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NASA NTRS2009

Advances in Land Data Assimilation at the NASA Goddard Space Flight Center

Research in land surface data assimilation has grown rapidly over the last decade. In this presentation we provide a brief overview of key research contributions by the NASA Goddard Space Flight Center (GSFC). The GSFC contributions to land assimilation primarily include the continued development and application of the Land Information System (US) and the ensemble Kalman filter (EnKF). In particular, we have developed a method to generate perturbation fields that are correlated in space, time, and across variables and that permit the flexible modeling of errors in land surface models and observations, along with an adaptive filtering approach that estimates observation and model error input parameters. A percentile-based scaling method that addresses soil moisture biases in model and observational estimates opened the path to the successful application of land data assimilation to satellite retrievals of surface soil moisture. Assimilation of AMSR-E surface soil moisture retrievals into the NASA Catchment model provided superior surface and root zone assimilation products (when validated against in situ measurements and compared to the model estimates or satellite observations alone). The multi-model capabilities of US were used to investigate the role of subsurface physics in the assimilation of surface soil moisture observations. Results indicate that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Building on this experience, GSFC leads the development of the Level 4 Surface and Root-Zone Soil Moisture (L4_SM) product for the planned NASA Soil-Moisture-Active-Passive (SMAP) mission. A key milestone was the design and execution of an Observing System Simulation Experiment that quantified the contribution of soil moisture retrievals to land data assimilation products as a function of retrieval and land model skill and yielded an estimate of the error budget for the SMAP L4_SM product. Terrestrial water storage observations from GRACE satellite system were also successfully assimilated into the NASA Catchment model and provided improved estimates of groundwater variability when compared to the model estimates alone. Moreover, satellite-based land surface temperature (LST) observations from the ISCCP archive were assimilated using a bias estimation module that was specifically designed for LST assimilation. As with soil moisture, LST assimilation provides modest yet statistically significant improvements when compared to the model or satellite observations alone. To achieve the improvement, however, the LST assimilation algorithm must be adapted to the specific formulation of LST in the land model. An improved method for the assimilation of snow cover observations was also developed. Finally, the coupling of LIS to the mesoscale Weather Research and Forecasting (WRF) model enabled investigations into how the sensitivity of land-atmosphere interactions to the specific choice of planetary boundary layer scheme and land surface model varies across surface moisture regimes, and how it can be quantified and evaluated against observations. The on-going development and integration of land assimilation modules into the Land Information System will enable the use of GSFC software with a variety of land models and make it accessible to the research community.

Reichle, Rolf↗

DOE OSTI2021

Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.

Skomski, Elliott↗

DOE OSTI2025

Real-time neutron multiplicity and source localization for criticality safety during fuel debris removal

Advancing neutron detection and analysis techniques for complex radiation environments is an ongoing focus in nuclear instrumentation and monitoring. This proposal presents research and development of a generalized real-time neutron monitoring and analysis system, applicable to any detector capable of producing time-tagged neutron count data. While the work is demonstrated using the Neutron Multiplication Analysis Detector (NoMAD), a modular 15-tube helium-3 (He-3) array, due to its availability, spatial resolution, and flexible deployment, the methods developed are extensible to other systems, including organic scintillators and fast digital detectors. This research investigates two complementary analytical techniques for real-time characterization of neutron emitting sources: neutron multiplicity estimation based on the Hage-Cifarelli formalism and spatial localization using supervised machine learning applied to spatial count rate patterns. These methods are designed to operate under dynamic, evolving conditions such as fuel debris retrieval or reactor startup, where neutron-emitting material geometries may be partially unknown or changing over time. By integrating statistical neutron emission data with spatial localization, this research aims to develop and evaluate methods for real time neutron monitoring, source characterization, and material verification. Key contributions include implementation of a low-latency data pipeline for continuous neutron multiplicity analysis, development and validation of machine learning models for spatial inference, and experimental evaluation of system performance under variable measurement conditions. The outcomes are intended to support applications in nuclear safeguards, verification, emergency response, and reactor startup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2018

A Multi-Satellite Assimilation and Modeling Platform to Construct a Global and Complete view of the Hydrologic Cycle

What captivates me about the research field of satellite remote sensing of the earth system science is the opportunity to help human beings by addressing challenging questions related to the current and future availability of natural resources for domestic, agricultural, and industrial needs. For example, a continuously varying climate poses a threat to the water resource, not only in developing countries, but also in economically well-developed regions, creating the urgent need to characterize and predict its spatial and temporal availability. Specifically, my curiosity is driven by the challenge of merging cutting-edge space technology and earth observations (i.e., remote sensing) with state-of-the-art models for the purpose of improving our scientific knowledge about the variability and the change of the hydrologic cycle.The only practical way to observe the land surface processes (e.g., hydrologic cycle) on continental to global scales is via satellite remote sensing. Though remote sensing can make spatially comprehensive measurements of various components of the land surface system, it cannot provide direct information on the entire system, and the measurements represent only a snapshot in time. Physical based models may be used to continuously predict the temporal and spatial earth processes, but these predictions are often poor, due to model initialization, parameter and meteorological forcing errors, and inadequate model physics and/or resolution. Thus, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses of both) to provide a superior land surface state estimate. This is the goal of land surface data assimilation. Data assimilation provides a better estimate of the environmental states than either models or observations could individually do. The broad concept of data assimilation can be applied to various disciplines such as hydrology, ecology, environmental hazards, agriculture and economy.My research is targeted to integrating multiple satellite information having multi-sensor and multi-resolution assimilation within today's state-of-the-art hydrologic models. Multi-sensor and multi-resolution assimilation techniques represent a necessary milestone in future earth science applications because they offer the capability of comprehensively integrating disparate types of earth observations via deep-learning techniques. A multi-satellite assimilation and modeling platform will ultimately provide a robust and complete dataset to more fully understand earth's dynamics. In fact, the multi-satellite assimilation platforms build a comprehensive description of the earth processes that is geared toward accurately representing the complexity of natural and anthropogenic interactions in land surface processes. Most importantly, these platforms are suitable as decision support tools for applications across different aspects of earth system science.My recent work has focused on recent multi-sensor data assimilation techniques targeted at improving snow, soil moisture, groundwater, and terrestrial water storage hydrological states.

Girotto, Manuela↗

NASA NTRS2001

A Nontriggered Burst Supplement to the BATSE Gamma-Ray Burst Catalogs

The Burst and Transient Source Experiment (BATSE) on the Compton Gamma Ray Observatory detects gamma-ray bursts (GRBs) with a real-time burst detection (or "trigger") system running onboard the spacecraft. Under some circumstances, however, a GRB may not activate the on-board burst trigger. For example, the burst may be too faint to exceed the on-board detection threshold, or it may occur while the on-board burst trigger is disabled for technical reasons. This paper describes a catalog of 873 "nontriggered" GRBs that were detected in a search of the archival continuous data from BATSE recorded between 1991 December 9.0 and 1997 December 17.0. For each burst, the catalog gives an estimated source direction, duration, peak flux, and fluence. Similar data are presented for 50 additional bursts of unknown origin that were detected in the 25-50 keV range; these events may represent the low-energy "tail" of the GRB spectral distribution. This catalog increases the number of GRBs detected with BATSE by 48% during the time period covered by the search.

Kommers, Jefferson M.↗

DOE OSTI2017

A Search for Additional Bodies in the GJ 1132 Planetary System from 21 Ground-based Transits and a 100-hr Spitzer Campaign

We present the results of a search for additional bodies in the GJ 1132 system through two methods: photometric transits and transit timing variations of GJ 1132b. We collected 21 transit observations of GJ 1132b with the MEarth-South array. We obtained 100 near-continuous hours of observations with the Spitzer Space Telescope, including two transits of GJ 1132b and spanning 60% of the orbital phase of the maximum (6.9-day) period at which bodies coplanar with GJ 1132b would transit. We exclude transits of additional Mars-sized bodies, such as a second planet or a moon, with a confidence of 99.7%. We find that the planet-to-star radius ratio inferred from the MEarth and Spitzer light curves are discrepant at the 3.7σ level, which we ascribe to the effects of starspots and faculae. When we combine the mass estimate of the star (obtained from its parallax and apparent K {sub s} band magnitude) with the stellar density inferred from our high-cadence Spitzer light curve (assuming zero eccentricity), we measure the stellar radius of GJ 1132 to be 0.2105{sub −0.0085}{sup +0.0102} R{sub ⊙}, and we refine the radius measurement of GJ 1132b to 1.130±0.056 R{sub ⊕}. Combined with HARPS RV measurements, we determine the density of GJ 1132b to be 6.2 ± 2.0 g cm{sup −3}. We refine the ephemeris of the system (improving the period determination by an order of magnitude) and find no evidence for transit timing variations, which would be expected if there was a second planet near an orbital resonance with GJ 1132b.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2016

Large-Scale and Global Hydrology

Powered by the sun, water moves continuously between and through Earths oceanic, atmospheric, and terrestrial reservoirs. It enables life, shapes Earths surface, and responds to and influences climate change. Scientists measure various features of the water cycle using a combination of ground, airborne, and space-based observations, and seek to characterize it at multiple scales with the aid of numerical models. Over time our understanding of the water cycle and ability to quantify it have improved, owing to advances in observational capabilities, the extension of the data record, and increases in computing power and storage. Here we present some of the most recent estimates of global and continental ocean basin scale water cycle stocks and fluxes and provide examples of modern numerical modeling systems and reanalyses.Further, we discuss prospects for predicting water cycle variability at seasonal and longer scales, which is complicated by a changing climate and direct human impacts related to water management and agriculture. Changes to the water cycle will be among the most obvious and important facets of climate change, thus it is crucial that we continue to invest in our ability to monitor it.

land hydrology↗

NASA NTRS2023

Universal Two-Phase Convection Heat Transfer Correlations for Cryogenic Pipe Chilldown

This paper presents a set of universal two-phase convection heat transfer correlations for modeling boiling heat transfer during pipe chilldown fit over the widest available range of cryogenic fluids and thermodynamic conditions. The correlations improve upon prior correlations that were developed separately for liquid nitrogen (LN2) and liquid hydrogen (LH2) pipe quenching datasets. The new correlations include equations to calculate the single-phase vapor heat transfer, film boiling heat transfer, transition boiling heat transfer, nucleate boiling heat transfer, single-phase liquid heat transfer, bulk vapor temperature during high quality film boiling, Leidenfrost temperature, critical heat flux, critical heat flux temperature, and the onset of nucleate boiling temperature. The correlations were validated against LH2, LN2, liquid methane, liquid oxygen, and liquid argon pipe quenching datasets. The eight datasets cover the following parameter ranges: pipe lengths of 0.1 to 6.5m; outer pipe diameters of 12.7 to 25.4 mm; pipe wall thicknesses from 0.51 to 1.64 mm; flow directions of upward, downward, and horizontal; gravity levels of 1g and 0g±0.01g. A numerical model assumes homogeneous mixing between the vapor and liquid and implicitly integrates the coupled energy equations for the pipe and fluid, as well as continuity for the fluid. The model was used to estimate the equilibrium quality and fluid mass accumulation along the pipes during chilldown from estimates of the fluid-pipe heat transfer extracted from temperature measurements. The correlations can be implemented in lumped parameter codes such as SINDA/FLUINT and the Generalized Fluid System Simulation Program (GFSSP) to improve the accuracy of chilldown time and chilldown boiloff mass predictions. Such predictions are useful for designing ground or in-space cryogenic liquid transfer systems.

Universal Correlations↗

DOE OSTI2026

Two-Level Sketching Alternating Anderson Acceleration for Complex Physics Applications

We present a novel two-level sketching extension of the Alternating Anderson–Picard (AAP) method for accelerating fixed-point iterations in challenging single- and multiphysics simulations governed by discretized PDEs. Our approach combines a static, physics-based projection that reduces the least-squares (LS) problem to the most informative field (e.g., via Schur-complement insight) with a dynamic, algebraic sketching stage driven by a backward stability analysis under Lipschitz continuity. We introduce inexpensive estimators for stability thresholds and cache-aware randomized selection strategies to balance computational cost against memory access overhead. The resulting algorithm solves reduced LS systems in place, minimizes memory footprints, and seamlessly alternates between low-cost Picard updates and Anderson mixing. Implemented in Julia, our two-level sketching AAP achieves up to 50% time-to-solution reductions compared to standard Anderson acceleration—without degrading convergence rates—on benchmark problems including Stokes, 𝑝-Laplacian, bidomain, and Navier–Stokes formulations at varying problem sizes. These results demonstrate the method’s robustness, scalability, and potential for integration into high-performance scientific computing frameworks. Our implementation is available open source in the AAP.jl library.

Barnafi, Nicolas [University of Chile, Santiago]↗

NASA NTRS1990

Investigation of air transportation technology at Princeton University, 1988-1989

The Air Transportation Technology Program at Princeton University, a program emphasizing graduate and undergraduate student research, proceeded along several avenues during the past year. A study of optimal trajectories for penetration of microbursts when encounter is unavoidable was conducted. The emphasis of current wind shear research is on developing an expert system for wind shear avoidance. A knowledge-based reconfigurable flight control system that is implemented with the Pascal programming language using parallel microprocessors was developed. This expert system could be considered a prototype for a failure-tolerant control system that can be constructed using existing hardware. Development of a real-time cockpit simulator continued during the year. The simulator provides a single-person crew station with both conventional and advanced control devices; it currently is programmed to simulate the Navion single-engine general aviation airplane. Alternatives for the air traffic control system giving particular attention to the institutional structure of the FAA are analyzed. A simple numerical procedure for estimating the stochastic robustness of control systems is being investigated. The revitalization of the general aviation industry is also discussed.

Stengel, Robert F.↗

NASA NTRS1998

A Non-Triggered Burst Supplement to the BATSE Gamma-Ray Burst Catalogs

The Burst and Transient Source Experiment (BATSE) on the Compton Gamma Ray Observatory detects gamma-ray bursts (GRBs) with a real-time burst detection (or "trigger") system running onboard the spacecraft. Under some circumstances, however, a GRB may not activate the onboard burst trigger. For example, the burst may be too faint to exceed the onboard detection threshold, or it may occur while the onboard burst trigger is disabled for technical reasons. This paper is a catalog of such "non-triggered" GRBs that were detected in a search of the archival continuous data from BATSE. It lists 873 non-triggered bursts that were recorded between 1991 December 9.0 and 1997 December 17.0. For each burst, the catalog gives an estimated source direction, duration, peak flux, and fluence. Similar data are presented for 50 additional bursts of unknown origin that were detected in the 25-50 keV range; these events may represent the low-energy "tail" of the GRB spectral distribution. This catalog increases the number of GRBs detected with BATSE by 48% during the time period covered by the search.

Kommers, J.↗

NASA NTRS2007

Detailed Analysis of Indian Summer Monsoon Rainfall Processes with Modern/High-Quality Satellite Observations

We examine, in detail, Indian Summer Monsoon rainfall processes using modernhigh quality satellite precipitation measurements. The focus here is on measurements derived from three NASA cloud and precipitation satellite missionslinstruments (TRMM/PR&TMI, AQUNAMSRE, and CLOUDSATICPR), and a fourth TRMM Project-generated multi-satellite precipitation measurement dataset (viz., TRMM standard algorithm 3b42) -- all from a period beginning in 1998 up to the present. It is emphasized that the 3b42 algorithm blends passive microwave (PMW) radiometer-based precipitation estimates from LEO satellites with infi-ared (IR) precipitation estimates from a world network of CEO satellites (representing -15% of the complete space-time coverage) All of these observations are first cross-calibrated to precipitation estimates taken from standard TRMM combined PR-TMI algorithm 2b31, and second adjusted at the large scale based on monthly-averaged rain-gage measurements. The blended approach takes advantage of direct estimates of precipitation from the PMW radiometerequipped LEO satellites -- but which suffer fi-om sampling limitations -- in combination with less accurate IR estimates from the optical-infrared imaging cameras on GEO satellites -- but which provide continuous diurnal sampling. The advantages of the current technologies are evident in the continuity and coverage properties inherent to the resultant precipitation datasets that have been an outgrowth of these stable measuring and retrieval technologies. There is a wealth of information contained in the current satellite measurements of precipitation regarding the salient precipitation properties of the Indian Summer Monsoon. Using different datasets obtained from the measuring systems noted above, we have analyzed the observations cast in the form of: (1) spatially distributed means and variances over the hierarchy of relevant time scales (hourly I diurnally, daily, monthly, seasonally I intra-seasonally, and inter-annually), (2) time series at these different time scales taken as area-averages over the hierarchy of relevant space scales (Indian sub-Division, Indian sub-continent, and Circumambient Indian Ocean), (3) principal autocorrelation and cross-correlation structures over various monsoon space-time domains, (4) diurnally modulated amplitude-phase properties of rain rates over different monsoon space-time domains, (5) foremost rain rate probability distributions intrinsic to monsoon precipitation, and (6) behavior of extreme events including occurrences of flood and drought episodes throughout the course of inter-annual monsoon processes.

Smith, Eric A.↗

DOE OSTI2017

Real-time fault detection and transient identification using extended Kalman filters - 254

In this paper, real-time fault detection and transient identification are studied with adaptive estimation methods, in particular parallel processing methods by implementing a bank of extended Kalman filters. Adaptive estimation is combined with probabilistic risk assessment to allow for real-time continuous monitoring of initiating events. The approach provides a probability of an initiating event happening. Real-time monitoring of initiating events can reduce labor demand. Each extended Kalman filter is configured to operate at a single configuration, either at normal operation or a faulted state. Fault detection is based on calculating a likelihood function for each extended Kalman filter to determine the posteriori conditional probability that the system configuration is the true configuration. The transient identification is determined by performing a multivariate statistical analysis on each extended Kalman filter to provide a posterior probability assessment that the reactor states and its dynamics are within the allowable limits. The probability distribution being outside the allowable limits indicate a transient has occurred. The combination of parallel processing methods with the multivariate statistical analysis for transient identification allows for the operator to perform transient identification with having a high level of certainty of the current system configuration. We demonstrate the real-time fault detection and transient identification on a simple reactor example to determine the reactor power, reactor temperature, and coolant temperature in the presence of various faults. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Optimizing TinyTPC Test Stand for Low Energy Event Reconstruction

Introducing dopants into liquid argon offers a promising avenue to enhance the sensitivity and performance of LAr detectors such as DUNE. To explore this opportunity, we are optimizing and deploying the TinyTPC test stand, a compact time projection chamber with LArPix readout to study the effects of photosensitive dopants on the charge detected. TinyTPC enables comparative measurements of low-energy responses under various operational configurations and provides a platform to study the behavior of pixel readouts at low energy. We ensured system reliability through continuous current and voltage monitoring, modeling the detector's electrical behavior, and indirectly estimating total resistance under testing conditions. We ran the Blanche cryostat under various thresholds and gain configurations to evaluate their potential impact to low-energy event reconstruction and advancing R&D efforts for dopant integration in neutrino detectors

Van Loenen, Drew [Franciscan U. Steubenville]↗

NASA NTRS1976

An algorithm for the weighting matrices in the sampled-data optimal linear regulator problem

The sampled-data optimal linear regulator problem provides a means whereby a control designer can use an understanding of continuous optimal regulator design to produce a digital state variable feedback control law which satisfies continuous system performance specifications. A basic difficulty in applying the sampled-data regulator theory is the requirement that certain digital performance index weighting matrices, expressed as complicated functions of system matrices, be computed. Infinite series representations are presented for the weighting matrices of the time-invariant version of the optimal linear sampled-data regulator problem. Error bounds are given for estimating the effect of truncating the series expressions after a finite number of terms, and a method is described for their computer implementation. A numerical example is given to illustrate the results.

Armstrong, E. S.↗

NASA NTRS2014

Constructing the "Best" Reliability Data for the Job

Modern business and technical decisions are based on the results of analyses. When considering assessments using "reliability data", the concern is how long a system will continue to operate as designed. Generally, the results are only as good as the data used. Ideally, a large set of pass/fail tests or observations to estimate the probability of failure of the item under test would produce the best data. However, this is a costly endeavor if used for every analysis and design. Developing specific data is costly and time consuming. Instead, analysts rely on available data to assess reliability. Finding data relevant to the specific use and environment for any project is difficult, if not impossible. Instead, we attempt to develop the "best" or composite analog data to support our assessments. One method used incorporates processes for reviewing existing data sources and identifying the available information based on similar equipment, then using that generic data to derive an analog composite. Dissimilarities in equipment descriptions, environment of intended use, quality and even failure modes impact the "best" data incorporated in an analog composite. Once developed, this composite analog data provides a "better" representation of the reliability of the equipment or component can be used to support early risk or reliability trade studies, or analytical models to establish the predicted reliability data points. Data that is more representative of reality and more project specific would provide more accurate analysis, and hopefully a better final decision.

DeMott, D. L.↗

NASA NTRS2014

Constructing the Best Reliability Data for the Job

Kleinhammer, R. K.↗

NASA NTRS2017

Multi-Source Sensor Fusion for Small Unmanned Aircraft Systems Using Fuzzy Logic

As the applications for using small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) continue to grow in the coming years, it is imperative that intelligent sensor fusion techniques be explored. In BVLOS scenarios the vehicle position must accurately be tracked over time to ensure no two vehicles collide with one another, no vehicle crashes into surrounding structures, and to identify off-nominal scenarios. Therefore, in this study an intelligent systems approach is used to estimate the position of sUAS given a variety of sensor platforms, including, GPS, radar, and on-board detection hardware. Common research challenges include, asynchronous sensor rates and sensor reliability. In an effort to realize these challenges, techniques such as a Maximum a Posteriori estimation and a Fuzzy Logic based sensor confidence determination are used.

fuzzy logic↗