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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Stellar s-process neutron capture cross sections on 78,80,84,86 Kr determined via activation, atom trap trace analysis and decay counting

We present a detailed account of neutron capture experiments of astrophysical relevance on 78,80,84,86 Kr (n, γ) reactions at the border between weak and main s process. The experiments were performed with quasi-Maxwellian neutrons from the Liquid-Lithium Target (LiLiT) and the mA-proton beam at 1.93 MeV (2-3 kW) of the Soreq Applied Research Accelerator Facility (SARAF). The setup yields high-intensity approximate to 40 keV quasi-Maxwellian neutrons (3-5 x10 10 n/s) closely reproducing the conditions of s-process stellar nucleosynthesis. A sample of 100 mg of atmospheric, pre-nuclear-age Kr gas contained in a Ti spherical shell was activated in the LiLiT neutron field. The abundances of long-lived Kr isotopes ( 81,85g Kr) were measured by atom counting via atom trap trace analysis (ATTA) at Argonne National Laboratory and low-level counting (LLC) at University of Bern. This work is the first measurement of a nuclear cross section using atom counting via ATTA. The activities of short-lived Kr isotopes ( 79,85m,87 Kr) were measured by gamma-decay counting with a high-purity germanium detector. Maxwellian-averaged cross sections for s-process thermal energies are extracted. By comparison to reference values, our nucleosynthesis network calculations show that the experimental cross sections have a strong impact on calculated abundances of krypton and neighboring nuclides, in some cases improving agreement between theory and observations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1986

The magnetic field of the BY Draconis flare star EQ Virginis

High resolution, high S/N-ratio line profiles of the BY Draconis-type flare star EQ Vir obtained with the MMT are presently subjected to a novel Zeeman analysis procedure which includes radiative transfer effects and compensation for blends. A mean field of 2500 + or - 300 G covering 80 + or 15 percent of EQ Vir is determined. This constitutes the first positive detection of a magnetic field in a BY Draconis-type flare star, confirming that magnetic fields are present on these stars. The value of 2500 G obtained for the photospheric field strength is similar to the value derived by assuming equipartition of magnetic and thermal energy densities in the photosphere and scaling from the solar network fields, suggesting that equipartition may determine the mean field strength in the nonspotted portion of the flare star photosphere.

Saar, . H.↗
DOE OSTI2022

NWAL Validation Report for U Assay by Potentiometric Titration (Modified Davies and Gray Technique) and U and PU Assay by Isotope Dilution Mass Spectrometry

SRNL Sensing & Metrology, previously Analytical Laboratory, is a member of the IAEA Network of Analytical Laboratories for Nuclear Material Measurement that is qualified to provide Uranium and Plutonium isotopic analysis of nuclear materials by Thermal Ionization Mass Spectrometry. As part of the membership, SRNL participated in the 2019 Nuclear Material Round Robin to maintain the qualification to provide isotopic analysis. SRNL also participated with the additional intent to qualify Uranium Assay by Potentiometric Titration (Davies and Gray method) and Uranium and Plutonium Isotope Dilution by Thermal Ionization Mass Spectrometry as part of the International Atomic Energy Agency’s Network of Analytical Laboratories. The proficiency test exercise was conducted by the Office of Safeguards Analytical Services, Department of Safeguards.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes

The current interconnection process and hosting capacity analysis for distributed energy resources (DERs), such as photovoltaics (PV) and battery energy storage systems, are based on analyzing grid network constraints (voltage and thermal) using only medium-voltage distribution network models. This is because most utilities do not have secondary low-voltage system models that connect service transformers and residential customers. This is important because in many cases the main impact of interconnecting DERs could occur on the low-voltage distribution systems. This paper proposes a supervised learning method to approximate local secondary models to improve the interconnection process. The proposed supervised learning method includes a decision tree model that predicts the secondary topology and a logistic regression model that predicts conductor types. The case studies demonstrate the benefits of including secondary low-voltage circuits in the interconnection process. We report the proposed modeling methodology is readily scalable and thus can reduce the cost and effort of PV interconnection for the industry and stakeholders.

14 SOLAR ENERGY↗
DOE OSTI2024

Maximizing energy efficiency in wastewater treatment plants: A data-driven approach for waste heat recovery and an economic analysis using Organic Rankine Cycle and thermal energy storage

Maximizing energy efficiency through waste heat recovery (WHR) processes is crucial for sustainable and ecofriendly operations across multiple industries, notably in wastewater treatment plants (WWTPs). This work proposes a comprehensive approach for assessing the WHR feasibility in WWTPs, structured in two main objectives. Firstly, an Artificial Neural Network (ANN) model is developed to accurately predict WHR based on operational data, including biogas temperature, biogas pressure, daily production in kWh, and WHR values in kWh th . The second objective focuses on economically evaluating the WHR feasibility based on the estimated WHR values obtained by the ANN model, and then realistically assessing the economic feasibility of integrating the Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage (STES) systems. With an application to the As-Samra WWTP located in Jordan, the developed ANN model demonstrates promising results in the validation phase, with a root mean square error (RMSE) of 2206 kWh/day, a mean absolute error (MAE) of 1674 kWh/day, and an R-squared (R 2 ) value of 68%. On the other hand, the economic analysis reveals that an optimal ORC system of 412.14 kW e capacity yields a Net Present Value (NPV) of 2.09 million US dollars, a Levelized Cost of Energy (LCOE) of 0.0749 USD/kWh, a Payback Period (PBP) of 4.8 years, and annual revenues of 428 kUSD. This work also investigates the techno-economic feasibility of integrating ORC-STES. Results indicate that the LCOE and PBP are highly affected by the ORC's capital cost, and integrating STES increases the LCOE to 0.0824 USD/kWh, rendering its integration with ORC infeasible. In conclusion, this study aims to advance the understanding and application of WHR in WWTPs, paving the way for more efficient and sustainable practices in the field.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Nanoengineering Porous Silica for Thermal Management

Thermal insulation of solid materials originates from the nanoscale porous architectures to regulate thermal management in energy-critical applications from energy-efficient buildings to heat-sensitive energy devices. Here, we show nanoengineering of porous silica materials to control the architecture transition from mesoporous to nanocage networks. A low thermal conductivity of such a porous silica network is achieved at 0.018 W/(m K) while exhibiting a porosity of 92.05%, specific surface area of 504 m 2 /g, and pore volume of 2.37 cm 3 /g after ambient pressure drying. Meanwhile, the crosslinking of the porous silica and ceramic fiber frameworks show a tensile Young’s modulus of 2.8 MPa while maintaining high thermal insulation, which provides an effective thermal runway mitigation strategy for rechargeable lithium-ion batteries. Furthermore, the nanoengineering strategy reported here would shed light on achieving superthermal insulation of nanostructures for energy-critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sensitivity Study of Mini-Batch Size on a Long Short-Term Memory Network for In-situ Sensing of Core-to-shell Ratio of Microencapsulated Phase Change Materials

Microencapsulated phase change materials are being studied for applications for thermal energy storage in concentrated solar fields. During fabrication, the thickness of the encapsulation cannot be readily measured for real-time control. Therefore, a machine learning network, specifically a Long Short-Term Memory network, is being developed to estimate the ratio of the shell radius to core radius based on a one second temperature history. The mini-batch size determines how often the algorithm weights are updated during network training, and shuffle indicates whether the training data is shuffled during training. A general factorial design is used to analyze the effects of varying mini-batch size and shuffle, along with the core-to-shell ratio, on the RMSE of the response from the Long Short-Term Memory network. It was found that the network performed better for smaller core to shell ratios (less than 0.6) and had the lowest RMSE when the minibatch size was 128. The minimum RMSE found was 0.00501.

Shannon, Rebecca↗
DOE OSTI2026

Achieving ultrahigh modulus of resilience and enhanced thermal stability in ZnO x /SU-8 interpenetrating network polymer nanocomposite nanopillars

The modulus of resilience, a mechanical property that quantifies the maximum strain energy density a material can store during elastic deformation, is a crucial parameter for materials used in flexible displays, micro/nano-electro-mechanical system (M/NEMS) actuators, and ultra-sensitive pressure sensors. In this study, ZnO x /SU-8 nanocomposite nanopillars with a diameter of 300 nm, fully infiltrated with a uniformly distributed, interpenetrating amorphous ZnO x filler network, were synthesized via vapor-phase infiltration (VPI). In-situ uniaxial nano-compression tests revealed that the modulus of resilience of ZnO x /SU-8 reaches ∼ 12 MJ/m 3 , which is an ultrahigh value among all engineering materials with comparable strength. In addition, the synthesis fidelity, inorganic infiltration depth, and mechanical performance were all significantly improved compared to VPI-synthesized AlO x nanocomposites. Thermal stability, another key requirement for M/NEMS device materials operating under extreme environments, was also notably enhanced. Furthermore, partial crystallization of the amorphous ZnO x fillers during annealing contributed to an additional increase in modulus of resilience, reaching up to ∼ 13.9 MJ/m 3 . This work presents an effective fabrication strategy for producing nanostructured organic–inorganic hybrid nanocomposites with ultrahigh modulus of resilience and superior thermal stability, paving the way for their integration into next-generation flexible displays and high-performance M/NEMS devices working under harsh environments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mechanically and Thermally Enhanced 3D-Printed Photocurable Polymer Nanocomposites Containing Functionalized Chitin Nanowhiskers by Stereolithography

Additive manufacturing, or simply 3D printing (3DP), where objects are built through layer-wise material deposition, has gained significant academic and industrial attention as a result of the development of advanced and functional materials requiring rapid, customized, and flexible solutions. In the context of green manufacturing, diversifying environmentally and economically sustainable material portfolios is an essential endeavor for the success of 3DP technology that uses widely available, highly valuable, and renewable materials. Here, we used stereolithography (SLA) for processing methacrylate-based photocurable resins containing crab shell-derived chitin nanowhiskers (CNWs), which are surface-functionalized by reactive acrylate groups. Results from full spectral, thermal, structural, and topological analyses corroborate not only the surface functionalization of CNWs but also indicate the presence of these photocurable CNW (pCNW) fillers in the 3D-printed nanocomposites. Owing to the strong interfacial bond induced by the physical and chemical crosslinking between the pCNW and methacrylate (MA) polymer matrix, the internally formulated nanocomposites displayed enhanced thermomechanical properties (e.g., storage modulus and glass transition temperature) compared to those of commercially available pure SLA resins. For instance, the inclusion of 0.5 wt % pCNW improved the tensile strength and stiffness to up to 78 and 71%, respectively, without compromising the toughness and ductility of the printed material. Accordingly, this result also evidences the compatibility between the filler and resin materials. Consequently, the formation of a crosslinked network in the nanocomposite structure results in a higher thermal stability and activation energy (i.e., up to ~79%) for all the hybrid materials than the pristine MA. Finally, the high-resolution SLA print features, dimensional accuracy, and enhanced mechanical performance of our microstructure-forming functionalized chitin-based nanocomposites make them promising materials for a wide range of robust and high-performance industrial applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Coal-derived conductive pavement for winter de-icing: prototype, modeling, and simulation

Existing pavement de-icing methods result in high installation and maintenance costs, traffic delays, excessive weariness and corrosion, and negative environmental and safety impacts. To overcome these challenges, this paper presents an innovative pathway to designing and constructing smart self-heating pavements using a low-cost coal-char bearing asphalt material. The conductive asphalt incorporates coal char, i.e., a key byproduct of the coal pyrolysis process, into the Stone Mastic Asphalt (SMA) mixture. This asphalt containing coal-derived solid carbon exhibits highly tailorable electrical conductivity, satisfactory mechanical and thermophysical properties, and superior cost-efficiency as compared to other conductive pavement materials. The de-icing performance was also demonstrated by laboratory experiments on a bench-scale prototype. Furthermore, an efficient thermal network model was developed and validated by experiments to investigate the transient thermal behavior and energy performance of the Ohmic heating pavement system. Furthermore, the whole-year energy simulation case studies were conducted on a bridge pavement with an annual energy use of 24.6–1444.8 kWh/m 2 , showcasing its potential in field applications across cool humid, cold humid, and subarctic/arctic climate zones.

42 ENGINEERING↗
DOE OSTI2020

A Topology Optimization Framework to Facilitate Adoption of Advanced District Thermal Energy Systems

Advanced district thermal energy systems, which circulate water at temperatures near ambient conditions, and facilitate the utilization of waste heat and renewable thermal sources, can lower the carbon-intensity of urban districts, advancing the U.N. Sustainable Development Goals. Optimization of the network topology — the selection of the best subset of buildings and the best network to connect them, to minimize life cycle cost — can increase adoption of these system in appropriate applications. The potential "solution space" of the topology optimization problem grows factorially with the number of buildings in the district, motivating the consideration of a design heuristic. In this study, a heuristic for the network selection was evaluated with an exhaustive search, for a prototypical four-building district. For the prototypical district considered, the heuristic was effective in selecting an optimal network topology. Additionally, it was found that, in this case, the selection of the subset of buildings was more influential on the life cycle cost than the selection of the network topology. This work is part of a larger effort to develop a topology optimization framework for district thermal energy systems, which is anticipated to address barriers to adoption of ambient-temperature systems.

28 EE - Advanced Manufacturing Office (EE-5A)↗
NASA NTRSDate not supplied

Electric Aircraft Thermal Management Using a Two-Phase Heat Transport System with Solid-State Thermal Switching Capability

Advanced Cooling Technologies, Inc. (ACT) is collaborating with NASA Glenn Research Center (GRC) to develop a heat pipe-based thermal delivery system to efficiently manage the waste heat generated onboard an electric aircraft. The heat pipe system will interface with NASA GRC’s thermoacoustic heat pump in order to recycle waste heat by transporting thermal energy to various end users onboard the aircraft. This paper discusses the layout of theoretical heat pipe networks for a MW-class commercial electric aircraft. This is followed by a discussion of progress made on the development of a novel two-phase heat transport system with solid-state thermal switching and control capabilities.

Exergy↗
DOE OSTI2021

Heat transport in liquid water from first-principles and deep neural network simulations

In this work, we compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Evolution and use of combined mechanical and thermal codes for cryogenic turbopump bearings

Shaft bearing system analysis codes were developed, improved, and used to investigate Space Shuttle Main Engine (SSME) Liquid Oxygen (LOX) turbopump bearing problems, and to support the Marshall Space Flight Center Bearing and Seal Materials Test (BSMT) program. Thermal network modeling uses the SINDA thermal code, and the modeling of bearing quasi-dynamic characteristics uses the SHABERTH bearing/shaft code. These codes are solved concurrently for a bearing/shaft system using software developed for this purpose. Simulation of the SSME LOX turbopump turbine and pump end bearings and the MSFC BSMT operating in liquid nitrogen (LN sub 2) and LOX was done. The thermal network models include the bearing components, bearing carriers, shaft, housing, frictional heat, and viscous fluid energy. A cage model was included to account for heat generation between the cage and rolling elements. Since most bearing surfaces operate at temperatures well above the coolant saturation temperature, and move at high speed relative to the coolant, forced convection boiling is the dominant mechanism for heat removal. Improved modeling of forced convection film boiling was incorporated to take into account the local vapor generation at the high temperature surfaces. Rearing preloads in the pump and tester are provided by preload springs. As bearing operating clearances and contact angles change due to thermal effects and loading, the bearing preload changes with these varying conditions. These characteristics were modeled and are included in the overall system models. Results from these models indicate an operational limit which, if exceeded, predicts a thermal excursion.

Cody, Joe C.↗
DOE OSTI2023

Thermal transport in warm dense matter revealed by refraction-enhanced x-ray radiography with a deep-neural-network analysis

Abstract Transport properties of high energy density matter affect the evolution of many systems, ranging from the geodynamo in the Earth’s core, to hydrodynamic instability growth in inertial confinement fusion capsules. Large uncertainties of these properties are present in the warm dense matter regime where both plasma models and condensed matter models become invalid. To overcome this limit, we devise an experimental platform based on x-ray differential heating and time-resolved refraction-enhanced radiography coupled to a deep neural network. We retrieve the first measurement of thermal conductivity of CH and Be in the warm dense matter regime and compare our measurement with the most commonly adopted models. The discrepancies observed are related to the estimation of a correction term from electron-electron collisions. The results necessitate improvement of transport models in the warm dense matter regime and could impact the understanding of the implosion performance for inertial confinement fusion.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Adaptive behavior and different thermal experiences of real people: A Bayesian neural network approach to thermal preference prediction and classification

Various observed and unquantifiable factors affect the thermal comfort of occupants in indoor environments and can lead to high uncertainty in the prediction and classification of their thermal preferences. The behavioral adaptation of occupants, by operating window systems for example, changes their thermal experience and expectations and therefore contributes to even higher prediction uncertainty. In this study, we applied a Bayesian neural network (BNN) algorithm to build a predictive model for occupant thermal preference using the ASHRAE Global Thermal Comfort Database II. The Bayesian method allows us to synthesize prior knowledge and available measurements into a unified modeling framework. It also offers a way to express and quantify uncertainty. Here we have performed a systematic study to test the efficiency and robustness of different BNN model configurations. In this study, the results show that the BNN model outperforms conventional thermal comfort models such as Predicted Mean Vote (PMV) and adaptive comfort model. The BNN model tends to produce more confident “prefer cooler” predictions with high possibility and low uncertainty. In contrast, the BNN model produces less certain predictions for “prefer no change” and “prefer warmer” across all occupants. Our findings suggest that linking occupants’ subjective evaluation measures and window opening/closing behavior to thermal comfort modeling effectively improves predictive performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Entry, Descent, and Landing Analysis of Low Mass Martian Probe for Surface Climate Network

Aeolus is a mission to provide the first direct measurements of Martian atmospheric wind speeds and correlate them with thermal and compositional data to bring together a complete systematic description for the global energy balance and climate cycles of Mars. Objective A of the Mars Exploration Program Analysis Group (MEPAG) Goal II cites a Science Investigation Area to develop a network of surface landers to provide global, diurnal and synoptic coverage of the near-surface environment to characterize present Mars climate [1]. Aeolus will characterize the present Martian global climate system via an orbiter element and a novel surface network element. A series of low mass, low power probes will be distributed across the Martian surface and measure pressure, sky opacity, temperature, wind speed, water vapor and other trace gases. Low mass and power electrical systems designed to operate in extreme cold and daily thermal cycling for one Martian year could facilitate lower cost, Class-D science probes. Aeolus is targeting a total per-probe mass of <5 kg. Recent advances in nanofabrication of chip-scale sensors that are highly robust to temperature and shock, combined with a novel packaging concept (the deployable sensor probe [2]) originally developed by the Aerospace Corp., for the first time enables a Mars surface climate sensor network. These probes are folded flat during transit to Mars and automatically deploy into the final reentry configuration once released from the orbiter. The capability to fold for stowage significantly increases the probe quantity carried by the orbiter.

EDL↗
DOE OSTI2023

Study of Storage Requirements and Costs for Shaping Renewables and Nuclear Energy (FY23 Summary Report)

To decarbonize electricity generation primarily by using wind and solar resources, it is likely that additional low (or zero) carbon dioxide (CO 2 ) alternatives will be required for ensuring a reliable electricity supply. This study extends the work and modeling framework that we consolidated in FY 2022, by assessing the zero-CO 2 pathways of a Texas power system in which the availability of variable renewable energy (VRE), nuclear energy, and energy storage types (i.e., battery and thermal energy storage [TES]) are considered the sole resources available for expansion. Using the Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON) frameworks, this study investigates the market of two nuclear energy technologies (i.e., large light-water reactors [LWRs] and LWR-type small modular reactors [SMRs]) under two plausible storage coupling scenarios (i.e., electric coupling and direct thermal coupling). We also explore optimal environments for nuclear energy deployment, and identify key performance characteristics that make nuclear energy economically viable in areas with significant intermittent energy sources. Our analysis encompasses 24 cases. We model the least-cost grid systems, highlight the potential for a coupled LWR-TES approach, and utilize SMRs to achieve deep decarbonization in an affordable, technically feasible manner. We also examine seasonal variations in balancing electricity supply and demand, and account for varying performance, costs, and grid constraints. Overall, the findings of our modeling afford valuable insights for supporting future technology investment decisions in the energy sector.

25 ENERGY STORAGE↗