• Energy Research
• 2021-2025close
• engineering and technology close
• US close
• ES close

This study focuses on formulating the most sustainable concrete by incorporating recycled concrete aggregates and other products retrieved from construction and demolition (C&D) activities. Both recycled coarse aggregates (RCA) and recycled fine aggregates (RFA) are firstly used to fully replace the natural coarse and fine aggregates in the concrete mix design. Later, the cement rich ultrafine particles, recycled glass powder and mineral fibres recovered from construction and demolition wastes (CDW) are further incorporated at a smaller rate either as cement substituent or as supplementary additives. Remarkable properties are noticed when the RCA (4–12 mm) and RFA (0.25–4 mm) are fully used to replace the natural aggregates in a new concrete mix. The addition of recycled cement rich ultrafines (RCU), Recycled glass ultrafines (RGU) and recycled mineral fibres (RMF) into recycled concrete improves the modulus of elasticity. The final concrete, which comprises more than 75% (wt.) of recycled components/materials, is believed to be the most sustainable and green concrete mix. Mechanical properties and durability of this concrete have been studied and found to be within acceptable limits, indicating the potential of recycled aggregates and other CDW components in shaping sustainable and circular construction practices. The authors wish to acknowledge the financial support from EU Horizon 2020 Project VEEP ‘‘Cost-Effective Recycling of C&DW in High Added Value Energy Efficient Prefabricated Concrete Compo-nents for Massive Retrofitting of our Built Environment” (No.723582).

Interest in the highly efficient energy hub (EH) model has been growing despite the high computational requirements of planning for a multi-energy, multi-device operation. To address both the device size limitation and the multi-scenario issue, we propose a new solution methodology for solving the EH planning problem. In the method, the decision variables are device sizes. First, a dimension reduction technique is proposed to address the curse of dimensionality based on the correlation of unknown variables such as the capacities of different devices in an EH. Second, to avoid local convergence, a solution method called the variable-sized unimodal searching (VUS) approach is proposed to assure a global optimal planning scheme for the one-dimensional non-convex optimization model obtained from the preceding dimension reduction process. The case study indicates that the proposed approach has a higher computing efficiency than the Benders decomposition (BD) algorithm to deal with a scenario-based stochastic planning problem with a large number of scenarios. Thus, the effectiveness of the EH planning approach is verified.

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Abstract AP1000® Generation III+ reactor bases its safety concept on passive systems, differently from the previous Generation II reactors. This fact has led the approximations and methodologies previously used for modeling active safety systems to be reviewed and adapted to simulate the physics of passive systems. Diverse studies about the AP1000 containment have demonstrated the difficulty to correctly model the occurring phenomenology. In this paper, an integral AP1000 3D containment GOTHIC model is presented, including the Passive Containment Cooling System (PCCS). The model includes the compartments inside and outside the metallic containment liner that influence the thermal–hydraulic behavior. The model is tested against a Large Break Loss of Coolant Accident (LBLOCA) to assess its thermal–hydraulic performance, assuming a PCS tank malfunction, what is a conservative hypothesis. The pressure and temperature evolution predicted by the 3D containment model is analyzed and compared with a single node Lumped Parameters model, allowing to evaluate some preliminary benefits of 3D modeling for containment safety analysis. The 3D containment model allows to predict the thermal evolution in each containment compartment capturing the heterogeneity of this phenomenon, with higher resolution than the lumped parameters models traditionally used in this kind of analyses. It allows to observe the thermohydraulic conditions locally at any time during the transient.

Hydraulic fracturing drilling technology can cause a high risk of surface spill accidents and thus water contamination. Climate change together with the high water demand and rapid increase in industrial and agricultural activities are valued reasons why we should all care about the availability of water resources and protect them from contamination. Hence, the purpose of this study is to estimate the risk associated with a site contaminated with benzene from oil spillage and its potential impact on groundwater. This study focused on investigating the impact of soil variability and water table depth on groundwater contamination. Temperature-dependent parameters, such as soil water content and the diffusion of pollutants, were considered as key input factors for the HYDRUS 1D numerical model to simulate benzene migration through three types of soil (loamy, sandy clay loam, and silt loam) and evaluate its concentration in the water aquifer. The results indicated that an anticipated increase in earth’s average surface temperature by 4 °C due to climate change could lead to a rise in the level of groundwater pollution in the study area by 0.017 mg/L in loamy soil, 0.00046 mg/L in sandy clay loam soil, and 0.00023 mg/L in silt loam soil. It was found that climate change can reduce the amount of benzene absorbed from 10 to 0.07% in loamy soil, 14 to 0.07% in sandy clay loam soil, and 60 to 53% in silt loam soil. The results showed that the soil properties and solute characteristics that depend on the temperature have a major and important role in determining the level of groundwater pollutants.

Abstract Molten salt reactors (MSRs) are a class of next-generation nuclear reactors that have received recent industrial and research interest. A generalized species transport solver was implemented in the Virtual Environment for Reactor Applications (VERA) computing suite to extend this tool to analyze liquid-fueled MSRs. This core simulator has been extended to model the transport of fission product gases into a collection of circulating gas bubbles with the purpose of removing the gases. This paper presents the governing species transport equation, along with various nuclear source terms. Development of the source term for phase migration is discussed, along with a simplified interfacial area tracking method. Finally, a case study on a simplified MSR loop is presented in which modeling parameters were varied to assess their impact on gas removal. The steady state results show that parameters such as bubble diameter, gas injection rate and mass transfer coefficient have a low to moderate effect on the fraction of xenon in the core region. Removal efficiency has the greatest effect on the fraction in the core region. After the pump bowl, bubble diameter has a minor effect on the fraction of xenon in the gas void. These results point out that increasing parameters such as mass transfer coefficient, gas injection rate, and removal efficiency drives the xenon into the circulating gas void, while decreasing bubble diameter also drives xenon into the gas void by increasing interfacial area.

As is the case with several other mechanical power takeoffs (PTOs), the mechanical-motion-rectifier-based PTO consists of components, such as one-way clutches, gears, a ball screw, mechanical couplings, and a generator. Equivalent circuit models have been created in this article to describe the dry frictions, viscous damping, and mechanical compliances in these components, so the nonideal efficiency and nonlinear force of the PTO can be predicted in electrical simulations by integrating these subcircuit models. The circuit model is simplified, and its parameters are categorized as dc and ac unknowns. The dc and ac tests on the PTO are performed sequentially to extract two sets of parameters through linear regression or nonlinear curve fitting. Then, the model is validated through its prediction capability over 25 test conditions on input forces, output voltages, and efficiencies, with correlation coefficients of 0.9, 0.98, and 0.981, respectively.

This paper proposes CONCERTS: Coverage competency-based target search, a failure-resilient path-planning algorithm for heterogeneous robot teams performing target searches for static targets in indoor and outdoor environments. This work aims to improve search completion time for realistic scenarios such as search and rescue or surveillance, while maintaining the computational speed required to perform online re-planning in scenarios when teammates fail. To provide high-quality candidate paths to an information-theoretic utility function, we split the sample generation process into two steps, namely Heterogeneous Clustering (H-Clustering) and multiple Traveling Salesman Problems (TSP). The H-Clustering step generates plans that maximize the coverage potential of each team member, while the TSP step creates optimal sample paths. In situations without prior target information, we compare our method against a state-of-the-art algorithm for multi-robot Coverage Path Planning and show a 9% advantage in total mission time. Additionally, we perform experiments to demonstrate that our algorithm can take advantage of prior target information when it is available. The proposed method provides resilience in the event of single or multiple teammate failure by recomputing global team plans online. Finally, we present simulations and deploy real hardware for search to show that the generated plans are sufficient for executing realistic missions.