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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Human Performance Models of Pilot Behavior

Five modeling teams from industry and academia were chosen by the NASA Aviation Safety and Security Program to develop human performance models (HPM) of pilots performing taxi operations and runway instrument approaches with and without advanced displays. One representative from each team will serve as a panelist to discuss their team s model architecture, augmentations and advancements to HPMs, and aviation-safety related lessons learned. Panelists will discuss how modeling results are influenced by a model s architecture and structure, the role of the external environment, specific modeling advances and future directions and challenges for human performance modeling in aviation.

Foyle, David C.↗
NASA NTRS1979

Mathematical concepts for modeling human behavior in complex man-machine systems

Many human behavior (e.g., manual control) models have been found to be inadequate for describing processes in certain real complex man-machine systems. An attempt is made to find a way to overcome this problem by examining the range of applicability of existing mathematical models with respect to the hierarchy of human activities in real complex tasks. Automobile driving is chosen as a baseline scenario, and a hierarchy of human activities is derived by analyzing this task in general terms. A structural description leads to a block diagram and a time-sharing computer analogy.

Johannsen, G.↗
NASA NTRS1991

Modeling methods for the design and evaluation of fault-tolerant systems

The authors describe an approach for using directed graph simulation models, behavioral simulation models, and semi-Markov analytic models to implement early- to mid-design analysis activities specified by the SDIO BM/C3 Processor and Algorithm Working Group. The use of the models was demonstrated for a mission scenario requiring parallel, reliable computations with a maximum probability of system failure between 10-4 and 10-2 over a 5-year-preengagement phase and between 10-7 and 10-5 over a half-hour engagement phase.

Scheper, Charlotte O.↗
DOE OSTI2025

Disturbance and Response Model (DRM)

The DRM is a framework to interpret ecosystem process model output for 3D fire behavior model input and interpret the 3D fire behavior model output for ecosystem process model inpu

Atchley, Adam↗
DOE OSTI2024

W2VPCA: A Machine Learning Method for Measuring Attitudes With Natural Language

Company strategy influences many decisions in freight transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables. However, strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent strategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which can be problematic. This study introduces a new machine learning method to generate strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses information on word context. We apply W2VPCA to generate measurements of latent strategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strategies, one focusing on distribution and the other on products, with truck fleet and distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies. While this study focuses on freight behavioral models, W2VPCA may also have applications in behavioral modeling in other domains.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2009

Towards Behavioral Reflexion Models

Software architecture has become essential in the struggle to manage today s increasingly large and complex systems. Software architecture views are created to capture important system characteristics on an abstract and, thus, comprehensible level. As the system is implemented and later maintained, it often deviates from the original design specification. Such deviations can have implication for the quality of the system, such as reliability, security, and maintainability. Software architecture compliance checking approaches, such as the reflexion model technique, have been proposed to address this issue by comparing the implementation to a model of the systems architecture design. However, architecture compliance checking approaches focus solely on structural characteristics and ignore behavioral conformance. This is especially an issue in Systems-of- Systems. Systems-of-Systems (SoS) are decompositions of large systems, into smaller systems for the sake of flexibility. Deviations of the implementation to its behavioral design often reduce the reliability of the entire SoS. An approach is needed that supports the reasoning about behavioral conformance on architecture level. In order to address this issue, we have developed an approach for comparing the implementation of a SoS to an architecture model of its behavioral design. The approach follows the idea of reflexion models and adopts it to support the compliance checking of behaviors. In this paper, we focus on sequencing properties as they play an important role in many SoS. Sequencing deviations potentially have a severe impact on the SoS correctness and qualities. The desired behavioral specification is defined in UML sequence diagram notation and behaviors are extracted from the SoS implementation. The behaviors are then mapped to the model of the desired behavior and the two are compared. Finally, a reflexion model is constructed that shows the deviations between behavioral design and implementation. This paper discusses the approach and shows how it can be applied to investigate reliability issues in SoS.

Ackermann, Christopher↗
NASA NTRS2010

Modeling Pilot Behavior for Assessing Integrated Alert and Notification Systems on Flight Decks

Numerous new flight deck configurations for caution, warning, and alerts can be conceived; yet testing them with human-in-the-Ioop experiments to evaluate each one would not be practical. New sensors, instruments, and displays are being put into cockpits every day and this is particularly true as we enter the dawn of the Next Generation Air Transportation System (NextGen). By modeling pilot behavior in a computer simulation, an unlimited number of unique caution, warning, and alert configurations can be evaluated 24/7 by a computer. These computer simulations can then identify the most promising candidate formats to further evaluate in higher fidelity, but more costly, Human-in-the-Ioop (HITL) simulations. Evaluations using batch simulations with human performance models saves time, money, and enables a broader consideration of possible caution, warning, and alerting configurations for future flight decks.

Cover, Mathew↗
NASA NTRS1995

Correlations of Different Surface Tests: Tire Behavior Math Model for the High Speed Civil Transport (HSCT) and Michelin Tire Properties Tests for Boeing 777

In the surfaces correlation study, several different volumetric and drainage measurement techniques for classifying surface texture were evaluated as part of a major study to develop and improve methods for predicting tire friction performance on all types of pavement. The objective of the evaluation was to seek relationships between the different techniques, and to relate those results to surface frictional characteristics. We needed to know how each of the tests could be related to each other. Another of my assigned projects was to make a tire behavior math model for the High Speed Civil Transport (HSCT) using the same methods used for the space shuttle a few years ago. A provided third order equation with two variables was used. This model will also be used for studies with the Boeing 777. Only a few changes will be necessary to adapt it for this other aircraft, which is the newest offered by Boeing. In my final project I was involved with testing the tires for this new aircraft using the Aircraft Landing Dynamics Facility (ALDF) test carriage within the carriage house at LaRC. A 50 inch diameter radial tire manufactured by Michelin Aircraft Tire Corporation had to be tested to double overload of 114,000 pounds. The rated load of each tire is 57,000 pounds, but Boeing required tests assuming failure of a companion tire that could have cost Michelin approximately $12 million to build a facility to provide the required test capability. Here at LaRC, only minimum modifications to the facility were required to perform this specific test.

Roman, Ivan↗
DOE OSTI2025

WHA Sphere Impacting RHA Plate: Modeling Brittle Behavior in Metal Alloys

Brittle behavior of metal alloys is often critical to modeling ballistic impact and penetration. The ALEGRA multiphysics finite element software incorporates calibrated models for the equation of state, elasticity, yield stress, plasticity and fracture, but simulations do not always capture expected metal fracture. Here we report concerted efforts to do so for one important case where experiments clearly show shear fractures: a tungsten sphere impacting a steel plate at various angles. Our best simulations show fractures that are qualitatively similar to experiments, but there are significant differences in quantitative metrics. Specifically, velocities of tracers used to quantify simulated plug parameters consistently fall short of measured plug velocities. Also, simulated plugs break apart more than expected from experimental evidence. We attribute these shortfalls to the lack of an explicit shear fracture mechanism in the material models, leading to over-estimated resistance to plug formation and movement.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Discrete time modelization of human pilot behavior

This modelization starts from the following hypotheses: pilot's behavior is a time discrete process, he can perform only one task at a time and his operating mode depends on the considered flight subphase. Pilot's behavior was observed using an electro oculometer and a simulator cockpit. A FORTRAN program has been elaborated using two strategies. The first one is a Markovian process in which the successive instrument readings are governed by a matrix of conditional probabilities. In the second one, strategy is an heuristic process and the concepts of mental load and performance are described. The results of the two aspects have been compared with simulation data.

Cavalli, D.↗
DOE OSTI2025

Remote Influence of Andean Convection on Amazonian Rainfall and Its Mechanisms

Models from Coupled Model Intercomparison Project Phase 6 produce too much precipitation over the Andes but too little over the Amazon or the Wet Andes-Dry Amazon (WADA) bias pattern. Unlike the conventional view that convection parameterization and land model deficiencies can contribute to Amazonian rainfall biases, we approach this long-standing biased model behavior through the lens of Andean convection. Using Community Earth System Model v1.1 and focusing on the wet season, our mechanism-denial experiments demonstrate that Andean convection notably reduces precipitation over the Amazon during austral summer. The Andean forced Amazonian response operates on weather timescale. Furthermore, the reduction of Amazonian rainfall is detectable within a few hours after initial Andean forcing. The precipitation response is primarily driven by variations in the moisture budget and is moderated by changes in convective available potential energy over the Amazon. Changes in the total advection of moisture over the Amazon are dominated by the vertical advection term and can be attributed to discrepancies in the dynamic omega field. In the experiments, the Andean east flank region is scrutinized where the vertical velocity and moisture fields play an intermediary role for the Andean driven WADA connection. The Andean forcing induces descending anomalies on the Andean east flank. The disturbances of wind and geopotential fields over the Andean east flank propagate eastward via Kelvin waves. Over the Amazon, descending anomalies and advective drying lead to reduction of mid-to-high level cloud, increase of shortwave cloud forcing and surface net radiation, and enhancement of themodynamic stability and rainfall reduction.

58 GEOSCIENCES↗
NASA NTRS2018

Linking the Pilot Structural Model and Pilot Workload

Behavioral models are developed that closely reproduced pulsive control response of two pilots using markedly different control techniques while conducting a tracking task. An intriguing find was that the pilots appeared to: 1) produce a continuous, internally-generated stick signal that they integrated in time; 2) integrate the actual stick position; and 3) compare the two integrations to either issue or cease a pulse command. This suggests that the pilots utilized kinesthetic feedback in order to sense and integrate stick position, supporting the hypothesis that pilots can access and employ the proprioceptive inner feedback loop proposed by Hess's pilot Structural Model. A Pilot Cost Index was developed, whose elements include estimated workload, performance, and the degree to which the pilot employs kinesthetic feedback. Preliminary results suggest that a pilot's operating point (parameter values) may be based on control style and index minimization.

Pilot workload↗
NASA NTRS1973

Modeling the behavior of the helmsman steering a ship

A supertanker is considered as a nonlinear system which responds very slowly to changes in the rudder position. Moreover this type of ship is often unstable in loaded condition. In order to model the helmsman's behavior, a number of tests were performed using a ship maneuvering simulator. The trained subjects had to steer a 200,000 tons tanker along a varying course. The results obtained from these trials are presented.

Veldhuyzen, W.↗
DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

The Advanced Modeling, Simulation and Analysis Capability Roadmap Vision for Engineering

This paper summarizes a subset of the Advanced Modeling Simulation and Analysis (AMSA) Capability Roadmap that was developed for NASA in 2005. The AMSA Capability Roadmap Team was chartered to "To identify what is needed to enhance NASA's capabilities to produce leading-edge exploration and science missions by improving engineering system development, operations, and science understanding through broad application of advanced modeling, simulation and analysis techniques." The AMSA roadmap stressed the need for integration, not just within the science, engineering and operations domains themselves, but also across these domains. Here we discuss the roadmap element pertaining to integration within the engineering domain, with a particular focus on implications for future observatory missions. The AMSA products supporting the system engineering function are mission information, bounds on information quality, and system validation guidance. The Engineering roadmap element contains 5 sub-elements: (1) Large-Scale Systems Models, (2) Anomalous Behavior Models, (3) advanced Uncertainty Models, (4) Virtual Testing Models, and (5) space-based Robotics Manufacture and Servicing Models.

Zang, Thomas↗
NASA NTRS2020

The GGCMI Phase 2 Experiment: Global Gridded Crop Model Simulations Under Uniform Changes in CO2, Temperature, Water, and Nitrogen Levels (Protocol Version 1.0)

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

GGCMI↗