Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “feature engineering”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Innovative, Versatile and Cost-Effective Solid Oxide Fuel Cell Stack Concept

This report summarizes the work performed for the U. S. Department of Energy/National Energy Technology Laboratory (DOE/NETL) under Cooperative Agreement DE-FE0026211 entitled “Innovative, Versatile and Cost-Effective Solid Oxide Fuel Cell Stack Concept”. The overall objective of this project was to develop and evaluate a solid oxide fuel cell (SOFC) stack configuration based on a novel prime-surface interconnect design for a broad range of power generation applications. The prime-surface is a one-piece metallic interconnect that incorporates both fuel and oxidant flow fields with peaks on one side of the interconnect serving as flow channels (valleys) on the other side. (One example is the egg-carton shaped interconnect design.) This stack technology has several attractive features including reduced stack weight and volume, decreased performance losses in stacking, improved sealing, versatility in incorporation of different types of cell construction (thin-film cells or sintered cells) and flexibility in gas flow configuration (cross-flow, co-flow or counter-flow). These features lead to lower cost, better performance, and enhanced reliability for the stack. The R&D work of this project focused on demonstrating fabrication, operability and cost effectiveness of the stack design and was conducted by University of California, San Diego (UCSD) in conjunction with OxEon Energy (OxEon). The key technical activities of this project involved prime-surface interconnect design and fabrication development; supported thin-film cell structure development; stack development; stack operation demonstration; and stack cost assessment. The results of these activities are summarized in this report.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Resilience Metrics Framework for Solar Photovoltaics

This presentation was given at the Photovoltaic Specialist Conference (PVSC) 54 in New Orleans, Louisiana. Photovoltaic (PV) systems are routinely exposed to extreme weather, including wind and hail storms. Historically, most systems have proven to be resilient to such events, but some storms have damaged PV systems, leading to physical and financial loss. Storm hardening measures and specific system attributes can reduce this risk. This work introduces a set of resilience metrics and a framework for quantifying, comparing, and predicting PV system resilience. The framework is divided into two parts: 1) predictive, attribute metrics based on site and component characteristics, and 2) impact metrics that assess post-storm performance. Metrics are weighted and aggregated, producing hazard-specific resilience scores. We derive damage functions from storm-impacted PV systems, establishing a baseline against which post-storm performance can be compared. This damage was widely variable across hail and wind intensities, and field hail damage was less than predicted by laboratory tests, suggesting that system features - in addition to storm conditions - influence damage likelihood. Finally, the metrics framework is demonstrated using three case studies of storm damaged PV systems. Although additional data are needed to create attribute specific damage functions and establish metric weights, this study presents a methodology for evaluating PV resilience and contributes new damage functions to the literature.

14 SOLAR ENERGY↗
DOE OSTI2020

Tailoring Surface Roughness Using Additive Manufacturing to Improve Internal Cooling

Abstract Surface roughness present on internal cooling channels produced with additive manufacturing has been previously shown to augment heat transfer and pressure loss to levels similar to traditionally cast turbulators. Given the ability of the surface roughness to improve the cooling performance of small cooling channels, the question arises on whether there is an optimal combination of random roughness features to maximize internal cooling performance. To investigate this question, test coupons with different surface roughness morphologies and magnitudes were manufactured by manipulating the parameters in the laser powder bed fusion additive manufacturing process. The coupons were tested to characterize the friction factor and Nusselt number of the cooling channels over a range of Reynolds numbers. Results showed that certain roughness combinations outperformed others, increasing the internal cooling performance of the channels. Additionally, manipulation of the performance using the process parameters allowed for reductions in build time, which could be useful for controlling component cost.

Engineering↗
DOE OSTI2025

Design and Fabrication of a Thyristor Using Ion Implantation of Anode and Cathode

A thyristor is a solid-state semiconductor switch made up of four alternating p and n type layers. When the switch is turned on by a small pulse, current can flow as long as the device stays on. When the voltage drops below the turn on level or the current reverses, the switch will turn off. Thyristors only allow current to flow in one direction. One of the benefits of a thyristor device is in its capacity to conduct large voltages with a small device and a small turn-on pulse requirement. Another distinguishing feature of thyristors compared to other semiconductor devices is that it can only be in the on or off state, there’s no in-between state for the device to exist within. These properties make thyristors very useful as switch devices, especially in applications where current flow is only needed in one direction and could potentially be damaging in the opposite direction. Currently, thyristors are most commonly used to control very high power loads and they are available commercially in many designs to control different amounts of current.

42 ENGINEERING↗
DOE OSTI2023

Deciphering the evolution of flavin-dependent monooxygenase stereoselectivity using ancestral sequence reconstruction

Controlling the selectivity of a reaction is critical for target-oriented synthesis. Accessing complementary selectivity profiles enables divergent synthetic strategies, but is challenging to achieve in biocatalytic reactions given enzymes’ innate preferences of a single selectivity. Thus, it is critical to understand the structural features that control selectivity in biocatalytic reactions to achieve tunable selectivity. Here, we investigate the structural features that control the stereoselectivity in an oxidative dearomatization reaction that is key to making azaphilone natural products. Crystal structures of enantiocomplementary biocatalysts guided the development of multiple hypotheses centered on the structural features that control the stereochemical outcome of the reaction; however, in many cases, direct substitutions of active site residues in natural proteins led to inactive enzymes. Ancestral sequence reconstruction (ASR) and resurrection were employed as an alternative strategy to probe the impact of each residue on the stereochemical outcome of the dearomatization reaction. These studies suggest that two mechanisms are active in controlling the stereochemical outcome of the oxidative dearomatization reaction: one involving multiple active site residues in AzaH and the other dominated by a single Phe to Tyr switch in TropB and AfoD. Moreover, this study suggests that the flavin-dependent monooxygenases (FDMOs) adopt simple and flexible strategies to control stereoselectivity, which has led to stereocomplementary azaphilone natural products produced by fungi. This paradigm of combining ASR and resurrection with mutational and computational studies showcases sets of tools for understanding enzyme mechanisms and provides a solid foundation for future protein engineering efforts.

Science & Technology - Other Topics↗
DOE OSTI2023

Dual-Purpose Canister Filling Demonstration Project Progress Report at ORNL, 2023

The US DOE Office of Nuclear Energy is investigating the feasibility of direct disposal of dual-purpose canisters (DPCs) in a hypothetical geological repository to offset the potential requirement to repackage spent nuclear fuel (SNF) from existing DPCs into smaller, disposal-ready canisters. Oak Ridge National Laboratory (ORNL) is currently evaluating the feasibility of filling void space in loaded DPCs with an engineered material to prevent a criticality event caused by groundwater/moderator intrusion. Metal alloys are being investigated as a filler material because of their relatively low viscosities when molten, which may facilitate their injection via an existing drainpipe that runs almost the full length of the DPC. ORNL’s strategy for evaluating filler viability includes simulations and physical demonstrations of filling and casting behavior, as well as evaluations of materials for compatibility in the repository environment. Filling of DPCs in this manner is expected to mitigate the risk associated with a post-closure criticality event during the repository performance assessment time frame (10,000 years or greater). Efforts in this fiscal year focused on (1) destructive analysis of experimental filler castings made in FY 2022, (2) a report outlining a conceptual design of a DPC filling facility (Fortner et al., 2023, M3SF 23OR010305044/ ORNL/SPR-2023/2921, May 31, 2023), (3) a report on affected features, events, and processes (FEPs) due to DPC filling (Price et al., 2023, M3SF-23SN010305093, issuance pending), (4) developing and testing a more practical alloy filler based upon a Sn-Al eutectic, and (5) modeling the heating and cooling dynamics of a DPC subjected to molten metal filling. The preliminary results from each of these tasks support the feasibility of filling DPCs with metal as a strategy against the possibility of criticality in the repository. The FY 2022 casting was found to penetrate even very small orifices in the mold and internal structures. Preliminary testing of the Sn-Al eutectic indicate little interaction of the melt with Zircaloy cladding. Thermal modelling shows that a filled DPC will cool to manageable temperatures within 2-3 days, which is likely manageable in a facility.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Multifidelity uncertainty quantification with models based on dissimilar parameters

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the highest-fidelity model, provided that the low-fidelity models are well correlated. However, maintaining a high level of correlation can be challenging, especially when models depend on different input uncertain parameters, which drastically reduces the correlation. Existing MF UQ approaches do not adequately address this issue. In this work, we propose a new sampling strategy that exploits a shared space to improve the correlation among models with dissimilar parameterization. We achieve this by transforming the original coordinates onto an auxiliary manifold using the adaptive basis (AB) method (Tipireddy and Ghanem, 2014). The AB method has two main benefits: (1) it provides an effective tool to identify the low-dimensional manifold on which each model can be represented, and (2) it enables easy transformation of polynomial chaos representations from high- to low-dimensional spaces. This latter feature is used to identify a shared manifold among models without requiring additional evaluations. Here we present two algorithmic flavors of the new estimator to cover different analysis scenarios, including those with legacy and non-legacy high-fidelity (HF) data. We provide numerical results for analytical examples, a direct field acoustic test, and a finite element model of a nuclear fuel assembly. For all examples, we compare the proposed strategy against both single-fidelity and MF estimators based on the original model parameterization.

42 ENGINEERING↗
DOE OSTI2021

A machine learning-based interaction force model for non-spherical and irregular particles in low Reynolds number incompressible flows

In this study, interaction force of non-spherical particles in low Reynolds number gas-solid flow is investigated by neural network approaches. An artificial neural network (ANN) model is developed to correlate the non-spherical particle shape and the flow conditions with the interaction force. To define the particle shape, spherical harmonic expansion is applied. Furthermore, variational autoencoder model is then used to extract latent geometric features. The latent vector is utilized as an input with the Reynolds number for the ANN. The interaction force data, which is used as output data of the ANN, is obtained by particle resolved direct numerical simulation for 5200 non-spherical particles. The proposed model enables unsupervised extraction for non-spherical particle shapes and accurate predictions on the interaction force without heavy computation. This study provides the model that can explain complicated shapes of particles and be applied to a large scale, computational fluid dynamics simulation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Randomized probe imaging through deep k-learning

Randomized probe imaging (RPI) is a single-frame diffractive imaging method that uses highly randomized light to reconstruct the spatial features of a scattering object. The reconstruction process, known as phase retrieval, aims to recover a unique solution for the object without measuring the far-field phase information. Typically, reconstruction is done via time-consuming iterative algorithms. In this work, we propose a fast and efficient deep learning based method to reconstruct phase objects from RPI data. The method, which we call deep k-learning, applies the physical propagation operator to generate an approximation of the object as an input to the neural network. This way, the network no longer needs to parametrize the far-field diffraction physics, dramatically improving the results. Deep k-learning is shown to be computationally efficient and robust to Poisson noise. The advantages provided by our method may enable the analysis of far larger datasets in photon starved conditions, with important applications to the study of dynamic phenomena in physical science and biological engineering.

Guo, Zhen (ORCID:0000000213473451)↗
DOE OSTI2025

Facets of hydro power and future trends in a Nordic Context

Hydropower technologies bolster high penetration of variable renewable energies (VREs) in the net zero emissions scenarios. Nevertheless, there are various challenges to meeting the ambitious goal, such as stability, reliability, resiliency, security, lack of reactive power, voltage support and inertia, large-scale storage deployment and coordination, interconnectedness, demand-side response, higher thermal cycles with increased start/stops, and inadequate Levelized Cost of Energy (LCOE) for system-wise VRE integration and profitability. This survey conducts a bottom-up analysis to unveil the opportunities to utilize hydropower facilities and disentangle the nested problem for intertwining design features, control algorithms, operation, optimization approaches, incentives, services, and market mechanisms using a three-pillar framework perspective: grid owners, power producers, and machine designers. The survey identified emerging trends in real-time and capacity markets, flexible power systems, and enhanced grid capabilities, including advanced voltage support and updated grid codes. These developments present significant opportunities for hydropower, such as achieving super-flexibility through hybridization, expanded reactive power capabilities, and advanced operational modes like a synchronous condenser and power adequator functionalities. These opportunities require novel design philosophies — including new winding, stator, and rotor configurations, optimized ventilation, and active cooling systems — to enhance performance under stressed grid and climate conditions. Finally, integrating climate and energy models for multi-basin optimization with finer spatial and temporal granularity enhances the planning accuracy for water management of hydropower while addressing environmental challenges. The review delivers helpful prospective suggestions and tools that would serve researchers, power engineers, and stakeholders in making decisions about hydropower technologies and services in 2050 and beyond.

13 HYDRO ENERGY↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2024

First Principles Evaluation of Phase Stability in the In-Sn Binary System

Abstract The In-Sn binary alloy system exhibits several unusual features that challenge crystallographic and thermodynamic expectations. We combine first principles total energy calculation with simple thermodynamic modeling to address two key points. First, we evaluate energies along the Bain path to interpret the discontinuous transition between the phases α-In (Pearson type tI2) and β-In 3 Sn (also Pearson type tI2) that are identical in symmetry. Second, we demonstrate that the solid solution phases β-In 3 Sn and γ-InSn 4 (Pearson type hP1) exist at high temperatures only, and they exhibit eutectoid decompositions at low temperatures.

Chemistry↗
DOE OSTI2020

Single-Step Shear-Based Deformation Processing of Electrical Conductor Wires

Commercial electrical conductor wires are currently produced from aluminum alloys by multi-step deformation processing involving rolling and drawing. These processes typically require 10 to 20 steps of deformation, since the plastic strain or reduction that can be imposed in a single step is limited by material workability and process mechanics. Here, we demonstrate a fundamentally different, single-step approach to produce flat wire aluminum products using machining-based deformation that also ensures adequate material workability in the formed product. Two process routes are proposed: (1) chip formation by free-machining (FM), with a post-machining, light drawing reduction (<20%) to achieve desired finish and (2) constrained chip formation by large strain extrusion machining (LSEM). Using commercially pure aluminum conductor alloys (Al 1100 and EC1350) as representative material systems, we demonstrate key features of the machining-based processing, including (a) single-step processing to achieve flat wire geometries, (b) surface finish (Ra = 0.2 to 1.0 μm) comparable to that of commercial wire products made by drawing/rolling, (c) deformation control independent of wire size, and (d) hardness increases of 50–150% over that of annealed wires, while retaining high electrical conductivity (>56% IACS). Here, the wire microstructure, which can also be varied via the large-strain deformation parameters, is correlated with mechanical and electrical properties. Implications for commercial manufacture of flat wire products are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Notes on Bayliss Taper for Monopulse Radar

A radar system’s antenna characteristics are fundamental to its performance. A principal defining feature of an antenna’s performance are its often problematic sidelobes. Often in monopulse antenna topologies, the emphasis has been on the sum channel sidelobes at the expense of difference channel sidelobes. Edward Bayliss published a paper detailing a design procedure for managing the difference channel sidelobes. The resulting aperture taper now is identified by his name. This report analyzes his taper derivation, and resulting performance attributes, after which some implementation comments are rendered.

42 ENGINEERING↗
DOE OSTI2022

A review of the fabrication methods and mechanical behavior of continuous thermoplastic polymer fiber–thermoplastic polymer matrix composites

Abstract Thermoplastic polymer fiber–thermoplastic polymer matrix composites (PPCs or PRFPs), often recognized as self‐reinforced or single polymer composites, are potential candidates for future advanced polymer composites because of various advantages ( e.g., recyclability, formability, low‐cost, ultra‐lightweight, environmental friendliness, etc.). The manufacturability and mechanical behavior of these composites compared to conventional carbon‐/glass‐/aramid‐fiber‐reinforced polymers is of great interest to the composites community, but there are a limited number of studies in this area. To this end, this paper reviewed fabrication methods with different processing parameters and mechanical behavior of uni‐/multi‐directional thermoplastic PPCs featuring continuous thermoplastic polymer fibers from limited data in the literature. It was shown that most specific behaviors (normalized by density) of these materials in various loading conditions (e.g., quasi‐static tension/shear/flexure, tension‐tension fatigue, and out‐of‐plane impacting, etc.) are comparable to or better than glass‐/aramid‐fiber‐reinforced polymers. Particularly, the specific ductility in the foregoing conditions outperforms all the carbon‐/glass‐/aramid‐fiber‐reinforced polymers. Thermoplastic PPCs with remarkable performance can be achieved through several uncomplicated methods (e.g., film stacking, hot compaction, powder and solution impregnations, matrix infusion and injection molding, additive manufacturing, etc.), which have some similarities to the methods used for carbon‐/glass‐/aramid‐fiber‐reinforced polymers. Moreover, several opportunities and challenging problems of thermoplastic PPCs were summarized at the end of this review paper. Efficient solutions may require countless efforts in the composites community to further strengthen the performance and understanding of thermoplastic PPCs for wide applications in various engineering fields in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Adaptive immersed isogeometric level-set topology optimization

Here, this paper presents for the first time an adaptive immersed approach for level-set topology optimization using higher-order truncated hierarchical B-spline discretizations for design and state variable fields. Boundaries and interfaces are represented implicitly by the iso-contour of one or multiple level-set functions. An immersed finite element method, the eXtended IsoGeometric Analysis, is used to predict the physical response. The proposed optimization framework affords different adaptively refined higher-order B-spline discretizations for individual design and state variable fields. The increased continuity of higher-order B-spline discretizations together with local refinement enables direct control over the accuracy of the representation of each field while simultaneously reducing computational cost compared to uniformly refined discretizations. A flexible mesh adaptation strategy enables local refinement based on geometric measures or physics-based error indicators. These adaptive discretization and analysis approaches are integrated into gradient-based optimization schemes, evaluating the design sensitivities using the adjoint method. Numerical studies illustrate the features of the proposed framework with static, linear elastic, multi-material, two- and three-dimensional problems. The examples provide insight into the effect of refining the design variable field on the optimization result and the convergence rate of the optimization process. Using coarse higher-order B-spline discretizations for level-set fields promotes the development of smooth designs and suppresses the emergence of small features. Moreover, adaptive mesh refinement for state variable fields results in a reduction of overall computational cost. Higher-order B-spline discretizations are especially interesting when evaluating gradients of state variable fields due to their higher inter-element continuity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multiscale operando X-ray investigations provide insights into electro-chemo-mechanical behavior of lithium intercalation cathodes

The electrochemical performance and cycle life of lithium-ion batteries (LIBs) depend on the electrochemical, chemical, and mechanical behavior of electrodes and electrolytes. Despite extensive studies conducted previously, challenges exist to decouple these behaviors, capture the evolution of electro-chemo-mechanical behavior in realistic conditions, and correlate atomic-scale stress evolution to micro-scale bulk mechanical degradation. Here, we report multiscale operando techniques to investigate polydisperse battery electrodes by integrating volume-averaged quantitative synchrotron X-ray scattering with high-resolution transmission X-ray microscopy (TXM). The former provides us information spanning a wide spatial range, from Angstrom-level atomic structures to micrometer-level particle scales, while the latter provides time-resolved 2D images of the particles during cycling. The complementarity of the two operando techniques is demonstrated by an over-lithiation test of LiCoO 2 electrodes, where particles crack and eventually pulverize. Additionally, the techniques are applied to study LiCoO 2 cycling stability from 3.0 V to 4.5 V. Operando X-ray scattering result shows nanometer-scale features keep forming in LiCoO 2 electrodes during cycling, resulting in an increased projected area observed by the TXM experiment. The formation of such features is inhibited by a polymer coating on the electrode, leading to vastly improved cycling stability. The polymer coating alleviates LiCoO 2 surface deterioration, reduces side product generation, and inhibits LiCoO 2 particles volume expansion during the cycling test. These operando multimodal X-ray techniques presented herein thus offer a novel, multiscale diagnostic modality for studying existing and emerging battery materials, aiding the development of next-generation LIBs.

25 ENERGY STORAGE↗
DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗