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At least 955 records · Page 53

Hydrogen Mitigation Process Testing at Nevada Solar One

The National Renewable Energy Laboratory (NREL) and Acciona Solar Power (ASP) developed and installed a process that addresses the issue of hydrogen buildup in Acciona's Nevada Solar One power plant. Our method selectively removes hydrogen from the expansion tanks of the power plant to control hydrogen levels in the circulating heat-transfer fluid (HTF). During previous work, we developed a sensor that measures hydrogen partial pressure in the expansion-tank headspace gas. We demonstrated that our sensor measures hydrogen levels over a wide range of partial pressure from 10 mbar down to 0.003 mbar. More recently, we conceived and developed an integrated process module that performs both hydrogen sensing and separating functions. The sensor/separator measures hydrogen partial pressure in the headspace gas in the same way as our original sensor design. Additionally, the integrated module separates hydrogen from the headspace gas to reduce hydrogen to the level needed to maintain the performance of receivers in the collector field. Laboratory testing at NREL showed that the sensor function had an accuracy of +/-7%, and the hydrogen extraction rate for separator function was consistent with our modeling predictions. The primary benefit of this module is its simple design, both in terms of function and incorporation into the HTF subsystem of the power plant. Most recently, NREL and ASP completed installation and initial testing of a mitigation process at ASP's Nevada Solar One power plant in Boulder City, Nevada. In this paper, we report on the completed installation, initial testing, and plans to bring the process to full automation, so that it can be operated unattended on a daily schedule.

chemical elements↗

Cold air quench control of local crystallization environment in fully air-processed carbon-based perovskite solar cells

Carbon-based perovskite solar cells (C-PSCs) present a low-cost route to efficient photovoltaic technology. Gas quenching is an essential process commonly used for solvent extraction in scalable module fabrication using solution processes, but film quality remains highly sensitive to the localized environmental processing temperature, especially during intermediate phase transitions before perovskite film high temperature annealing. In this study, we introduce a cold air quench strategy to precisely control the local crystallization temperature that simultaneously promotes uniform crystallization and facilitates strain relaxation in fully air-processed C-PSCs. It is found that implementing dry air at 10 °C as the quenching gas yields a champion power conversion efficiency (PCE) of 20.52 %, with preferential (110) orientation and reduced, homogenized in-plane strain. This newly developed technique of a cost-effective low temperature air quenching process also enhances carrier lifetime, reduces interface recombination, and improves charge extraction. Furthermore, this work presents cold air quenching as a scalable, economic method to control perovskite crystallization and strain in C-PSC fabrication, advancing the industrial viability of high-performance perovskite modules.

14 SOLAR ENERGY↗

Pulsar Based Timing for Grid Synchronization

Existing synchronization systems in the power grid, such as the global positioning system, are susceptible to temporary or permanent failures due to various unpredictable and uncontrollable factors such as cyber-attack and electromagnetic interferences, thus affecting the accuracy and reliability of generated timing signal. In this article, a pulsar astronomy-based timing system is proposed to provide an alternative synchronization signal. Further, this clock will offer significant security improvements to power grid applications, such as a wide-area monitoring system, which depends on a precise timing signal. The hardware and software frameworks are described in detail. First, a high-speed sampling hardware platform is designed to collect signals from radio telescopes. Then a periodic pulse extraction method with three steps is proposed to process the pulsar signal, including polyphase filterbanks, incoherent de-dispersion, and sliding window folding. Lastly, three experiments are conducted to verify the effectiveness of the frameworks. The generated pulsar timing pulse is presented, and the factors affecting its accuracy are also discussed. The analysis results demonstrate that the pulsar signals can provide high-accurate timing pulses for grid synchronization.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Collapsar Black Holes Are Likely Born Slowly Spinning

Collapsing stars constitute the main black hole (BH) formation channel, and are occasionally associated with the launch of relativistic jets that power γ-ray bursts (GRBs). Thus, collapsars offer an opportunity to infer the natal (before spin-up/down by accretion) BH spin directly from observations. We show that once the BH saturates with a large-scale magnetic flux, the jet power is dictated by the BH spin and mass accretion rate. Core-collapse simulations by Halevi et al. and GRB observations favor stellar density profiles that yield an accretion rate of $\dot m\approx 10^{-2} M_{\odot}s^{-1}$, weakly dependent on time. This leaves the spin as the main factor that governs the jet power. By comparing the jet power to characteristic GRB luminosities, we find that the majority of BHs associated with jets are likely born slowly spinning with a dimensionless spin of a ≃ 0.2, or a ≃ 0.5 for wobbling jets, with the main uncertainty originating in the unknown γ-ray radiative efficiency. This result could be applied to the entire core-collapse BH population, unless an anticorrelation between the stellar magnetic field and angular momentum is present. In a companion paper, Jacquemin-Ide et al., we show that regardless of the natal spin, the extraction of BH rotational energy leads to spin-down to a ≲ 0.2, consistent with gravitational-wave observations. We verify our results by performing the first 3D general-relativistic magnetohydrodynamic simulations of collapsar jets with characteristic GRB energies, powered by slowly spinning BHs. We find that jets of typical GRB power struggle to escape from the star, providing the first numerical indication that many jets fail to generate a GRB.

79 ASTRONOMY AND ASTROPHYSICS↗

Machine Learning Using a Simple Feature for Detecting Multiple Types of Events From PMU Data

This paper describes simple and efficient machine learning (ML) methods for efficiently detecting multiple types of power system events captured by PMUs scarcely placed in a large power grid. It uses a single feature from each PMU based on a rectangle area enclosing the event in a given data window. This single feature is sufficient to enable commonly used ML models to detect different types of events quickly and accurately. The feature is used by five ML models on four different data-window sizes. The results indicated a tradeoff between the execution speed and detection accuracy in variety of data-window size choices. Here, the proposed method is insensitive to most data quality issues typical for data from field PMUs, and thus it does not require major data cleansing efforts prior to feature extraction.

Big data↗

Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Development of a High-Rate Front-End ASIC for X-Ray Spectroscopy and Diffraction Applications

We developed a new front-end application-specific integrated circuit (ASIC) to upgrade the Maia X-ray microprobe. The ASIC instruments 32 configurable channels that perform either positive or negative charge amplification, pulse shaping, peak amplitude, and time extraction along with buffered analog storage. At a gain of 3.6 V/fC, 1-μs peaking time, and a temperature of 248 K, an electronic resolution of 13 and 10 e- rms was measured with and without a silicon drift detector (SDD) sensor, respectively. A spectral resolution of 170-eV full-width at half-maximum (FWHM) at 5.9 keV was obtained with an 55Fe source. The channel linearity was better than ± 1% with rate capabilities up to 40 kcps. The ASIC was fabricated in a commercial 250-nm process with a footprint of 6.3 mm × 3.9 mm and dissipates 167 mW of static power.

Vernon, Emerson↗

Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge

Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. Here, to protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Fermi Level Engineering of Passivation and Electron Transport Materials for p-Type CuBi 2 O 4 Employing a High-Throughput Methodology

Metal oxide semiconductors are promising for solar photochemistry if the issues of excessive charge carrier recombination and material degradation can be resolved, which are both influenced by surface quality and interface chemistry. Coating the semiconductor with an overlayer to passivate surface states is a common remedial strategy but is less desirable than application of a functional coating that can improve carrier extraction and reduce recombination while mitigating corrosion. Here, a data-driven materials science approach utilizing high-throughput methodologies, including inkjet printing and scanning droplet electrochemical cell measurements, is used to create and evaluate multi-element coating libraries to discover new classes of candidate passivation and electron-selective contact materials for p-type CuBi 2 O 4 . The optimized overlayer (Cu 1.5 TiO z ) improves the onset potential by 110 mV, the photocurrent by 2.8×, and the absorbed photon-to-current efficiency by 15.5% compared to non-coated photoelectrodes. It is shown that these enhancements are related to reduced surface recombination through passivation of surface defect states as well as improved carrier extraction efficiency through Fermi level engineering. This work presents a generalizable, high-throughput method to design and optimize passivation materials for a variety of semiconductors, providing a powerful platform for development of high-performance photoelectrodes for incorporation into solar-fuel generation systems.

36 MATERIALS SCIENCE↗

Atikokan Digital Twin: Machine learning in a biomass energy system

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

09 BIOMASS FUELS↗

A contextual sensor system for non-intrusive machine status and energy monitoring

Event-driven contexts in manufacturing occur pervasively as a result of interactions among involved entities such as machines, workers, materials, and environment. One of the primary tasks in smart manufacturing is to derive a context-aware system conveniently incorporating worker knowledge for generating timely actionable intelligence for workers on factory floor and supervisors to respond. In this paper, we propose to design a human-and-machine interaction recognition framework by using a causality concept to collect contextual data for classifications of normal and abnormal machine operations. The causes and effects are between workers and machines for this initial research. To apply the causality to recognize worker interactions, initially a reliable way to identify the states of machines is necessary. The proposed contextual sensor system, consisting of a power meter for measuring machine operation conditions, a visual camera for capturing worker and machine interactions via a finite state machine model, and an algorithm for determining power signatures of individual components via energy disaggregation is implemented on semiconductor fabrication machines (manual or PLC controlled) each with multiple components. The experiment results demonstrate its context extraction capability such as components states and their corresponding energy usage in real time as well as its ability to identify anomalous operation conditions.

47 OTHER INSTRUMENTATION↗

Chemometric modeling of power conversion efficiency of organic dyes in dye sensitized solar cells for the future renewable energy

The present study reports chemometric modeling of power conversion efficiency (PCE) of dye sensitized solar cells (DSSCs) using the biggest available data set till date which comprises around 1200 dyes covering 7 chemical classes. To extract the best structural features required for higher PCE, we have developed multiple partial least squares (PLS) quantitative structure-property relationship (QSPR) models for the Triphenylamine, Phenothiazine, Indoline, Porphyrin, Coumarin, Carbazole and Diphenylamine chemical classes using descriptors derived from the best subset selection method followed by selection of best five models in each dataset based on the Mean Absolute Error (MAE) values. The models were validated both internally and externally followed by the consensus predictions employing “Intelligent Consensus Predictor” tool to examine whether the quality of predictions can be improved with the “intelligent” selection of multiple PLS models. The quality of predictions for the respective external sets showed that the consensus models (CM) are better than the individual models (IM) in most of the cases. From the insights of the developed models, we concluded that attributes like a packed structure toward higher conductivity of electrons, auxiliary donor fragment of aromatic tertiary amines, number of thiophenes inducing the bathochromic shift and augmenting the absorption, presence of additional electron donors, enhancement of electron-donating abilities, number of non-aromatic conjugated C(sp 2 ) which helps as conjugation extension units to broaden the absorption and highly conjugated $π$-systems exert positive contributions to the PCE. On the contrary, features negatively contributing to PCE are the followings: fragments which lower the tendency of localized $π–π*$ transition, fragments related to larger volume and surface area of dyes along with hydrophobicity resulting in poor adhesion, fragment RC = N causing dye hydrolysis, steric hindrance for π electronic mobility, fragments enhancing polarity, etc. Finally, the identified features from the best QSPR model of the coumarin dataset was employed in designing of ten more efficient coumarin dyes (predicted %PCE ranging from 8.93 to 10.62) than the existing ones.

14 SOLAR ENERGY↗

Iodine’s Wild Ride Leading to Photoinstability in Halide Perovskite Solar Cells

Understanding processes that contribute to efficiency losses during long-term operation of perovskite solar cells is crucial for achieving operational stability. Although maximum power point tracking optimizes outdoor performance, it is not uncommon that devices are kept under open-circuit conditions during sunny hours. Under these conditions, charge carriers accumulate at the interfaces rather than flowing through the circuit. In the case of the MAPbI 3 /spiro-OMeTAD interface studied here, hole accumulation leads to formation of I2 and subsequent oxidation of spiro-OMeTAD. By employing in situ absorption measurements, we show that the decrease in power conversion efficiency follows the spiro-OMeTAD oxidation while operating the device under open-circuit conditions. In short-circuit conditions, where photogenerated charge carriers are extracted in the external circuit, the I 2 -induced oxidation of spiro-OMeTAD and the device instability are minimized. The photoinduced expulsion of iodine from MAPbI 3 into spiro-OMeTAD discussed in this work provides new insight into the photoinstability of perovskite solar cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

IDAES-PSE 2.6.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.6.0 Release Highlights Upcoming Changes IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of. The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset. By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain. IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers. The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated. New Models, Tools and Features New Intersphinx extension automatically linking Jupyter notebook examples to project documentation New end-to-end diagnostics example demonstrated on a real problem New complementarity formulation for VLE with cubic equations of state, backward compatibility for old formulation New solver interface with presolve (ipopt_v2) in support of upcoming changes to the initialization and APIs methods, with default set to ipopt to maintain backwards compatibility; this will deprecate once all examples have been updated New forecaster and parameterized bidder methods within grid integration library Updated surrogates API and examples to support Keras 3, with backwards compatibility for older formats such as TensorFlow SavedModel (TFSM) Updated costing base dictionary to include the 2023 cost year index value Updated ProcessBlock to include information on the constructing block class Updated Flowsheet Visualizer to allow visualize() method to return value and functions Bug Fixes Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria. Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria. Fixed typos flagged by June update to crate-ci/typos and removed DMF-related exceptions Minor corrections of units of measurement handling in power plant waste/transport costing expressions, control volume material holdup expressions, and BTX property package parameters Fixed throwing >7500 numpy deprecation warnings by replacing scalar value assignment with element extraction and item iteration calls Testing and Robustness Migrated slow tests (>10s) to integration, impacting test coverage but also yielding a nearly 30% decrease in local test runtime Pinned pint to avoid issues with older supported Python versions Pinned codecov versions to avoid tokenless upload behavior with latest version Bumped extensions to version 3.4.2 to allow pointing to non-standard install location Deprecations and Removals Python 3.8 is no longer supported. The supported Python versions are 3.9 through 3.12 The Data Management Framework (DMF) is no longer supported. Importing idaes.core.dmf will cause a deprecation warning to be displayed until the next release The SOFC Keras surrogates have been removed. The current version of the SOFC surrogate model in the examples repository is a PySMO Kriging model.

AS↗

An absolute recoil-carbon polarimeter for polarized light-ions at the BNL Booster

We describe a recoil-carbon polarimeter for the BNL Booster synchrotron capable of measuring the transverse polarization of both the polarized proton beam and the polarized 3 He ion beam throughout the acceleration cycle. The instrument addresses a critical gap in the BNL polarized beam program: no independent polarization measurement currently exists in the injector chain between the 200 MeV Linac polarimeter and the AGS. The proposed new polarimeter will provide continuous, absolute polarization measurements at the Booster stage, supplying additional anchor points for the polarization transmission through the accelerator chain. The polarimeter 9 is based on elastic pC and 3 He C scattering from a thin internal carbon target, with detection of the recoil 12 C nucleus in a fixed six-station silicon detector ring. A single detector geometry provides continuous kinematic coverage from injection to extraction for both beam species without mechanical adjustment. For pC scattering, the available data and the Bonin parametrization provide a well-established basis for estimating the analyzing power and figure of merit over the Booster energy range; for 3 He C the sole experimental anchor is a measurement at 443 MeV, and the Booster polarimeter itself is identified as the instrument to map the analyzing power across the remainder of the acceleration ramp by a ramp-and-return calibration that anchors the absolute 3 He polarization scale to a few percent. Provision for future deuteron polarimetry is incorporated in the chamber design without modification to the existing geometry. With appropriate detector upgrades, the same recoil-carbon technique can be extended to deuteron beams, for which analyzing power data are already available, and, with future analyzing-power measurements, also to 6 Li and 7 Li beams, making the polarimeter a versatile instrument for the full range of light polarized ion species anticipated at BNL.

43 PARTICLE ACCELERATORS↗

Melting and Re-Freezing Leads to Irreversible Changes in the Morphology and Molecular-Level Dynamics of Pfizer-BioNTech COVID-19 Vaccine

As an mRNA-based vaccine, the Pfizer-BioNTech COVID-19 vaccine has stringent cold storage requirements to preserve functionality of the mRNA active ingredient. To this end, lipid components of the vaccine formulation play an important role in stabilizing and protecting the mRNA molecule for long-term storage. The purpose of the current study was to measure molecular-level dynamics as a function of temperature in the Pfizer-BioNTech COVID-19 vaccine to gain microscopic insight into its thermal stability. Materials and Methods: We used quasielastic and inelastic neutron scattering to probe (1) the vaccine extracted from the manufacturer-supplied vials and (2) unperturbed vaccine in the original manufacturer-supplied vials. The latter measurement was possible due to the high penetrative power of neutrons. Upon warming from the low-temperature frozen state, the vaccine in its original form exhibits two-step melting, indicative of a two-phase morphology. Once the melting is completed (above 0 °C), vaccine re-freezing cannot restore its original two-phase state. This observation is corroborated by the changes in the molecular vibrational spectra. The molecular-level mobility measured in the resulting single-phase state of the re-frozen vaccine greatly exceeds the mobility measured in the original vaccine. Even a brief melting (above 0 °C) leads to an irreversible alteration of the two-phase morphology of the original vaccine formulation. Re-freezing of the vaccine results in a one-phase morphology with much increased molecular-level mobility compared to that in the original vaccine, suggesting irreversible deterioration of the vaccine’s in-storage stability. Neutron scattering can be used to distinguish between the vibrational spectra characteristic of the original and deteriorated vaccines contained in the unperturbed original manufacturer-supplied vials.

60 APPLIED LIFE SCIENCES↗

Lunar oxygen and metal for use in near-earth space - Magma electrolysis

The unique conditions on the moon, such as vacuum, absence of many reagents common on the earth, and presence of very nontraditional 'ores', suggest that a unique and nontraditional process for extracting materials from the ores may prove the most practical. An investigation has begun into unfluxed silicate electrolysis as a method for extracting oxygen, Fe, and Si from lunar regolith. The advantages of the process include simplicity of concept, absence of need to supply reagents from the earth, and low power and mass requirements for the processing plant. Disadvantages include the need for uninterrupted high temperature and the highly corrosive nature of the high-temperature silicate melts, which has made identifying suitable electrode and container materials difficult.

Colson, Russell O.↗

Physical Processes Governing Atmospheric Trace Constituents Measured from an Aircraft on PEM-Tropics

Before the mission, the PI (principal investigator) was instrumental in securing real-time use of the new 51-level ECMWF (European Centre for Medium Range Weather Forecasts) meteorological data. During the mission, he provided flight planning and execution guidance as meteorologist for the P-3B. Mr. Yong Zhu computed and plotted meteorological forecast maps using the ECMWF data and transmitted them to the field from MIT (Massachusetts Institute of Technology). Dr. John Cho was in the field for the Christmas Island portion to extract data from the on-site NOAA (National Oceanic and Atmospheric Administration) radars for local wind profiles that were used at the flight planning meetings. When the power supply for the VHF radar failed, he assisted the NOAA engineer in its repair. After the mission, Mr. Zhu produced meteorological data memos, which were made available to the PEM (Pacific Exploratory Mission)-Tropics B science team on request. An undergraduate student, Ms. Danielle Morse, wrote memos annotating the cloud conditions seen on the aircraft external monitor video tapes. Dr. Cho and the PI circulated a memo regarding the status (and associated problems) of the meteorological measurement systems on the DC-8 and P-3B to the relevant people on the science team. Several papers by members of our project were completed and accepted by JGR (Journal of Geophysical Research) for the first special section on PEM-Tropics B. These papers included coverage of the following topics: 1) examination of boundary layer data; 2) water vapor transport; 3) tropospheric trace constituent layers; 4) summarizations of the meteorological background and events during PEM-Tropics B; 5) concomitant lidar measurements of ozone, water vapor, and aerosol.

Newell, Reginald E.↗