in-situ measurements. These ionizing particles (electrons, protons, and cosmic rays) have a strong impact on the chemistry, hence must be modeled. Aims. We revisit our computation of ionization in the atmosphere of Titan by cosmic rays. The high-energy high-mass ions are taken into account to improve the precision of the calculation of the ion production profile. Methods. The Badhwahr and O Neill model for cosmic ray spectrum was adapted for the Titan model. We used the TransTitan model coupled with the Planetocosmics model to compute the ion production by cosmic rays. We compared the results with the NAIRAS/HZETRN ionization model used for the first time for a body that differs from the Earth. Results. The cosmic ray ionization is computed for five groups of cosmic rays, depending on their charge and mass: protons, alpha, Z = 8 (oxygen), Z = 14 (silicon), and Z = 26 (iron) nucleus. Protons and alpha particles ionize mainly at 65 km altitude, while the higher mass nucleons ionize at higher altitudes. Nevertheless, the ionization at higher altitude is insufficient to obscure the impact of Saturn s magnetosphere protons at a 500 km altitude. The ionization rate at the peak (altitude: 65 km, for all the different conditions) lies between 30 and 40/cu cm/s. Conclusions. These new computations show for the first time the importance of high Z cosmic rays on the ionization of the Titan atmosphere. The updated full ionization profile shape does not differ significantly from that found in our previous calculations (Paper I: Gronoff et al. 2009, 506, 955) but undergoes a strong increase in intensity below an altitude of 400 km, especially between 200 and 400 km altitude where alpha and heavier particles (in the cosmic ray spectrum) are responsible for 40% of the ionization. The comparison of several models of ionization and cosmic ray spectra (in intensity and composition) reassures us about the stability of the altitude of the ionization peak (65 km altitude) with respect to the solar activity.
Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. Furthermore, we demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.
Excalibur is a nonparametric, hierarchical framework for precision wavelength calibration of spectrographs. It is designed with the needs of extreme-precision radial-velocity (EPRV) instruments in mind, which require calibration or stabilization to better than 10{sup −4} pixels. Instruments vary along only a few dominant degrees of freedom, especially EPRV instruments that feature highly stabilized optical systems and detectors. Excalibur takes advantage of this property by using all calibration data to construct a low-dimensional representation of all accessible calibration states for an instrument. Excalibur also takes advantage of laser-frequency combs or etalons, which generate a dense set of stable calibration points. This density permits the use of a nonparametric wavelength solution that can adapt to any instrument or detector oddities better than parametric models, such as a polynomial. We demonstrate the success of this method with data from the Extreme Precision Spectrograph (EXPRES), which uses a laser-frequency comb. When wavelengths are assigned to laser comb lines using excalibur, the rms of the residuals is about one-fifth that of wavelengths assigned using polynomial fits to individual exposures. Radial-velocity measurements of HD 34411 show a reduction in rms scatter over a 10 month time baseline from 1.17 to 1.05 m s{sup −1}.
The OpenSE Cookbook is an open-sourced collection of patterns, procedures, and best practices targeted for systems engineers who seek guidance on applying model-based and executable systems engineering (MBSE) using SysML. Its content has emerged from the system level modeling effort on the European Framework Program 6 (FP6) and the Thirty Meter Telescope (TMT). The TMT MBSE approach applied the Executable Systems Engineering Method (ESEM) and the open-source Engineering Environment (OpenMBEE) to specify, analyze, and verify requirements of TMT’s Alignment and Phasing System (APS) and the Narrow Field Infrared Adaptive Optics System (NFIRAOS). In these applications, implicit dependencies are made explicit in a formal model through the use of ESEM, OpenMBEE, and SysML modeling constructs. The value proposition for applying this MBSE approach was to establish precise requirements and fine-grained traceability to system designs, and to verify key requirements beginning early in development. The integration of ESEM and the OpenMBEE tooling infrastructure (providing linked-data and web-operability) is a significant added value for the MBSE approach. The APS is responsible for the overall pre-adaptive optics wavefront quality, using starlight to measure wavefront errors and align the TMT optics. In the formally integrated and executable SysML model, simulations are performed to analyze the impact of changed requirements and verify specified constraints for various operational scenarios. The APS team used several modeling patterns to capture information such as the requirements, the operational scenarios, involved subsystems and their interaction points, the estimated or required time durations, and the mass and power consumption. Adaptive optics systems are designed to sense real-time atmospheric turbulence and correct the telescope’s optical beam to remove its effect. The system model for the adaptive optics operational modes was developed to capture sequence behaviors and operational scenarios to run Monte-Carlo simulations for verifying acquisition time, observing efficiency, and operational behavior requirements. The model is particularly useful for investigating the effect of parallelization, identifying interface issues, and re-ordering sequence acquisition tasks. A former version of the Cookbook (which is now updated to MBSE challenges, goals, and lessons learned) included modeling guidelines and conventions for all system aspects, hierarchy levels, and views, which were developed during for the Active Phasing Experiment (APE), an opto-mechatronical system technology demonstrator for the Extremely Large Telescope (ELT). The Cookbook utilizes the above mentioned system models as real-world case-studies to demonstrate and document the applications of the recipes, providing also instructional examples and addressing the available tooling support. The Cookbook is accompanied by a number of SysML models and aodel libraries which facilitate model authoring and maintenance. The Cookbook covers the different aspects of Systems Engineering such as management of Requirements, Design (behavior and structure), Interfaces, Interdisciplinary Integration, Analysis, Trade Studies, and Technical Resources. This paper presents the background, motivation, architecture, and highlights some key content of the Cookbook. For example, interface management, error budget management, requirements verification, Monte Carlo driven analysis, and timing analysis of operational scenarios. The paper discusses how the capabilities of OpenMBEE contributed significantly to the adoption of executable systems engineering.
Due to their ultra lightweight and high packaging efficiency, membrane reflectors are getting more and more attentions for mission architectures that need extremely large inspace deployable antennas. However how to maintain the surface shape of a membrane reflector to the instrument precision requirements is a very challenging problem. This experimental study investigated using PVDF membrane piezoelectric material as actuators to control the surface figures of membrane reflectors. The feasibility of this approach is demonstrated by several sets of test results.
Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features. Ultrasound images previously collected in a tissue phantom with or without neurovascular features were labeled with ground truth masks. These image sets were used to train two different segmentation AI frameworks: YOLOv7 and U-Net segmentation models. Overall, both approaches were successful in identifying shrapnel in the image set, with U-Net outperforming YOLOv7 for single-class segmentation. Both segmentation models were also evaluated with a more complex image set containing shrapnel, artery, vein, and nerve features. YOLOv7 obtained higher precision scores across multiple classes whereas U-Net achieved higher recall scores. Using each AI model, a triage distance metric was adapted to measure the proximity of shrapnel to the nearest neurovascular feature, with U-Net more closely mirroring the triage distances measured from ground truth labels. Overall, the segmentation AI models were successful in detecting shrapnel in ultrasound images and could allow for improved injury triage in emergency medicine scenarios.
Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.
Trends in spectropolarimetry as applied to the problem of Zeeman effect measurement are discussed. The use of detector arrays to improve observing efficiency is obtained. Which required new polarization modulation schemes that match the time required to read detector arrays. Another significant trend is narrowband filters, to improve angular and temporal coverage, and to Fourier transform spectrometers, to improve spectral coverage and precision. Low-polarization designs and improved methods for compensating instrumental polarization were developed. A requirement for high angular resolution suggests using adaptive optical devices to subdue the effects of bad seeing. The ultimate strategy to beat the seeing is to loft the telescope above the atmosphere such as is planned with a 30-cm telescope in 1985 and a 1250-cm telescope in 1990.
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.
Adaptive optics and interferometry, two techniques that will improve the limiting resolution of optical and infrared observations by factors of tens or even thousands, are discussed. The real-time adjustment of optical surfaces to compensate for wavefront distortions will improve image quality and increase sensitivity. The phased operation of multiple telescopes separated by large distances will make it possible to achieve very high angular resolution and precise positional measurements. Infrared and optical interferometers that will manipulate light beams and measure interference directly are considered. Angular resolutions of single telescopes will be limited to around 10 milliarcseconds even using the adaptive optics techniques. Interferometry would surpass this limit by a factor of 100 or more. Future telescope arrays with 100-m baselines (resolution of 2.5 milliarcseconds at a 1-micron wavelength) are also discussed.