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At least 685 records · Page 38

Barriers to Balcony Solar and Plug-In Distributed Energy Resources in the United States

Plug-in distributed energy resources (DERs), such as balcony solar, backfeed power to the home through a standard plug. These systems may represent the future of residential solar and storage, particularly as recent net metering policies have reduced the economic appeal of rooftop solar. While plug-in DERs have seen widespread success in Europe, their U.S. market is stagnant. This paper reviews the technical, interconnection, and regulatory barriers hindering the adoption of plug-in DERs. We first discuss the technical barriers, which include touch safety, breaker masking, and bidirectional ground-fault circuit interrupters. We then examine utility perspectives on plug-in DERs and strategies for navigating interconnection challenges. Finally, we discuss regulatory hurdles related to UL standards and the National Electrical Code.

Gerber, Daniel L. (ORCID:0000000219720679)↗

A Comprehensive Overview of Postbiotics with a Special Focus on Discovery Techniques and Clinical Applications

The increasing interest in postbiotics, a term gaining recognition alongside probiotics and prebiotics, aligns with a growing number of clinical trials demonstrating positive outcomes for specific conditions. Postbiotics present several advantages, including safety, extended shelf life, ease of administration, absence of risk, and patentability, making them more appealing than probiotics alone. This review covers various aspects, starting with an introduction, terminology, classification of postbiotics, and brief mechanisms of action. It emphasizes microbial metabolomics as the initial step in discovering novel postbiotics. Commonly employed techniques such as NMR, GC-MS, and LC-MS are briefly outlined, along with their application principles and limitations in microbial metabolomics. The review also examines existing research where these techniques were used to identify, isolate, and characterize postbiotics derived from different microbial sources. The discovery section concludes by highlighting challenges and future directions to enhance postbiotic discovery. In the second half of the review, we delve deeper into numerous published postbiotic clinical trials to date. We provide brief overviews of system-specific trial applications, their objectives, the postbiotics tested, and their outcomes. The review concludes by highlighting ongoing applications of postbiotics in extended clinical trials, offering a comprehensive overview of the current landscape in this evolving field.

60 APPLIED LIFE SCIENCES↗

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.

97 MATHEMATICS AND COMPUTING↗

A Multicontinuum-Theory-Based Approach to the Analysis of Fiber-Reinforced Polymer Composites with Degraded Stiffness and Strength Properties Due to Moisture Absorption

Marine energy generation technologies such as wave and tidal power have great potential in meeting the need for renewable energy in the years ahead. Yet, many challenges remain associated with marine-based systems because of the corrosive environment. Conventional materials like metals are subject to rapid corrosive breakdown, crippling the lifespan of structures in such environments. Fiber-reinforced polymer composites offer an appealing alternative in their strength and corrosion resistance, but can experience degradation of mechanical properties as a result of moisture absorption. An investigation is conducted to test the application of a technique for micromechanical analysis of composites, known as multicontinuum theory and demonstrated in past works, as a mechanism for predicting the effects of prolonged moisture absorption on the performance of fiber-reinforced composites. Experimental tensile tests are performed on composite coupons with and without prolonged exposure to a salt water solution to obtain stiffness and strength properties. Multicontinuum theory is applied in conjunction with micromechanical modeling to deduce the effects of moisture absorption on the behavior of constituent materials within the composites. The results are consistent with experimental observations when guided by known mechanisms and trends from previous studies, indicating multicontinuum theory as a potentially effective tool in predicting the long-term performance of composites in marine environments.

36 MATERIALS SCIENCE↗

Investigation of Fe-Ni Battery/Module for Grid Service Duty Cycles

Iron–nickel (Fe-Ni) batteries are renowned for their durability and resilience against overcharging and operating temperatures. However, they encounter challenges in achieving widespread adoption for energy storage applications due to their low efficiency and the need for regular maintenance and electrolyte replacement, which adds to maintenance costs. This study evaluates and demonstrates the capabilities of Fe-Ni batteries for participating in grid energy storage applications. Stable performance was observed frequency regulation (FR) testing at 100% and 50% state of charge (SOC)s, while at 50% SOC, there was a 14% increase in efficiency compared to 100% SOC. Although 25% SOC achieved higher efficiency, limited cyclability was observed due to reaching the discharge cutoff voltage. Optimal SOC selection, battery monitoring, maintenance, and appropriate charging strategies of Fe-Ni batteries seem to be crucial for their FR applications. Fe-Ni batteries exhibit stable peak shaving (PS) results, indicating their suitability and reliability under various load conditions for PS testing. Extended cycling tests confirm their potential for long-term grid-scale energy storage, enhancing their appeal for PS and FR applications.

25 ENERGY STORAGE↗

Nanosystems, Edge Computing, and the Next Generation Computing Systems

It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.

97 MATHEMATICS AND COMPUTING↗

Formation of Stable Interphase of Polymer-in-Salt Electrolyte in All-Solid-State Lithium Batteries

The integration of solid-polymer electrolytes into all-solid-state lithium batteries is highly desirable to overcome the limitations of current battery configurations that have a low energy density and severe safety concerns. Polyacrylonitrile is an appealing matrix for solid-polymer electrolytes; however, the practical utilization of such polymer electrolytes in all-solid-state cells is impeded by inferior ionic conductivity and instability against a lithium-metal anode. In this work, we show that a polymer-in-salt electrolyte based on polyacrylonitrile with a lithium salt as the major component exhibits a wide electrochemically stable window, a high ionic conductivity, and an increased lithium-ion transference number. The growth of dendrites from the lithium-metal anode was suppressed effectively by the polymer-in-salt electrolyte to increase the safety features of the batteries. In addition, we found that a stable interphase was formed between the lithium-metal anode and the polymer-in-salt electrolyte to restrain the uncontrolled parasitic reactions, and we demonstrated an all-solid-state battery configuration with a LiFePO 4 cathode and the polymer-in-salt electrolyte, which exhibited a superior cycling stability and rate capability.

Gao, Hongcai↗

SPECTRUM AND ANISOTROPY OF TURBULENCE FROM MULTI-FREQUENCY MEASUREMENT OF SYNCHROTRON POLARIZATION

We consider turbulent synchrotron-emitting media that also exhibit Faraday rotation and provide a statistical description of synchrotron polarization fluctuations. In particular, we consider these fluctuations as a function of the spatial separation of the direction of the measurements and as a function of wavelength for the same line of sight. On the basis of our general analytical approach, we introduce several measures that can be used to obtain the spectral slopes and correlation scales of both the underlying magnetic turbulence responsible for emission and the spectrum of the Faraday rotation fluctuations. We show the synergetic nature of these measures and discuss how the study can be performed using sparsely sampled interferometric data. We also discuss how additional characteristics of turbulence can be obtained, including the turbulence anisotropy and the three-dimensional direction of the mean magnetic field. In addition, we consider the cases when the synchrotron emission and Faraday rotation regions are spatially separated. Appealing to our earlier study, we explain that our new results are applicable to a wide range of spectral indexes of relativistic electrons responsible for synchrotron emission. We expect wide application of our techniques, both with existing synchrotron data sets and with big forthcoming data sets from LOFAR and SKA.

79 ASTRONOMY AND ASTROPHYSICS↗

A SEARCH FOR SUB-SECOND RADIO VARIABILITY PREDICTED TO ARISE TOWARD 3C 84 FROM INTERGALACTIC DISPERSION

We empirically evaluate the scheme proposed by Lieu and Duan in which the light curve of a time-steady radio source is predicted to exhibit increased variability on a characteristic timescale set by the sightline’s electron column density. Application to extragalactic sources is of significant appeal, as it would enable a unique and reliable probe of cosmic baryons. We examine temporal power spectra for 3C 84, observed at 1.7 GHz with the Karl G. Jansky Very Large Array and the Robert C. Byrd Green Bank Telescope. These data constrain the ratio between standard deviation and mean intensity for 3C 84 to less than 0.05% at temporal frequencies ranging between 0.1 and 200 Hz. This limit is 3 orders of magnitude below the variability predicted by Lieu and Duan and is in accord with theoretical arguments presented by Hirata and McQuinn rebutting electron density dependence. We identify other spectral features in the data consistent with the slow solar wind, a coronal mass ejection, and the ionosphere.

79 ASTRONOMY AND ASTROPHYSICS↗

Quantifying Environmental and Line-of-sight Effects in Models of Strong Gravitational Lens Systems

Matter near a gravitational lens galaxy or projected along the line of sight (LOS) can affect strong lensing observables by more than contemporary measurement errors. We simulate lens fields with realistic three-dimensional mass configurations (self-consistently including voids), and then fit mock lensing observables with increasingly complex lens models to quantify biases and uncertainties associated with different ways of treating the lens environment (ENV) and LOS. We identify the combination of mass, projected offset, and redshift that determines the importance of a perturbing galaxy for lensing. Foreground structures have a stronger effect on the lens potential than background structures, due to nonlinear effects in the foreground and downweighting in the background. There is dramatic variation in the net strength of ENV/LOS effects across different lens fields; modeling fields individually yields stronger priors for H {sub 0} than ray tracing through N-body simulations. Models that ignore mass outside the lens yield poor fits and biased results. Adding external shear can account for tidal stretching from galaxies at redshifts z⩾z{sub lens}, but it requires corrections for external convergence and cannot reproduce nonlinear effects from foreground galaxies. Using the tidal approximation is reasonable for most perturbers as long as nonlinear redshift effects are included. Even then, the scatter in H {sub 0} is limited by the lens profile degeneracy. Asymmetric image configurations produced by highly elliptical lens galaxies are less sensitive to the lens profile degeneracy, so they offer appealing targets for precision lensing analyses in future surveys like LSST and Euclid.

79 ASTRONOMY AND ASTROPHYSICS↗

Successive Homologous Coronal Mass Ejections Driven by Shearing and Converging Motions in Solar Active Region NOAA 12371

We study the magnetic field evolution in AR 12371, related to its successive eruptive nature. During the disk transit of seven days, the active region (AR) launched four sequential fast coronal mass ejections (CMEs), which are associated with long duration M-class flares. Morphological study delineates a pre-eruptive coronal sigmoid structure above the polarity inversion line (PIL) similar to Moore et al.’s study. The velocity field derived from tracked magnetograms indicates persistent shear and converging motions of polarity regions about the PIL. While these shear motions continue, the crossed arms of two sigmoid elbows are being brought to interaction by converging motions at the middle of the PIL, initiating the tether-cutting reconnection of field lines and the onset of the CME explosion. The successive CMEs are explained by a cyclic process of magnetic energy storage and release referred to as “sigmoid-to-arcade-to-sigmoid” transformation driven by photospheric flux motions. Furthermore, the continued shear motions inject helicity flux with a dominant negative sign, which contributes to core field twist and its energy by building a twisted flux rope (FR). After a limiting value, the excess coronal helicity is expelled by bodily ejection of the FR, which is initiated by some instability as realized by intermittent CMEs. This AR is in contrast with the confined AR 12192 with a predominant negative sign and larger helicity flux, but much weaker (−0.02 turns) normalized coronal helicity content. While predominant signed helicity flux is a requirement for CME eruption, our study suggests that the magnetic flux normalized helicity flux is a necessary condition accommodating the role of background flux and appeals to a further study of a large sample of ARs.

79 ASTRONOMY AND ASTROPHYSICS↗

Surface Treatment Strategies for Mitigating Gas Hydrate & Asphaltene Formation, Growth, and Deposition in Flowloops

Abstract Numerous solids including gas hydrates, waxes, and asphaltenes have the potential to form in the production lines of gas and oil fields. This creates a highly non-ideal scenario as the accumulation of said species leads to flow assurance issues, especially with long-term processes like deposition. Since an ever-increasing amount of material is deposited in place at the pipe surface, production stoppage or active mitigation efforts become inevitable. The latter production issues result in increased safety risks and operational expenditures. Therefore, a cost-effective, passive deposition mitigation technology, such as a pipeline coating or surface treatment is especially appealing. The ability to address multiple pipeline flow assurance issues simultaneously without actively disrupting production would represent a dramatic step forward in this area. This study is part of a long-term ongoing effort that evaluates the performance and application of an omniphobic surface treatment for solids deposition prevention in industrially relevant systems. In particular, this specific work concentrates on the efficacy and robustness of the treatment under fully flowing conditions. The apparatuses utilized for this include two flowloops: a lab-scale, high-pressure flowloop for gas hydrate and surface treatment durability studies, and a bench-scale, atmospheric pressure loop for crude oil and asphaltene experiments. Film growth in high-pressure flowloop tests corroborated previous reports of delayed gas hydrate nucleation observed in rocking cells. Without the aid of the memory effect, treated oil-dominated experiments never experienced hydrate formation, spending upwards of a week in the hydrate stability zone (at the subcooled/fluid test conditions). Subsequent tests which utilized the memory effect then revealed that the hydrate formation rate reduced in the presence of the surface treatment compared to a bare stainless-steel surface. This testing was part of a larger set of trials conducted in the flowloop, which lasted about one year. The surface treatment durability under flowing conditions was evaluated during this time. Even after experiencing ∼4000 operating hours and 2 full pressure cycles, no evidence of delamination or damage was detected. Finally, as part of an extension to previous work, corroded surface asphaltene deposition experiments were performed in a bench-top flowloop. Treated experiments displayed an order of magnitude reduction in both total oil (all fractions of crude oil) and asphaltene fraction deposited.

03 NATURAL GAS↗

Progress in Modeling and Simulation of Batteries

Modeling and simulation of batteries, in conjunction with theory and experiment, are important research tools that offer opportunities for advancement of technologies that are critical to electric motors. The development of data from the application of these tools can provide the basis for managerial and technical decision-making. Together, these will continue to transform batteries for electric vehicles. This collection of nine papers presents the modeling and simulation of batteries and the continuing contribution being made to this impressive progress, including topics that cover: * Thermal behavior and characteristics * Battery management system design and analysis * Moderately high-fidelity 3D capabilities * Optimization Techniques and Durability As electric vehicles continue to gain interest from manufacturers and consumers alike, improvements in economy and affordability, as well as adoption of alternative fuel sources to meet government mandates are driving battery research and development. Progress in modeling and simulation will continue to contribute to battery improvements that deliver increased power, energy storage, and durability to further enhance the appeal of electric vehicles.

batteries↗

sympl (v. 0.4.0) and climt (v. 0.15.3) – towards a flexible framework for building model hierarchies in Python

sympl (System for Modelling Planets) andclimt (Climate Modelling and Diagnostics Toolkit) are anattempt to rethink climate modelling frameworks from the ground up. The aimis to use expressive data structures available in the scientific Pythonecosystem along with best practices in software design to allow scientists toeasily and reliably combine model components to represent the climate systemat a desired level of complexity and to enable users to fully understandwhat the model is doing. sympl is a framework which formulates the model in terms of astate that gets evolved forward in time or modified within a specifictime by well-defined components. sympl's design facilitates buildingmodels that are self-documenting, are highly interoperable, and providefine-grained control over model components and behaviour. symplcomponents contain all relevant information about the input they expect andoutput that they provide. Components are designed to be easily interchanged,even when they rely on different units or array configurations.sympl provides basic functions and objects which could be used inany type of Earth system model. climt is an Earth system modelling toolkit that contains scientificcomponents built using sympl base objects. These include both purePython components and wrapped Fortran libraries. climt providesfunctionality requiring model-specific assumptions, such as stateinitialization and grid configuration. climt's programming interfacedesigned to be easy to use and thus appealing to a wide audience. Model building, configuration and execution are performed through a Pythonscript (or Jupyter Notebook), enabling researchers to build an end-to-endPython-based pipeline along with popular Python data analysis andvisualization tools.

97 MATHEMATICS AND COMPUTING↗

Flutter behavior of highly flexible blades for two- and three-bladed wind turbines

Abstract. With the progression of novel design, material, and manufacturing technologies, the wind energy industry has successfully produced larger and larger wind turbine rotor blades while driving down the levelized cost of energy (LCOE). Though the benefits of larger turbine blades are appealing, larger blades are prone to aeroelastic instabilities due to their long, slender, highly flexible nature, and this effect is accentuated as rotors further grow in size. In addition to the trend of larger rotors, non-traditional rotor concepts are emerging including two-bladed rotors and downwind configurations. In this work, we introduce a comprehensive evaluation of flutter behavior including classical flutter, edgewise vibration, and flutter mode characteristics for two-bladed, downwind rotors. Flutter speed trends and characteristics for a series of both two- and three-bladed rotors are analyzed and compared in order to illustrate the flutter behavior of two-bladed rotors relative to more well-known flutter characteristics of three-bladed rotors. In addition, we examine the important problem of blade design to mitigate flutter and present a solution to mitigate flutter in the structural design process. A study is carried out evaluating the effect of leading edge and trailing edge reinforcement on flutter speed and hence demonstrates the ability to increase the flutter speed and satisfy structural design requirements (such as fatigue) while maintaining or even reducing blade mass.

Chetan, Mayank (ORCID:0000000241978801)↗

Arctic Mixed-Phase Cloud Base Ice Precipitation Properties During the M-PACE Field Campaign

Cloud-climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long-lived, and nearly-continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud-base properties a more appealing target for inference and evaluation of model simulations. This dataset provides a set of 25 samples from the M-PACE field campaign, all of which were retrieved using the synthesis of ARM radar and lidar measurements. The retrieved ice precipitation variables in this dataset include, among others, the ice number concentration, water content, PSD parameters, precipitation rate, mass-weighted fall velocity, vertical air motion, and effective radius, all of which are highly valuable for model evaluation and a general understanding of polar cloud sink processes. Each variable sample includes its mean value and associated uncertainty. Additional variables based on ARM measurements (liquid layer statistics, etc.) are included in this dataset as well. The retrieval algorithm and analysis of this dataset are described in Silber (JGR, 2023, https://doi.org/10.1029/2022JD038202).

54 ENVIRONMENTAL SCIENCES↗

Arctic Mixed-Phase Cloud Base Ice Precipitation Properties Over the NSA Site

Cloud-climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long-lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud-base properties a more appealing target for inference and evaluation of model simulations. This data set provides more than 1800 samples of cloud-base ice precipitation properties over Utqiagvik, North Slope of Alaska, all of which were retrieved using the synthesis of ARM radar and lidar measurements. The retrieved ice precipitation variables in this data set include, among others, the ice number concentration, water content, PSD parameters, precipitation rate, mass-weighted fall velocity, vertical air motion, and effective radius, all of which are highly valuable for model evaluation and a general understanding of polar cloud sink processes. Each variable sample includes its mean value and associated uncertainty. Additional variables based on ARM measurements (liquid layer statistics, etc.) are included in this data set. The retrieval algorithm and analysis of this data set are described in Silber (JGR, 2023, https://doi.org/10.1029/2022JD038202).

54 ENVIRONMENTAL SCIENCES↗