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DOE OSTI2024

Development of a platform for experimental and computational studies of magnetic and radiative effects on astrophysically relevant jets at OMEGA

Accurate modeling of astrophysical jets is critical for understanding accretion systems and their impact on the interstellar medium. While astronomical observations can validate models, they have limitations. Controlled laboratory experiments offer a complementary approach for qualitative and quantitative demonstration. Laser experiments offer a complementary approach. This article introduces a new platform on the OMEGA laser facility for high-velocity (1500 kms−1), high-aspect-ratio (∼36) jet creation with strong cylindrical symmetry. This platform's capabilities bridge observational gaps, enabling controlled initial conditions and direct measurements of plasma characteristics, crucial for refining astrophysical jet dynamics and improving the models accuracy.

Rigon, G. (ORCID:0000000256664725)↗

DOE OSTI2022

FLASC (FLORIS-based Analysis for SCADA data)

FLASC provides a rich suite of analysis tools for SCADA data filtering & analysis, wind farm model validation, field experiment design, and field experiment monitoring. The repository is centrally built around NRELs in-house floris wind farm model, available at https://github.com/nrel/floris. FLASC also largely relies on the energy ratio to, among others, quantify wake losses in synthetic and historical data, to perform turbine northing calibrations, and for model parameter estimation.

Fleming, Paul↗

DOE OSTI2021

Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO 2 capture process

In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO 2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO 2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Modeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.x

Abstract. Developing accurate and efficient modeling techniques for streamflow at the tens-of-kilometers spatial scale and multi-year temporal scale is critical for evaluating and predicting the impact of climate- and human-induced discharge variations on river hydrodynamics. However, achieving such a goal is challenging because of limited surveys of streambed hydraulic roughness, uncertain boundary condition specifications, and high computational costs. We demonstrate that accurate and efficient three-dimensional (3-D) hydrodynamic modeling of natural rivers at 30 km and 5-year scales is feasible using the following three techniques within OpenFOAM, an open-source computational fluid dynamics platform: (1) generating a distributed hydraulic roughness field for the streambed by integrating water-stage observation data, a rough wall theory, and a local roughness optimization and adjustment strategy; (2) prescribing the boundary condition for the inflow and outflow by integrating precomputed results of a one-dimensional (1-D) hydraulic model with the 3-D model; and (3) reducing computational time using multiple parallel runs constrained by 1-D inflow and outflow boundary conditions. Streamflow modeling for a 30 km long reach in the Columbia River (CR) over 58 months can be achieved in less than 6 d using 1.1 million CPU hours. The mean error between the modeled and the observed water stages for our simulated CR reach ranges from −16 to 9 cm (equivalent to approximately ±7 % relative to the average water depth) at seven locations during most of the years between 2011 and 2019. We can reproduce the velocity distribution measured by the acoustic Doppler current profiler (ADCP). The correlation coefficients of the depth-averaged velocity between the model and ADCP measurements are in the range between 0.71 and 0.83 at 75 % of the survey cross sections. With the validated model, we further show that the relative importance of dynamic pressure versus hydrostatic pressure varies with discharge variations and topography heterogeneity. Given the model's high accuracy and computational efficiency, the model framework provides a generic approach to evaluate and predict the impacts of climate- and human-induced discharge variations on river hydrodynamics at tens-of-kilometers and decadal scales.

58 GEOSCIENCES↗

DOE OSTI2024

Enhancing Power Grid Resilience with Causal Loops Diagram and Bayesian Networks

Enhancing power grid resilience through improved analysis and planning of Distributed Energy Resources is a key for power system planner. This paper explores the integration of Causal Loop Diagrams (CLDs) and Bayesian Networks (BNs) for enhancing resilience in power systems, focusing on Distributed Energy Resources (DER) planning. By automating CLD analysis in Python's matplotlib, we present a tool for rapid model validation and structural accuracy, crucial for power system planners. This hybrid approach utilizes BNs for inferential depth and CLDs for dynamic system modeling, offering a comprehensive framework for policy formulation and collaborative strategy development against disruptions. Here, we highlight the tool's capability to identify and analyze interconnected feedback loops, facilitating a deeper understanding of DER integration's impact on network resilience. This work aims to bridge quantitative analysis and qualitative insights, addressing the limitations of each method while providing a robust model for power system resilience assessment.

14 SOLAR ENERGY↗

DOE OSTI2023

Generalizing the Gurson model using symbolic regression and transfer learning to relax inherent assumptions

Abstract To generate material models with fewer limiting assumptions while maintaining closed-form, interpretable solutions, we propose using genetic programming based symbolic regression (GPSR), a machine learning (ML) approach that describes data using free-form symbolic expressions. To maximize interpretability, we start from an analytical, derived material model, the Gurson model for porous ductile metals, and systematically relax inherent assumptions made in its derivation to understand each assumption’s contribution to the GPSR model forms. We incorporate transfer learning methods into the GPSR training process to increase GPSR efficiency and generate models that abide by known mechanics of the system. The results show that regularizing the GPSR fitness function is critical for generating physically valid models and illustrate how GPSR allows a high level of interpretability compared with other ML approaches. The method of systematic assumption relaxation allows the generation of models that address limiting assumptions found in the Gurson model, and the symbolic forms allow conjecture of decreased material strength due to void interaction and non-symmetric void shapes.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Defect Kinetics and Control for Module Reliability

Potential induced degradation is currently one of the most important module degradation mechanisms. It has been suggested that stacking faults decorated with sodium from the module glass are responsible for this effect and authors have also shown the reversibility of this effect upon reverse biasing of the module. The importance of sodium in the failure mechanism is clear, however, little is known regarding the factors that control its diffusion into the wafer, making it nearly impossible to predict the performance of a given module and engineer it to be better. Sodium migration from module glass into silicon cells and the resulting module degradation is a clear example of how defect kinetics can determine overall module performance and long-term reliability. To the detriment of the industry and its bankability, no quantitative models yet exist to predict defect-assisted module degradation, limiting the progress in improving reliability. In particular, the understanding of defect behavior under high electric fields, under stresses imparted by encapsulation or temperature, and under real operating conditions over long periods of time is a crucial gap in the current state-of-the-art. In this work we developed a Defect-Device-Degradation model to predict defect behavior and its impact on device performance over the module operational lifetime using experimentally-determined defect parameterizations. The validated model will provide a platform for manufacturing process optimization across input materials and architectures to avoid deleterious defects upstream and enable enhanced module robustness.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Development and Validation of Predictive Models for In-Cylinder Radiation and Wall Heat Transfer

The lack of accurate submodels for in-cylinder radiation and heat transfer has been identified as a key shortcoming in developing truly predictive, physics-based computational fluid dynamics (CFD) models that can be used to develop and design combustion systems for advanced high-efficiency, low-emissions engines. Measurements of wall boundary layers in engines show discrepancies of up to 100% with respect to standard CFD boundary layer models. And analysis of in-cylinder radiation based on the most recent spectral property databases and high-fidelity radiative transfer equation solvers has shown that at operating pressures and exhaust-gas recirculation levels typical of modern compression-ignition truck engines, molecular gas radiation (mainly carbon dioxide and water vapor) can be more important than soot particle radiation, and that a significant fraction of the emitted radiation (50% or more) can be reabsorbed before reaching the walls, thereby acting to redistribute energy in the combustion chamber. In this project, an integrated experimental and simulation approach has been used to generate new physical insight into in-cylinder heat-transfer processes in engines, and to develop computationally practicable CFD-based models that accurately represent the physics. New experimental data have been taken in an optically accessible single-cylinder research engine, and in a multi-cylinder production engine. Those data have provided new insight into the radiative environment in engines. New CFD models have been developed and validated for in-cylinder heat transfer, focusing mainly on radiative heat transfer and coupling between turbulent boundary layer wall heat transfer and radiative heat transfer. The CFD models have been developed and implemented in an open-source software framework that can be shared with other researchers. The models are compatible both with unsteady Reynolds-averaged Navier Stokes (URANS) formulations and with large-eddy simulations (LES) of in-cylinder processes in engines.

02 PETROLEUM↗

DOE OSTI2025

Informed Investments in Clean Energy Technologies

Governments and companies face consequential decisions about allocating resources to the research, development, demonstration and deployment of energy technologies to meet environmental, economic and social goals. Here we discuss how research insights can inform and potentially improve these decisions to make effective use of limited resources and time in shaping the next-generation energy infrastructure. We outline three key research steps: forecasting technological change, relating investments to economic, social and environmental outcomes and informing decision-making processes. We recommend advances to address uncertainty as well as to make methods and results more practicable, emphasizing the importance of model validation, streamlining and interactivity. Progress has been made, yet further work is needed-for example, in the development of reduced-order, testable models and more comprehensive data collection. Overall, this research is beginning to inform decisions but could be adopted more widely by governments and the private sector to help support technological progress for energy affordability, equitable climate change mitigation, health benefits and other objectives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Energy Model to Evaluate Thermal Energy Storage Integrated with Air Source Heat Pumps: Preprint

Full decarbonization in buildings requires the replacement of combustion appliances with electric ones, and air source heat pumps (ASHP) are a candidate alternative. However, technical limitations, such as the efficiency decrease when operating in cold weather, limit their adoption in the global heating market. Among several options to improve ASHP efficiency operating in colder climates, thermal energy storage (TES) has been considered, as it may provide heating when it is cold and shift ASHP operation to times when the weather is warmer. It may also take advantage of time of use electricity rates and support defrosting when necessary. The evaluation of ASHP-TES systems, however, is still limited because traditional metrics do not capture their full economic and environmental benefits. In this work, a python framework is presented to model the ASHP with and without the presence of TES. Metrics are proposed to analyze the system performance in terms of costs, equivalent CO2 emission, and efficiency metrics to evaluate and compare alternative systems. Model validation against experimental data obtained for a commercial heat pump is provided, as well as an application example using Denver, Colorado, to highlight the model capabilities.

cold climate↗

DOE OSTI2024

Development of a rate-based ENRTL-RK process model for a water-lean solvent

Advanced water-lean solvents (WLS) for post-combustion CO2 capture offer several advantages over the aqueous amine solvents . WLS have lower parasitic energy penalty, lower corrosion, lower temperature and high-pressure CO2 regeneration leading to lower cost of CO2 capture. RTI International, with funding from the US Department of Energy, has been developing its novel water-lean solvent, that has shown specific reboiler duty of 2.3 GJ/t-CO2 at the 60-kWe pilot testing unit (Tiller Plant, SINTEF, Norway) and 2.6 GJ/t-CO2 at the engineering scale testing system (12 MWe) at the Technology Centre Mongstad (TCM) in Norway. All heat duties, including the one from TCM testing, were consistent with Aspen Plus modeling of the specific configuration of each test plant. This work focuses on the development of a detailed process model using in-house laboratory measurements and process data at pilot scale. The eNTRL-RK model used in this work is based on an unsymmetric activity coefficient model with the reference states chosen to be pure liquids for solvents and ideal dilute solution at unit solute molality (resulting in activity coefficient of unity at infinite dilution) for electrolytes. It uses the Redlich-Kwong equation of state for vapor phase properties and Henry’s law for solubility of supercritical gases. The model was validated using process data from the pilot-scale campaign at the Tiller plant, and the engineering scale test campaign at TCM. Data on CO2 capture rate, absorber, and regenerator temperature profiles and specific reboiler duties from two different test campaigns at Tiller and TCM, were used to further refine and validate the model and the model compares favorably to experimental data. The validation results against TCM campaign will be presented in this work.

CO2 capture↗

DOE OSTI2023

Controls on Barite (BaSO 4 ) Precipitation in Unconventional Reservoirs

Barite (BaSO 4 ) precipitation is one of the most ubiquitous examples of secondary sulfate mineral scaling in shale oil and gas reservoirs. Often, a suite of chemical additives is used during fracturing operations to inhibit the accumulation of mineral scales, though their efficacy is widely varied and poorly understood. This study combines experimental data and multi-component numerical reactive transport modeling to offer a more comprehensive understanding of the geochemical behavior of barite accumulation in shale matrices under conditions typical of fracturing operations. A variety of additives and conditions are individually tested in batch reactor experiments to identify the factors controlling barite precipitation. Our experimental results demonstrate a pH dependence in the rate of barite precipitation, which we use to develop a predictive model including a pH-dependent term that satisfactorily reproduces our observations. Further, this model is then extended to consider the behavior of three major shale samples of highly variable mineralogy (Eagle Ford, Marcellus, and Barnett). This data-validated model offers a reliable tool to predict and ultimately mitigate against secondary mineral accumulation in unconventional shale reservoirs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Abbreviated Technical Report: Experimentally Interrogating Detonation Chemistry on Sub-Nanosecond to Nanosecond Timescales

Direct experimental measurement of chemical reactions during high explosive detonation remains challenging. Theory and modeling have long preceded experiment in the fundamental physical and chemical kinetic properties of detonation, and experimentation at the relevant timescales are needed to both validate models and provide fundamental understanding of detonation. In this LDRD-ER project, two approaches, x-ray diffraction and core-level x-ray Raman, were developed and used to further experimental capabilities to address this gap. We further developed dynamic x-ray diffraction to directly detect nanodiamond formation during detonation, providing experimental data towards resolving longstanding controversy in the scientific literature, and although the full kinetics have not yet been fully mapped, diamond diffraction appears on the same timescales as detonation soot formation. In the second research thrust, we have developed core-level x-ray Raman for use with high explosives. This technique provides information analogous to x-ray absorption spectroscopy and electron energy loss spectroscopy, but uses inelastic scattering of hard x-rays that can interrogate chemistry around light elements much deeper into the material. The low cross section and requisite high solid angle collection have hindered its use for ultra-fast spectroscopy. We developed and tested a high-q spectrometer which will substantially increase cross section and signal-to-noise, showing this method will also not dramatically alter, compared to x-ray absorption, the most discriminating spectral features of C, N, and O from various high explosives and expected detonation products. We have also used x-ray Raman combined with OCEAN electronic structure calculations to explore dynamic photodegradation mechanisms in PETN and CL-20 explosives. This provides a pathway towards implementing capability to dynamically explore chemistry at an x-ray free electron laser.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Use of a Compliant Tether to Decouple Observation Buoy Motion for Auxiliary Wave Power

With the growth of the Blue Economy, the volume of data collection within the ocean environment has been rapidly increasing. Larger numbers of oceanographic, meteorological, and floating Light Detection And Ranging (LiDAR) buoys have been collecting high fidelity measurements while pushing against power budget limits. Power limitations lead to infrequent transmission of reduced data sets or recording data to local storage that must be physically collected when the buoy is serviced. Triton Systems, Inc. and its partners are developing a retrofittable wave energy converter (WEC) to provide auxiliary power to these observation buoys to increase mission duration and power budget, improve reliability, and reduce the need for service trips. One of the greatest challenges has been developing a method to interface Triton's WEC with these buoys without impacting measurement fidelity. This is especially critical for inertial wave and LiDAR wind measurements collected with sensors that could be adversely affected by additional buoy dynamics introduced by an integrated WEC. To address this, Triton and EOM Offshore developed a compliant tether to pair an observation buoy with a floating WEC while decoupling relative motion. Based on EOM's proven stretch hose technology, this compliant tether transmits power and data between the buoy-WEC system. In conclusion, modeling shows that the system has the potential to minimally adversely affect oceanographic, meteorological, wind resource characterization, and other measurements, with future testing scheduled to validate modeling efforts.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

A Scientist-in-the-Loop Data Analytics Framework for Intelligent Simulation Model Tuning and Validation

This project developed a scientist-in-the-loop data analytics framework for intelligent simulation model tuning and validation, targeting the Weather Research and Forecasting (WRF) model and its solar energy variant, WRF-Solar-BNL. Domain experts, such as climate scientists, depend on large-scale numerical simulations for knowledge discovery and decision-making, yet the complexity of parameter tuning and the disconnect between automated optimization and domain expertise pose significant challenges. We extended an interactive visual analytics framework that enables domain experts to observe and intervene in the computational steering process by identifying disagreements between the simulation model, surrogate model, and the expert’s domain knowledge. Using Bayesian Optimization with Gaussian Process Regression as the surrogate model, our system allows users to probe parameter relationships, analyze correlation patterns, and adjust tuning parameters in real time. We developed use cases for solar irradiance forecasting through sustained collaboration with Brookhaven National Laboratory, resolving critical model configuration challenges and achieving meaningful reductions in prediction error. The project supported one PhD student, one MS student, and eight undergraduate students across three Data Science Capstone projects, resulting in one master’s thesis.

Dasgupta, Aritra [New Jersey Institute of Technolo↗

DOE OSTI2023

Vertical control of DIII-D discharges with strong negative triangularity

Through predictive modeling validated by a series of experiments on DIII-D, the vertical stability of low β diverted plasmas with strong negative triangularity (NT) ($\delta\sim-0.6$) is assessed. As a result of their unique magnetic geometry, NT plasmas feature larger Shafranov shifts and more elongated inner flux surfaces than positive triangularity counterparts, typically leading to enhanced vertical instability growth rates. However, coupling with the non-conformal vessel DIII-D wall reduces these growth rates to controllable values, providing a path forward for stabilizing strongly NT plasma with a diverted geometry. These discharges are used to validate GSdesign (part of the TokSys code suite) stability calculations in strong NT plasmas on DIII-D, with errors of no more than ~20% observed between modeled and experimentally measured growth rates. Additions of diagnostic noise and power supply tuning to the TokSys model are needed to accurately capture the time dependence of DIII-D NT discharges, assisting with the design of control schemes specific to the DIII-D poloidal field coils. Lastly, implications of these results on a future NT reactor are briefly described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

WINDPROF: Merged Best-Estimate Wind Profile Data – Site A1 (AWAKEN Campaign)

WINDPROF provides 10-minute wind and turbulence profiles, integrating Doppler lidars and anemometers during the AWAKEN campaign. Key data include wind speed, direction, vertical velocity, and turbulence parameters, with standardized quality control (e.g., instrument-specific thresholds and inter-instrument validation). Profiles are interpolated to a height grid (20 m spacing below 100 m; 30 m above) and include uncertainty estimates, offering reproducible methods for atmospheric research, model validation, and wind energy studies.

17 WIND ENERGY↗

DOE OSTI2026

WINDPROF: Merged Best-Estimate Wind Profile Data – Site A2 (AWAKEN Campaign)

17 WIND ENERGY↗