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At least 235 records · Page 13

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DOE OSTI2025

HP-FLEX: Field demonstration of the semantics-driven configuration of a Model Predictive Control system to make heat pumps flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗

DOE OSTI2025

HP-FLEX: Field Demonstration of the Semantics-Driven Configuration of a Model Predictive Control System to Make Heat Pumps Flexible

Paul, Lazlo↗

DOE OSTI2025

Accelerating eigenvalue computation for nuclear structure calculations via perturbative corrections

Subspace projection methods utilizing perturbative corrections have been proposed for computing the lowest few eigenvalues and corresponding eigenvectors of large Hamiltonian matrices. In this paper, we build upon these methods and introduce the term Subspace Projection with Perturbative Corrections (SPPC) method to refer to this approach. We tailor the SPPC for nuclear many-body Hamiltonians represented in a truncated configuration interaction subspace, i.e., the no-core shell model (NCSM). We use the hierarchical structure of the NCSM Hamiltonian to partition the Hamiltonian as the sum of two matrices. The first matrix corresponds to the Hamiltonian represented in a small configuration space, whereas the second is viewed as the perturbation to the first matrix. Eigenvalues and eigenvectors of the first matrix can be computed efficiently. Because of the split, perturbative corrections to the eigenvectors of the first matrix can be obtained efficiently from the solutions of a sequence of linear systems of equations defined in the small configuration space. These correction vectors can be combined with the approximate eigenvectors of the first matrix to construct a subspace from which more accurate approximations of the desired eigenpairs can be obtained. We show by numerical examples that the SPPC method can be more efficient than conventional iterative methods for solving large-scale eigenvalue problems such as the Lanczos, block Lanczos and the locally optimal block preconditioned conjugate gradient (LOBPCG) method. The method can also be combined with other methods to avoid convergence stagnation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Introduction to Microgrids

A microgrid is simply a typical power system that has distributed energy resources (DERs), like small diesel generators, with loads in the network. The resiliency offered by microgrids to retain service during system events makes them a suitable support for the electric grid during natural disasters. One key factor that differentiates microgrids from a typical distribution grid is its ability to disconnect itself in the event of a blackout and sustain its loads through DERs. Microgrid systems will reconnect to the main grid when the grid comes back up. The resiliency offered by microgrids to retain service during system events makes them a suitable support for the electric grid during natural disasters. This presentation discusses the typical key steps involved in microgrid planning, installation, operation in a interoperable and interconnectable fashion.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Examining Aerosol Vertical Transport and Removal during Deep Convective Events

Atmospheric aerosols affect the global energy budget by scattering and absorbing sunlight (direct effects) and by changing the microphysical structure, lifetime, and coverage of clouds (indirect effects). Globally, the free troposphere is a major source of nucleation- and Aitken-mode aerosols due to the enhanced new particle formation rates at high altitudes. Recent studies have shown deep convective systems are capable of transporting these small aerosols from the free troposphere to the boundary layer by strong convective downdrafts and weaker downward motions in the stratiform regions. These vertically transported aerosols can grow into cloud condensation nuclei (CCN) and play a significant role in the global climate. During the deep convective processes, existing accumulation-mode aerosols that act as coagulation sinks of smaller particles are also removed by wet scavenging. Compared to the vertical transport of these particles by entrainment mixing, which is slower but more prevalent, the deep convective downdraft processes may be more rapid and efficient in the vertical transport of aerosols. However, most of the current climate models do not include this mechanism as a source of CCN, mainly because the frequency of deep convective events varies significantly with geographic location and thus their contributions to CCN are unpredictable. We target this critical gap in understanding the vertical transport and removal of aerosols by deep convections. We proposed to analyze a multi-year, multi-site measurement record available from the U.S. Department of Energy (DOE) ARM program, including the observations from the 2014/15 Observations and Modeling of the Green Ocean Amazon (GoAmazon) field campaign, the 2017/18 Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign, the 2018/19 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign, the 2021/22 Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign, and the long-term measurements collected at the Southern Great Plains (SGP) atmospheric observatory, where deep convective clouds were frequently observed. This project is aimed at the following three objectives: (1) Gaining a detailed and quantitative understanding of the aerosols transported by a convective downdraft and their evolution in the atmosphere; (2) Examining the wet scavenging mechanisms and efficiencies of aerosols at altitudes of deep convective systems based on ground and aircraft measurements; (3) Evaluating the contribution of deep convective systems to CCN as both a source and a sink of atmospheric aerosols and its seasonal variabilities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Techno-economic assessment of distributed wellhead RO water treatment for nitrate removal and salinity reduction: A field study in small disadvantaged communities

Techno-economic analysis of distributed wellhead water treatment and desalination (DWTD) systems was carried out based on a three-year field study in three small, disadvantaged communities (DACs) to evaluate the reliability and affordability of upgrading their impaired well water. The local water supplies of the three study DACs, located in Salinas Valley, California, were contaminated with nitrate at levels (~ 12–87 mg/L NO$^{-}_{3}$ - N) ) above the California maximum contaminant level (MCL) of 10 mg/L NO$^{-}_{3}$ - N , and had elevated water salinity (~600–1,600 mg/L total dissolved solids(TDS)) above its secondary MCL (SMCL) of 500 mg/L TDS. Well water nitrate removal and salinity reduction were accomplished via reverse osmosis (RO) based DWTD systems that operated autonomously, supported by remote monitoring and supervisory cyberinfrastructure. Reliable DWTD operation provided treated water quality, with respect to nitrate and salinity, in the range of 0.5–6.3 mg/L NO$^{-}_{3}$ - N and 57–161 mg/L TDS, respectively, which were well below the respective MCL and SMCL. The levelized cost of water treatment was in the range of ~$$2/m 3 - $$2.9/m 3 which aligns with typical residential water costs in California and in the study region, and monthly residential water costs (39 dollars-74 dollars/residential unit/month) were also within the range in California. The study showcased the DWTD approach as a viable and potentially scalable solution for upgrading impaired local potable water supply of communities lacking centralized water delivery infrastructure. However, streamlined permitting processes and standardized regulatory frameworks are critical to promoting wider adoption and maximizing the socio-economic benefits of the DWT approach. Moreover, DACs are likely to require government subsidies in order to cover the CapEx of DWTD systems in addition to upgrade of site infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Predicting Flow in Fracture Networks With Quantum Algorithms

Uncertainty quantification plays a crucial role in the modeling of subsurface flow. For instance, uncertainties in the properties of geologic fracture networks significantly impact flow, requiring numerous simulations to accurately estimate quantities of interest. However, each simulation is computationally expensive because it requires solving a large linear system to capture features that involve both small and large fractures. An example is in percolation, where the interaction of many small fractures (which cumulatively can have a large surface area) with the rock matrix must be modeled precisely. Quantum computing is an emerging tool with the potential to address this issue. Quantum algorithms offer a significant speedup in solving linear systems, achieving efficiencies that are challenging to match with classical approaches. These classical approaches include direct solvers, such as LU decomposition, and iterative methods, notably preconditioned conjugate gradient, commonly used in subsurface modeling to solve large sparse systems. However, applying quantum algorithms to geologic fracture flow requires careful attention to algorithmic and problem-specific constraints to fully realize this quantum advantage. In this work we describe a quantum algorithm for generalized Monte Carlo applications with a quadratic speedup over the classical approaches which can be combined with the quantum speedup, currently under investigation, for solving quantum linear systems for subsurface flow. We show that for quantum algorithms the computational cost of estimating a quantity of interest for a statistical ensemble of networks is roughly the same as that of a single realization, essentially implying that one can get uncertainty quantification for free.

58 GEOSCIENCES↗

DOE OSTI2026

Enhancing dimensionality prediction in hybrid metal halides via feature engineering and class-imbalance mitigation

We present a machine learning (ML) framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. This study is motivated by the small and highly imbalanced nature of experimentally available HMH datasets, which limits the applicability and reliability of conventional ML approaches. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. To mitigate this limitation, the dataset was augmented to 1336 samples using the synthetic minority oversampling technique, enabling improved learning of underrepresented dimensionality classes while preserving chemically meaningful feature relationships. We developed interaction-based descriptors designed to capture coupled steric and polarity effects relevant to dimensionality prediction, which are not readily captured by standard single-parameter or composition-only descriptors. These descriptors are integrated into a multi-stage workflow combining feature selection, ensemble stacking, and performance optimization. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities. This work demonstrates a generalizable strategy for extracting reliable and interpretable structure–dimensionality relationships from limited experimental data, enabling pre-synthesis screening of organic cations and providing a practical blueprint for small-data ML in hybrid materials systems.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Toward Trustworthy Detectors Detector Characterization for SuperCDMS SNOLAB Commissioning

SuperCDMS SNOLAB is a next generation direct detection experiment searching for low mass dark matter using cryogenic germanium and silicon detectors operated at millikelvin temperatures. As commissioning begins, establishing stable, predictable detector response to energy deposits is a prerequisite for future physics analysis. This work studies detector stability and calibration for four SuperCDMS SNOLAB detectors, det 7 and det 15 (germanium) and det 11 and det 14 (silicon), using Barium-133 calibration data (356 keV gamma ray reference) and low background data (ambient radiation, no external source). The Ba-133 data show no distinct line at the expected energy, and the low background data show a baseline that drifts and oscillates rather than remaining flat, consistently across multiple channels, suggesting a shared, detector wide cause. These observations point to the cryogenic support system as the likely source, since small temperature fluctuations could couple into the temperature sensitive detectors, informing the ongoing commissioning effort.

O'Hanlon, Vika [Skidmore Coll.]↗

DOE OSTI2025

Nonlinear Optimal Control of Electron Dynamics Within Hartree-Fock Theory

Consider the problem of determining the optimal applied electric field to drive a molecule from an initial state to a desired target state. For even moderately sized molecules, solving this problem directly using the exact equations of motion—the time-dependent Schrödinger equation (TDSE)—is numerically intractable. Here, we present a solution of this problem within time-dependent Hartree-Fock (TDHF) theory, a mean field approximation of the TDSE. Optimality is defined in terms of minimizing the total control effort while maximizing the overlap between desired and achieved target states. We frame this problem as an optimization problem constrained by the nonlinear TDHF equations; we solve it using trust region optimization with gradients computed via a custom-built adjoint state method. For three molecular systems, we show that with very small neural network parametrizations of the control, our method yields solutions that achieve desired targets within acceptable constraints and tolerances.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

PIP-II Linac Cryogenic Distribution System Design Challenges

The PIP-II linac Cryogenic Distribution System (CDS) is characterized by extremely small heat inflows and robust mechanical design. It consists of a Distribution Valve Box (DVB), Intermediate Transfer Line, Tunnel Transfer Line, comprising 25 Bayonet Cans, and ends with a Turnaround Can. Multiple helium streams, each characterized by distinct helium parameters, flow through each of these elements. The CDS geometry allows maintaining an acceptable pressure drop for each helium stream, considering the planned flows and helium parameters in different operation modes. This is particularly crucial for the return line of helium vapors, which return from cryomodules to the cold compressors and thus have very restrictive pressure drop requirements. On both sides of the DVB there are fixed supports for process pipes. One of the DVB design challenges was to route the process pipes in such a way that their shape provided sufficient compensation for thermal shrinkage. This ensures th at the forces resulting from thermal shrinkage acting on the cryogenic valves remain at a level acceptable to the manufacturer. The required thermal budget of the CDS was achieved by thermo-mechanical optimization of its components, like process pipes fixed supports in Bayonet Cans.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Gas-phase activation of holmium tetranitrato complexes likely leads to formation of hydroxylated species through the loss of nitric acid, not water splitting

It has been observed that water can react with activated gas-phase lanthanide tetranitrato complexes ([Ln(NO3)4]-) to form a hydroxylated species. These reactions, which are proposed to involve water splitting, are observed for most -- but not all -- of the lanthanides. Thus, study of this system could yield insight into how small changes in electronic structure (through variation of the lanthanide species) influences water-splitting reactions, help guide development of new materials used to convert water to hydrogen using electricity. The mechanism proposed in the literature is: (Step 1) [Ln(NO3)4]- ? [LnO(NO3)3]- + ?NO2 (Step 2) [LnO(NO3)3]- + H2O ? [LnOH(NO3)3]- + ?OH The purpose of this study is to determine if the reaction mechanism really does involve splitting of water or proceeds through an alternative pathway.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Temperature Dependent Spin Dynamics in La 0.67 Sr 0.33 MnO 3 /Pt Bilayers

Complex ferromagnetic oxides such as La 0.67 Sr 0.33 MnO 3 (LSMO) offer pathways for creating energy‐efficient spintronic devices with new functionalities. LSMO exhibits high‐temperature ferromagnetism, half metallicity, sharp resonance linewidth, low damping, and a large anisotropic magnetoresistance response. Combined with Pt, a proven material with high spin‐charge conversion efficiency, LSMO can be used to create robust nano‐oscillators for neuromorphic computing. Ferromagnetic resonance (FMR) and device‐level spin‐pumping FMR measurements are performed to investigate the magnetization dynamics and spin transport in NdGaO 3 (110)/LSMO(15 nm)/Pt(0 and 5 nm) thin films ranging from 300 K to 90 K and compare the device performance with Py(7 nm)/Pt(5 nm) sample. The spin current pumped into Pt is quantified to determine the temperature‐dependent influence of interfacial interactions. The generated spin current in the micro‐device is maximum at 170 K for the optimally grown LSMO/Pt films. Additionally, this bilayer system exhibits low magnetic Gilbert damping (0.002), small linewidth (12 Oe), and a large spin Hall angle (≈3.2%) at 170 K. Ex situ deposited LSMO/Pt bilayers demonstrate excellent dynamic response, exhibiting fourfold enhancement in signal output, eightfold reduction in damping, and a threefold reduction in linewidth as compared to the Pt/Py system. Such robust device‐level performance can pave way for energy‐efficient spintronic‐based devices.

complex perovskite thin films↗

DOE OSTI2025

Estimating time in quantum chaotic systems and black holes

We characterize new universal features of the dynamics of chaotic quantum many-body systems, by considering a hypothetical task of "time estimation". Most macroscopic observables in a chaotic system equilibrate to nearly constant late-time values. Intuitively, it should become increasingly difficult to estimate the precise value of time by making measurements on the state. We use a quantity called the Fisher information from quantum metrology to quantify the minimum uncertainty in estimating time. Due to unitarity, the uncertainty in the time estimate does not grow with time if we have access to optimal measurements on the full system. Restricting the measurements to act on a small subsystem or to have low computational complexity leads to results expected from equilibration, where the time uncertainty becomes large at late times. With optimal measurements on a subsystem larger than half of the system, we regain the ability to estimate the time very precisely, even at late times. Hawking's calculation for the reduced density matrix of the black hole radiation in semiclassical gravity contradicts our general predictions for unitary quantum chaotic systems. Hawking's state always has a large uncertainty for attempts to estimate the time using the radiation, whereas our general results imply that the uncertainty should become small after the Page time. This gives a new version of the black hole information loss paradox in terms of the time estimation task. By restricting to simple measurements on the radiation, the time uncertainty becomes large. This indicates from a new perspective that the observations of computationally bounded agents are consistent with the semiclassical effective description of gravity.

Black holes↗

DOE OSTI2024

Impact of the Exciter and Governor Parameters on Forced Oscillations

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability.

42 ENGINEERING↗

DOE OSTI2025

Characterization and Stimulation of Selenium Reducing Microbial Consortia from Real Flue Gas Desulfurization Wastewater Biological Treatment System

Selenium is released into the environment via anthropogenic activities such as coal combustion. The U.S. Environmental Protection Agency’s Effluent Limitation Guideline (ELG) Rule limits the release of selenium species into U.S. waterways. To meet the ELG limit for selenium, coal-fired power plants must treat the wastewater produced by the flue gas desulfurization (FGD) systems. Selenium removal is of interest because small changes of the concentrations of selenium in the environment can cause detrimental ecological consequences. Biological treatment is used to remediate this wastewater, but little is known about the individual species in the microbial consortium that reduce toxic water-soluble selenium oxyanions to insoluble elemental selenium. To optimize current biological treatment capabilities of this water, the selenium-reducing capabilities must be leveraged. In order to do so, the selenium-reducing bacteria (SeRB) in biological treatment systems must be characterized. This study will be the first to characterize the microbial consortia from a real FGD WW biological treatment system and provide insights on how to better achieve selenium removal via biological treatment. This work will provide insights into the microorganisms predominantly responsible for the reduction of selenium oxyanions in FGD wastewater biological treatment systems, which can be used to better optimize more efficient biological treatment strategies of this industrial waste.

geomicrobiology↗

DOE OSTI2024

Understanding vortex dynamics in CaK(Fe,Ni) 4 As 4 and Ba(Fe,Co) 2 As 2 single crystals under the influence of random point disorder

We report on the influence of doping on vortex dynamics in 3 MeV proton-irradiated single crystals of CaK(Fe 1–x Ni x ) 4 As 4 (1144, x = 0.015, 0.025, and 0.03) and Ba(Fe 1–x Co x ) 2 As 2 (x = 0.04, 0.062, 0.066 and 0.074). Non-irradiated crystals of the 1144 system display superconducting critical temperatures ranging from 31 K for x = 0.015–20.5 K, as doping increases to 0.03. On the other hand, pristine crystals of the 122 system show T c values between 14.6 and 23.6 K, with the maximum T c occurring at intermediate doping levels. The fluence was set at 3 × 10 16 p cm –2 , resulting in a decrease in the T c by around 1.5 K for all samples and significantly affecting the vortex dynamics by reducing the flux creep relaxation compared to previously reported values for unirradiated crystals. Parameters such as vortex pinning energy U 0 and the glassy exponent μ dependencies on doping and magnetic field strength are identified. For the 1144 system, U 0 reaches values approaching 500 K for small fields in samples with T c = 29.3 K (x = 0.015), systematically decreasing to around 200 K as T c falls below 20 K. Furthermore, U 0 decreases as the field increases to 3 T for the same sample, varying from approximately 250 K to 100 K as T c decreases. These changes are typically accompanied by modifications in μ, gradually increasing from values around 1 towards 1.5, corresponding to small bundle relaxation in the collective creep theory. Despite differences in the substitutional disorder and magnetic phase diagram with respect to the 1144 system, the results for 122 single crystals follow a similar tendency in which U 0 usually reduces and μ increase rise as the applied magnetic field is increased. Due to moderate U 0 in these systems (few hundreds of kelvins), the resulting decay of persistent current at liquid helium temperatures is primarily determined by a balance between U 0 and bundle size contribution. Furthermore, these findings provide valuable insights for potential applications of these systems, particularly in the context of intrinsic superconducting parameters and the resulting pinning landscape.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Mixed-mode oscillations in a three-timescale coupled Morris–Lecar system

Mixed-mode oscillations (MMOs) are complex oscillatory behaviors of multiple-timescale dynamical systems in which there is an alternation of large-amplitude and small-amplitude oscillations. It is well known that MMOs in two-timescale systems can arise either from a canard mechanism associated with folded node singularities or a delayed Andronov–Hopf bifurcation (DHB) of the fast subsystem. While MMOs in two-timescale systems have been extensively studied, less is known regarding MMOs emerging in three-timescale systems. In this work, we examine the mechanisms of MMOs in coupled Morris–Lecar neurons with three distinct timescales. We investigate two kinds of MMOs occurring in the presence of a singularity known as canard-delayed-Hopf (CDH) and in cases where CDH is absent. In both cases, we examine how features and mechanisms of MMOs vary with respect to variations in timescales. Our analysis reveals that MMOs supported by CDH demonstrate significantly stronger robustness than those in its absence. Moreover, we show that the mere presence of CDH does not guarantee the occurrence of MMOs. This work yields important insights into conditions under which the two separate mechanisms in two-timescale context, canard and DHB, can interact in a three-timescale setting and produce more robust MMOs, particularly against timescale variations.

97 MATHEMATICS AND COMPUTING↗