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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluating spectral cloud effective radius retrievals from the Enhanced MODIS Airborne Simulator (eMAS) during ORACLES

Satellite remote sensing retrievals of cloud effective radius (CER) are widely used for studies of aerosol–cloud interactions. Such retrievals, however, rely on forward radiative transfer (RT) calculations using simplified assumptions that can lead to retrieval errors when the real atmosphere deviates from the forward model. Here, coincident airborne remote sensing and in situ observations obtained during NASA's ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign are used to evaluate retrievals of CER for marine boundary layer stratocumulus clouds and to explore impacts of forward RT model assumptions and other confounding factors. Specifically, spectral CER retrievals from the Enhanced MODIS Airborne Simulator (eMAS) and the Research Scanning Polarimeter (RSP) are compared with polarimetric retrievals from RSP and with CER derived from droplet size distributions (DSDs) observed by the Phase Doppler Interferometer (PDI) and a combination of the Cloud and Aerosol Spectrometer (CAS) and the Two-Dimensional Stereo Probe (2D-S). The sensitivities of the eMAS and RSP spectral retrievals to assumptions about the DSD effective variance (CEV) and liquid water complex index of refraction are explored. CER and CEV inferred from eMAS spectral reflectance observations of the backscatter glory provide additional context for the spectral CER retrievals. The spectral and polarimetric CER retrieval agreement is case dependent, and updating the retrieval RT assumptions, including using RSP polarimetric CEV retrievals as a constraint, yields mixed results that are tied to differing sensitivities to vertical heterogeneity. Moreover, the in situ cloud probes, often used as the benchmark for remote sensing CER retrieval assessments, themselves do not agree, with PDI DSDs yielding CER values 1.3–1.6 µm larger than CAS and with CEV roughly 50 %–60 % smaller than CAS. Implications for the interpretation of spectral and polarimetric CER retrievals and their agreement are discussed.

Meyer, Kerry [NASA Goddard Space Flight Center (GS↗
DOE OSTI2026

Effective medium approximation for the refractive index of stratified metal oxide composites synthesized by atomic layer deposition

Atomic layer deposition (ALD) is a unique method for synthesizing conformal layers with precise composition. It is especially useful for the synthesis of mixed metal oxides for functional materials. One application of interest is the use of ALD for tailoring the refractive index of coatings. In homogeneously distributed composites of metal oxides, the refractive properties can be approximated as the average of the indices of the components. This is known as an effective medium approximation (EMA) and can be used to design the macroscopic properties of composites. ALD produces layered, anisotropic films, so the validity of an EMA in describing these films is not clear. Here, we use optical simulations and experimental characterization of stratified composites of TiO 2 and Al 2 O 3 to study the application of an EMA to the transmission and reflection behavior of ALD-prepared mixed metal oxide thin films. We found that when the characteristic layer thickness is smaller than roughly 10 nm, the optical spectra of theoretical and experimental ALD films match the equivalent theoretical spectra for a material with a refractive index calculated from a simple, compositionally weighted EMA. Hence, this EMA could describe the transmission and reflectance spectra in ALD-derived TiO2–Al 2 O 3 nanolaminates when the films were sufficiently stratified even without thorough characterization of the real layers in the films. As a result, we demonstrated that ALD can be used to prepare effectively homogenous mixed metal oxide films with a predictable, tailorable refractive index of any value between those of the TiO 2 and Al 2 O 3 component materials.

42 ENGINEERING↗
DOE OSTI2021

Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

Abstract The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Conduction Heat Transfer through Solid in Porous Materials: A Comparative Study by Finite-Element Simulations and Effective Medium Approximations

The development of insulation materials with low effective thermal conductivity is essential for energy savings in various applications, including buildings, food services, pipe insulation, and refrigeration. Such materials can be developed by using micro- or nanoporous structures, as well as low-thermal conductivity gases and/or reduced pressure. A variety of effective medium approximation (EMA) models have been developed to study thermal transport through solid in porous structures. However, in many cases, the impacts of porosity on solid conduction are not well predicted by EMA models because of the assumptions made for simplification. Furthermore, the results can vary by a factor of up to 1.5, depending on the morphology of the pores. Hence, proper guidance is needed to choose the appropriate EMA model for a given morphology. This work presents a finite element method study using COMSOL Multiphysics software for various geometries, including hollow cubes, overlapping hollow spheres (normally stacked and tightly packed), and Voronoi structures, and compares the results to those obtained from various EMA models. Simulation results for the hollow cubes and Voronoi structures matched well with the Maxwell-Eucken and Russell model. The results for hollow spheres closely matched the Bauer model and the Glicksman model with fitting parameters. This work provides guidance on analyzing and designing insulation materials for energy savings in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Full circle mechanical dynamic characterization including experimental modal analysis and finite element analysis

During operation, it was observed that a specific mechanical system experienced undesirable vibration and it became necessary to understand and mitigate this phenomenon. This document investigates the tools, methodology, and results of the dynamic characterization of the system. The characterization makes use of the experimental modal analysis (EMA) methods of single input multiple output (SIMO) and single input single output (SISO). The validity of the theory of reciprocity is confirmed to minimize measurement error, cost, and time of repeat testing. Finite element analysis (FEA) is used in choosing transducer and modal impact locations to adequately characterize the system. Single degree of freedom (SDOF) and multiple degree of freedom (MDOF) curve fitting is used to fully characterize the system’s mode shapes and natural frequencies. The EMA characterization results are used to modify and validate the FEA model so that FEA can be used to model potential structural modifications to the system to mitigate the undesirable vibration. Structural modifications are chosen, implemented, and their effectiveness is quantified using EMA. Finally, a qualitative evaluation of the methodology of FEA validation by EMA and tuning of the model to match the experimental results is discussed.

42 ENGINEERING↗
DOE OSTI2024

Proactive and Reactive Thermal Comfort Behaviors

The expansion of renewable electricity generation, growing demands due to electrification, greater prevalence of working from home, and increasing frequency and severity of extreme weather events, will place new demands on the electric supply and distribution grid. Broader adoption of demand response programs (DRPs) for the residential sector may help meet these challenges; however, experience shows that occupant overrides in DRPs compromises their effectiveness. There is a lack of formal understanding of how discomfort, routines, and other motivations affect DRP overrides and other related human building interactions (HBI). This paper reports preliminary findings from a study of 20 households in Colorado and Massachusetts, US over three months. Participants responded to ecological momentary assessments (EMA) triggered by thermostat interactions and at random times throughout the day. EMAs included Likert-scale questions of thermal preference, preference intensity, and changes to 7 different activity types that could affect thermal comfort, and an opened ended question about motivations of such actions. Twelve tags were developed to categorize motivation responses and analyzed statistically to identify associations between motivations, preferences, and HBI actions. Reactions to changes in the thermal environment were the most frequently observed motivation (118 of 220 responses). On the other hand, almost half (47%) responses were at least partially motivated by non-thermal factors, suggesting limited utility for occupant behavior models founded solely on thermal comfort. Changes in activity level and clothing were less likely to be reported when EMAs were triggered by thermostat interactions, while fan interactions were more likely. Windows, shades, and portable heater interactions had no significant dependence on how the EMA was triggered. These results suggest that better understanding of motivations for HBI may improve effectiveness of demand response programs.

Pathak, Maharshi↗
DOE OSTI2023

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15

Abstract Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter‐chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per‐target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per‐target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi‐model method (PSS) with the complementary single‐model method (ICPS) is a promising approach to EMA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Global harmonization of immediate‐release solid oral drug product bioequivalence recommendations and the impact on generic drug development

Immediate-release (IR) solid oral drug products constitute a significant portion of approved drug products and products under development. Bioequivalence (BE) assessment for these oral products is important for establishing therapeutic equivalence for generic products to their respective comparator products. In December 2022, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) published the first new draft guideline on BE for IR solid oral dosage forms (M13A). To support the development of ICH M13A, we comprehensively reviewed the landscape of oral IR products approved by the U.S. Food and Drug Administration (FDA) and compared BE recommendations for these products in the current U.S. FDA and European Medicines Agency (EMA) BE guidances. We utilized databases including Drugs@FDA, Orange Book, and product-specific guidances (PSGs) published on the U.S. FDA and EMA websites to collect information. Oral IR products account for 46% of all FDA-approved new drug applications currently listed in Orange Book with 82.5% solids, 0.9% semi-solids, and 16.6% liquids. For all published U.S. FDA PSGs for solid oral IR products, in vivo BE studies with pharmacokinetic (PK) endpoints account for 88% of BE approaches recommended. Of these PK BE studies, 86.5% recommended fasting and fed BE studies, while only 15.9% EMA PSGs recommended both fasting and fed BE studies. This review helps clarify the scope of U.S. solid oral IR products impacted by the new ICH M13A draft guideline and demonstrates how recommendations in draft ICH M13A could significantly harmonize BE recommendations for IR oral products to facilitate global drug development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Computational design of isotropic and anisotropic ultralow thermal conductivity polymer foams

Current state-of-the-art commercial polymer thermal insulation foam exhibits a thermal conductivity of 24 mW·m -1 ·K -1 (equivalently thermal resistivity of R-6/in.), similar to that of static air. To further optimize building energy efficiency, achieving even lower thermal conductivity is needed, which is, however, highly challenging. This paper presents computational evidence that demonstrates the feasibility of achieving an ultra-low thermal conductivity of less than 14.4 mW·m -1 ·K -1 (equivalently R-10/in.) using isotropic and anisotropic foam cell designs. For the isotropic design, we have identified analytical effective medium approximation (EMA) models within the accuracy of ±5% as finite element analysis (FEA) in predicting the effective thermal conductivity of foams with various porosities and filler gases. For the anisotropic design, we have developed and validated new EMA models against FEA in predicting the effective thermal conductivity of general anisotropic cuboids and Voronoi foams. For both isotropic and anisotropic designs, the design spaces for 18, 16, and 14.4 mW·m -1 ·K -1 (equivalently R-8, R-9 and R-10/in.) using various filler gases are obtained. It is found that polymer foams can be improved to achieve ultralow thermal conductivity by reducing CO 2 concentration, reducing radiation, increasing porosity, and using anisotropic pore geometry. In conclusion, these findings contribute to the development of highly efficient thermal insulation materials, enhancing building energy efficiency and promoting sustainable construction practices.

42 ENGINEERING↗
DOE OSTI2021

Emerging Energy Market Analysis Initiative, Methodological Framework

Planning and operations of the electric power sector are undergoing radical changes. Climate change mitigation efforts have forced rapid changes to the technology mix. Technologies like wind and solar have experienced rapid growth, while investment in fossil sources has peaked or is declining. These foundational changes are forcing changes to energy systems. Demand-side adoption of electrified technologies, including electric vehicles, is changing load profiles and opening up new avenues for consumer participation in the power systems. The implications of an evolving power system pertain to more than environmental and technical dimensions. Changes to the generation mix and its consequent upstream and downstream impacts such as fuel production have significant and highly concentrated consequences on economies and employment. Shifts towards distributed (or decentralized) generating assets offer the potential to reshape economic and employment opportunities associated with the energy sector across space and socioeconomic groups. The Emerging Energy Market Analysis (EMA) initiative aims to identify sustainable, regionally acceptable, and high-value energy solutions that are secure and equitable. Unlike short-term, least-cost choices that can narrowly account for traditional options, EMA’s focus on emerging energy markets recognizes that new or adapted practices and technologies can alter the frontier of solutions and advance a community’s social, economic, and natural pathways. Such change requires a more comprehensive analysis of societal input, resources, capabilities, and infrastructure. These considerations lay the foundation for community decision-making models that are responsive to community values as well as the history and drivers. The result is a community-based decision and engagement model that will be valuable to decisionmakers and developers of advanced and emerging energy solutions, seeking a social license to operate prior to project development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Biological toxicity and environmental hazards associated with PLGA nanoparticles

There have been tremendous advances in nanotechnology. More recently, the use of nanoparticles has expanded to applications including materials, packaging, energy, and medical uses such as drug delivery, diagnostics, and therapeutics. The American Society for Testing and Materials (ASTM) and the International Organization for Standardization (ISO) define nanoparticles as particles with at least one dimension measuring between 1 and 100 nm (2), but this view is generally too limited, and recommendations have been made to consider all materials with a dimension measuring under 1,000 nm as nanoparticles. The biomedical uses of nanoparticles are particularly promising because of their ability to reach and target various sites and organs. However, some nanoparticles can be composed of toxic materials or are limited by issues of biodistribution and bioaccumulation, which have hampered their use in biomedicine. The copolymer poly lactic-co-glycolic acid (PLGA) has gained use in biomedical applications as a delivery system because it is considered biocompatible and can be formulated with controlled degradation in physiological environments. PLGA has a history in biomedicine that dates to the 1970s when biodegradable sutures were developed using PLGA. In addition, the United States (US) Food and Drug Administration (FDA) and the European Medicine Agency (EMA) have approved various PLGA particle formulations as therapeutic delivery vehicles. Interest in biocompatible materials to deliver a range of therapeutic drugs, proteins, nucleic acids, and other molecules has risen recently. In addition to drug delivery, sustained drug release can be achieved by tuning the physical properties of PLGA, such as the molecular weight, ratio of lactic to glycolic acid, drug concentration, stabilizing molecules, and particle size. Given the rise of nanoparticles, human and environmental exposure to them is inevitable as more applications use free, unbound, and highly mobile particles. Although PLGA is generally considered safe, a detailed understanding of how PLGA and the other components used to help formulate this copolymer into a nanoparticle delivery vehicle is useful for assessing potential harmful effects. Despite recent advances in PLGA nanoparticle formulations, residual stabilizing molecules, inconsistent preparations, and batch-to-batch variations can lead to toxicity. Poor preparations of PLGA can lead to common mechanisms of toxicity observed with nanoparticles, such as inflammation and oxidative stress. Consistency in PLGA formulations and accurate methods to evaluate nanoparticle toxicity are needed to ensure safety. In order to better assess PLGA nanoparticles as non-toxic delivery systems, it is critical to understand their physical and chemical properties and the formulation protocols that may introduce toxic components into particles.

Biology, nanoparticle, toxicity↗
DOE OSTI2023

Implications of Electron Transport Layer and Back Metal Contact Variations in Tin–Lead Perovskite Solar Cells Assessed by Spectroscopic Ellipsometry and External Quantum Efficiency

The structural and optical properties of hybrid organic–inorganic metal halide perovskite solar cells are measured by spectroscopic ellipsometry to reveal an optically distinct interfacial layer among the back contact metal, charge transport, and absorber layers. Understanding how this interfacial layer impacts performance is essential for developing higher performing solar cells. Here, this interfacial layer is modeled by Bruggeman effective medium approximations (EMAs) to contain perovskite, C 60 , BCP, and metal. External quantum efficiency (EQE) simulations that consider scattering, electronic losses, and the formation of nonparallel interfaces are created with input derived from ellipsometry structural-optical models and compared with experimental EQE to estimate optical losses. This nonplanar interface causes optical losses in short circuit current density (JSC) of up to 1.2 mA cm –2 . A study of glass/C 60 /SnO 2 /Ag or Cu and glass/C 60 /BCP/Ag film stacks shows that C 60 and BCP mix, but replacing BCP with SnO 2 can prevent mixing between the ETLs to prevent contact between C 60 and back contact metal and enable the formation of a planar interface between ETLs and back contact metals.

14 SOLAR ENERGY↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
DOE OSTI2025

Characterization and Damping Control of Mechanical Connections to Improve Performance of Horn Stripline

Magnetic focusing horns are critical components for creating a stable beam of neutrinos for neutrino facilities, such as the Long Baseline Neutrino Facility (LBNF) and the Neutrinos and Main Injector (NuMI) beam lines at Fermilab. The pulsed magnetic horns are powered by high current electricity through long striplines, which must survive in a harsh radiation environment for the operational life of the component. Each stripline assembly consists of four layers of Al 6101-T6. As an electro-mechanical system, the stripline layers are bolted together with ceramic isolators for electrical insulation and to facilitate passive cooling and mechanical stability. The striplines experience vibrational force in addition to clamping force, repetitive thermal and electro-magnetic loading. The holes of stripline plates for ceramic joints represent one of the weak links for potential failure. Damping can be achieved by adding damp material to the joint. By characterizing behavior of the joint and optimizing their damping properties through FEA and EMA, the stripline performance and longevity can be improved. Appropriate damp materials are the key.

Liu, Zunping [Fermilab]↗
DOE OSTI2023

Studying Response to Light in Offices: A Literature Review and Pilot Study

Researchers have been exploring the influence of light on health in office settings for over two decades; however, a greater understanding of physiological responses and technology advancements are shifting the way researchers study the influence of light in realistic environments. New technologies paired with Ecological Momentary Assessments (EMAs) administered via smartphones provide ways to collect information about individual light exposure and occupant response throughout the day. The study aims to document occupant response to tunable lighting in a real office environment, including potential beneficial or adverse health and well-being effects. Twenty-three office employees agreed to participate in a twelve-week study examining occupant response to two lighting conditions (static vs. dynamic). No significant differences were observed for any of the measures, highlighting the importance and complexity of in-situ studies conducted in realistic environments. While prior office studies have shown a significant influence on daytime sleepiness and sleep quality, research has not shown mood or stress to be significantly impacted by lighting conditions. Correlation analyses regarding lighting satisfaction, environmental satisfaction, and visual comfort demonstrate a significant relationship between certain items that may inform future studies. Further, the high correlation means it is reasonable to assume that many environmental factors in offices can influence occupant behavior and well-being.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

‪A Novel Methodology for Longitudinal Studies of Home Thermal Comfort Perception and Behavior

Human-building interactions significantly influence building energy consumption and affect peak energy demand. For example, heating and cooling contribute 46% of daily peak residential energy demand. Grid-interactive efficient buildings (GEBs) can potentially increase energy-demand flexibility and accelerate the adoption of renewables. However, traditional demand response (DR) programs focused on shedding peak loads disregard the human-building interactions leading to occupant thermal frustration. Specifically, they do not model occupants’ ability to override thermostat controls, nor how indoor environmental conditions and sociocultural factors affect the timing and magnitude of overrides. Studies have found 20% of occupants override thermostat setpoints during DR events longer than 6 hours yet lack detail about the motivation that might guide more successful efforts. Balancing energy-demand flexibility with occupant thermal comfort requires understanding dynamic occupant behavior, the underlying psychophysiological drivers, in the context of homes. This paper presents methods for scalable longitudinal studies of residential occupant behavior dynamics to inform the development of psychophysiological occupant-centric building models. Smart sensors were installed to measure local environmental conditions in 20 homes in two regions of the United States. Just-in-time ecological momentary assessments (EMAs) provided qualitative data on occupant thermal comfort and local environmental conditions not captured in Internet-of-Things (IoT) based studies or existing datasets. Participant interviews provided insight into environmental attitudes, mental models, and the role of economics in comfort and behavior, which in turn may affect thermostat interactions. Based on these data, interventions in the next phase of the study will collect data and monitor occupant behavior during simulated DR events.

Kane, Michael↗