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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

NMF-Based Anomaly Detection in CMS 2D Tracking Occupancy Histograms

The CMS experiment relies on Data Quality Monitoring (DQM) to ensure that recorded collision data are suitable for physics analysis. During LHC Run 3, each run contains many lumisections and tracking monitoring elements, making offline inspection challenging, especially for localized detector effects that may appear only for short periods of time. This poster presents an unsupervised machine-learning approach to identify anomalous lumisections in CMS tracking occupancy histograms using Non-Negative Matrix Factorization (NMF). The workflow uses offline CMS DQMIO tracking histograms retrieved with the CMS DIALS API and organized as two-dimensional occupancy maps for each lumisection. After selecting stable lumisections, the occupancy maps are normalized and arranged into a non-negative data matrix. The NMF model learns a compact set of basis patterns describing normal tracking occupancy. Each lumisection is then reconstructed from these learned components, and the reconstruction error is used as an anomaly score. Large residuals indicate occupancy patterns that deviate from normal detector behavior and are flagged for further inspection. This NMF-based approach provides a fast and interpretable way to flag lumisections whose tracking occupancy patterns differ from normal detector behavior. Preliminary studies show sensitivity to known tracking anomalies, and ongoing work is focused on validating the method across additional Run 3 Pixel and Strip detector issues.

Rodríguez Ramos, Iliomar [Puerto Rico U., Mayaguez↗
DOE OSTI2022

Conversion of Resonance Parameters Between Wigner-Eisenbud R-matrix and Transition Matrix Pole Representation

The goal of this research is to develop a numerical code for translation between the current standard, Wigner-Eisenbud R-matrix parameters and a novel pole-representation of cross sections. The method follows Ducru et al. and constructs the conversion between these parametric representations of the scattering and reaction data. This pole representation, which expresses cross sections as a sum of simple poles (and their attendant residues), is examined in the context of the advantages provided by this compact, computationally efficient representation. These advantages include the ability to doppler broaden cross sections on-the-fly as well as a lower memory footprint when generating cross sections, an attractive feature to those interested in parallel GPU based Monte Carlo. Some conversion methods between R-matrix parameters and S-matrix poles have relied on curve fitting techniques. It is unknown weather these numerical methods necessarily preserve certain underlying physics properties of the standard R-matrix cross section model (such as unitarity). The direct conversion studied here aim to preserve such properties. This knowledge is of interest for future work into the generation of resonance parameter sensitivities.

CLUTCH↗
DOE OSTI2018

Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach

The significantly increasing deployment of wind power necessitates that system operation considers the spatial-temporal correlation of power forecast from different wind power plants. How to model this spatial-temporal correlation in the actual system dispatch is challenging. In this paper, a sparse correlation matrix is utilized to efficiently model the spatial-temporal correlation of wind power forecast in the generation dispatch model. A novel, adjustable, and distributionally-robust chance-constrained multi-interval optimal power flow (ADRCC-MIOPF) model is proposed to obtain reliable economic dispatch (ED) solutions. The spatial-temporal correlation of wind power plants power forecasts is represented by the sparse correlation covariance matrix obtained from historical, time series wind power forecast error data. The chance constraints in the ADRCC-MIOPF model are transformed into a set of second-order-cone (SOC) constraints in which an adjustable coefficient in the chance constraints controls the robustness of the ADRCC-MIOPF model to the wind power forecast errors distribution. Case studies performed on the PJM 5-bus system and IEEE 118-bus system verify the proposed method to improve the system security and reduce the cost especially under the high wind penetration levels. All the cases can be solved within several minutes for both the small and large cases which validates the efficiency of the proposed sparse matrix model. In addition, considering the spatial-temporal correlation of wind power forecast and the distributional robustness of wind power forecast error leads to a more reliable economic dispatch with lower system violations.

17 WIND ENERGY↗
DOE OSTI2020

Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5

Abstract Earth system models (ESMs) have been rapidly developed in recent decades to advance our understanding of climate change‐carbon cycle feedback. However, those models are massive in coding, require expensive computational resources, and have difficulty in diagnosing their performance. It is highly desirable to develop ESMs with modularity and effective diagnostics. Toward these goals, we implemented a matrix approach to the Community Land Model version 5 (CLM5) to represent carbon and nitrogen cycles. Specifically, we reorganized 18 balance equations each for carbon and nitrogen cycles among the 18 vegetation pools in the original CLM5 into two matrix equations. Similarly, 140 balance equations each for carbon and nitrogen cycles among the 140 soil pools were reorganized into two additional matrix equations. The vegetation carbon and nitrogen matrix equations are connected to soil matrix equations via litterfall. The matrix equations fully reproduce simulations of carbon and nitrogen dynamics by the original model. The computational cost for forwarding simulation of the CLM5 matrix model was 26% more expensive than the original model, largely due to calculation of additional diagnostic variables, but the spin‐up computational cost was significantly saved. We showed a case study on modeled soil carbon storage under two forcing data sets to illustrate the diagnostic capability that the matrix approach uniquely offers to understand simulation results of global carbon and nitrogen dynamics. The successful implementation of the matrix approach to CLM5, one of the most complex land models, demonstrates that most, if not all, the biogeochemical models can be reorganized into the matrix form to gain high modularity, effective diagnostics, and accelerated spin‐up.

58 GEOSCIENCES↗
DOE OSTI2024

Analog-antianalog isospin mixing in 47 K 𝛽−decay

Here, we have measured the isospin mixing of the 𝐼 𝜋 =1/2 + , 𝐸 𝑥 = 2.599 MeV state in nearly doubly magic 47 Ca with the isobaric analog 1/2 + state of 47 K . Using the TRIUMF atom trap for 𝛽 decay, we have measured a nonzero asymmetry of the progeny 47 Ca with respect to the initial 47 K spin polarization, which together with the 𝛽 asymmetry implies a nonzero ratio of Fermi to Gamow-Teller matrix elements 𝑦 = 0.098 ± 0.037 for the 1/2 + → 1/2 + transition. Interpreting 𝑦 as mixing between this state and the isobaric analog state implies a Coulomb matrix element magnitude 101 ± 37 keV. This relatively large matrix element supports a model from the literature of analog-antianalog isospin mixing, which predicts large matrix elements in cases involving excess neutrons over protons occupying more than one major shell. The result supports pursuing a search for time-reversal odd, parity-even, isovector interactions using a correlation in 47 K 𝛽 decay.

beta decay↗
DOE OSTI2025

Effect of vector meson spin coherence on the observables for the chiral magnetic effect in heavy-ion collisions

The chiral magnetic effect (CME) in heavy-ion collisions reflects the local violation of P and CP symmetries in strong interactions and manifests as electric charge separation along the direction of the magnetic field created by the wounded nuclei. The experimental observables for the CME, such as the γ 112 correlator, the R Ψ$_2$ ⁡ (Δ⁢S) correlator, and the signed balance functions, however, are also subject to non-CME backgrounds, including those from resonance decays. A previous study showed that the CME observables are affected by the diagonal component of the spin density matrix, the ρ 00 for vector mesons. Here, in this work, we study the contributions from the other elements of the spin density matrix using a toy model and a multiphase transport model. We find that the real part of the ρ 1-1 component, Re ⁡ρ 1-1 , affects the CME observables in a manner opposite to that of the ρ 00 . All three aforementioned CME observables show a linear dependence on Re ⁡ρ 1-1 in the model calculations, supporting our analytical derivations. The rest elements of the spin density matrix do not contribute to the CME observables. The off-diagonal terms in the spin density matrix indicate spin coherence and may be nonzero in heavy-ion collisions due to local spin polarization or spin-spin correlations. Thus, Re ⁡ρ 1-1 , along with ρ 00 , could play a significant role in interpreting measurements in search of the CME.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Modeling the direct sun component in buildings using matrix algebraic approaches: Methods and validation

Simulation tools that enable annual energy performance analysis of optically-complex fenestration systems have been widely adopted by the building industry for use in building design, code development, and the development of rating and certification programs for commercially-available shading and daylighting products. The tools rely on a three-phase matrix operation to compute solar heat gains, using as input low-resolution bidirectional scattering distribution function (BSDF) data (10–15° angular resolution; BSDF data define the angle-dependent behavior of light-scattering materials and systems). Measurement standards and product libraries for BSDF data are undergoing development to support solar heat gain calculations. Simulation of other metrics such as discomfort glare, annual solar exposure, and potentially thermal discomfort, however, require algorithms and BSDF input data that more accurately model the spatial distribution of transmitted and reflected irradiance or illuminance from the sun (0.5° resolution). This study describes such algorithms and input data, then validates the tools (i.e., an interpolation tool for measured BSDF data and the five-phase method) through comparisons with ray-tracing simulations and field monitored data from a full-scale testbed. Simulations of daylight-redirecting films, a micro-louvered screen, and venetian blinds using variable resolution, tensor tree BSDF input data derived from interpolated scanning goniophotometer measurements were shown to agree with field monitored data to within 20% for greater than 75% of the measurement period for illuminance-based performance parameters. The three-phase method delivered significantly less accurate results. We discuss the ramifications of these findings on industry and provide recommendations to increase end user awareness of the current limitations of existing software tools and BSDF product libraries.

14 SOLAR ENERGY↗
DOE OSTI2020

Evaluation of a Geotechnical Compaction Model to Estimate Sediment Deformation

When surface ruts from vehicle activity occur above archeological deposits, there is a potential for an impact to the archeological record to occur. While impacts to the soil surface are relatively straightforward to describe, sub-surface disturbances are more difficult to quantify. In particular, disturbances to buried archeological materials can be difficult to characterize. Materials can be damaged by surface disturbances, but they can also be impacted even if they stay intact. Interpretation of intact materials relocated within the sediment column can be problematic. Traditional characterization methods (e.g. excavation) can also disturb archaeological materials. Here, we present a geotechnical compaction model that can be used to assist with sub-surface characterization under vehicle ruts formed during off-road travel. The model is based on a Boussinesq analysis of stress increase below a surface load. The change in bulk density resulting from compaction is assumed to be proportional to the stress increase. The model results were compared against pilot scale tests conducted with homogeneous sediment and field scale in-situ tests. The results indicate that the model generally agreed with measurements to within 20%, although only a single soil type was evaluated in these studies, with a narrow range of initial bulk densities and rut depths. While application of the model across a broader matrix of soil types, bulk densities and rut depths would increase confidence in the model, it appears that the model may be suitable for use in archeological field investigations. Potential uses include both compliance and research based field applications.

Fritz, Brad G.↗
DOE OSTI2021

Ab Initio Neutrinoless Double-Beta Decay Matrix Elements for Ca 48 , Ge 76 , and Se 82

We calculate basis-space converged neutrinoless ββ-decay nuclear matrix elements for the lightest candidates: 48 Ca, 76 Ge, and 82 Se. Starting from initial two- and three-nucleon forces, we apply the ab initio in-medium similarity renormalization group to construct valence-space Hamiltonians and consistently transformed ββ-decay operators. Here, we find that the tensor component is non-negligible in 76 Ge and 82 Se, and the resulting nuclear matrix elements are overall 25%–45% smaller than those obtained from the phenomenological shell model. While a final matrix element with uncertainties still requires substantial developments, this work nevertheless opens a path toward a true first-principles calculation of neutrinoless ββ decay in all nuclei relevant for ongoing large-scale searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗
DOE OSTI2022

High-fidelity micromechanical modeling of the effects of defects on damage and creep behavior in single tow ceramic matrix composite

Despite the superior properties of ceramic matrix composites (CMCs), their fabrication process generates inevitable defects with high density that significantly influence material integrity and residual useful life. Recent efforts have focused on CMC property prediction and investigation of their different inelastic mechanisms, but very limited work exists on understanding the influence of defects on inelastic responses of CMCs. Here, we introduce a three-dimensional micromechanics computational framework that includes experimentally informed material microstructure and architectural variabilities to investigate CMC response in the presence of manufacturing-induced defects. A developed microstructure generation algorithm is used to generate the representative volume elements in the micromechanics models from complex material morphology information obtained from extensive characterization studies of C/SiNC and SiC/SiNC CMCs. A fracture mechanics-informed matrix damage model is reformulated, in which the model considers the growth of porosity and microcracks in the as-received material. A progressive fiber damage model as well as Orthotropic viscoplasticity creep formulation are also utilized for the study. This methodology is implemented within the micromechanics framework to investigate the complex temperature- and time-dependent load transfer and damage mechanisms of CMCs under operation loading conditions. The developed framework explores the influence of as-received defects on the damage and creep behavior in service conditions. It also provides new insights into the effect of size, shape, distribution, and location of these defects on material response. The framework is then calibrated with unidirectional CMC minicomposite results available in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Probabilistic neural networks for improved analyses with phenomenological R -matrix

Here we present a method for measurement analyses based on probabilistic deep neural networks that provide several advantages over conventional analyses with phenomenological models. These include predicting physical quantities directly from data, the rapid generation of statistically robust uncertainties, and the ability to bypass some parameters that may induce ambiguities and complications in data analysis. As deep learning methods make predictions through “black boxes,” the uncertainty quantification is typically challenging. We use a probabilistic framework that provides thorough uncertainty quantification and is straightforward to follow in practice. With the network architecture based on the Transformer, we demonstrate the current method for predicting nuclear resonance parameters from scattering data using the phenomenological R-matrix model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Level-set Modeling Simulations of Chemical Vapor Infiltration for Ceramic Matrix Composites Manufacturing

Silicon-carbide (SiC) reinforced ceramic matrix composites (CMCs) are a key enabling technology to reduce fuel consumption and emissions of gas turbine engines. In one manufacturing approach, chemical vapor infiltration (CVI) is limited to only coating SiC fibers. The preform is then fabricated using a lay-up of basic plys or 2-D woven sheets composed of the precoated fibers. At the other extreme, CVI is used to completely densify a 3-D woven preform shaped almost like the gas turbine component itself. The latter approach is more suitable for highly engineered components which sit directly in the gas path of the engine, for example, a high pressure turbine blade. In this case, the geometry is necessarily complex for aerodynamic, stress, and lifing (multi-physics) requirements. Presently, optimizing the CVI-dominated manufacturing approach is largely by trial-and-error. In this work, a first-principles modeling of CVI is performed to realize optimization of SiC/SiC CMC manufacturing. The modeling is based on a level-set framework to describe the interface between the vapor and solid phases. A finite-difference numerical scheme using an immersed boundary method is developed for fixed, structured meshes. Massively parallel direct numerical simulations (DNS) of CVI through fiber-woven geometries are performed using one-step chemistry, and over a range of Thiele moduli. Illustrative applications of the resulting large DNS data sets are given, including the development of fiber-weave specific infiltration models and structure functions for mean-field (porous media) Computational Fluid Dynamics (CFD) simulations of CVI.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Search for a standard model Higgs boson produced in association with a top-quark pair and decaying to bottom quarks using a matrix element method

A search for a standard model Higgs boson produced in association with a top-quark pair and decaying to bottom quarks is presented. Events with hadronic jets and one or two oppositely charged leptons are selected from a data sample corresponding to an integrated luminosity of 19.5fb -1 collected by the CMS experiment at the LHC in pp collisions at a centre-of-mass energy of 8TeV. In order to separate the signal from the larger tt¯ + jets background, this analysis uses a matrix element method that assigns a probability density value to each reconstructed event under signal or background hypotheses. The ratio between the two values is used in a maximum likelihood fit to extract the signal yield. The results are presented in terms of the measured signal strength modifier, μ, relative to the standard model prediction for a Higgs boson mass of 125GeV. The observed (expected) exclusion limit at a 95 % confidence level is μ < 4.2 (3.3), corresponding to a best fit value μ^ = 1.2 +1.6 -1.5 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Toward shell model interactions with credible uncertainties

Background: The nuclear shell model is a powerful framework for predicting nuclear structure observables, but relies on interaction matrix elements fit to experimental data as its inputs. Extending the shell model's applicability, particularly toward dripline nuclei, requires efficient fitting methods and credible uncertainty quantification. Traditional approaches face computational challenges and may underestimate uncertainties. Purpose: We develop and test a framework combining eigenvector continuation and Markov chain Monte Carlo to efficiently fit shell model interaction matrix elements and quantify their uncertainties. Methods: Eigenvector continuation is used to emulate shell model calculations, reducing computational costs. The emulator enables Markov chain Monte Carlo sampling to optimize interaction matrix elements and rigorously assess parametric uncertainties. Here, the framework is benchmarked using the USDB interaction in the 𝑠⁢𝑑 shell. Results: The emulator reproduces the USDB interaction with negligible error, validating its use in shell model fitting applications. However, we find that to obtain credible predictive intervals, the model defect of the shell model itself, rather than experimental or emulator error, must be taken into account in order to obtain credible uncertainties. Conclusions: The proposed framework provides an efficient and rigorous approach for fitting shell model interactions and quantifying uncertainties. Further, the normality assumption used in the past appears sufficient to describe the distribution of interaction matrix elements. However, it is crucial to account for model correlations to avoid underestimating uncertainties.

Nuclear forces↗
DOE OSTI2024

Reactive-transport modeling of fracture flow to quantify the changes in flow pathways from matrix thermal contraction and mineral precipitation and dissolution; influence of grid resolution

A series of reactive-transport models of Enhanced Geothermal Systems (EGS) were constructed using the reactive transport code PFLOTRAN to examine the effect of matrix thermal contraction and mineral dissolution/precipitation on fracture flow in the context of grid cell size and model complexity. It was found that for thermal drawdown at production well, the impact of fracture zone grid cell size is negligible.

Gatz-Miller, Hannah Sarah↗
DOE OSTI2017

P -wave π π scattering and the ρ resonance from lattice QCD

Here we calculate the parameters describing elastic I = 1, P-wave ππ scattering using lattice QCD with 2 +1 flavors of clover fermions. Our calculation is performed with a pion mass of m π ≈ 320 MeV and a lattice size of L ≈ 3.6 fm. We construct the two-point correlation matrices with both quark-antiquark and two-hadron interpolating fields using a combination of smeared forward, sequential and stochastic propagators. The spectra in all relevant irreducible representations for total momenta |$\vec{P}$| ≤ $\sqrt{3}\frac{2π}{L}$ are extracted with two alternative methods: a variational analysis as well as multiexponential matrix fits. We perform an analysis using Lüscher’s formalism for the energies below the inelastic thresholds, and investigate several phase shift models, including possible nonresonant contributions. We find that our data are well described by the minimal Breit-Wigner form, with no statistically significant nonresonant component. In determining the ρ resonance mass and coupling we compare two different approaches: fitting the individually extracted phase shifts versus fitting the t-matrix model directly to the energy spectrum. We find that both methods give consistent results, and at a pion mass of am π = 0.18295 (36) stat obtain gρππ = 5.69(13) stat (16) sys , am ρ = 0.4609(16) stat (14) sys , and am ρ /am N = 0.7476(38) stat (23) sys , where the first uncertainty is statistical and the second is the systematic uncertainty due to the choice of fit ranges.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗