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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Experimental Characterization of High-Surface Area Thermal Energy Storage

There is growing interest in energy storage technologies due to the expansion of renewable energy sources that are inherently intermittent and the increasing frequency of extreme weather events that disturb the power grid. Power consumption in buildings makes up approximately 76% of all electricity usage on the grid and is primarily used for thermal applications such as space conditioning, hot water, and cooking. This makes thermal energy storage (TES) an ideal solution for many of these applications. Many TES technologies rely on latent energy storage, which utilizes the melting/solidification of phase change materials (PCM) to store energy. Typically, TES designs suffer from low power density due to their low inherent thermal conductivity. This limitation makes the deployment of TES in active applications difficult as the ease of access to energy is essential for effective use. Common routes for improving power density include high thermal conductivity additives or extended features such as fins that increase cost. This study presents an alternative approach to improving performance through increasing the overall surface area to volume ratio of the device, to increase the available area for convection to occur between the working fluid and PCM. In the study a commercial PCM was selected with a transition temperature ideal for space heating applications. The heat exchanger design utilizes a unique application of triply periodic minimal surfaces (TPMS) for macro-encapsulation of the PCM. The use of TPMS for heat exchangers has been growing in interest due to their high-surface area to volume ratios, which were previously unmanufacturable until the development of additive manufacturing. A modular system was designed and manufactured with a stereolithography resin printing system that is then backfilled with PCM. An experiment test set-up is designed to test the charge and discharge performance of the thermal storage using a conditioned air stream. The pressure drop of the design is tested across a variety of flow rates. When compared to existing experimental data within literature, there is excellent agreement based on the Reynolds number at similar hydraulic diameters. Several inlet temperatures are tested at consistent temperature differences from the phase change temperature for both charging and discharging. Additionally, the volumetric flow rate is varied for each temperature set point. It was found that increasing flow rate had diminishing returns in reducing the overall charge time of the TES. The temperature delta from the melting temperature was the primary contributor to the change in average heat flux with limited variation in average heat transfer rate between charging and discharging at similar inlet temperatures and flow rates. The TPMS heat exchanger design has a high air-side pressure drop but it provides high heat transfer rates. This helps maintain a high outlet temperature during discharge, which is important to thermal comfort applications. The design, manufacturing, and experimental characterization of the TES device will be presented as part of this study.

25 ENERGY STORAGE↗
DOE OSTI2024

Evaluation of RANS vs. LES simulation of fluid flow through 3 × 3 rod bundle with a simple spacer grid as a precursor to coupled fluid–structure interaction simulations

The research literature on Computational Fluid Dynamics (CFD) of coolant flow through rod bundles with spacer-grids and mixing vanes is replete, ranging from high fidelity Large Eddy Simulation (LES)/Direct Numerical Simulation (DNS) simulations to Reynolds-Averaged Navier–Stokes (RANS) modeled studies. The mixing of flow between subchannels and the pressure drop through the bundle are fundamental quantities useful for comparing and evaluating CFD methods. Less commonly observed and compared are the forces exerted onto the structure by the fluid. The present study seeks to evaluate the use of RANS simulations for predicting the structural response to fluid flow. Wall resolved RANS simulations are benchmarked against LES simulations of fluid flow at a Reynolds number of 15,000 through a 3 × 3 fuel rod bundle with a simple spacer grid. Velocity line-plots are compared showing good agreement between RANS and LES results, ascertaining that the former is capable of capturing the essential time-averaged velocity profile. Additionally, the distribution of forces on the spacer grid and fuel rods are collected as a function of time and space. The RANS methods are evaluated using the frequency and magnitude of the fluctuating forces on various portions of the structure as compared to LES. In conclusion, the power spectral density evaluation of the models reveal underprediction of force amplitude on the rod walls by RANS and also discrepancy in the prediction of high frequency spectra, especially in the immediate vicinity of spacer-grid structure, which may be attributed to the lack of random turbulence fluctuation or insufficient modeling of small-scale eddies in RANS simulation.

FIV↗
DOE OSTI2024

Accelerating the design of lattice structures using machine learning

Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R 2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rupture Dynamics Simulations of Shallow Crustal Earthquakes on Reverse Slip Faults

The main goal of this study was to perform dynamic rupture simulations in order constrain the characterization of kinematic rupture models implemented in techniques for simulating strong ground motion for crustal earthquakes. First, we investigated the rupture process of the 2008 Iwate-Miyagi Nairiku earthquake by developing a dynamic rupture model on a reverse fault using a trial-and-error technique that produced a slip distribution and near-fault ground motion that matched the recorded ones. The simulations were performed in the frequency range 0-2 Hz, using a 3D staggered grid finite-difference method and a linear slip weakening friction law. Constrained by the observed slip distribution and consistent with dynamic rupture models, the derived kinematic rupture model of the Iwate-Miyagi Nairiku earthquake contains areas with relatively large slip rate, representing strong motion generation areas, set against lower amplitude heterogeneous background slip, and a relatively low slip rate in the weak zone of the top-most crust (upper 3km). Second, we performed rupture dynamics modeling to constrain shallow slip characterization in rupture models for strike-slip crustal earthquakes. The objective was to establish general rules about the characterization of slip rate function and slip at shallow depths and in the strong motion generation areas (SMGAs). The simulations of spontaneous rupture were performed in the frequency range 0-2.0Hz, using a 3D staggered-grid finite-difference method and a layer over half-space 1D crustal velocity model with a minimum shear-wave velocity of 2.8 km/s. In order to account for changes in material ductility and reduction of stress drop, observed in the shallow crust (upper 3-5 km), and the transition from ductile state to brittle state in the upper seismogenic zone, in our stress models we included a shallow weak zone (<4km). In this zone the stress drop was set to zero at the free surface and gradually increased with depth, while the slip weakening distance was set to 75 cm at the free surface and decreased to 50 cm at the base of the weak zone. As in the case of reverse faulting during the Iwate-Miyagi Nairiku earthquake, from these computations we found a systematic change in the shape of the slip-rate function, from Kostrov-type in the deeper part of the fault to a more symmetric cosine-type in the upper few kilometers, near the free-surface. Moreover, the average slip duration in the weak zone, with respect to slip duration in the deeper parts of the fault, increases by at most a factor of 1.5. We found a systematic and gradual change in the shape of the slip-rate function from Kostrov-type in the asperity areas (SMGAs), to more symmetric cosine-type in the upper few km near the free-surface, on the long period motion generation area (LMGA). The effective rise time in the LMGA, with respect to that in the SMGAs, increases by at most a factor of 2. Effective rise time is the time difference between the time at which the slip rate drops to a level that is equal to 25% of its peak and the onset time of the signal. In addition, the slip in the LMGA located above the SMGAs is almost the same as the one in the SMGAs, and about 1.5 times larger than the average slip.

58 GEOSCIENCES↗
DOE OSTI2020

Mobile Sorption-based Thermal Battery for Harvesting Low-Temperature Geothermal Energy

Around 20% of the total primary energy in the United States is consumed for thermal demands of buildings such as space cooling, dehumidification, and space heating (EIA 2018). Low-temperature geothermal energy is abundant and can effectively satisfy buildings’ thermal demands. However, low-temperature geothermal energy is underutilized because the energy density of geothermal fluid is too low to justify the costs associated with transporting it between existing geothermal resources and buildings. The mobile sorption-based thermal battery (MSTB) system has been developed using three-phase (i.e., vapor–liquid, solution–solid, crystal) sorption technology to harvest low-temperature heat and store it with a much higher energy density than the geothermal fluid. The energy density of salt crystals is over six times higher than geothermal fluid, which makes long-distance transportation of salt crystals economically feasible. Salt crystals can be used to dehumidify air or provide space cooling in buildings, which alleviates peak demand on the electricity grid by offsetting electricity use for these end uses. This helps improve the grid’s stability and resilience. High-energy storage density, fast crystallization, and dissolution of salt crystals are all critical to the viability and performance of the MSTB system. Therefore, the design and operation of MSTB systems need to ensure effective generation and dissolution of salt crystals inside the MSTB. To achieve this target, this seedling project developed an experimental apparatus for characterizing the crystallization and dissolution processes. The energy density and potential latent cooling capacity of the MSTB are also evaluated based on lab test results. The crystallization results showed that the generated lithium chloride hydrate crystals are fluffy, the crystallization process lasts about 50 min, and the maximum crystal fraction (i.e., the ratio of crystal mass to the mass in the MSTB) can be up to 51.1% of the total mass in the MSTB at a solution flow rate of 1.58 g/s. The dissolution results show that the salt crystals in the MSTB can be fully dissolved within 15–28 min, based on different test conditions. Reducing solution flow rate and cooling water temperature can achieve increased energy storage density and crystal fraction. While the increase in the discharge rate (i.e., latent cooling capacity for dehumidifying air) is achieved by increasing flow rate and temperature of inlet diluted solution, as well as by using a pump for internal solution circulation, the discharge rate increases by 38%, from 0.95 kW to 1.31 kW. Compared with increasing the inlet solution flow rate, power consumption of salt solution transportation can be reduced by using a pump for internal solution circulation. The crystallization test results also showed that the maximum energy storage density is 981.8 kJ/kg, and the maximum discharge rate of the dissolution tests is ≤1.79 kW. Both are above the target values of 900 kJ/kg and 1.75 kW) for this project. The work reported here proves the feasibility and advancement of the MSTB system, which is helpful to the further study and improvement of the MSTB system.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Cold Climate Integrated Heat Pump with Energy Storage for Grid-Responsive Control

An air-source integrated heat pump (ASIHP) is a multifunctional unit, capable of space cooling, space heating, and water heating. We developed a packaged cold climate ASIHP, using a multi-stage compressor, capable of working down to -25°C, and providing 100% rated capacity down to -15°C with a heating COP > 2.2. An innovative system configuration and related controls were developed to solve charge balance in the integrated heat pump, smooth charge migration, and mode transition among multiple working modes. Extensive laboratory investigations for individual modes were performed to verify the performance. Its integrated heating capability provides speedy water heating to meet domestic hot water use and store heating energy in a phase change material ceiling/panel. The heating energy storage can be used to shift the load when the electricity price is high. Based on the performance data and a calibrated model from the laboratory testing, we conducted building energy simulations driven by a model predictive control, using EnergyPlus in one U.S. cold climate zone to investigate the grid-responsive control strategy and estimate the utility cost reduction potential.

Shen, Bo↗
DOE OSTI2021

Restoring Critical Loads In Resilient Distribution Systems using A Curriculum Learned Controller

In this paper, we propose a curriculum learned reinforcement learning (RL) controller to facilitate distribution system critical load restoration (CLR), leveraging RL's fast online response and its outstanding optimal sequential control capability. Like many grid control problems, CLR is complicated due to the large control action space and renewable uncertainty in a heavily constrained non-linear environment with strong intertemporal dependency. The nature of the problem oftentimes causes the RL policy to converge to a poor-performing local optimum if learned directly. To overcome this, we design a two-stage curriculum in which the RL agent will learn generation control and load restoration decision under different scenarios progressively. Via curriculum learning, the trained RL controller is expected to achieve a better control performance, with critical loads restored as rapidly and reliably as possible. Using the IEEE 13-bus test system, we illustrate the performance of the RL controller trained by the proposed curriculum-based method.

curriculum learning↗
DOE OSTI2021

Restoring Critical Loads in Resilient Distribution Systems Using a Curriculum Learned Controller: Preprint

In this paper, we propose a curriculum learned reinforcement learning (RL) controller to facilitate distribution system critical load restoration (CLR), leveraging RL's fast online response and its outstanding optimal sequential control capability. Like many grid control problems, CLR is complicated due to the large control action space and renewable uncertainty in a heavily constrained non-linear environment with strong intertemporal dependency. The nature of the problem oftentimes causes the RL policy to converge to a poor-performing local optimum if learned directly. To overcome this, we design a two-stage curriculum in which the RL agent will learn generation control and load restoration decision under different scenarios progressively. Via curriculum learning, the trained RL controller is expected to achieve a better control performance, with critical loads restored as rapidly and reliably as possible. Using the IEEE 13-bus test system, we illustrate the performance of the RL controller trained by the proposed curriculum-based method.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Volumetric Rendering on Wavelet-Based Adaptive Grid

Numerical modeling of physical phenomena frequently involves processes across a wide range of spatial and temporal scales. In the last two decades, the advancements in wavelet-based numerical methodologies to solve partial differential equations, combined with the unique properties of wavelet analysis to resolve localized structures of the solution on dynamically adaptive computational meshes, make it feasible to perform large-scale numerical simulations of a variety of physical systems on a dynamically adaptive computational mesh that changes both in space and time. Volumetric visualization of the solution is an essential part of scientific computing, yet the existing volumetric visualization techniques do not take full advantage of multi-resolution wavelet analysis and are not fully tailored for visualization of a compressed solution on the wavelet-based adaptive computational mesh. Our objective is to explore the alternatives for the visualization of time-dependent data on space-time varying adaptive mesh using volume rendering while capitalizing on the available sparse data representation. Two alternative formulations are explored. The first one is based on volumetric ray casting of multi-scale datasets in wavelet space. Rather than working with the wavelets at the finest possible resolution, a partial inverse wavelet transform is performed as a preprocessing step to obtain scaling functions on a uniform grid at a user-prescribed resolution. As a result, a solution in physical space is represented by a superposition of scaling functions on a coarse regular grid and wavelets on an adaptive mesh. An efficient and accurate ray casting algorithm is based just on these coarse scaling functions. Additional details are added during the ray tracing by taking an appropriate number of wavelets into account based on support overlap with the interpolation point, wavelet coefficient magnitude, and other characteristics, such as opacity accumulation (front to back ordering) and deviation from frontal viewing direction. The second approach is based on complementing of wavelet-based adaptive mesh to the traditional Adaptive Mesh Refinement (AMR) mesh. Both algorithms are illustrated and compared to the existing volume visualization software for Rayleigh-Benard thermal convection and electron density data sets in terms of rendering time and visual quality for different data compression of both wavelet-based and AMR adaptive meshes.

Vezolainen, Alexei V.↗
DOE OSTI2024

Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics

One of the prominent effects of space weather is the formation of rapid geomagnetic field variations on Earth's surface driven by the magnetosphere-ionosphere system. These geomagnetic disturbances (GMDs) cause geomagnetically induced currents to run through ground conducting systems. In particular, localized GMDs (LGMDs) can be high amplitude and can have an effect on scale sizes less than 100 km, making them hazardous to power grids and difficult to predict. In this study, we examine the ability of the Space Weather Modeling Framework (SWMF) to reproduce LGMDs in the 7 September 2017 event using both existing and new metrics to quantify the success of the model against observation. We show that the high-resolution SWMF can reproduce LGMDs driven by ionospheric sources, but struggles to reproduce LGMDs driven by substorm effects. We calculate the global maxima of the magnetic fluctuations to show instances when the SWMF captures LGMDs at the correct times but not the correct locations. To remedy these shortcomings we suggest model developments that will directly impact the ability of the SWMF to reproduce LGMDs, most importantly updating the ionospheric conductance calculation from empirical to physics-based.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Repurposing Inactive Oil and Gas Wells for Energy Storage: Maximizing the Potential Via Optimal Drivetrain Control: Preprint

In recent years, there has been a growing emphasis on utilizing energy storage to enhance grid resilience against disruptive events. While renewable energy supply continues to expand, gravity-based solutions like pumped hydro remain dominant in the commercial space. However, their geographical limitations constrain availability, scalability, and increase costs for co-locating solar and wind energy. An alternative approach proposes repurposing idle oil and gas wells located closer to existing grid infrastructure, offering a promising and cost-effective solution. This paper addresses the optimization and control of a regenerative drive system coupled to an interior permanent magnet synchronous machine in a 300-meter well with a 100-Newton weight. The study employs a dynamic MATLAB/Simulink model to simulate the operation of the electric drivetrain system during storage and discharge operations. The results demonstrate an initial round-trip efficiency of 85.9% for the electrical system alone and identify crucial factors for maximizing efficiency. The optimized operation and control of the electromechanical drivetrain system hold great potential for minimizing the levelized cost of storage while maximizing efficiency and revenue generation.

ENERGY STORAGE,ENGINEERING↗
DOE OSTI2022

Wind Energy Costs in Puerto Rico Through 2035

In 2019, Puerto Rico codified into law a 100% renewable electricity by 2050 target. Decision makers there need information about the costs and performance of wind energy technologies to incorporate into planning efforts for the future grid. After consulting with key technical stakeholders and accounting for unique conditions in Puerto Rico like increased hurricane risks, we customize NREL's Renewable Energy Potential model to calculate how capital expenditures, operational expenditures, net annual energy generation, and levelized cost of energy (LCOE) vary in time and space for wind energy projects around Puerto Rico. Wind turbines can add value by providing grid services through grid-forming inverters and complement solar energy generation with evening and nighttime electricity production, especially offshore. By 2035, we expect average costs of land-based and offshore wind energy in Puerto Rico to reach $\$$69/MWh (6.9 cents/kWh) and $\$$100/MWh (10 cents/kWh), respectively. While these projections do not include transmission system upgrade costs, they do suggest that low-cost, clean electricity is possible for Puerto Rico.

17 WIND ENERGY↗
DOE OSTI2021

A Sparse-Grid Probabilistic Scheme for Approximation of the Runaway Probability of Electrons in Fusion Tokamak Simulation

Runaway electrons (RE) generated during magnetic disruptions present a major threat to the safe operation of plasma nuclear fusion reactors. A critical aspect of understanding RE dynamics is to calculate the runaway probability, i.e., the probability that an electron in the phase space will runaway on, or before, a prescribed time. Such probability can be obtained by solving the adjoint equation of the underlying Fokker-Planck equation that controls the electron dynamics. In this effort, we present a sparse-grid probabilistic scheme for computing the runaway probability. The key ingredient of our approach is to represent the solution of the adjoint equation as a conditional expectation, such that discretizing the differential operator reduces to the approximation of a set of integrals. Adaptive sparse grid interpolation is utilized to approximate the map from the phase space to the runaway probability. The main novelties of this effort are the integration of the sparse-grid method into the probabilistic numerical scheme for computing escape probability, and the application of the proposed method in computing RE probabilities. Two numerical examples are given to illustrate that the proposed method can achieve O(Δt) convergence, and that the local anisotropic adaptive refinement strategy (M. Stoyanov, Adaptive sparse grid construction in a context of local anisotropy and multiple hierarchical parents. In: Sparse Grids and Applications-Miami 2016, Springer, Berlin, 2018, pp. 175–199) can effectively handle the sharp transition layer between the runaway and non-runaway regions.

Yang, Minglei↗
DOE OSTI2022

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Impact analysis of heating electrification in US buildings with geothermal heat pumps

Few studies have investigated the impacts of large-scale deployment of geothermal heat pumps (GHPs, also called ground source heat pumps) on the electric grid. GHPs utilize the ground as a heat source to warm buildings more efficiently than other space-heating systems. The coupling with the ground offers seasonal thermal storage so that GHPs can also cool buildings in summer more efficiently than other space-cooling systems. This study simulated the performance of GHP systems for various commercial and residential buildings in 15 climate zones in the United States. Combined with the latest End-Use Load Profiles of the US building stock and grid modeling, this study aims to assess the impacts of a national deployment of GHP systems on the US electric grid in terms of energy consumption, emissions, and operational resilience. The preliminary results show that the GHP deployment can save 429 billion kWh of electricity (a 19% reduction from baseline) and reduce carbon emissions by 496 million tons per year (a 31 % reduction from baseline). A geographical view of the results indicates that retrofitting existing HVAC systems with new GHP systems can lead to further reductions in annual electricity consumption and peak electricity demand in the southern regions of the United States than in other parts of the country. On the other hand, GHP retrofits result in higher percentages of site energy savings and carbon emission reduction in the north (cold climates) than in the south (warm climates).

Liu, Xiaobing↗
DOE OSTI2021

Development and validation of a second-order thermal network model for residential buildings

Heating, Ventilation, and Air Conditioning (HVAC) systems can maintain the space air temperature of residential buildings, either directly by heating/cooling the air, or indirectly via heat transfer to and from the building structure that acts as a thermal mass. Hence, HVAC systems can help achieve load shifting, peak load reduction, and/or energy cost saving, thus enabling grid-interactive HVAC operation. A home thermal model that can accurately reflect the dynamics of the space air and interior wall surface temperatures, is therefore valuable. This paper develops such a model using the standard RC (resistance-capacitance) approach. The model contains a virtual envelope node and an internal space node and is thus second-order. A hybrid parameter identification scheme, made up of the least-squares and optimal search methods, is also developed. The proposed model and scheme were validated using data collected from a test home. It was found that a modest amount of training data was sufficient to yield reliable parameter estimates and accurate prediction. It was also found that when making 24-hour-ahead prediction of the space air temperature, both methods had comparable performances when the training data began in a transition season. However, when they began in an HVAC season, the optimal search method performed better. Furthermore, the least-squares method is recommended during a transition season due to its lower computational burden, while the optimal search method is recommended during an HVAC season due to its better estimation performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Simple Space Vector Modulation of High-Frequency AC Linked Three-Phase-to-Single-Phase/DC Converter

High frequency (HF) ac linked converter functions well to interface between buses with different voltage types. Using a three-phase-to-single-phase matrix converter, a single-phase cyclo converter, and a HF transformer, we can compose a HF ac linked converter to interface between three-phase ac grids and single-phase ac or dc distributed generations (DGs). In this paper, a novel space vector modulation (SVM) method is proposed for such kind of converter. The proposed SVM method converts the mains three-phase ac voltage to a HF ac voltage at the primary side of transformer, and then unfolds the HF ac voltage to a single-phase ac or dc voltage at the secondary side of transformer. Without need to compute separate switching signals for the primary and secondary converters and then coordinate their operations elaborately, the proposed SVM method can be easily implemented by incorporating the HF chopping into the SVM of traditional indirect matrix converter. Therefore, no complicated duty cycle computation or elaborate switching states combination is required. Simulation and hardware-in-loop implementation demonstrate the validity of the proposed method.

42 ENGINEERING↗
DOE OSTI2020

Phase behavior of continuous-space systems: A supervised machine learning approach

The phase behavior of complex fluids is a challenging problem for molecular simulations. Supervised machine learning (ML) methods have shown potential for identifying the phase boundaries of lattice models. In this work, we extend these ML methods to continuous-space systems. We propose a convolutional neural network model that utilizes grid-interpolated coordinates of molecules as input data of ML and optimizes the search for phase transitions with different filter sizes. We test the method for the phase diagram of two off-lattice models, namely, the Widom–Rowlinson model and a symmetric freely jointed polymer blend, for which results are available from standard molecular simulations techniques. The ML results show good agreement with results of previous simulation studies with the added advantage that there is no critical slowing down. We find that understanding intermediate structures near a phase transition and including them in the training set is important to obtain the phase boundary near the critical point. The method is quite general and easy to implement and could find wide application to study the phase behavior of complex fluids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗