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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data-Driven Modeling and Optimization of Building Energy Consumption: a Case Study

Installing sensors and Building Automation Systems (BAS) allows controlling the facility operations while generating data that can be analyzed for model development. This work focuses on data-driven modeling of the building to optimize energy consumption. The City of Orlando aims to reduce its energy consumption so, they provided us access to their BAS for data and studying the operation of its facilities. We selected a mid-size pilot building to conduct data analysis and modeling. We develop an Application Programming Interface (API) to login to the servers and scrape data. The scraped data contains features ranging from environmental conditions to equipment activity. This dataset is a time series so, it's handled in accordance and analyzed to investigate patterns and relations between data points that help choose parameters for predictive models for building and equipment. Finally, the models are optimized to reduce the energy consumption of the facility.

Grover, Divas↗
DOE OSTI2020

A Data-driven Approach for Predicting Nepheline Crystallization in High-Level Waste Glasses

High-level waste (HLW) glasses with high aluminum content are prone to nepheline crystallization during the slow canister cooling that is experienced during large-scale production. Due to its detrimental effects on glass durability, nepheline precipitation must be avoided; however, developing robust, predictive models for nepheline crystallization behavior in HLW glasses is difficult due to their compositional complexity. Using overly conservative constraints to predict nepheline formation can limit the waste loading to lower than the achievable capacity. In this study, a robust data-driven model using five compositional features has been developed to predict nepheline formation. The analysis of the model and the data show that there is an overlap, instead of a distinct compositional boundary, between glasses that form and do not form nepheline. As a result, the model’s predictive accuracy is not the same throughout the feature space and instead is dependent on the location of the glass composition in the dimensionally reduced feature space.

Sargin, Irmak↗
DOE OSTI2020

Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts

Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH 3 decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Demonstration of Electrochemically-Driven CO 2 Separation Using Hydroxide Exchange Membranes

Hydroxide exchange membrane fuel cells (HEMFCs) are a potentially lower-cost hydrogen fuel cell technology; however, ambient levels of CO 2 in air significantly reduce HEMFCs’ performance. In this work, we demonstrate an electrochemically-driven CO 2 separator (EDCS) which can be used to remove ambient levels of CO 2 from air upstream of the HEMFC stack in fuel cell vehicles, protecting it from CO 2 -related performance losses. The EDCS operating window was explored for current density, anode flow, and cathode flow with respect to its impact on CO 2 separation performance. Additionally, gas-phase mass transport was improved by selecting flow fields and gas diffusion layers conducive to the EDCS operating regime. The use of a carbon-ionomer interlayer at the cathode was explored and improved CO 2 removal performance from 77.7% to 98.2% at 20 mA cm −2 . An analytical, 1-D model is used to explain the experimental observations and design improvements. A single-cell, 25 cm 2 EDCS using the aforementioned improved design demonstrated greater than 98% CO 2 removal at a cathode flow rate of 1300 sccm for 100 h with 2.7% hydrogen stack consumption.

Matz, Stephanie↗
NASA NTRS1982

Measurements in a pressure-driven and a shear-driven three-dimensional turbulent boundary layer

Results of mean velocity field, wall static pressure field and simultaneous, direct force measurements of the local wall shear stress magnitude and direction are reported for a pressure-driven and a shear-driven three-dimensional turbulent boundary layer. These data, particularly the direct force local wall shear data, were obtained to test the validity of several of the near-wall similarity models proposed in the literature for such flows.

Pierce, F. J.↗
NASA NTRS1990

Current-driven plasma acceleration versus current-driven energy dissipation. I - Wave stability theory

The dominant unstable electrostatic wave modes of an electromagnetically accelerated plasma are investigated. The study is the first part of a three-phase program aimed at characterizing the current-driven turbulent dissipation degrading the efficiency of Lorentz force plasma accelerators such as the MPD thruster. The analysis uses a kinetic theory that includes magnetic and thermal effects as well as those of an electron current transverse to the magnetic field and collisions, thus combining all the features of previous models. Analytical and numerical solutions allow a detailed description of threshold criteria, finite growth behavior, destabilization mechanisms and maximized-growth characteristics of the dominant unstable modes. The lower hybrid current-driven instability is implicated as dominant and was found to preserve its character in the collisional plasma regime.

Kelly, A. J.↗
NASA NTRS1996

Studies of Radiation-Driven and Buoyancy-Driven Fluid Flows and Transport

It is well known that radiative heat transport influences many types of buoyant flows due to its effect on the temperature and thus density field in the fluid medium. It is of interest to study gaseous flows driven solely by radiation in the absence of buoyancy, particularly because of its application to astrophysical flows that are well known from astronomical observations and numerical simulation. However, no laboratory-scale experiments of this phenomenon have ever been conducted. To study the possibility of obtaining such flows in the laboratory, an apparatus was built to produce large temperature differences (Delta (T)) up to 300 K in a gas confined between flat parallel plates. SF6 was used as the radiatively-active gas because its Planck absorption length is much shorter than that of any other common non-reactive gas. The NASA-Lewis 2.2 second drop tower was used to obtain reduced gravity in order to suppress buoyancy effects. To image the resulting flows, a laser shearing interferometer was employed. Initial results indicate the presence of flow that does not appear to be attributable to the residual flow resulting from buoyancy influences before the drop. For Delta(T) greater than 70 K, slight deformations in the interferometer fringes seen at lower Delta(T) became large unsteady swirls. Such behavior did not occur for radiatively-inactive gases, suggesting that a flow driven solely by radiation was obtained in SF6 and to a lesser extent in CO2. This was more pronounced at higher pressures and plate spacings, consistent with our scaling predictions.

Ronney, Paul D.↗
NASA NTRS1994

Studies of Radiation-Driven and Buoyancy-Driven Fluid Flows and Transport

It is well known that radiative heat transport influences many types of buoyant flows due to its effect on the temperature and thus density field in the fluid medium. It is of interest to study gaseous flows driven solely by radiation in the absence of buoyancy, particularly because of its application to astrophysical flows that are well known from astronomical observations and numerical simulation. However, no laboratory-scale experiments of this phenomenon have ever been conducted. To study the possibility of obtaining such flows in the laboratory, an apparatus was built to produce large temperature differences (Delta T) up to 300 K in a gas confined between flat parallel plates. SF6 was used as the radiatively-active gas because its Planck absorption length is much shorter than that of any other common non-reactive gas. The NASA-Lewis 2.2 second drop tower was used to obtain reduced gravity in order to suppress buoyancy effects. To image the resulting flows, a laser shearing interferometer was employed. Initial results indicate the presence of flow that does not appear to be attributable to the residual flow resulting from buoyancy influences before the drop. For Delta T greater than 70 K, slight deformations in the interferometer fringes seen at lower Delta T became large unsteady swirls. Such behavior did not occur for radiatively-inactive gases, suggesting that a flow driven solely by radiation was obtained in SF6 and to a lesser extent in CO2 This was more pronounced at higher pressures and plate spacings, consistent with our scaling predictions.

Ronney, Paul D.↗
NASA NTRSDate not supplied

Enabling Mission Flexibility to Battery Driven Deep Space Endeavors With Generalized Battery-Health-Monitoring Using Physics-Based and Data-Driven Reduced-Order Models

The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.

Automation↗
DOE OSTI2021

Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS

With recent developments in computational techniques, Data-driven Machine Learning Models (DMLs) have shown great potential in simulating streamflow and capturing the rainfall-runoff relationship in given watersheds, which are traditionally fulfilled by Process-based Hydrologic Models (PHMs). There are debates on whether the DMLs can outperform and possibly replace the classical PHMs for streamflow simulation and river forecasting, but no clear conclusions have been made. This study aims to investigate whether the newer DMLs have any potential in further improving the simulation accuracy of classical PHMs, and vice versa. To do this, we compared a few popular PHMs and DMLs over four watersheds across the Continental US (CONUS) that are associated with different input, climate, and regional conditions. A total of five hydrologic models were chosen, including (1) two classical lumped models, i.e., the Sacramento Soil Moisture Accounting (SAC-SMA) and Xinanjiang (XAJ); (2) one modern distributed model, termed Coupled Routing and Excess Storage (CREST); (3) and two DMLs including an Artificial Neural Networks (ANN) and a deep learning model, termed Long Short Term Memory (LSTM). Our results demonstrated that the DMLs still significantly biased when using the baseline input scenario with the PHMs. However, the DMLs fed with delayed input scenarios had great potential and can reach high simulation accuracy. The DMLs, especially the ANN, outperformed other employed models under the rainfall-runoff relationship in which rainfall dominantly drives. Furthermore, the DMLs also showed better performance in the high-flow regime, while the PHMs had a better performance for the low-flow regime, implying both PHMs and DMLs have their own merits and are worthy of joint development. In general, our study indicated a great potential of using DMLs to simulate streamflow, but further studies are still needed to verify the transferability and scalability of DMLs in large-scale experiments, such as the Distributed Model Intercomparison Projects 1&2 conducted by National Weather Services but to compare modern DMLs and PHMs.

58 GEOSCIENCES↗
DOE OSTI2021

Data‐Driven Safety Risk Prediction of Lithium‐Ion Battery

Abstract Inevitable safety issues have pushed battery engineers to become more conservative in battery system design; however, battery‐involved accidents still frequently are reported in headlines. Identifying, understanding, and predicting safety risks have become priorities to further accelerate technology and industry development. However, diverse loading scenarios, significantly varied stress‐induced short circuit mechanisms, and highly coupled mechanical–electrochemical safety behaviors have remained grand challenges. Herein, the safety risk is termed as the probability of the mechanical triggering of an internal short circuit, to reflect the safety related behaviors of lithium‐ion batteries. Based on a mechanical model and experimental results, a sufficient dataset is generated consisting of strain states and their corresponding safety risks, covering both cylindrical and pouch cells, various states of charges, and loading conditions. Machine‐learning tools combined with the established finite element mechanical model are applied to predict the safety risks of the cells. The results achieve a high level of accuracy on the test data (the relative error of the average short circuit prediction deviation is less than 6.2%.). This work underpins the safety risk concept and highlights the promise of physics combined with data‐driven modeling methodology to predict the safety behaviors of energy storage systems.

Jia, Yikai↗
DOE OSTI2020

Iodine Immobilization by Materials through Sorption and Redox-Driven Processes: A Literature Review

Radioiodine-129 (129I) in the subsurface is mobile and limited information is available on treatment technologies. Scientific literature was reviewed to compile information on materials that could potentially be used to immobilize 129I through sorption and redox-driven processes, with an emphasis on ex-situ processes. Candidate materials to immobilize 129I include iron minerals, sulfur-based materials, silver-based materials, bismuth-based materials, ion exchange resins, activated carbon, modified clays, and tailored materials (metal organic frameworks (MOFS), layered double hydroxides (LDHs) and aerogels). Where available, compiled information includes material performance in terms of (i) capacity for 129I uptake; (ii) long-term performance (i.e., solubility of a precipitated phase); (iii) technology maturity; (iv) cost; (v) available quantity; (vi) environmental impact; (vii) ability to emplace the technology for in situ use at the field-scale; and (viii) ex situ treatment (for media extracted from the subsurface or secondary waste streams). Because it can be difficult to compare materials due to differences in experimental conditions applied in the literature, Part II of this review describes results of laboratory studies for selected materials using a standardized batch loading test.

Moore, Robert C.↗
DOE OSTI2020

Modeling Nonperturbative Field-Driven Vibronic Dynamics: Selective State Preparation and Nonlinear Spectroscopy

The partially linearized density matrix formalism for nonadiabatic dynamics is adapted to incorporate a classical external electromagentic field into the system Hamiltonian. This advancement encompasses the possibility of describing field-driven dynamics and computing a variety of linear and nonlinear spectroscopic signals beyond the perturbative limit. Here, the capabilities of the developed approach are demonstrated on a simple two-state vibronic model coupled to a bath, for which we (a) perform an exhaustive search in the field parameter space for optimal state preparation and (b) compute time-resolved transient absorption spectroscopy to monitor the effect of different pulse shapes on measurable experimental signals. While no restrictions on the form of the field have to be assumed, we focus here on Gaussian shaped (linearly) chirped pulses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ion Solvation-Driven Liquid–Liquid Phase Separation in Divalent Electrolytes with Miscible Organic Solvents

Liquid–liquid phase separation (LLPS) is a common phenomenon, but LLPS of electrolytes prepared in miscible organic solvents is rarely documented. Here we report four cases of LLPS that occur in MgTFSI 2 or ZnTFSI 2 electrolytes in mixed organic solvents. The conditions for the formation of this LLPS share four common features: cations with high charge density; bulky anions with low charge density; a strongly coordinating solvent with high compressibility; and a relatively weak coordinating solvent with low relative permittivity and high mobility. With these conditions, the cations tend to draw the strongly coordinating solvents together to form a densely-packed, energetically-favorable ion solvation region, while the cosolvents with low relative permittivity tend to repel ions to form an ion-depleted dilute phase. Furthermore, in the dilute upper layer with more than 300 solvent molecules per cation, ion pairs and large ion aggregates are clearly evidenced, due to the incomplete screening of electrostatic interactions by the weaker cosolvent. This LLPS driven by ion solvation may be more common in multivalent electrolytes and is a design consideration with mixed solvents that should not be overlooked.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

In Silico Design of Methyl-Driven Overhauser Dynamic Nuclear Polarization Agents

Overhauser effect (OE) dynamic nuclear polarization (DNP) has drawn attention owing to its enhanced performance at ultrahigh magnetic fields. The lack of design principles has, nevertheless, limited the development of OE polarizing agents when compared to those used for cross-effect DNP. Here, we measured the 19 F OE DNP performance of a series of CF 3 -functionalized Blatter-type radicals. Using density functional theory calculations, we accurately predict the methyl-driven OE performance, paving the way for computer-aided design of optimized OE DNP polarizing agents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Proximity Spin–Orbit Torque on a Two-Dimensional Magnet within van der Waals Heterostructure: Current-Driven Antiferromagnet-to-Ferromagnet Reversible Nonequilibrium Phase Transition in Bilayer CrI 3

The recently discovered two-dimensional magnetic insulator CrI 3 is an intriguing case for basic research and spintronic applications since it is a ferromagnet in the bulk but an antiferromagnet in bilayer form, with its magnetic ordering amenable to external manipulations. Using the first-principles quantum transport approach, we predict that injecting unpolarized charge current parallel to the interface of the bilayer-CrI 3 /monolayer-TaSe 2 van der Waals (vdW) heterostructure will induce spin–orbit torque and thereby drive the dynamics of magnetization on the first monolayer of CrI 3 in direct contact with TaSe 2 . By combining the calculated complex angular dependence of spin–orbit torque with the Landau-Lifshitz-Gilbert equation for classical dynamics of magnetization, we demonstrate that current pulses can switch the direction of magnetization on the first monolayer to become parallel to that of the second monolayer, thereby converting CrI 3 from antiferromagnet to ferromagnet while not requiring any external magnetic field. We explain the mechanism of this reversible current-driven nonequilibrium phase transition by showing that first monolayer of CrI 3 carries current due to evanescent wave functions injected by metallic transition metal dichalcogenide TaSe 2 , while concurrently acquiring strong spin–orbit coupling via such a proximity effect, whereas the second monolayer of CrI 3 remains insulating. Furthermore, the transition can be detected by passing vertical read current through the vdW heterostructure, encapsulated by a bilayer of hexagonal boron nitride and sandwiched between graphite electrodes, where we find a tunneling magnetoresistance of ≃240%.

2D magnetic materials↗