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NASA NTRS1983

Spectral decontamination of a real-time helicopter simulation

Nonlinear mathematical models of a rotor system, referred to as rotating blade-element models, produce steady-state, high-frequency harmonics of significant magnitude. In a discrete simulation model, certain of these harmonics may be incompatible with realistic real-time computational constraints because of their aliasing into the operational low-pass region. However, the energy is an aliased harmonic may be suppressed by increasing the computation rate of an isolated, causal nonlinearity and using an appropriate filter. This decontamination technique is applied to Sikorsky's real-time model of the Black Hawk helicopter, as supplied to NASA for handling-qualities investigations.

Mcfarland, R. E.↗

NASA NTRS1993

Case-based approach to handling aircraft malfunctions

Reasoning about physical systems in operation is a difficult task, and any attempt to automate the process must overcome the problems of modeling normal behavior, diagnosing faults, and predicting future behavior. This paper describes a prototypical case-based reasoner (CBR) that operates in the domain of in-flight fault diagnosis and prognosis of aviation subsystems, particularly jet engines. The reasoner operates on the observation that the ability of a CBR program to reason about physical systems can be significantly enhanced by the addition to the CBR program of a model of the physical system to describe the system's structural, functional, and causal behavior.

Karamouzis, Stamos T.↗

NASA NTRS2006

A Bayesian Framework for Landing Site Selection During Autonomous Spacecraft Descent

The success of a landed space exploration mission depends largely on the final landing site. Factors influencing site selection include safety, fuel-consumption, and scientific return. This paper addresses the problem of selecting the best available landing site based on these factors in real-time during autonomous spacecraft descent onto a planetary surface. The problem is modeled probabilistically using Bayesian Networks (BNs). BNs provide a means of representing the causal relationships between variables that impact the quality of a landing site. The final landing site is determined via probabilistic reasoning based on terrain safety derived from on-board sensors, available fuel based on spacecraft descent dynamics, and regions of interest defined by mission scientists.

entry, descent, landing (EDL)↗

NASA NTRS2007

The Aviation System Monitoring and Modeling (ASMM) Project: A Documentation of its History and Accomplishments: 1999-2005

The Aviation System Monitoring and Modeling (ASMM) Project was one of the projects within NASA s Aviation Safety Program from 1999 through 2005. The objective of the ASMM Project was to develop the technologies to enable the aviation industry to undertake a proactive approach to the management of its system-wide safety risks. The ASMM Project entailed four interdependent elements: (1) Data Analysis Tools Development - develop tools to convert numerical and textual data into information; (2) Intramural Monitoring - test and evaluate the data analysis tools in operational environments; (3) Extramural Monitoring - gain insight into the aviation system performance by surveying its front-line operators; and (4) Modeling and Simulations - provide reliable predictions of the system-wide hazards, their causal factors, and their operational risks that may result from the introduction of new technologies, new procedures, or new operational concepts. This report is a documentation of the history of this highly successful project and of its many accomplishments and contributions to improved safety of the aviation system.

Statler, Irving C.↗

NASA NTRS2014

Modeling Increased Complexity and the Reliance on Automation: FLightdeck Automation Problems (FLAP) Model

This paper highlights the development of a model that is focused on the safety issue of increasing complexity and reliance on automation systems in transport category aircraft. Recent statistics show an increase in mishaps related to manual handling and automation errors due to pilot complacency and over-reliance on automation, loss of situational awareness, automation system failures and/or pilot deficiencies. Consequently, the aircraft can enter a state outside the flight envelope and/or air traffic safety margins which potentially can lead to loss-of-control (LOC), controlled-flight-into-terrain (CFIT), or runway excursion/confusion accidents, etc. The goal of this modeling effort is to provide NASA's Aviation Safety Program (AvSP) with a platform capable of assessing the impacts of AvSP technologies and products towards reducing the relative risk of automation related accidents and incidents. In order to do so, a generic framework, capable of mapping both latent and active causal factors leading to automation errors, is developed. Next, the framework is converted into a Bayesian Belief Network model and populated with data gathered from Subject Matter Experts (SMEs). With the insertion of technologies and products, the model provides individual and collective risk reduction acquired by technologies and methodologies developed within AvSP.

Ancel, Ersin↗

NASA NTRS2015

Bayesian Safety Risk Modeling of Human-Flightdeck Automation Interaction

Usage of automatic systems in airliners has increased fuel efficiency, added extra capabilities, enhanced safety and reliability, as well as provide improved passenger comfort since its introduction in the late 80's. However, original automation benefits, including reduced flight crew workload, human errors or training requirements, were not achieved as originally expected. Instead, automation introduced new failure modes, redistributed, and sometimes increased workload, brought in new cognitive and attention demands, and increased training requirements. Modern airliners have numerous flight modes, providing more flexibility (and inherently more complexity) to the flight crew. However, the price to pay for the increased flexibility is the need for increased mode awareness, as well as the need to supervise, understand, and predict automated system behavior. Also, over-reliance on automation is linked to manual flight skill degradation and complacency in commercial pilots. As a result, recent accidents involving human errors are often caused by the interactions between humans and the automated systems (e.g., the breakdown in man-machine coordination), deteriorated manual flying skills, and/or loss of situational awareness due to heavy dependence on automated systems. This paper describes the development of the increased complexity and reliance on automation baseline model, named FLAP for FLightdeck Automation Problems. The model development process starts with a comprehensive literature review followed by the construction of a framework comprised of high-level causal factors leading to an automation-related flight anomaly. The framework was then converted into a Bayesian Belief Network (BBN) using the Hugin Software v7.8. The effects of automation on flight crew are incorporated into the model, including flight skill degradation, increased cognitive demand and training requirements along with their interactions. Besides flight crew deficiencies, automation system failures and anomalies of avionic systems are also incorporated. The resultant model helps simulate the emergence of automation-related issues in today's modern airliners from a top-down, generalized approach, which serves as a platform to evaluate NASA developed technologies

Ancel, Ersin↗

NASA NTRS1996

Comprehensive Analysis of Two Downburst-Related Aircraft Accidents

Although downbursts have been identified as the major cause of a number of aircraft takeoff and landing accidents, only the 1985 Dallas/Fort Worth (DFW) and the more recent (July 1994) Charlotte, North Carolina, landing accidents provided sufficient onboard recorded data to perform a comprehensive analysis of the downburst phenomenon. The first step in the present analysis was the determination of the downburst wind components. Once the wind components and their gradients were determined, the degrading effect of the wind environment on the airplane's performance was calculated. This wind-shear-induced aircraft performance degradation, sometimes called the F-factor, was broken down into two components F(sub 1) and F(sub 2), representing the effect of the horizontal wind gradient and the vertical wind velocity, respectively. In both the DFW and Charlotte cases, F(sub 1) was found to be the dominant causal factor of the accident. Next, the aircraft in the two cases were mathematically modeled using the longitudinal equations of motion and the appropriate aerodynamic parameters. Based on the aircraft model and the determined winds, the aircraft response to the recorded pilot inputs showed good agreement with the onboard recordings. Finally, various landing abort strategies were studied. It was concluded that the most acceptable landing abort strategy from both an analytical and pilot's standpoint was to hold constant nose-up pitch attitude while operating at maximum engine thrust.

Shen, J.↗

NASA NTRS2001

Separating Spatial and Temporal Variations of the Aurora Using Two Nearly Colocated Satellites

This final report describes the efforts accomplished during the grant's period of performance, covering the period of 1 May 1997 to 30 April 2001, of a NASA Supporting Research and Technology Program grant under the Ionospheric, Thermospheric, and Mesospheric Physics component of the Sun-Earth Connections program. We have met and exceeded the goals set forth in the proposed research objectives. Referred publications have appeared in the scientific literature and several others are in the review process. In addition, numerous invited and contributed presentations of these studies were presented at national and international meetings during the performance period. One graduate student completed his PhD and won two AGU Best Student Paper awards based on research funded by this grant. These studies are summarized below. The science goal delineated in the initial proposal was "to systematically explore the temporal and spatial characteristics of the aurora in a way heretofore impossible, using data from two coplanar DMSP spacecraft." We accomplished this goal through a series of related studies. One study used these unique data to establish the role of Ps6 waves in coupling between the magnetosphere and the auroral ionosphere (omega bands) during the recovery phase of a magnetic storm; the published paper demonstrated the causal relationships between geospace processes occurring in different regions and established a simple conceptual model based on the fortuitous constellation of observations. In the second string of papers, we used these data to explore velocity-dispersed ions (VDIS) in and near the cusp, to test region identification models, and to look at space/time structure of auroral precipitation. On the first topic, the unique DMSP data revealed a remarkable double VDIS with a latitudinal overlap. This could only be explained in terms of a unified reconnection geometry that builds on several earlier unrelated models. The paper outlining this discovery has drawn considerable attention from the community and is currently in press - it adds significantly to the debate over whether reconnection is study state versus bursty and patchy versus global. The second paper develops the model further by incorporating the electron signature - these ionospheric particle precipitation signatures reveal the presence of magnetospheric "fossilized" FTEs, demonstrating the power of ionospheric measurements as a remote diagnostic of magnetospheric processes. Finally, the general nature of aurora] stability and coherence and region identification by particle characteristics were fully explored in a final paper. We identify candidate mechanisms controlling coherence time scales and length scales and refine boundary region identification criteria. We also use the dual-DMSP observations to identify the open and closed LLBL region and related its significance to the generalized bursty, multiple x-line model developed in the first paper. All of these topics are chapters of Dr. Boudouridis' recently completed PhD thesis.

Spence, Harlan E.↗

NASA NTRS1989

Processes responsible for the compositional structure of the thermosphere

The relative importances of the various physical and chemical mechanisms that force changes in neutral thermospheric composition for a given geophysical situation were quantified using a diagnostic postprocessor analysis package in conjunction with runs of the NCAR thermospheric general circulation model (TGCM) that was extended to include the terms of the neutral composition equation. New information was obtained on the causal mechanisms responsible for changes in the concentrations of the three neutral species, O2, O, and N2, whose time-dependent mass mixing ratios were calculated within the TGCM. Principal results calculated for F-region altitudes are described, and thermospheric compositions calculated using the NCAR-TGCM model are compared with the predictions of the empirical thermospheric model of Hedin (1987).

Burns, A. G.↗

NASA NTRS2007

Productivity Analysis of Public and Private Airports: A Causal Investigation

Around the world, airports are being viewed as enterprises, rather than public services, which are expected to be managed efficiently and provide passengers with courteous customer services. Governments are, increasingly, turning to the private sectors for their efficiency in managing the operation, financing, and development, as well as providing security for airports. Operational and financial performance evaluation has become increasingly important to airport operators due to recent trends in airport privatization. Assessing performance allows the airport operators to plan for human resources and capital investment as efficiently as possible. Productivity measurements may be used as comparisons and guidelines in strategic planning, in the internal analysis of operational efficiency and effectiveness, and in assessing the competitive position of an airport in transportation industry. The primary purpose of this paper is to investigate the operational and financial efficiencies of 22 major airports in the United States and Europe. These airports are divided into three groups based on private ownership (7 British Airport Authority airports), public ownership (8 major United States airports), and a mix of private and public ownership (7 major European Union airports. The detail ownership structures of these airports are presented in Appendix A. Total factor productivity (TFP) model was utilized to measure airport performance in terms of financial and operational efficiencies and to develop a benchmarking tool to identify the areas of strength and weakness. A regression model was then employed to measure the relationship between TFP and ownership structure. Finally a Granger causality test was performed to determine whether ownership structure is a Granger cause of TFP. The results of the analysis presented in this paper demonstrate that there is not a significant relationship between airport TFP and ownership structure. Airport productivity and efficiency is, however dependent upon the level of competition, choice of the market, and regulatory control.

Vasigh, Bijan↗

DOE OSTI2024

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Markov decision process↗

DOE OSTI2024

Changing effects of external forcing on Atlantic–Pacific interactions

Recent studies have highlighted the increasingly dominant role of external forcing in driving Atlantic and Pacific Ocean variability during the second half of the 20th century. This paper provides insights into the underlying mechanisms driving interactions between modes of variability over the two basins. We define a set of possible drivers of these interactions and apply causal discovery to reanalysis data, two ensembles of pacemaker simulations where sea surface temperatures in either the tropical Pacific or the North Atlantic are nudged to observations, and a pre-industrial control run. We also utilize large-ensemble means of historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to quantify the effect of external forcing and improve the understanding of its impact. A causal analysis of the historical time series between 1950 and 2014 identifies a regime switch in the interactions between major modes of Atlantic and Pacific climate variability in both reanalysis and pacemaker simulations. A sliding window causal analysis reveals a decaying El Niño–Southern Oscillation (ENSO) effect on the Atlantic as the North Atlantic fluctuates towards an anomalously warm state. The causal networks also demonstrate that external forcing contributed to strengthening the Atlantic's negative-sign effect on ENSO since the mid-1980s, where warming tropical Atlantic sea surface temperatures induce a La Niña-like cooling in the equatorial Pacific during the following season through an intensification of the Pacific Walker circulation. The strengthening of this effect is not detected when the historical external forcing signal is removed in the Pacific pacemaker ensemble. The analysis of the pre-industrial control run supports the notion that the Atlantic and Pacific modes of natural climate variability exert contrasting impacts on each other even in the absence of anthropogenic forcing. The interactions are shown to be modulated by the (multi)decadal states of temperature anomalies of both basins with stronger connections when these states are “out of phase”. We show that causal discovery can detect previously documented connections and provides important potential for a deeper understanding of the mechanisms driving changes in regional and global climate variability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Sensitivities of Large Eddy Simulations of Aerosol Plume Transport and Cloud Response

Abstract Cloud responses to surface‐based sources of aerosol perturbation partially depend on how turbulent transport of the aerosol to cloud base affects the spatial and temporal distribution of aerosol. Here, scenarios of plume injection below a marine stratocumulus cloud are modeled using large eddy simulations coupled to a prognostic bulk aerosol and cloud microphysics scheme. Both passive plumes, consisting of an inert tracer, and active plumes are investigated, where the latter are representative of saltwater droplet plumes such as have been proposed for marine cloud brightening. Passive plume scenarios show higher in‐plume cloud brightness (relative to out‐of‐plume) due to the predominant transport of the passive plume tracer from the near‐surface to the cloud layer within updrafts. These updrafts rise into brighter areas within the cloud deck, even in the absence of an aerosol perturbation associated with an active plume. Comparing albedo at in‐plume to out‐of‐plume locations associates the inert plume with the brightest cloud locations, without any causal effect of the plume on the cloud. Numerical sensitivities are first assessed to establish a suitable model configuration. Then sensitivity to particle injection rate is investigated. Trade‐offs are identified between the number of injected particles and the suppressive effect of droplet evaporation on plume loft and spread. Furthermore, as the near‐field in‐plume brightening effect does not depend significantly on injection rate given a suitable definition of perturbed versus unperturbed regions of the flow, plume area is a key controlling factor on the overall cloud brightening effect of an aerosol perturbation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

DG2DAG: Learning Directed Acyclic Graphs from Functional Priors

Physics-based systems-of-systems models are computationally expensive. Reduced graphical models can decrease computational complexity, but may not proffer an end-to-end model from upstream inputs to downstream outputs. We consequently are interested in reducing models on directed graphs to models on a directed acyclic subgraph such that preserves accurate reconstruction of nodes. The consequence is a model with a topological ordering, providing a one-way flow of computation, and a causal interpr

Voronin, Alexey [Sandia National Laboratories (SNL↗

DOE OSTI2025

Increasing Mosquito Abundance Under Global Warming

Mosquitoes are a key virus vector that poses significant health threats globally, affecting 700 million individuals and causing 1 million deaths annually. Accurately predicting mosquito abundance and dispersion remains a challenge. Complex interactions between mosquito dynamics and various environmental factors, notably hydrology, contribute to this challenge. Existing models typically focus on precipitation and temperature and often overlook further impacts of hydrological variables within mosquito modeling. In this study, we developed an artificial intelligence‐based model for mosquito dynamics, explicitly accounting for different hydrological variables, such as precipitation, soil moisture and streamflow. Using Toronto, Canada, as a case study, we identified causal relationships between changes in mosquito populations, hydrological factors, vegetation (e.g., leaf area index), and climate variables (e.g., daylight length, precipitation, and temperature). We embedded these relationships into a Long Short‐Term Memory (LSTM) Neural Network Model capable of accurately detecting mosquito dynamics across annual, seasonal, and monthly time scales. The LSTM is able to explain, on average, approximately 40% of the variance in the observed mosquito abundance data. Using the calibrated model, we predicted that the summer season mosquito abundance would increase by ∼16% and ∼19% under an intermediate greenhouse emission scenario, Shared Socioeconomic Pathway (SSP) 2–4.5, and a high greenhouse emission scenario, SSP5‐8.5, respectively. We expect that this model can serve as a valuable tool and inform science‐based decisions affecting mosquito dynamics and public health. It can also build a foundation for future risk analysis at the regional and larger scales.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

High-Resolution X-Ray Imaging of Colliding Radio-Jet Galaxies

We received ROSAT data for four program objects:3C31,3C278,3C449,and NGC1044. The first three sources were observed with the ROSAT HRI instrument. Our plan was to use the HRI to image the hot gas distribution in a few pairs of strongly disturbed interacting elliptical galaxies which are also strong radio sources having a bent-jet source morphology. The PSPC was used for NGC1044 in order to obtain a flux measurement to use in planning future High Resolution Imager (HRI) observations of that source. Though we never requested such HRI observations of NGC1044, others have used those archival PSPC data from our project for other research projects and analyses. The goal of the program was to elucidate the detailed distribution of hot gas into which the jets flow. The X-ray data were consequently analyzed in conjunction with existing VLA radio maps, optical broad-band and H-alpha Charge Couple device (CCD) images, and optical kinematic data to constrain models for the propagation of ballistic jets in interacting galaxies. We were able to test and validate the claimed causal connection between tidal interaction, the presence of gas, and the onset of activity in galaxies. The full multi-wavelength multi-observatory analyses described here are still on-going and will be published in the future. Because of the relevance of this research to on-going work in the field of active galaxies, the grant was used to support travel to several scientific meetings where our x-ray analysis, numerical modeling, and related radio results were presented and discussed.

Born, Kirk D.↗

NASA NTRS2005

Prediction of Turbulence-Generated Noise in Unheated Jets: JeNo Technical Manual (Version 1.0) - Part 1

The model-based approach, used by the JeNo code to predict jet noise spectral directivity, is described. A linearized form of Lilley's equation governs the non-causal Green s function of interest, with the non-linear terms on the right hand side identified as the source. A Reynolds-averaged Navier-Stokes (RANS) solution yields the required mean flow for the solution of the propagation Green s function in a locally parallel flow. The RANS solution also produces time- and length-scales needed to model the non-compact source, the turbulent velocity correlation tensor, with exponential temporal and spatial functions. It is shown that while an exact non-causal Green s function accurately predicts the observed shift in the location of the spectrum peak with angle as well as the angularity of sound at low to moderate Mach numbers, the polar directivity of radiated sound is not entirely captured by this Green s function at high subsonic and supersonic acoustic Mach numbers. Results presented for unheated jets in the Mach number range of 0.51 to 1.8 suggest that near the peak radiation angle of high-speed jets, a different source/Green s function convolution integral may be required in order to capture the peak observed directivity of jet noise. A sample Mach 0.90 heated jet is also discussed that highlights the requirements for a comprehensive jet noise prediction model.

Khavaran, Abbas↗

DOE OSTI2025

Quantum Gravity and Laser Interferometry: Towards Observable Predictions

Understanding quantum gravity remains one of the deepest challenges in modern physics, as direct experimental access to Planck-scale effects is beyond current technological reach. However, recent theoretical advances indicate that quantum fluctuations of spacetime may produce measurable effects in precision experiments, particularly near causal horizons. This opens new avenues for testing quantum gravity phenomena through high-precision measurement techniques. This dissertation develops multiple theoretical models to characterize these effects and examines their potential observational signatures in future gravitational wave interferometers. We begin by investigating the role of quantum fluctuations in near-horizon geometries through the lens of the AdS/CFT correspondence, which provides a powerful framework for understanding the interplay between quantum field theory and general relativity via holographic principles. By modeling stochastic energy-momentum sources in Rindler-AdS spacetime, we demonstrate that vacuum fluctuations transform the Einstein equations into a Langevin-type stochastic differential equation, leading to potentially observable fluctuations in photon traversal times. Extending this approach to Minkowski spacetime, we establish a correspondence between gravitational shockwaves and fluid dynamics, showing that near-horizon perturbations satisfy an equation analogous to that governing incompressible fluids, thereby reinforcing the membrane paradigm and hydrodynamic analogies in the context of the fluid/gravity duality. Furthermore, we construct the covariant phase space of a spherically symmetric causal diamond in Minkowski spacetime, identifying two fundamental charges that govern its evolution. These results provide a foundation for quantizing causal horizons and understanding their microscopic degrees of freedom. Building upon these theoretical developments, we further examine a related stochastic phenomenon: the gravitational wave memory background arising from the cumulative memory steps produced by supermassive black hole mergers. After reviewing the standard stochastic gravitational wave background, gravitational memory effects, and BMS symmetries, we model the stochastic memory background using a Brownian motion framework. We show that while the cumulative memory background initially appears above the sensitivity curve of space-based interferometers like LISA, the realistic subtraction of individually resolvable merger events substantially suppresses the residual signal, making its detection more challenging. This highlights the critical importance of source subtraction when evaluating the detectability of gravitational memory effects. By bridging fundamental theory with experimental prospects, this dissertation contributes to the ongoing effort to uncover the quantum nature of spacetime through precision measurement techniques. Whether through detecting quantum spacetime fluctuations, gravitational memory backgrounds, or probing the symmetries of causal horizons, the pursuit of observable quantum gravity phenomena continues to expand the frontiers of both theory and experiment.

Zhang, Yiwen [Caltech] (ORCID:0000000323559416)↗