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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Electric-vehicle battery second-life and recycling pathways: How economics depend on chemistry, processing, and application

We assess the economics of repurposing and recycling electric vehicle (EV) batteries by estimating the maximum acquisition price repurposers and recyclers could pay for used EV packs across cathode chemistries, first-life conditions, second-life applications, and recycling processes. We develop a novel open-source process-based cost model of a UL-1974-certified repurposing facility and leverage battery degradation models to estimate the maximum acquisition price repurposers could pay for used EV batteries while producing second-life battery energy storage systems with life-adjusted costs equivalent to new systems. We compare these maximum price estimates to maximum prices for recyclers based on cost and revenue estimates from the EverBatt model. We find that repurposing is more economical than recycling for lithium iron phosphate (LFP) batteries, due to their relatively long life and low value materials; recycling is generally more economical than repurposing for lithium nickel cobalt aluminum oxide (NCA) batteries, due to their shorter life and higher value materials; and the economics for lithium nickel manganese cobalt oxide (NMC) batteries depend more heavily on first life retirement conditions and second life application intensity. These results suggest an overall strategy: reuse LFP, recycle NCA, and sort NMC into recycling or repurposing pathways based on state of health and second-life application.

25 ENERGY STORAGE↗
DOE OSTI2023

Process Modeling and Analysis of a Novel Sorbent Material for Direct Air Capture Applications

This poster presents results of Task 9.0, Advanced Modeling Support of CDR Pilot Projects, of the Carbon Dioxide Removal program (CDR FWP23). Specifically, the poster presents the multi-scale modelling framework to investigate alternate processes configuration for DAC applications including Vacuum-Assisted Temperature Swing Adsorption (TVSA) process and sweep gas for regeneration.

Caballero, Daison↗
DOE OSTI2020

A Novel Model of Thyristor in Reverse Recovery Process for Current Interruption Test of HVDC Circuit Breakers

The high voltage thyristor is widely used in high voltage DC (HVDC) grids due to its large current capability. Recently, the high voltage thyristor is implemented to produce a high current in the HVDC circuit breaker (CB) testing process. Unfortunately, the reverse recovery process (RRP) of a thyristor could induce significant turn-off overvoltage and power losses, jeopardizing the safety of the devices. The existing models of a thyristor cannot describe such hard-switching transient accurately. Therefore, this paper divides this process into four stages based on the physical mechanism and proposes a novel cosine exponential (CE), simulating the voltage and current of the thyristor with analytical equations, which are derived from the equivalent circuit. Moreover, the extraction procedure of the proposed model is illustrated in this paper. Finally, we develop an HVDC CB test platform to validate the proposed model. Experimental results show that the proposed CE model can accurately predict the overvoltage with a relative error of less than 8% at various current varying rates.

Dongye, Zhonghao↗
DOE OSTI2024

A Bayesian Learning Approach to Wireless Outdoor Heatmap Construction using Deep Gaussian Process

We present a novel Bayesian learning approach to outdoor radio heatmap construction utilizing deep Gaussian process (GP). The proposed approach employs a two-layer hierarchy which consists of two cascaded Gaussian processes that are capable of modeling more complex input-output relations than standard single-layer Gaussian processes. Since deriving the exact model likelihood is challenging, a lower bound is optimized instead so that gradient descent-based methods can be performed to find out the optimal model parameters. Typically, inducing points are used in GPs to facilitate low-rank approximation of covariance (kernel) matrices for computation speedup. However, the inaccuracy induced by inducing points can accumulate when stacking multiple layers of GP which may hinder the performance of deep GP. Moreover, since inducing points need to be learned, having them at all layers of deep GP also incurs computational burden. To overcome the above challenges, in contrast to the canonical deep GP model, we use a modified architecture where a full standard GP resides in the first layer and inducing points are only introduced for the second layer. This modified architecture strikes a balance between model accuracy and training complexity. In the proposed model, the noise parameter of the first GP layer is also eliminated to improve the training efficiency as the noise parameter at the output of the second layer suffices to model the uncertainty in the output. The proposed approach is evaluated on real-world datasets, in the form of location-Received Signal Strength (RSS) pairs, collected from the Platform for Open Wireless Data-driven Experimental Research (POWDER) located at the campus of the University of Utah. Experiment results show that the proposed approach can achieve smaller prediction errors on various training and testing data configurations than DNN-based and GP-based methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A Novel Continuous Ultrasound-Assisted Leaching Process for Rare Earth Element Extraction: Environmental and Economic Assessment

Rare earth elements (REEs) make up integral components in personal electronics, healthcare instrumentation, and modern energy technologies. REE leaching with organic acids is an environmentally friendly alternative to traditional extraction methods. Our previous study demonstrated that batch ultrasound-assisted organic acid leaching of REEs can significantly decrease environmental impacts compared to traditional bioleaching. The batch method is limited to small volumes and is unsuitable for industrial implementation. This study proposes a novel approach to increase reaction volume using a continuous ultrasound-assisted organic acid leaching method. Laboratory experiments showed that continuous ultrasound-assisted leaching increased the leaching rate (µg/h) 11.3–24.5 times compared to our previously reported batch method. Techno-economic analysis estimates the cost of the continuous approach using commercially purchased organic acids is $\$$9465/kg of extracted REEs and $\$$4325/kg of extracted REEs, using gluconic acid and citric acid, respectively. The sensitivity analysis reveals that substituting commercially purchased organic acids with microbially produced biolixiviant can reduce the process cost by approximately 99% while minimally increasing energy consumption. Environmental assessment shows that most of the emissions stemmed from the energy required to power the ultrasound reactor. We concluded that increased leaching capacity using a continuous ultrasound-assisted approach is feasible, but process modifications are needed to reduce the environmental impact.

54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗
DOE OSTI2024

Interfacially Enhanced Superconductivity in Fe(Te,Se)/Bi 4 Te 3 Heterostructures

Realizing topological superconductivity by integrating high-transition-temperature (T C ) superconductors with topological insulators can open new paths for quantum computing applications. Here, a new approach is reported for increasing the superconducting transition temperature $T^{onset}_C$ by interfacing the unconventional superconductor Fe(Te,Se) with the topological insulator Bi–Te system in the low-Se doping regime, near where superconductivity vanishes in the bulk. The critical finding is that the $T^{onset}_C$ of Fe(Te,Se) increases from nominally non-superconducting to as high as 12.5 K when Bi 2 Te3 is replaced with the topological phase Bi 4 Te 3 . Interfacing Fe(Te,Se) with Bi4Te3 is also found to be critical for stabilizing superconductivity in monolayer films where $T^{onset}_C$ can be as high as 6 K. Measurements of the electronic and crystalline structure of the Bi 4 Te 3 layer reveal that a large electron transfer, epitaxial strain, and novel chemical reduction processes are critical factors for the enhancement of superconductivity. This novel route for enhancing T C in an important epitaxial system provides new insight on the nature of interfacial superconductivity and a platform to identify and utilize new electronic phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression

We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML(KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML(KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, and GDB-13-T) and open-shell (QMSpin) molecules.

Chemistry↗
DOE OSTI2020

High-Throughput Analysis of Materials for Chemical Looping Processes

Chemical looping is a promising approach for improving the energy efficiency of many industrial chemical processes. However, a major limitation of modern chemical looping technologies is the lack of suitable active materials to mediate the involved subreactions. Identification of suitable materials has been historically limited by the scarcity of high-temperature (>600 °C) thermochemical data to evaluate candidate materials. An accuratethermodynamic approach is demonstrated here to rapidly identify active materials which is applicable to a wide variety of chemical looping chemistries. Application of this analysis to chemical looping combustion correctly classifies 17/17 experimentally studied redox materials by their viability and identifies over 1300 promising yet previously unstudied active materials. This approach is further demonstrated by analyzing redox pairs for mediating a novel chemical looping process for producing pure SO2 from raw sulfur and air which could provide a more efficient and lower emission route to sulfuric acid. 12 promising redox materials for this process are identified, two of which are supported by previous experimental studies of their individual oxidation and reduction reactions. This approach provides the necessary foundation for connecting process design with high-throughput material discovery to accelerate the innovation and development of a wide range of chemical looping technologies.

chemical looping↗
DOE OSTI2021

GeoMicro3D: A novel numerical model for simulating the reaction process and microstructure formation of alkali-activated slag

Highlights: • The GeoMicro3D model was innovatively developed for simulating the reaction process and microstructure formation of AAS. • In GeoMicro3D, the dissolution of slag and reactions of aqueous ions are fundamentally described in the local lattice cell. • A novel approach is proposed to speed up the nucleation simulations. • GeoMicro3D was implemented and verified with the relevant experimental data and thermodynamic calculation results. For the first time, this study developed a novel model, named GeoMicro3D, to simulate the reaction process and microstructure formation of alkali-activated slag. The GeoMicro3D model consists of four modules that are designed to simulate, respectively: (i) the initial spatial distribution of real-shape slag particles in alkaline activator, (ii) the dissolution of slag and diffusion of ions via the transition state theory and lattice Boltzmann method, respectively, (iii) the spatial distribution of reaction products using a nucleation probability theory, and (iv) the chemical reactions with thermodynamic modelling. Afterwards the GeoMicro3D model was implemented and verified. The simulation results were discussed and compared with the relevant experimental data and thermodynamic calculation results using GEMS. A good agreement was found in the comparisons, showing the strong simulation capability of GeoMicro3D.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling Novel Aqueous Particle and Cloud Chemistry Processes of Biomass Burning Phenols and Their Potential to Form Secondary Organic Aerosols

Phenols emitted from biomass burning contribute significantly to secondary organic aerosol (SOA) formation through the partitioning of semivolatile products formed from gas-phase chemistry and multiphase chemistry in aerosol liquid water and clouds. The aqueous-phase SOA (aqSOA) formed via hydroxyl radical (˙OH), singlet molecular oxygen ( 1 O 2 *), and triplet excited states of organic compounds ( 3 C*), which oxidize dissolved phenols in the aqueous phase, might play a significant role in the evolution of organic aerosol (OA). However, a quantitative and predictive understanding of aqSOA has been challenging. Here, we develop a stand-alone box model to investigate the formation of SOA from gas-phase ˙OH chemistry and aqSOA formed by the dissolution of phenols followed by their aqueous-phase reactions with ˙OH, 1 O 2 *, and 3 C* in cloud droplets and aerosol liquid water. Here, we investigate four phenolic compounds, i.e., phenol, guaiacol, syringol, and guaiacyl acetone (GA), which represent some of the key potential sources of aqSOA from biomass burning in clouds. For the same initial precursor organic gas that dissolves in aerosol/cloud liquid water and subsequently reacts with aqueous phase oxidants, we predict that the aqSOA formation potential (defined as aqSOA formed per unit dissolved organic gas concentration) of these phenols is higher than that of isoprene-epoxydiol (IEPOX), a well-known aqSOA precursor. Cloud droplets can dissolve a broader range of soluble phenols compared to aqueous aerosols, since the liquid water contents of aerosols are orders of magnitude smaller than cloud droplets. Further, our simulations suggest that highly soluble and reactive multifunctional phenols like GA would predominantly undergo cloud chemistry within cloud layers, while gas-phase chemistry is likely to be more important for less soluble phenols. But in the absence of clouds, the condensation of low-volatility products from gas-phase oxidation followed by their reversible partitioning to organic aerosols dominates SOA formation, while the SOA formed through aqueous aerosol chemistry increases with relative humidity (RH), approaching 40% of the sum of gas and aqueous aerosol chemistry at 95% RH for GA. Our model developments of biomass-burning phenols and their aqueous chemistry can be readily implemented in regional and global atmospheric chemistry models to investigate the aqueous aerosol and cloud chemistry of biomass-burning organic gases in the atmosphere.

09 BIOMASS FUELS↗
DOE OSTI2022

EpIC: novel Monte Carlo generator for exclusive processes

Abstract We present the EpIC Monte Carlo event generator for exclusive processes based on generalised parton distributions. EpIC utilises the PARTONS framework, which provides a flexible software architecture and a variety of modelling options for the partonic description of the nucleon. The generator offers a comprehensive set of features, including simulation of a variety of exclusive processes and radiative corrections. It may be used both in the analysis of experimental data, as well as for impact studies, especially for the future electron-ion colliders.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2024

7 Innovations in high-rate composite manufacturing: integrating additive manufacturing with compression molding process

Advanced composites play a pivotal role in modern engineering, offering exceptional strength-to-weight ratios and tailored properties, essential for various industries. High-rate composite manufacturing techniques have rapid production capabilities, which are essential for meeting the demands of industries requiring cost-saving, efficiency, and quick turnaround times. This chapter explores the Additive Manufacturing- Compression Molding (AM-CM) system developed by Oak Ridge National Laboratory (ORNL) for advanced composites manufacturing. The AM-CM system integrates additive manufacturing with compression molding, facilitating the production of polymer composite parts with superior mechanical properties and meticulously controlled microstructures. This innovative system not only ensures precise material deposition but also operates as a fast composite manufacturing process, enhancing productivity and performance, which are needed attributes across industrial applications. Through comprehensive mechanical testing and microstructural analysis, AM-CM promotes remarkable fiber alignment and reduced porosity in composite parts compared to alternative thermoplastic high-rate composite manufacturing methods. Furthermore, AM-CM enables overmolding reinforcement using continuous carbon fiber and supports selective reinforcement through customizable toolpaths. It also facilitates the production of hybrid materials to achieve tailored mechanical properties. Future advancements in AM-CM technology aim to enhance process efficiency, broaden material versatility, and improve part performance. This involves exploring novel materials, advancing process monitoring, implementing automation technologies, and integrating artificial intelligence (AI) and machine learning (ML) for predictive modeling and real-time optimization in composite manufacturing. These developments will establish the AM-CM system as a transformative technology in composite manufacturing, driving innovation across industries.

Hassen, Ahmed [ORNL] (ORCID:0000000328521222)↗
DOE OSTI2022

An Adaptive Geometry-Free Thermo-Mechanical Model for Directed Energy Deposition Process Modeling

This presentation describes a novel, geometry-free thermo-mechanical model with adaptive subdomain con- struction to accurately predict the thermal conditions, distortions, and residual stresses throughout the directed energy deposition (DED) process. A novel finite element workflow is designed to con- duct the numerical analysis, based on the multi-app and data transfer capabilities in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). Unlike with traditional methods, the part geometry in this model is not predefined. Instead, it is a combined effect of the processing parameters and material properties. At each time step, the model utilizes a subdomain construction paradigm to model the material deposition. A specialized mesh adaptivity scheme is incorporated to provide an accurate prediction while reducing the overall computational cost. The results generated by the proposed model show general agreement with the experimental measurements for the single track scan with varying processing parameters and demonstrate reasonable predictions for higher material buildups.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach

Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia. A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects’ neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants’ demographic characteristics. Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

neuropsychological↗
DOE OSTI2026

NGEE Arctic Authorship Guidelines

Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗
DOE OSTI2021

Mineral-Ground Micro-Fibrillated Cellulose Reinforcement for Polymer Compounds

Additive Manufacturing (AM) is quickly becoming a mainstream, energy efficient manufacturing technique for complex and custom components. ORNL has taken polymer AM into the next level with Big Area Additive Manufacturing (BAAM). However, we need to develop the strength of AM materials (specifically polymers) to satisfy the mechanical requirements of structural parts. Also, it is crucial that the developed process can easily be scaled up to have a significant impact in the manufacturing industry of US. Furthermore, renewable, bio-derived AM materials are desirable from a sustainability perspective. In the first phase of this project the mechanical properties of polymer AM components were significantly improved developing a micro fibrillated cellulose (MFC)-reinforced composite material system that is derived from biomass and compatible with BAAM. Unfortunately, the improvements were observed only with freeze-dried MFCs, and freeze-drying is a very slow process and is not industrially scalable. Furthermore, MFCs were not available in large quantities at low cost. However, FiberLean has reached 12,000 tons/year capacity (60,000 tons/year including the minerals). FiberLean offers MFCs at a scale and order of magnitude lower price than current market participants. With their recently developed novel (patent pending) drying process FiberLean can achieve over 50% solid content commercially. In the 2nd phase of the project, the original objective was to investigate this new novel drying process along with compounding with different formulations to optimize and adapt to large scale additive manufacturing. However, the compounded materials prepared via this approach were very brittle and demonstrated poor mechanical performance. Therefore, the project was focused more on a scalable chemical surface modification of MFCs to enable their drying via conventional oven process with minimal agglomeration and better dispersion in PLA polymer matrix. Fiberlean and ORNL have worked together in this project to develop mineral-ground microfibrillated cellulose (MFC) - reinforced additive manufacturing feedstock materials with significantly improved mechanical properties. Properly surface modified and dried Fiberlean cellulose microfibrils led to tensile strength increases up to ~40% and elastic modulus increases up to 70% with the addition of 30% MFC into PLA matrix. The work has led to a high impact journal article and a provisional patent. Opportunities for a follow up work to demonstrate the technology in large scale and application of it to AM applications are currently being searched.

36 MATERIALS SCIENCE↗