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At least 1,063 records · Page 59

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DOE OSTI2021

Iron speciation in blast furnace slag cements

Slag-containing pastes and concretes were analysed by element-specific synchrotron-based techniques to determine the speciation of iron on crushed materials through spatially resolved micro-spectroscopic studies. The investigated cement samples were hydrated either in the laboratory, or exposed to river or sea water. Metallic iron, along with minor proportions of iron sulphide and magnetite was detected in the laboratory sample. Iron sulphide, goethite, and siliceous hydrogarnet were discovered in the blended slag cements hydrated in contact with river water for up to 7 years. In contrast, no Fe(0) was observed in blended concretes exposed to sea water. Instead, iron sulphide, iron(II)-hydroxide and -oxide, hematite, magnetite, siliceous hydrogarnet, and goethite were detected as well as ilmenite (FeTiO{sub 3}) in the aggregates. The strong acceleration of Fe oxidation in samples exposed to sea water and the long-term passivation observed in the other samples indicate comparable processes as those occurring on steel bars.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Effect of moderate temperatures on compressive strength of ultra-high-performance concrete: A microstructural analysis

Highlights: • All coarse aggregates and steel fibres were surrounded by the binder. • X-ray diffraction is useful for explaining the increase in compressive strength with increasing temperature. • Rosenhahnite and/or quartz Dauphiné-twinned phases improved the compressive strength. • Polypropylene fibres prevented spalling and preserved the compressive strength. • Concrete with steel and polypropylene fibres did not exhibit spalling at 300 °C. Concrete with two types of steel fibres and a polypropylene fibre prevented spalling and preserved the compressive strength at 300 °C, which makes these concretes suitable for long-term applications up to 300 °C, such as for steam collectors or thermal energy storage systems. The compressive strength behaviour of three types of ultra-high-performance fibre-reinforced concrete manufactured with the same matrix was investigated. For this purpose, a complete characterisation of all the raw materials and the three types of fibres used was performed. The morphology of all concrete mixtures at room temperature was analysed using scanning electron microscopy–energy-dispersive X-ray spectroscopy. From the results, it was ascertained that the steel fibres and coarse siliceous aggregates were not in contact (being separated by ≥3.41 μm) and were surrounded by the binder (of ≥1 μm in thickness) for all the mixtures studied. Rosenhahnite and/or quartz Dauphiné-twinned phases improved the compressive strength (as determined by X-ray diffraction).

36 MATERIALS SCIENCE↗

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 OSTI2021

DEM analysis of the effect of interface transition zone on dynamic splitting tensile behavior of high-strength concrete based on multi-phase model

Highlights: • Crushable aggregates with the real geometry shape are generated by using clump-cluster method. • Flat-Joint Model is adopted as the constitutive law. • The tensile strength is almost not affected by ITZ strength when its strength exceeds 0.8 times that of mortar. • A modified formula that can reflect the functional relationship between the ITZ strength and the TDIF is proposed. The tensile strength is one of the main parameters in the design of concrete structures and has an important influence on the cracking performance and durability of concrete materials. As the weak region in concrete, the strength of the interface transition zone (ITZ) directly affects the dynamic mechanical response of concrete. Meanwhile, considering that the tensile performance of concrete is far weaker than its compressive performance, the study of the strength of ITZ is of great significance to the dynamic tensile fracture characteristics and mechanical properties of concrete. In this paper, a method combining 3D scanning technology and Clump-Cluster was proposed to generate a real geometry shape model of crushable aggregate. On this basis, the 3D discrete element model (DEM) of high-strength concrete was constructed using the Flat-Joint Model (FJM). The effects of the ITZ strength on the failure mode, number of microcracks, tensile strength and strain rate effect of high-strength concrete under strain rates from 0.01/s to 100/s were studied by splitting tensile test. The simulation results indicate that the number of microcracks generated decreases with the increase of ITZ strength, and the proportion of ITZ cracks decreases obviously. When the ITZ strength exceeds the critical value of 0.8 times that of mortar, the tensile strength is almost not affected by ITZ strength. In addition, based on the numerical simulation results, a modified formula that can reflect the functional relationship between the ITZ strength and the dynamic increasing factor of the concrete tensile strength (TDIF) is proposed, and the rationality of the TDIF modified formula is verified.

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 OSTI2023

Thermal energy storage in concrete: Review, testing, and simulation of thermal properties at relevant ranges of elevated temperature

This study examines the thermal performance of concrete used for thermal energy storage (TES) applications. The influence of concrete constituents (aggregates, cementitious materials, and fibers) on the thermal conductivity and specific heat are summarized based on literature and via experimentation at elevated temperatures. It is indicated that concrete with siliceous aggregate, low water/cement ratios, and steel fibers produces better overall thermal properties. The results of literature review and laboratory testing are used for numerical simulation to evaluate the impact of concrete thermal property variability on TES capabilities. The TES module consists of the heat exchanger and surrounding concrete as the storage media. Both idealized cases without heat loss and realistic cases with insulation and heat losses are considered. Furthermore, the result shows that conventional concrete mixes can be tailored to decrease the module charge/discharge durations and increase the amount of thermal energy storage.

25 ENERGY STORAGE↗

DOE OSTI2022

Sensitivity of the strength and toughness of concrete to the properties of the interfacial transition zone

We report Civil infrastructure is made primarily of concrete structures or components and therefore understanding durability and fracture behavior of concrete is of utmost importance. Concrete contains an interfacial transition zone (ITZ), a porous region surrounding the aggregates, that is often considered to be the weakest region in the concrete. The ITZ is poorly characterized and property estimates for the ITZ differ considerably. In this simulation study, representative concrete mesostructures are produced by packing coarse aggregates with realistic geometries into a mortar matrix. A meshless numerical method, peridynamics, is utilized to simulate the mechanical response including fracture under uniaxial compression and tension. The sensitivity of the stiffness and fracture toughness of the samples to the ITZ properties is computed, showing strong relationships between the ITZ properties and the effective modulus and effective yield strength of the concrete. These results provides insight into the influence of the poorly characterized ITZ on the stiffness and strength of concrete. This work showcases the applicability of peridynamics to concrete systems, matching experimental strength and modulus values. Additionally, relationships between the ITZ’s mechanical properties and the overall concrete strength and stiffness are presented to enable future design decisions.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A mesoscale 3D model of irradiated concrete informed via a 2.5 U-Net semantic segmentation

The concrete biological shield in light-water reactors is exposed to neutron and gamma irradiation, which deteriorates the concrete’s mechanical properties in the long term. To assess the irradiation-induced damage, predictive mechanical models are developed and used in parallel with the characterization of irradiated concrete samples. Realistic 3D simulation domains can drastically improve a model’s prediction. In this work, we utilized x-ray computed tomography (XCT) data of a concrete specimen to reconstruct its 3D microstructure. The XCT data shows low contrast between the concrete’s aggregates and cement paste, resulting in poor image segmentation when using traditional unsupervised techniques. To address this issue, we developed and trained a 2.5D U-Net model on only 24 pre-labeled XCT layers to segment 651 layers of the XCT data. The overall F1-score of the model is approximately 96%. Then, we created a 3D finite element (FE) mesh based on the stack of segmented images. The FE model contains radiation-induced expansion, damage, and creep. The constitutive equations are adapted to each phase (aggregates and cement paste). Here, we simulated the effects of neutron irradiation in the concrete specimen as well as the specimen’s mechanical response to uniaxial compression. Finally, model validation was performed using experimental data on similar concrete specimens in the literature.

2.5D U-Net↗

DOE OSTI2021

A Flexible and Generic Functional Mock-up Unit Based Threat Injection Framework for Grid-interactive Efficient Buildings: A Case Study in Modelica

Grid-interactive efficient buildings (GEBs) have been considered as an important asset to support the power grid reliability by utilizing the demand flexibility offered by GEBs. GEBs are enabled by advances in sensors and controls, and the communication between building equipment, whole buildings, and the grid. The integration of different building technologies and network-based communication system makes GEBs vulnerable to passive threats such as equipment failure and active threats such as cyber-attacks. Modeling and simulation is an effective way to evaluate the impact of threats on the system performance. This paper proposes a generic and flexible threat injection framework for commonly-used building energy simulators such as EnergyPlus and Modelica to support threat modeling and evaluation. This framework leverages functional mock-up unit (FMU) to develop a general modeling interface for threat injection and simulation. A numerical case study using Modelica as a building energy simulator is conducted to demonstrate the capability of the framework for supporting single/multiple-order threat modeling and simulation of a GEB. Four threats and their combinations are injected on a Modelica-based threat-free building energy and control system, including operating supply fan at its full speed, remotely cycling the chiller on and off, blocking the chiller from receiving the chilled water supply temperature setpoints, and hijacking the global zone air temperature setpoint. Simulation results show that the cyber-attack that leads to short-term signal blocking has small effects on the system operation due to the "self-healing" feature of the heating, ventilation, and air-conditioning (HVAC) interactive control system. The threat that takes control of resetting the global zone air temperature setpoints has the most adverse impact on the system energy use, peak power demand, thermal comfort and the provision of demand flexibility. The combination of four threats have aggregative effects on the system but the effects are less than the additive effects of the individual threat.

Fu, Yanyang↗

DOE OSTI2023

Developing and tuning a community scale energy model for a disadvantaged community

This work describes the development of a community-scale energy model for a mixed-use low-income community located in Huntington Beach, CA. An accurate community-scale energy model is useful for evaluating the use of limited capital resources used to invest in clean energy technologies. This work lays out the process of developing such a model while relying primarily on publicly available data and highlighting critical partnerships necessary for model development success. The primary contribution of this work is the demonstration of the process used to develop an accurate energy model for a disadvantaged community when minimal building and energy use data is available. The heart of the model is the physics-based community scale energy modeling platform URBANopt. Using a bottom-up load modeling approach, energy simulated energy use falls within 3% or less of aggregate annual utility data, and within 10% or less aggregate monthly utility data. The demonstrated model development and tuning process can be used by others to characterize other atypical communities, which may differ significantly from prototypical models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗

DOE OSTI2023

Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting

Here, the growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.

14 SOLAR ENERGY↗

DOE OSTI2021

An assessment of power flexibility from commercial building cooling systems in the United States

Understanding varying characteristics and aggregate potential of power flexibility from different building types considering regional diversity is critically important to actively engaging building resources in future eco-friendly, low-cost, and sustainable power systems. This paper presents a comprehensive characteristics analysis and potential assessment of the power flexibility from heating, ventilation, and air conditioning (HVAC) loads in commercial buildings in the U.S. using a simulation-based method. In this method, commercial buildings are first grouped by building types and climate regions. The U.S. Department of Energy Commercial Prototype Building Models are used to represent an average building in each group and are simulated to characterize corresponding power flexibility. Based on building survey data, the number of commercial buildings in each group is estimated and used to calculate aggregate power flexibility. It is found that HVAC loads in commercial buildings offer more flexibility for increasing power consumption than for decreasing it. The power consumption of commercial buildings in the U.S. can be increased by 46 GW and decreased by 40 GW on peak summer days. Among all commercial building types, standalone retail buildings provide the most absolute flexibility while the medium office buildings have the most flexibility as a percentage of the rated power consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Identification of engine oil-derived ash nanoparticles and ash formation process for a gasoline direct-injection engine

Engine oil-derived ash particles emitted from internal combustion (IC) engines are unwanted by-products, after oil is involved in in-cylinder combustion process. Since they typically come out together with particulate emissions, no detail has been reported about their early-stage particles other than agglomerated particles loaded on aftertreatment catalysts and filters. To better understand ash formation process during the combustion process, here differently formulated engine oils were dosed into a fuel system of a gasoline direct injection (GDI) engine that produces low soot mass emissions at normal operating conditions to increase the chances to find stand-alone ash particles separated from soot aggregates in the sub-20-nm size range. In addition to them, ash/soot aggregates in the larger size range were examined using scanning transmission electron microscopy (STEM)-X-ray electron dispersive spectroscopy (XEDS) to present elemental information at different sizes of particles from various oil formulations. The STEM-XEDS results showed that regardless of formulated oil type and particle size, Ca, P and C were always contained, while Zn was occasionally found on relatively large particles, suggesting that these elements get together from an early stage of particle formation. The S, Ca and P K-edge X-ray absorption near edge structure (XANES) analyses were performed for bulk soot containing raw ash. The linear combination approach & cross-checking among XANES results proposed that Ca 5 (OH)(PO 4 ) 2 , Ca 3 (PO 4 ) 2 and Zn 3 (PO 4 ) 2 are potentially major chemical compounds in raw ash particles, when combined with the STEM-XEDS results. Despite many reports that CaSO 4 is a major ash chemical when ash found in DPF/GFP systems was examined, it was observed to be rarely present in raw ashes using the S K-edge XANES analysis, suggesting ash transformation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

In situ deformation of antigorite-olivine two-phase mixtures: Implications for dynamics and seismic anisotropy in the mantle wedge

Water released from hydrous minerals in subducting slabs reacts with the overlying plate, resulting in widespread serpentinization in the mantle wedge. Deformation of serpentinized peridotites has been invoked to explain forearc seismic anisotropy, yet studies of the mechanical properties and deformation behaviors of serpentine-bearing multiphase aggregates remain limited. Here we deformed olivine-antigorite mixtures containing 70, 50, and 20 vol.% of antigorite at 2.5 – 7.6 GPa, 673 K and strain rates of ∼10 –5 –10 –4 s –1 . Elasto-viscoplastic self-consistent simulations, constrained by synchrotron X-ray diffraction (XRD) data, were used to estimate lattice strain, stress–strain partitioning, crystallographic preferred orientations (CPO) development, and aggregate strength. Selected run products were also analyzed by electron backscatter diffraction for comparison with the CPO results obtained from XRD experiments. We found olivine transitions from A- or B-type to C-type when antigorite fraction drops to 20 vol.%, coinciding with a microstructural change from interconnected weak layers to a load-bearing framework (LBF). An additional run on a sample Atg50/Ol50 with preexisting microstructures suggested the formation of LBF was promoted by these microstructures, although the preexisting antigorite CPO has been overprinted at 20.8 % strain and could be erased completely by subsequent deformation in nature. Estimated viscosity of the two-phase mixtures suggests that low-degree serpentinization (≤20 %) in the mantle wedge may increase the strength of olivine-rich peridotite and hinder slab-mantle decoupling, whereas high-degree serpentinization (≥50–70 %) weakens the peridotite and favors decoupling if sufficient viscosity contrast (>10) develops. Seismic anisotropy shows a nonlinear dependence on antigorite fraction: antigorite CPO governs the anisotropy of the mixtures with ≥50 vol.% antigorite, whereas olivine CPO dominates at low fractions (∼20 vol.%). The presence of pre-existing microstructures reduces seismic anisotropy of the deformed mixtures, however the persistence of pre-existing CPO in actively subducting slabs remains uncertain, making their significance over geological timescales questionable.

Crystallographic preferred orientation↗

DOE OSTI2024

Levelized cost of charging of extreme fast charging with stationary LMO/LTO batteries

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

25 ENERGY STORAGE↗

DOE OSTI2026

Roadmap and Benchmarking: Privacy in Federated Load Forecasting

Data-driven techniques for energy demand forecasting continue to emerge with promising impacts on distribution grid planning. However, the development of robust and generalizable machine learning models requires that representative high quality training data are available. Distributed energy resources have begun to embed intelligence, gathering large amounts of data on customer demand, behavior, and household devices that are connected to the grid. Though utilities aggregate meter-level demand data for load shaping, demand response, outage management, reliability planning, and billing applications, there lies an inherent privacy concern in sharing consumption data that may identify individual consumer behavioral patterns. Hence, while sharing the data is crucial, the private sensitive customer data must be safeguarded from being exposed or manipulated. In this study, we propose a roadmap for implementing a based privacy preserving framework to support the advancement of data-driven analytics in data-sensitive distributed energy resources environments. The roadmap incorporates federated learning–a distributed training framework, differential privacy–a statistical framework that provides guarantees to safeguard the leakage of sensitive data, secure multiparty computation and homomorphic encryption– techniques for encrypting model gradients and applying secure aggregation on the server. Moreover, we perform baseline experiments on the federated short-term load forecasting (STLF) task using open-source residential load profile datasets, offering insights into the challenges of integrating differential privacy into federated learning.

Abebe, Waqwoya [Oak Ridge National Laboratory (ORN↗

DOE OSTI2022

Tri-level hybrid interval-stochastic optimal scheduling for flexible residential loads under GAN-assisted multiple uncertainties

Various building loads, such as heating, ventilation, and air conditioners (HVACs), electric water heaters (EWHs), and electric vehicles (EVs), can introduce opportunities for improving the flexibility of electricity consumption while satisfying the needs of building owners as well as benefiting the resilience of distribution system. To utilize such flexibility, a tri-level distribution market framework is established, including residential consumers, load aggregators (LAs), and the distribution system operator (DSO). In this work, the uncertainties from all three levels are considered. The random consumption behavior at the consumer level is modeled as a Gaussian noise that is also aggregated and transmitted to the LA level. The weather temperature in the LA level is forecasted as an interval, and the photovoltaic (PV) power in the market-clearing level is modeled by a set of power scenarios generated by Generative Adversarial Networks (GANs). Then, a hybrid interval-stochastic programming is proposed to transform the uncertain problems in the first two levels into deterministic ones. For real-time implementations, a rolling horizon optimization (RHO) scheme is employed to continuously optimize the power consumption based on the latest operating information. Finally, case studies on a modified IEEE 69-bus system validate the effectiveness of the proposed uncertainty modeling strategies and the RHO scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗