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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predicting High Energy Arcing Fault Zones of Influence for Aluminum Using an Arc Flash Modeling Approach: Evaluation of a model bias, uncertainty, parameter sensitivity and zone of influence estimation

This report documents the development of an arc flash hazard model to calculate the incident energy and zone of influence from high energy arcing faults involving aluminum. The NRC has identified the potential for (HEAFs) involving aluminum to increase the damage zone beyond what is currently postulated in fire probabilistic risk assessment (PRA) methodologies. To estimate the hazard from HEAFs involving aluminum an arc flash model was developed. Differences between the initial model and nuclear power plant (NPP) fire PRA scenarios were identified. Modification of the initial model established from existing literature and test data was used to minimize these differences. The developed model was evaluated against NRC datasets to understand the model prediction and relative uncertainties. Finally, a range of fire PRA zone of influences (ZOI) were developed based on the developed model, target fragility estimates and update HEAF PRA methodology. The results were developed to support an NRC LIC-504 evaluation in tandem with other modeling efforts. The report documents the effort and provides a reference for any future advancements in arc flash modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Model Calibration with Censored Data

Here, the purpose of model calibration is to make the model predictions closer to reality. The classical Kennedy-O'Hagan approach is widely used for model calibration, which can account for the inadequacy of the computer model while simultaneously estimating the unknown calibration parameters. In many applications, the phenomenon of censoring occurs when the exact outcome of the physical experiment is not observed, but is only known to fall within a certain region. In such cases, the Kennedy-O'Hagan approach cannot be used directly, and we propose a method to incorporate the censoring information when performing model calibration. The method is applied to study the compression phenomenon of liquid inside a bottle. The results show significant improvement over the traditional calibration methods, especially when the number of censored observations is large.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Stan : A Probabilistic Programming Language

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can also be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows

Active- and transfer-learning are applied to microscale dynamics of polymer flows for the multiscale discovery of effective constitutive approximations required in viscoelastic flow simulation. The result is macroscopic rheology directly connected to a microstructural model. Micro and macroscale simulations are adaptively coupled by means of Gaussian process regression (GPR) to run the expensive microscale computations only as necessary. This multiscale method is demonstrated with flows of a polymer solution as a model system. At the microscale level dissipative particle dynamics (DPD) is employed to model the fluid as a suspension of bead-spring micro-structures subjected to steady shear flow. The results yield the non-Newtonian viscosity and the first normal stress difference at strain rates as training data used in a GPR model. DPD parameters are calibrated with respect to experimental data for a real polymer solution. Compliance with these data requires adjustment of the DPD model's cutoff radius, which then becomes a function of the second invariant of the strain rate tensor. The FENE-P model is chosen for the macroscale description using the spectral element method (SEM) to simulate channel flow and flow past a circular cylinder. The DPD results at the lowest possible shear strain rate yield an estimate of the zero-shear rate viscosity, which allows the initiation of the macroscale flow by SEM as a Newtonian fluid. The resulting strain-rate field is surveyed to determine additional shear strain rate sampling points for the DPD system. This new information allows an initial fitting of parameters of the constitutive equation followed by new SEM simulations at the macroscale. Additionally, guided by active-learning GPR to select new sampling points, this process continues until convergence is achieved. The effectiveness of this new simulation paradigm for viscoelastic flows is tested with different macroscale operating conditions. The effective closure learned in the channel simulation is then transferred directly to the flow past a circular cylinder at low Reynolds number, where the results show that only two additional DPD simulations are required to achieve a satisfactory constitutive model. With an increase of the Reynolds number, the active-learning scheme automatically detects the inaccuracy of the learned constitutive model, and initiates additional DPD simulations for the extra data needed to once again close the microscale-macroscale coupled system. This new paradigm of active- and transfer-learning for multiscale modeling is readily applicable to other microscale-macroscale coupled simulations of complex fluids and other materials. Furthermore, the coupling between microscale and macroscale solvers can be seamlessly implemented with our open source multiscale universal interface (MUI) library.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

14 SOLAR ENERGY↗
DOE OSTI2018

Surface Composition of (99942) Apophis

On 2029 April 13, near-Earth asteroid (NEA) (99942) Apophis will pass at a distance of ∼6 Earth radii from Earth. This event will provide researchers with a unique opportunity to study the effects of tidal forces experienced by an asteroid during a close encounter with a terrestrial planet. Binzel et al. predicted that close flybys of terrestrial planets by NEAs would cause resurfacing of their regolith due to seismic shaking. In this work, we present the best pre-encounter near-infrared spectra of Apophis obtained so far. These new data were obtained during the 2013 apparition using the NASA Infrared Telescope Facility (IRTF). We found that our spectral data is consistent with previous observations by Binzel et al. but with a much higher signal-to-noise ratio. Spectral band parameters were extracted from the spectra and were used to determine the composition of the asteroid. Using a naïve Bayes classifier, we computed the likelihood of Apophis being an LL chondrite to be >99% based on mol% of Fa versus Fs. Using the same method, we estimated a probability of 89% for Apophis being an LL chondrite based on ol/(ol+px) and Fs. The results from the dynamical model indicate that the most likely source region for Apophis is the ν {sub 6} resonance in the inner main belt. Data presented in this study (especially Band I depth) could serve as a baseline to verify seismic shaking during the 2029 encounter.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A revised dosimetric characterization of the model S700 electronic brachytherapy source containing an anode-centering plastic insert and other components not included in the 2006 model

Purpose: The model S700 Axxent electronic brachytherapy source by Xoft, Inc., was characterized by Rivard et al. in 2006. Since then, the source design was modified to include a new insert at the source tip. Current study objectives were to establish an accurate source model for simulation purposes, dosimetrically characterize the new source and obtain its TG-43 brachytherapy dosimetry parameters, and determine dose differences between the original simulation model and the current model S700 source design. Methods: Design information from measurements of dissected model S700 sources and from vendor-supplied CAD drawings was used to aid establishment of an updated Monte Carlo source model, which included the complex-shaped plastic source-centering insert intended to promote water flow for cooling the source anode. These data were used to create a model for subsequent radiation transport simulations in a water phantom. Compared to the 2006 simulation geometry, the influence of volume averaging close to the source was substantially reduced. A track-length estimator was used to evaluate collision kerma as a function of radial distance and polar angle for determination of TG-43 dosimetry parameters. Results for the 50 kV source were determined every 0.1 cm from 0.3 to 15 cm and every 1° from 0° to 180°. Photon spectra in water with 0.1 keV resolution were also obtained from 0.5 to 15 cm and polar angles from 0° to 165°. Simulations were run for 10{sup 10} histories, resulting in statistical uncertainties on the transverse plane of 0.04% at r = 1 cm and 0.06% at r = 5 cm. Results: The dose-rate distribution ratio for the model S700 source as compared to the 2006 model exceeded unity by more than 5% for roughly one quarter of the solid angle surrounding the source, i.e., θ ≥ 120°. The radial dose function diminished in a similar manner as for an {sup 125}I seed, with values of 1.434, 0.636, 0.283, and 0.0975 at 0.5, 2, 5, and 10 cm, respectively. The radial dose function ratio between the current and the 2006 model had a minimum of 0.980 at 0.4 cm, close to the source sheath and for large distances approached 1.014. 2D anisotropy function ratios were close to unity for 50° ≤ θ ≤ 110°, but exceeded 5% for θ < 40° at close distances to the sheath and exceeded 15% for θ > 140°, even at large distances. Photon energy fluence of the updated model as compared to the 2006 model showed a decrease in output with increasing distance; this effect was pronounced at the lowest energies. A decrease in photon fluence with increase in polar angle was also observed and was attributed to the silver epoxy component. Conclusions: Changes in source design influenced the overall dose rate and distribution by more than 2% in several regions. This discrepancy is greater than the dose calculation acceptance criteria as recommended in the AAPM TG-56 report. The effect of the design change on the TG-43 parameters would likely not result in dose differences outside of patient applicators. Adoption of this new dataset is suggested for accurate depiction of model S700 source dose distributions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model

Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15,000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters in high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO2), nitrogen oxides (NOx), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset Results demonstrated that high resolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate measured microscopic vehicle movement in tandem with microscopic emissions modeling for a substantial study domain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A New Approach for Obtaining Cosmological Constraints from Type Ia Supernovae using Approximate Bayesian Computation

Cosmological parameter estimation techniques that robustly account for systematic measurement uncertainties will be crucial for the next generation of cosmological surveys. We present a new analysis method, superABC, for obtaining cosmological constraints from Type Ia supernova (SN Ia) light curves using Approximate Bayesian Computation (ABC) without any likelihood assumptions. The ABC method works by using a forward model simulation of the data where systematic uncertainties can be simulated and marginalized over. A key feature of the method presented here is the use of two distinct metrics, the `Tripp' and `Light Curve' metrics, which allow us to compare the simulated data to the observed data set. The Tripp metric takes as input the parameters of models fit to each light curve with the SALT-II method, whereas the Light Curve metric uses the measured fluxes directly without model fitting. We apply the superABC sampler to a simulated data set of $\sim$1000 SNe corresponding to the first season of the Dark Energy Survey Supernova Program. Varying $\Omega_m, w_0, \alpha$ and $\beta$ and a magnitude offset parameter, with no systematics we obtain $\Delta(w_0) = w_0^{\rm true} - w_0^{\rm best \, fit} = -0.036\pm0.109$ (a $\sim11$% 1$\sigma$ uncertainty) using the Tripp metric and $\Delta(w_0) = -0.055\pm0.068$ (a $\sim7$% 1$\sigma$ uncertainty) using the Light Curve metric. Including 1% calibration uncertainties in four passbands, adding 4 more parameters, we obtain $\Delta(w_0) = -0.062\pm0.132$ (a $\sim14$% 1$\sigma$ uncertainty) using the Tripp metric. Overall we find a $17$% increase in the uncertainty on $w_0$ with systematics compared to without. We contrast this with a MCMC approach where systematic effects are approximately included. We find that the MCMC method slightly underestimates the impact of calibration uncertainties for this simulated data set.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Analysis of the Pressure-Pulse Propagation in Rock: a New Approach to Simultaneously Determine Permeability, Porosity, and Adsorption Capacity

Permeability estimation from pressure-pulse decay method is complicated by two facts: (1) the decay curve often deviates from the single-exponential behavior in the early time period and (2) possible existence of gas adsorption. Both the two factors cause significant permeability error in most of pressure-pulse decay methods. In this paper, we first present a thorough analysis of pressure-pulse propagation process to reveal the mechanism behind the early time and later time behaviors of pressure decay curve. Inspired by the findings from these analyses, a new scaled pressure is proposed which can: (1) be easily used to distinguish the early time and later time data and (2) make the decay curves of all cases into a single 1:1 straight line for later time. A new data-proceeding method, which calculates the apparent porosity and permeability using the same set of measured data, is then developed. The new method could not only remove the effects of the adsorption on the permeability estimation, but also identify the apparent porosity as well as proper adsorption model and parameters. The proposed method is verified by comparing with true values and calculated values through numerical simulations that cover variations in typical rock properties (porosity, permeability, slippage, and adsorption) and the experiment configurations. It is found that the new method is accurate and reliable for all test cases, whereas the Brace’s and Cui’s approaches may cause permeability error in some cases. Lastly, the new method has been successfully applied to real data measured in pressure-pulse decay experiments involving different types of rocks and gases.

58 GEOSCIENCES↗
DOE OSTI2014

The effect of weak lensing on distance estimates from supernovae

Using a sample of 608 Type Ia supernovae from the SDSS-II and BOSS surveys, combined with a sample of foreground galaxies from SDSS-II, we estimate the weak lensing convergence for each supernova line of sight. We find that the correlation between this measurement and the Hubble residuals is consistent with the prediction from lensing (at a significance of 1.7σ). Strong correlations are also found between the residuals and supernova nuisance parameters after a linear correction is applied. When these other correlations are taken into account, the lensing signal is detected at 1.4σ. We show, for the first time, that distance estimates from supernovae can be improved when lensing is incorporated, by including a new parameter in the SALT2 methodology for determining distance moduli. The recovered value of the new parameter is consistent with the lensing prediction. Using cosmic microwave background data from WMAP7, H {sub 0} data from Hubble Space Telescope and Sloan Digital Sky Survey (SDSS) Baryon acoustic oscillations measurements, we find the best-fit value of the new lensing parameter and show that the central values and uncertainties on Ω {sub m} and w are unaffected. The lensing of supernovae, while only seen at marginal significance in this low-redshift sample, will be of vital importance for the next generation of surveys, such as DES and LSST, which will be systematics-dominated.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Measuring the largest angular scale CMB B-mode polarization with galactic foregrounds on a cut sky

We consider the effectiveness of foreground cleaning in the recovery of Cosmic Microwave Background (CMB) polarization sourced by gravitational waves for tensor-to-scalar ratios in the range 0<r<0.1. Using the planned survey area, frequency bands, and sensitivity of the Cosmology Large Angular Scale Surveyor (CLASS), we simulate maps of Stokes Q and U parameters at 40, 90, 150, and 220 GHz, including realistic models of the CMB, diffuse Galactic thermal dust and synchrotron foregrounds, and Gaussian white noise. We use linear combinations (LCs) of the simulated multifrequency data to obtain maximum likelihood estimates of r, the relative scalar amplitude s, and LC coefficients. We find that for 10,000 simulations of a CLASS-like experiment using only measurements of the reionization peak (ℓ⩽23), there is a 95% C.L. upper limit of r<0.017 in the case of no primordial gravitational waves. For simulations with r=0.01, we recover at 68% C.L. r=0.012{sub −0.006}{sup +0.011}. The reionization peak corresponds to a fraction of the multipole moments probed by CLASS, and simulations including 30⩽ℓ⩽100 further improve our upper limits to r<0.008 at 95% C.L. (r=0.010{sub −0.004}{sup +0.004} for primordial gravitational waves with r = 0.01). In addition to decreasing the current upper bound on r by an order of magnitude, these foreground-cleaned low multipole data will achieve a cosmic variance limited measurement of the E-mode polarization’s reionization peak.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Integrated systems-based approach to monitoring environmental remediation

The U.S. Department of Energy is responsible for risk reduction and cleanup of its nuclear weapons complex. Remediation strategies for some of the contamination may include techniques that mitigate risk, but leave contaminants in place. Monitoring to verify remedy performance and long-term mitigation of risk is key to implementing these strategies and can be a large portion of the total cost of remedy implementation. Especially in these situations, there is a need for innovative monitoring approaches that move away from the cost- and labor-intensive point-source monitoring. In this paper, alternative approaches for monitoring are presented for vadose zone, groundwater, groundwater/surface water interface, and surface water. To illustrate integrated, systems-based monitoring, this paper focuses on vadose zone contaminant remediation to mitigate impact to groundwater. In this context, vadose zone contamination is a source, or potential source, to groundwater plumes. The monitoring design uses a systems-based approach focused on developing a conceptual site model that highlights key features that control contaminant flux to groundwater. These features are derived considering the unsaturated flow and contaminant transport processes in the vadose zone and the nature of the waste discharge. Diagnostic properties and/or parameters related to both short- and long-term contaminant flux to groundwater can be identified and targeted for monitoring. The resolution of monitoring data needed to correspond to a functionally useful indicator of flux to groundwater can be estimated using quantitative analyses and the associated unsaturated flow properties relevant to the targeted site and vadose zone features. This monitoring design approach follows the process of developing a quantitative conceptual model suitable for supporting projections of future flux to groundwater. Support for such projections is important because it is likely that, in many cases, remediation decisions for the vadose zone will need to be made based all or in part on projected impacts to groundwater, and monitoring will then be applied to verify that remedy goals are being met. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Efficient calibration for imperfect computer models

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L 2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Here, numerical examples show that the proposed method outperforms the existing ones.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Visualizing Wind Farm Wakes Using SCADA Data

As wind farms scale to include more and more turbines, questions about turbine wake interactions become increasingly important. Turbine wakes reduce wind speed and downwind turbines suffer decreased performance. The cumulative effect of the wakes throughout a wind farm will therefore decrease the performance of the entire farm. These interactions are dynamic and complicated, and it is difficult to quantify the overall effect of the wakes. This problem has attracted some attention in terms of computational modelling for siting turbines on new farms, but less attention in terms of empirical studies and performance validation of existing farms. In this report, Supervisory Control and Data Acquisition (SCADA) data from an existing wind farm is analyzed in order to explore methods for documenting wake interactions. Visualization techniques are proposed and used to analyze wakes in a 67 turbine farm. The visualizations are based on directional analysis using power measurements, and can be considered to be normalized capacity factors below rated power. Wind speed measurements are not used in the analysis except for data pre-processing. Four wake effects are observed; including wake deficit, channel speed up, and two potentially new effects, single and multiple shear point speed up. In addition, an attempt is made to quantify wake losses using the same SCADA data. Power losses for the specific wind farm investigated are relatively low, estimated to be in the range of 3-5%. Finally, a simple model based on the wind farm geometrical layout is proposed. Key parameters for the model have been estimated by comparing wake profiles at different ranges and making some ad hoc assumptions. A preliminary comparison of six selected profiles shows excellent agreement with the model. Where discrepancies are observed, reasonable explanations can be found in multi-turbine speedup effects and landscape features, which are yet to be modelled.

17 WIND ENERGY↗
DOE OSTI2026

Development, Monitoring, and Control of Fracture Thermal Energy Storage (FTES) in Crystalline Rock Formations (DEMO-FTES) (CRADA Final Report)

The DEMO-FTES project sought to demonstrate the thermal efficiency of fracture thermal energy storage (FTES) through numerical simulations, laboratory and meso-scale field tests. A detailed dimensional and scaling analysis was performed to identify key parameters and how they can be most effectively scaled to the laboratory and decameter scale. Numerical models were developed and used for three purposes: 1. Before field experiments, numerical modelling can be used to estimate fracture properties based on previous data from the EGS Collab experiment and then predict thermal hydrological behaviors of the fracture system with hot water injection/withdrawal, therefore, help to design the experiments (e.g., to decide the duration of the cycles based on the flow rate the pump can provide, and the estimated fracture properties); 2. After the field experiment, to estimate the system properties during the experiment (as the size and shape of a fracture could change over time), and understand why system performance is different than what has been predicted, i.e., to help understand the meso-scale test; and, 3. To model the lab experiments and estimate fracture properties and storage efficiency. Ultimately, the experiment and numerical models could shed light on the processes and uncertainty happening during fracture activation and help understand the scaling between lab and field test, and finally, the design and optimization of potential fracture thermal energy storage systems.

25 ENERGY STORAGE↗