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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A methodology for selection of solid desiccants in energy recovery ventilators

Controlling indoor humidity levels is essential for maintaining acceptable indoor air quality in buildings. The use of energy recovery ventilators (ERVs) is an energy-efficient way to regulate indoor air humidity. Fixed-bed regenerators and rotary wheels are widely used ERVs because of their high sensible and latent effectiveness. These ERVs are made of desiccant-coated substrates, which enable them to transfer moisture between the supply and exhaust air streams. However, the moisture transfer ability of ERVs depends on the physiochemical and sorption properties of desiccants. Extensive, full-scale experiments are required to determine the best desiccant material for these systems. This paper presents a simplified method of selecting suitable desiccant materials for ERVs. The methodology involves important characterization methods, literature correlations for performance prediction, and cost-effective testing methods prior to full-scale testing, and full-scale test methods are discussed in detail. Furthermore, the performance of a few newly derived materials is evaluated and compared with that of conventional desiccants such as silica gel and molecular sieves. The highest latent effectiveness was obtained for composite of super absorbent polymer (SAP) with potassium formate (SAP-HCO2K-50 %), all-polymer porous solid desiccant (APPSD) and metal organic framework (MOF)–MIL–101 (Cr), followed by activated carbon fibre felt (ACFF) Silica sol-LiCl30, SAP, silica gel, MOF–303, and molecular sieve. Researchers and manufacturers would benefit from the proposed methodology and presented data in developing new desiccant materials for ERV applications.

Energy recovery↗
DOE OSTI2020

Detect and correct bias in multi-site neuroimaging datasets

The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The most obvious way to increase sample size is by pooling scans from independent studies. However, simple pooling is often ill-advised as selection, measurement, and confounding biases may creep in and yield spurious correlations. In this work, we combine 35,320 magnetic resonance images of the brain from 17 studies to examine bias in neuroimaging. In the first experiment, Name That Dataset, we provide empirical evidence for the presence of bias by showing that scans can be correctly assigned to their respective dataset with 71.5% accuracy. Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies. In practice, we neither know all potential confounders nor do we have data on them. Hence, we model confounders as unknown, latent variables. Kolmogorov complexity is then used to decide whether the confounded or the causal model provides the simplest factorization of the graphical model. Finally, we present methods for dataset harmonization and study their ability to remove bias in imaging features. In particular, we propose an extension of the recently introduced ComBat algorithm to control for global variation across image features, inspired by adjusting for unknown population stratification in genetics. Overall, our results demonstrate that harmonization can reduce dataset-specific information in image features. Further, confounding bias can be reduced and even turned into a causal relationship. However, harmonization also requires caution as it can easily remove relevant subject-specific information. Code is available at https://github.com/ai-med/Dataset-Bias.

42 ENGINEERING↗
DOE OSTI2025

Cloud radiative effects significantly increase wintertime atmospheric blocking in the Euro-Atlantic sector

Reliable simulation, prediction, and complete theoretical understanding of atmospheric blocking remain challenging despite its significant socio-economic impacts. Generations of climate models have notoriously underestimated blocking frequency, particularly over the Euro-Atlantic sector. Identifying factors controlling blocking frequency and dynamics is therefore essential for improving its simulation. Here, using a cloud-locking experiment, we show that cloud radiative effects (CREs) significantly increase the frequency of Euro-Atlantic blocking. CREs enhance upstream diabatic source of wave activity, both directly through longwave heating and indirectly through their feedback on latent heating, with the latter playing the dominant role. The resulting increase in the upstream diabatic source feeds into local wave activity downstream and promotes blocking formation. Qualitatively similar results are shown by multi-model experiments with radiatively inactive clouds to longwave radiation, albeit with a larger impact from mean-state changes. The results underscore the necessity of accurately representing cloud-radiation interactions in weather and climate models for improved prediction of blocking events.

Atmospheric dynamics↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Review of liquid desiccant air dehumidification systems coupled with heat pump: System configurations, component design, and performance

Vapor Compression Systems (VCS) are the most common air conditioning technology. VCS cool the air to its dew point temperature (overcooling) to remove water vapor in the air through condensation and then reheats the air back to the comfort temperature for direct use. The VCS process is inefficient due to overcooling and reheating. Liquid Desiccant Dehumidification (LDD) is a potentially energy-efficient air conditioning. LDD removes water vapor in the process air using liquid desiccant’s high-water affinity. It hybrids with sensible cooling to control temperature and humidity separately. The LD in the LDD becomes weak after dehumidification. The LDD needs additional heating to regenerate the weak Liquid Desiccant (LD) to a high concentration for dehumidification. Earlier versions of the LDD systems use highly concentrated liquid desiccant (large water removal capability) to dehumidify the air by only dealing with latent load. It leads to highly elevated temperatures above 60 °C of heat sources (combustion or electric resistance-based heating) for regeneration. The energy needed for the elevated temperature heat resource significantly reduces or demolishes the benefit of LDD systems. In the recent two decades, researchers have investigated a new configured LDD system that couples an LDD with a heat pump at both dehumidification and regeneration sides for better efficiency. The heat pump provides cooling (from the evaporator) for both dehumidification and sensible cooling and simultaneous heating from the condenser for regeneration. The highly integrated system (HP-LDD) with improved efficiency enables the LDD to operate at lower concentrations and temperatures in dehumidification and regeneration. This paper depicts the working principle behind HP-LDD and its heating and cooling requirements. It reviews the comparison between the HP-LDD systems and the conventional LDD systems regarding system configurations, component design, energy efficiency, and dehumidification performance characteristics. The main findings from the review include the preferred use of packed bed over membrane-based dehumidifiers, the use of internally cooled dehumidifiers enabled by the HP cooling capacity, the high dispersion of HP operation conditions, and the dependence of dehumidification performance on various dehumidifiers. Finally, an outlook for future research on HP-LDD strategies is presented based on the reviewed works and their limitations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Electrochemical carbon deposition from CO 2 in molten carbonates: substrate-dependent growth

Electrochemical carbon deposition from molten carbonate salts using inert electrodes and no additional catalyst is systematically examined. At sufficiently cathodic potentials, carbon formation occurs within 30 min and transitions from substrate-dependent nucleation to carbon-on-carbon growth. Although deposits formed on either mild-steel or graphite cathodes exhibit comparable thicknesses and salt contents, their macrostructures differ, reflecting differences in early-stage growth rates and surface chemistry. Characterization by ICP-OES, Raman spectroscopy, SEM/EDS, synchrotron powder X-ray diffraction, and C K-edge X-ray absorption near edge structure reveals that the as-deposited carbon is predominantly disordered sp 2 -bonded material. However, post-treatment reveals latent turbostratic ordering in select samples, indicating that short-range order is established during deposition but is obscured by an entrained electrolyte and kinetically formed amorphous phases. No anode-derived contamination is detected, and carbon grown on graphite cathodes shows minimal metallic impurities. These results clarify the role of cathode surface chemistry and provide pathways for controlling electrode-derived impurity incorporation and carbon structure in catalyst-free molten carbonate systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.

Skomski, Elliott↗
DOE OSTI2022

Learning in continuous action space for developing high dimensional potential energy models

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a “window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

End-Use Savings Shapes Measure Documentation: Variable Refrigerant Flow with Heat Recovery and Dedicated Outdoor Air System

Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy's ResStock™ and ComStock™ models over the past 3 years, the objective of this work is to produce national data sets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to energy efficiency variables that can be applied to buildings during modeling. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. This documentation focuses on a single heating, ventilation, and air-conditioning (HVAC) end-use savings shape measure - a variable refrigerant flow with heat recovery (VRF HR) heating and cooling system coupled with a dedicated outdoor air system (DOAS) for ventilation. This measure replaces existing multi-zone variable air volume (VAV) systems or single-zone rooftop units (RTU) with a VRF HR system coupled with a DOAS that includes an energy/heat recovery ventilator (E/HRV). The measure covers 53% of exisiting building stock's floor area and is not applicable to HVAC system types using district heating or cooling or buildings/spaces that include high-ventilation spaces such as kitchens where the amount of exhaust air is large. A DOAS with E/HRV is used to provide required outdoor ventilation air to spaces since ventilation air is generally not supplied by a VRF HR system. An exhaust air energy recovery ventilator (ERV ) with sensible and latent heat exchange is added to humid climate zones while a heat recovery ventilator (HRV ) with sensible only exchange is added to drier climate zones. The ERV is modeled as a fixed membrane plate counterflow heat exchanger, while the HRV is modeled as a sensible-only fixed aluminum plate counterflow heat exchanger. Both systems include a bypass (for temperature control and economizer lockout) and minimum exhaust temperature control for frost prevention.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Development of a two-zone zero-dimensional mixing model for actively fueled prechamber engines

In this numerical work, a zero-dimensional, two-zone mixing model was developed and validated to capture the mixture preparation process of an actively fueled prechamber engine. The model was developed for ethanol-gasoline fuel blends but is applicable to various fuels of interest relevant to prechamber applications. The model was exercised to demonstrate the effects of boundary conditions, fuel composition, and residual content on the mixture stoichiometry predictions. The 0D model showed reasonable agreement in prechamber equivalence ratio predictions with higher fidelity computational fluid dynamics modeling, but the accuracy of model was constrained to the governing assumptions of the injection process. The modeling assumptions surrounding the non-dimensional injection of mass into the prechamber were scrutinized to identify key areas of model improvement. The results indicate heat transfer effects play a crucial role in the evaporation of the fuel within the confined auxiliary chamber, especially when considering fuels with high latent heat of vaporization. Additionally, the presence of fuel films that collect on the prechamber surfaces complicated the modeled evaporative cooling loses as the modeling suggests the films evaporate primarily by means of conduction from the boundary. Furthermore, the developed model serves as a simplified framework to generate calibration maps of prechamber fueling or run real time as an open loop control strategy such that desired prechamber mixture proportions are effectively targeted.

Zero-dimensional↗
DOE OSTI2026

Effect of NO on DME-Methanol HCCI Combustion Using a Reduced Chemical Kinetics Mechanism

Methanol is an attractive fuel for the maritime sector due to its wide availability. Its direct use as a fuel, however, is accompanied by challenges such as high latent heat of vaporization and low cetane number. A potential solution to overcome the ignition properties of methanol could be through on-board generation of dimethyl ether (DME) via catalytic dehydration of methanol. The resulting mixture from dehydration can be mixed in with the intake air to generate a homogenous charge compression ignition (HCCI) preburn for subsequent direct injection (DI) mixing controlled compression ignition (MCCI) of neat methanol. Within that context, complementary experimental work found that the influence of combustion residuals on the heat release rate (HRR) was significant, specifically for residual NO. This finding motivated the present computational and kinetic evaluation of the effects of NO on the low (LTHR) and high (HTHR) temperature heat release rates. The strong influence of small quantities of NO on the combustion process of a DME/methanol/H2O mixture (low catalyst or reactor efficiency) necessitated a kinetics-based investigation into this phenomenon. A mechanism sourced from the existing literature with NO had 172 species and 1375 reactions, making it computationally expensive for use. Hence, a mechanism reduction effort was implemented, and a rate constant (k) tuning effort based on sensitivity analysis was needed to validate experimental results using a zero-dimensional engine model in Cantera. The reduced mechanism was able to successfully capture the negligible influence of NO addition on DME HCCI combustion, whereas an advancement in LTHR and HTHR for a DME/methanol/H2O mixture was kinetically confirmed. Reaction pathway analysis showed that addition of NO chemically counteracted the OH sink created by alcohols like methanol, increasing the effectiveness of DME ignition.

Tyrewala, Daanish [ORNL] (ORCID:0000000208599324)↗
DOE OSTI2020

Thermal Image Processing for Feature Extraction from Encapsulated Phase Change Materials

Encapsulated inorganic particles with high melting points (>300 °C) are desired as high-temperature Phase Change Materials (PCMs) for next-generation Latent Heat Thermal Energy Storage (LHTES) systems. One of the many challenges during the development of PCMs is to achieve a high throughput that in turn depends on accurately modeling the relation between process parameters and geometric & thermal properties of the PCMs particle. During the production of the PCMs, a high-speed infrared camera is used to acquire images of the encapsulated material under controlled illumination conditions. This research article focuses on the development of image processing techniques for both geometric and thermal feature extraction during the development of the PCMs. A user-friendly GUI has been designed in MATLAB and preliminary experimental results have demonstrated that the method is fast, accurate and reliable for a high throughput production. The extracted features will be used to develop Machine Learning (ML) models to predict the geometric and thermal properties of the PCM based on the process parameter settings. The ML model will accelerate the search for the optimized process settings to boost the throughput of the production.

25 ENERGY STORAGE↗
DOE OSTI2022

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize ( Zea mays ) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest.

Tross, Michael C.↗
DOE OSTI2020

A Performance analysis of the Claridge-Culp-Liu dehumidification process: A novel approach for drying moist air based on membrane separation, vacuum compression and sub-atmospheric condensation

This article covers a basic model for analyzing the performance of the Claridge-Culp-Liu dehumidification process. The fundamental process efficiency limit for dehumidification is close to COP Carnot , but for the eight dehumidification cases examined, the limiting or ideal energy use required is 26% to 56% that of a Carnot condensing system as shown in an earlier paper. The model presented in this paper is used to show the membrane system performance reduction caused by finite membrane area, finite water vapor permeance, non-zero air permeance, non-zero system air pressure drop, non-ideal compressors, vacuum pumps, and condensers. The performance of a “conservative” membrane system based on the use of existing components is computed for eight specific conditions along with that of a “target” system that assumes expected component performance after additional future component development. The “conservative” membrane system would use 36% to 66% as much energy as a system with a COP=7 chiller to produce the same dehumidification for the eight cases examined while the “target” system would use 15% to 40% the energy of a system with a COP=7 chiller. In addition to the significant energy reduction over conventional technology, the membrane system offers the advantages of: 1) no HFC refrigerant use; 2) direct isothermal control over humidity ratio setpoint; 3) maximum capacity occurs at design conditions; and 4) system generates pure water extracted from air as a by-product.

42 ENGINEERING↗
DOE OSTI2023

Efficient Air Dehumidification Can Save 15%-50% of Cooling Energy in Commercial Buildings

Current approach to dehumidification in commercial buildings with chilled/hot water AHU systems is to overcool return + supply air to dewpoint, and then reheat before supply to zone. This is energy inefficient, with reheat often being gas-fired. To reduce energy consumption, building operators will often operate at little-to-no outside air, sacrificing IAQ. Decarbonizing commercial buildings requires solutions to reduce and fully electrify dehumidification energy consumption. The Altaire ADAPT and Conservant HEDS systems aim to solve this problem by decoupling humidity control from cooling reduces energy consumption while enabling improved IAQ management.

commercial buildings↗
DOE OSTI2021

A first principles framework to predict the transient performance of latent heat thermal energy storage

Thermal energy storage (TES) is increasingly recognized as an essential component of efficient Combined Heat and Power (CHP), Concentrated Solar Power (CSP), Heating Ventilation and Air Conditioning (HVAC), and refrigeration as it reduces peak demand while helping to manage intermittent availability of energy (e.g., from solar or wind). Latent Heat Thermal Energy Storage (LHTES) is a viable option because of its high energy storage density. Parametric analysis of LHTES in terms of dimensionless numbers is highly desired as a tool to model LHTES systems. One approach is to develop a model equation so as to minimize the error between the model and data obtained from experiments or simulations. While this approach can produce an accurate correlation applicable within the range of data used for its creation, it does not provide physical understanding of the rate-limiting process controlling the transient behavior of the device. In this paper we present an alternative approach whereby the potential rate-limiting processes are identified from first principles and then the key process is determined as a function of time as a LHTES device is charged. For example, in a simple geometry, the melt-fraction can be expected to vary linearly in time if the heat transfer rate is limited by natural convection of the phase changing material and we show it scales with the PCM Grashof number as $Gr^1_p$ and PCM Prandtl number as $Pr_p^{(1/3)}$. On the other hand, if surface area of solid PCM limits the heat transfer rate, the melt fraction increases asymptotically to reach full melting. The existence of these linear and asymptotic regions and the $Gr^1_pP r^{1/3}_p$ shape of the melt fraction curve is verified using our database of 64 simulations. Of practical importance in designing LHTES devices is the melt fraction at which the heat transfer rate ceases to be limited by convection, after which the heat storage rate deteriorates. For our geometry, this is found to be about 90%. This test case of our methodology shows the value of our approach, that predicting heat storage rate based on the rate-limiting physical phenomenon as a function of time is an effective approach to modeling LHTES devices.

25 ENERGY STORAGE↗
DOE OSTI2023

Bio-Based Phase Change Materials (PCMs) for Thermal Energy Storage (Final Scientific/Technical Report)

The goal of this project is to develop a novel bio-based phase change material (PCM) derived from squid ring teeth (SRT) proteins, and design a prototype heat exchanger based on these bio-based PCMs for energy storage and dynamic heat exchange for building thermal energy storage systems. The PCMs, derived from SRT proteins, will demonstrate room temperature energy storage capacities and switchable thermal conductivities that redefine the current state-of-the-art (SOA) for building thermal energy storage. Further, in a single material and with a single manufacturing processes, our proposed thermal battery will be developed with “on demand” thermal conductivity for rapid charging/discharging but superior insulation during energy storage periods. More specifically, our bio-based PCMs have demonstrated: 1) the potential for increased energy storage capacity (i.e., greater than paraffines at room temperature); 2) tunability in thermal conductivity with the largest thermal conductivity switching ratios for an intrinsic material reported to date, and 3) a 100% recyclable and biodegradable PCM with low volatility and toxicity and scalability in its manufacture for both residential and commercial applications. The already established ability for roll-to-roll processing of these non-toxic, non-flammable SRT composites along with their carbon-neutral manufacturing processes that has been pioneered by our team make this solution a disruptive and impactful technology to redefine the current SOA of energy storage technologies. The outcomes of this effort have been: 1) the identification of new bio-based PCM that establishes a new SOA for thermal conductivity and energy storage density at room temperature and 2) the development of a new experimental technique to measure the energy storage density via latent heat measurements during melting of thin films. This new technique can be applied to thin films (as thin as < 1 micrometer), and represents a novel approach for thin film energy storage density measurements. Dynamic thermal energy storage is the missing technology that will enable a grid- interactive efficient building (GEB). For a “smart” building capable of connecting with the power grid, the media must be inexpensive and possess thermal switching capabilities to control the time-dependent heat flow into (and out of) the PCM on demand. Unique to the material system in this program, programmable SRT-based PCM show improvements as compared to traditionally used PCMs, and thus its technical performance will represent a leap forward for the future GEB concept.

25 ENERGY STORAGE↗