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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

2022 Prototype Manufacture and Installation Awardee: Sonsight Wind

The Sonsight Wind 3.5-kilowatt (kW) prototype turbine is designed to compete with solar power, meeting energy needs for a wide range of smaller-scale applications, such as powering individual homes and irrigation systems on farms and ranches in remote areas. The technology's innovative high-torque, low-rotations-per-minute generator permits the use of longer blades that, at moderate wind speed sites, generate the energy of a 5-kW turbine with lower thrust forces than similarly rated turbines. The 3.5-kW turbine is projected to ofer greater durability at a signifcantly lower levelized cost of energy. Sonsight Wind advanced the development of the prototype with a previous Competitiveness Improvement Project (CIP) funding award.

competitiveness improvement project↗
DOE OSTI2024

WEC-Sim Modeling of Laminar Scientific's Patented Seesaw Wave Energy Converter: Preprint

Laminar Scientific's patented seesaw wave energy converter was modeled in WEC-Sim to predict performance. The device operates by utilizing ocean surface waves to rotate a truss in pitch about a pivot. The pivot is located at the top of two pylons, which are embedded in the seafloor. The seesaw has a float on either end, and the buoyancy forces from each float cause the system to rise or fall with passing waves. Device performance relies upon seesaw length and ocean wavelength creating an antiphase effect. The seesaw truss has an adjustable length intended to achieve this effect. The operation method enforces a narrow band of wavelengths which induce the largest rotational motion from the device. The hydrodynamic analysis of the device was performed using Capytaine, and the results confirmed that the device operates best in a narrow frequency band. Four float-to-float spacing cases and three pylon radii were examined. The hydrodynamic results indicate a match between the model and the physical expectations for the device, and that varying the pylon radii by 0.1-m increments for three instances creates minimal changes in hydrodynamic properties. Power matrices for three float spacing cases of the device were simulated with Joint North Sea Wave Project spectra waves and optimal power take-off damping in WEC-Sim. The maximum average power production for the 15-m spacing case was 14.1 kW with a 5.0-s peak wave period and 4-m significant wave heights. Plots of capture-width ratios indicated that the device performance was linear and confirmed that the device is optimal in a narrow frequency band. The maximum percentage of the available wave power produced by the 15-m device was approximately 16%. Simulations of the device in regular waves were used to produce plots of average power compared to a ratio of float spacing to wavelength. These plots indicate that the power production is maximized at a ratio of 0.5, and further confirm that the device has a narrow frequency response. The device was simulated at an example field location, where the device produced an annual average power rating of 1.6 kW given an average omnidirectional wave climate of 10.3 kW m-1 and an optimal, linearized power take-off model. While the maximum predicted device performance is reliant upon a narrow band of wave frequencies, the conducted analysis provides an opportunity to improve device design prior to prototyping and testing. Modifying the design to respond to a broader frequency range would improve device performance.

numerical modeling↗
DOE OSTI2024

Dynamic Modeling of Power Conversion Stages for an Exascale Supercomputer

In this paper a power conversion and energy consumption model for an exascale supercomputer is investigated. Power consumption, energy loss and efficiency are derived for the 27.2 MW liquid-cooled, centralized, High Performance Computing (HPC) power system, which is supplied directly from the 480 V three-phase mains. Two energy conversion stages are analyzed, measured and modeled. The model is developed in order to be adapted and implemented in a digital twin platform utilizing a Resource Allocator and Power Simulator (RAPS) module. RAPS enables estimation of potential energy savings in the direct AC power supply architecture via both conventional rectifier load sharing (commonly used in HPC systems), as well as smart rectifier load sharing. Moreover, besides the direct AC supply architecture analysis, the full direct DC supply architecture with with 1 kV DC bus were also studied. Comparison of 10 hour time frame operation of the system, with direct 480 V AC voltage supply with conventional and smart load sharing and medium dc voltage supply were done. For the direct AC supply architecture, with conventional and smart load sharing the predicted power loss was approximately 840 kW and 820 kW, respectively and the predicted total system efficiency was 92.87% and 93.05%, respectively. For the direct DC supply architecture with the 1000 V DC supply bus power loss was approximately 340 kW and the predicted total system efficiency was 97.02%.

Wojda, Rafal↗
DOE OSTI2021

SOFC Prototype System Test

The goal of this U.S. Department of Energy (DOE) sponsored project is to test a 200 kWe thermally self-sustaining atmospheric-pressure solid oxide fuel cell (SOFC) prototype system at a prominent site. Fuel Cell Energy Inc. (FCE) utilized the state-of-the-art SOFC technology of its wholly-owned subsidiary Versa Power Systems (VPS) to design, fabricate and test the 200 kW prototype system. The specific objectives of this project were to achieve an SOFC stack power degradation rate of ≤1.5% per 1000 hours for the 400 kW stand-alone prototype power system undergoing ≥ 5000 hours of steady state tests at thermally self-sustained normal operating conditions (NOC), and to verify the prospects for a high volume SOFC stack production cost below the DOE target of 225 dollars per kilowatt. Achieving these goals enables commercial natural gas fueled SOFC system deployment in the 2020 timeframe, which will eventually lead to SOFC technology that is viable for large scale central power generation applications.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

High-moisture pelleting of corn stover using pilot- and commercial-scale systems: Impact of moisture content, L / D ratio and hammer mill screen size on pellet quality and energy consumption

Here, high-moisture pelleting of corn stover was tested in pilot- and commercial-scale systems. For the pilot-scale study, a 6.35 mm screen size hammer mill ground corn stover was reconditioned to moisture between 14 and 29.6% (w.b.), and 5, 7 and 9 L/D ratio pellet die were used. At 29% corn stover moisture content and L/D ratios of 5 and 9, the bulk density and durability of the pellets were ≤450 kg/m 3 and <90% and >540 kg/m 3 and >90%, respectively. Increasing the feedstock moisture and L/D ratio increased the pelleting energy. Increasing the hammer mill screen size to 11.11 mm reduced the bulk density but not the durability. The response surface models adequately described the pelleting process (R 2 ≥ 0.88), and analysis of variance showed a strong interaction between the process variables and pellet properties. Commercial-scale testing of high-moisture pelleting using 6.35 mm ground corn stover bales in the moisture range 24–26% (w.b.) produced pellets with a density of >675 kg/m 3 and durability of >98%, whereas increasing the hammer mill screen size to 11.11 mm reduced the bulk density by about 100 kg/m 3 but not the durability. The energy consumption of the commercial-scale unit operations for the high-moisture pelleting process was in the range 98–124 kW h/ton, which is 64–72% less energy than conventional pelleting that requires energy about 350 kW h/ton for biomass drying from 30% (w.b.) to 10% (w.b.) moisture content before pelleting. Pellets produced using high-moisture pelleting met International Organization for Standardization and Pellets Fuel Institute international standards. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

09 BIOMASS FUELS↗
DOE OSTI2024

A semi–automatic analytical methodology for characterizing the energy consumption of MRI systems using load duration curves

Background and purpose: Magnetic resonance imaging (MRI) scanners are a major contributor to greenhouse gas emissions from the healthcare sector, and efforts to improve energy efficiency and reduce energy consumption rely on quantification of the characteristics of energy consumption. The purpose of this work was to develop a semi-automatic analytical methodology for the characterization of the energy consumption of MRI systems using only the load duration curve (LDC). LDCs are a fundamental tool used across various fields to analyze and understand the behavior of loads over time. Methods: An electric current transformer sensor and data logger were installed on two 3T MRI scanners from two vendors, termed M1 (outpatient scanner) and M2 (inpatient/emergency scanner). Data was collected for 1 month (7/11/2023 to 8/11/2023). Active power was calculated, assuming a balanced three-phase system, using the average current measured across all three phases, a 480 V reference voltage for both machines, and vendor-provided power factors. An LDC was constructed for each system by sorting the active power values in descending order and computing the cumulative time (in units of percentage) for each data point. The first derivative of the LDC was then computed (LDC’), smoothed by convolution with a window function (sLDC’), and used to detect transitions between different system modes including (in descending power levels): scan, prepared-to-scan, idle, low-power, and off. The final, segmented LDC was used to measure time (% total time), total energy (kWh), and mean power (kW) for each system mode on both scanners. The method was validated by comparing mean power values, computed using the segmented 1-month LDC, for each nonproductive system mode (i.e., prepared-to-scan, idle, lower-power, and off) against power levels measured after a deliberate system shutdown was performed for each scanner (1 day worth of data). Results: The validation revealed differences in mean power values <1.4% for all nonproductive modes and both scanners. In the scan system mode, the mean power values ranged from 29.8 to 37.2 kW and the total energy consumed for 1 month ranged from 11 106 to 14 466 kWh depending on the scanner. Over the course of 1 month, the portion of time the scanners were in nonproductive modes ranged from 76% to 80% across scanners and the nonproductive energy consumption ranged from 8010 to 6722 kWh depending on the scanner. The M1 (outpatient) scanner consumed 99.9 and 183.9 kWh/day in idle mode for weekdays and weekends, respectively, because the scanner spent 23% more time proportionally in idle mode on the weekends. Conclusions: A semi-automatic method for quantifying energy consumption characteristics of MRI scanners was introduced and validated. This method is relatively simple to implement as it requires only power data from the scanners and avoids the technical challenges associated with extracting and processing scanner log files. Finally, the methodology enables quantitative evaluation of the power, time, and energy characteristics of MRI scanners in scan and nonproductive system modes, providing baseline data and the capability of identifying potential opportunities for enhancing the energy efficiency of MRI scanners.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Optimization of the FRIB beam dump: a hybrid genetic algorithm and reinforcement learning approach

The operational envelope of high-power-density systems, such as particle accelerators and advanced nuclear energy systems, is critically constrained by the need to manage extreme thermal loads. To address this, we present a novel hybrid optimization framework combining a genetic algorithm (GA) with a soft actor-critic (SAC) deep reinforcement learning agent. This framework was applied to a practical high-heat-flux problem: redesigning the beam dump at the Facility for Rare Isotope Beams (FRIB) for a power upgrade from 20 kW to 50 kW. The resulting design, validated by three-dimensional conjugate heat transfer simulations, suppresses hazardous hot spots and yields a markedly more uniform temperature distribution. This provides a robust operating margin, increasing the average power-handling capability by 72% relative to the current design, demonstrating the framework’s potential to solve complex thermal management challenges in both accelerator technology and advanced nuclear systems.

Accelerator↗
DOE OSTI2020

Peak load reduction and load shaping in HVAC and refrigeration systems in commercial buildings by using a novel lightweight dynamic priority-based control strategy

Reducing peak power demand in a building can reduce electricity expenses for the building owner and contribute to the efficiency and reliability of the electrical power grid. For the building owner, reduced expenses come from the reduction or elimination of peak power charges on electricity bills. For the power system operator, reducing peak power demand leads to a more predictable load profile and reduces stress on the electric grid system. Herein we present a computationally inexpensive, dynamic, and retrofit-deployable control strategy to effect peak load reduction and load shaping. The effectiveness of the control strategy is examined in a simulation with 80 air-conditioning units and 40 refrigeration units. The results show that a peak demand reduction of 60 kW can be achieved relative to peak demand in a typical set point–based approach. The proposed strategy was deployed in a gymnasium building with four rooftop HVAC units, where it showed over 15% peak demand (kW) reduction savings while maintaining or lowering energy consumption (in kilowatt-hours) relative to the set point–based thermostat controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

OR-AGENT framework – Architecting electrified heavy-duty drayage applications

The widespread adoption of zero-emission vehicles in heavy-duty (HD) commercial freight transportation faces considerable technoeconomic challenges. For heavy-duty trucks, ensuring high uptime, cost parity with diesel, and safety standards is especially critical as these vehicles operate over long distances with heavy loads, where any downtime or off-nominal behaviors significantly impacts logistics, productivity, and the total cost of ownership. Unlike traditional diesel refueling, BEV charging infrastructure must be co-optimized with vehicle deployment, operational demands, and grid capacity to ensure cost-effective and reliable freight operations. However, the lack of a standardized ownership and service model has led to a fragmented approach—where commercial vehicle operators may invest in, own, and maintain both vehicle/batteries and charging/energy infrastructure. This disconnect may exclude energy service providers from the equation, forcing fleet operators to explore ‘behind-the-fence’ energy solutions that increase capital investment, operational downtime, overhead costs, and, in some cases, net carbon emissions. To address these issues, this study introduces OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport), a comprehensive modeling framework that integrates powertrain architectures, charging infrastructures, and energy backbone systems into a cohesive strategy. In this paper, OR-AGENT is applied to develop an interconnected systems architecture for energy efficiency and resiliency enhancement of heavy-duty drayage vehicles at the Port of Savannah, GA. This framework showcases an interconnected systems approach to electrifying heavy-duty drayage vehicles at the Port of Savannah, GA. The study assessed BEVs with 400–1200 kWh battery capacities, accounting for seasonal variations in weather and freight routing. A diverse charging mix (150 kW–1250 kW) was evaluated alongside grid capacity constraints, cost, and carbon intensity analysis, leading to the development of a strategic microgrid/Distributed Energy Resources (DER) deployment architecture to ensure a reliable and sustainable transition. However, the findings also highlight the need for alternative zero-emission solutions for remaining trips, such as larger batteries, electrified roadways, hydrogen powertrains, or net-zero emission fuels. In conclusion, the findings are incorporated into a Total Cost of Ownership (TCO) model to identify optimal architectures for an interconnected electrified ecosystem.

Commercial vehicles↗
DOE OSTI2020

A microchannel shell-and-tube absorber for ammonia-water absorption

A shell-and-tube absorber with microscale geometric features that yields significant volumetric heat duties is investigated. A vertically-oriented baffled shell-and-tube heat exchanger with a tightly packed tube bundle is designed to serve as the absorber for a 10.5 kW nominal cooling capacity absorption chiller. The heat and mass transfer in this absorber is investigated experimentally at operating conditions spanning a wide range of working fluid and coupling fluid flow rates and ambient temperatures. An experimental facility is fabricated to closely simulate the operating conditions in an absorption refrigeration system. Absorption heat duties in the range of 5-15 kW are measured. In addition to operating the absorber in the baseline co-current flow configuration with premixed inlet, fluid ports are provided to enable operation in counter-current flow, where the dilute solution enters from the top and vapor enters from the bottom. The performance of these two flow configurations is compared. A preliminary heat and mass transfer model is developed to quantify the local temperatures, concentrations, and mass flow rates of the solution and vapor streams along the length of the absorber. The model predictions for total heat duty agree with the experimental results within 10% for almost all the data points, with an AAD of ~3.4%. Finally, the results of this study, along with the modeling framework, can guide further development of highly compact and efficient absorbers absorption systems.

42 ENGINEERING↗
DOE OSTI2026

Parametric optimization of PCM-enhanced underground thermal energy storage for buildings in moderate cold climates

This study presents a novel underground thermal energy storage (UTES) system designed for space heating in buildings located in moderate cold climates. The proposed UTES features a borehole with a depth of 25 ft. (7.62 m) and a diameter of 3 ft. (0.91 m), containing two helical pipe loops-one for discharge fluid and another for recharge fluid-and a thermally enhanced phase change material (PCM) layer that provides high energy storage capacity. The system requires daily thermal recharging using a low-grade heat source for a few hours and delivers continuous heating at a nearly constant discharge rate without significant performance degradation over a 24-h period. This study demonstrates that the optimized UTES, when recharged with hot fluid at an inlet temperature of 40 degrees C for 6 h daily, can provide a continuous heat discharge rate of approximately 2.9 kW for 24 h or 3.4 kW if operated under a shorter discharge period of 15 h (17:00-08:00). With a round-trip efficiency of 72-97%, the proposed UTES offers an efficient and reliable solution for short- and long-duration thermal energy storage technology, making it a promising technology for cold climates.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Chemical looping air separation with Sr 0.8 Ca0.2Fe 0.9 Co 0.1 O 3-δ perovskite sorbent: Packed bed modeling, verification, and optimization

Chemical looping air separation (CLAS) represents a promising approach for efficient O 2 production from the air. This present study aims at optimizing the absorber/desorber operations and the separation process with extensive experimental validation. Specifically, a one-dimensional packed bed model was developed to investigate the CLAS operation with a Sr 0.8 Ca 0.2 Fe 0.9 Co 0.1 O 3-δ perovskite sorbent. The redox thermodynamics of perovskite sorbent was measured by TGA and then incorporated into a linear driving force model to describe the O 2 absorption and desorption rates. Both 4-step and 5-step air separation cycle configurations, with various cyclic structures, were performed in a subpilot-scale packed bed. The model predicted O2 purity and productivity were consistent with experimental results, supporting its accuracy and applicability. Parametric analysis and multi-objective optimization were further carried out to assess the performance of CLAS. Both O 2 purity and recovery increased monotonically with the cycle time, airflow rate, steam flow rate, and absorption pressure. Meanwhile, optimal O 2 productivity and power consumption can only be achieved by specific combinations of these parameters. The optimized results showed that CLAS can be highly competitive when compared to conventional pressure swing adsorption (PSA) or cryogenic distillation. The 5-step cycle configuration achieved a minimum power consumption of 118 kW·h for producing 1 ton O 2 with ≥ 95% purity. The maximum O 2 productivity reached 0.0932 g O2 /(g sorbent ·h) with 390 kW·h/ton O 2 of energy consumption (95% pure). The optimization results also indicate that CLAS can potentially be more efficient than cryogenic distillation even when the required O 2 purity is above 99%.

42 ENGINEERING↗
DOE OSTI2022

Design optimization of an additively manufactured prototype recuperator for supercritical CO 2 power cycles

Supercritical CO 2 (sCO 2 ) power cycles are being developed due to their potential for high efficiency and reduced capital cost. It is necessary that these recuperators operate at high pressures and temperatures, up to 30 MPa and 900 K, with effectiveness values > 95% and pressure drops <1% to achieve high cycle efficiencies. Moreover, it is also necessary to have reasonable cost recuperators to control the capital costs of the sCO 2 power cycles. In this study, a Plate Pin-Fin (PPF) heat exchanger has been proposed as an sCO 2 recuperator. This preliminary recuperator design leverages capabilities enabled by additive manufacturing. Although the PPF design has characteristics similar to those of a plate heat exchanger, small diameter and relatively long fins are used to increase surface area, enhance heat transfer, and provide structural support for the partition plates that separate the fluid streams. Existing correlations for heat transfer and pressure drop were adapted for the PPF heat exchanger. These correlations were implemented in a 1D analytical model and used for the optimization of a 5-kW th high temperature recuperator for an indirect sCO 2 cycle by varying the design parameters to minimize the quantity of material required. A 3D conjugate heat transfer numerical simulations were conducted to validate the heat transfer and pressure loss correlations. A steepest descent method was used to minimize heat exchanger mass for a 5-kW prototype recuperator subject to a maximum specified pressure drop. Additionally, the design analysis indicated that an optimum PPF recuperator would be attained for the minimum allowable pin transverse spacing, minimum pin width, minimum pin height and near maximum cell aspect ratio. Moreover, at a low material requirement of 0.216 kg/kW and a pressure drop, which is almost five times lower than the allowable pressure drop design target, the optimized PPF heat exchanger has the high potential to be an alternative to a printed circuit heat exchanger, which is a conservative design basis for the current state-of-the-art sCO 2 recuperators.

42 ENGINEERING↗
DOE OSTI2022

Near isothermal compressed air energy storage system in residential and commercial buildings: Techno-economic analysis

Electrical energy storage systems offer a wide range of options to efficiently manage our power supply infrastructure. Energy and environmental security via renewable resources and energy storage devices will also play a critical role in addressing the supply-demand challenges. A novel energy efficient storage system based on near isothermal compressed air energy storage concept, named as Ground-Level Integrated Diverse Energy Storage (GLIDES) is analyzed for integration with residential and commercial buildings. Additionally, the influence of different configurational aspects on key performance and cost attributes is presented in this study. GLIDES modules in the range of 0.5 kWh–15 kWh configurations were considered for residential buildings whereas 25 kW h–500 kW h modules were considered for commercial buildings. Detailed cost and performance analysis of the GLIDES module proved them to be an effective resource in lowering the peak energy demand and corresponding utility bills.

25 ENERGY STORAGE↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2020

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Aluminum-doped calcium manganite particles for solar thermochemical energy storage: Reactor design, particle characterization, and heat and mass transfer modeling

A two-step cycle is discussed for solar thermochemical energy storage based on particulate aluminum-doped calcium manganite reduction/oxidation reactions for direct integration into Air Brayton cycles. The two steps encompass 1) the storage of concentrated solar irradiation within endothermic reduction of aluminum-doped calcium manganite and 2) the delivery of heat to an Air-Brayton cycle via exothermic re-oxidation of oxygen-deficient aluminum-doped calcium magnanite. A 5 kW th scale solar thermochemical reactor operating under vacuum was designed, modeled, and optimized to thermally reduce a continuous, gravity-driven flow of aluminum-doped calcium manganite particles. The granular flows were characterized in a tilt-flow rig, and particle image velocimetry was used to determine flow properties via frictional and velocity scaling relationships. Flow properties were integrated into a detailed heat and mass transfer model of the solar thermochemical reactor. A reactor design with 31° inclination angle, 230 g/min of particles, and 5.2 kW th radiative input from the high-flux solar simulator was discovered to produce an outlet flow temperature of 1158 K, with stoichiometric deviations of 0.076 and a storage efficiency of 0.628 while avoiding particle overheating and promoting longer particle residence times.

14 SOLAR ENERGY↗