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At least 487 records · Page 27

Ocean Variability Beneath the Filchner-Ronne Ice Shelf Inferred From Basal Melt Rate Time Series

Fourteen phase-sensitive radars (ApRES) were deployed on the Filchner-Ronne Ice Shelf (FRIS) to measure variability in its basal melt rate. Melt rates from sites along the Ronne Depression vary seasonally, consistent with the dynamics of the propagation of seasonal dense water from the western ice front into the cavity. Several sites at the back of the FRIS cavity feature a signal with two seasonal maxima. Sub-ice shelf oceanographic data available from one of the sites indicate that this signal is caused by two different pathways followed by the same source water. Inter-annual variability is strongest along a direct flow pathway between western Ronne Ice Front and western Berkner Island. Highest melting occurred in 1999 and 2018, following anomalously low summer sea-ice concentrations in front of the ice shelf. Inter-annual melt rate variability at the back of the FRIS cavity is limited. If present, it is expressed as a suppression or delay in the arrival of the seasonal melt rate minimum, which can be understood in terms of inter-annual stratification changes and variable inflow pathways toward the sites. Long term mean ApRES melt rates agree with estimates from satellite data over eastern FRIS. However, the satellite estimates overstate the area of active basal freezing in the western part of the ice shelf. Furthermore, the temporal melt rate variability from the satellite estimates exaggerates the range of variability at both seasonal and inter-annual time scales with any correspondence between the in-situ and remotely derived inter-annual variability being limited to a single site.

54 ENVIRONMENTAL SCIENCES↗

The Potential to Improve the Value of U.S. Geothermal Electricity Generation Through Flexible Operations

Geothermal power plants have typically been operated as baseload plants. The recent expansion of wind and solar power generation creates a potential opportunity to increase the value of geothermal generation through flexible operations. In recent years, California’s wholesale electricity markets have exhibited frequent, short-term periods of negative pricing, indicating that additional flexibility would be valued in the market. Here, we examine local nodal hourly price records at all geothermal plants located in the western United States. We describe how the frequency and temporal characteristics of negative pricing episodes have changed over recent years. Based on these price series, we calculate the value of multiple strategies of flexible operations. Additionally, we use the estimates of future prices, developed through a capacity-expansion model and a dispatch model, to explore how the value of such flexible operations might change along with further penetration of variable renewable power sources. Based on the historical pricing records, we find that simple curtailment of operations during negative pricing episodes could increase the average energy value by 1–2 dollars per MWh and that allowing for increased production during limited high-priced hours, in addition to curtailment during negative priced hours, could potentially double the increase in value (up to 4 dollars per MWh). The forward-looking simulations indicate reduced values of flexibility from geothermal power despite higher penetrations of variable renewable energy. This result highlights the possibility that increasing flexibility options throughout the system may counteract the influence of increased variable renewable energy deployment.

15 GEOTHERMAL ENERGY↗

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, G. C.↗

Fragmentation, protostellar winds, and star formation

Aspects of the current theory of molecular-cloud (MC) evolution and star formation are reviewed, with consideration of recent star and MC observations. The problem of MC collapse is discussed in terms of fragmentation in spherically symmetric collapse, the role of magnetic fields and density fluctuations, anisotropic collapse, and nonlinear interactions between fragments. Protostellar winds are identified as the source of the energy supporting MC against collapse, sweeping shells of mass into MC to produce low-mass stars continuously, and sustaining the production of massive stars once it is triggered by the collision of MCs, as in the spiral density-wave peaks. The clumping and coagulation processes are considered for the case of the MC in Orion. The protostellar-wind mass input required to inhibit systematic MC-core collapse is estimated at 10 to the -6th solar mass/cu pc year.

Silk, J.↗

A Regional-Scale Assessment of Satellite Derived Precipitable Water Vapor Across The Amazon Basin

Atmospheric water vapor is widely recognized as a key climate variable, linking an assortment of poorly understood and complex processes. It is a major element of the hydrological cycle and provides a mechanism for energy exchange among many of the Earth system components. Reducing uncertainty in our current knowledge of water vapor and its role in the climate system requires accurate measurement, improved modeling techniques, and long-term prediction. Satellites have the potential to satisfy these criteria, as well as provide high resolution measurements that are not available from conventional sources. The focus of this paper is to examine the temporal and mesoscale variations of satellite derived precipitable water vapor (PW) across the Amazon Basin. This region is pivotal in the functioning of the global climate system through its abundant release of latent heat associated with heavy precipitation events. In addition, anthropogenic deforestation and biomass burning activities in recent decades are altering the conditions of the atmosphere, especially in the planetary boundary layer. A physical split-window (PSW) algorithm estimates PW using images from the GOES satellites along with the NCEP/NCAR Reanalysis data that provides the first guess information. Retrievals are made at a three-hourly time step during daylight hours in the Amazon Basin and surrounding areas for the months of June and October in 1988 (dry year) and 1995 (wet year). Spatially continuous fields are generated 5 times daily at 12Z, 15Z, 18Z, 21Z, and 00Z. These fields are then averaged to create monthly and 3 hourly monthly grids. Overall, the PSW estimates PW reasonable well in the Amazon with MAE ranging from 3.0 - 9.0 mm and MAE/observed mean around 20% in comparison to radiosonde observations. The distribution of PW generally mimics that of precipitation. Maximum values (42 - 52 mm) are located in the Northwest whereas minimum values (18 - 27 mm) are found along Brazil's East coast. Aside from the East coast, PW has a stronger north-south gradient than that of rainfall. As for the temporal variation of PW, June (1988 and 1995) experiences a peak about 1400 local time, corresponding to a maximum in air temperature. In contrast, October (1988 and 1995) experiences a maximum early in the day, 1100 local time, with a gradual decrease toward nighttime.

DeLiberty, Tracy↗

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗

Forearc Carbon Sink Reduces Long-Term Volatile Recycling Into the Mantle

Carbon and other volatiles are transported from Earth’s surface into the mantle at subduction margins. The efficiency of this transfer has profound implications for the nature and scale of geochemical heterogeneities in Earth’s deep (mantle) and shallow (crustal) reservoirs, as well as Earth’s oxidation state. However, the proportion of volatiles released in the forearc and backarc are not well constrained compared to fluxes from the volcanic front. Here, we use helium and carbon isotope data from deeply sourced springs along two cross-arc transects to show that ~91% of carbon released from the slab/mantle beneath the Costa Rica forearc is sequestered within the crust by calcite deposition, and an additional ~3% is incorporated into biomass through microbial chemolithoautotrophy. We estimate that ~1.2 × 10(exp 8) to 1.3 × 10(exp 10) mol CO2/yr are released from the slab beneath the forearc, resulting in up to ~19% less carbon being transferred to Earth’s deep mantle than previously estimated.

Carbon cycle↗

Aluminum Electrolytic Capacitor Vulnerability Evaluation in DC Power Supplies at the Spallation Neutron Source

The service life of electrolytic capacitors is a concern for long-term reliable operation of power supplies. The Power Conversion Group at the Spallation Neutron Source (SNS) facility of Oak Ridge National Laboratory is responsible for more than 500 power supplies with over 1000 electrolytic capacitors installed in these power supplies. Most of the electrolytic capacitors have been in operation since 2007 and are either well over or close to the manufacturer provided lifetime. This paper addresses this vulnerability to ensure continued reliable operation of the SNS. The power supply circuit is simulated in a MATLAB/Simulink environment to quantify the capacitor ripple current and operating voltage. Combined with manufacturer data, this information is used to estimate capacitor lifetime. Utilizing the simulation results and lifetime projections of the capacitors, capacitor replacements for the power supplies is prioritized. To assess end-of-life, the ambient temperature and ripple current of the capacitor is used. Based on the manufacturer's data and scientific literature review, capacitor end-of-life is established. The results of the simulations and analysis is presented. Data on the highest priority supplies will also be presented, and statistics on the capacitor performance, reliability and remaining lifetime will be shown.

Harave, Sudarshan↗

Flood and Landslide Applications of High Time Resolution Satellite Rain Products

Experimental, potentially real-time systems to detect floods and landslides related to heavy rain events are described. A key basis for these applications is high time resolution satellite rainfall analyses. Rainfall is the primary cause for devastating floods across the world. However, in many countries, satellite-based precipitation estimation may be the best source of rainfall data due to insufficient ground networks and absence of data sharing along many trans-boundary river basins. Remotely sensed precipitation from the NASA's TRMM Multi-satellite Precipitation Analysis (TMPA) operational system (near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25deg x 0.25deg) is used to monitor extreme precipitation events. Then these data are ingested into a macro-scale hydrological model which is parameterized using spatially distributed elevation, soil and land cover datasets available globally from satellite remote sensing. Preliminary flood results appear reasonable in terms of location and frequency of events, with implementation on a quasi-global basis underway. With the availability of satellite rainfall analyses at fine time resolution, it has also become possible to assess landslide risk on a near-global basis. Early results show that landslide occurrence is closely associated with the spatial patterns and temporal distribution of TRMM rainfall characteristics. Particularly, the number of landslides triggered by rainfall is related to rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms. For the purpose of prediction, an empirical TMPA-based rainfall intensity-duration threshold is developed and shown to have skill in determining potential areas of landslides. These experimental findings, in combination with landslide surface susceptibility information based on satellite-based land surface information, form a starting point towards a potential operational landslide monitoring/warning system around the globe.

Adler, Robert F.↗

Assessing High Burnup U-19Pu-10Zr Fuel Performance against Historical and Modeled Behavior

Advancing the deployment of sodium-cooled fast reactors (SFRs) requires thorough testing of metallic fuel pins under accident conditions to establish safe operational limits of high burnup fuel. To conduct transient testing, a comprehensive understanding of steady-state fuel behavior obtained through both experimental characterization and accurate predictive capabilities is needed. This study comparatively assesses the steady-state irradiation performance of two high burnup U-19Pu-10Zr fuel pins, DP-36 and DP-40, irradiated under prototypic fast reactor conditions in preparation for planned safety testing at the Transient Reactor Test Facility. Since DP-40 was designated for use in the test and DP-36 serves as its sibling pin, non-destructive, engineering-scale post-irradiation examinations (PIE) were conducted on both pins while destructive examinations were performed exclusively on DP-36. The results were then assessed against historical performance data from similar fuel pins irradiated in the Experimental Breeder Reactor-II. Additionally, the steady-state irradiation of each pin was modeled using the BISON fuel performance code to assess the accuracy of current modeling capabilities in predicting the baseline irradiation behavior. Non-destructive examinations included neutron radiography to measure fuel column elongation, gamma scanning to verify pin integrity and fission product migration, and profilometry to assess dimensional changes. Benchmarking against existing PIE data revealed consistent patterns in axial fuel column growth and cladding diametral strain, though both pins exhibited longer low-density “fluff” structures, which can have implications for core reactivity and source term calculations. Destructive examinations on DP-36 included fission gas release analysis and sectioning for optical microscopy, which showed more complex constituent redistribution patterns than the traditionally accepted 3-ring model. The axial evolution of fractional areas and porosities of each of the redistributed zones were quantified and presented. Modeling comparisons showed agreement in fractional fission gas release but consistently overestimated axial and radial swelling and disagreed with measured axial porosity patterns. These conservative overpredictions suggested that the pins would appear closer to failure or operational limits at the start of transient tests, potentially leading to higher strain accumulation during the transient. While conservative estimates provide safety margins, they can negatively impact fuel economics. A review of the swelling models identified areas for improvement in the gaseous swelling, solid swelling, and fuel hot-pressing models when applied to ternary fuel. The results of this study highlight the critical importance of conducting pre-test characterization on both test and sibling pins to accurately capture steady-state fuel behavior, providing a precise baseline for post-test evaluations and essential inputs for transient modeling of the planned experiments. The analysis also revealed significant data gaps that require further investigation to enhance the understanding and prediction of fuel swelling and pore dynamics. Collecting comprehensive data across different irradiation conditions, burnup levels, and fuel compositions are essential for refining existing models and developing mechanistic models for both binary and ternary metallic fuels, ultimately improving the integration of modeling and experimental approaches in accident testing.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts

Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ~ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ~ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.

54 ENVIRONMENTAL SCIENCES↗

Modeling Dust Mineralogical Composition: Sensitivity to Soil Mineralogy Atlases and Their Expected Climate Impacts

Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ∼ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ∼ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.

Soil dust↗

Fire, dust, air and water: Improving aerosol biogeochemistry interactions in ACME. Final report

Aerosol-biogeochemistry interactions will have important consequences for human health, ecosystem services, and climate feedbacks over the next several decades. In this proposal we seek to link how human activities on land, including direct industrial emissions and landscape fires, impact the atmosphere and the biogeochemistry of the ocean and land. Our overall goal was to complement existing work at DOE labs to improve the capability and performance of the E3SM model in short-term, high-resolution projections of climate. Projections of ocean uptake of anthropogenic carbon are highly dependent on the details of the ocean physical and biogeochemistry parameterizations. Potentially equally important are the estimates of increases in iron and soluble nitrogen deposition in aerosols to the open ocean, but there are substantial uncertainties in these feedbacks. The E3SM land model simulates natural and anthropogenic fires, but improvement is needed to more accurately simulate fire responses to climate variability as well as fire contributions to terrestrial climate feedbacks. This proposal addressed four of the six priorities within the ACME/E3SM call by improving the representation of fire, industrial combustion sources, and desert dust and adding iron and soluble iron to the atmospheric chemistry (1). Our work focused on improving ocean biogeochemistry and the response of the oceans to anthropogenic atmospheric iron and nitrogen (3). We also improved the representation of land disturbance on fires and fire mediated carbon cycle feedbacks in the land model (4). Finally we had planned to couple the land, atmosphere and ocean biogeochemical systems (6) and explore how this coupling changes the feedbacks in the climate system. Our work under this proposal was separated into the following 5 tasks: i) improve representation of fire dynamics in both boreal and tropical ecosystems, ii) improve estimates of industrial emissions of iron and phosphorus, iii) improve representation of atmospheric iron, phosphorus and nitrogen transport, chemistry and deposition, iv) improve representation of iron, phosphorus and nitrogen deposition impacts on ocean biogeochemistry, and v) couple emissions into atmosphere, to deposition onto land and ocean biogeochemistry. For all 5 tasks, we conducted offline experiments of the historical period to enable comparison with available aerosol and land surface observations. We had planned to conduct fully coupled simulations, however, these will be conducted as part of the coupling in version 3 and 4 of the E3SM model, and thus have not been completed.

54 ENVIRONMENTAL SCIENCES↗

Risk Considerations of Transitioning CO2-EOR Field to CO2 storage Field: Case Study

In the United States (U.S.), carbon dioxide (CO2) injection wells at EOR sites are currently regulated as Class II wells under the U.S. Environmental Protection Agency’s (EPA) Underground Injection Control (UIC) program while dedicated geological CO2 storage (GCS) wells are considered Class VI wells. A CO2-EOR facility considering a transitioning from tertiary oil recovery to injecting CO2 for the primary purpose of long-term storage is required to obtain a Class VI permit where this transition poses an increased risk to underground sources of drinking water. This study considers how transitioning operations from CO2-EOR to storage can impact reservoir plume and pressure transient in the storage envelope, and how these changes could impact area of review and potential unwanted fluid migration. We developed a case study to assess subsurface response and leakage risks associated with a representative, hypothetical operation in a carbonate reservoir. This reservoir transitions from tertiary hydrocarbon recovery to dedicated GCS. Reservoir simulations were run for a set of credible CO2-EOR scenarios to estimate distributions of fluids phases and pressures throughout the model domain after CO2 flooding as well as forecasting the behavior of the reservoir after the transition to a dedicated storage phase. The evolutions from all simulated scenarios were used as the basis for leakage risk quantification using the National Risk Assessment Partnership’s Open-Source Integrated Assessment Model (NRAP-Open-IAM) with a novel reduced-order model to estimate time-dependent leakage of CO2, brine, and hydrocarbon fluids through potentially leaky wells. Results include a description of reservoir response, an estimate of the areal extent that could potentially be impacted by leakage to underground sources of drinking water, and estimates of the magnitude of potential leakage. Considerations for dedicated storage injection well selection, injectivity, and injection scheme performance and potential leakage risk are presented, with implications for risk assessment of well transition discussed. This study presents a risk-based workflow for the Class II to Class VI well transition. Integrating credible numerical simulation of viable CO2-EOR to dedicated CO2 storage with quantitative risk assessment tools, such as the NRAP-Open-IAM, will provide a valuable means to devise operational scenarios and inform decision-making related to storage benefit, leakage risk, and liability. Presented at the SPE/AAPG/SEG Carbon Capture Utilization and Storage Conference in Houston, TX, March 11-13, 2024.

Liu, Guoxiang↗

The long term stability of VLBI earth orientation measurements

Tectonic motions will, in general, change the orientation as well as the length of baselines used in Very Long Baseline Interferometry (VLBI), and will thus cause slow divergences between earth orientation results obtained with different VLBI networks, as well as between VLBI results and those obtained by satellite laser ranging and lunar laser ranging. Such drifts (on the order of a milliarcsecond/year) are inherently interesting as well as being significant in combinations of orientation results from different sources. The geodetic study of tectonic motions is also closely connected to research into the nature and causes of systematic errors in data from the modern techniques of space geodesy. Both a special coordinate system found to be of use in the analysis of VLBI data and tectonic motion estimates for a VLBI baseline stretching from California to Australia are described.

Eubanks, T. M.↗

An analysis of carrier dynamics in methylammonium lead triiodide perovskite solar cells using cross correlation noise spectroscopy

Using cross correlation current noise spectroscopy, we have investigated carrier dynamics in methylammonium lead triiodide solar cells. This method provides space selectivity for devices with a planar multi-layered structure, effectively amplifying current noise contributions coming from the most resistive element of the stack. In the studied solar cells, we observe near full-scale shot noise, indicating the dominance of noise generation by a single source, likely the interface between the perovskite and the spiro-organic 2,2',7,7'-tetrakis(N,N-di-p-methoxyphenyl-amine)9,9'-spirobifluorene hole-transport layer. In this paper, we argue that the strong 1/f noise term has contributions from both the perovskite layer and interfaces. It displays a non-ideal dependence on photocurrent, S $\propto$ I 1.4 (instead of usual S $\propto$ I 2 ), which is likely due to current-induced halide migration. Finally, we observe generation–recombination noise. We argue that this contribution is due to bimolecular recombination in the perovskite bulk absorption layer. Extrapolating our results, we estimate that at standard 1 sun illumination, the electron–hole recombination time is 5 microseconds.

14 SOLAR ENERGY↗

The time-varying geomagnetic field

Information (obtained from Magsat and other sources) published by the U.S. authors between 1983 and 1986 on the large-scale geomagnetic field of deep internal origin and its secular variation (SV) is reviewed. Results on the main field modeling, including reference fields, and on the separaton of core and crustal fields are discussed together with the advances made in geomagnetic field theory and applications of novel methods. Consideration is also given to global SV, short-term global SV, and regional CV analyses; nonsecular impulses, featuring the sudden geomagnetic jerk of 1969; and the frozen-flux core approximation, including downward continuation to the core-mantle boundary constraints, westward drift, and estimates of the fluid velocity at the top of the core.

Voorhies, Coerte V.↗