Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “hierarchical models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Object-Oriented Bayesian Networks (OOBN) for Aviation Accident Modeling and Technology Portfolio Impact Assessment

The concern for reducing aviation safety risk is rising as the National Airspace System in the United States transforms to the Next Generation Air Transportation System (NextGen). The NASA Aviation Safety Program is committed to developing an effective aviation safety technology portfolio to meet the challenges of this transformation and to mitigate relevant safety risks. The paper focuses on the reasoning of selecting Object-Oriented Bayesian Networks (OOBN) as the technique and commercial software for the accident modeling and portfolio assessment. To illustrate the benefits of OOBN in a large and complex aviation accident model, the in-flight Loss-of-Control Accident Framework (LOCAF) constructed as an influence diagram is presented. An OOBN approach not only simplifies construction and maintenance of complex causal networks for the modelers, but also offers a well-organized hierarchical network that is easier for decision makers to exploit the model examining the effectiveness of risk mitigation strategies through technology insertions.

Shih, Ann T.↗
DOE OSTI2022

A deep learning approach to identify missing is-a relations in SNOMED CT

Abstract Objective SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT. Materials and Methods Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances to train a model predicting whether an is-a relation exists between 2 concepts with containment pattern. The model is a binary classifier leveraging concept name features, hierarchical features, enriched lexical attribute features, and logical definition features. We introduce a cross-validation inspired approach to identify missing is-a relations among all hierarchically unrelated containment concept-pairs. Results We trained and applied our model on the Clinical finding subhierarchy of SNOMED CT (September 2019 US edition). Our model (based on the validation sets) achieved a precision of 0.8164, recall of 0.8397, and F1 score of 0.8279. Applying the model to predict actual missing is-a relations, we obtained a total of 1661 potential candidates. Domain experts performed evaluation on randomly selected 230 samples and verified that 192 (83.48%) are valid. Conclusions The results showed that our deep learning approach is effective in uncovering missing is-a relations between containment concept-pairs in SNOMED CT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

ERF: Energy Research and Forecasting

The Energy Research and Forecasting (ERF) code is a new model that simulates the mesoscale and microscale dynamics of the atmosphere using the latest high-performance computing architectures. It employs hierarchical parallelism using an MPI+X model, where X may be OpenMP on multicore CPU-only systems, or CUDA, HIP, or SYCL on GPU-accelerated systems. ERF is built on AMReX (Zhang et al., 2019, 2021), a block-structured adaptive mesh refinement (AMR) software framework that provides the underlying performance-portable software infrastructure for block-structured mesh operations. The "energy" aspect of ERF indicates that the software has been developed with renewable energy applications in mind. In addition to being a numerical weather prediction model, ERF is designed to provide a flexible computational framework for the exploration and investigation of different physics parameterizations and numerical strategies, and to characterize the flow field that impacts the ability of wind turbines to extract wind energy. The ERF development is part of a broader effort led by the US Department of Energy's Wind Energy Technologies Office.

17 WIND ENERGY↗
NASA NTRS2010

The Role of Hierarchy in Response Surface Modeling of Wind Tunnel Data

This paper is intended as a tutorial introduction to certain aspects of response surface modeling, for the experimentalist who has started to explore these methods as a means of improving productivity and quality in wind tunnel testing and other aerospace applications. A brief review of the productivity advantages of response surface modeling in aerospace research is followed by a description of the advantages of a common coding scheme that scales and centers independent variables. The benefits of model term reduction are reviewed. A constraint on model term reduction with coded factors is described in some detail, which requires such models to be well-formulated, or hierarchical. Examples illustrate the consequences of ignoring this constraint. The implication for automated regression model reduction procedures is discussed, and some opinions formed from the author s experience are offered on coding, model reduction, and hierarchy.

DeLoach, Richard↗
DOE OSTI2020

Bottom-Up Meets Top-Down: The Crossroads of Multiscale Chromatin Modeling

Chromatin can be viewed as a hierarchically structured fiber that regulates gene expression. It consists of a complex network of DNA and proteins whose characteristic dynamical modes facilitate compaction and rearrangement in the cell nucleus. These modes stem from chromatin's fundamental unit, the nucleosome, and their effects are propagated across length scales. Understanding the effects of nucleosome dynamics on the chromatin fiber, primarily through post-translational modifications that occur on the histones, is of central importance to epigenetics. Within the last decade, imaging and chromosome conformation capture techniques have revealed a number of structural and statistical features of the packaged chromatin fiber at a hitherto unavailable level of resolution. Such experiments have led to increased efforts to develop polymer models that aim to reproduce, explain, and predict the contact probability scaling and density heterogeneity. At nanometer scales, available models have focused on the role of the nucleosome and epigenetic marks on local chromatin structure. At micrometer scales, existing models have sought to explain scaling laws and density heterogeneity. Less work, however, has been done to reconcile these two approaches: bottom-up and top-down models of chromatin. Here, we highlight the multiscale simulation models that are driving toward an understanding of chromatin structure and function, from the nanometer to the micron scale, and we highlight areas of opportunity and some of the prospects for new frameworks that bridge these two scales. Taken together, experimental and modeling advances over the last few years have established a robust platform for the study of chromatin fiber structure and dynamics, which will be of considerable use to the chromatin community in developing an understanding of the interplay between epigenomic regulation and molecular structure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

How to Obtain the Redshift Distribution from Probabilistic Redshift Estimates

Abstract A reliable estimate of the redshift distribution n ( z ) is crucial for using weak gravitational lensing and large-scale structures of galaxy catalogs to study cosmology. Spectroscopic redshifts for the dim and numerous galaxies of next-generation weak-lensing surveys are expected to be unavailable, making photometric redshift (photo- z ) probability density functions (PDFs) the next best alternative for comprehensively encapsulating the nontrivial systematics affecting photo- z point estimation. The established stacked estimator of n ( z ) avoids reducing photo- z PDFs to point estimates but yields a systematically biased estimate of n ( z ) that worsens with a decreasing signal-to-noise ratio, the very regime where photo- z PDFs are most necessary. We introduce Cosmological Hierarchical Inference with Probabilistic Photometric Redshifts ( CHIPPR ), a statistically rigorous probabilistic graphical model of redshift-dependent photometry that correctly propagates the redshift uncertainty information beyond the best-fit estimator of n ( z ) produced by traditional procedures and is provably the only self-consistent way to recover n ( z ) from photo- z PDFs. We present the chippr prototype code, noting that the mathematically justifiable approach incurs computational cost. The CHIPPR approach is applicable to any one-point statistic of any random variable, provided the prior probability density used to produce the posteriors is explicitly known; if the prior is implicit, as may be the case for popular photo- z techniques, then the resulting posterior PDFs cannot be used for scientific inference. We therefore recommend that the photo- z community focus on developing methodologies that enable the recovery of photo- z likelihoods with support over all redshifts, either directly or via a known prior probability density.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Network-Aware Distributed Energy Resource Aggregation Framework for Flexible, Cost-Optimal, and Resilient Operation

To efficiently use the ubiquitous behind-the-meter distributed energy resources (DERs) in distribution systems for providing grid services, this paper presents a hierarchical control framework for DER optimal aggregation and control. We first develop a convex optimization model to evaluate the DER flexibility, and then use a convex model-predictive-control based approach to dispatch those DERs. The hierarchical control framework consists of a utility controller, community aggregators and multiple home energy management systems. The flexibility of the DERs is evaluated by each controller in the hierarchy such that the resultant flexibility is feasible given its operational domain. Based on the determined flexibility, the hierarchical controllers then compute optimal setpoints for the DERs to help the distribution system regulate node voltages and provide other distribution grid services. Numerical simulations performed on a model of a real distribution feeder in Colorado, using actual DER data in a residential community demonstrate that the proposed approach can effectively alleviate voltage issues and support resilient operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

Abstract Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ( $${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Software for Checking Statecharts

HiVy is a software tool set that enables verification through model checking of designs represented as finite-state machines or statecharts. HiVy provides automated translation of (1) statecharts created by use of the MathWorks Stateflow program to (2) Promela, the input language of the Spin model checker, which can then be used to verify, or trace logical errors in, distributed software systems. HiVy can operate directly on Stateflow models, or its abstract syntax of hierarchical sequential automata (HSA) can be used independently as an intermediate format for translation to Promela. In a typical design application, HiVy parses and reformats Stateflow model file data using the programs SfParse and sf2hsa, respectively. If the parsing effort is successful, an abstract syntax tree is delivered into a file named with the extension .hsa. If the design comprises several model files, they may be merged into one .hsa file before translation into Promela. Stateflow scope is preserved, and name clashes are avoided in the merge process. The HiVy program hsa2pr translates the model from the intermediate HSA format into Promela. Additionally, HiVy provides through translation a list of all statechart model propositions that are the means for formalizing linear temporal logic (LTL) properties about the model for Spin verification.

Pingree, Paula↗
DOE OSTI2025

Accelerating eigenvalue computation for nuclear structure calculations via perturbative corrections

Subspace projection methods utilizing perturbative corrections have been proposed for computing the lowest few eigenvalues and corresponding eigenvectors of large Hamiltonian matrices. In this paper, we build upon these methods and introduce the term Subspace Projection with Perturbative Corrections (SPPC) method to refer to this approach. We tailor the SPPC for nuclear many-body Hamiltonians represented in a truncated configuration interaction subspace, i.e., the no-core shell model (NCSM). We use the hierarchical structure of the NCSM Hamiltonian to partition the Hamiltonian as the sum of two matrices. The first matrix corresponds to the Hamiltonian represented in a small configuration space, whereas the second is viewed as the perturbation to the first matrix. Eigenvalues and eigenvectors of the first matrix can be computed efficiently. Because of the split, perturbative corrections to the eigenvectors of the first matrix can be obtained efficiently from the solutions of a sequence of linear systems of equations defined in the small configuration space. These correction vectors can be combined with the approximate eigenvectors of the first matrix to construct a subspace from which more accurate approximations of the desired eigenpairs can be obtained. We show by numerical examples that the SPPC method can be more efficient than conventional iterative methods for solving large-scale eigenvalue problems such as the Lanczos, block Lanczos and the locally optimal block preconditioned conjugate gradient (LOBPCG) method. The method can also be combined with other methods to avoid convergence stagnation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Multi-resolution enhancement for full-spectrum neural representations

Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-basedrepresentations increasingly intractable. Implicit neural representations (INRs) offer a promising solutionby encoding signals through coordinate-based neural networks, serving as surrogates of data, withcomputational and storage requirements scaling with network complexity rather than data dimensionality.However, smaller INRs struggle to faithfully represent multiscale structures, high-frequency informationand fine textures that constitute a large proportion of scientific measurements. We propose WIEN-INR, atheoretically guided hierarchical INR framework that distributes modelling across resolution scales andenables improved representation capacity through a novel enhancement network to recover subtle details.This multiscale architecture allows smaller networks to retain the full spatial-frequency content of thesignal as well as preserve training efficiency and lower storage cost. Evaluated on distinct raw experimentalmeasurements across scales and complexities, WIEN-INR represents a practical step towards a broaderadoption of neural representations in scientific workflows, delivering compact, robust and high-fidelityrepresentations.

Ni, Yuan [SLAC National Accelerator Laboratory (SL↗
DOE OSTI2022

pnnl/TransMED

Transfer Learning From Existing Diseases Via Hierarchical Multi-Modal BERT Models

Ham, Colby↗
DOE OSTI2024

PROVABGS: The Probabilistic Stellar Mass Function of the BGS One-percent Survey

We present the probabilistic stellar mass function (pSMF) of galaxies in the DESI Bright Galaxy Survey (BGS), observed during the One-percent Survey. The One-percent Survey was one of DESI's survey validation programs conducted from 2021 April to May, before the start of the main survey. It used the same target selection and similar observing strategy as the main survey and successfully observed the spectra and redshifts of 143,017 galaxies in the r < 19.5 magnitude-limited BGS Bright sample and 95,499 galaxies in the fainter surface-brightness- and color-selected BGS Faint sample over z < 0.6. We derive pSMFs from posteriors of stellar mass, M*, inferred from DESI photometry and spectroscopy using the Hahn et al. PRObabilistic Value-Added BGS (PROVABGS) Bayesian spectral energy distribution modeling framework. We use a hierarchical population inference framework that statistically and rigorously propagates the M* uncertainties. Furthermore, we include correction weights that account for the selection effects and incompleteness of the BGS observations. We present the redshift evolution of the pSMF in BGS, as well as the pSMFs of star-forming and quiescent galaxies classified using average specific star formation rates from PROVABGS. Overall, the pSMFs show good agreement with previous stellar mass function measurements in the literature. Our pSMFs showcase the potential and statistical power of BGS, which in its main survey will observe >100 × more galaxies. Moreover, we present the statistical framework for subsequent population statistics measurements using BGS, which will characterize the global galaxy population and scaling relations at low redshifts with unprecedented precision.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1987

An overview of controls research on the NASA Langley Research Center grid

The NASA Langley Research Center has assembled a flexible grid on which control systems research can be accomplished on a two-dimensional structure that has many physically distributed sensors and actuators. The grid is a rectangular planar structure that is suspended by two cables attached to one edge so that out of plane vibrations are normal to gravity. There are six torque wheel actuators mounted to it so that torque is produced in the grid plane. Also, there are six rate gyros mounted to sense angular motion in the grid plane and eight accelerometers that measure linear acceleration normal to the grid plane. All components can be relocated to meet specific control system test requirements. Digital, analog, and hybrid control systems capability is provided in the apparatus. To date, research on this grid has been conducted in the areas of system and parameter identification, model estimation, distributed modal control, hierarchical adaptive control, and advanced redundancy management algorithms. The presentation overviews each technique and presents the most significant results generated for each area.

Montgomery, Raymond C.↗
NASA NTRS1989

Distributed earth model/orbiter simulation

Distributed Earth Model/Orbiter Simulation (DEMOS) is a network based application developed for the UNIX environment that visually monitors or simulates the Earth and any number of orbiting vehicles. Its purpose is to provide Mission Control Center (MCC) flight controllers with a visually accurate three dimensional (3D) model of the Earth, Sun, Moon and orbiters, driven by real time or simulated data. The project incorporates a graphical user interface, 3D modelling employing state-of-the art hardware, and simulation of orbital mechanics in a networked/distributed environment. The user interface is based on the X Window System and the X Ray toolbox. The 3D modelling utilizes the Programmer's Hierarchical Interactive Graphics System (PHIGS) standard and Raster Technologies hardware for rendering/display performance. The simulation of orbiting vehicles uses two methods of vector propagation implemented with standard UNIX/C for portability. Each part is a distinct process that can run on separate nodes of a network, exploiting each node's unique hardware capabilities. The client/server communication architecture of the application can be reused for a variety of distributed applications.

Geisler, Erik↗
NASA NTRS2003

Development of a Dynamically Configurable,Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation

This research is aimed at developing a neiv and advanced simulation framework that will significantly improve the overall efficiency of aerospace systems design and development. This objective will be accomplished through an innovative integration of object-oriented and Web-based technologies ivith both new and proven simulation methodologies. The basic approach involves Ihree major areas of research: Aerospace system and component representation using a hierarchical object-oriented component model which enables the use of multimodels and enforces component interoperability. Collaborative software environment that streamlines the process of developing, sharing and integrating aerospace design and analysis models. . Development of a distributed infrastructure which enables Web-based exchange of models to simplify the collaborative design process, and to support computationally intensive aerospace design and analysis processes. Research for the first year dealt with the design of the basic architecture and supporting infrastructure, an initial implementation of that design, and a demonstration of its application to an example aircraft engine system simulation.

Afjeh, Abdollah A.↗