Search NASA⌕ Search

SEARCH · Search NASA

Results for “Concrete Modeling”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Cosmological constraints on dark matter interactions with ordinary matter

Dark matter interactions with electrons or protons during the early Universe leave imprints on the cosmic microwave background and the matter power spectrum, and can be probed through cosmological and astrophysical observations. These interactions lead to momentum and heat exchange between the ordinary and dark matter components, which in turn results in a transfer of pressure from the ordinary to the dark matter. Here, we explore these interactions using a diverse suite of data: cosmic microwave background anisotropies, baryon acoustic oscillations, the Lyman-α forest, and the abundance of Milky-Way subhalos. We derive constraints using model-independent parameterizations of the dark matter–electron and dark matter–proton interaction cross sections and map these constraints onto concrete dark matter models. Our constraints are complementary to other probes of dark matter interactions with ordinary matter, such as direct detection, big bang nucleosynthesis, various astrophysical systems, and accelerator-based experiments. They exclude sufficiently large cross sections for a large range of dark matter masses, which cannot be accessed by direct-detection experiments due to the overburden from the Earth’s atmosphere or crust.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2022

Cosmological constraints on dark matter interactions with ordinary matter

Dark matter interactions with electrons or protons during the early Universe leave imprints on the cosmic microwave background and the matter power spectrum, and can be probed through cosmological and astrophysical observations. These interactions lead to momentum and heat exchange between the ordinary and dark matter components, which in turn results in a transfer of pressure from the ordinary to the dark matter. We explore these interactions using a diverse suite of data: cosmic microwave background anisotropies, baryon acoustic oscillations, the Lyman-α forest, and the abundance of Milky-Way subhalos. We derive constraints using model-independent parameterizations of the dark matter–electron and dark matter–proton interaction cross sections and map these constraints onto concrete dark matter models. Our constraints are complementary to other probes of dark matter interactions with ordinary matter, such as direct detection, big bang nucleosynthesis, various astrophysical systems, and accelerator based experiments. They exclude sufficiently large cross sections for a large range of dark matter masses, which cannot be accessed by direct-detection experiments due to the overburden from the Earth’s atmosphere or crust.

Manuel A. Buen-Abad↗
DOE OSTI2025

PyCMG-based Simulation of Volumetric Concrete Microstructure

Concrete is a complex, heterogeneous material with a microstructure composed of aggregates, cement paste, and pores spanning multiple length scales. Understanding this microstructure is critical for advancing the performance, durability, and modeling of concrete-based systems. While experimental imaging such as X-ray computed tomography (XCT) provides valuable insights, generating large datasets with detailed ground truth annotations is both costly and labor-intensive due to challenges in segmenting similar phases, such as aggregates and cement paste, that often share similar attenuation properties. To address this, we developed a pipeline to simulate realistic 3D concrete microstructures using the open-source Python package PyCMG. This simulation effort focuses on generating high-fidelity, annotated microstructures that can serve as training or benchmarking datasets for image analysis, segmentation algorithms, and machine learning models, particularly in scenarios where experimental data is scarce.

Ziabari, Amir [Oak Ridge National Laboratory; ORNL↗
DOE OSTI2021

A traversable wormhole teleportation protocol in the SYK model

In this paper, we propose a concrete teleportation protocol in the SYK model based on a particle traversing a wormhole. The required operations for the communication, and insertion and extraction of the qubit, are all simple operators in terms of the basic qubits. We determine the effectiveness of this protocol, and find a version achieves almost perfect fidelity. Many features of semiclassical traversable wormholes are manifested in this setup.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A time-dependent chloride diffusion model for predicting initial corrosion time of reinforced concrete with slag addition

The effect of granulated blast furnace slag (GBFS) addition on the threshold chloride concentration (TCC) for rebar corrosion was investigated. A modified diffusion model, coupled with a time-dependent effective diffusion coefficient and surface chloride concentration, was proposed to predict the chloride profile. The corrosion initiation time was estimated based on the model predictions and the measured TCC. The results indicate that adding GBFS decreases the TCC by lowering the pH value of the pore solution. The evolution of corrosion potential and current density is found to obey a 3-parameter Weibull distribution. MIP tests show that adding GBFS contributes to refinement of pore structure by decreasing the fraction of large capillary pores. The time-dependent model exhibits good predictive strength and helps understand how GBFS addition delays the corrosion initiation by retarding the chloride diffusion, though a lower TCC is obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Loop-current charge density wave driven by long-range Coulomb repulsion on the kagomé lattice

Recent experiments on vanadium-based nonmagnetic kagomé metals A⁢V 3 ⁢Sb 5 ⁢(A=K,Rb,Cs) revealed evidence for possible spontaneous time-reversal symmetry (TRS) breaking in the charge density wave (CDW) ordered state. The long-sought-after quantum order of loop currents has been suggested as a candidate for the TRS breaking state. However, a microscopic model for the emergence of the loop-current CDW due to electronic correlations is still lacking. Here, in this work, we calculate the susceptibility of the real and imaginary bond orders on the kagomé lattice near van Hove filling, and reveal the importance of next-nearest-neighbor Coulomb repulsion V 2 in triggering the instability toward imaginary bond ordered CDW. The concrete effective single-orbital t–V 1 –V 2 model on the kagomé lattice is then studied, where t and V 1 are the hopping and Coulomb repulsion on the nearest-neighbor bonds. We obtain the mean-field ground states, analyze their properties, and determine the phase diagram in the plane spanned by V 1 and V 2 at van Hove filling. The region dominated by V 1 is occupied by a 2⁢a 0 ×2⁢a 0 real CDW insulator with the inverse of Star-of-David (ISD) bond configuration. Increasing V 2 indeed drives a first-order transition from ISD to stabilized loop-current insulators that exhibit four possible current patterns of different topological properties, leading to orbital Chern insulators. We then extend these results away from van Hove filling and show that electron doping helps the stabilization of loop currents, and gives rise to doped orbital Chern insulators with emergent Chern Fermi pockets carrying large Berry curvature and orbital magnetic moment. Our findings provide a concrete model realization of the loop-current Chern metal at the mean-field level for the TRS breaking normal state of the kagomé superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

New Laboratory and Astrophysical Probes of Physics Beyond the Standard Model (Final Technical Report)

The first of these directions centers on new ideas in dark matter detection. As experimental scrutiny narrows the window for new physics at the weak scale, it has become apparent that the solution to the dark matter mystery may not reside there. Theoretical developments such as the hidden sector/valley paradigm have, at the same time, shown that compelling theories of dark matter reside below the weak scale. Searching for dark matter at lower mass scales requires looking beyond the current paradigm of dark matter direct detection based on nuclear recoils. It is the goal of this project to provide new ideas to guide the experimental program looking for light dark matter. I am actively proposing ideas for detecting dark matter as light as a meV, well beyond the current focus of GeV-TeV scale dark matter. Some of the ideas I have proposed, such as superconducting, superfluid, and polar material targets, are actively being developed into experiments. My group will provide the relevant calculations to determine dark matter reach, providing crucial input to experiment on which targets should be developed. We will also provide an effective field theory framework to understand which types of experiments are most sensitive to each interaction type. The second of these directions is on probes of dark matter substructure as a mean to constrain, or observe, models of dark matter. The standard ΛCDM paradigm assumes that the dark matter density perturbations are adiabatic, scale invariant, and produced during inflation. However, many standard models of dark matter will produce modifications of this prediction, such as axion models with symmetry broken below the inflationary scale. Remarkably, we have limited direct measurements of the dark matter clumpiness at mass scales below dwarf galaxies. This allows for the possibility of observing modifications from vanilla ΛCDM due to particle dynamics. In many cases, simulations of dark matter structure in the presence of non-scale invariant fluctu- ations are understudied or completely lacking. I plan to develop both theoretical predictions for small scale structure in models with additional matter power on small scales, as well as observa- tional probes of small scale halos or clumps. One idea I have been recently focused on is pulsar timing, though my group will pursue a variety of lensing and astrometric probes. Lastly, the most risky direction involves spacetime fluctuations from quantum gravity. I showed that if one assumes that metric fluctuations in the Minkowski vacuum, at a surface separating a region in and out of causal contact (which we call a “horizon”), are determined by standard thermodynamic considerations at horizon, these metric fluctuations are large enough to observe in an interferometer with similar sensitivity to LIGO. In a follow-up paper we showed that these assumption holds for the vacuum in AdS/CFT. In future work, I plan to connect this work to recent soft graviton results, to frame this work in a concrete Randall-Sundrum model, and to work out concrete phenomenological predictions from the model. This work unquestionably takes a less trodden path, and works well as part of a well-rounded portfolio of less and more risky ideas. Particle physics is currently at a juncture which requires bold exploration of qualitatively new directions. This proposal outlines some of my plans over the coming years in these directions, leaving room also for surprises.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

"Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models"

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components. Yet, in many applications, particularly macroscopic traffic flow modeling and optimization, structurally simple approximations to the complex vehicle dynamics are needed that nevertheless reproduce the correct effective energy behavior. This work presents a systematic model reduction pipeline that starts from complex vehicle models based on the Autonomie software and derives a hierarchy of simplified models that are fast to evaluate, easy to disseminate in open-source frameworks, and compatible with optimization frameworks. The pipeline, based on a virtual chassis dynamometer and subsequent approximation strategies, is reproducible and is applied to six different vehicle classes to produce concrete explicit energy models that represent an average vehicle in each class and leverage the accuracy and validation work of the Autonomie software.

Khoudari, Nour↗
DOE OSTI2022

Nondestructive Damage Detection of Concrete With Alkali-Silica Reactions Using Coda Wave and Anomaly Detection

An anomaly detection model for early damage detection for concrete structures undergoing alkali-silica reaction (ASR) is presented. It is difficult to detect ASR initiation and early damage without a reference expansion measurement. Coda waves, or the multiply scattered portion of ultrasonic waves, have been found to be indicative of small changes in complex material such as concrete. The relationship between concrete damage and relative velocity change and decorrelation of coda waves has been studied, but a generalized model which detects when damage occurs in a concrete structure is still lacking. The presented method uses features extracted from coda waves to detect early damage in concrete structures. The model uses unsupervised learning and only requires data from undamaged structures for training. During the training process, the reconstruction error of the training data is minimized. When the data collected from damaged concrete structures is used as an input of the model, it returns high reconstruction errors that indicate the occurrence of damage in the structures. The performance of the model is validated using experimental studies and has been shown to generalize across two different ASR specimens.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Wrinkles in the Froggatt-Nielsen mechanism and flavorful new physics

Abstract When the Froggatt-Nielsen mechanism is used to explain the Standard Model flavor hierarchy, new physics couplings are also determined by the horizontal symmetry. However, additional symmetries or dynamics in the UV can sometimes lead to a departure from this naïve scaling for the new physics couplings. We show that an effective way to keep track of these changes is by using the new spurions of the U(3) 5 global flavor symmetry, where we parameterize extra suppression or enhancement factors, referred to aswrinkles, using the same power counting parameter as in the original Froggatt-Nielsen model. As a concrete realization, we consider two flavor spurions of theS 1 leptoquark, and demonstrate that wrinkles can be used to make an enhanced value of$$ \textrm{BR}\left({B}^{+}\to {K}^{+}\nu \overline{\nu}\right) $$ BR B + → K + ν ν ¯ consistent with other flavor observables. We also present example UV models that realize wrinkles, and comment on choosing consistent charges in ordinary Froggatt-Nielsen models without the typical monotonicity condition.

Physics↗
DOE OSTI2023

A structural model of the long-term degradation of the concrete biological shield

The concrete biological shield (CBS) of light water reactors is exposed to high neutron radiation dose in the long term, which may lead to the degradation of the concrete’s mechanical properties. Given the important shielding role of the CBS, it is necessary to investigate the irradiation effects at the structural scale and provide estimates of the damage extent from the wall’s inner surface to study potential license renewals. For this purpose, we developed a mechanical model accounting for radiation-induced expansion, creep, and damage in concrete using the Grizzly finite element code, informed by ex-core neutron flux calculations using the VERA tool. The model was applied to a 3D CBS structure represented by the CBS wall, a steel liner, reinforcement bars, and a concrete base mat and evaluated damage at 40, 60, and 80 years of operation. The VERA model predicted a maximum fluence of approximately 2 x 10 19 ncm -2 at 80 years of operation. The results showed that damage is highest at the inner surface of the CBS wall and gradually decreases with depth. It extends beyond the rebar after 60 years and reaches a depth of approximately 12 cm at 80 years.

42 ENGINEERING↗
DOE OSTI2021

COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Development and Validation of Quantitative Model-Based Image Reconstruction for NDE of Reinforced Steel-Lined Concrete Structures: Kal-El FY 2020

This project studied the capabilities of Model-Based Image Reconstruction (MBIR) and Machine Learning (ML) algorithms in the imaging and estimation of rebar corrosion from ultrasound signals. The application was challenging due to the introduction of a steel liner between the sensor and the concrete containing the rebar. The study focused in a synthetic specimen with a 6.35 mm thick steel liner, and a 19.05 mm diameter rebar at a depth of 44.5 mm and with four levels of corrosion; 0%, 20%, 50%, and 100% corrosion level. The corrosion was applied uniformly around the rebar, generating a ring in the perimeter of the rebar with different acoustic density. In order to generate a realistic synthetic dataset, we added random texture to the concrete of the synthetic specimens. This texture will mimic the variations encountered in real concrete specimens.We adapted our MBIR algorithm for the application and enhanced the algorithm to integrate in the physical model known specimen features, such as the steel liner thickness. The new MBIR method was able to cancel out reverberation from the steel liner and properly image the rebar. In particular, corrosion for the 50% and 100% cases were easily visible and quantitative metrics showed a slight separation for the 0% and 20% levels. The quantitative results were in agreement with the qualitative assessment. We also developed a ML XGBoost model to estimate corrosion level from the ultrasound signals. By combining the ML method with MBIR, we can pinpoint in the ultrasound signals the section that corresponds to the echoes from the rebar. The extract signals are processed by the XGBoost model for prediction. From each system scan, we obtain 15 predictions. The median prediction value is used as the final prediction. The True Acceptance Rate for the method is over 99%.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Estimation of constituent properties of concrete materials with an artificial neural network based method

Multi-scale models are developed for heterogeneous concrete materials to estimate their macroscopic mechanical properties in terms of micro-structural data. One crucial challenge of those models is the identification of local properties of constituent phases. In this paper, we present an efficient method based on Artificial Neural Networks (ANN). Typical concrete materials are taken as example. A macroscopic analytical strength criterion is established from three steps of nonlinear homogenization procedure. The macroscopic strength of materials is determined as a function of the frictional coefficient and cohesion of solid cement particles at nanometer scale, intra-particle pores, inter-particle pores and aggregates (inclusions). The objective is to identify the nanoscopic frictional coefficient and cohesion of cement particle from measured macroscopic values of uniaxial compression and tensile strengths. For this purpose, a numerical method based on the ANN is developed. With the analytical macroscopic strength criterion, sensitivity studies are first realized to identify the most important micro-structural parameters influencing the macroscopic strength of concrete. A simplified analytical macroscopic strength criterion is then proposed. A large dataset is further constructed through the inversion of the analytical strength criterion by using the aggregates volume fraction, porosity, macroscopic uniaxial tensile and compressive strengths as input variables and the frictional coefficient and cohesion of cement particles as output unknowns. An ANN model containing four hidden layers and 100 neurons in each layer is constructed and trained by using this dataset. Various types of validation of the ANN model are performed. It is found that the proposed ANN based model can effectively predict the frictional coefficient and cohesion of porous cement paste at the microscopic scale with a very good accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Engineering Corner States from Two-Dimensional Topological Insulators

We theoretically demonstrate that the second-order topological insulator with robust corner states can be realized in two-dimensional $\mathbb{Z}_2$ topological insulators by applying an in-plane Zeeman field. The Zeeman field breaks the time-reversal symmetry and thus destroys the $\mathbb{Z}_2$ topological phase. Nevertheless, it respects some crystalline symmetries and thus can protect the higher-order topological phase. By taking the Kane-Mele model as a concrete example, we find that spin-helical edge states along zigzag boundaries are gapped out by the Zeeman field whereas the in-gap corner state at the intersection between two zigzag edges arises, which is independent of the field orientation. In this work, we further show that the corner states are robust against the out-of-plane Zeeman field, staggered sublattice potentials, Rashba spin-orbit coupling, and the buckling of honeycomb lattices, making them experimentally feasible. Similar behaviors can also be found in the well-known Bernevig-Hughes-Zhang model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗