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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Impact of Calibrated Land Surface Model Parameters on the Accuracy and Uncertainty of Land-Atmosphere Coupling in WRF Simulations

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (LIS-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Santanello, Joseph A., Jr.↗
NASA NTRS2021

Large-scale features of Last Interglacial climate: results from evaluating the lig127k simulations for the Coupled Model Intercomparison Project (CMIP6)–Paleoclimate Modeling Intercomparison Project (PMIP4)

The modeling of paleoclimate, using physically based tools, is increasingly seen as a strong out-of-sample test of the models that are used for the projection of future climate changes. New to the Coupled Model Intercomparison Project (CMIP6) is the Tier 1 Last Interglacial experiment for 127 000 years ago (lig127k), designed to address the climate responses to stronger orbital forcing than the midHolocene experiment, using the same state-of-the-art models as for the future and following a common experimental protocol. Here we present a first analysis of a multi-model ensemble of 17 climate models, all of which have completed the CMIP6 DECK (Diagnostic, Evaluation and Characterization of Klima) experiments. The equilibrium climate sensitivity (ECS) of these models varies from 1.8 to 5.6°C. The seasonal character of the insolation anomalies results in strong summer warming over the Northern Hemisphere continents in the lig127k ensemble as compared to the CMIP6 piControl and much-reduced minimum sea ice in the Arctic. The multi-model results indicate enhanced summer monsoonal precipitation in the Northern Hemisphere and reductions in the Southern Hemisphere. These responses are greater in the lig127k than the CMIP6 midHolocene simulations as expected from the larger insolation anomalies at 127 than 6 ka. New synthesis for surface temperature and precipitation, targeted for 127 ka, have been developed for comparison to the multi-model ensemble. The lig127k model ensemble and data reconstructions are in good agreement for summer temperature anomalies over Canada, Scandinavia, and the North Atlantic and for precipitation over the Northern Hemisphere continents. The model–data comparisons and mismatches point to further study of the sensitivity of the simulations to uncertainties in the boundary conditions and of the uncertainties and sparse coverage in current proxy reconstructions. The CMIP6–Paleoclimate Modeling Intercomparison Project (PMIP4) lig127k simulations, in combination with the proxy record, improve our confidence in future projections of monsoons, surface temperature, and Arctic sea ice, thus providing a key target for model evaluation and optimization.

Last Interglacial climate↗
NASA NTRS1991

Development of life prediction capabilities for liquid propellant rocket engines. Post-fire diagnostic system for the SSME system architecture study

This system architecture task (1) analyzed the current process used to make an assessment of engine and component health after each test or flight firing of an SSME, (2) developed an approach and a specific set of objectives and requirements for automated diagnostics during post fire health assessment, and (3) listed and described the software applications required to implement this system. The diagnostic system described is a distributed system with a database management system to store diagnostic information and test data, a CAE package for visual data analysis and preparation of plots of hot-fire data, a set of procedural applications for routine anomaly detection, and an expert system for the advanced anomaly detection and evaluation.

Gage, Mark↗
NASA NTRS2010

Fault Management Technology Maturation for NASA's Constellation Program

This slide presentation reviews the maturation of fault management technology in preparation for the Constellation Program. There is a review of the Space Shuttle Main Engine (SSME) and a discussion of a couple of incidents with the shuttle main engine and tanking that indicated the necessity for predictive maintenance. Included is a review of the planned Ares I-X Ground Diagnostic Prototype (GDP) and further information about detection and isolation of faults using Testability Engineering and Maintenance System (TEAMS). Another system that being readied for use that detects anomalies, the Inductive Monitoring System (IMS). The IMS automatically learns how the system behaves and alerts operations it the current behavior is anomalous. The comparison of STS-83 and STS-107 (i.e., the Columbia accident) is shown as an example of the anomaly detection capabilities.

Waterman, Robert D.↗
DOE OSTI2025

Diagnostic Systems in the Muon $g-2$ experiment at Fermilab

The muon anomalous magnetic moment, $a_\mu=\frac{g-2}{2}$, is a low-energy observable which can be both measured and computed to high precision, making it a sensitive test of the Standard Model and a probe for new physics. This anomaly was measured with a precision of $0.20$~parts per million (ppm) by the Fermilab's Muon g-2 (E989) experiment. The final goal of the E989 experiment is to reach a precision of $0.14$~ppm. The experiment is based on the measurement of the muon spin anomalous precession frequency, $\omega_a$, based on the arrival time distribution of high-energy decay positrons observed by 24 electromagnetic calorimeters, placed around the inner circumference of a $14$~m diameter storage ring, and on the precise knowledge of the storage ring magnetic field and of the beam time and space distribution. Achieving this level of precision requires strict control over systematics, which is ensured through several diagnostic devices. At the accelerator level, these devices monitor the quality of the injected beam (e.g., verifying that it has the correct momentum), while at the detector level, they track both the magnetic field and the gain of the calorimeters. In this work the devices and techniques used by the E989 experiment will be presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Standardising the “Gregory method” for calculating equilibrium climate sensitivity

The equilibrium climate sensitivity (ECS) – the equilibrium global mean temperature response to a doubling of atmospheric CO 2 – is a high-profile metric for quantifying the Earth system's response to human-induced climate change. A widely applied approach to estimating the ECS is the “Gregory method” (Gregory et al., 2004), which uses an ordinary least squares (OLS) regression between the net radiative flux, N, and surface air temperature anomalies, ΔT, from a 150 year experiment in which atmospheric CO 2 concentrations are quadrupled. The ECS is determined by extrapolating the linear fit to N=0, i.e. the ΔT-intercept, indicating the point at which the system is back in equilibrium. This method has been used to compare ECS estimates across the CMIP5 and CMIP6 ensembles and will likely be a key diagnostic for CMIP7. Despite its widespread application, there is little consistency or transparency between studies in how the climate model data is processed prior to the regression, leading to potential discrepancies in ECS estimates. We identify 32 alternative data processing pathways, varying by differences in global mean weighting, net radiative flux variable, anomaly calculation method, and linear regression fit. Using 44 CMIP6 models, we systematically assess the impact of these choices on ECS estimates and calculate uncertainty ranges using two bootstrap approaches. While the inter-model ECS range is insensitive to the data processing pathway, individual outlier models exhibit notable differences. Approximating a model's native grid cell area (if irregular) with cosine of the latitude can decrease the ECS by 11 %, the choice of N-variable can change the ECS by 6 %, and some anomaly calculation methods can introduce spurious temporal correlations in the processed data. Beyond data processing choices, we also evaluate an alternative linear regression method – total least squares (TLS) – which has a more statistically robust basis than OLS. However, for consistency with previous literature, and given TLS may reduce the ECS compared to OLS (by up to 24 %), thereby making a known bias in the Gregory method worse, we do not feel there is sufficient clarity to recommend a transition to TLS in all cases. To improve reproducibility and comparability in future studies, we recommend a standardised Gregory method: weighting the global mean by cell area, using the top of the atmosphere (as opposed to the top of model) N-variable, and calculating anomalies by first applying a rolling average to the preindustrial control timeseries then subtracting from the raw CO 2 quadrupling experiment. This approach accounts for model drift while reducing noise in the data to best meet the pre-conditions of the linear regression. While CMIP6 results of the multi-model mean ECS appear insensitive to these processing choices, similar assumptions may not hold for CMIP7, underscoring the need for standardised data preparation in future climate sensitivity assessments.

Geosciences↗
DOE OSTI2026

Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels (Final Scientific/Technical Report)

The project, "Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels," represents a significant advancement in nuclear fuel recycling technology. It integrates cutting-edge multimodal sensor fusion, machine learning (ML), and digital twin (DT) technologies to address challenges in material safeguarding, process optimization, and regulatory compliance for pyroprocessing facilities. This research has significantly enhanced the understanding of pyrochemical fuel recycling processes by developing innovative tools and methodologies. The Multimodal Safeguards Monitoring Unit (MSMU) combines electroanalytical techniques, Raman spectroscopy, and differential thermal analysis (DTA) to enable high-fidelity, near-real-time material accountancy measurements. Machine learning techniques, such as Long Short-Term Memory (LSTM) autoencoders, are utilized to detect anomalies in material balances and sensor data, improving the reliability of safeguards monitoring. Additionally, digital twin technology has been established to provide real-time system-level monitoring and diagnostics, integrating physics-based models with sensor data to optimize process safety and efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2008

Inductive System Monitors Tasks

The Inductive Monitoring System (IMS) software developed at Ames Research Center uses artificial intelligence and data mining techniques to build system-monitoring knowledge bases from archived or simulated sensor data. This information is then used to detect unusual or anomalous behavior that may indicate an impending system failure. Currently helping analyze data from systems that help fly and maintain the space shuttle and the International Space Station (ISS), the IMS has also been employed by data classes are then used to build a monitoring knowledge base. In real time, IMS performs monitoring functions: determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Successful applications have ranged from real-time monitoring of aircraft engine and control systems to anomaly detection in space shuttle and ISS data. IMS was used on shuttle missions STS-121, STS-115, and STS-116 to search the Wing Leading Edge Impact Detection System (WLEIDS) data for signs of possible damaging impacts during launch. It independently verified findings of the WLEIDS Mission Evaluation Room (MER) analysts and indicated additional points of interest that were subsequently investigated by the MER team. In support of the Exploration Systems Mission Directorate, IMS is being deployed as an anomaly detection tool on ISS mission control consoles in the Johnson Space Center Mission Operations Directorate. IMS has been trained to detect faults in the ISS Control Moment Gyroscope (CMG) systems. In laboratory tests, it has already detected several minor anomalies in real-time CMG data. When tested on archived data, IMS was able to detect precursors of the CMG1 failure nearly 15 hours in advance of the actual failure event. In the Aeronautics Research Mission Directorate, IMS successfully performed real-time engine health analysis. IMS was able to detect simulated failures and actual engine anomalies in an F/A-18 aircraft during the course of 25 test flights. IMS is also being used in colla

Source record↗
NASA NTRS1992

Remote maintenance monitoring system

A remote maintenance monitoring system retrofits to a given hardware device with a sensor implant which gathers and captures failure data from the hardware device, without interfering with its operation. Failure data is continuously obtained from predetermined critical points within the hardware device, and is analyzed with a diagnostic expert system, which isolates failure origin to a particular component within the hardware device. For example, monitoring of a computer-based device may include monitoring of parity error data therefrom, as well as monitoring power supply fluctuations therein, so that parity error and power supply anomaly data may be used to trace the failure origin to a particular plane or power supply within the computer-based device. A plurality of sensor implants may be rerofit to corresponding plural devices comprising a distributed large-scale system. Transparent interface of the sensors to the devices precludes operative interference with the distributed network. Retrofit capability of the sensors permits monitoring of even older devices having no built-in testing technology. Continuous real time monitoring of a distributed network of such devices, coupled with diagnostic expert system analysis thereof, permits capture and analysis of even intermittent failures, thereby facilitating maintenance of the monitored large-scale system.

Simpkins, Lorenz G.↗
NASA NTRS2011

Diagnosing the Nature of Land-Atmosphere Coupling During the 2006-7 Dry/Wet Extremes in the U. S. Southern Great Plains

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address deficiencies in numerical weather prediction and climate models due to improper treatment of L-A interactions, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land-PBL coupling at the process-level. In this study, a diagnosis of the nature and impacts of local land-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of2006-7 in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to NASA's Land Information System (LIS), which provides a flexible and high resolution representation and initialization of land surface physics and states. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation are examined for the dry/wet regimes of this region, along with the behavior and accuracy of different land-PBL scheme couplings under these conditions. Results demonstrate how LoCo diagnostics can be applied to coupled model components in the context of their integrated impacts on the process-chain connecting the land surface to the PBL and support of hydrological anomalies.

Santanello, Joseph A.↗
NASA NTRS1993

Intelligent neuroprocessors for in-situ launch vehicle propulsion systems health management

Efficacy of existing on-board propulsion systems health management systems (HMS) are severely impacted by computational limitations (e.g., low sampling rates); paradigmatic limitations (e.g., low-fidelity logic/parameter redlining only, false alarms due to noisy/corrupted sensor signatures, preprogrammed diagnostics only); and telemetry bandwidth limitations on space/ground interactions. Ultra-compact/light, adaptive neural networks with massively parallel, asynchronous, fast reconfigurable and fault-tolerant information processing properties have already demonstrated significant potential for inflight diagnostic analyses and resource allocation with reduced ground dependence. In particular, they can automatically exploit correlation effects across multiple sensor streams (plume analyzer, flow meters, vibration detectors, etc.) so as to detect anomaly signatures that cannot be determined from the exploitation of single sensor. Furthermore, neural networks have already demonstrated the potential for impacting real-time fault recovery in vehicle subsystems by adaptively regulating combustion mixture/power subsystems and optimizing resource utilization under degraded conditions. A class of high-performance neuroprocessors, developed at JPL, that have demonstrated potential for next-generation HMS for a family of space transportation vehicles envisioned for the next few decades, including HLLV, NLS, and space shuttle is presented. Of fundamental interest are intelligent neuroprocessors for real-time plume analysis, optimizing combustion mixture-ratio, and feedback to hydraulic, pneumatic control systems. This class includes concurrently asynchronous reprogrammable, nonvolatile, analog neural processors with high speed, high bandwidth electronic/optical I/O interfaced, with special emphasis on NASA's unique requirements in terms of performance, reliability, ultra-high density ultra-compactness, ultra-light weight devices, radiation hardened devices, power stringency, and long life terms.

Gulati, S.↗
NASA NTRSDate not supplied

Insights Into the Hydrology of the Congo Peatlands Through Land Surface Modeling and Data Assimilation

The 16.8 million ha of peatlands in the Cuvette Centrale wetland complex in the Congo Basin is one of the largest peatland regions on Earth but still highly understudied. Understanding the hydrological functioning of these peatlands and the effects of external disturbances thereon remains a major challenge. Recent research suggested fundamental hydrological differences between the Congo peatlands and the well-studied Southeast Asian peatlands. The Congo peatlands have a doming gradient that is up to ten times smaller, and they are influenced by river hydrology to some extent. In this study, we explore the Congo peatland hydrology through land surface modeling and data assimilation. We build upon our recently developed tropical PEATCLSM module (Apers et al., 2022) that was parameterized based on data from Southeast Asian peatlands due to the lack of field data from other tropical peatland regions. In a first step, we derive Congo-specific peat hydraulic and discharge function parameters from a scalar parametrization of water level dynamics in the Congo peatlands, using observed water level data at two locations. These Congo-specific parameters differ considerably from the original literature-based parameters from Southeast Asian peatlands. In a second step, we apply our original and Congo-specific parameters in an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture and Ocean Salinity (SMOS) mission. The data assimilation results are used in two ways. First, the effect of these parameters on the simulated peatland hydrology and the observation-minus-forecast Tb residuals is evaluated. It is hypothesized that the new parameters reduce the previously reported modeling errors over the Congo peatlands and reduce the residuals in Tb as well. Second, we analyze the data assimilation diagnostics to learn about other model improvement possibilities. In preliminary results, we found long periods of temporally autocorrelated total water storage increments (difference of pre- and post-update) that coincided with anomalies in river stages measured upstream of the peatlands. Since PEATLCSM neglects possible river influence, this concurrence suggests that the typically used grid-based approach of land surface models should be combined with a river routing scheme over the Congo peatlands.

Sebastian Apers↗
NASA NTRS1993

Space Shuttle Main Engine plume diagnostics: OPAD approach to vehicle health monitoring

The process of applying spectroscopy to the Space Shuttle Main Engine (SSME) for plume diagnostics, as it exists today, originated at Marshall Space Flight Center in Huntsville, Alabama, and its implementation was assured largely through the efforts of Sverdrup, AEDC, in Tullahoma, Tennessee. This process, Optical Plume Anomaly Detection (OPAD), has formed the basis for various efforts in the development of in-flight plume spectroscopy and in addition produced a viable test stand vehicle health monitor. The purpose of this paper will be to provide an introduction to the OPAD system by discussing the process of obtaining data as well as the methods of examining and interpreting the data.

Powers, W. T.↗
NASA NTRS2005

Health Management Applications for International Space Station

Traditional mission and vehicle management involves teams of highly trained specialists monitoring vehicle status and crew activities, responding rapidly to any anomalies encountered during operations. These teams work from the Mission Control Center and have access to engineering support teams with specialized expertise in International Space Station (ISS) subsystems. Integrated System Health Management (ISHM) applications can significantly augment these capabilities by providing enhanced monitoring, prognostic and diagnostic tools for critical decision support and mission management. The Intelligent Systems Division of NASA Ames Research Center is developing many prototype applications using model-based reasoning, data mining and simulation, working with Mission Control through the ISHM Testbed and Prototypes Project. This paper will briefly describe information technology that supports current mission management practice, and will extend this to a vision for future mission control workflow incorporating new ISHM applications. It will describe ISHM applications currently under development at NASA and will define technical approaches for implementing our vision of future human exploration mission management incorporating artificial intelligence and distributed web service architectures using specific examples. Several prototypes are under development, each highlighting a different computational approach. The ISStrider application allows in-depth analysis of Caution and Warning (C&W) events by correlating real-time telemetry with the logical fault trees used to define off-nominal events. The application uses live telemetry data and the Livingstone diagnostic inference engine to display the specific parameters and fault trees that generated the C&W event, allowing a flight controller to identify the root cause of the event from thousands of possibilities by simply navigating animated fault tree models on their workstation. SimStation models the functional power flow for the ISS Electrical Power System and can predict power balance for nominal and off-nominal conditions. SimStation uses realtime telemetry data to keep detailed computational physics models synchronized with actual ISS power system state. In the event of failure, the application can then rapidly diagnose root cause, predict future resource levels and even correlate technical documents relevant to the specific failure. These advanced computational models will allow better insight and more precise control of ISS subsystems, increasing safety margins by speeding up anomaly resolution and reducing,engineering team effort and cost. This technology will make operating ISS more efficient and is directly applicable to next-generation exploration missions and Crew Exploration Vehicles.

Alena, Richard↗
NASA NTRS2012

Diagnosing the Nature of Land-Atmosphere Coupling: A Case Study of Dry/Wet Extremes in the U.S. Southern Great Plains

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land-PBL coupling at the process level. In this paper, a diagnosis of the nature and impacts of local land-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation is applied to the dry/wet regimes exhibited in this region, and in the process, a thorough evaluation of nine different land-PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling test bed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L-A coupling is stronger toward the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g., reanalysis products) in the context of their integrated impacts on the process chain connecting the land surface to the PBL and in support of hydrological anomalies.

southern plains↗
NASA NTRS1997

Consistency Between Divergent Circulations from Reanalysis Data Sets and Satellite-Derived Precipitation, Radiation, and Surface Fluxes

Large-scale divergent circulations are part of the atmospheric dynamic response to diabatic heating from condensation, radiative processes, and surface heat fluxes. Vertical motion and the associated divergent wind is thus intimately tied to the hydrologic cycle and the global heat balance. Despite its importance, the divergent circulation is too small in comparison to the rotational flow to measure directly with any accuracy. Vertical motions are recovered diagnostically from reanalyses and, as such, are subject to shortcomings in model physics, numerics, and data availability. We use several GEWEX pre-EOS data sets derived from satellite data to assess interannual divergent flow anomalies in the NCEP, GSFC, and GSFC DAO analyse. Among the data sets are monthly, 2.5 degree gridded precipitation (MSU and SSM/I-based), TOA radiative fluxes from ERBS, surface radiative fluxes from the SRB project, and surface latent and sensible flux estimates from SSM/I. Most of these data sets can be considered as independent of the reanalysis fields. We focus largely on the period 1987-1989 encompassing a strong El Nino / la Nina couplet. Consequently we emphasize interannual changes rather than climatological aspects of the reanalyses. In the processes of this study we use simple integral constraints enforced through the satellite-derived data sets to derive corrections to the divergent circulation produced from the reanalyses. We examine the implications of these corrections in describing how perturbations to the tropical heat balance evolve during a warm / cool couplet. In particular the perturbations to the planetary scale water vapor transport, and the resulting changes in TOA and surface radiative fluxes are considered.

Robertson, Franklin R.↗
NASA NTRS2012

Diagnosing the Nature of Land-Atmosphere Coupling: A Case Study of Dry/Wet Extremes in the U. S. Southern Great Plains

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land- PBL coupling at the process-level. In this paper, a diagnosis of the nature and impacts of local land-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. Southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation are applied to the dry/wet regimes exhibited in this region, and in the process a thorough evaluation of nine different land-PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling testbed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L-A coupling is stronger towards the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g. reanalysis products) in the context of their integrated impacts on the process-chain connecting the land surface to the PBL and in support of hydrological anomalies.

Santanello, Joseph A. Jr.↗
DOE OSTI2025

Model-driven prediction for accelerator magnet diagnostics to improve operation reliability

Reliability is one of the most critical metrics for accelerator operation, especially in user facilities. To reduce costly facility downtime and provide an operational environment where system performance can be reliably predicted in support of scientific studies, we are developing a model-driven approach for prediction and anomaly detection. Here, in this study, we present the application of a model-driven method that employs a linear regression model to predict the future temperature, in real time, of accelerator magnets at the NSLS-II light source. This approach enables proactive identification of magnet-heating issues, facilitating magnet flushing prior to the occurrence of permanent damage without interrupting machine operation. The implementation of this method in the NSLS-II control room is described and the analysis of the online results is presented. The results demonstrate the model’s effectiveness in providing early alerts to engineers and improving the reliability of accelerator operations.

36 MATERIALS SCIENCE↗