Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model Inference”

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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

INTEGRAL Observations of GW170104

We used data from the International Gamma-Ray Astrophysics Laboratory ( INTEGRAL ) to set upper limits on the γ -ray and hard X-ray prompt emission associated with the gravitational-wave event GW170104, discovered by the Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo collaboration. The unique omnidirectional viewing capability of the instruments on board INTEGRAL allowed us to examine the full 90% confidence level localization region of the LIGO trigger. Depending on the particular spectral model assumed and the specific position within this region, the upper limits inferred from the INTEGRAL observations range from F {sub γ} = 1.9 × 10{sup −7} erg cm{sup −2} to F {sub γ} = 10{sup −6} erg cm{sup −2} (75 keV–2 MeV energy range). This translates into a ratio between the prompt energy released in γ -rays along the direction to the observer and the gravitational-wave energy of E {sub γ} / E {sub GW} < 2.6 × 10{sup −5}. Using the INTEGRAL results, we cannot confirm the γ -ray proposed counterpart to GW170104 by the Astro—Rivelatore Gamma a Immagini Leggero (AGILE) team with the mini-Calorimeter (MCAL) instrument. The reported flux of the AGILE/MCAL event, E2, is not compatible with the INTEGRAL upper limits within most of the 90% LIGO localization region. There is only a relatively limited portion of the sky where the sensitivity of the INTEGRAL instruments was not optimal and the lowest-allowed fluence estimated for E2 would still be compatible with the INTEGRAL results. This region was also observed independently by Fermi /Gamma-ray Burst Monitor and AstroSAT, from which, as far as we are aware, there are no reports of any significant detection of a prompt high-energy event.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Three-dimensional Collapse of a Rapidly Rotating 16 M ⊙ Star

I report on the three-dimensional (3D) hydrodynamic evolution of a rapidly rotating 16 M ⊙ star to iron core collapse. For the first time, I follow the 3D evolution of the angular momentum (AM) distribution in the iron core and convective shell burning regions for the final 10 minutes up to and including gravitational instability and core collapse. In 3D, convective regions show efficient AM transport that leads to an AM profile that differs in shape and magnitude from MESA within a few shell convective turnover timescales. For different progenitor models, such as those with tightly coupled Si/O convective shells, efficient AM transport in 3D simulations could lead to a significantly different AM distribution in the stellar interior affecting estimates of the natal neutron star or black hole spin. The results suggest that 3D AM transport in convective and rotating shell burning regions are critical components in models of massive stars and could qualitatively alter the explosion outcome and inferred compact remnant properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Accelerating Transformer-based Deep Learning Models on FPGAs using Column Balanced Block Pruning

Although Transformer-based language representations achieve state-of-the-art accuracy on various natural language processing (NLP) tasks, the large model size has been challenging the resource constrained computing platforms. Weight pruning, as a popular and effective technique in reducing the number of weight parameters and accelerating the Transformer, has been investigated on GPUs. However, the Transformer acceleration using weight pruning on field-programmable gate array (FPGAs) remains unexplored. This paper investigates the column balanced block-wise pruning on Transformer and designs an FPGA acceleration engine to customize the balanced blockwise matrix multiplication. We implement the Transformer model with proper hardware scheduling, and the experiments show that the Transformer inference on FPGA achieves 10.35 ms latency with the batch size of 32, which is 10.96 × speed up comparing to CPU platform and 2.08 × speed up comparing to GPU platform.

Peng, Hongwu↗
DOE OSTI2023

Bayesian optimal experimental design for constitutive model calibration

Computational simulation is increasingly relied upon for high/consequence engineering decisions, which necessitates a high confidence in the calibration of and predictions from complex material models. However, the calibration and validation of material models is often a discrete, multi-stage process that is decoupled from material characterization activities, which means the data collected does not always align with the data that is needed. To address this issue, an integrated workflow for delivering an enhanced characterization and calibration procedure—Interlaced Characterization and Calibration (ICC)—is introduced and demonstrated. Further, this framework leverages Bayesian optimal experimental design (BOED), which creates a line of communication between model calibration needs and data collection capabilities in order to optimize the information content gathered from the experiments for model calibration. Eventually, the ICC framework will be used in quasi real-time to actively control experiments of complex specimens for the calibration of a high-fidelity material model. This work presents the critical first piece of algorithm development and a demonstration in determining the optimal load path of a cruciform specimen with simulated data. Calibration results, obtained via Bayesian inference, from the integrated ICC approach are compared to calibrations performed by choosing the load path a priori based on human intuition, as is traditionally done. The calibration results are communicated through parameter uncertainties which are propagated to the model output space (i.e. stress–strain). In these exemplar problems, data generated within the ICC framework resulted in calibrated model parameters with reduced measures of uncertainty compared to the traditional approaches.

42 ENGINEERING↗
DOE OSTI2018

Two-temperature GRRMHD Simulations of M87

Here, we present axisymmetric two-temperature general relativistic radiation magnetohydrodynamic simulations of the inner region of the accretion flow onto the supermassive black hole M87. We address uncertainties from previous modeling efforts through inclusion of models for (1) self-consistent dissipative and Coulomb electron heating (2) radiation transport (3) frequency-dependent synchrotron emission, self-absorption, and Compton scattering. We adopt a distance D = 16.7 Mpc, an observer angle θ = 20°, and consider black hole masses M/M ⊙ = (3.3 x 10 9 , 6.2 x 10 9 ) and spins a star = (0.5, 0.9375) in a four-simulation suite. For each (M, a star ), we identify the accretion rate that recovers the 230 GHz flux from very long baseline interferometry measurements. We report on disk thermodynamics at these accretion rates (M · /M · Edd ~ 10 -5 ). The disk remains geometrically thick; cooling does not lead to a thin disk component. While electron heating is dominated by Coulomb rather than dissipation for r ≳ 10GM/c 2 , the accretion disk remains two-temperature. Radiative cooling of electrons is not negligible, especially for r ≲ 10GM/c 2 . The Compton y parameter is of order unity. We then compare derived and observed or inferred spectra, millimeter images, and jet powers. Simulations with M/M ⊙ = 3.3 × 10 9 are in conflict with observations. These simulations produce millimeter images that are too small, while the low-spin simulation also overproduces X-rays. For M/M ⊙ = 6.2 x 10 9 , both simulations agree with constraints on radio/IR/X-ray fluxes and millimeter image sizes. Simulation jet power is a factor 10 2 –10 3 below inferred values, a possible consequence of the modest net magnetic flux in our models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

OBSERVATIONS OF INFALLING AND ROTATIONAL MOTIONS ON A 1000 AU SCALE AROUND 17 CLASS 0 AND 0/I PROTOSTARS: HINTS OF DISK GROWTH AND MAGNETIC BRAKING?

We perform imaging and analyses of SMA 1.3 mm continuum, C{sup 18}O (2-1) and {sup 12}CO (2-1) line data of 17 Class 0 and 0/I protostars to study their gas kinematics on a 1000 AU scale. Continuum and C{sup 18}O (2-1) emission are detected toward all the sample sources and show central primary components with sizes of ∼600-1500 AU associated with protostars. The velocity gradients in C{sup 18}O (2-1) have wide ranges of orientations from parallel to perpendicular to the outflows, with magnitudes from ∼1 to ∼530 km s{sup –1} pc{sup –1}. We construct a simple kinematic model to reproduce the observed velocity gradients, estimate the infalling and rotational velocities, and infer the disk radii and the protostellar masses. The inferred disk radii range from <5 AU to >500 AU with estimated protostellar masses from <0.1 M {sub ☉} to >1 M {sub ☉}. Our results hint that both large and small disks are possibly present around Class 0 protostars, which could be a sign of disk growth at the Class 0 stage. In addition, the directions of the overall velocity gradients in 7 out of the 17 sources are close to perpendicular to their outflow axes (Δθ > 65°), which is a signature of significant rotational motions. From our model fitting, the specific angular momenta in these sources are estimated to be >2 × 10{sup –4} km s{sup –1} pc, suggesting that magnetic braking is unlikely efficient on a 1000 AU scale in these Class 0 and 0/I sources. In a sub-sample with observed magnetic field orientations, we find no source with large specific angular momenta together with closely aligned magnetic field and outflow axes. This possibly hints that the magnetic field, if originally aligned with the rotational axis, can play a role in removing angular momentum from infalling material at the Class 0 stage. We discuss our results in comparison with theoretical models of collapsing dense cores with and without magnetic fields in the context of disk formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

H β and H γ Absorption-line Profile Inconsistencies in Laboratory Experiments Performed at White Dwarf Photosphere Conditions

The spectroscopic method relies on hydrogen Balmer absorption lines to infer white dwarf (WD) masses. These masses depend on the choice of atmosphere model, hydrogen atomic line shape calculation, and which Balmer series members are included in the spectral fit. In addition to those variables, spectroscopic masses disagree with those derived using other methods. Here we present laboratory experiments aimed at investigating the main component of the spectroscopic method: hydrogen line shape calculations. These experiments use X-rays from Sandia National Laboratories’ Z-machine to create a uniform ~15 cm 3 hydrogen plasma and a ~4 eV backlighter that enables recording high-quality absorption spectra. The large plasma, volumetric X-ray heating that fosters plasma uniformity, and the ability to collect absorption spectra at WD photosphere conditions are improvements over past laboratory experiments. Analysis of the experimental absorption spectra reveals that electron density (n e ) values derived from the Hγ line are ~34% ± 7.3% lower than from Hβ. Two potential systematic errors that may contribute to this difference were investigated. A detailed evaluation of self-emission and plasma gradients shows that these phenomena are unlikely to produce any measurable Hβ–Hγ n e difference. WD masses inferred with the spectroscopic method are proportional to the photosphere density. Hence, the measured Hβ–Hγ n e difference is qualitatively consistent with the trend that WD masses inferred from their Hβ line are higher than that resulting from the analysis of Hβ and Hγ. In conclusion, this evidence may suggest that current hydrogen line shape calculations are not sufficiently accurate to capture the intricacies of the Balmer series.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

A Deep Generative Deconvolutional Image Model

A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic unpooling is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the top-layer features, yielding max-margin discrimination. Deep deconvolutional inference is employed when testing, to infer the latent features, and the top-layer features are connected with the max-margin classifier for discrimination tasks. The model is efficiently trained using a Monte Carlo expectation-maximization (MCEM) algorithm; the algorithm is implemented on graphical processor units (GPU) to enable large-scale learning, and fast testing. Excellent results are obtained on several benchmark datasets, including ImageNet, demonstrating that the proposed model achieves results that are highly competitive with similarly sized convolutional neural networks.

convolutional neural network↗
DOE OSTI2023

Extending Parsimonious Bayesian Inference

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Constraining the Higgs potential with neural simulation-based inference for di-Higgs production

Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity LHC. We explore how to improve the sensitivity to physics beyond the Standard Model through per-event kinematics for di-Higgs events. In particular, we employ machine learning through simulation-based inference to estimate per-event likelihood ratios and gauge potential sensitivity gains from including this kinematic information. In terms of the Standard Model Effective Field Theory, we find that adding a limited number of observables can help to remove degeneracies in Wilson coefficient likelihoods and significantly improve the experimental sensitivity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2022

A Model of Double Coronal Hard X-Ray Sources in Solar Flares

A number of double coronal X-ray sources have been observed during solar flares by RHESSI, where the two sources reside at different sides of the inferred reconnection site. However, where and how these X-ray-emitting electrons are accelerated remains unclear. Here we present the first model of the double coronal hard X-ray (HXR) sources, where electrons are accelerated by a pair of termination shocks driven by bidirectional fast reconnection outflows. We model the acceleration and transport of electrons in the flare region by numerically solving the Parker transport equation using velocity and magnetic fields from the macroscopic magnetohydrodynamic simulation of a flux rope eruption. We show that electrons can be efficiently accelerated by the termination shocks and high-energy electrons mainly concentrate around the two shocks. The synthetic HXR emission images display two distinct sources extending to >100 keV below and above the reconnection region, with the upper source much fainter than the lower one. The HXR energy spectra of the two coronal sources show similar spectral slopes, consistent with the observations. Our simulation results suggest that the flare termination shock can be a promising particle acceleration mechanism in explaining the double-source nonthermal emissions in solar flares.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

How to constrain your M dwarf: Measuring effective temperature, bolometric luminosity, mass, and radius

Precise and accurate parameters for late-type (late K and M) dwarf stars are important for characterization of any orbiting planets, but such determinations have been hampered by these stars’ complex spectra and dissimilarity to the Sun. We exploit an empirically calibrated method to estimate spectroscopic effective temperature (T{sub eff}) and the Stefan–Boltzmann law to determine radii of 183 nearby K7–M7 single stars with a precision of 2%–5%. Our improved stellar parameters enable us to develop model-independent relations between T{sub eff} or absolute magnitude and radius, as well as between color and T{sub eff}. The derived T{sub eff}–radius relation depends strongly on [Fe/H], as predicted by theory. The relation between absolute K{sub S} magnitude and radius can predict radii accurate to ≃3%. We derive bolometric corrections to the VR{sub C}I{sub C}grizJHK{sub S} and Gaia passbands as a function of color, accurate to 1%–3%. We confront the reliability of predictions from Dartmouth stellar evolution models using a Markov chain Monte Carlo to find the values of unobservable model parameters (mass, age) that best reproduce the observed effective temperature and bolometric flux while satisfying constraints on distance and metallicity as Bayesian priors. With the inferred masses we derive a semi-empirical mass–absolute magnitude relation with a scatter of 2% in mass. The best-agreement models overpredict stellar T{sub eff} values by an average of 2.2% and underpredict stellar radii by 4.6%, similar to differences with values from low-mass eclipsing binaries. These differences are not correlated with metallicity, mass, or indicators of activity, suggesting issues with the underlying model assumptions, e.g., opacities or convective mixing length.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Towards a Semantically-Enabled Control Strategy for Building Simulations: Integration of Semantic Technologies and Model Predictive Control

State-of-the-art building simulation control methods incorporate physical constraints into their mathematical models, but omit implicit constraints associated with policies of operation and dependency relationships among rules representing those constraints. To overcome these shortcomings, there is a recent trend in enabling the control strategies with inference-based rule checking capabilities. One solution is to exploit semantic web technologies in building simulation control. Such approaches provide the tools for semantic modeling of domains, and the ability to deduce new information based on the models through use of Description Logic (DL). In a step toward enabling this capability, this paper presents a cross-disciplinary data-driven control strategy for building energy management simulation that integrates semantic modeling and formal rule checking mechanisms into a Model Predictive Control (MPC) formulation. The results show that MPC provides superior levels of performance when initial conditions and inputs are derived from inference-based rules.

Delgoshaei, Parastoo↗
DOE OSTI2025

Joint state-parameter estimation for the reduced fracture model via the united filter

Here, in this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured porous media. Fluid flow and transport in fractured porous media are critical in subsurface hydrology, geophysics, and reservoir geomechanics. Reduced fracture models, which represent fractures as lower-dimensional interfaces, enable efficient multi-scale simulations. However, reduced fracture models also face accuracy challenges due to modeling errors and uncertainties in physical parameters such as permeability and fracture geometry. To address these challenges, we propose a United Filter method, which integrates the Ensemble Score Filter (EnSF) for state estimation with the Direct Filter for parameter estimation. EnSF, based on a score-based diffusion model framework, produces ensemble representations of the state distribution without deep learning. Meanwhile, the Direct Filter, a recursive Bayesian inference method, estimates parameters directly from state observations. The United Filter combines these methods iteratively: EnSF estimates are used to refine parameter values, which are then fed back to improve state estimation. Numerical experiments demonstrate that the United Filter method surpasses the state-of-the-art Augmented Ensemble Kalman Filter, delivering more accurate state and parameter estimation for reduced fracture models. This framework also provides a robust and efficient solution for PDE-constrained inverse problems with uncertainties and sparse observations.

Bayesian inference↗
DOE OSTI2015

MAGNETIC FLUX CONSERVATION IN THE HELIOSHEATH INCLUDING SOLAR CYCLE VARIATIONS OF MAGNETIC FIELD INTENSITY

In the heliosheath (HS), Voyager 2 has observed a flow with constant radial velocity and magnetic flux conservation. Voyager 1, however, has observed a decrease in the flow’s radial velocity and an order of magnitude decrease in magnetic flux. We investigate the role of the 11 yr solar cycle variation of the magnetic field strength on the magnetic flux within the HS using a global 3D magnetohydrodynamic model of the heliosphere. We use time and latitude-dependent solar wind velocity and density inferred from Solar and Heliospheric Observatory/SWAN and interplanetary scintillations data and implemented solar cycle variations of the magnetic field derived from 27 day averages of the field magnitude average of the magnetic field at 1 AU from the OMNI database. With the inclusion of the solar cycle time-dependent magnetic field intensity, the model matches the observed intensity of the magnetic field in the HS along both Voyager 1 and 2. This is a significant improvement from the same model without magnetic field solar cycle variations, which was over a factor of two larger. The model accurately predicts the radial velocity observed by Voyager 2; however, the model predicts a flow speed ∼100 km s{sup −1} larger than that derived from LECP measurements at Voyager 1. In the model, magnetic flux is conserved along both Voyager trajectories, contrary to observations. This implies that the solar cycle variations in solar wind magnetic field observed at 1 AU does not cause the order of magnitude decrease in magnetic flux observed in the Voyager 1 data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A deep learning-based Bayesian framework for high-resolution calibration of building energy models

Calibrating building energy models (BEMs), i.e., closing discrepancy between modeling and field measurements, is of significance to support its applications in building sustainability and resilience analysis. However, as being widely used in practice, current Bayesian calibration is mostly performed in low-resolution (annual or monthly), instead of high-resolution (hourly or sub-hourly), which is crucial to support emerging BEM applications, such as building-renewable energy integration (demand response) and smart control. This is attributable to the gaps in current Bayesian calibration process, including (1) difficulty in supporting reliable high-resolution calibration with over-parameterization and multi-solution issues, (2) inadequacy of meta-model to capture temporal building dynamics in high-resolution, and (3) excessive computational burdens of covariance matrix calculation in Bayesian inference. Therefore, to close these gaps, this research proposes a novel deep learning-based Bayesian calibration framework, involving pre-calibration mechanism, Long Short-Term Memory as surrogate models, and simplified covariance matrix calculation, to calibrate BEMs in high temporal resolution (i.e., hourly) with enhanced accuracy and computational efficiency. Finally, the case study demonstrates its effectiveness to match modeling outcomes with measurements and realize CV-RMSE of < 30 % and NMBE of < 6 % in hourly resolution, as well as a significant reduction of calibration time (by > 99 %, from > 600 h to ~ 1.5 h).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗