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All sources NASA NTRS DOE OSTI

DOE OSTI2025

GPR_calculator: An on-the-fly surrogate model to accelerate massive nudged elastic band calculations

We present GPR_calculator, a package based on Python and C++ programming languages to build an on-the-fly surrogate model using Gaussian Process Regression (GPR) to approximate computationally expensive electronic structure calculations. The key idea is to dynamically train a GPR model during the simulation that can accurately predict energies and forces with uncertainty quantification. When the uncertainty is high, the costly electronic structure calculation is performed to obtain the ground truth data, which is then used to update the GPR model. To illustrate the effectiveness of GPR_calculator, we demonstrate its application in Nudged Elastic Band (NEB) simulations of surface diffusion and reactions, achieving 3-10 times acceleration compared to pure ab initio calculations. The source code is available at https://github.com/MaterSim/GPR_calculator.

Gaussian process regression↗

NASA NTRS1993

Programs Help Create And Evaluate Markov Models

Pade Approximation With Scaling (PAWS) and Scaled Taylor Exponential Matrix (STEM) computer programs provide flexible, user-friendly, language-based interface for creation and evaluation of Markov models describing behaviors of fault-tolerant reconfigurable computer systems. Produce exact solution for probabilities of system failures and provide conservative estimates of numbers of significant digits in solutions. Also offer as part of bundled package with SURE and ASSIST, two other reliable analysis programs developed by Systems Validation Methods group at Langley Research Center.

Butler, Ricky W.↗

NASA NTRS1972

A Management Information System Model for Program Management

The development of a model to simulate the information system of a program management type of organization is reported. The model statistically determines the following parameters: type of messages, destinations, delivery durations, type processing, processing durations, communication channels, outgoing messages, and priorites. The total management information system of the program management organization is considered, including formal and informal information flows and both facilities and equipment. The model is written in General Purpose System Simulation 2 computer programming language for use on the Univac 1108, Executive 8 computer. The model is simulated on a daily basis and collects queue and resource utilization statistics for each decision point. The statistics are then used by management to evaluate proposed resource allocations, to evaluate proposed changes to the system, and to identify potential problem areas. The model employs both empirical and theoretical distributions which are adjusted to simulate the information flow being studied.

Shipman, D. L.↗

NASA NTRS1990

Construction of dynamic stochastic simulation models using knowledge-based techniques

Over the past three decades, computer-based simulation models have proven themselves to be cost-effective alternatives to the more structured deterministic methods of systems analysis. During this time, many techniques, tools and languages for constructing computer-based simulation models have been developed. More recently, advances in knowledge-based system technology have led many researchers to note the similarities between knowledge-based programming and simulation technologies and to investigate the potential application of knowledge-based programming techniques to simulation modeling. The integration of conventional simulation techniques with knowledge-based programming techniques is discussed to provide a development environment for constructing knowledge-based simulation models. A comparison of the techniques used in the construction of dynamic stochastic simulation models and those used in the construction of knowledge-based systems provides the requirements for the environment. This leads to the design and implementation of a knowledge-based simulation development environment. These techniques were used in the construction of several knowledge-based simulation models including the Advanced Launch System Model (ALSYM).

Williams, M. Douglas↗

NASA NTRS2012

Getting a Cohesive Answer from a Common Start: Scalable Multidisciplinary Analysis through Transformation of a System Model

One of the challenges of systems engineering is in working multidisciplinary problems in a cohesive manner. When planning analysis of these problems, system engineers must tradeoff time and cost for analysis quality and quantity. The quality is associated with the fidelity of the multidisciplinary models and the quantity is associated with the design space that can be analyzed. The tradeoff is due to the resource intensive process of creating a cohesive multidisciplinary system model and analysis. Furthermore, reuse or extension of the models used in one stage of a product life cycle for another is a major challenge. Recent developments have enabled a much less resource-intensive and more rigorous approach than handwritten translation scripts or codes of multidisciplinary models and their analyses. The key is to work from a core system model defined in a MOF-based language such as SysML and in leveraging the emerging tool ecosystem, such as Query-View- Transform (QVT), from the OMG community. SysML was designed to model multidisciplinary systems and analyses. The QVT standard was designed to transform SysML models. The Europa Hability Mission (EHM) team has begun to exploit these capabilities. In one case, a Matlab/Simulink model is generated on the fly from a system description for power analysis written in SysML. In a more general case, a symbolic mathematical framework (supported by Wolfram Mathematica) is coordinated by data objects transformed from the system model, enabling extremely flexible and powerful tradespace exploration and analytical investigations of expected system performance.

Cole, Bjorn↗

NASA NTRS1996

A convertor and user interface to import CAD files into worldtoolkit virtual reality systems

Virtual Reality (VR) is a rapidly developing human-to-computer interface technology. VR can be considered as a three-dimensional computer-generated Virtual World (VW) which can sense particular aspects of a user's behavior, allow the user to manipulate the objects interactively, and render the VW at real-time accordingly. The user is totally immersed in the virtual world and feel the sense of transforming into that VW. NASA/MSFC Computer Application Virtual Environments (CAVE) has been developing the space-related VR applications since 1990. The VR systems in CAVE lab are based on VPL RB2 system which consists of a VPL RB2 control tower, an LX eyephone, an Isotrak polhemus sensor, two Fastrak polhemus sensors, a folk of Bird sensor, and two VPL DG2 DataGloves. A dynamics animator called Body Electric from VPL is used as the control system to interface with all the input/output devices and to provide the network communications as well as VR programming environment. The RB2 Swivel 3D is used as the modelling program to construct the VW's. A severe limitation of the VPL VR system is the use of RB2 Swivel 3D, which restricts the files to a maximum of 1020 objects and doesn't have the advanced graphics texture mapping. The other limitation is that the VPL VR system is a turn-key system which does not provide the flexibility for user to add new sensors and C language interface. Recently, NASA/MSFC CAVE lab provides VR systems built on Sense8 WorldToolKit (WTK) which is a C library for creating VR development environments. WTK provides device drivers for most of the sensors and eyephones available on the VR market. WTK accepts several CAD file formats, such as Sense8 Neutral File Format, AutoCAD DXF and 3D Studio file format, Wave Front OBJ file format, VideoScape GEO file format, Intergraph EMS stereolithographics and CATIA Stereolithographics STL file formats. WTK functions are object-oriented in their naming convention, are grouped into classes, and provide easy C language interface. Using a CAD or modelling program to build a VW for WTK VR applications, we typically construct the stationary universe with all the geometric objects except the dynamic objects, and create each dynamic object in an individual file.

Wang, Peter Hor-Ching↗

NASA NTRS2018

Conceptual Design Shop: A Tool for Rapid Airframe Structural Modeling

This paper presents an innovative approach to rapidly generate finite element (FE) models of a complete airframe for a variety of airframe concepts. The current implementation of this software includes all airfoil surfaces and the fuselage, and is limited to FE modeling of low-wing designs with T-tails or twin tails. This tool, called the Conceptual Design Shop (CDS), was developed using the PATRAN command language (PCL) within the PATRAN finite element modeling software. CDS is an attempt to fill a gap in current finite element modeling software to automatically connect wings and tails to the fuselage in airframe models. The CDS software is demonstrated on two airframe designs: a generic transport aircraft and an advanced aircraft design with a boundary-layer ingestion engine.

Mason, Brian H.↗

NASA NTRS2017

Scenario Development Process at the Vertical Motion Simulator

There has been a significant effort within the simulation community to standardize many aspects of flight simulation. More recently, an effort has begun to develop a formal scenario definition language for aviation. A working group within the AIAA Modeling and Simulation Technical Committee has been created to develop a standard aviation scenario definition language, though much of the initial effort has been tailored to training simulators. Research and development (R&D) simulators, like the Vertical Motion Simulator (VMS), and training simulators have different missions and thus have different scenario requirements. The purpose of this paper is to highlight some of the unique tasks and scenario elements used at the VMS so they may be captured by scenario standardization efforts. The VMS most often performs handling qualities studies and transfer of training studies. Three representative handling qualities simulation studies and two transfer of training simulation studies are described in this paper. Unique scenario elements discussed in this paper included special out-the-window (OTW) targets and environmental conditions, motion system parameters, active inceptor parameters, and configurable vehicle math model parameters.

Vertical Motion Simulator↗

DOE OSTI2022

PyPVRPM: Photovoltaic Reliability and Performance Model in Python

The ability to perform accurate techno-economic analysis of solar photovoltaic (PV) systems is essential for bankability and investment purposes. Most energy yield models assume an almost flawless operation (i.e., no failures); however, realistically, components fail and get repaired stochastically. This package, PyPVRPM, is a Python translation and improvement of the Language Kit (LK) based PhotoVoltaic Reliability Performance Model (PVRPM), which was first developed at Sandia National Laboratories in Goldsim software (Granata et al., 2011) (Miller et al., 2012). PyPVRPM allows the user to define a PV system at a specific location and incorporate failure, repair, and detection rates and distributions to calculate energy yield and other financial metrics such as the levelized cost of energy and net present value (Klise, Lavrova, et al., 2017). Our package is a simulation tool that uses NREL’s Python interface for System Advisor Model (SAM) (National Renewable Energy Laboratory, 2020b) (National Renewable Energy Laboratory, 2020a) to evaluate the performance of a PV plant throughout its lifetime by considering component reliability metrics. Besides the numerous benefits from migrating to Python (e.g., speed, libraries, batch analyses), it also expands on the failure and repair processes from the LK version by including the ability to vary monitoring strategies. These failures, repairs, and monitoring processes are based on user-defined distributions and values, enabling a more accurate and realistic representation of cost and availability throughout a PV system’s lifetime.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2022

Kamodo’s Model-Agnostic Satellite Flythrough: Lowering the Utilization Barrier for Heliophysics Model Outputs

Heliophysics model outputs are increasingly accessible, but typically are not usable by the majority of the community unless directly collaborating with the relevant model developers. Prohibitive factors include complex file output formats, cryptic metadata, unspecified and often customized coordinate systems, and non-linear coordinate grids. Some pockets of progress exist, giving interfaces to various simulation outputs, but only for a small set of outputs and typically not with open-source, freely available packages. Additionally, the increasing array of tools built upon these sporadic interfaces are typically model-specific. We present Kamodo’s model-agnostic satellite flythrough capabilities as the solution to the utilization barrier for heliophysics model outputs. Developed at the Community Coordinated Modeling Center, these flythrough capabilities are built in Python upon a network of model-agnostic interfaces developed in collaboration with model developers, providing interpolation results the community can trust. Kamodo’s flythrough capabilities present the user with a growing variety of flythrough tools based upon a rapidly expanding library of heliophysics model outputs in several domains, currently including a variety of Ionosphere-Thermosphere-Mesosphere and global magnetosphere model outputs. Each capability is designed to be easily accessible via simplistic model-agnostic syntax, with the entire package freely available in the cloud on Github. Here, we describe the tools developed, include several sample applications for common science questions, demonstrate interoperability with selected packages, and summarize ongoing developments.

Software↗

NASA NTRS1991

Plan recognition and generalization in command languages with application to telerobotics

A method for pragmatic inference as a necessary accompaniment to command languages is proposed. The approach taken focuses on the modeling and recognition of the human operator's intent, which relates sequences of domain actions ('plans') to changes in some model of the task environment. The salient feature of this module is that it captures some of the physical and linguistic contextual aspects of an instruction. This provides a basis for generalization and reinterpretation of the instruction in different task environments. The theoretical development is founded on previous work in computational linguistics and some recent models in the theory of action and intention. To illustrate these ideas, an experimental command language to a telerobot is implemented. The program consists of three different components: a robot graphic simulation, the command language itself, and the domain-independent pragmatic inference module. Examples of task instruction processes are provided to demonstrate the benefits of this approach.

Yared, Wael I.↗

NASA NTRS1992

High level cognitive information processing in neural networks

Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

Barnden, John A.↗

NASA NTRS1990

AiGERM: A logic programming front end for GERM

AiGerm (Artificially Intelligent Graphical Entity Relation Modeler) is a relational data base query and programming language front end for MCC (Mission Control Center)/STP's (Space Test Program) Germ (Graphical Entity Relational Modeling) system. It is intended as an add-on component of the Germ system to be used for navigating very large networks of information. It can also function as an expert system shell for prototyping knowledge-based systems. AiGerm provides an interface between the programming language and Germ.

Hashim, Safaa H.↗

DOE OSTI2022

Real-Time Drilling Optimization System for Improved Overall Rate of Penetration and Reduced Cost Per Foot in Geothermal Drilling

The key to success in geothermal drilling is economic feasibility, and a major cost in the development of geothermal resources is the actual drilling of the wells. In this project, a real-time drilling optimization system for geothermal drilling was developed. The system couples three individual components while drilling. The first component is a drill stem vibration analysis model, the second is Mechanical Specific Energy (MSE) analyses, and the third is a detailed PDC Rate of Penetration (ROP) drill bit model for optimum RPM and WOB combinations. The benefit of the coupled system is that the range of WOB and RPM could be selected to avoid drill stem vibrations. Secondly, MSE is used as an efficiency measure and the detailed PDC drill bit model ensures the drill bit does not endure temperatures that exceed the temperature at which the PDC cutters experience accelerated wear. The new detailed PDC bit model is based on rock/bit interaction that physically tracks the PDC cutter wear flats as the bit drills ahead giving the capability to calculate the temperature being generated underneath the worn cutters to better advise on operational parameters to avoid accelerated cutter wear and failure and to ensure that operational parameters are applied so that overall ROP is maximized. By combining the drill stem vibrations and the detailed PDC bit cutter wear and “safe” non-accelerated cutter wear temperature and optimum ranges of operating parameters, it results in higher ROP and lower cost drilling. Single cutter PDC testing performed in different lithologies at Sandia was utilized to verify the PDC cutter forces and depth of cut for new and worn cutters. Based on single cutter PDC temperature modeling, verification using single cutter data from the testing done by National Oilwell Varco (NOV) was performed. Sandia’s Hard-Rock Drilling Facility (HRDF) was utilized to test different drill bit configurations with different cutter designs and wear status with different induced modes of vibration to obtain the critical bit RPM/WOB ranges resulting in ineffective drilling and low ROP. The collected test data were further used to verify and calibrate the full hole PDC ROP model that was developed based on single cutter interaction data. A full coupled drill stem vibration model was formulated and verified with geothermal field data from the Chocolate Mountain Aerial Gunnery Range (CMAGR). A graphical user interface (GUI) was developed using Tkinter library in the computer programming language Python, which integrates all the developed models in one system. The developed system consists mainly of the PDC ROP model, PDC bit wear model, PDC cutter temperature model, Mechanical Specific Energy (MSE) model, and drillstring vibration model integrated into one system. The developed system can be used for both, post well analysis and real-time optimization using different criteria such as ROP maximization or MSE minimization. The software uses Differential Evolution Algorithm (DEA) to find optimum values for operational parameters based on last foot drilled while avoiding the drillstring vibration and cutter temperature critical operating parameters.

15 GEOTHERMAL ENERGY↗

NASA NTRS2007

The DaveMLTranslator: An Interface for DAVE-ML Aerodynamic Models

It can take weeks or months to incorporate a new aerodynamic model into a vehicle simulation and validate the performance of the model. The Dynamic Aerospace Vehicle Exchange Markup Language (DAVE-ML) has been proposed as a means to reduce the time required to accomplish this task by defining a standard format for typical components of a flight dynamic model. The purpose of this paper is to describe an object-oriented C++ implementation of a class that interfaces a vehicle subsystem model specified in DAVE-ML and a vehicle simulation. Using the DaveMLTranslator class, aerodynamic or other subsystem models can be automatically imported and verified at run-time, significantly reducing the elapsed time between receipt of a DAVE-ML model and its integration into a simulation environment. The translator performs variable initializations, data table lookups, and mathematical calculations for the aerodynamic build-up, and executes any embedded static check-cases for verification. The implementation is efficient, enabling real-time execution. Simple interface code for the model inputs and outputs is the only requirement to integrate the DaveMLTranslator as a vehicle aerodynamic model. The translator makes use of existing table-lookup utilities from the Langley Standard Real-Time Simulation in C++ (LaSRS++). The design and operation of the translator class is described and comparisons with existing, conventional, C++ aerodynamic models of the same vehicle are given.

Hill, Melissa A.↗

NASA NTRSDate not supplied

Graph Convolutional Network-Strengthened Topic Modeling for Scientific Papers

Machine learning has been woven into statistics to modernize topic modeling over textual documents written in natural language, and scientific paper search and recommendation can consequently offer higher accuracy instead of counting on traditional keyword-based search. However, topic distribution of a paper resulted from existing topic modeling techniques only relies on the statistics of words contained in the paper itself. We argue that community users’ views of a paper may also provide insights at the time of recommendation. For example, if a paper on fake image detection has been cited heavily by machine learning papers, such a feature should be absorbed in the embedding of this paper, so that it can be recommended for future query on machine learning. In this paper, we present a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) method, which employs GCN technique to refine topic modeling of scientific papers. A citation-oriented knowledge graph is constructed, and topic modeling is mapped to feature embedding of the comprising papers. On top of its own topics carried in its content, each paper learns topics from its neighbors and revise its embedding accordingly. Our empirical studies over real-life scientific literature has proved the necessity and effectiveness of our proposed approach.

Jia Zhang↗

DOE OSTI2024

A Compound Data Poisoning Technique with Significant Adversarial Effects on Transformer-based Sentiment Classification Tasks

Transformer-based models have demonstrated much success in various natural language processing tasks. However, they are often vulnerable to adversarial attacks, such as data poisoning, which can intentionally fool the model into generating incorrect results. In this article, we present a novel, compound variant of a data poisoning attack on a transformer-based model that maximizes the poisoning effect while minimizing the scope of poisoning. Here we do so by combining the established data poisoning technique (label flipping) with a novel adversarial artifact selection and insertion technique aimed at minimizing detectability and the scope of the poisoning footprint. We find that by using a combination of these two techniques, we achieve a state-of-the-art attack success rate of approximately 90% while poisoning only 0.5% of the original training set, thus minimizing the scope and detectability of the poisoning action. These findings have the potential to advance the development of better data poisoning detection methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

59 BASIC BIOLOGICAL SCIENCES↗