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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

New approaches to Bayesian uncertainty quantification for Nuclear Science (Final Technical Report)

Inverse problems play a central role in experimentation and theory/data comparisons for many areas of modern Nuclear Physics (NP) and High-Energy Physics (HEP). Bayes’s Theorem is a powerful tool for solving Inverse Problems, providing conceptually transparent and unbiased constraints on theoretical parameters and their uncertainties (“Bayesian Inference”) and enabling the quantification of agreement or tension between models and data. However, analyses based on Bayesian Inference are often challenging for NP and HEP applications, either because of the large number of parameters in the problem, the high computational cost, or both. We propose a multi-institutional collaboration to develop and deploy novel Bayesian analysis tools that advance the scientific scope of a broad range of current and future NP experiments. This project brings together NP domain scientists working on several high-profile NP projects for which new, high-performance Bayesian Uncertainty Quantification (“Bayesian UQ”) methods are essential to carry out the science, and data scientists who are developing state-of-the-art methods applicable to these problems. The NP projects in this proposal comprise measurements of the mass and fundamental nature of the neutrino; study of the Quark-Gluon Plasma that filled the early universe; and mapping of natural and anthropogenic radiation environments. While these NP projects have very different scientific goals, with datasets and analysis approaches that differ significantly, they share common requirements for improving computationally intensive Bayesian analyses using advanced Machine Learning algorithms and will benefit strongly from a coherent effort to develop general solutions. This proposal brings together these projects and forefront ML-based data science algorithms to develop such general solutions. The methods developed in this project will also be more widely applicable, thereby advancing science in the larger Nuclear Physics portfolio.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Bayesian Characterization of Main-sequence Binaries in the Old Open Cluster NGC 188

The binary fractions of open and globular clusters yield powerful constraints on their dynamical state and evolutionary history. We apply publicly available Bayesian analysis tools to a UBV RIJHK {sub S} photometric catalog of the open cluster NGC 188 to detect and characterize photometric binaries along the cluster main sequence. This technique has the advantage of self-consistently handling photometric errors, missing data in various bandpasses, and star-by-star prior constraints on cluster membership. Simulations are used to verify uncertainties and quantify selection biases in our analysis, illustrating that among binaries with mass ratios >0.5, we recover the binary fraction to better than 7% in the mean, with no significant dependence on binary fraction and a mild dependence on assumed mass-ratio distribution. Using our photometric catalog, we recover the majority (65% ± 11%) of spectroscopically identified main-sequence binaries, including eight of the nine with spectroscopically measured mass ratios. Accounting for incompleteness and systematics, we derive a mass-ratio distribution that rises toward lower mass ratios (within our q > 0.5 analysis domain). We observe a raw binary fraction for solar-type main-sequence stars with mass ratios q > 0.5 of 42% ± 4%, independent of the assumed mass-ratio distribution to within its uncertainties, consistent with literature values for old open clusters but significantly higher than the field solar-type binary fraction. We confirm that the binaries identified by our method are more concentrated than single stars, in agreement with previous studies, and we demonstrate that the binary nature of those candidates that remain unidentified spectroscopically is strongly supported by photometry from Gaia DR2.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Software reliability through fault-avoidance and fault-tolerance

Strategies and tools for the testing, risk assessment and risk control of dependable software-based systems were developed. Part of this project consists of studies to enable the transfer of technology to industry, for example the risk management techniques for safety-concious systems. Theoretical investigations of Boolean and Relational Operator (BRO) testing strategy were conducted for condition-based testing. The Basic Graph Generation and Analysis tool (BGG) was extended to fully incorporate several variants of the BRO metric. Single- and multi-phase risk, coverage and time-based models are being developed to provide additional theoretical and empirical basis for estimation of the reliability and availability of large, highly dependable software. A model for software process and risk management was developed. The use of cause-effect graphing for software specification and validation was investigated. Lastly, advanced software fault-tolerance models were studied to provide alternatives and improvements in situations where simple software fault-tolerance strategies break down.

Vouk, Mladen A.↗
DOE OSTI2023

Preliminary investigation of an uncertainty budget for uranium isotope ratio analysis using a liquid sampling—atmospheric pressure glow discharge—orbitrap mass spectrometer system

The liquid sampling-atmospheric pressure glow discharge (LS-APGD) ionization source has proven to be an effective analysis tool for making uranium isotope ratio measurements when coupled to high-resolution mass spectrometers, such as the Orbitrap. While previous studies have shown the capabilities of the LS-APGD for isotope ratio determination, a systematic evaluation of the measurement uncertainty of the technique has not been conducted. To this end, the International Standards Organizations (ISO) guidelines to the expression of uncertainty in measurement (GUM analysis) have been applied to generate an uncertainty budget. For this work, a preliminary assessment derived from the GUM analysis was performed. The uncertainty in the instrument blank determination has been identified as a primary factor contributing to measurement uncertainty for the LS-APGD-Orbitrap method. These findings for the specific test case of uranium isotopic analysis will be invaluable in applications across the breadth of isotope ratio mass spectrometry performed on this unique instrumental platform.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS1987

The NESSUS finite element code

The objective of this development is to provide a new analysis tool which integrates the structural modeling versatility of a modern finite element code with the latest advances in the area of probabilistic modeling and structural reliability. Version 2.0 of the NESSUS finite element code was released last February, and is currently being exercised on a set of problems which are representative of typical Space Shuttle Main Engine (SSME) applications. NESSUS 2.0 allows linear elastostatic and eigenvalue analysis of structures with uncertain geometry, material properties and boundary conditions, which are subjected to a random mechanical and thermal loading environment. The NESSUS finite element code is a key component in a broader software system consisting of five major modules. NESSUS/EXPERT is an expert system under development at Southwest Research Institute, with the objective of centralizing all component-specific knowledge useful for conducting probabilistic analysis of typical Space Shuttle Main Engine (SSME) components. NESSUS/FEM contains the finite element code used for the structural analysis and parameter sensitivity evaluation of these components. The task of parametrizing a finite element mesh in terms of the random variables present is facilitated with the use of the probabilistic data preprocessor in NESSUS/PRE. An external database file is used for managing the bulk of the data generated by NESSUS/FEM.

Dias, J. B.↗
NASA NTRS2010

An Overview of NASA's Oribital Debris Environment Model

Using updated measurement data, analysis tools, and modeling techniques; the NASA Orbital Debris Program Office has created a new Orbital Debris Environment Model. This model extends the coverage of orbital debris flux throughout the Earth orbit environment, and includes information on the mass density of the debris as well as the uncertainties in the model environment. This paper will give an overview of this model and its implications for spacecraft risk analysis.

Matney, Mark↗
NASA NTRS2011

Pattern Recognition for a Flight Dynamics Monte Carlo Simulation

The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.

Restrepo, Carolina↗
DOE OSTI2022

Using Calibrated Water Data for Preliminary Validation of the SRT Code for Advanced Reactors

Recent interest and corresponding progress worldwide regarding advanced nuclear reactors has renewed focus on their performance and related safety assessments. Specifically, the U.S. Nuclear Regulatory Commission has emphasized the importance of mechanistic approaches to source term analysis for advanced reactor licensing applications, which attempt to realistically account radionuclide transport and retention phenomena. Further model development is required due to the numerous and complex physical and chemical phenomena associated with mechanistic source term analyses. Reflecting the need for modeling advancement, Argonne National Laboratory developed a mechanistic source term analysis tool for sodium fast reactors. The Simplified Radionuclide Transport (SRT) code describes fuel pin failure (for simulating the initial condition at the point of fuel pin breach), bubble scrubbing, deposition, leakage and following environmental impact. In the current work, a validation study of the SRT bubble scrubbing model is performed using a water-loop experiment performed at the University of Wisconsin-Madison. Through the analysis, the approach and fundamental bubble scrubbing models in SRT, which examine the removal of aerosols within the bubble as it is transported through a pool, have been widely evaluated. The results of the assessment demonstrate a high level of agreement in the regions of greater aerosol size. In the parameter range of minimal aerosol removal, the simulation slightly underpredicts the experiment results; however, considering the scale of plots and huge uncertainties inherently included, the deviation can be judged to be minor and would produce a conservative result. In addition, uncertainty analysis has been further refined to reflect the experimental distribution of parameters including aerosol sizes, which induces a span of performance for each representative aerosol size. Based upon the initial validation results along with uncertainty effects, SRT is expected to provide meaningful insights for the analysis of bubble scrubbing. Future sodium-loop tests will provide further validation basis.

Kam, Dong Hoon↗
DOE OSTI2024

Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion

Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require $\mathcal{O}(10^3)$ parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

WI Fast Stats: a collection of web apps for the visualization and analysis of WI Fast Plants data

WI Fast Stats is an integrated animated web page which serves as a medium to a collection of R-developed web apps that provide Data Visualization and Data Analysis tools for WI Fast Plants data. Each web app corresponds to an educational unit linked to a specific WI Fast Plants webinar and it serves two main functions: 1) K-16 teachers attending the WI Fast Plants webinar will learn how to design Data Science exercises through the web app for their students and 2) students can use the web app independently to learn about visualization and analysis via the publicly available sample datasets and educational materials.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1972

A comparison of the capabilities of three finite element programs

Three finite element programs are compared to assess their capabilities as an analysis tool in a structural design process. Because of the need for repetitive analyses as an integral part of a design loop, a candidate program must be capable of handling large problems, operate efficiently, and be readily adaptable for use in computer-aided design. The three programs considered in the study, ELAS,SNAP, and NASTRAN, range from a relatively small finite element program limited to static structural analysis (ELAS) to a large complex general analysis system (NASTRAN). Results are given for comparative speeds and computer resources required for each program in the analysis of sample fuselage problems representative of practical aircraft design.

Loendorf, D. D.↗
NASA NTRS2016

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases.1 Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission.2 Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an Advanced Design Methods (ADM) based approach. This approach applies the concepts of Design of Experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development e ort. In order to t a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of Ground Rules and Assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated \what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are t to the outputs of each environment and are integrated via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any \what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will have the potential to lower LCC of launch vehicle designs originating from ACO.

Zwack, Mathew R.↗
NASA NTRS2016

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are fit to the outputs of each environment and are "integrated" via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any ''what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will help ensure lower LCC of launch vehicle designs originating from ACO.

Zwack, Mathew R.↗
DOE OSTI2024

Powered By ReEDS™ [Slides]

The National Renewable Energy Laboratory's flagship Regional Energy Deployment System (ReEDS) electric grid planning model is informing the answers to some of the biggest questions surrounding electricity sector research. Powered By ReEDS is the third webinar in the Powered By series. Each webinar highlights an innovative NREL grid planning and analysis tool and its real-world applications. The series is an exciting opportunity to learn directly from NREL's grid experts, so make sure to bring your questions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Preparing Distribution Utilities for the Future - Unlocking Demand-Side Management Potential: A Novel Analytical Framework

The balance of supply and demand in the power systems has traditionally been served solely through generation and network capacity planning and operations. However, with increased requirements for flexibility due to the uptake in variable renewable generation sources such as wind and solar there is a need to increased demand-side flexibility. In addition, there are increased communications and flexibility capabilities emerging on the demand-side from the adoption of advanced metering infrastructures and smart meter deployment and intelligent loads such as smart thermostats and schedulable white goods (e.g. dishwashers and washing machines). Unlocking demand-side flexibility can bring system benefits from peak load reduction bringing about generation capacity and network upgrade deferral, to reducing demand and more efficient utilization of generation and network capacity. Unlocking demand-side flexibility is an evolving process for utilities and solutions must be tailored to each specific customer group. Demand-side management (DSM) is a broad set of tools that can include demand response (both dispatchable and non-dispatchable), energy efficiency and distributed energy resources and demand-side technologies. The National Renewable Energy Laboratory (NREL), in collaboration with BSES Rajdhani Power Ltd. (BRPL) and Deloitte, examined the potential of DSM in BRPL’s service territory, developing detailed information on customer classes and willingness to participate in DSM. The study developed modeling frameworks for load analysis and the analysis tools to assess the potential of time-of-use tariffs in motivating customers to reduce their peak period energy consumption. The study shows that BRPL customers, specifically their domestic customers, are willing to participate in DSM programs and that time-of-use pricing can help BRPL reduce their peak demand and help unlock demand-side flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1986

Fault-free validation of a fault-tolerant multiprocessor: Baseline experiments and workoad implementation

In the future, aircraft employing active control technology must use highly reliable multiprocessors in order to achieve flight safety. Such computers must be experimentally validated before they are deployed. This project outlines a methodology for doing fault-free validation of reliable multiprocessors. The methodology begins with baseline experiments, which test single phenomenon. As experiments progress, tools for performance testing are developed. This report presents the results of interrupt baseline experiments performed on the Fault-Tolerant Multiprocessor (FTMP) at NASA-Langley's AIRLAB. Interrupt-causing excepting conditions were tested, and several were found to have unimplemented interrupt handling software while one had an unimplemented interrupt vector. A synthetic workload model for realtime multiprocessors is then developed as an application level performance analysis tool. Details of the workload implementation and calibration are presented. Both the experimental methodology and the synthetic workload model are general enough to be applicable to reliable multi-processors besides FTMP.

Feather, F.↗
NASA NTRS1998

A Case Study for Probabilistic Methods Validation (MSFC Center Director's Discretionary Fund, Project No. 94-26)

Probabilistic method is not a universally accepted approach for the design and analysis of aerospace structures. The validity of this approach must be demonstrated to encourage its acceptance as it viable design and analysis tool to estimate structural reliability. The objective of this Study is to develop a well characterized finite population of similar aerospace structures that can be used to (1) validate probabilistic codes, (2) demonstrate the basic principles behind probabilistic methods, (3) formulate general guidelines for characterization of material drivers (such as elastic modulus) when limited data is available, and (4) investigate how the drivers affect the results of sensitivity analysis at the component/failure mode level.

Price J. M.↗
NASA NTRS2016

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases [1]. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission [2]. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions.

Zwack, Mathew R.↗