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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Calculation of Friction Factors in Heat Pipes Using CFD in Support of the Sockeye Code

A heat pipe is two-phase heat transfer device which relies on surface tension and capillary pressure to provide a very efficient heat transfer mechanism. Currently, there is interest in designing micro nuclear power reactors using high temperature heat pipes to provide totally passive cooling. To support this, Sockeye is being developed as a heat pipe simulation capability. To support the development of Sockeye, individual phenomena must be tested. This work focuses on applying a CFD model to evaluate friction factors for use in Sockeye. A CFD model has been developed in Nek5000 which simulates each component fluid, i.e. sodium liquid and vapor, independently and captures the effects of the wick on friction factor. The model tests the effect of the wick permeability and the thickness of the wick occupied by each fluid component. The thickness of the wick occupied by each component can be directly related to the local vapor volume fraction from the Sockeye heat pipe model. Results from the CFD model indicate that friction factor in the liquid flow decreases with increasing wick permeability, while the friction factor in the vapor flow increases with increasing wick permeability. In the liquid, this is likely a direct result of the increased cross sectional area available for the flow. For the vapor, this is explained as the wick acting like a rough wall, while the increased area has only a negligible impact. Results for the tests of the thickness of the wick occupied by each fluid showed a thresholding behavior for both liquid and vapor. When the thickness of the wick occupied by each fluid was increased, it increased the effect of the wick, but only up to a certain point. Once a certain thickness was reached, the friction factors remained constant. When the fluid occupied only a very small fraction of the wick, friction factors returned to nominal values.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Update to the Finite Cloud Dose Correction Factors in MACCS

In WASH - 1400, external exposure from the finite radioactive cloud (cloudshine) is calculated by assuming that the cloud is semi-infinite, the concentration of radioactive material is uniform, and by using a correction factor to account for these approximations. This correction factor is originally based upon formulations by Healy and depends on the effective size of the plume and the distance from the plume center to the receptor. The range of the finite cloud dose correction factor table from WASH - 1400 developed using Healy formulations can be exceeded in certain situations. When the range of the table is exceeded, no extrapolation is performed; rather interpolation at the edge of the table is performed per WASH - 1400. The tabulated values of these finite cloud dose correction factors from WASH - 1400 and the interpolation at the edge of the table have been used in MACCS since its creation. An expanded table of finite cloud dose correction factors is one way to reduce the need of using interpolation at the edge of the table. The generation of an expanded finite cloud dose correction factor table for future use in MACCS is documented in this report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Impact of K-factor on Short Circuit Program Convergence for Inverter-Based Resources during Faults

Classical short-circuit programs that linearize the power network are no longer applicable for inverter based resources (IBRs), necessitating an iterative approach. Phasor domain programs can model the IBRs using an iterative approach considering nonlinear fault responses. In phasor domain models, the IBR can be modeled as a voltage controlled current source (VCCS) in tabular form with positive, negative, and zero sequence information for balanced and unbalance short-circuit faults. In the VCCS modeling of the IBR, positive and negative incremental reactive current, also known as the k-factor, plays an important role in short circuit program convergence. In this work, a few approaches: conventional VCCS modeling with a k-factor of 2, conventional VCCS modeling with a k-factor of 2 with modified pre-fault voltages, VCCS characteristics based on the power flow solution with a k-factor of 2, and VCCS characteristics based on the power flow solution with a k-factor of 1 are investigated for short circuit program convergence under higher IBR penetration. The IEEE 39 bus New England Test System is taken as the test system, and simulations are carried out in PSS®CAPE 15.0.26 simulation software. Simulation results demonstrate that IBR penetration is higher for the VCCS model, which corresponds to the power flow solution with k-factor 1, compared to other approaches.

42 ENGINEERING↗
DOE OSTI2024

SAGAbg. I. A Near-unity Mass-loading Factor in Low-mass Galaxies via Their Low-redshift Evolution in Stellar Mass, Oxygen Abundance, and Star Formation Rate

Measuring the relation between star formation and galactic winds is observationally difficult. In this work we make an indirect measurement of the mass-loading factor (the ratio between the mass outflow rate and star formation rate) in low-mass galaxies using a differential approach to modeling the low-redshift evolution of the star-forming main sequence and mass–metallicity relation. We use Satellites Around Galactic Analogs (SAGA) background galaxies, i.e., spectra observed by the SAGA Survey that are not associated with the main SAGA host galaxies, to construct a sample of 11,925 spectroscopically confirmed low-mass galaxies from 0.01 ≲ z ≤ 0.21 and measure auroral line metallicities for 120 galaxies. The crux of the method is to use the lowest-redshift galaxies as the boundary condition of our model, and to infer a mass-loading factor for the sample by comparing the expected evolution of the low-redshift reference sample in stellar mass, gas-phase metallicity, and star formation rate against the observed properties of the sample at higher redshift. We infer a mass-loading factor of η m = $0.92^{+1.76}_{-0.74}$, which is in line with direct measurements of the mass-loading factor from the literature despite the drastically different sets of assumptions needed for each approach. While our estimate of the mass-loading factor is in good agreement with recent galaxy simulations that focus on resolving the dynamics of the interstellar medium, it is smaller by over an order of magnitude than the mass-loading factor produced by many contemporary cosmological simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Measurement of the Neutron Electromagnetic Form Factor Ratio from a Rosenbluth Technique with Simultaneous Detection of Neutrons and Protons

The internal structure of protons and neutrons provides insight into both the dynamical behavior of the constitute quarks and gluons, and emergent properties of the nucleons (such as mass, spin, and electromagnetic distributions). Elastic electron-nucleon scattering can probe the elastic electromagnetic form factors of the nucleon. The electric and magnetic form factors, respectively, encode information about the internal charge and magnetization distributions within the nucleon. Precision data for these form factors, over a broad range of the four-momentum transfer squared, Q^2, can benchmark theoretical models describing the strong interaction of nuclear physics. The Super BigBite Spectrometer (SBS) program in Hall A at Jefferson Lab, is a series of high-precision experiments which seek to significantly extend the Q^2 reach of previous data for the nucleon electromagnetic form factors. The first two experiments of this program are known as G_M^n and the neutron Two Photon Exchange (nTPE) and the data were collected from October 2021 to February 2022. Both experiments were conducted with the simultaneous measurement of D(e,e'n) and D(e,e'p) reactions for quasi-elastic electron-deuteron scattering. The scattered electrons were detected in the BigBite Spectrometer, which features multiple large-acceptance Gas Electron Multiplier (GEM) detectors. The Super BigBite Spectrometer provided simultaneous detection of scattered nucleons, and utilized a large acceptance dipole magnet and Hadron Calorimeter (HCal). The G_M^n experiment provides precision measurements of the neutron magnetic form factor, via the ratio method, over a Q^2 range of 3.0 to 13.5 (GeV/c)2. From this data analysis, preliminary values for G_M^n/µ_n G_D are extracted. For Q^2=4.48 (GeV/c)2 we find G_M^n/µ_n G_D=0.9546±0.0132 and for Q^2=4.476 (GeV/c)2 we find G_M^n/µ_n G_D=0.9563±0.0110. These preliminary G_M^n/µ_n G_D values are more precise than existing world data in this Q^2 regime and are consistent with the most recent parameterization of the G_M^n/µ_n G_D world data. The nTPE experiment provides a first measurement of the neutron Rosenbluth Slope and seeks to quantify the two-photon exchange(TPE) contribution to elastic electron-neutron scattering at a fixed Q^2=4.5 (GeV/c)2 with two different beam energies and scattering angle values. For data of the proton form factor ratio, µ_p G_E^p/G_M^p, significant discrepancies exist between values obtained from Rosenbluth Separation and polarization transfer measurement, particularly at large Q^2, and TPE contributions are thought to resolve this discrepancy. The impacts of TPE contributions have not yet been experimentally established for the neutron. From the data analysis presented in this dissertation, a preliminary result for the neutron Rosenbluth Slope is found as S^n=(G_E^n )^2/t_n (G_M^n )^2=0.0916±0.0476 for Q^2=4.48 (GeV/c)2. This value of the neutron Rosenbluth Slope is consistent with the world data extrapolation and the absence of large TPE corrections.

Wertz, Ezekiel [Thomas Jefferson National Accelera↗
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

This dissertation explores factors influencing pooled rideshare (PR) adoption to provide actionable insights for transportation network companies (TNCs) and policymakers. PR allows travelers to share rides with unknown passengers, offering benefits such as cost reduction and congestion relief. However, adoption remains limited due to safety concerns, privacy issues, and trust in rideshare platforms. A national U.S. survey with 5,385 respondents examined transportation preferences and barriers to PR adoption. Exploratory and confirmatory factor analyses identified five key factors influencing PR consideration—safety, service experience, privacy, traffic/environment, and time/cost. Second factor analyses examined ways to optimize PR experiences, revealing four factors—comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Privacy concerns, for instance, using regression analysis, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. The Pooled Rideshare Acceptance Model (PRAM), based on the Technology Acceptance Model, assessed the impact of these factors using the Structural Equation Model (SEM). Privacy, safety, trust, and convenience had a large effect (Cohen's f2 > 0.35) on PR acceptance, while multigroup analyses (PRAMMA) explored 16 demographic variables such as gender, generation, and income, emphasizing the need for tailored strategies. Based on all the statistical analysis and workshops using descriptive statistics, 95 actionable recommendations were made from the riders' perspective. Findings highlight the importance of customized services, user experience improvements, and policy interventions to enhance PR adoption. This dissertation provides a roadmap for future research and policy development, ensuring evidence-based, practical strategies to improve PR services in the U.S. and beyond.

Gangadharaiah, Rakesh↗
DOE OSTI2020

Quantitative Analysis of the Effects of Natural and Human Factors on a Hydrological System in Zhangweinan Canal Basin

Drought is a water deficit state caused by large-scale climate change that cannot be avoided by a water resource management system. Water scarcity refers to the unsustainable utilization of water resources over a long time, which is the result of water management policies. However, water deficits caused by drought and water scarcity often occur simultaneously and are indistinguishable. This study proposes a model-based simulation framework that can quantitatively distinguish natural factors (drought) from human factors (water scarcity) in a hydrological system. The simulation was applied to the Zhangweinan Canal Basin, based on the runoff sequences from 1950 to 2004. The results show that the runoff curve number, soil depth, soil available water, soil evaporation compensation coefficient, base runoff α coefficient and the maximum canopy interception have the highest sensitivity to runoff, and that the calibrated and validated SWAT model can effectively simulate the runoff process in the Zhangweinan Canal Basin and similar areas. Abrupt changes in human activities in 1975 and water scarcity led to the disappearance of the summer peak runoff period in both wet and dry years. Human factors are the main reason for the change in the hydrological system in the study area; the runoff loss caused by human factors is four times that caused by natural factors according to the proposed variable threshold. This study proposes a model-based simulation framework that can help water resource managers to distinguish the effects of drought and water scarcity in water-stressed areas and adjust management accordingly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Computing rank‐revealing factorizations of matrices stored out‐of‐core

This paper describes efficient algorithms for computing rank-revealing factorizations of matrices that are too large to fit in main memory (RAM), and must instead be stored on slow external memory devices such as disks (out-of-core or out-of-memory). Traditional algorithms for computing rank-revealing factorizations (such as the column pivoted QR factorization and the singular value decomposition) are very communication intensive as they require many vector-vector and matrix-vector operations, which become prohibitively expensive when data is not in RAM. Randomization allows to reformulate new methods so that large contiguous blocks of the matrix are processed in bulk. The paper describes two distinct methods. The first is a blocked version of column pivoted Householder QR, organized as a “left-looking” method to minimize the number of the expensive write operations. The second method results employs a UTV factorization. It is organized as an algorithm-by-blocks to overlap computations and I/O operations. As it incorporates power iterations, it is much better at revealing the numerical rank. Numerical experiments on several computers demonstrate that the new algorithms are almost as fast when processing data stored on slow memory devices as traditional algorithms are for data stored in RAM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Factorization of Binary Matrices: Rank Relations, Uniqueness and Model Selection of Boolean Decomposition

The application of binary matrices are numerous. Representing a matrix as a mixture of a small collection of latent vectors via low-rank decomposition is often seen as an advantageous method to interpret and analyze data. In this work, we examine the factorizations of binary matrices using standard arithmetic (real and nonnegative) and logical operations (Boolean and $\mathbb{Z}$ 2 ). We examine the relationships between the different ranks, and discuss when factorization is unique. In particular, we characterize when a Boolean factorization X = W$\land$H has a unique W, a unique H (for a fixed W), and when both W and H are unique, given a rank constraint. We introduce a method for robust Boolean model selection, called BMFk, and show on numerical examples that BMFk not only accurately determines the correct number of Boolean latent features but reconstruct the pre-determined factors accurately.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Mechanisms in the Synthesis of S -Alcohols with 1,4-NADH Biomimetic Co-factor N-Benzyl-1,4-dihydronicotinamide using Horse Liver Alcohol Dehydrogenase: A Hybrid Computational Study

The enantioselective reduction of prochiral ketones catalyzed by horse liver alcohol dehydrogenase (HLADH), was investigated via a hybrid computational approach, for molecular reactions involved in chiral synthesis of S-alcohols, when the natural co-factor, 1,4-dihyronicotinamide adenine dinucleotide, 1,4-NADH, was replaced with biomimetic co-factor, N-benzyl-1,4-dihydronicotinamide, 1. We surmised that different hydride and proton transfer mechanisms were involved using co-factor, 1. An alternative mechanism, where the hydride transfer step occurred, via an η 1 -keto-S-η 2 -5,6-1,4-dihydronicotinamide-Zn(II) complex, was previously investigated with a model of the HLADH-Zn(II) catalytic site (J. Organometal. Chem. 2021, 943, 121810). Presently, we studied canonical and alternative mechanisms compared to models of the entire enzyme structure. We disproved the η 2 -Zn(II) complex, and discovered a canonical hydride transfer from biomimetic 1,4-NADH, 1, to the Zn(II) bound prochiral ketone substrate, followed by a new proton relay, consisting of a water chain connecting His51 to Ser48 that accomplished the S-alkoxy anion's protonation to yield the final S-alcohol product. The HLADH catalysis, with biomimetic co-factor, 1, that replaced the ribose group, the 5'-diphosphate groups, and the adenine nucleotide with a N-benzyl group, has provided a new paradigm for the design of other structures of 1,4-NADH biomimetic co-factors, including their economic value in biocatalysis reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach .

Despite widespread use of factor fixing in environmental modeling, its effect on model predictions has received little attention and is instead commonly presumed to be negligible. We propose a proof-of-concept adaptive method for systematically investigating the impact of factor fixing. The method uses Global Sensitivity Analysis methods to identify groups of sensitive parameters, then quantifies which groups can be safely fixed at nominal values without exceeding a maximum acceptable error, demonstrated using the 21-dimensional Sobol’ G-function. Furthermore, three error measures are considered for quantities of interest, namely Relative Mean Absolute Error, Pearson Product-Moment Correlation and Relative Variance. Results demonstrate that factor fixing may cause large errors in the model results unexpectedly, when preliminary analysis suggests otherwise, and that the default value selected affects the number of factors to fix. To improve the applicability and methodological development of factor fixing, a new research agenda encompassing five opportunities is discussed for further attention.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Individual low-carbon behaviors and influencing factors: Insights from a behavior survey study in China

Climate change is a major global concern. Greenhouse gas emissions that cause global climate change are directly or indirectly affected by human activities. Individual low-carbon behaviors are crucial in reducing CO 2 emissions and improving environmental and ecological health. To effectively promote individual low-carbon behavior, this study designed a questionnaire on the factors influencing individual low-carbon intentions and behavior based on theoretical models of environmental behavior. A total of 2430 valid questionnaires were collected in China. This study focuses on analyzing the impact of demographic characteristics, internal and external factors on individual low-carbon behaviors and their interrelationships. The research shows correlations between internal and external factors in determining low-carbon intention or behaviors. Internal factors-related low-carbon behavior is not closely linked with demographic variables, whereas the external factors-related low-carbon behavior vary significantly by age, residence, education, marital status, occupation, and income. The findings have important implications for designing effective policies to promote low-carbon behaviors, such as creating a more favorable external environment and increasing the use of policy tools for reducing CO 2 emission.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A semiparametric latent factor model for large scale temporal data with heteroscedasticity

Large scale temporal data have flourished in a vast array of applications, and their sophisticated structures, especially the heteroscedasticity among subjects with inter- and intra-temporal dependence, have fueled a great demand for new statistical models. In this paper, with covariate information, we consider a flexible model for large scale temporal data with subject-specific heteroscedasticity. Formally, the model employs latent semiparametric factors to simultaneously account for the subject-specific heteroscedasticity and the contemporaneous and/or serial correlations. The subject-specific heteroscedasticity is modeled as the product of the unobserved factor process and subject’s covariate effect, which is further characterized via additive models. For estimation, we propose a two-step procedure. First, the latent factor process and nonparametric loading are recovered through projection-based methods, and following, we estimate the regression components by approaches motivated from the generalized least squares. By scrupulously examining the non-asymptotic rates for recovering the factor process and its loading, we show the consistency and efficiency of estimated regression coefficients in the absence of prior knowledge of latent factor process and subject’s covariate effect. Here, the statistical guarantees remain valid even for finite time points that makes our method particularly appealing when the subjects significantly outnumber the observation time points. Using comprehensive simulations, we demonstrate the finite sample performance of our method, which corroborates the theoretical findings. Finally, we apply our method to a data set of air quality and energy consumption collected at 129 monitoring sites in the United States in 2015.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Pion gravitational form factors in the QCD instanton vacuum. I

The pion form factors of the QCD energy-momentum tensor (EMT) are studied in the instanton liquid model (ILM) of the QCD vacuum. In this approach, the breaking of conformal symmetry is encoded in the form of stronger-than-Poisson fluctuations in the number of instantons. For the trace of the EMT, it is shown that the gluonic trace anomaly term contributes half the pion mass, with the other half coming from the quark-mass-dependent σ term. The Q 2 dependence of the form factors is governed by glueball and scalar meson exchanges. For the EMT, the spin-0 (trace) and spin-2 (traceless rank-2 tensor) form factors are computed at next-to-leading order in the instanton density using effective quark operators. Relations between the gluon and quark contributions to the EMT form factors are derived. The form factors are also expressed in terms of the pion light-front wave functions in the ILM. The results at the low resolution scale of the inverse instanton size are evolved to higher scales using the renormalization group equation. The ILM results compare well with those of recent lattice QCD calculations. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Nucleon axial form factor from elementary target data

Precise neutrino-nucleon amplitudes are essential ingredients for predicting neutrino event rates in current and upcoming long-baseline neutrino oscillation experiments. A common neutrino interaction with a low reaction threshold and with most of the energy carried by two final state particles is quasielastic scattering, for which the nucleon axial form factor, 𝐹 𝐴⁡ (𝑄 2 ), is a dominant source of uncertainty. Improvements to the nucleon axial form factor rely on neutrino scattering data with elementary targets to reduce or eliminate the need for nuclear modeling systematics. This work examines constraints on the nucleon axial form factor that can be achieved from datasets of neutrino scattering on deuterium targets, Lattice QCD predictions, and from the recent hydrogen target data from the MINERvA Collaboration. Significant tension is found between hydrogen and deuterium target data, suggesting that extractions from deuterium underestimate both the central value and uncertainty of the form factor. Parametrizations for and uncertainties of the nucleon axial form factor using the 𝑧 expansion are provided.

FOS: Physical sciences↗
DOE OSTI2021

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A User-Centered Design Exploration of Factors That Influence the Rideshare Experience

The rise of real-time information communication through smartphones and wireless networks enabled the growth of ridesharing services. While personal rideshare services (individuals riding alone or with acquaintances) initially dominated the market, the popularity of pooled ridesharing (individuals sharing rides with people they do not know) has grown globally. However, pooled ridesharing remains less common in the U.S., where personal vehicle usage is still the norm. Vehicle design and rideshare services may need to be tailored to user preferences to increase pooled rideshare adoption. Based on a large, national U.S. survey (N = 5385), the results of exploratory and confirmatory factor analyses suggested that four key factors influence riders’ willingness to consider pooled ridesharing: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. A binomial logistic regression was conducted to determine how the four factors influence one’s willingness to consider pooled ridesharing. The two factors that positively influence riders’ willingness to consider pooled ridesharing are vehicle technology/accessibility (B = 1.10) and convenience (B = 0.94), while lack of passenger safety (B = –0.63) and comfort/ease of use (B = –0.17) are pooled ridesharing deterrents. Understanding user-centered design and service factors are critical to increase the use of pooled ridesharing services in the future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Understanding Bifacial PV Modeling: Raytracing and View Factor Models

As part of the PV Magazine Webinar 'View-factor vs. ray tracing - which bifacial modelling techniques should you use?,' the second part lead by NREL will explore the different rear-irradiance calculation software, which fall into two categories: view-factor and ray-tracing models. View factor models assume isotropic scattering of reflected rays, allowing for calculation of irradiance by integration. Due-diligence software such as PVSyst or SAM use the view-factor model. Ray-tracing models simulate multipath reflection and absorption of individual rays entering a scene. Raytracing software such as bifacial_radiance, which is the only available open-source toolkit, offers the possibility of reproducing complex scenes, including shading or finite-system edge effects. This can be useful to evaluate specific system geometries, shading and obstructions, areas of different albedo and electrical mismatch, all topics of interest to the prediction of bifacial performance. Model agreement for view factors and bifacial_radiance software is improving and has been shown to be between 2% (absolute) when compared with measured results.

14 SOLAR ENERGY↗