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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Framework to select robust energy retrofit measures for residential communities

Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework’s robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework’s effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework’s potential for broader applications in optimizing community-scale residential energy retrofits.

Shu, Lei↗
DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗
DOE OSTI2024

Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales

Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6–4.9%, R 2 = 0.53–0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Alternative strategies of nutrient acquisition and energy conservation map to the biogeography of marine ammonia-oxidizing archaea

Ammonia-oxidizing archaea (AOA) are among the most abundant and ubiquitous microorganisms in the ocean, exerting primary control on nitrification and nitrogen oxides emission. Although united by a common physiology of chemoautotrophic growth on ammonia, a corresponding high genomic and habitat variability suggests tremendous adaptive capacity. Here, we compared 44 diverse AOA genomes, 37 from species cultivated from samples collected across diverse geographic locations and seven assembled from metagenomic sequences from the mesopelagic to hadopelagic zones of the deep ocean. Comparative analysis identified seven major marine AOA genotypic groups having gene content correlated with their distinctive biogeographies. Phosphorus and ammonia availabilities as well as hydrostatic pressure were identified as selective forces driving marine AOA genotypic and gene content variability in different oceanic regions. Notably, AOA methylphosphonate biosynthetic genes span diverse oceanic provinces, reinforcing their importance for methane production in the ocean. Together, our combined comparative physiological, genomic, and metagenomic analyses provide a comprehensive view of the biogeography of globally abundant AOA and their adaptive radiation into a vast range of marine and terrestrial habitats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A multitree approach for global solution of ACOPF problems using piecewise outer approximations

Electricity markets rely on the rapid solution of the optimal power flow (OPF) problem to determine generator power levels and set nodal prices. Traditionally, the OPF problem has been formulated using linearized, approximate models, ignoring nonlinear alternating current (AC) physics. These approaches do not guarantee global optimality or even feasibility in the real ACOPF problem. We introduce an outer-approximation approach to solve the ACOPF problem to global optimality based on alternating solution of upper- and lower-bounding problems. The lower-bounding problem is a piecewise relaxation based on strong second-order cone relaxations of the ACOPF, and these piecewise relaxations are selectively refined at each major iteration through increased variable domain partitioning. Our approach is able to efficiently solve all but one of the test cases considered to an optimality gap below 0.1%. This approach opens the door for global solution of MINLP problems with AC power flow equations.

42 ENGINEERING↗
DOE OSTI2026

Seasonal performance evaluation of zero-superheat active refrigerant charge control for variable-speed heat pumps

Heat pumps account for a significant portion of electricity consumption in buildings, making their energy efficiency a critical area of research. Control optimization is recognized as an effective approach for enhancing the efficiency of building HVAC systems. Specifically, since the efficiency of heat pumps increases with reduced superheat and the optimal refrigerant charge level varies with operating conditions, zero-superheat active charge control is a promising strategy to maximize the system’s COP. However, due to its complexity, this control strategy remains in the research phase and has only been tested under limited operating conditions. This paper proposes a methodology to evaluate the seasonal performance improvement of zero-superheat active charge control across a full range of heating conditions in buildings. The methodology includes the development of an optimal controller that utilizes an accumulator to store excess refrigerant and manages the active charge level at zero superheat through subcooling control, as well as the creation of a detailed physics-based heat pump model. Targeted a 3-ton variable-speed heat pump with R410A refrigerant, we selected ten U.S. cities representing different climate regions for seasonal performance evaluation. Our simulations indicate that, depending on the climate conditions, the optimal control strategy could achieve energy savings ranging from 5.8 % to 8.0 % over the entire heating season. These results suggest that zero-superheat active charge control, combined with coordinated compressor and fan speeds, could substantially improve the energy efficiency of heat pump systems while maintaining the required heating capacity.

Guo, Fangzhou↗
DOE OSTI2021

Dimensional Control of Octahedral Tilt in SrRuO 3 via Infinite-Layered Oxides

Manipulation of octahedral distortion at atomic scale is an effective means to tune the ground states of functional oxides. Previous work demonstrates that strain and film thickness are variable parameters to modify the octahedral parameters. However, selective control of bonding geometry by structural propagation from adjacent layers is rarely studied. Here we propose a new route to tune the ferromagnetism in SrRuO 3 (SRO) ultrathin layers by oxygen coordination of adjacent SrCuO 2 (SCO) layers. The infinite-layered CuO 2 exhibits a structural transformation from “planar-type” to “chain-type” with reduced film thickness. Two orientations dramatically modify the polyhedral connectivity at the interface, thus altering the octahedral distortion of SRO. The local structural variation changes the spin state of Ru and orbital hybridization strength, leading to a significant change in the magnetoresistance and anomalous Hall resistivity. Furthermore, these findings could launch investigations into adaptive control of functionalities in quantum oxide heterostructures using oxygen coordination.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Highly Active Hydrogen Evolution Reaction (HER) Catalysts Formed by Energetic Pt n Cluster Deposition: Deposition Dynamics and the HER Mechanism

Mass-selected Pt n + (n ≤ 7) were deposited at variable energies on highly oriented pyrolytic graphite (HOPG), creating highly active hydrogen evolution reaction (HER) electrocatalysts. HER mass activities were ~2 to >10 times higher than those for the surface atoms in bulk Pt and for Pt n deposited on several other supports. Thus, high activity reflects the Pt-C structures formed by energetic Pt n -HOPG impacts, in addition to high Pt surface availability. The Pt n /HOPG electrodes were probed by X-ray photoelectron spectroscopy, low energy ion scattering, and electron microscopy. Born-Oppenheimer molecular dynamics (BOMD) was used to simulate Pt n - HOPG impacts, revealing the types of structures formed at different energies, then DFT was used to probe their most important HER pathways. For low deposition energies, the Pt n deposit onto the HOPG surface with sub-unit sticking probability, aggregating at defects. With increasing deposition energy, the sticking probability initially decreases, then rises to unity as subplantation and defect creation allow formation of strongly bonded platinum-carbon structures. Barriers for HER on these structures were found to be low and weakly dependent on Pt n size, consistent with experiment. The activities were highest for small covalently-bonded Pt-C structures created at high deposition energies. The larger aggregated structures formed at low energies were less active, but still substantially better than the bulk Pt surface monolayer. The catalysts were stable in repeated potential cycling at reducing potentials, but electrodes containing subplanted Pt became more active when scanned to oxidizing potentials, due to emergence of subplanted Pt onto the surface.

08 HYDROGEN↗
DOE OSTI2018

Determination and Assessment of Optimum Internal Thermal Insulation for Masonry Walls in Historic Multifamily Buildings

A large portion of existing building stock is comprised of load-bearing masonry buildings, particularly in the Midwest and on the East Coast, though such buildings are distributed across the US. Most of them have uninsulated walls or suffer from insufficient thermal insulation materials. These buildings are inseparable parts of the cultural, social, and economic characteristics of their cities. Due to heritage and preservation concerns, zoning, and space restrictions, they cannot be retrofitted with exterior insulation. Interior insulation must therefore be considered as an alternative. Lack of insulation directly results in energy waste as well as heat loss and gain. Furthermore, it adversely affects heating, ventilation, and air conditioning (HVAC) loads, equipment sizing, indoor air quality, and thermal comfort for residents. The objective of this study is to simulate and evaluate the effects of various internal thermal insulation materials and strategies on mass masonry load-bearing walls in multifamily buildings with attention to various variables and parameters. It not only addresses the energy performance and cost savings associated with an interior insulation retrofit, but also outlines other variables and parameters that are factors in the selection of optimum insulation material, including installations costs, payback periods, useful internal space loss, and options for the depth of the interior insulation retrofit. In conclusion, the audience for this paper is diverse: homeowners, contractors and remodelers, architects and designers, engineers, housing and preservation authorities, and more can benefit from understanding the results outlined in this study.

42 ENGINEERING↗
DOE OSTI2019

Relaxed Bell inequalities with arbitrary measurement dependence for each observer

Bell's inequality was originally derived under the assumption that experimenters are free to select detector settings independently of any local “hidden variables” that might affect the outcomes of measurements on entangled particles. This assumption has come to be known as “measurement independence” (also referred to as “freedom of choice” or “settings independence”). For a two-setting, two-outcome Bell test, we derive modified Bell inequalities that relax measurement independence, for either or both observers, while remaining locally causal. We describe the loss of measurement independence for each observer using the parameters M 1 and M 2 , as defined by Hall in 2010, and also by a more complete description that adds two new parameters, which we call M 1 ^ and M 2 ^ , deriving a modified Bell inequality for each description. These “relaxed” inequalities subsume those considered in previous work as special cases, and quantify how much the assumption of measurement independence needs to be relaxed in order for a locally causal model to produce a given violation of the standard Bell-Clauser-Horne-Shimony-Holt (Bell-CHSH) inequality. We demonstrate that both relaxed Bell inequalities are tight bounds on the CHSH parameter by constructing locally causal models that saturate them. For any given Bell inequality violation, the new two-parameter and four-parameter models each require significantly less mutual information between the hidden variables and measurement settings than previous models. We conjecture that the new models, with optimal parameters, require the minimum possible mutual information for a given Bell violation. We also argue that, contrary to various claims in the literature, relaxing freedom of choice need not imply superdeterminism.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A Graphics Processing Unit–Based, Industrial Grade Compositional Reservoir Simulator

Summary Recently, graphics processing units (GPUs) have been demonstrated to provide a significant performance benefit for black-oil reservoir simulation, as well as flash calculations that serve an important role in compositional simulation. A comprehensive approach to compositional simulation based on GPUs has yet to emerge, and the question remains as to whether the benefits observed in black-oil simulation persist with a more complex fluid description. We present a positive answer to this question through the extension of a commercial GPU-based black-oil simulator to include a compositional description based on standard cubic equations of state (EOSs). We describe the motivations for the selected nonlinear formulation, including the choice of primary variables and iteration scheme, and support for both fully implicit methods (FIMs) and adaptive implicit methods (AIMs). We then present performance results on an example sector model and simplified synthetic case designed to allow a detailed examination of runtime and memory scaling with respect to the number of hydrocarbon components and model size, as well as the number of processors. We finally show results from two complex asset models (synthetic and real) and examine performance scaling with respect to GPU generation, demonstrating that performance correlates strongly with GPU memory bandwidth. NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Engineering↗
DOE OSTI2018

Advanced Reaction System Program: Materials for ARS Reaction Chambers (FY18 Milestone, Subtask 3.3)

The objective of this effort is to identify materials and manufacturing technology that will allow the rapid construction and evaluation of computer modeled advanced reactor system designs to process specific carbon feedstock materials. Before full scale reaction systems can be built, prototypes need to be constructed and operated to validate the proposed new system performance against target criteria. The first steps, however, are to understand what type of materials are needed that can withstand the targeted process environments and determine how structures using those materials can be built. A wide range of reaction vessel conditions may exist in the processes being considered that could involve temperatures up to 1200°C, abrasive materials (high velocity coal and coal ash), different carbon feedstock materials (coal, biomass, and process wastes such as petcoke), corrosive and oxidizing/reducing gases, different material phases (solid, liquid, and gas), cyclic temperatures, microwave energy, customized vessel construction material needs (process stability, cost, weight, and compatibility with other materials), along with other known/unknown variables – all of which must be considered in selecting materials and considering manufacturing processes. The current focus of the research is to develop a reaction vessel sidewall structure consisting of a metal outer support and an inner ceramic (or cermet) protective surface. Ceramic coatings are desired because of their good corrosion and wear resistance, and because they are the material of choice to reduce metal shell temperature in high temperature processes. However significant property mismatches between metals and ceramics have been a roadblock to the joining of metals and ceramics.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Event Selections in the NOvA 2024 Analysis

The NuMI Off-axis $\nu_e$ Experiment (NOvA) is a long-baseline neutrino oscillation experiment that studies a neutrino beam produced by the Neutrinos at the Main Injector (NuMI) facility at Fermilab to constrain the PNFS mixing angles, the neutrino mass hierarchy, and the CP-violating phase $\delta_{CP}$. These parameters are extracted by comparing the spectra of muon neutrinos and electron neutrinos measured at the near and far detectors, using an extrapolation of the Near Detector spectra. Accurate event selection is critical to this process, as only interactions with well-reconstructed energies and interaction types should be included in the oscillation analysis to reduce systematic uncertainties. In this work, we describe the selection criteria used for muon and electron neutrinos in the Near and Far Detector. We demonstrate the effectiveness of the criteria by examining the efficiency and purity of the event selections as well as their impact on key variables used in the oscillation analysis.

Chen, Hanyi [Indiana U.]↗
DOE OSTI2023

Development of a Machine-Learned Cruise Guide Indicator for Rotorcraft

This paper presents a machine-learned virtual cruise guide indicator (vCGI) for Chinook helicopters. Two temporal neural networks were trained and evaluated on measured data from 55 flight tests, one for the fore rotor and another for the aft rotor, to predict a vCGI value, which protects 23 components from fatigue damage during steady-state conditions. Three different classes of machine learning architectures were evaluated for prediction of the vCGI from time sequences: a temporal convolutional neural network with 1D dilated causal convolutions, a long short-term memory recurrent neural network, and an attention-based transformer architecture. The final average model accuracy on unseen flight data is currently greater than 93% for CGI values which could result in fatigue damage and 90% for normal operation CGI values. Model accuracy was improved through a series of advancements in:(1) selection of optimal training data using temporal collective variables and unsupervised learning, (2) dataset augmentation with maximum-entropy temporal collective variables, and (3) implementation of a mixture-of-experts classification- regression approach using an adversarial classification approach to assign maneuver labels. The results are presented for each advancement in model development along with lessons learned in training machine learning models on real- world, time-dependent rotorcraft data.

Boyer, Mathew↗
DOE OSTI2017

NONPARAMETRIC EMPIRICAL BAYES ESTIMATION THROUGH DECONVOLUTION IN PROBABILISTIC RISK ANALYSIS

The advent of Markov Chain Monte Carlo (MCMC) simulations and availability of open source software made full hierarchical Bayesian analysis tractable and a popular choice in parameter estimation for Probabilistic Risk Assessment (PRA). However, despite its theoretical attractiveness, the hierarchical Bayesian analysis is not without its problems. Two of the most prominent practical difficulties in applying hierarchical Bayes analysis in practice to analyze source-to-source variability, for example, is its sensitivity to the selection of the first-stage prior and dependence of the rate of convergence on the selection of the first-stage prior. The first-stage prior in hierarchical Bayesian analysis is usually assumed having a parametric form, often conjugate to the likelihood function (aleatory model). To facilitate convergence for hierarchical Bayes models, the first-stage prior is frequently set through empirical Bayes estimate, which can use available historical data to find the parameters of the first-stage prior. The paper discusses a new nonparametric empirical Bayes estimation and compare it to several well-known parametric estimates such as method of moments and maximum likelihood. The new nonparametric technique exploits the prior predictive distribution as integral equation and proceeds to solve it with respect to prior assuming the sampling data distribution and aleatory model are available. The discussion covers topics such as selection of aleatory model for future data, selection of regularization parameter, and density estimation for historical data. For comparison, the paper is using contrived as well as real-world data from utilities. Utilities data represent failure data with Poisson distribution used for aleatory model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Millimeter-wave Detections of Symbiotic Stars in SPT and ACT Data

We present the results of a joint targeted search of candidate symbiotic stars at millimeter wavelengths using the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT). Candidates are selected from the New Online Database of Symbiotic Variables, restricting to objects that are within either the SPT-3G or ACT~DR6 footprint, covering most of the southern hemisphere and up to a declination of $+20^\circ$. Forced photometry on the 828 candidate symbiotic star locations in SPT and ACT data results in 31 unique objects detected with more than a $3σ$ significance using two frequency bands: 18 confirmed and 13 suspected symbiotic stars. We provide the SPT and ACT 95/98, 150, and 220~GHz light curves, along with optical and infrared light curves from 2016--2026, as well as spectral energy distributions, physical parameters from the literature, and brief summaries regarding the nature of each individual object. Using Herschel SPIRE data from 2013, we place upper limits on millimeter flux for CN Cha near the beginning of the optical rise in its 2012/2013 nova, which suggests a strong variability and lag at millimeter wavelengths and results in a rare observance of a Galactic millimeter slow transient. In addition, we provide coadded thumbnails and light curves for the remaining 797 candidate symbiotic stars that did not pass our detection thresholds. Millimeter-wave emission from symbiotic stars is primarily a combination of free-free emission of the ionization region and optically thick blackbody emission of the cooler dust components of the system. When combined with contemporaneous multi-wavelength observations, millimeter-wave observations can be used to test binary models of symbiotic stars and provide insight on the geometry and physical properties of these systems.

Tandoi, C. [Illinois U., Urbana, Astron. Dept.] (O↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗
DOE OSTI2020

Development of Control System Functional Capabilities within the IES Plug-and-Play Simulation Environment

The concept of an Integrated Energy System (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. A new RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization (HERON) has been developed for optimizing the production variables of the IES given the demand profile. Currently, only the limits that affect the production variables and their corresponding time rates of change are considered (explicit constraints). However, other variables are additionally subject to constraints, but the associated limits are not accounted for (implicit constraints). In particular, for the power dispatch problem, the optimization algorithm takes into account the limits on the electrical power output and the corresponding hourly power variations but does not consider other constraints on process variables whose response effects the service life of the IES. This report describes a scheme that allows accounting for implicit constraints without increasing the size of the optimization problem. The Reference Governor (RG) algorithm is traditionally used for enforcing state and control constraints by modifying the set-point trajectories supplied to the feedback regulators. In our application, the RG is coupled within an iterative loop with the HERON-power dispatcher to generate optimal trajectories that ensure the operational constraints are met. A data-driven procedure to derive a representation of the dynamics of the controlled system was developed. First, the variables that represented the state of the system are selected (PCA-based approach), and then state-space representation matrices are derived from the collected measurements (DMDc algorithm). A preliminary version of the developed workflow based on Linear Time Invariant matrices was assessed by adopting a two-unit test case. More sophisticated versions of this workflow foreseeing the on-line derivation of system matrices will be deployed in FY 2021. Finally, a “plug-and-play” library of controllers and state observers was developed in Dymola. Some aspects of the current configuration of the IES unit components, e.g., the encapsulation of the control schemes into dedicated blocks, are consistent with the “plug-and-play” philosophy. Other features, e.g., the system buses collecting the input and the output variables, are not. For this reason, once listed and described the limits of the current configuration, necessary modifications to the plant model interface are presented. As a test-case, the interfaces of the SES model in the RAVEN-based HYBRID framework were reworked accordingly, and two different control schemes were applied to the same plant model.

24 POWER TRANSMISSION AND DISTRIBUTION↗