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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A graphical language for reliability model generation

A graphical interface capability of the hybrid automated reliability predictor (HARP) is described. The graphics-oriented (GO) module provides the user with a graphical language for modeling system failure modes through the selection of various fault tree gates, including sequence dependency gates, or by a Markov chain. With this graphical input language, a fault tree becomes a convenient notation for describing a system. In accounting for any sequence dependencies, HARP converts the fault-tree notation to a complex stochastic process that is reduced to a Markov chain which it can then solve for system reliability. The graphics capability is available for use on an IBM-compatible PC, a Sun, and a VAX workstation. The GO module is written in the C programming language and uses the Graphical Kernel System (GKS) standard for graphics implementation. The PC, VAX, and Sun versions of the HARP GO module are currently in beta-testing.

Howell, Sandra V.↗
NASA NTRS1993

Model-based reasoning for system and software engineering: The Knowledge From Pictures (KFP) environment

This paper presents a discussion of current work in the area of graphical modeling and model-based reasoning being undertaken by the Automation Technology Section, Code 522.3, at Goddard. The work was initially motivated by the growing realization that the knowledge acquisition process was a major bottleneck in the generation of fault detection, isolation, and repair (FDIR) systems for application in automated Mission Operations. As with most research activities this work started out with a simple objective: to develop a proof-of-concept system demonstrating that a draft rule-base for a FDIR system could be automatically realized by reasoning from a graphical representation of the system to be monitored. This work was called Knowledge From Pictures (KFP) (Truszkowski et. al. 1992). As the work has successfully progressed the KFP tool has become an environment populated by a set of tools that support a more comprehensive approach to model-based reasoning. This paper continues by giving an overview of the graphical modeling objectives of the work, describing the three tools that now populate the KFP environment, briefly presenting a discussion of related work in the field, and by indicating future directions for the KFP environment.

Bailin, Sydney↗
NASA NTRS1987

CONFIG: Qualitative simulation tool for analyzing behavior of engineering devices

To design failure management expert systems, engineers mentally analyze the effects of failures and procedures as they propagate through device configurations. CONFIG is a generic device modeling tool for use in discrete event simulation, to support such analyses. CONFIG permits graphical modeling of device configurations and qualitative specification of local operating modes of device components. Computation requirements are reduced by focussing the level of component description on operating modes and failure modes, and specifying qualitative ranges of variables relative to mode transition boundaries. Simulation processing occurs only when modes change or variables cross qualitative boundaries. Device models are built graphically, using components from libraries. Components are connected at ports by graphical relations that define data flow. The core of a component model is its state transition diagram, which specifies modes of operation and transitions among them.

Malin, Jane T.↗
NASA NTRS2023

Unifying Radiative Transfer Models in Computer Graphics and Remote Sensing, Part II: A Differentiable, Polarimetric Forward Model and Validation

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 2 of this work, we demonstrate that Monte Carlo forward models in computer graphics are capable of sufficient accuracy for remote sensing by extending Mitsuba 3, a forward and inverse modeling framework recently developed in the computer graphics community, to simulate simple atmosphere-ocean systems and show that our framework is capable of achieving error on par with codes currently used by the remote sensing community on benchmark results.

Radiative transfer↗
NASA NTRS1992

View generated database

This document represents the final report for the View Generated Database (VGD) project, NAS7-1066. It documents the work done on the project up to the point at which all project work was terminated due to lack of project funds. The VGD was to provide the capability to accurately represent any real-world object or scene as a computer model. Such models include both an accurate spatial/geometric representation of surfaces of the object or scene, as well as any surface detail present on the object. Applications of such models are numerous, including acquisition and maintenance of work models for tele-autonomous systems, generation of accurate 3-D geometric/photometric models for various 3-D vision systems, and graphical models for realistic rendering of 3-D scenes via computer graphics.

Downward, James G.↗
DOE OSTI2020

Detect and correct bias in multi-site neuroimaging datasets

The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The most obvious way to increase sample size is by pooling scans from independent studies. However, simple pooling is often ill-advised as selection, measurement, and confounding biases may creep in and yield spurious correlations. In this work, we combine 35,320 magnetic resonance images of the brain from 17 studies to examine bias in neuroimaging. In the first experiment, Name That Dataset, we provide empirical evidence for the presence of bias by showing that scans can be correctly assigned to their respective dataset with 71.5% accuracy. Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies. In practice, we neither know all potential confounders nor do we have data on them. Hence, we model confounders as unknown, latent variables. Kolmogorov complexity is then used to decide whether the confounded or the causal model provides the simplest factorization of the graphical model. Finally, we present methods for dataset harmonization and study their ability to remove bias in imaging features. In particular, we propose an extension of the recently introduced ComBat algorithm to control for global variation across image features, inspired by adjusting for unknown population stratification in genetics. Overall, our results demonstrate that harmonization can reduce dataset-specific information in image features. Further, confounding bias can be reduced and even turned into a causal relationship. However, harmonization also requires caution as it can easily remove relevant subject-specific information. Code is available at https://github.com/ai-med/Dataset-Bias.

42 ENGINEERING↗
DOE OSTI2020

On the Solution of ℓ 0 -Constrained Sparse Inverse Covariance Estimation Problems

The sparse inverse covariance matrix is used to model conditional dependencies between variables in a graphical model to fit a multivariate Gaussian distribution. Estimating the matrix from data are well known to be computationally expensive for large-scale problems. Sparsity is employed to handle noise in the data and to promote interpretability of a learning model. Although the use of a convex ℓ 1 regularizer to encourage sparsity is common practice, the combinatorial ℓ 0 penalty often has more favorable statistical properties. In this paper, we directly constrain sparsity by specifying a maximally allowable number of nonzeros, in other words, by imposing an ℓ 0 constraint. Here, we introduce an efficient approximate Newton algorithm using warm starts for solving the nonconvex ℓ 0 -constrained inverse covariance learning problem. Numerical experiments on standard data sets show that the performance of the proposed algorithm is competitive with state-of-the-art methods.

$\ell_0$-Constrained↗
NASA NTRS1987

Design modeling for shape optimization

Some important aspects of design modeling for shape optimization are discussed for both stamped sheet metal components and cast solid components. For stamped components the basis for the modeling approach is a boundary design function. Design parameters control the shape of 2-D regions. For more complex, folded plate components, the 2-D regions can be assembled using translation and rotation operations. The analysis model is automatically created using a mesh generation procedure requiring only boundary data. For less complex solid components, it was found that this approach is not suitable. For these structures, the finite element models are typically created using very sophisticated graphical modeling systems. A new approach which overlays a parameterized surface design model on an existing analysis model is described. To summarize, the future needs for solid shape design is described in terms of an extension of the previously described 2-D capability.

Botkin, M. E.↗
DOE OSTI2022

Scalable probabilistic estimates of electric vehicle charging given observed driver behavior

To prepare for rapid growth in global electric vehicle adoption, grid and policy planners depend on detailed forecasts of future charging demand. In this paper we propose a novel holistic, scalable, probabilistic framework to produce large-scale estimates of electric vehicle charging load for long-term planning that capture real drivers’ charging patterns. Our framework captures the uncertainty and stochasticity in charging demand by taking a graphical modeling approach. It has three core elements: driver groups, charging segment choices, and charging session time and energy requirements. The framework uses hierarchical clustering to group drivers by their charging histories, capturing their heterogeneous behaviors and preferences across different segments or types of charging. The framework uses probabilistic mixture models for each driver group’s sessions to identify the unique charging behaviors observed within each segment. We illustrate its application with a large data set from California, profiling the charging patterns and unique driver clusters it identifies. Using the model knobs representing drivers’ battery capacities, behavior, and segment access we present scenarios for California’s charging demand in 2030 with 8 million passenger electric vehicles. Peak charging demand ranged from 3.3 to 8.7 GW across scenarios. Furthermore, each was calculated in under 45 s on a laptop computer.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Risk-Informed Condition Evaluation of Solar-centered Energy Generation and Distribution Networks through Bayesian Learning and Inference

We develop a methodology based on Bayesian inference over Probabilistic Graphical Models (PGMs) to understand and quantify risk in solar-centered grids using targeted measurements and learned system behavior. Being non-prescriptive but, rather, able to infer system behavior and, ultimately, address risk queries from data, our machine learning-type paradigm is tailored for diverse topologies and threat scenarios often associated with distributed energy generation and photovoltaic distributed energy resources (PV-DERs) in particular. We describe algorithmic processes for: (i) learning the structure of PGMs that result from attack-prone PV-DER-proliferated distribution systems, (ii) quantifying cause-effect relationships, and (iii) evaluating risk queries based on diverse evidence. The contributions are illustrated on a residential grid subject to output impairment attacks on its PV-DER infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1994

The use of interactive computer vision and robot hand controllers for enhancing manufacturing safety

Current available robotic systems provide limited support for CAD-based model-driven visualization, sensing algorithm development and integration, and automated graphical planning systems. This paper describes ongoing work which provides the functionality necessary to apply advanced robotics to automated manufacturing and assembly operations. An interface has been built which incorporates 6-DOF tactile manipulation, displays for three dimensional graphical models, and automated tracking functions which depend on automated machine vision. A set of tools for single and multiple focal plane sensor image processing and understanding has been demonstrated which utilizes object recognition models. The resulting tool will enable sensing and planning from computationally simple graphical objects. A synergistic interplay between human and operator vision is created from programmable feedback received from the controller. This approach can be used as the basis for implementing enhanced safety in automated robotics manufacturing, assembly, repair and inspection tasks in both ground and space applications. Thus, an interactive capability has been developed to match the modeled environment to the real task environment for safe and predictable task execution.

Marzwell, Neville I.↗
NASA NTRS2000

Challenges in Integrating Nondestructive Evaluation and Finite Element Methods for Realistic Structural Analysis

Capabilities and expertise related to the development of links between nondestructive evaluation (NDE) and finite element analysis (FEA) at Glenn Research Center (GRC) are demonstrated. Current tools to analyze data produced by computed tomography (CT) scans are exercised to help assess the damage state in high temperature structural composite materials. A utility translator was written to convert velocity (an image processing software) STL data file to a suitable CAD-FEA type file. Finite element analyses are carried out with MARC, a commercial nonlinear finite element code, and the analytical results are discussed. Modeling was established by building MSC/Patran (a pre and post processing finite element package) generated model and comparing it to a model generated by Velocity in conjunction with MSC/Patran Graphics. Modeling issues and results are discussed in this paper. The entire process that outlines the tie between the data extracted via NDE and the finite element modeling and analysis is fully described.

Abdul-Aziz, Ali↗
NASA NTRS1993

Design strategies and functionality of the Visual Interface for Virtual Interaction Development (VIVID) tool

Development of interactive virtual environments (VE) has typically consisted of three primary activities: model (object) development, model relationship tree development, and environment behavior definition and coding. The model and relationship tree development activities are accomplished with a variety of well-established graphic library (GL) based programs - most utilizing graphical user interfaces (GUI) with point-and-click interactions. Because of this GUI format, little programming expertise on the part of the developer is necessary to create the 3D graphical models or to establish interrelationships between the models. However, the third VE development activity, environment behavior definition and coding, has generally required the greatest amount of time and programmer expertise. Behaviors, characteristics, and interactions between objects and the user within a VE must be defined via command line C coding prior to rendering the environment scenes. In an effort to simplify this environment behavior definition phase for non-programmers, and to provide easy access to model and tree tools, a graphical interface and development tool has been created. The principal thrust of this research is to effect rapid development and prototyping of virtual environments. This presentation will discuss the 'Visual Interface for Virtual Interaction Development' (VIVID) tool; an X-Windows based system employing drop-down menus for user selection of program access, models, and trees, behavior editing, and code generation. Examples of these selection will be highlighted in this presentation, as will the currently available program interfaces. The functionality of this tool allows non-programming users access to all facets of VE development while providing experienced programmers with a collection of pre-coded behaviors. In conjunction with its existing, interfaces and predefined suite of behaviors, future development plans for VIVID will be described. These include incorporation of dual user virtual environment enhancements, tool expansion, and additional behaviors.

Nguyen, Lac↗
DOE OSTI2016

A tracking system to calculate patient skin dose in real-time during neurointerventional procedures using a biplane x-ray imaging system

Purpose: Neurovascular interventional procedures using biplane fluoroscopic imaging systems can lead to increased risk of radiation-induced skin injuries. The authors developed a biplane dose tracking system (Biplane-DTS) to calculate the cumulative skin dose distribution from the frontal and lateral x-ray tubes and display it in real-time as a color-coded map on a 3D graphic of the patient for immediate feedback to the physician. The agreement of the calculated values with the dose measured on phantoms was evaluated. Methods: The Biplane-DTS consists of multiple components including 3D graphic models of the imaging system and patient, an interactive graphical user interface, a data acquisition module to collect geometry and exposure parameters, the computer graphics processing unit, and functions for determining which parts of the patient graphic skin surface are within the beam and for calculating dose. The dose is calculated to individual points on the patient graphic using premeasured calibration files of entrance skin dose per mAs including backscatter; corrections are applied for field area, distance from the focal spot and patient table and pad attenuation when appropriate. The agreement of the calculated patient skin dose and its spatial distribution with measured values was evaluated in 2D and 3D for simulated procedure conditions using a PMMA block phantom and an SK-150 head phantom, respectively. Dose values calculated by the Biplane-DTS were compared to the measurements made on the phantom surface with radiochromic film and a calibrated ionization chamber, which was also used to calibrate the DTS. The agreement with measurements was specifically evaluated with variation in kVp, gantry angle, and field size. Results: The dose tracking system that was developed is able to acquire data from the two x-ray gantries on a biplane imaging system and calculate the skin dose for each exposure pulse to those vertices of a patient graphic that are determined to be in the beam. The calculations are done in real-time with a typical graphic update time of 30 ms and an average vertex separation of 3 mm. With appropriate corrections applied, the Biplane-DTS was able to determine the entrance dose within 6% and the spatial distribution of the dose within 4% compared to the measurements with the ionization chamber and film for the SK150 head phantom. The cumulative dose for overlapping fields from both gantries showed similar agreement. Conclusions: The Biplane-DTS can provide a good estimate of the peak skin dose and cumulative skin dose distribution during biplane neurointerventional procedures. Real-time display of this information should help the physician manage patient dose to reduce the risk of radiation-induced skin injuries.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1997

Meta-Modeling: A Knowledge-Based Approach to Facilitating Model Construction and Reuse

In this paper, we introduce a new modeling approach called meta-modeling and illustrate its practical applicability to the construction of physically-based ecosystem process models. As a critical adjunct to modeling codes meta-modeling requires explicit specification of certain background information related to the construction and conceptual underpinnings of a model. This information formalizes the heretofore tacit relationship between the mathematical modeling code and the underlying real-world phenomena being investigated, and gives insight into the process by which the model was constructed. We show how the explicit availability of such information can make models more understandable and reusable and less subject to misinterpretation. In particular, background information enables potential users to better interpret an implemented ecosystem model without direct assistance from the model author. Additionally, we show how the discipline involved in specifying background information leads to improved management of model complexity and fewer implementation errors. We illustrate the meta-modeling approach in the context of the Scientists' Intelligent Graphical Modeling Assistant (SIGMA) a new model construction environment. As the user constructs a model using SIGMA the system adds appropriate background information that ties the executable model to the underlying physical phenomena under investigation. Not only does this information improve the understandability of the final model it also serves to reduce the overall time and programming expertise necessary to initially build and subsequently modify models. Furthermore, SIGMA's use of background knowledge helps eliminate coding errors resulting from scientific and dimensional inconsistencies that are otherwise difficult to avoid when building complex models. As a. demonstration of SIGMA's utility, the system was used to reimplement and extend a well-known forest ecosystem dynamics model: Forest-BGC.

Keller, Richard M.↗
DOE OSTI2021

Artificial Reasoning System for Symptom-Based Conditional Failure Probability Estimation Using Bayesian Network

Advances in nuclear power technologies require enhanced capabilities for operator advice and autonomous control. One of the first tasks in the development of such capabilities is the formulation of symptom-based conditional failure probabilities for structures, systems, and components (SSCs) of interest, for which the primary goal is to aid plant personnel in deducing the probabilistic performance status of the monitored SSCs and in detecting impending faults/failure. The task of conditional failure probability estimation is a bidirectional inference problem and shall be logically tackled by the Bayesian network (BN) approach. As a knowledge-based artificial intelligence tool and a probabilistic graphical model, BN offers the capability of reasoning under uncertainty and graphical representation emulating the physical behavior of the target SSC. This paper provides a systematic overview of the BN technique and the software tools for handling implementation of BN models, along with the associated knowledge representation and reasoning paradigm. Both operational data and expert judgement can be readily incorporated into the knowledge base of a BN model. The challenges with data availability are highlighted, and the general approach to target SSC identification is presented. Our focus is upon failure-prone and risk-important balance of plant assets, especially cases having strong operator involvement. An exemplary case study on the failure of a motor-driven centrifugal pump is also conducted to demonstrate the usefulness and technical feasibility of the proposed artificial reasoning system using an expert system shell.

Zhao, Xingang↗
NASA NTRS1990

A study on diagnosability of space station ECLSS

The use is demonstrated of the Multigraph Architecture (MGA) for studies on the Environment Control and Life Support System (ECLSS). The objective was the following: (1) to create an updated set of models of the Potable Water Subsystem (PWS) by using the graphical model building tools of the Multigraph Programming Environment (MPE); (2) to derive a real time alarm simulator from the models; and (3) to demonstrate the effects of sensor allocation on the diagnosability of the PWS. This work may serve as a preliminary study for the detailed analysis of the sensor allocation and diagnosability problems in the ECLSS.

Padalkar, S.↗