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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Acquire and Test IDAES Framework Components

The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Bio-based solutions to transform the climate and clean energy systems [Slides]

The study and use of non-model microbes in both natural and engineered systems, to tackle a range of national and global challenges. Unique combination of expertise from synthetic biology tool development and high throughput screening to aquatic and terrestrial environmental simulation systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A hybrid method to evaluate the life cycle climate performance of heat pumps

Heat pumps have a significant impact on the climate, and numerous studies exist discussing the environmental impact of heat pumps quantitatively. Life Cycle Climate Performance (LCCP) is a widely accepted metric and can evaluate the carbon footprint of heat pumps from cradle to grave. LCCP calculation typically requires inputs like annual energy consumption, the material used, refrigerant type, and charge level, which are usually the intermediate or final results from heat pump simulation tools. Thus, a platform linking the heat pump simulation software, local weather conditions, and LCCP evaluation tool can simplify the heat pump design process, especially for complicated systems. This paper presents an LCCP evaluation tool accessing the outputs from the DOE Heat Pump Design Model and the building loads and temperature bin data from AHRI standard 210/240. As for case studies, this paper investigates the LCCP values and life cycle electricity costs (LCEC) of a given cooling-based heat pump with R410A in five typical US cities. In conclusion, this cooling-based heat pump has the highest LCCP value at Chicago and lowest LCCP value at Seattle. In contrast, the heat pump has the highest LCEC at Seattle and the lowest LCEC at Miami. As a future work, a more comprehensive carbon emission and cost analysis of heat pump systems will be conducted through all major cities around the world.

Shen, Bo↗
DOE OSTI2023

Electrochemical Characterization of Molten Salt Chemistry During Atmospheric Ingressions

This report describes the salt chemistry and sedimentation studies that were completed in FY23 as part of the Pyrochemical Fuel Cycles – ANL project. The primary objective of these activities was to employ electrochemical methods in order to quantify and gain insights into the reaction mechanisms associated with the interaction of O2 and moisture impurities with the molten chloride salts used for the pyrochemical processing of nuclear materials. If not monitored and controlled, the ingression of these atmospheric impurities can lead to changes in the salt redox conditions and initiate the formation of oxide particles, potentially resulting in operational and safeguards challenges. By elucidating the mechanisms underlying the formation of oxide particles, we can effectively monitor the conditions of the salt and design systems to mitigate the influence of atmospheric O2 and H2O. To achieve these goals, we designed a test system that allowed for precisely controlled gas ingressions into the molten salt vessel. This system included on-line monitoring provided by electrochemical probes that enable near real-time measurements of the salt conditions throughout the course of the experiment. Using this system, we systematically varied experimental conditions, including O2/Ar flow rates and O2 concentrations, to comprehensively understand their impact on solid particle formation. We also used a particle size and shape analyzer to characterize the particles that were generated. Tests in FY23 concentrated on the formation of CeO2 particles, but we also prepared a separate apparatus targeting UO2 particles for use in FY24. We additionally conducted extensive modeling activities in support of this work. This included multiphysics simulations of the gas ingressions along with the development of a machine learning workflow to enable accelerated molecular dynamics modeling of the molten salt chemistry of LiCl-KCl-UCl3. The combination of experimental and modeling tools has allowed us to begin to get a more complete understanding of the reactions that occur when atmospheric ingressions occur in pyroprocessing systems.

Guo, Jicheng↗
DOE OSTI2022

The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation

Abstract. The E3SM Diagnostics Package (E3SM Diags) is a modern, Python-based Earth system model (ESM) evaluation tool (with Python module name e3sm_diags), developed to support the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). E3SM Diags provides a wide suite of tools for evaluating native E3SM output, as well as ESM data on regular latitude–longitude grids, including output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) Atmosphere Model Working Group (AMWG, 2022) diagnostics package. In its version 1 release, E3SM Diags included a set of core essential diagnostics to evaluate the mean physical climate from model simulations. As of version 2.7, more process-oriented and phenomenon-based evaluation diagnostics have been implemented, such as analysis of the quasi-biennial oscillation (QBO), the El Niño–Southern Oscillation (ENSO), streamflow, the diurnal cycle of precipitation, tropical cyclones, ozone and aerosol properties. An in situ dataset from DOE's Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating the representation of simulated cloud and precipitation processes. This tool is designed with enough flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. Additional features include customizable figures; streamlined installation, configuration and execution; and multiprocessing for fast computation. The package uses an up-to-date observational data repository maintained by its developers, where recent datasets are added to the repository as they become available. Finally, several applications for the E3SM Diags module were introduced to fit a diverse set of use cases from the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The ParFlow Sandtank: An interactive educational tool making invisible groundwater visible

Physical aquifer models are a highly effective teaching tool for hydrology education, however they come with inherent limitations that include the high cost to purchase, the static configuration of the model materials, the time required to visualize hydrogeological phenomena, and the effort to reset and clean them over time. To address these and other limitations, we have developed an interactive computer simulation of a physical aquifer model called the ParFlow Sandtank. In this gamified interface, users run the simulation using a familiar web-app like interface with sliders and buttons while learning real hydrologic concepts. Our user interface allows participants to dive into the world of hydrology, understanding assumptions about model parameters such as hydraulic conductivity, making decisions about inputs to groundwater aquifer systems such as pumping rates, visualizing outputs such as stream flow, transport, and saturation, and exploring various factors that impact real environmental systems such as climate change. The ParFlow Sandtank has already been used in a variety of educational settings with more than 9,000 users per year, and we feel this emerging educational tool can be used broadly in educational environments and can be scaled-up to provide greater accessibility for students and educators. Here we present the capabilities and workflow of the ParFlow Sandtank, two use cases, and additional tools and custom templates that have been developed to support and enhance the reach of the ParFlow Sandtank.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assimilating Scanning Radar Data into Cloud-Scale Models

The aim of this project is to improve the initial conditions of large eddy simulations (LES) of deep convective storms by assimilating scanning radar reflectivity and radial velocity data as well as local environmental observations. Accomplishing this goal often results in more accurate numerical forecasts of clouds and precipitation than if the background initial meteorological conditions are solely relied upon for convection initiation and intensification, as is typical of most operational and research models. Conducted in a storm-scale LES framework, these more accurate numerical analyses can serve as tool to better understand and parameterize land-atmosphere coupling, boundary layer turbulence, and cloud processes, each used for model parameterization development of convection. Our work examined the sensitivity of the numerical representation of cloud-scale wind and microphysical structures to: i) the assimilation of radiosonde observations characterizing the near-storm inflow, ii) the assimilation of radar data of varying resolution for model simulations with grid spacings of 1-km and 0.25-km, and iii) to the assimilation of three-dimensional plan-projection-indicator (PPI) and range-height indicator (RHI) radar volumes. Few studies have explored deep convective storm analyses and forecasts produced by assimilation of radar observations to cloud-scale LES. Furthermore, to our knowledge, no storm-scale DA study has explored the value of assimilating high-resolution sector RHI volumes to determine if they can improve the retrieval of the vertical kinematic and microphysical storm structure aloft over the use of traditionally utilized PPI scans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Demonstrating SolarPILOT's Python API Through Heliostat Optimal Aimpoint Strategy Use Case: Preprint

SolarPILOT is a software package that generates heliostat field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. SolarPILOT was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL's System Advisor Model (SAM) in a simplified format. Prior means for user interaction with SolarPILOT have included the application's graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called "LK." This paper presents a new, full-featured Python-based application programmable interface (API) for SolarPILOT, which we hereafter refer to as CoPylot. CoPylot provides access to all SolarPILOT's capabilities to generate and characterize power tower CSP systems seamlessly through Python. Supported capabilities include (i) creating and destroying a model instances with message reporting tools; (ii) accessing and setting any SolarPILOT variable including custom land boundaries for field layout; (iii) programmatically managing receiver and heliostat objects with varied attributes for systems with multiple receiver or heliostat types; (iv) generating, assigning, and modifying heliostat field layouts including the ability to set individual heliostat locations, aimpoints, soiling rates, and reflectivity levels; (v) simulating heliostat field performance; (vi) returning detailed results describing performance of individual heliostats, the aggregate field, and receiver flux; and, (vii) exporting Python-based model instances to multiple file formats. CoPylot enables Python users to perform detailed tower CSP analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. In addition to CoPylot's functionality, Python users have access to the over 100,000 open-source libraries to develop, analyze, optimize, and visualize CSP tower research.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

SASHIMI-SIDM: semi-analytical subhalo modelling for self-interacting dark matter at sub-galactic scales

We combine the semi-analytical structure formation model, SASHIMI, which predicts subhalo populations in collisionless, cold dark matter (CDM), with a parametric model that maps CDM halos to self-interacting dark matter (SIDM) halos. The resulting model, SASHIMI-SIDM, generates SIDM subhalo populations down to sub-galactic mass scales, for an arbitrary input cross section, in minutes. We show that SASHIMI-SIDM agrees with SIDM subhalo populations from high-resolution cosmological zoom-in simulations in resolved regimes. Crucially, we predict that the fraction of core-collapsed subhalos peaks at a mass scale determined by the input SIDM cross section and decreases toward higher halo masses, consistent with the predictions of gravothermal models and cosmological simulations. For the first time, we also show that the core-collapsed fraction decreases toward lower halo masses. While the dependence of the collapse time on mass and concentration implies such behaviour, our semi-analytical approach allows us to quantify and illustrate this trend clearly across the full mass spectrum of subhalos, including for subhalo masses below the resolution limit of any current cosmological SIDM simulation. As a proof of principle, we apply SASHIMI-SIDM to predict the boost to the local dark matter density and annihilation rate from core-collapsed SIDM subhalos, which can be enhanced relative to CDM by an order of magnitude for viable SIDM models. Thus, SASHIMI-SIDM provides an efficient and reliable tool for scanning SIDM parameter space and testing it with astrophysical observations. The code is publicly available at https://github.com/shinichiroando/sashimi-si.

cosmological simulations↗
DOE OSTI2021

Demonstrating SolarPILOT’s Python API Through Heliostat Optimal Aimpoint Strategy Use Case

SolarPILOT is a software package that generates solar field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. SolarPILOT was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL’s1 System Advisor Model (SAM) in a simplified format. Prior means for user interaction with SolarPILOT have included the application’s graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called “LK.” This paper presents a new, full-featured, Python-based application programmable interface (API) for SolarPILOT, which we hereafter refer to as CoPylot. CoPylot provides access to all SolarPILOT’s capabilities to generate and characterize power tower CSP systems seamlessly through Python. Supported capabilities include (i) creating and destroying a model instance with message reporting tools; (ii) accessing and setting any SolarPILOT variable including custom land boundaries for field layouts; (iii) programmatically managing receiver and heliostat objects with varied attributes for systems with multiple receiver or heliostat types; (iv) generating, assigning, and modifying solar field layouts including the ability to set individual heliostat locations, aimpoints, soiling rates, and reflectivity levels; (v) simulating solar field performance; (vi) returning detailed results describing performance of individual heliostats, the aggregate field, and receiver flux distribution; and, (vii) exporting Python-based model instances to multiple file formats. CoPylot enables Python users to perform detailed CSP tower analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. In addition to CoPylot’s functionality, Python users have access to the over 100,000 open-source libraries to develop, analyze, optimize, and visualize power tower CSP research. This enables CSP researchers to perform analysis that was previously not possible through SolarPILOT’s existing interfaces. This paper discusses the capabilities of CoPylot and presents a use case wherein we demonstrate optimal solar field aiming strategies.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Hydrogen Production System Scaling Using a High-Fidelity Simulation-Optimization Framework

Proton exchange membrane (PEM) electrolyzers are widely used for hydrogen production, yet few validated, high-fidelity tools can reliably guide scale-up. Using measured performance from a 50-hour hardware-in-the-loop pilot test, a physics-based, plant-level model of a 1.25 MW PEM electrolyzer and its balance-of-plant (BoP) subsystems is developed and validated. The model couples electrochemistry and thermal/flow submodels and is calibrated against pilot test data via a genetic algorithm (GA) workflow. Validation yields a mean absolute percentage error (APE) of 0.43% for cell voltage and stack power. Two scale-out strategies are then benchmarked under a common 7-day wind-and-photovoltaic (PV) profile: (i) linear duplication of 1.25 MW blocks and (ii) shared-BoP architectures. Sharing BoP between stacks reduces BoP energy by 27% at 10 MW and 34% at 100 MW (vs. linear duplication) and improves system specific energy consumption (SEC) to 52.9 and 52.6 kWh/kg, respectively (from 54.0 kWh/kg with linear duplication). Partial-load studies (25-100% set-point) show that cumulative hydrogen production remains nearly constant down to 50% load because all cases use the same weekly renewable-energy input. Below 50%, the power cap limits how much energy can be used within 168 h, which reduces hydrogen output. The model further indicates that the practical operating optimum lies between 50% and 85% load, where efficiency gains begin to appear without significant loss in hydrogen output. Moreover, the efficiency gains at lower loads are offset by reduced production. The validated framework supports scenario-based engineering trade-off studies for large configurations (10-100 MW) and for operating policies under variable renewables.

08 HYDROGEN↗
DOE OSTI2025

Capability in Theory, Modeling, and Validation for a Range of Innovative Fusion Concepts using High-Fidelity Moment-Kinetic Models

A computational modeling capability is created and available to the fusion community to understand and design lower-cost and innovative fusion concepts. The approach uses high- fidelity kinetic, moment-kinetic, and moment models and includes sophisticated plasma- boundary interactions. A majority of fusion-relevant simulations are performed with magnetohydrodynamic models and hybrid particle-in-cell codes, with limited-fidelity electron and kinetic physics. However, in fusion configurations like Z-pinches, field-reversed- configurations, plasma jet magneto-inertial fusion, spinning mirrors, and others, kinetic effects (both electron and ions) are critical to understand the physics and design scaling into the highly kinetic regime of a burning fusion plasma. Furthermore, as present fusion machines move towards a burning plasma regime, liquid-metal blankets are needed to handle first-wall heat- flux, reduce erosion, and eventually for energy conversion and fuel breeding. The work performed under this ARPA-E BETHE Capability Team advances the state-of-the-art in modeling and understanding plasma dynamics in fusion devices and its coupling with liquid-metal dynamics. These are critical areas of research for fusion energy to become realizable. To address these complex problems, we have leveraged and extended computational capabilities through the code, Gkeyll (developed jointly with Princeton Plasma Physics Laboratory and academic partners), for kinetic and moment modeling of fusion plasmas. The Concept Teams supported by this Capability Team include the Wisconsin High-field Axisymmetric Mirror (WHAM), Centrifugal Mirror Experiment (CFME), Plasma-Jet Magneto- Inertial Fusion (PJMIF), and solid and liquid wall plasma-material interaction studies relevant to a number of fusion concepts including Zap Energy’s Z-pinch. This software is open-source and available to the fusion community as a high-fidelity tool for the design of lower-cost fusion experiments. 3D gyrokinetic simulations of WHAM are now possible for long enough time scales to understand the evolution of interchange instabilities. 3D multi-fluid simulations of CMFE at higher Mach numbers are now possible for detailed design iterations with the goal of stability. The state-of-the-art in understanding shock formation and shock mitigation regimes in merging liners for PJMIF have been furthered by our kinetic simulations. Our novel models and frameworks studying plasma-material interaction by incorporating wall emission for various solid wall materials of relevance to pulsed and steady fusion concepts have advanced the state-of-the-art in our understanding of particle fluxes, heat fluxes, and other quantities at cathodes and anodes. The results from this work may explain discrepancies between experimental and theoretical predictions of achieved current densities in pulsed concepts such as Z-pinches. Another significant contribution of this Capability Team is the development and deployment of a novel experimental platform, LEX (Liquid Electrode eXperiment), at Virginia Tech to understand liquid metal free-surface response to electromagnetic pulses. The novel experiments along with model validation quantified the effect of different materials and sizes of liquid metal droplets on the radiative power balance of fusion plasmas for pulsed concepts. Furthermore, these experiments provided mitigation strategies for violent liquid metal response for high current pulses as would be expected in fusion regimes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. However, previous works such as Deep Fluids (Kim et al., 2019) show that large simulation speed-ups and low error are achievable by replacing CFD simulators with neural-network (NN) surrogates that mimic the CFD simulation process. This raises the possibility for a fast, accurate replacement for a CFD simulator, and thus computationally feasible IA-based CCS-design optimization. As such, here, we explore whether an existing NN-surrogate approach (and variants we develop) can successfully be applied to our complex carbon-capture CFD simulations, with the ultimate goal of obtaining a fast and accurate simulator for our CCS-design application. Our experiments build on the Deep Fluids approach and find that resulting surrogates can produce large speed ups (4000x) while maintaining IA relative errors as low as 4% on unseen CCS configurations (interpolating between configurations seen during training). Thus, despite less faithfulness to the underlying physics of the problem, NN surrogates may be a promising tool for our CCS design optimization problem. Notably, though, the Deep Fluids approach has limitations for our application (such as model non-transferability to CCS-packing changes), which we discuss. We conclude with potential directions for future work that may, like innovations we introduced here (e.g., transformer-based dynamics prediction), improve performance on our complicated dataset of CCS CFD simulations.

AI Surrogate Simulation, Carbon Capture, design op↗
DOE OSTI2026

Mauka Energy FEVER Tool Dataset

Mauka Energy’s dataset, developed under the Forestry Electric Vehicle Energy Routing (FEVER) project and funded by the U.S. Department of Energy’s Small Business Innovation Research program, is a high-resolution geospatial resource designed to support energy modeling for electric log trucks in complex forestry environments. The dataset integrates detailed spatial and road network data to enable accurate simulation of vehicle performance across varied terrain. At its core, the dataset incorporates lidar-derived elevation models, road alignments, and surface classifications from Oregon State University’s McDonald-Dunn Research Forest. These data capture fine-scale variations in slope, curvature, and surface conditions across forest road systems, allowing for vehicle-level analysis of energy consumption and recovery. The dataset also includes data collected on the surrounding public and private road networks in Benton County, Oregon, used in real-world haul routes. These connecting segments provide critical context for modeling transitions between forest operations and regional transportation infrastructure, incorporating attributes such as grade profiles, elevation change, and speed constraints. This combined dataset underpins the development of Mauka Energy’s rolldown tool, which quantifies energy use and regenerative braking potential on downhill and variable-grade segments. By leveraging high-resolution terrain and road data, the FEVER project enables more accurate assessment of electric vehicle feasibility and performance in forestry applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. Here in this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Temperature Homogeneity under Selective and Localized Microwave Heating in Structured Flow Reactors

Selective heating of different phases of multiphase systems via microwaves can result in energy savings and suppression of side reactions. However, materials properties and operating conditions that maximize temperature gradients are poorly understood. Here we utilize computational fluid dynamics (CFD) computations and temperature measurements in structured flow reactors (monoliths) in a monomodal microwave cavity to assess the temperature difference between the walls and the fluid and develop a simple lumped model to estimate when temperature gradients exist. We also explore the material’s thermal and electrical properties of structured reactors for isothermal catalyst conditions. We propose that CFD simulations can be used as a nonintrusive, predictive tool of temperature homogeneity. Importantly, we demonstrate that localized heating in the bed under several conditions rather than selective heating is responsible for the selectivity enhancement. Furthermore, our results indicate that structured beds made of high thermal conductivity materials avoid arcing and enable temperature homogeneity and low electrical conductivity materials allow microwaves to penetrate the domain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Simulation of hydropower at subcontinental to global scales: a state-of-the-art review

Abstract Hydroelectric power is playing a new and often expanded role in the world’s major power grids, offering low carbon generating capacity in industrializing, dam-building economies while providing reserve and flexibility to co-manage fledgling wind and solar resources in high income countries. Driven by river flows, conventional hydropower is exposed to the vagaries of weather and climate, motivating drought and climate change hydropower impact studies at large spatial scales. Here we review methods of climate-driven hydropower simulation at large spatial scales, specifically multi-basin regions to global. We identify four types of approach based on complexity of tools and richness of data applied to the problem. Since the earliest attempts to model climate-driven hydropower at continental scale almost two decades ago, the field has transitioned from one of scientific curiosity to practical application, with studies increasingly motivated by the need to inform power grid expansion planning and operation. As the hydrological and water management models used in large-scale hydropower studies become more sophisticated, new opportunities will emerge to study the impacts of changing hydropower on power system reliability and performance at large power grid scale. To grasp these opportunities, the water resources community must continue to enhance data and models for representing river flows and anthropogenic water use and management at subcontinental to global scales.

13 HYDRO ENERGY↗