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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 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Dietary microalgae on poultry meat and eggs: explained versus unexplained effects

Different types and sources of microalgae are used to feed broiler chickens and laying hens. This report provides a concise update on various impacts of feeding these novel ingredients on physical, chemical, and nutritional attributes of the resultant meat and eggs. Some of the observed effects may be associated with biochemical and molecular mechanisms derived from unique chemical compositions and nutritional values of microalgae. However, the full potential and the accurate mechanism of microalgae in producing health-promoting poultry foods remain to be explored.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

PyOECP: A flexible open-source software library for estimating and modeling the complex permittivity based on the open-ended coaxial probe (OECP) technique

Here, we present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on three different methods. The software library contains the dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) in the field of science and engineering are required only so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov Chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, electrode polarization, a typical measurement artifact for interpreting dielectric spectra, can be incorporated into the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem), is also included. This open-source software enables users to freely adjust the physical parameters to obtain physical insight into their materials under test and will be consistently updated for more accurate measurement and interpretation of dielectric spectra in an automated manner. This work describes the theoretical and mathematical background of the software, lays out the workflow, and validates the software functionality based on both synthetic and empirical data included in the software.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Numerical modeling of electromagnetic field spatiotemporal evolution to evaluate the effects on calcium carbonate crystallization

Calcium carbonate (CaCO 3 ) scaling is a significant impediment to water systems. Electromagnetic field (EMF) treatment is a promising approach to control scaling owing to its simplicity and low or no energy requirements. However, the underlying mechanisms by which EMF impacts CaCO 3 crystallization remain unclear due to the challenges in measuring the EMFs in feed solutions and the lack of a fundamental understanding of the applied EMFs and the observed physicochemical phenomena. To fill this knowledge gap, a high-fidelity COMSOL model was first developed to simulate EMFs in bulk solutions for three alternating current-induced EMF devices with different configurations and properties. These were then integrated with experimental data to unveil the underlying mechanism by which applied EMFs alter the physicochemical processes. The study revealed that even low-strength EMFs (e.g., electric fields <0.15 V/m and magnetic fields <0.03 mT) promoted CaCO 3 precipitation in bulk solutions. The electric fields created by these EMF devices resulted in higher Lorentz force compared to their induced magnetic fields. In conclusion, the methodology of this study offers the capability to predict the effectiveness of different EMF devices in facilitating crystallization processes, and these mechanistic insights lay the foundation for the smart design of EMF devices for diverse water treatment applications.

COMSOL simulation↗

DOE OSTI2021

Characterizing Long COVID: Deep Phenotype of a Complex Condition

Background: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. Methods: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. Funding: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. Interpretation: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

State-by-state energy-water-land-health impacts of the US net-zero emissions goal

As decisionmakers at various scales begin to design strategies to implement the US net-zero goal, a holistic understanding of its broader economic and sustainability implications at subnational scales is important to shape public support and facilitate implementation. Here, we use an integrated assessment model to explore four different pathways toward the US net-zero goal and investigate their energy-water-land-health implications at the state level. In this study, we show that achieving the net-zero goal implies significant capital turnover (170–200 billion USD/year capital investment and 16–29 billion USD/year stranded assets in the power sector), reduced water withdrawal (120–210 km 3 /year), avoided air pollution damages (220–300 billion USD/year), and expanded forests (300–500 thousand km 2 ). However, the economic and sustainability implications of achieving the net-zero goal at the state-level may not be correlated to a state's contribution to national emission reductions. Our study lays the foundations for a deeper understanding of the broader implications of the US net-zero goal to facilitate cost-effective and environmentally sustainable transitions toward that goal.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Approximating a linear multiplicative objective in watershed management optimization

Implementing management practices in a cost-efficient manner is critical for regional efforts to reduce the amount of pollutants entering the Chesapeake Bay. We study the problem of selecting a subset of practices that minimizes pollutant load—subject to budgetary and environmental constraints—as simulated in a widely used regulatory watershed model. Mimicking the computation of pollutant load in the regulatory model, we formulate this problem as a continuous optimization model with a linear multiplicative objective function and linear constraints. To lay the groundwork for incorporating additional stakeholder requirements in the future, especially those that would require integer variables, we present and study a continuous linear optimization model that approximates the nonlinear model. The linear model, which requires an exponential number of variables, arises naturally as an alternative model for the same underlying physical process. We examine the theoretical behavior of these optimization models and investigate restrictions of the linear model to handle its large number of variables. Through extensive computational tests on real and randomly generated instances, we demonstrate that the linear model and its restrictions provide optimal solutions close to those of the nonlinear model in practice, despite poor approximation properties in the worst case. We conclude that the linear model—together with our approach to handling its large number of variables—provides a viable framework from which to extend the optimization model to better meet the needs of the Chesapeake Bay watershed management stakeholders.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

(CrMnCoNiTi) 3 O 4 high-entropy spinel oxide as a high-performance electrode for supercapacitors

In this study, spinel-structured (CrMnCoNiTi)₃O₄ high-entropy oxides (HEOs) have been successfully synthesized using the solution combustion method, and their performance as supercapacitor electrode materials was investigated. These HEOs exhibit excellent performance, stemming from the unique high-entropy effect and lattice distortion. This structure effectively buffers volumetric strain during cycling, conferring the material with extremely high structural stability and specific capacity. The material demonstrates a high specific capacity of 491.5 C∙g⁻¹ at a current density of 0.5 A∙g⁻¹. Furthermore, it shows extraordinary cycling stability: after 10,000 charge/discharge cycles at 5 A∙g⁻¹, the capacity retention rate is 99%, and the Coulombic efficiency consistently remains close to 100%. Ex-situ structural characterizations further reveal that this excellent cycling durability originates from a cooperative multination valence reconstruction mechanism within an entropy-stabilized spinel framework. Additionally, a symmetric supercapacitor assembled using this material and 1 M KOH electrolyte successfully extended the working voltage to 1.2 V. This research highlights the great potential of high-entropy oxides in synergizing high energy density and ultra-long cycling life, laying a solid foundation for the development of next-generation high-performance supercapacitor electrode materials.

High entropy oxides↗

DOE OSTI2022

A reaction–diffusion model for grayscale digital light processing 3D printing

We report that digital light processing (DLP) 3D printing is an additive manufacturing process that utilizes light patterns to photopolymerize a liquid resin into a solid. Due to the accuracy of modern digital micromirror devices (DMD) and recent advances in resin chemistry, it is now possible to create functionally graded structures using different light intensity values, also known as grayscale DLP (g-DLP). Different intensities of light lead to differences in the polymer crosslinking density after curing, which ultimately produces a part with gradients of material properties. However, g-DLP is a complicated process. First, the DLP printing is a highly coupled chemical and physical process that involves light propagation, chemical reactions, species diffusion, heat transfer, volume shrinkage, and changes in mechanical behaviors of the curing resin. Second, in g-DLP, light gradients create strong in plane gradients of chemical species concentrations in the curing liquid resin due to the strong dependence of light intensity on the rate of monomer crosslinking. Furthermore, light gradients through the depth create concentration gradients due to the degree of cure dependent light absorption and the use of photoabsorbers. These complex physical features of the printing process must be understood in order to properly control printing parameters such as light exposure time, printing speed, and grayscale variations to achieve accurate mechanical properties. In this paper, a photopolymerization reaction–diffusion model is developed and used in conjunction with experiments to investigate the coupled effects of light propagation, chemical reaction rates, and species diffusion during g-DLP 3D printing. The model is implemented numerically utilizing the finite difference method and simulation results are compared to experimental findings of simple printed structures. The agreement between experimental and model predictions of simple quantities of interest, such as geometric feature sizes, shows that the model can capture the overcure due to free-radical and other species diffusion during printing when grayscale patterns are employed. This model lays the groundwork for future extensions that can incorporate more complex coupled physics such as heat transfer, volume shrinkage, and material property evolution, which are critically important in utilizing g-DLP 3D printing for the fabrication of high-performance parts which excellent geometric and material property tolerances.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Developing and tuning a community scale energy model for a disadvantaged community

This work describes the development of a community-scale energy model for a mixed-use low-income community located in Huntington Beach, CA. An accurate community-scale energy model is useful for evaluating the use of limited capital resources used to invest in clean energy technologies. This work lays out the process of developing such a model while relying primarily on publicly available data and highlighting critical partnerships necessary for model development success. The primary contribution of this work is the demonstration of the process used to develop an accurate energy model for a disadvantaged community when minimal building and energy use data is available. The heart of the model is the physics-based community scale energy modeling platform URBANopt. Using a bottom-up load modeling approach, energy simulated energy use falls within 3% or less of aggregate annual utility data, and within 10% or less aggregate monthly utility data. The demonstrated model development and tuning process can be used by others to characterize other atypical communities, which may differ significantly from prototypical models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Dirty dishes or dirty laundry? Comparing two methods for quantifying American consumers' preferences for load management in a smart home

One challenge of transitioning to renewable energy is that household electricity use and renewable generation are often misaligned. Smart home energy management systems hold promise for shifting usage to match generation, but these systems need to be designed with the occupants’ preferences in mind. The purpose of the present research is to compare two approaches for collecting and modeling consumers’ load management preferences, both of which are amenable to use in a home energy management system. Specifically, we examine the performance of Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) and Analytic Hierarchy Process (AHP) in quantifying consumers’ preferences regarding air temperature (air conditioning and heating), water heating, dishwashing, clothes washing and drying, monetary costs, environmental impacts, and comfort/convenience. Two studies are presented: Study 1 examines the SMARTER approach, and Study 2 focuses on the AHP approach. In both studies, online surveys (N SMARTER = 956 and N AHP = 1023) were conducted to elicit preferences from participants across the United States. The preferences modeled by both approaches were validated based on (a) their ability to predict participants’ choices in a Discrete Choice Experiment and (b) their convergence with previous research on load-shifting behavior. The validation procedure suggests that the SMARTER approach is superior in modeling consumers’ preferences for load management. Overall, this research lays the groundwork for designing a smart home interface capable of collecting occupants’ preferences and using those preferences to deliver improved occupant comfort, lower operating costs, reduced environmental impact, and more significant demand response than exists today.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Flow regime and Reynolds number variation effects on the mixing behavior of parallel flows

The hydraulic single-phase mixing of three parallel rectangular channels is experimentally investigated at various Reynolds numbers (Re) and flow regime combinations. Particle Image Velocimetry results for seven mixing cases are presented and discussed with varying Re combinations ranging from 1,824 to 20,844. While all cases result in the same Re ratio of ~0.69 between the inner and outer flows, two cases represent multi-regime mixing with the inner-outer regime pair of laminar-transitional and transitional-turbulent, while the other 5 cases are all characteristic of turbulent mixing with varying levels of turbulence. The outer channels initially share characteristics with a backward facing step. The center channel is found to initially behave like a slot jet, but then sees a significant increase in velocity decay. This inner flow velocity decay increased dramatically in the laminar-transitional mixing case, whose centerline velocity decay was ~6 times larger than the decay in the turbulent mixing cases. Second order statistics revealed a consistent mixing layer thickness of ~0.1 hydraulic diameters for all the cases but showed more intense shearing in the multi-regime mixing cases. The combined point and thereby the mixing layer length is determined using centerline velocity decay profiles, which show a much more aggressive mixing in multi-regime flows. Multi-regime mixing demonstrated superior characteristics relative to turbulent mixing due to a more dramatic velocity decay in the inner flow and a shorter mixing length. The contributions of this work include communicating the benefits of multi-regime mixing and providing detailed characterization efforts that can serve future efforts for validating computational models. Here this research also lays the groundwork for future studies aimed at achieving high levels of mixing without a severe penalty in pressure drop.

42 ENGINEERING↗

DOE OSTI2021

Investigating feasible light configurations for fish restoration: an ethological insight

Light environment significantly impacts the effectiveness of fish population restoration in developed watersheds, subserving fish passage reconstruction in hydraulic complex and habitat rehabilitation. To develop an interior connection between light-related fish response from the laboratory and field fish conservation practice, we employed a new indexing system, consisting of phototaxis rate, relative swimming speed (RSS), and optic evasion coefficient (OEC), to quantify the light-induced behaviors in Ptychobarbus kaznakovi, a representative rare cyprinid in Tibet, China, at four experimental illuminance levels (15 lx, 30 lx, 60 lx, 120 lx) and four wavelengths (red, yellow, green, blue). The fish showed negative phototaxis at all given illuminances and wavelengths. Under red light, the OEC increased significantly from 0.642 at 15 lx to 0.782 at 30 lx (P = 0.029), while the RSS decreased significantly from 3.752 to 2.383 (P < 0.001). Behaviorally, the fish shifted from sprint to wandering around, indicating the alteration of dominant physiological activity from behavioral stress response to negative phototaxis. Under the green treatment, P. kaznakovi swam around quickly and presented slight negative phototaxis, and OEC values were uniform among all illuminances, probably because of the similarity of green light to the ambient color of the habitat. Accordingly, ecologists and fisheries practitioners can utilize red light to exclude fish from dangerous waters and green light to guide them to ideal habitats, laying the necessary groundwork for the light-driven fish recovery effort. Furthermore, the appropriate light configuration can yield economic benefits through the tradeoff among fish protection, power generation, and investment costs.

Lin, Chenyu↗

DOE OSTI2021

State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data

Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, a group of experts have worked together to highlight and establish a baseline set of these common concerns, which can be used as motivation to propose innovative analytics and data-driven solutions. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Critical review and analysis of hydrogen safety data collection tools

The wider adoption of hydrogen in multiple sectors of the economy requires that safety and risk issues be rigorously investigated. Quantitative Risk Assessment (QRA) is an important tool for enabling safe deployment of hydrogen fueling stations and is increasingly embedded in the permitting process. QRA requires reliability data, and currently hydrogen QRA is limited by the lack of hydrogen specific reliability data, thereby hindering the development of necessary safety codes and standards [1]. Four tools have been identified that collect hydrogen system safety data: H2Tools Lessons Learned, Hydrogen Incidents and Accidents Database (HIAD), National Renewable Energy Lab's (NREL) Composite Data Products (CDPs), and the Center for Hydrogen Safety (CHS) Equipment and Component Failure Rate Data Submission Form. This work critically reviews and analyzes these tools for their quality and usability in QRA. It is determined that these tools lay a good foundation, however, the data collected by these tools needs improvement for use in QRA. Areas in which these tools can be improved are highlighted, and can be used to develop a path towards adequate reliability data collection for hydrogen systems.

08 HYDROGEN↗

DOE OSTI2025

Tailoring Cu-Zr gradient nanoglass structures: Influence of nanoparticle size and cooling rates on glass-glass interfaces

The study of gradient nanoglasses (GNGs) has gained attention due to their unique mechanical properties and potential applications in advanced materials. This study employs molecular dynamics simulations to synthesize a GNG using Cu-Zr metallic glass nanoparticles (NPs) sized from 3 to 15 nm. The NPs were produced by melting and quenching metallic clusters at a relatively slow quench rate of 10 9 K/s. The synthesis of GNG is elucidated along with the characterization of its heterogeneous metallic glass nanostructure. A seamless GNG structure is formed through cold compression of Cu 64 Zr 36 amorphous NPs of varying sizes. The influence of NP size on the GNG structure is investigated, utilizing deeply relaxed NPs, which exhibit a characteristic Cu segregation pattern on their surfaces. The results highlight an increase in structural heterogeneity due to heterogeneous mass transport and the development of local composition and density variations caused by Cu segregation at glass-glass interfaces (GGIs). A reduction in NP size is correlated with decreased Cu atomic displacements and local density at GGIs, suggesting that larger NPs may produce stronger GGIs. This research presents a novel methodology for synthesizing heterogeneous metallic glasses, demonstrating the capacity to control and customize nanostructure heterogeneity through the manipulation of NP sizes and cooling rates. Furthermore, these findings enhance our understanding of structural evolution during nanoglass synthesis and lay the foundation for further exploration in nanomaterial synthesis and characterization.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Colloidal structure and proton conductivity of the gel within the electrosensory organs of cartilaginous fishes

Cartilaginous fishes possess gel-filled tubular sensory organs called Ampullae of Lorenzini (AoL) that are used to detect electric fields. Although recent studies have identified various components of AoL gel, it has remained unclear how the molecules are structurally arranged and how their structure influences the function of the organs. Here we describe the structure of AoL gel by microscopy and small-angle X-ray scattering and infer that the material is colloidal in nature. To assess the relative function of the gel’s protein constituents, we compared the microscopic structure, X-ray scattering, and proton conductivity properties of the gel before and after enzymatic digestion with a protease. We discovered that while proteins were largely responsible for conferring the viscous nature of the gel, their removal did not diminish proton conductivity. The findings lay the groundwork for more detailed studies into the specific interactions of molecules inside AoL gel at the nanoscale.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Crystal structure, magnetism, and specific heat of lightly depleted layered honeycomb oxides, Na$_2$(Ni$_{2-x}$D$_x$)TeO$_6$ ($D$ = Mg, Ga, Co; 0.10 ≤ $x$ ≤ 0.25)

Layered oxides, in which transition metal atoms form a honeycomb lattice, are known for complex magnetic phase diagrams. In this work we study the doped compounds, Na$_2$(Ni$_{2-x}$D$_x$)TeO$_6$, $D$ = Mg, Ga, and Co (0.10 ≤ $x$ ≤ 0.25), to understand how chemical dilution at the nickel site influences the stability of magnetic exchanges within the honeycomb layer of the parent, Na$_2$ Ni$_2$ TeO$_6$. Here, the studied compounds were found to crystallize in $P6_3/mcm$ space group common to P2 type layered oxides. In general, the lattice parameters and the unit cell volume expanded with increased doping. The oxidation states determined from X-ray photoelectron spectroscopy were consistent with bond valence sum estimates, that confirmed the presence of Ni$^{2+}$, Mg$^{2+}$, Ga$^{3+}$, Co$^{2+}$ in our samples. Irrespective of magnetic or non-magnetic doping, the magnetic phase transition temperature ($T_N$) of the doped compounds were reduced from that of Na$_2$ Ni$_2$ TeO$_6$ or Na$_2$ Co$_2$ TeO$_6$, and lay between 21.7 K and 26.8 K. The effective paramagnetic moments extracted from Curie–Weiss analysis were in the range 3.17 μ B to 3.84 μ B and the Weiss temperatures were between $-15$ K and $-25$ K, suggesting antiferromagnetism. Mg- and Ga-doped compounds showed free spin formation, indicated by Curie–Weiss tails in their magnetic susceptibility. Our results suggest emergence of short-range magnetic correlations in Na$_2$(Ni$_{2-x}$D$_x$)TeO$_6$, enhanced due to the depletion of the Ni honeycomb lattice.

75 - CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY A↗