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Abstract for CRADA between Mati Car bon PBC and NETL
The National Energy Technology Laboratory (NETL) will assist Mati Carbon PBC (participant) in the design and execution of their community benefits plan (CBP) for scaling Enhanced Rock Weathering (ERW) carbon dioxide removal (CDR) in Arkansas. ERW involves spreading finely crushed rocks in agricultural fields to speed up the dissolution and release of certain minerals that amend soil acidity, replenish soil nutrients, and simultaneously converting CO 2 from the atmosphere to Dissolved Inorganic Carbon (DIC). ERW not only removes CO 2 from the atmosphere but also has the potential to generate co-benefits for famers through cost reductions and increased crop yields. The collaboration between NETL and Mati Carbon, PBC (participant) will entail a detailed review of the economic, environmental and community impact of ERW in agricultural settings. This review will guide all project related decisions. It will provide a better understanding of the scale of these potential co-benefits and quantify community needs that ERW can address.
Periodic boundary conditions for arbitrary deformations in molecular dynamics simulations
A generalization of the Lees-Edwards periodic boundary conditions (gLE-PBC) for molecular dynamics (MD) simulations is developed to allow for arbitrary deformations to be applied to the domain. The gLE-PBC domain remains a rectangular cuboid regardless of the applied deformation in contrast with the Lagrangian-rhomboid periodic boundary conditions (LR-PBC) where the domain deforms according to the applied deformation. Furthermore, the kinematics of gLE-PBC are validated against pure shear. The gLE-PBC method for interacting systems is then validated against the LR-PBC method and analytical solutions for a solid under isotropic compression, one-dimensional shearing, three-dimensional extension and shearing and for a liquid under Couette flow. Bulk physical properties extracted from the gLE-PBC simulations agree well with values calculated from equilibrium MD simulations. Three dimensional shearing and a deformation with a full velocity gradient matrix are also simulated, showing the range of problems gLE-PBC can explore.
Phasor Based Control with the Distributed, Extensible Grid Control Platform
This paper describes how to implement Phasor Based Control (PBC) using the Distributed, Extensible Grid Control (DEGC) Platform. PBC is a novel method for controlling distributed energy resources (DER) that coordinates a centralized optimization with distributed feedback controllers to enforce voltage phasor targets. DEGC is an open source software and communication platform designed for general DER control. We deployed PBC at Lawrence Berkeley Lab's FLEXLAB test site, conducting multiple hardware-in-the-loop test runs. Here, we describe how DEGC was used to implement PBC on hardware, and results demonstrating successful deployment.
Plutonium Basket Counter Measurement Control Q3-Q4 2023
The Plutonium Basket Counter (PBC) is a spent fuel nuclear safeguards instrument that was developed to measure spent-fuel elements from MAGNOX-type research reactors and determine their plutonium content. The instrument is designed to measure fuel elements in spent fuel cooling pools through underwater operation, but it is also able to measure radiation sources in air. The PBC determines fuel plutonium content by detecting neutrons with an array of Helium-3 detectors. The electronics make use of a JSR-15 shift register for data collection and IAEA Neutron Coincidence Counting (INCC) software for data analysis. The PBC is used to determine the 240 Pu content in spent MAGNOX fuel elements grouped into basket-like bundles, thus the instrument’s name. Reactor burnup calculations can be used in conjunction with the data from the PBC to estimate the total plutonium content in the fuel.
Conformal high-entropy oxide coatings enable fast and durable surface oxygen reactions
Developing active and durable air electrodes for efficient oxygen reactions is challenging for protonic ceramic cells (PCCs), especially at temperatures below 550°C. Here, in this study, we report a rationally designed conformal coating with a high-entropy PrNi 0.2 Mn 0.2 Co 0.2 Fe 0.2 Cu 0.2 O 3−δ (PNMCFC) perovskite structure on the surface of a state-of-the-art PrBaCo 2 O 5+δ (PBC) air electrode. The formed hybrid air electrode (PNMCFC-PBC) shows faster surface oxygen kinetics and a more stable phase structure in high-humidity air than the bare PBC electrode. Further density functional theory calculations suggest that the conformal coating mitigates Ba segregation at the interface and improves oxygen-related reactions, enhancing overall stability and electrocatalytic performance. The cells with the developed hybrid electrodes show encouraging electrochemical performance at 550°C: a polarization resistance of 0.72 Ω cm 2 , a peak power density of 1.30 W cm −2 , an electrolysis current density of −1.36 A cm −2 at 1.3 V, and reasonable operating stabilities (∼200 h at 550°C).
On the closedness and geometry of tensor network state sets
Tensor network states (TNS) are a powerful approach for the study of strongly correlated quantum matter. The curse of dimensionality is addressed by parametrizing the many-body state in terms of a network of partially contracted tensors. These tensors form a substantially reduced set of effective degrees of freedom. In practical algorithms, functionals like energy expectation values or overlaps are optimized over certain sets of TNS. Concerning algorithmic stability, it is important whether the considered sets are closed because, otherwise, the algorithms may approach a boundary point that is outside the TNS set and tensor elements diverge. Here we discuss the closedness and geometries of TNS sets, and we propose regularizations for optimization problems on non-closed TNS sets. We show that sets of matrix product states (MPS) with open boundary conditions, tree tensor network states, and the multiscale entanglement renormalization ansatz are always closed, whereas sets of translation-invariant MPS with periodic boundary conditions (PBC), heterogeneous MPS with PBC, and projected entangled pair states are generally not closed. The latter is done using explicit examples like the W state, states that we call two-domain states, and fine-grained versions thereof.
Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control
This paper evaluates the virtual storage capability of a residential air-conditioning (AC) system by utilizing the building mass as a thermal storage to enable sustainable cities through model-based predictive control (MPC). The control-oriented building model was developed with a grey-box model structure based on the experimental data to predict the indoor air temperature. Based on the model, the operation cost-saving potential of the MPC was investigated in a single house with different comfort levels, electricity prices, and prediction horizons. The cost-saving of the MPC was approximately 10.6 % compared to the conventional control. The MPC was expanded to a residential building cluster considering the peak demand charge. In an hourly shedding and load-up scenario, the performances of the MPC and the heuristic prior-based control (PBC) are promising. When shedding was enforced all day, the peak demand saving of the MPC was 36~38 %, whereas that of the PBC was 25.7 % compared to the baseline. In addition, the monthly electricity bill, including the operation and peak demand cost, was investigated with different demand charge rates and weather conditions. The total saving decreased with a low demand charge rate and hot conditions, and the operation cost saving was compromised with more aggressive demand shedding.
Visual Tool for Assessing Stability of DER Configurations on Three-Phase Radial Networks
Here, we present a method and tool for evaluating the placement of Distributed Energy Resources (DER) on distribution circuits in order to control voltages and power flows. Our previous work described Phasor-Based Control (PBC), a novel control framework where DERs inject real and reactive power to track voltage magnitude and phase angle targets. Here, we employ linearized power flow equations and integral controllers to develop a linear state space model for PBC acting on a three-phase unbalanced network. We use this model to evaluate whether a given inverter-based DER configuration admits a stable set of controller gains, which cannot be done by analyzing controllability nor by using the Lyapunov equation. Instead, we sample over a parameter space to identify a stable set of controller gains. Our stability analysis requires only a line impedance model and does not entail simulating the system or solving an optimization problem. We incorporate this assessment into a publicly available visualization tool and demonstrate three processes for evaluating many control configurations on the IEEE 123-node test feeder (123NF).
Residential Building Stock Characterization in Palm Beach County, Florida
This building stock characterizations is intended to help Palm Beach County (PBC) Office of Resilience (OOR) and municipalities composing the Municipal Resilience Partnership prioritize building energy efficiency investments to reduce energy costs and increase resilience for the county's most vulnerable residents. The analysis utilizes NREL’s ResStock model to characterize PBC’s residential building stock, including building type, renter/owner status, size (square footage), age of buildings (vintage), HVAC system types, and, for multi-family buildings, number of units. Building energy efficiency, weatherization, and electrification upgrade packages were assessed for approximate cost, customer bill-savings, and emissions reductions potential and findings will help guide OOR financial assistance program design.
Development of neural network force fields for corrosion studies
To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.
Sorption kinetics and stability of conventional adsorbents for mercury remediation
In-situ remediation of mercury at numerous contaminated sites worldwide is a challenging and costly endeavor due to the persistency of this contaminant. In this study, we evaluated eight commercially available sorbent media ranging from carbon-, clays- and silica-based materials (PBC– Biochar, eSorb – Sorbster, nsPAC – Powdered Activated Carbon, fsPAC – Powdered Activated Carbon with Mackinawite, F300 – Filtrasorb 300, Si-SH – Silica Thiol, eBind – RemBind, Q-Clay – Organoclay PM-199), for their effectiveness in sorbing mercury (Hg 2+ ) and mercury complexed with dissolved organic matter (Hg-DOM). Under the chosen experimental conditions of this study, results showed that in the absence of DOM, the kinetic rates of Hg 2+ sorption onto the evaluated sorbents were in the order of 0.31 min-1 (Si-SH) to 2.98 min -1 (nsPAC), whereas in the presence of DOM, the rates varied from 0.16 min-1 (F300) to 0.95 min -1 (nsPAC). The measured sorption capacity for Hg 2+ in the absence of DOM varied from 3.02 mg/g (Q-Clay) to 35.15 mg/g (Si-SH), whereas in the presence of DOM, calculated partition coefficient (KD) ranged from 69.7 mL/g (Q-Clay) to 41,510 mL/g (Si-SH). Furthermore, kinetic data suggest liquid film diffusion was the rate-limiting steps governing mercury sorption onto the studied media. Overall, the obtained study parameters (kinetics/isotherm) are particularly important in informing robust engineering designs for deployment of the vast majority of evaluated sorbents. Thus, sorbent-based strategies offer viable solutions for cost-effective cleanup of mercury at industrially contaminated sites.
A methodology to generate crystal-based molecular structures for atomistic simulations
Abstract We propose a systematic method to construct crystal-based molecular structures often needed as input for computational chemistry studies. These structures include crystal ‘slabs’ with periodic boundary conditions (PBCs) and non-periodic solids such as Wulff structures. We also introduce a method to build crystal slabs with orthogonal PBC vectors. These methods are integrated into our code, Los Alamos Crystal Cut ( LCC ), which is open source and thus fully available to the community. Examples showing the use of these methods are given throughout the manuscript.
Iterative Linearization for Phasor-Defined Optimal Power Dispatch
Optimal power flow (OPF) problems, which dispatch power targets to controllable generating units across a network, must generally account for non-convex constraints on power flow. Furthermore, adapting those problems so as to make them solvable with convex optimization techniques is an area of much academic and operational interest. In this paper, we present a method for solving OPF as a quadratic program by iteratively refining and re-initializing a linearized model of power flow based on the outputs of an associated nonlinear solver. The linear model on which we demonstrate this method is an adapted version of an approximation designed for use with unbalanced distribution networks. As an important benefit, the model allows for the explicit inclusion of nodal voltage phasor values in both the OPF problem's objective and its constraints, which opens the door to the idea of phasor-based control (PBC) design. We show in simulations on the IEEE 13-node test feeder that our method quickly converges to a set of phasor targets that are sufficiently precise for use in operations at the distribution level.
Phasor-Based Control for Scalable Solar PV Integration
This project demonstrated that solar PV can be recruited to stabilize the grid, smooth out disturbances, manage power flows, and assist circuit switching operations. It developed a radically new, layered control framework for Distributed Energy Resources (DER) to act in response to real-time, measured conditions on their local distribution circuit, rather than waiting for a price signal to indicate preferred behavior. By enabling resources to act as good citizens on the electric grid, Phasor-Based Control (PBC) facilitates arbitrarily high solar penetration levels.
Phasor-Based Control for Scalable Solar PV Integration (Final Technical Report)
This project demonstrated that solar PV can be recruited to stabilize the grid, smooth out disturbances, manage power flows, and assist circuit switching operations. It developed a radically new, layered control framework for Distributed Energy Resources (DER) to act in response to real-time, measured conditions on their local distribution circuit, rather than waiting for a price signal to indicate preferred behavior. By enabling resources to act as good citizens on the electric grid, Phasor-Based Control (PBC) facilitates arbitrarily high solar penetration levels.
HydroForecast Long-term: Improving hydropower’s resilience to climate change through accurate climate-scale
With hydrologic patterns and water availability across the globe shifting due to climate change, advancements in hydrologic prediction systems can help significantly reduce the uncertainties that utilities and water supply entities have in their decision making. Understanding and estimating hydrology at the climate scale is critical for managing water resources under changing climate scenarios. This project focuses on integrating state-of-the-art neural network modeling with downscaled climate projections to deliver the reliable water supply projections decades into the future to meet an urgent need from hydropower operators and water utilities. In this Phase 1 DOE SBIR proposal, we developed and validated a theory-guided neural network model, HydroForecast Long-term, for climate-scale hydrology and implemented the model within existing HydroForecast infrastructure. HydroForecast Long-term combines the most accurate streamflow modeling system with a flexible and scalable data architecture to generate water supply projections out to the year 2100. This report illustrates that we have achieved our four objectives: 1) create a prototype of HydroForecast Long-term, building the neural network prediction model, 2) build an automated data input pipeline that processes large amounts of data from the latest global temperature and precipitation climate models; 3) benchmark the accuracy of the hydrologic model over the recent two decades over a large set of diverse basins, and 4) create a set of output visuals and summary metrics informed by customer feedback that connect the data to critical decision points. This work empowers water users to make data-informed decisions supporting a resilient, renewable-powered grid and water system. The results advance the Department of Energy’s mission by addressing critical gaps in water supply planning under climate change.
Kivalina Biomass Reactor
This report summarizes work performed under DOE Award DE-EE00010149 to support the reliable operation of a community-scale biochar reactor system in Kivalina, Alaska. The project focused on improving sanitation and waste management in a remote community by assessing the installed system, identifying spare parts, defining key performance indicators (KPIs), preparing operator and maintenance manuals, and developing mobile reporting tools for operational data and KPI tracking. The team also produced training materials and recorded videos to support operator onboarding and continuity. The project demonstrated progress in system readiness, documentation, and digital reporting, while also identifying challenges common to remote deployments, including travel constraints, upstream system failures, and local resource limitations. This work provides a practical framework for improving the operation, monitoring, and future replication of biomass reactor systems in remote communities.