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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Assessing the Effectiveness of Large-Scale Environmental Restoration: Challenges and Opportunities

A recent National Academies consensus report addresses monitoring and assessment of cumulative effects of large-scale and multiple restoration projects within the context of long-term environmental change. Fines and penalties from the Deepwater Horizon oil spill in the Gulf of Mexico (GoM) have supported hundreds of restoration projects at spatial scales not often possible in the past. Here, in this paper, the report committee members and staff provide personal reflections from our time working on the study. We found that gaps in data collection, issues with data accessibility, and a lack of synthesis and analysis are hindering the ability to answer a basic question: What are the impacts of these many restoration efforts on improving ecosystem health and productivity in the GoM at the regional and Gulf wide scale? Restoration efforts are occurring in environments where many trends are changing and exhibiting higher variability than in the past, suggesting that previously successful restoration practices may no longer be adequate to compensate for the effects of environmental changes and variability. Our proposed approach to these challenges includes employing emerging monitoring technologies; using conceptual models; devising an adaptive management framework; rethinking restoration outcome goals; assessing cumulative effects; and undertaking rigorous synthesis and analysis of existing information on long-term environmental trends and restoration efforts. Restoration scientists and practitioners working in the GoM have an unprecedented opportunity to demonstrate large-scale environmental recovery if advances in monitoring, synthesis, assessment, and action are taken quickly. We are cautiously optimistic that, with mid-course adjustments, continued progress toward large-scale environmental recovery is possible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Potential annual daylighting performance of a high-efficiency daylight redirecting slat system

While the primary role of window attachments is often to moderate glare and solar heat gains, they are also able to provide additional daylight to interior spaces. For this purpose, a variety of daylight-redirecting window systems have been developed over the past 150 years. Fixed reflective systems (slats/light shelves) or prismatic systems that rely on total internal reflection work well under specific solar conditions, but generally sacrifice performance over a much wider range of incident solar angles and sky conditions. Dynamic systems - typically reflective slats - are more responsive to sun angles but have not been able to achieve optimal performance for glare and daylight redirection efficiency. A previous investigation into an adjustable, reflective blind concept first conceived of in the late 1970s showed promise but was not reduced to practice due to lack of adequate simulation and analysis tools. In this paper, this concept is further developed and its energy and visual comfort performance evaluated for four mid-latitude, temperate climates using ray-tracing simulation techniques. Overall, results indicate significant potential lighting energy savings when compared with conventional automated reflective blinds (2.1–4.9 kWh/(m 2 ·a), or 14%–42%, depending on climate and orientation) or, especially, manually-operated matte white venetian blinds (1.4–7.9 kWh/(m 2 ·a), or 9%–54%, depending on climate and orientation), while maintaining acceptable or better visual comfort conditions throughout the interior space.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control

Occupants are the core of the built environment. Traditional Heating, Ventilation, and Air-Conditioning (HVAC) systems operate with predefined schedules and maximum occupancy assumptions with no consideration of specific occupant information. These generalized assumptions usually do not align with the actual demand and result in over-conditioning and occupant discomfort. In recent years, with the aid of Information & Communication Technology (ICT) and Computer Science (CS), it is possible to acquire real-time and accurate occupant information to satisfy the exact thermal requirement through specific HVAC control in one particular built environment. This mechanism is called HVAC “Occupant-centric Control (OCC).” HVAC OCC strategy starts with collecting the occupant’s information (e.g., presence/absence) and then applies it to meet the occupant’s requirement (e.g., thermal comfort). However, even though some research studies and field pilot demonstrations have been devoted to the field of OCC, there is a lack of systematic knowledge about occupant data, which is the principal component of OCC for HVAC researchers and practitioners. To fill this gap, this review paper discusses OCC with a particular emphasis on occupant information and investigates how this information can assist HVAC operation in providing an acceptable built environment in required spaces during the required time. Finally, we provide a fine-grained, comprehensive picture of occupant information, discuss its features, the modalities of information feed-in into the HVAC control, and the application of commonly utilized occupant information for OCC.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A new database of building-space-specific internal loads and load schedules for performance based code compliance modeling of commercial buildings

Building-level loads and load profiles prescribed by current modeling rules save modelers time and avoid gaming during whole building performance modeling. However, recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load profiles for different space types. As a solution to this issue, this paper develops a database of building-space-specific loads and load profiles used in code compliance modeling. The existing sets of loads and load profiles are reviewed and the challenges behind using them for specific research topics are discussed. Then, the proposed method to develop the building-space-specific loads and load profiles is introduced. After that, the database for these building-space-specific loads and load profiles is presented. In addition, one case is studied to demonstrate the applications of these loads and load profiles. In this case study, three methods are used to develop building energy models: space-specific (using knowledge of the distribution and location of space types and applying the space-specific data in the developed database), building-level (assuming a lack of knowledge of the space types and using the building-level data in the developed database), and calculated-ratio (assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database). Finally, the energy results simulated by using these three methods are compared, which show building-level methods can produce energy results up to 20% different than the space-specific methods. Finally, this paper discusses the application scope and maintenance of this new database.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Integration of a grey-box refrigerated case model in EnergyPlus via Python plugin

Commercial buildings, in particular grocery stores (due mainly to their large refrigeration load), provide opportunities for energy cost reductions. Grocery stores could offer substantial load flexibility to the power grid through participation in demand response programs because of their usage patterns and relatively high energy intensity. This load flexibility could come from modifying the control of heating, ventilation, and air conditioning (HVAC) systems, refrigeration systems, or both. Although estimation of the HVAC system’s load flexibility potential is relatively targeted in the literature, estimating load flexibility of refrigeration systems is nascent and has been a challenge, in part because of the lack of proper simulation tools that capture the dynamics in the refrigeration cases. The existing refrigerated case model within EnergyPlus, a whole building energy simulation program, assumes a constant case temperature throughout the simulation period and does not explicitly model the cycling of the compressor serving the refrigerated case. In addition, it does not encompass modeling of temperatures of the product inside the refrigerated case. This difference between modeled and actual operation can be a barrier to the development of demand control algorithm and accurate analysis of load flexibility potential. In this paper, we present a grey-box model for modeling refrigerated cases in grocery stores, which include medium temperature and low temperature. Four cases are modeled; two are low-temperature closed cases and two are medium-temperature cases with one closed and one open. Data from an experimental facility are used to train and test the models. Results demonstrate the efficacy of the grey-box models in predicting the temperatures. This model is integrated into EnergyPlus to capture the dynamic effects of case temperature on the environment and enhance the calculation of sensible and latent heat exchange with the environment (case credits). These enhancements can be leveraged more broadly to model advanced refrigeration controls such as defrost, develop and test unique algorithms that could affect refrigeration interactions with HVAC, and refine store design for any commercial building with refrigeration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

High temperature ceramic thermal insulation material

Flexible and lightweight thermal insulation materials with hierarchical microstructures are ubiquitous in thermal management and protection systems. Ceramic aerogels promise high-temperature thermal insulation but lack mechanical robustness, while the fibrous materials with excellent mechanical elasticity display modest thermal insulation. Here we describe flexible hierarchical superhydrophobic ceramic insulation nanocomposites through the densified architectured hierarchical nanostructures, radiative insulation coating, and interfacial cross-linking among composites. The lightweight flexible ceramic nanocomposites exhibit a density of 0.13 g/cm 3 , high-temperature fire resistance with thermal conductivity of 0.024 W/(m·K), and super-hydrophobicity with the water contact angle of 152°. The mechanical robustness and high-temperature thermal insulation of ceramic nanocomposites, together with its soundproof performance, shed light on the low-cost flexible insulation materials manufacturing with scalability for high-temperature thermal insulation applications under high mechanical loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Combined DSC/TGA Analysis to Rationalize the As-Sintered Microstructure in Binder Jet Additively Manufactured H13 Tool Steel

In this study, a combined DSC/TGA analysis has been used to rationalize the constant liquid channel width at higher sintering temperatures in H13 tool steel manufactured by binder jet additive manufacturing. Using enthalpy conservation along with coupled DSC + TGA analysis, the phase fractions of liquid and austenite phases during heating and cooling respectively were estimated. The estimates from the DSC/TGA analysis were compared with equilibrium phase fraction predictions from ThermoCalc. The resulting phase evolution was correlated to the as-sintered microstructure to rationalize the unexpected lack of change in liquid channel width at higher sintering temperatures.

Kannan, Rangasayee↗
DOE OSTI2020

Alkaline Ethanol Oxidation Reaction on Carbon Supported Ternary PdNiBi Nanocatalyst using Modified Instant Reduction Synthesis Method

Direct ethanol fuel cells (DEFC) still lack active and efficient electrocatalysts for the alkaline ethanol oxidation reaction (EOR). In this work, a new instant reduction synthesis method was developed to prepare carbon supported ternary PdNiBi nanocatalysts with improved EOR activity. Synthesized catalysts were characterized with a variety of structural and compositional analysis techniques in order to correlate their morphology and surface chemistry with electrochemical performance. The modified instant reduction synthesis results in well-dispersed, spherical Pd85Ni10Bi5 nanoparticles on Vulcan XC72R support (Pd85Ni10Bi5/C(II-III)), with sizes ranging from 3.7 +/- 0.8 to 4.7 +/- 0.7 nm. On the other hand, the common instant reduction synthesis method leads to significantly agglomerated nanoparticles (Pd85Ni10Bi5/C-(I)). EOR activity and stability of these three different carbon supported PdNiBi anode catalysts with a nominal atomic ratio of 85:10:5 were probed via cyclic voltammetry and chronoamperometry using the rotating disk electrode method. Pd85Ni10Bi5/C-(II) showed the highest electrocatalytic activity (150 mA.cm(-2); 2678 mA.mg(-1)) with low onset potential (0.207 V) for EOR in alkaline medium, as compared to a commercial Pd/C and to the other synthesized ternary nanocatalysts Pd85Ni10Bi5/C-(I) and Pd85Ni10Bi5/C-(III). This new synthesis approach provides a new avenue to developing efficient, carbon supported ternary nanocatalysts for future energy conversion devices. The modified instant reduction method for synthesis of ternary Pd85Ni10Bi5/C-(II) nanocatalyst using Vulcan XC72R as carbon support initiates an agglomeration reduction, provides low average particle size, and enables enhanced activity for the alkaline ethanol oxidation reaction (EOR) compared to the common instant reduction method and to a commercial Pd/C catalyst.

Alkaline direct ethanol fuel cell↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗
DOE OSTI2021

Nitrogen-doped char as a catalyst for wet oxidation of phenol-contaminated water

Catalytic wet oxidation (CWO) of aqueous effluents rich in organic compounds is a very promising technology for the treatment of liquid wastes from biomass conversion processes. CWO reactions occur through the formation of free radical species, produced in the presence of an oxidant, which act on organic contaminates in the effluent. Although the reaction is well known, there exists a lack of affordable catalysts to conduct this process at the lower temperatures and pressures in novel bioenergy processes. Here, this study assessed the catalytic effect of nitrogen-doped chars as such an option. Phenol in aqueous solution was used as a model waste effluent. Treatment was conducted at moderate temperatures (190 to 260 °C), oxygen partial pressure of 1 MPa, and reaction times of 15, 30, and 45 min in stainless steel and glass-lined tube reactors. High pressure liquid chromatography (HPLC) analyses of the products quantified phenol and by-product concentrations used in the calculation of reaction activation energy. The char catalyst was studied by X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and scanning electron microscopy (SEM) in order to gain insight into its structure and surface composition. The results indicate that nitrogen-doped char catalysts accelerate the oxidation of phenol by decreasing its reaction activation energy from 82.2 kJ/mol (non-catalyzed) to 40.4 kJ/mol (catalyzed). An analysis from first principles using density functional theory (DFT) was conducted to ascertain which N functional group has the most significant impact on free radical formation in the presence of oxygen. Among all the N functional groups studied, the dipyridinic functional groups showed the most promising characteristics to facilitate the formation of hydroxyl free radicals.

Advanced Oxidation Processes for Organic Pollutant↗
DOE OSTI2023

Molecular Dynamics Simulation of Hygroscopic Aging Effects in Epoxy Polymer

The automobile industry is incorporating more lightweight content in car designs to boost fuel-economy. New structural adhesives are needed to mitigate the corrosion and thermal expansion issues associated with joining dissimilar lightweight materials, but adhesive developers lack a fundamental understanding of the chemistry that occurs in the adhesive as the joint ages. In this study, we developed structural adhesive molecular models and applied classical molecular dynamics simulations and density functional theory calculations to gain molecular insights into the influence of water molecules on the properties of epoxy-based adhesives (DGEBA + Jeffamine (JD230)). The simulations were complemented by experimental synthesis and characterization. Our work underscores the impact of water molecules on the local structure of the epoxy network as well as resulting mechanical properties. Water molecules were mainly coordinated with hydroxyls, primary amines and secondary amines, but also weakly coordinated with ether linkages, which were found most probable to be labile. Simulated stress–strain data indicates that increasing the water content deteriorates the mechanical properties. The Young’s modulus decreased by ~ 30% when the water content increased to 3 wt%. We conclude, this integration of molecular-level chemical insights with mechanical property simulations of the hydrated epoxy system and experimental validation holds the promise to advance lightweight joint technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Phase Composition and Phase Transformation of Additively Manufactured Nickel Alloy 718 AM Bench Artifacts

Additive manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation during post-build processing. As part of the 2022 AM Bench Challenge, sponsored by the National Institute of Standards and Technology, this study focuses on the phase composition and phase evolution of AM nickel alloy 718, a nickel-based superalloy, to provide benchmark data essential for the validation of computational models for microstructural predictions. Here, we employed high-energy synchrotron X-ray diffraction, in situ synchrotron X-ray scattering, as well as high-resolution transmission electron microscopy for our analyses. The study uncovers critical aspects of the microstructure in its as-built state, its transformation during homogenization, and its phase evolution during subsequent aging heat treatment. Specifically, we identified secondary phases, monitored the dissolution and coarsening of microstructural elements, and observed the formation and stability of γ ’ and γ ” phases. The results provide the rigorous benchmark data required to understand the atomic and microstructural transformations of AM nickel alloy 718, thereby enhancing the reliability and applicability of AM models for predicting phase evolution and mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-Speed X-Ray Stereo Digital Image Correlation in a Shock Tube

X-ray stereo digital image correlation (DIC) measurements were performed at 10 kHz on the internal surface of a jointed structure in a shock tube at a shock Mach number of 1.42 and compared with optical stereo DIC measurements on the outer, visible surface of the structure. The shock tube environment introduces temperature and density gradients in the gas through which the structure was imaged, resulting in spatial and temporal index of refraction variations. These variations cause bias errors in optical DIC measurements due to beam-steering but have minimal influence on x-ray DIC measurements. These results demonstrate the utility of time-resolved x-ray DIC measurements in complicated environments where optical measurements suffer severe errors and/or are precluded by lack of optical access.

42 ENGINEERING↗
DOE OSTI2025

Quantitative Comparisons of Image Quality for Flash X-Ray Detectors

Due to X-rays’ ability to penetrate materials, flash X-ray radiography can be used for high-speed measurements where direct optical access is not possible. Choice of detector has a pronounced impact on resulting image quality. Four different detector systems were evaluated with a 450kVp flash source to quantitatively compare image quality metrics. The scintillating digital detector had less image noise than the three different storage phosphor computed radiography detectors across all transmission levels, but lacked the spatial resolution of the computed radiography detectors. For the screens tested here, the HPX-DR digital system had the highest signal to noise ratio of 68.24 and contrast to noise ratio of 35.53, but had the lowest spatial resolution, resolving 2.5 line pairs per millimeter at 1.78% contrast. At a value of 37.59, the Flex GP imaging plate had a signal to noise value above its storage phosphor counterparts under a 450kVp flash source. For radiographic setups typically used for dynamic experiments, the Flex XL Blue and Flex HR detectors had signal to noise ratios of 18.44 and 26.56 respectively. The highest resolved spatial frequencies of the Flex GP, Flex XL Blue, and Flex HR with the flash source are 3.85, 5.00, and 3.85 line pairs per millimeter, respectively. In conclusion, the Flex GP detector had the best combination of signal to noise ratio, contrast to noise ratio, and spatial resolution under a flash source.

computed radiography↗
DOE OSTI2022

Importance of Standardizing Analytical Characterization Methodology for Improved Reliability of the Nanomedicine Literature

Understanding the interaction between biological structures and nanoscale technologies, dubbed the nano-bio interface, is required for successful development of safe and efficient nanomedicine products. The lack of a universal reporting system and decentralized methodologies for nanomaterial characterization have resulted in a low degree of reliability and reproducibility in the nanomedicine literature. As such, there is a strong need to establish a characterization system to support the reproducibility of nanoscience data particularly for studies seeking clinical translation. Here, we discuss the existing key standards for addressing robust characterization of nanomaterials based on their intended use in medical devices or as pharmaceuticals. We also discuss the challenges surrounding implementation of such standard protocols and their implication for translation of nanotechnology into clinical practice. We, however, emphasize that practical implementation of standard protocols in experimental laboratories requires long-term planning through integration of stakeholders including institutions and funding agencies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Effect of directed energy deposition process parameter on build quality of tantalum

Tantalum is a refractory metal used in a variety of harsh environment applications. Additive manufacturing of tantalum is limited based on its high melting point and affinity for oxygen. Directed Energy Deposition is an additive manufacturing technique with rapid deposition time and compositional flexibility within builds. Additively manufactured tantalum is susceptible to a variety of material and process-based defects. Various lack-of-fusion defects were identified resulting from excessive powder feed rates or insufficient laser power. Oxygen impurities in some samples caused cracking and increased material hardness. Precipitates were identified in the highly oxidized samples which were printed immediately after the build chamber was opened. High-density, low-defect parts were successfully produced. The effects of scan speed, laser power, and powder feed rate on density and defects were analyzed. A processing window was identified for producing high-quality parts which requires adequately high laser power and lower powder feed rate.

DED↗