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

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

gLaSDI: Parametric physics-informed greedy latent space dynamics identification

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic nonlinear latent representations of high-dimensional data, while dynamics identification (DI) models capture local latent-space dynamics. Here, an interactive training algorithm is adopted for the autoencoder and local DI models, which enables identification of simple latent-space dynamics and enhances accuracy and efficiency of data-driven reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual-based error indicator and random-subset evaluation is introduced to search for the optimal training samples on the fly. Further, to exploit local latent-space dynamics captured by the local DI models for an improved modeling accuracy with a minimum number of local DI models in the parameter space, a -nearest neighbor convex interpolation scheme is employed. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including Burgers equations, nonlinear heat conduction, and radial advection. The proposed adaptive greedy sampling outperforms the conventional predefined uniform sampling in terms of accuracy. Compared with the high-fidelity models, gLaSDI achieves 17 to 2,658× speed-up with 1 to 5% relative errors.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

An experimental study of nonlinear dynamic system identification

A technique based on the Minimum Model Error optimal estimation approach is employed for robust identification of a nonlinear dynamic system. A simple harmonic oscillator with quadratic position feedback was simulated on an analog computer. With the aid of analog measurements and an assumed linear model, the Minimum Model Error Algorithm accurately identifies the quadratic nonlinearity. The tests demonstrate that the method is robust with respect to prior ignorance of the nonlinear system model and with respect to measurement record length, regardless of initial conditions.

Stry, Greselda I.↗
DOE OSTI2017

Data-Driven Nonlinear Subspace Modeling for Prediction and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

Blast furnace (BF) in ironmaking is a nonlinear dynamic process with complicated physical-chemical reactions, where multi-phase and multi-field coupling and large time delay occur during its operation. In BF operation, the molten iron temperature (MIT) as well as Si, P and S contents of molten iron are the most essential molten iron quality (MIQ) indices, whose measurement, modeling and control have always been important issues in metallurgic engineering and automation field. This paper develops a novel data-driven nonlinear state space modeling for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques. First, to improve modeling efficiency, a data-driven hybrid method combining canonical correlation analysis and correlation analysis is proposed to identify the most influential controllable variables as the modeling inputs from multitudinous factors would affect the MIQ indices. Then, a Hammerstein model for the prediction of MIQ indices is established using the LS-SVM based nonlinear subspace identification method. Such a model is further simplified by using piecewise cubic Hermite interpolating polynomial method to fit the complex nonlinear kernel function. Compared to the original Hammerstein model, this simplified model can not only significantly reduce the computational complexity, but also has almost the same reliability and accuracy for a stable prediction of MIQ indices. Last, in order to verify the practicability of the developed model, it is applied in designing a genetic algorithm based nonlinear predictive controller for multivariate MIQ indices by directly taking the established model as a predictor. Industrial experiments show the advantages and effectiveness of the proposed approach.

Blast furnace (BF)↗
NASA NTRS1987

Sensitivity, optimal scaling and minimum roundoff errors in flexible structure models

Traditional modeling notions presume the existence of a truth model that relates the input to the output, without advanced knowledge of the input. This has led to the evolution of education and research approaches (including the available control and robustness theories) that treat the modeling and control design as separate problems. The paper explores the subtleties of this presumption that the modeling and control problems are separable. A detailed study of the nature of modeling errors is useful to gain insight into the limitations of traditional control and identification points of view. Modeling errors need not be small but simply appropriate for control design. Furthermore, the modeling and control design processes are inevitably iterative in nature.

Skelton, Robert E.↗
DOE OSTI2021

Data-Driven Distributed Algorithms for Estimating Eigenvalues and Eigenvectors of Interconnected Dynamical Systems

Here, the paper presents data-driven algorithms to estimate in a distributed manner the eigenvalues, right and left eigenvectors of an unknown linear (or linearized) interconnected dynamic system. In particular, the proposed algorithms do not require the identification of the system model in advance before performing the estimation. As a first step, we consider interconnected dynamical system with distinct eigenvalues. The proposed strategy first estimates the eigenvalues using the well-known Prony method. The right and left eigenvectors are then estimated by solving distributively a set of linear equations. One important feature of the proposed algorithms is that the topology of communication network used to perform the distributed estimation can be chosen arbitrarily, given that it is connected, and is also independent of the structure or sparsity of the system (state) matrix. The proposed distributed algorithms are demonstrated via a numerical example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Attribution of heterogeneous stress distributions in low-grain polycrystals under conditions leading to damage

In high-purity polycrystalline metallic materials, voids tend to favor grain boundaries as nucleation sites due to the elevated stress states produced by granular interactions and the weakened grain boundary from the relative atomic disorder. To quantify the key factors of this elevated stress state, simple compression of a small multi-grain cylinder of body-centered cubic tantalum was simulated using a single crystal plasticity model that incorporates non-Schmid effects. Four increasingly complex synthetic microstructures were created to tractably incorporate grain boundary interactions, and a statistically significant number of combinations were performed by varying the initial crystallographic orientations of the microstructure. Most of these simulations produce the maximum von Mises stress on a grain boundary and less frequently at the multi-grain junctions. To build a statistical model for the maximum von Mises stress at the grain boundary, physically based features that could contribute to the elevated stress state were selected. Then, a learning algorithm based on information theory was used to identify which of these features contributed the most information to the data set. The identified features include a grain’s propensity to accommodate both elastic and plastic deformations and their directional components. The misalignment of the direction of each grain’s mechanical response was found to be strongly correlated to the magnitude of the stress near the grain boundary. For all of the synthetic microstructures, the statistical models produce a residual distribution that is nearly Gaussian with a variance of, at most, 10% of the prior distribution. The successful performance of the statistical model implies the correct identification of the physical features that cause severe stress localization in polycrystalline materials. The statistical models constructed here can be used to formulate a physically motivated void nucleation model which is sensitive to a microstructure’s propensity to produce elevated stress states. As a result, these statistical models also enable the design of material microstructures, in which the crystallographic orientation is chosen to resist void nucleation.

36 MATERIALS SCIENCE↗
NASA NTRS1991

A signal model for GPS

As the development of GPS continues, there will be an increasing need for a software-centered signal model. This model must accurately generate the observed pseudorange that would typically be encountered. The observed pseudorange varies from the true geometric range because of range measurement errors, which stem from a variety of hardware and environmental factors. In this paper, these errors are classified as either deterministic or random, and, where appropriate, their models are summarized. Of particular interest is the model for Selective Availability, which was derived from actual GPS data. The procedure for determination of this model, known as system identification theory, is briefly outlined. The synthesis of these error sources into the final signal model is given, along with simulation results.

Braasch, Michael S.↗
DOE OSTI2018

Electromagnetic Shower from Cosmic Muons in NOvA Detector

NO$\nu$A (NuMI Off-Axis $\nu_e$ Appearance) experiment is a neutrino $\nu_\mu \rightarrow \nu_e$ oscillation experiment with a baseline of 810 km. The signature for a $\nu_\mu \rightarrow \nu_e$ oscillation in the far detector is the production of an electromagnetic (EM) shower due to the charge current (CC) interaction of $\nu_e$ with the detector material. Cosmic muon induced electromagnetic showers can serve as a source of EM shower sample that can be used as a data-driven method for validating the EM shower modelling and EM shower identification algorithms at NO$\nu$A. The work reported in the thesis is the first ever implementation of cosmic muon induced EM shower for such a purpose in the NO$\nu$A experiment. We extracted EM shower sample from two sources - muon undergoing bremsstrahlung (Brem) and muon decay in flight to electron (DiF). Brem sample, extracted from cosmic muon, turns out to be a very useful data-driven method for testing EM shower modelling and the part icle identification (PID) algorithms for the detection of $\nu_e$-CC interactions. A new muon removal (MR) technique is developed and applied to remove the muon hits inside the bremsstrahlung shower. However, a complete removal of the muon energy in the event is not possible with high efficiency. DiF induced EM shower, on the other hand, is exactly similar to $\nu_e$-CC EM shower because DiF is a pure electron initiated EM shower. Therefore, DiF EM shower, once extracted, mimicks the shower of a $\nu_e$-CC interaction event. We present the extraction methods for both brem and DiF events in MC and cosmic data in far and near detector of NO$\nu$A and its use in data driven validation of $\nu_e$ EM shower modelling and $\nu_e$ signal selection efficiency across the detector. A systematic uncertainty is also assigned on $\nu_e$ appearance signal selection based on this study to take into account the difference in EM shower modelling in data and MC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Parameter identification methods for low-order gray box building energy models: A critical review

The body of knowledge on gray box building energy modeling (GBBEM) has been developed over the past few decades and has undergone some important changes recently. Starting with simple methods and simple buildings, the science of GBBEM has grown to encompass complex and more computationally intensive techniques and complex commercial buildings. Numerous works including a recent review have considered model structure and inputs in a nearly systematic way, but no extant work systematically reviews the approaches for GBBEM parameter search initialization and final identification, despite this being arguably the most difficult and impactful part of the modeling process. To this end, we critically review 55 extant works describing advantages, limitations, and domain of applicability of several classes of parameter initialization and optimization techniques specifically for GBBEM. We categorize the classes of methods and analyze the evidence of their applicability for different applications within the field of GBBEM. We find an emerging consensus that initialization of parameter searches for anything other than the simplest building elements is often challenging and sometimes requires a stochastic approach to begin the parameter identification process. After this initial process, faster methods have been used in some cases but often the nature of the problem requires stochastic methods for this portion of the process as well. For less complex systems, more deterministic and efficient methods have been shown to be effective. Finally, we draw conclusions as to the domain of applicability of different classes of initialization and optimization techniques for GBBEEM and offer suggestions for research directions that are likely to prove fruitful.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Simplified metrics for the identification of the Madden-Julian oscillation in models

We propose simplified metrics to evaluate the fidelity with which the Madden–Julian oscillation (MJO) is simulated in climate models. These metrics are based on lag correlation analysis of principal component time series (PCs). The PCs are obtained by projecting simulated 20–100 day bandpass filtered daily outgoing longwave radiation onto the two leading empirical orthogonal functions of observed MJO variability. The simplified MJO metrics, the maximum positive correlation and time lag at which it occurs, provide consistent information relative to more complex diagnostics developed by the Madden–Julian Oscillation Working Group (CLIVAR MJOWG) and by Kim et al.

58 GEOSCIENCES↗
DOE OSTI2021

Black-Box Neural System Identification and Differentiable Programming to Improve Earth System Model Predictions February

Focal Area(s): Focal Area 2: AI for predictive modeling, including AI-derived model components, and AI-enabled applications including parameter optimization, data assimilation, and uncertainty quantification. Focal Area 1: AI-assisted data assimilation using numerical Earth system models. Science Challenge: Earth system models have structural biases that lead to uncertain predictions, and their complexity and expense makes it difficult constraining the models with data or improved physical understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Exposed Phosphatidylserine as a Biomarker for Clear Identification of Breast Cancer Brain Metastases in Mouse Models

Brain metastasis is the most common intracranial malignancy in adults. The prognosis is extremely poor, partly because most patients have more than one brain lesion, and the currently available therapies are nonspecific or inaccessible to those occult metastases due to an impermeable blood–tumor barrier (BTB). Phosphatidylserine (PS) is externalized on the surface of viable endothelial cells (ECs) in tumor blood vessels. In this study, we have applied a PS-targeting antibody to assess brain metastases in mouse models. Fluorescence microscopic imaging revealed that extensive PS exposure was found exclusively on vascular ECs of brain metastases. The highly sensitive and specific binding of the PS antibody enables individual metastases, even micrometastases containing an intact BTB, to be clearly delineated. Furthermore, the conjugation of the PS antibody with a fluorescence dye, IRDye 800CW, or a radioisotope, 125I, allowed the clear visualization of individual brain metastases by optical imaging and autoradiography, respectively. In conclusion, we demonstrated a novel strategy for targeting brain metastases based on our finding that abundant PS exposure occurs on blood vessels of brain metastases but not on normal brain, which may be useful for the development of imaging and targeted therapeutics for brain metastases.

Oncology↗
DOE OSTI2017

Magnetic Flux Rope Identification and Characterization from Observationally Driven Solar Coronal Models

Formed through magnetic field shearing and reconnection in the solar corona, magnetic flux ropes are structures of twisted magnetic field, threaded along an axis. Their evolution and potential eruption are of great importance for space weather. Here we describe a new methodology for the automated detection of flux ropes in simulated magnetic fields, utilizing field-line helicity. Our Flux Rope Detection and Organization (FRoDO) code, which measures the magnetic flux and helicity content of pre-erupting flux ropes over time, as well as detecting eruptions, is publicly available. As a first demonstration, the code is applied to the output from a time-dependent magnetofrictional model, spanning 1996 June 15–2014 February 10. Over this period, 1561 erupting and 2099 non-erupting magnetic flux ropes are detected, tracked, and characterized. For this particular model data, erupting flux ropes have a mean net helicity magnitude of 2.66×10{sup 43} Mx{sup 2}, while non-erupting flux ropes have a significantly lower mean of 4.04×10{sup 42} Mx{sup 2}, although there is overlap between the two distributions. Similarly, the mean unsigned magnetic flux for erupting flux ropes is 4.04×10{sup 21} Mx, significantly higher than the mean value of 7.05×10{sup 20} Mx for non-erupting ropes. These values for erupting flux ropes are within the broad range expected from observational and theoretical estimates, although the eruption rate in this particular model is lower than that of observed coronal mass ejections. In the future, the FRoDO code will prove to be a valuable tool for assessing the performance of different non-potential coronal simulations and comparing them with observations.

79 ASTRONOMY AND ASTROPHYSICS↗