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DOE OSTI2025

A nonhydrostatic formulation for MPAS-Ocean

The Model for Prediction Across Scales-Ocean (MPAS-Ocean) is an open-source, global ocean model and is one component of a family of climate models within the MPAS framework, including atmosphere, sea-ice, and land-ice models. Here, in this work, a new formulation for the ocean model is presented that solves the nonhydrostatic, incompressible Boussinesq equations on an unstructured, staggered, z-level grid. The introduction of this nonhydrostatic capability is necessary for the resolution of internal wave dynamics and large eddy simulations. Compared to the standard, hydrostatic formulation, a nonhydrostatic pressure solver and a vertical momentum equation are added, where the PETSc (Portable Extensible Toolkit for Scientific Computation) library is used for the inversion of a large sparse system for the nonhydrostatic pressure. Numerical results on a stratified seiche, internal solitary wave, overflow and lock-exchange test cases are presented, and the parallel efficiency of the code is evaluated using up to 1024 processors.

3D Poisson equation↗

DOE OSTI2025

A nonhydrostatic formulation for MPAS-Ocean

3D Poisson equation↗

DOE OSTI2021

Teaching a neural network to attach and detach electrons from molecules

Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2–3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Temporal Characterization and Filtering of Sensor Data to Support Anomaly Detection

Here, we present an approach for characterizing complex temporal behavior in the sensor measurements of a system in order to support detection of anomalies in that system. We first characterize typical behavior by extending a hidden Markov model-based approach to time series alignment. We then use a trace of that learned behavior to develop a particle filter that enables efficient estimation of the filtering distribution on the state space. This produces filtered residuals that can then be used in an anomaly detection framework. Our motivating example is the daily behavior of a building’s heating, ventilation, and air conditioning (HVAC) system, using sensor measurements that arrive every minute and induce a state space with 15,120 states. We provide an end-to-end demonstration of our approach showing improved performance of anomaly detection after application of alignment and filtering compared to the unaligned data. The proposed model is implemented as a computationally efficient R package alignts (align time series) built with R and Fortran 95 with OpenMP support.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Core of 25 F studied by the 25 F(–p) proton-removal reaction

The 9 Be( 25 F(5/2 + ), 24 O)X proton-removal reaction was studied at the NSCL using the S800 spectrometer. The experimental spectroscopic factor for the ground-state to ground-state transition indicates a substantial depletion of the proton d 5/2 strength compared to shell-model expectations, similar to the findings of an inverse-kinematics (p,2p) measurement performed at RIBF. The 25 F to 24 O ground-states overlap is considerably less than anticipated if the core nucleons behaved as rigid, doubly-magic 24 O within 25 F. We interpret the new results within the framework of the Particle-Vibration Coupling (PVC) model, of a d 5/2 proton coupled to a quadrupole phonon of an effective core. This approach provides a good description of the experimental data, requiring an effective 24 O* core with a phonon energy of ℏω 2 = 3.2 MeV and a B(E2) ≈ 2.7 W.u. – softer and more collective than a bare 24 O. Here, both the Nilsson deformed mean field and the PVC models appear to capture the properties of the effective core of 25 F, suggesting that the additional proton polarizes 24 O in such a way that it becomes either slightly deformed or a quadrupole vibrator.

20 ≤ A ≤ 38↗

DOE OSTI2021

Three-Wave Mixing Kinetic Inductance Traveling-Wave Amplifier with Near-Quantum-Limited Noise Performance

We present a theoretical model and experimental characterization of a microwave kinetic inductance traveling-wave (KIT) amplifier, whose noise performance, measured by a shot-noise tunnel junction (SNTJ), approaches the quantum limit. Biased with a dc current, this amplifier operates in a three-wave mixing fashion, thereby reducing by several orders of magnitude the power of the microwave pump tone and associated parasitic heating compared to conventional four-wave mixing KIT amplifier devices. It consists of a 50 Ω artificial transmission line whose dispersion allows for a controlled amplification bandwidth. We measure $16.5^{+1}_{–1.3}$ dB of gain across a 2 GHz bandwidth with an input 1 dB compression power of –63 dBm, in qualitative agreement with theory. Using a theoretical framework that accounts for the SNTJ-generated noise entering both the signal and idler ports of the KIT amplifier, we measure the system-added noise of an amplification chain that integrates the KIT amplifier as the first amplifier. This system-added noise, 3.1 ± 0.6 quanta (equivalent to 0.66 ± 0.15 K) between 3.5 and 5.5 GHz, is the one that a device replacing the SNTJ in that chain would see. This KIT amplifier is therefore suitable to read large arrays of microwave kinetic inductance detectors and promising for multiplexed superconducting qubit readout.

42 ENGINEERING↗

DOE OSTI2026

Safe Deep Reinforcement Learning for Active Distribution System Model Predictive Control with EVs and DERs

The temporal and spatial mismatch between PV generation and electric vehicle (EV) charging and discharging may cause voltage violations in active distribution networks. Despite the widespread use of deep reinforcement learning (DRL) in power system optimization and control, it lacks guarantees on constraint satisfaction during both training and deployment. This paper proposes a Lagrangian-based safe DRL approach for model predictive control (MPC) of active distribution systems with large-scale integration of PVs, EVs, and energy storage systems (ESSs). A Transformer-LSTM time-series model is proposed to forecast EV charging demand, which is then formulated as a constraint to ensure charging requirements are met. Using this prediction, a Lagrangian-based safe soft actor-critic (SAC) framework is developed for real-time control in a three-phase unbalanced distribution system, enforcing voltage safety constraints while optimizing the cumulative net reward. By integrating the forecasting model with multi-period constraints, the proposed framework jointly coordinates PV systems, EV charging and discharging, and ESS scheduling within the MPC horizon. Numerical experiments on a modified IEEE 123-bus system with real-world data show that, under a high PV penetration scenario, the proposed method increases the net reward by 30.74% and reduces average voltage violations from 0.0011 p.u. to 0.0002 p.u. compared with standard SAC. Compared with the optimal power flow (OPF) approach, it achieves similar voltage security while yielding lower line losses. It also maintains real-time control capability, reducing operation latency to 53.21 ms per 15-minute control interval. The proposed method remains effective under varying PV/EV penetrations and load conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Bandgap and electronic structure of CaSiN 2 : Experiment and theory

Abstract We interrogate the electronic properties of the nitridosilicate CaSiN 2 as determined using soft X‐ray absorption and emission spectroscopy, which probe directly the partial electronic density of states. We compare our measurements to full potential, all electron density functional theory (DFT) calculations. Good agreement between experiment and theory is obtained, and the electronic bandgap of CaSiN 2 is determined to be 3.95 ± 0.3 eV, in contrast with our predicted value of 4.78 eV that was calculated using the modified Becke–Johnson exchange‐correlation functional with the DFT framework.

Materials Science↗

DOE OSTI2023

COUPLING SMOOTHED PARTICLE HYDRODYNAMICS WITH FINITE ELEMENT METHOD TO SIMULATE RESIDUAL STRESSES FROM FRICTION STIR PROCESSING

Friction stir processing (FSP) is a solid-state material processing technique that locally modifies the microstructure but also induces undesirable residual stresses. A robust numerical model for the FSP can help in mitigating these residual stresses. Heat source models within a finite element method (FEM) framework suffer from inaccuracies. In contrast, smoothed particle hydrodynamics (SPH) model that explicitly captures the material flow near the tool and the associated heat generation are accurate. However, the computational expense of SPH simulations can be prohibitive. In this work, we propose a coupled SPH-FEM framework. SPH is used to model the heat generation accurately near the tool and which is then inserted into to the FEM model as a heat source. To verify this proposed coupling approach, a test case is set up with typical FSP conditions and it is modeled in both SPH and SPH-FEM. The temperatures profiles were compared after the simulations have reached steady-state temperatures. The similarity of the temperature profiles from SPH-FEA and SPH validated the proposed coupling approach. This proposed approach achieves the accuracy of the SPH method while potentially retaining the low computational expense of FEM.

smoothed particle hydrodynamics, Finite Element Me↗

DOE OSTI2022

Electron dynamics in extended systems within real-time time-dependent density-functional theory

Due to a beneficial balance of computational cost and accuracy, real-time time-dependent density-functional theory has emerged as a promising first-principles framework to describe electron real-time dynamics. Here we discuss recent implementations around this approach, in particular in the context of complex, extended systems. Results include an analysis of the computational cost associated with numerical propagation and when using absorbing boundary conditions. Here we extensively explore the shortcomings for describing electron–electron scattering in real time and compare to many-body perturbation theory. Modern improvements of the description of exchange and correlation are reviewed. In this work, we specifically focus on the Qb@ll code, which we have mainly used for these types of simulations over the last years, and we conclude by pointing to further progress needed going forward.

2D materials↗

DOE OSTI2024

Optimization of Solid Oxide Electrolysis Cell Systems Accounting for Long-Term Performance and Health Degradation

This study focuses on optimizing solid oxide electrolysis cell (SOEC) systems for efficient and durable long-term hydrogen (H2) production. While the elevated operating temperatures of SOECs offer advantages in terms of efficiency, they also lead to chemical degradation, which shortens cell lifespan. To address this challenge, dynamic degradation models are coupled with a steady-state, two-dimensional, non-isothermal SOEC model and steady-state auxiliary balance of plant equipment models, within the IDAES modeling and optimization framework. A quasi-steady state approach is presented to reduce model size and computational complexity. Long-term dynamic simulations at constant H2 production rate illustrate the thermal effects of chemical degradation. Dynamic optimization is used to minimize the lifetime cost of H2 production, accounting for SOEC replacement, operating, and energy expenses. Several optimized operating profiles are compared by calculating the Levelized Cost of Hydrogen (LCOH).

Giridhar, Nishant↗

DOE OSTI2024

Synergistic Thermo-Microbial-Electrochemical (T-MEC) Approach for Drop-In Fuel Production from Wet Waste

This project successfully developed and demonstrated the synergistic thermo-microbial-electrochemical (T-MEC) process, converting food waste into sustainable biofuels while achieving self-sustaining wastewater treatment and hydrogen production. By integrating hydrothermal liquefaction (HTL) and microbial electrolysis cells (MECs), the project advanced waste-to-fuel technology and expanded the understanding of sustainable waste valorization. It established a scalable framework for achieving high carbon efficiency, effective pollutant removal, and energy recovery, showcasing the potential of combining biological, thermal, and electrochemical systems to optimize resource recovery and reduce environmental impacts. The project demonstrated the technical effectiveness of the T-MEC process, achieving over 50% improvement in carbon efficiency and reducing waste processing costs by more than 25% compared to anaerobic digestion (AD). The HTL pilot reactor processed food waste at 90 kg/h, producing up to 200 L/day of biocrude oil with high conversion efficiency. A critical desalting step in pretreatment prevented catalyst fouling, enabling efficient hydrotreating with 100% deoxygenation and denitrogenation and sulfur reduction to <15 ppm. This positioned the kerosene fraction as a strong candidate for sustainable aviation fuel (SAF). The MECs achieved rapid startup, 86.4% COD removal, and hydrogen production rates of 1.8 L H 2 /L cat /day, among the highest recorded for pilot-scale systems. The integrated process achieved 65% carbon efficiency to biocrude and 58% to finished fuels, outperforming AD's 41% and 33% efficiencies for biogas and natural gas vehicle fuels. System analysis highlighted economic potential, with minimum fuel selling prices (MFSP) decreasing from $\$$25/GGE at 5 tpd to $\$$10/GGE at 500 tpd due to economies of scale. Future work will focus on reducing MEC material and membrane costs, enhancing performance through higher current densities, and creating tailored operational strategies for diverse feedstocks. Optimization of the integrated system will improve scalability and feasibility, positioning the T-MEC process as a competitive solution for converting wet waste into sustainable fuels and clean water. Beyond its technical and economic achievements, the project offers significant public benefits. The T-MEC process provides a sustainable alternative to landfilling and incineration, reducing greenhouse gas emissions and conserving resources. Converting waste into SAF and renewable fuels supports decarbonization in the transportation sector, advancing energy independence and reducing reliance on fossil fuels. Additionally, the process minimizes environmental pollutants, transforming them into valuable products like hydrogen and fuels, contributing to a cleaner and more sustainable future.

09 BIOMASS FUELS↗

DOE OSTI2026

Virtual Power Plant Architecture and Resilient Design

Virtual Power Plants (VPPs) represent a fundamental shift in electric grid operations, aggregating distributed energy resources (DERs) such as solar panels and battery storage to deliver utility-scale grid services traditionally provided by centralized power plants. This report examines the unique architectural, operational, and digital assurance considerations that distinguish VPPs from conventional utility infrastructure as they scale from pilot projects to mainstream deployment across the United States. While VPPs offer significant opportunities for grid modernization and enhanced flexibility, their distributed, multi-stakeholder architecture introduces distinct security challenges that differ fundamentally from traditional generation facilities. The analysis identifies risks in VPP operations, including device-level security gaps, platform vulnerabilities, and communication protocol weaknesses that create expanded attack surfaces compared to centralized power plants. Through examination of real-world incidents and emerging threat patterns, the report demonstrates how some VPPs' reliance on consumer-owned devices, public internet infrastructure, and complex vendor ecosystems require new approaches to digital assurance and operational security. The findings provide practical guidance for utilities, regulators, and aggregators to implement robust security frameworks and operational best practices essential for maintaining grid reliability as VPP deployment accelerates under the Federal Energy Regulatory Commission (FERC) Order 2222 and related regulatory initiatives.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

A New Complex Borohydride LiAl(BH4)2Cl2

A new mixed alkali metal–aluminum borohydride LiAl(BH4)2Cl2 has been prepared via mechanochemical synthesis from the 2LiBH4–AlCl3 mixture. Structural characterization, performed using a combination of X-ray powder diffraction and solid-state NMR methods, indicates that the LiAl(BH4)2Cl2 phase adopts a unique 3D framework and crystallizes in an orthorhombic structure with the space group C2221, a = 11.6709(6) Å, b = 8.4718(4) Å, c = 7.5114(3) Å. The material shows excellent dehydrogenation characteristics, where hydrogen evolution starts at Tons = 70 °C, releasing approximately 2 wt.% of nearly pure (99.8 vol.%) hydrogen and a very small amount (~0.2 vol.%) of diborane. When compared to halide-free mixed alkali metal–aluminum borohydrides, the presence of Al‒Cl bonding in the LiAl(BH4)2Cl2 structure likely prevents the formation of Al(BH4)3 upon decomposition, thus suppressing the formation of diborane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Optimization of Solid Oxide Electrolysis Cell Systems Accounting for Long-Term Performance and Health Degradation

Giridhar, Nishant↗

DOE OSTI2023

Cost‐benefit assessment framework for robotics‐driven inspection of floating offshore wind farms

Abstract Operations and maintenance (O&M) of floating offshore wind farms (FOWFs) poses various challenges in terms of greater distances from the shore, harsher weather conditions, and restricted mobility options. Robotic systems have the potential to automate some parts of the O&M leading to continuous feature‐rich data acquisition, operational efficiency, along with health and safety improvements. There remains a gap in assessing the techno‐economic feasibility of robotics in the FOWF sector. This paper investigates the costs and benefits of incorporating robotics into the O&M of a FOWF. A bottom‐up cost model is used to estimate the costs for a proposed multi‐robot platform (MRP). The MRP houses unmanned aerial vehicle (UAV) and remotely operated vehicle (ROV) to conduct the inspection of specific FOWF components. Emphasis is laid on the most conducive O&M activities for robotization and the associated technical and cost aspects. The simulation is conducted in Windfarm Operations and Maintenance cost‐Benefit Analysis Tool (WOMBAT), where the metrics of incurred operational expenditure (OPEX) and the inspection time are calculated and compared with those of a baseline case consisting of crew transfer vessels, rope‐access technicians, and divers. Results show that the MRP can reduce the inspection time incurred, but this reduction has dependency on the efficacy of the robotic system and the associated parameterization e.g., cost elements and the inspection rates. Conversely, the increased MRP day rate results in a higher annualized OPEX. Residual risk is calculated to assess the net benefit of incorporating the MRP. Furthermore, sensitivity analysis is conducted to find the key parameters influencing the OPEX and the inspection time variation. A key output of this work is a robust and realistic framework which can be used for the cost‐benefit assessment of future MRP systems for specific FOWF activities.

17 WIND ENERGY↗

DOE OSTI2022

Tuning Photoexcited Charge Transfer in Imine-Linked Two-Dimensional Covalent Organic Frameworks

The generation of a long-lived charge-separated state in versatile π-conjugated two-dimensional covalent organic frameworks (2D COFs), a process essential to extending their great potentials in advanced semiconducting applications, is yet fully elucidated. Herein, we report a systematic investigation of the photophysical properties of three highly crystalline imine-linked 2D COFs using steady-state and transient absorption spectroscopy accompanied by time-dependent density functional theory (TDDFT) calculations. The different electron affinity between 5,5',5''-(1,3,5-benzenetriyl)-tris(2-pyridinecarboxaldehyde) (BTPA) and three tunable electron-donating/accepting triamine monomers dominated the formation of the excited state, charge transfer direction and lifetime. A prominent charge transfer from electron-rich 4,4',4''-triaminotriphenylamine (TAPA) to BTPA in COFTAPA-BTPA led to the long-lived charge-separated state, which was attributed to a greater degree of delocalization compared to the two other COFs. Furthermore, these results provide fundamental insight into the importance of structure-property correlation for designing advanced photoactive 2D COF materials with the efficient charge transfer and long-lived charge-separated state.

14 SOLAR ENERGY↗

DOE OSTI2024

Using Median Point in Keeling Plot to Reduce the Uncertainty of the Isotopic Composition of Evapotranspiration

Abstract The isotopic composition of evapotranspirationδ ET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculateδ ET , though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested theδ ET uncertainty using the mean point [σ ET (mean)] and median point [σ ET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. Theσ ET (mean) would be greater thanσ ET (median) when the mean value of inverse vapor concentration () is greater than the median value of inverse vapor concentration []. When applying the filter ofr 2 > 0.8, around 70% ofσ ET (mean) was greater thanσ ET (median). This phenomenon might be due to the normality of the vapor concentrationC υ producing the asymmetric distribution of 1/C υ . The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentrationC υ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.

Meteorology & Atmospheric Sciences↗