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.

SourceResearch connectionFrom yearThrough year

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

At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Reconciling and Improving Formulations for Thermodynamics and Conservation Principles in Earth System Models (ESMs)

Abstract This paper provides a comprehensive derivation of the total energy equations for the atmospheric components of Earth System Models (ESMs). The assumptions and approximations made in this derivation are motivated and discussed. In particular, it is emphasized that closing the energy budget is conceptually challenging and hard to achieve in practice without resorting to ad hoc fixers. As a concrete example, the energy budget terms are diagnosed in a realistic climate simulation using a global atmosphere model. The largest total energy errors in this example are spurious dynamical core energy dissipation, thermodynamic inconsistencies (e.g., coupling parameterizations with the host model) and missing processes/terms associated with falling precipitation and evaporation (e.g., enthalpy flux between components). The latter two errors are not, in general, reduced by increasing horizontal resolution. They are due to incomplete thermodynamic and dynamic formulations. Future research directions are proposed to reconcile and improve thermodynamics formulations and conservation principles.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Resiliency of Degraded Built Infrastructure

Infrastructure resiliency depends on the ability of infrastructure systems to withstand, adapt, and recover from chronic and extreme stresses. In this white paper, we address the resiliency of infrastructure assets and discuss improving infrastructure stability through development of our understanding of cement and concrete degradation. The resiliency of infrastructure during extreme events relies on the condition, adaptability, and recoverability of built infrastructure (roads, bridges, dams), which serves as the backbone of existing infrastructure systems. Much of the built infrastructure in the US has consistently been rated D+ by the American Society of Civil Engineers (ASCE). Aged infrastructure introduces risk to the system, since unreliable infrastructure increases the likelihood of failures under chronic and extreme stress and are particularly concerning when extreme events occur. To understand and account for this added risk from poor infrastructure quality, more research is needed on (i) how the changing environment alters the aging of new and existing built infrastructure and (ii) how degradation causes unique failure mechanisms. The aging of built infrastructure is based on degradation of the structural materials, such as concrete and steel supports, which causes failure. Current work in cement/concrete degradation is based on (i) the development of high strength and degradation resistance concrete mixtures, (ii) methods of assessing the age and reliability of existing structures, and (3) modeling of structural stability and the microstructural evolution of concrete/cement from degradation mechanisms (sulfide attack, carbonation, decalcification). Sandia National Laboratories (SNL) has made several investments in studying the durability and degradation of cement based materials, including using SNL-developed codes and methodologies (peridynamics, PFLOTRAN) to focus on chemo-mechanical fracture of cement for energy applications. Additionally, a recent collaboration with the University of Colorado Boulder has included fracture of concrete gravity dams, scaling the existing work to applications in full sized infrastructure problems. Ultimately, SNL has the experience in degradation of cementitious materials to extend the current research portfolio and answer concerns about the resilience of aging built infrastructure.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Monitoring of dielectric permittivity in accelerated alkali-silica reaction concrete with microwave backscattering

Deterioration of concrete due to the alkali–silica reaction (ASR) involves a reaction between alkaline ions in the cement pore solution and non-crystalline silica found in many aggregates. The product of reaction is a porous hydrophilic ASR gel, which causes expansion and cracking of concrete structure. Currently, there is no reliable method for non-destructive evaluation of ASR. We have investigated the feasibility of using relative dielectric permittivity obtained from X-band microwave backscattering of concrete. Advantages of this method of concrete evaluation are that measurements are non-contact and one-sided, and the approach is scalable to arbitrary-size concrete structures. The exploratory study was performed using an unrestrained set of accelerated ASR concrete prism specimens developed according to the ASTM1293 standard. One set of specimens contained no entrained air, while another set was contained entrained air. The specimens were removed from the environmental chamber at different times during a year-long study to create a set of different ASR maturity specimens. Strain measurements were performed on all specimens. Strain data both air entrained and non-air entrained sets were shown to be in close agreement with Larive model of isothermal unrestrained ASR expansion with similar fitting parameters. Furthermore, relative dielectric permittivity of concrete specimens was correlated with strain data. Both sets show correlation between dielectric permittivity and strain, with stronger linear correlation observed for the air-entrained specimens. Development of such correlation would enable estimating expansion of actual concrete structures from microwave backscattering measurements, which could be used for risk stratification to guide and minimize coring.

36 MATERIALS SCIENCE↗

DOE OSTI2021

VERA-Grizzly Ex-Core Calculations: Watts Bar Unit 1 Cycles 1-2

The critical structures that comprise light-water reactor (LWR) nuclear power plants are subjected to operating environments that can challenge their integrity. Structures in close proximity to the reactor core, such as the reactor pressure vessel (RPV) and the biological shield wall, are subjected to high levels of radiation emanating from the core, as well as elevated temperatures. As the US fleet of operating LWRs ages, the effects of these operating environments on the integrity of these structures must be considered to ensure their continued safe operation. Extending the lifetime of commercial reactors and maintaining the aging reactor fleet require accurate prediction of the exposure of ex-core components to neutron and photon radiation. In particular, concrete degradation studies must be performed to evaluate the safety and long-term operation of reactors with lifetime extensions. The concrete reactor bioshield is important for providing radiological protection during operation and must last for the entire lifetime of the reactor. Recent interest in lifetime extensions furthers the need to accurately simulate concrete material degradation in the reactor bioshield. As a result of this need, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has funded this study to couple its tools, Virtual Environment for Reactor Applications (VERA) and Grizzly. VERA allows users to set up models to calculate time-dependent and fully coupled solutions (with thermal feedback) for ex-core quantities of interest such as vessel and coupon fluence and detector responses for multiple statepoints and cycles. Grizzly is a finite-element application based on the Multiphysics Object Oriented Simulation Environment (MOOSE) framework that is used to enable aging materials calculations. This report highlights the work performed to calculate the fluence in the vessel and concrete for Watts Bar Nuclear Plant Unit 1 (WBN1) Cycles 1 and 2. The fluences obtained from VERA were successfully transferred to Grizzly using a Python script. Four simulations were run with Grizzly: (1) the Mazars model with the initial Young’s modulus being the instantaneous modulus, (2) the Mazars model with the initial Young’s modulus being the delayed modulus, (3) the Mazars model with the initial Young’s modulus being the delayed modulus with the addition of the effects of micro-damage caused by irradiation, and (4) the Mazars model with the initial Young’s modulus being the instantaneous modulus, and with the addition of micro-damage and creep. Details regarding the methods used to obtain the fluence and the statistical errors associated with the VERA Monte Carlo Shift calculations are discussed in greater detail in this report. The results obtained from the four Grizzly models are also presented in this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Techno-Economic Modelling with H2A and H2FAST

This presentation introduces audience to techno-economic modelling using the H2A and H2FAST models. ARPA-E will use these models for evaluating technologies producing hydrogen and solid carbon. Solid carbon would be produced in a form suitable for construction materials such as high strength concrete.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Mechanism of drying-induced change in the physical properties of concrete: A mesoscale simulation study

Although many studies have found that drying alters the mechanical properties of concrete, the mechanism behind this change remains unclarified. The aim of this study is to elucidate the mechanism of change in properties of concrete after drying through the numerical calculation: a 3D mesoscale rigid-body-spring model (RBSM) with three phases, i.e. mortar, aggregate, and the interfacial transition zone while considering the properties changes of mortar due to drying. Based on the RBSM results, it is concluded that the change in compressive strength due to drying and heating is determined by a balance of the impact of drying-induced microcracking around coarse aggregates and the change in mechanical properties of the mortar due to drying. These mechanisms change the applied load required to reach the critical crack width and distribution, at which rim of the specimen begins to isolate from the core region and the load sustained by the rim decreases.

36 MATERIALS SCIENCE↗

DOE OSTI2024

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Average-atom model with Siegert states

In plasmas, electronic states can be well-localized bound states or itinerant free states, or something in between. In self-consistent treatments of plasma electronic structure such as the average-atom model, all states must be accurately resolved in order to achieve a converged numerical solution. Furthermore, this is a challenging numerical and algorithmic problem in large part due to the continuum of free states which is relatively expensive and difficult to resolve accurately. Siegert states are an appealing alternative. They form a complete eigenbasis with a purely discrete spectrum while still being equivalent to a representation in terms of the usual bound states and free states. However, many of their properties are unintuitive, and it is not obvious that they are suitable for self-consistent plasma electronic structure calculations. Here it is demonstrated that Siegert states can be used to accurately solve an average-atom model and offer advantages over the traditional finite-difference approach, including a concrete physical picture of pressure ionization and continuum resonances.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Tensor renormalization group approach to critical phenomena via symmetry-twisted partition functions

The locality of field theories strongly constrains the possible behaviors of symmetry-twisted partition functions, and thus they serve as order parameters to detect low-energy realizations of global symmetries, such as spontaneous symmetry breaking (SSB). We demonstrate that the tensor renormalization group (TRG) offers an efficient framework to compute the symmetry-twisted partition functions, which enables us to detect the symmetry-breaking transition and also to study associated critical phenomena. As concrete examples of SSB, we investigate the two-dimensional (2D) classical Ising model and the three-dimensional (3D) classical 𝑂⁡(2) nonlinear sigma model, and we identify their critical points solely from the twisted partition function. By employing the finite-size scaling argument, we find the critical temperature 𝑇 𝑐 = 2.2017⁢(2) with the critical exponent 𝜈 = 0.663⁢(33) for the 3D 𝑂⁡(2) model. In addition, we also study the Berezinskii–Kosterlitz–Thouless (BKT) criticality of the 2D classical 𝑂⁡(2) model by extracting the helicity modulus from the twisted partition functions, and we obtain the BKT transition temperature, 𝑇 BKT = 0.8928⁢(2).

lattice field theory↗

DOE OSTI2023

Bremsstrahlung from neutrino scattering via magnetic dipole moments

In this paper, we discuss bremsstrahlung induced by neutrino scattering. This process should exist since neutrinos are expected to couple to photons via magnetic dipole and transition moments. These moments are loop-induced and tiny in the Standard Model with neutrino masses but could be significantly enhanced in extended theories. As concrete example, we study the scattering of the two largest neutrino fluxes on earth, solar neutrinos and Cosmic Neutrino Background (CNB). Finally, it is tempting to consider this as a potential signature for CNB searches but it turns out that the signal is extremely small and unlikely to be observed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Seismic Tomography 2024

Seismic tomography is the most abundant source of information about the internal structure of the Earth at scales ranging from a few meters to thousands of kilometers. It constrains the properties of active volcanoes, earthquake fault zones, deep reservoirs and storage sites, glaciers and ice sheets, or the entire globe. It contributes to outstanding societal problems related to natural hazards, resource exploration, underground storage, and many more. The recent advances in seismic tomography are being translated to nondestructive testing, medical ultrasound, and helioseismology. Nearly 50 yr after its first successful applications, this article offers a snapshot of modern seismic tomography. Focused on major challenges and particularly promising research directions, it is intended to guide both Earth science professionals and early-career scientists. The individual contributions by the coauthors provide diverse perspectives on topics that may at first seem disconnected but are closely tied together by a few coherent threads: multiparameter inversion for properties related to dynamic processes, data quality, and geographic coverage, uncertainty quantification that is useful for geologic interpretation, new formulations of tomographic inverse problems that address concrete geologic questions more directly, and the presentation and quantitative comparison of tomographic models. Finally, it remains to be seen which of these problems will be considered solved, solved to some extent, or practically unsolvable over the next decade.

58 GEOSCIENCES↗

DOE OSTI2023

Physical Education Teachers’ Representations of Their Training to Promote the Inclusion of Students with Disabilities

School inclusion is based on the need to adopt and implement a holistic view of education, training, and human development embodied in the idea of everyone, for everyone. In the context of Physical Education (PE), there are still several constraints to the realization of this universal desideratum. Among these, teacher training and qualification for the inclusion of students with Specific Health Needs (SHNs) stands out. That is, students with physical and mental health problems whose impact is significantly manifested in the learning process. Thus, the objective of this study was to identify the representations of PE teachers about their training to develop inclusive processes with students with SES. Participants in this study were 151 PE teachers from different regions and districts of Portugal (Algarve, Aveiro, Castelo Branco, Lisbon, Porto, and Viseu) who had 23.6 ± 8.1 years of teaching service. Teachers answered an online questionnaire, on the Google Forms platform, with open and closed questions about their education and training to develop inclusive processes in PE. The results indicate two significant dimensions: (1) initial training for teaching inclusive PE and (2) continuous training for inclusion. Regarding initial training, a large majority of the teachers under study, at the end of their initial training, did not have the essential skills to teach PE to students with SES. It was also identified that a large majority reported not having had any contact with students with SES throughout their training process for teaching. It was also recognized that this training was not adjusted to the development of intervention skills with students with SHN. Regarding continuous training, it was identified that attendance at this training increased their skills to teach PE to students with SHN. Workshops/actions/training courses are the main training models adopted. However, it is recognized that the training provided does not respond concretely to their training needs to intervene with students with SHN, since teachers essentially seek to improve intervention in the context of inclusive physical education. We conclude that teacher training for inclusion is not yet fully adjusted to the reality of the inclusive school paradigm. In this sense, in practical terms, the following are suggested: (1) the need for reinforcement in study plans with specific and long-term curricular units; (2) the introduction of real practice components in context; and (3) supervised pedagogical practice in diverse contexts.

Celestino, Tadeu (ORCID:0000000163087981)↗

DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗

DOE OSTI2024

Pretest Modeling A Spent Nuclear Fuel Seismic Shake Test

The U.S. Department of Energy Spent Fuel and Waste Science and Technology (SFWST) program is planning to conduct a series of full-scale shake table tests to simulate hypothetical earthquake conditions and record the response of surrogate spent nuclear fuel (SNF) assemblies in a dry canister storage system mockup. The shake table motions will represent a range of hypothetical earthquake conditions at hypothetical locations in the continental U.S. to generally define the range of mechanical loads that SNF can be expected to experience during extended dry storage periods. This paper describes the pretest predictions made with LS-DYNA models of the mockup storage systems. The test will use two dry storage system configurations, a mockup vertical concrete cask (VCC) and a mockup horizontal storage module (HSM). The test will use a production-quality canister and basket. Within the canister will be four instrumented fuel assemblies with fuel rods containing surrogate mass and 28 instrumented dummy assemblies that are intended to match the mass and outer dimension of a fuel assembly. The finite element models include models of the VCC and HSM on the shake table to calculate the system level dynamic responses and separate single fuel assembly models to calculate stress and strain in fuel assembly components. Both types of models include nonlinear behavior like rod-to-rod contact and the ability for VCC’s to rock and slide. This paper presents the expected response of the VCC, HSM, and fuel assemblies to the shake table testing that is planned to start in April of 2024. The earthquake conditions represent seismic hazards in the 2,000-to-20,000-year return period range. The test data is expected to confirm the expectation that fuel rod cladding will remain intact, fuel assembly structural components like guide tubes will remain intact, and no significant VCC sliding or tipping will occur in the range of conditions to be tested with the shake table.

Klymyshyn, Nicholas A.↗

DOE OSTI2026

Trigonometric continuous-variable gates and hybrid quantum simulations of the sine-Gordon model

Hybrid qubit-qumode quantum computing platforms provide a natural setting for simulating interacting bosonic quantum field theories. However, existing continuous-variable gate constructions rely predominantly on polynomial functions of canonical quadratures. In this work, we introduce a complementary universality paradigm based on trigonometric continuous-variable gates, which enable a Fourier-like representation of bosonic operators and are particularly well suited for periodic and non-perturbative interactions. We present an ancilla-based framework for implementing trigonometric gates with arguments given by arbitrary Hermitian functions of qumode quadratures. The protocol yields unitary gates deterministically, and non-unitary gates through probabilistic post-selection. As a concrete application, we develop a hybrid qubit-qumode quantum simulation of the lattice sine-Gordon model. Using these gates, we prepare ground states via quantum imaginary-time evolution, simulate real-time dynamics, compute time-dependent vertex two-point correlation functions, and extract quantum kink profiles under topological boundary conditions. Our results demonstrate that trigonometric continuous-variable gates provide a physically natural framework for simulating interacting field theories on near-term hybrid quantum hardware, while establishing a parallel route to universality beyond polynomial gate constructions. We expect that the trigonometric gates introduced here to find broader applications, including quantum simulations of condensed matter systems, quantum chemistry, and biological models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Highlights: • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μm and a R{sup 2} value of 0.984. High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R{sup 2} > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Entanglement Capacity Estimates and Throughput Measurements of Quantum Channels

The throughput is an important performance metric of entangled qubit distribution quantum networks, and may be characterized by the number of distributed entangled qubit pairs per second (ebps). It is measured over physical quantum network connections using specialized instruments, including photonic entanglement sources and single photon detectors. Extensive theory has been developed to estimate the entangled qubit capacity of quantum channels using abstractions of physical connections. These two quantities both characterize the throughput performance but in different ways, and typically have been hard to relate to each other in concrete terms, in part due to the lack of precise measurements with matching analytical models and derivations. We describe measurements on a physical testbed with fiber connections of lengths 0-75 kilometers. We obtain the normalized analytic capacity estimates using the transmissivity approximations derived using single photon coincidence measurements, and convert them to bounds on throughput (measured in ebps) using a multiplier derived from co-located detector measurements. The results indicate consistent throughput measurements upper-bounded by their analytical capacity estimates across all connections. We show that previous capacity estimates using light measurements are below ebps measurements for some connections, due to the inclusion of non-representative decrease of light levels outside C-band with distance.

Rao, Nageswara↗

DOE OSTI2020

Optimization of Archimedes Screw for Use in Hydroelectric Projects

In 2017, the Department of Energy’s (DOE) Water Power Technologies Office (WPTO) made federal funding available to several awardees to investigate and provide innovative technological solutions to reduce capital costs of installed hydroelectric facilities at non-powered dam’s (NPD’s) across the United States, while maintaining a high level of efficiency. Awardees were to develop specific technologies that showed promise of reducing installed capital cost (ICC) by at least 20% over a given baseline and maintain a water-to-wire efficiency of at least 80% at head levels of 50 feet or less and flows of 1000 cubic feet per second (cfs) or less. Canyon Hydro (Canyon) was selected as an awardee for this funding and worked to refine the implementation of the Archimedes screw turbine for use at these site conditions. Canyon and its project partners analyzed several dam inventory datasets to determine the most common head and flow range of current NPD’s in order to best select design conditions for a turbine that would perform successfully at common heads and flows. Computational Fluid Dynamic (CFD) analyses were conducted after site conditions had been selected to optimize the turbine geometry for efficiency. Through this effort we were able to estimate water-to-wire efficiency over a range of flows. Equipment design focused on concepts of modularity and pre-fabricated construction. These methodologies were used where possible to reduce costly on-site construction and shorten deployment timelines. Cost modeling was conducted with input from industry experts in general construction, pre-fabricated concrete construction, and composite fabrication. As a hydroelectric turbine manufacturer of 35 years, Canyon drew upon its own knowledge base in steel fabrication, water-to-wire component specification and ancillary systems to develop the remaining cost matrix for the project. As a result of these efforts, Canyon was able to present findings that indicated a modular Archimedes screw turbine system could be designed that reduced ICC by 23.9% and maintain a maximum water-to-wire efficiency of 82.3%, meeting the goals of the project.

13 HYDRO ENERGY↗