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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Delay-Throughput Performance of the Deep-Space Ka-band Link

In this paper, performance of a first-in, first-out (FIFO), selective retransmission scheme for the deep-space Ka-band link is presented and compared to the performance of a comparable X-band link. In this analysis, 16 months of water vapor radiometer (WVR) and advanced water vapor radiometer (AWVR) data from the three Deep Space Network (DSN) Communication Complexes (DSCC) were used to emulate weather effects on X-band and Ka-band links from Mars. Mars Reconnaissance Orbiter (MRO) X-band and Ka-band telecommunications parameters were used for spacecraft telecommunications capabilities. One pass per week per complex was selected from MRO's Deep Space Network (DSN) schedule from April 1, 2006 to August 31, 2007 for a total of 207 passes (69 passes per complex) for this analysis. For each pass both X-band and Ka-band links were designed using at most two data rates so that the expected pass capacity would be maximized subject to a minimum availability requirement (MAR). In conjunction with the WVR/AWVR data, elevation profiles of the selected passes and models for the performance of the antennas in the DSN were used to emulate the performance of both links. It was assumed that the retransmission of the data takes place not on the same pass as the original transmission but during subsequent passes. The data collected before a pass was assumed to be a fraction of the expected capacity of the pass as calculated through the link design process. Infinite spacecraft storage was assumed to obtain an upper bound on the spacecraft storage requirement. The independent parameters of this analysis were MAR and the ratio of data collected before a pass to the expected pass capacity. Since the selected passes did not occur at regular intervals, the delay in this analysis was measured in terms of number of passes. The throughput was measured in terms of number of bits received successfully on the ground. The results indicate that reasonable delay performance could be achieved with very high throughput for relatively low MAR values for data collection to expected pass capacity ratio of around 97% for Ka-band. The results indicate that, except for very low average delay requirements, the Ka-band link provides more than twice the throughput of the X-band link for the same amount of power consumed by the spacecraft. In addition, the results indicate that the required storage onboard the spacecraft is not prohibitive and good performance could be achieved by using a buffer size less than three times the maximum amount of data collected before a pass.

Shambayati, Shervin↗
DOE OSTI2025

Root genetics in the field to understand drought adaptation and carbon sequestration (Final Scientific/Technical Report)

For all crop plants, roots play a critical role in growth. Roots anchor the plants, and are the primary site of nutrient and water uptake. Roots are also the main source of C to soil in the form of root tissues and exudates, and thus greatly influence SOM stocks. To perform these functions, primary roots extend into soil, producing a network of branching roots of characteristic form, known as its root system architecture (RSA). RSA varies among species, and among varieties within a species that are adapted to different environments. Root traits are major targets for the second green revolution because of their potential to improve crop productivity, increase drought tolerance and nutrient acquisition, and increase C capture of soil. Improving the quality of roots in maize will be particularly valuable, since this crop is planted on over 92 million acres annually in the US. The future sustainability of agricultural systems relies on their ability to enhance soil organic matter (SOM) storage and reduce GHG emissions, while maintaining or enhancing productivity. This program had two components, Sensors and Models. For the first component, we designed and built a high-throughput phenotyping platform for root pulling of maize plants. This eliminated the physical labor of manually pulling up plants and reduced the number of personnel required down to one. The standardized pulling mechanism allowed recording force curves during the pulling process, providing additional information. We validated that the maximum force for pulling the root system was well-correlated with the root system mass and provided root crowns for further RSA analysis. These root crowns identified significant correlations with 2D root area and root depth, along with 3D root volume, total root length and number of root tips. We then used this system for field-based studies in maize on the genetics of root system architecture and its relation to nitrogen-use efficiency (NUE), including using lines relevant to the Corteva breeding program. Varieties were also evaluated at Corteva sites in the cornbelt and Danforth farm in Missouri, to establish responses across sites. From these studies we have identified genetic loci associated with root traits and created mutant lines for these loci and correlations of root traits with NUE. For the Models component, we worked to incorporate root and soil characteristics into the MEMS 2.0 soil and ecosystem biogeochemical model. Existing soil C models, such as Century, are unable to represent specific root trait interactions with the soil environment and therefore to accurately forecast the potential C sequestration benefits of root breeding under different climatic and soil type conditions. We have developed the MEMS 2.0 ecosystem biogeochemical model to improve quantification of farm-scale soil carbon and greenhouse gas emissions. The new knowledge and large datasets produced by this project will be used to develop and drive an innovative model capable of forecasting the impacts on soil C stocks and nutrient dynamics. An innovation was to use the empirical data from the field studies (in 1, above) to model genetic variation in nitrogen use efficiencies and soil C input. Our work demonstrated that maize root-derived C rapidly replaces existing soil C and after 3 years of continuous maize, up to 20% of soil organic C in the topsoil (0-15cm) and 3% in the subsoil (15-30cm) was contributed by maize. However, this contribution did not entirely represent a net increase. Root C contribution to soil was affected by maize genetics. We have analyzed soils derived from the CSU field trials for C and N stocks, in the different soil physical fractions represented by the MEMS model, using both physical fractionation with elemental analyses, and Fourier transformed infrared spectroscopy. Data will be used to link crop nitrogen use efficiencies with soil C sequestration and provide data to bridge the field trials with the model development, for verification of model predictions. The project had a number of successful outcomes: we have used the new phenotyping platform to identify new genetic loci that can enhance root phenotypes; we have partnered with multiple maize seed companies phenotype varieties in their breeding programs; we have developed the MEMS model that can help inform industry on the potential for carbon sequestration in the agricultural sector, and which is now available at the CSU Soil Carbon Solutions Center for use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rapid sensing of hidden objects and defects using a single-pixel diffractive terahertz sensor

Abstract Terahertz waves offer advantages for nondestructive detection of hidden objects/defects in materials, as they can penetrate most optically-opaque materials. However, existing terahertz inspection systems face throughput and accuracy restrictions due to their limited imaging speed and resolution. Furthermore, machine-vision-based systems using large-pixel-count imaging encounter bottlenecks due to their data storage, transmission and processing requirements. Here, we report a diffractive sensor that rapidly detects hidden defects/objects within a 3D sample using a single-pixel terahertz detector, eliminating sample scanning or image formation/processing. Leveraging deep-learning-optimized diffractive layers, this diffractive sensor can all-optically probe the 3D structural information of samples by outputting a spectrum, directly indicating the presence/absence of hidden structures or defects. We experimentally validated this framework using a single-pixel terahertz time-domain spectroscopy set-up and 3D-printed diffractive layers, successfully detecting unknown hidden defects inside silicon samples. This technique is valuable for applications including security screening, biomedical sensing and industrial quality control.

Li, Jingxi (ORCID:0000000165958680)↗
NASA NTRS1992

A bipolar population counter using wave pipelining to achieve 2.5 x normal clock frequency

Wave pipelining is a technique for pipelining digital systems that can increase clock frequency in practical circuits without increasing the number of storage elements. In wave pipelining, multiple coherent waves of data are sent through a block of combinational logic by applying new inputs faster than the delay through the logic. The throughput of a 63-b CML population counter was increased from 97 to 250 MHz using wave pipelining. The internal circuit is flowthrough combinational logic. Novel CAD methods have balanced all input-to-output paths to about the same delay. This allows multiple data waves to propagate in sequence when the circuit is clocked faster than its propagation delay.

Wong, Derek C.↗
DOE OSTI2025

Unveiling Feedstock Variability: Insights into Corn Stover Conversion - Part I: Physicochemical Properties and Self-Degradation

Transforming agricultural waste into biofuels and bioproducts is crucial to advancing a low-carbon bioeconomy. However, the inherent variability in the composition and quality introduces uncertainties in the conversion efficiency and poses challenges in process development. Through integrating a high-throughput conversion system, material characterization techniques, and advanced data analysis tools, this study investigates the variability of corn stover and its subsequent impacts on carbohydrate conversion. The findings reveal that indoor storage substantially reduces the moisture and ash content and soil contamination, while other properties remain largely unchanged. Self-degradation due to microbial activity during storage decreases the carbohydrate content of corn stover but enhances glucose and xylose yields. A negative correlation is observed between sugar yields and lignin content across samples with varying ash and moisture content. The inhibitory effect of lignin diminishes in self-degraded samples likely due to the disrupted cell wall structure. Although self-degradation slightly increases cellulose crystallinity, no strong correlation was observed between the crystallinity and sugar yield. Hot water pretreatment under mild conditions effectively mitigates inherent variability, consistently improving the sugar yield from corn stover by up to 50%. By elucidating the feedstock variability and its impact on convertibility, these findings offer valuable insights into appropriate feedstock handling and management, highlighting potential strategies to address variability challenges.

09 BIOMASS FUELS↗
DOE OSTI2023

High-throughput Li plating quantification for fast-charging battery design

Fast charging of most commercial lithium-ion batteries is limited due to fear of lithium plating on the graphite anode, which is difficult to detect and poses considerable safety risk. In this report we demonstrate the power of simple, accessible and high-throughput cycling techniques to quantify irreversible Li plating spanning data from over 200 cells. We first observe the effects of energy density, charge rate, temperature and state of charge on lithium plating, use the results to refine a mature physics-based electrochemical model and provide an interpretable empirical equation for predicting the plating onset state of charge. We then explore the reversibility of lithium plating and its connection to electrolyte design for preventing irreversible Li accumulation. Finally, we design a method to quantify in situ Li plating for commercially relevant graphite|LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC) cells and compare with results from the experimentally convenient Li|graphite configuration. The hypotheses and abundant data herein were generated primarily with equipment universal to the battery researcher, encouraging further development of innovative testing methods and data processing that enable rapid battery engineering.

25 ENERGY STORAGE↗
DOE OSTI2024

Artificial-Intelligence Aided Design and Synthesis of Novel Layered 2D Multi-Principal Element Materials for Energy Storage (Final Report)

This DOE-EPSCoR project aimed to predict, synthesize, and characterize novel layered two-dimensional (2D) high-entropy materials (HEMs). These 2D-HEMs, composed of multiple principal elements in nearly equal concentrations, are distinct from traditional 2D materials (typically containing two or three elements) and conventional alloys (dominated by a single primary element with minor secondary additions). Their unique structural and compositional features enable significant lattice strain accommodation, resulting in enhanced electrode performance and potential applications in catalysis, hydrogen storage, sensing, quantum information technologies, and flexible electronics. The research focused on addressing four fundamental questions: (i) What combinations of elements can form stable and synthesizable 2D-HEMs? (ii) What mechanisms drive the stability and synthesizability of crystalline single-phase 2D-HEMs? (iii) How do local chemical disorder and defects influence the macroscopic electronic and mechanical properties? and (iv) What charge storage mechanisms are active in selectively synthesized 2D-HEMs for battery and supercapacitor electrode applications? To achieve these goals, the project employed an integrated theory-experiment approach, incorporating high-throughput first-principles calculations, theoretical modeling, data mining, experimental synthesis, and advanced characterization techniques. The advanced computing resources and state-of-the-art experimental characterization facilities at Oak Ridge National Laboratory (ORNL) were leveraged through collaboration. Beyond scientific advancements, the project contributed to workforce development. Two postdoctoral researchers and three graduate students at the University of Maine were trained through co-advising by ORNL scientists and collaborative interactions, strengthening their expertise in cutting-edge materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Incorporating Flexibility Effects into Metal–Organic Framework Adsorption Simulations Using Different Models

High-throughput calculations based on molecular simulations to predict the adsorption of molecules inside metal–organic frameworks (MOFs) have become a useful complement to experimental efforts to identify promising adsorbents for chemical separations and storage. For computational convenience, all existing efforts of this kind have relied on simulations in which the MOF is approximated as rigid. In this paper, we use extensive adsorption–relaxation simulations that fully include MOF flexibility effects to explore the validity of the rigid framework approximation. We also examine the accuracy of several approximate methods to incorporate framework flexibility that are more computationally efficient than adsorption–relaxation calculations. We first benchmark various models of MOF flexibility for four MOFs with well-established CO 2 experimental consensus isotherms. We then consider a range of adsorption properties, including Henry’s constants, nondilute loadings, and adsorption selectivity, for seven adsorbates in 15 MOFs randomly selected from the CoRE MOF database. Our results indicate that in many MOFs adsorption–relaxation simulations are necessary to make quantitative predictions of adsorption, particularly for adsorption at dilute concentrations, although more standard calculations based on rigid structures can provide useful information. Finally, we investigate whether a correlation exists between the elastic properties of empty MOFs and the importance of including framework flexibility in making accurate predictions of molecular adsorption. Our results did not identify a simple correlation of this type.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling scale-up of particle coating by atomic layer deposition

Atomic layer deposition (ALD) is a promising technique to functionalize particle surfaces for energy applications including energy storage, catalysis, and decarbonization. In this work, we present a set of models of ALD particle coating to explore the transition from lab scale to manufacturing. Our models encompass the main particle coating manufacturing approaches including rotary bed, fluidized bed, and continuously vibrating reactors. These models provide key metrics, such as throughput and precursor utilization, required to evaluate the scalability of ALD manufacturing approaches and their feasibility in the context of energy applications. Our results show that designs that force the precursor to flow through fluidized particles transition faster to a transport-limited regime where throughput is maximized. They also exhibit higher precursor utilization. In the context of continuous processes, our models indicate that it is possible to achieve self-extinguishing processes with almost 100% precursor utilization. A comparison with past experimental results of ALD in fluidized bed reactors shows excellent qualitative and quantitative agreement.

25 ENERGY STORAGE↗
DOE OSTI2024

Enabling Low-Overhead HT-HPC Workflows at Extreme Scale using GNU Parallel

GNU Parallel is a versatile and powerful tool for process parallelization widely used in scientific computing. This paper demonstrates its effective application in high-performance computing (HPC) environments, particularly focusing on its scalability and efficiency in executing large-scale high-throughput high-performance computing (HT-HPC) workflows. Through real-world examples, we highlight GNU Parallel’s performance across various HPC workloads, including GPU computing, container-based workloads, and node-local NVMe storage. Our results on two leading supercomputers, OLCF’s Frontier and NERSC’s Perlmutter, showcase GNU Parallel’s rapid process dispatching ability and its capacity to maintain low overhead even at extreme scales. We explore GNU Parallel’s application in massive parallel file transfers using a scheduled Data Transfer Node (DTN) cluster, emphasizing its broad utility in diverse scientific workflows. Beyond its direct application as a viable workflow manager, GNU Parallel can be employed in conjunction with other workflow systems as a "last-mile" parallelizing driver and as a quick prototyping tool to design and extract parallel profiles from application executions. We then argue that the potential for GNU Parallel to transform workflow management at extreme scales is substantial, paving the way for more efficient and effective scientific discoveries.

Maheshwari, Ketan↗
NASA NTRSDate not supplied

Cost and Throughput Analysis for the NASA Ames Arc Jet Modernization Program

NASA Ames Center is currently evaluating alternatives to modernize the Arc Jet Complex, a critical part of testing for NASA’s planetary missions. NASA’s Arc Jet Complex facilities “are used to simulate the aerothermodynamic heating that a spacecraft endures throughout hypersonic atmospheric entry, and to test candidate thermal protection system (TPS) materials and systems. “Because planetary mission schedules often have tight windows due to planetary alignment constraints, a small increase in schedule could result in a two-year delay. Such a delay could increase the cost of a $1billionmissionbyhundreds of millions of dollars due to project personnel pay and clean room storage. To avoid these costs, the authors support NASA Ames in evaluating return on investment (ROI) and effectiveness of alternatives for modernizing the complex. The first input into the ROI is the deconstruction and construction cost estimates, which are developed using independent research on highly specialized subsystems, vendor quotes, and Unified Facilities Criteria (UFC), depending on the facility and work package. One of the measures of effectiveness is throughput analysis of the test bays, as a main goal of the modernization is to increase the number of possible test runs per year. This analysis is conducted via a probabilistic simulation and accounts for a variety of stochastic factors that influence the sequence of test runs, such as the facility availability; test complexity; the need to pause to assess test results; test failure; and the possibility of a system failure. The methodologies for both these analyses are discussed, along with the challenges presented due to the unique nature of the highly specialized test equipment.

Jennifer Scheel↗
DOE OSTI2020

On Performance Prediction of Big Data Transfer in High-performance Networks

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

Liu, Wuji↗
DOE OSTI2025

Toward High-Voltage Cathodes for Zinc-Ion Batteries: Discovery Pipeline and Material Design Rules

Efficient energy storage systems are crucial to address the intermittency of renewable energy sources. As multivalent batteries, Zn-ion batteries (ZIBs), while inherently low voltage, offer a promising low-cost alternative to Li-ion batteries due to the viable use of zinc as the anode. However, to maximize the potential impact of ZIBs, rechargeable cathodes with improved Zn diffusion are needed. To better understand the chemical and structural factors influencing Zn-ion mobility within battery electrode materials, we employ a high-throughput computational screening approach to systematically evaluate candidate intercalation hosts for ZIB cathodes, expanding the chemical search space on empty intercalation hosts that do not contain Zn. We leverage a high-throughput screening funnel to identify promising cathodes in ZIBs, integrating screening criteria with density functional theory (DFT)-based calculations of Zn2+ intercalation and diffusion inside the host materials. Using these data, we identify the design principles that favor Zn-ion mobility in candidate cathode materials. Building on previous work on divalent-ion cathodes, this study broadens the chemical space for next-generation multivalent energy storage systems.

electrodes↗
DOE OSTI2023

Rapid and Energy‐Efficient Synthesis of Disordered Rocksalt Cathodes

Abstract Lithium‐rich transition metal oxides with a cation‐disordered rocksalt structure (disordered rocksalt oxides or DRX) are promising candidates for sustainable, next‐generation Li‐ion cathodes due to their high energy densities and compositional flexibility, enabling Co‐ and Ni‐free battery chemistries. However, current methods to synthesize DRX compounds require either high temperature (≈1000 °C) sintering for several hours, or high energy ball milling for several days in an inert atmosphere. Both methods are time‐ and energy‐intensive, limiting the scale up of DRX production. The present study reports the rapid synthesis of various DRX compositions in ambient air via a microwave‐assisted solid‐state technique resulting in reaction times as short as 5 min, which are more than two orders of magnitude faster than current synthesis methods. The DRX compounds synthesized via microwave are phase‐pure and have a similar short‐ and long‐range structure as compared to DRX materials synthesized via a standard solid‐state route, resulting in nearly identical electrochemical performance. In some cases, microwave heating allows for better particle size and morphology control. Overall, the rapid and energy‐efficient microwave technique provides a more sustainable route to produce DRX materials, further incentivizes the development of next‐generation DRX cathodes, and is key to accelerating their optimization via high‐throughput studies.

25 ENERGY STORAGE↗
DOE OSTI2023

Safe Electrolytes for Batteries

The demand for reliable and safe lithium-ion batteries (LIBs) for electric vehicles (EVs) and energy storage systems (ESS) necessitates the exploration of nonflammable phosphorus-based electrolytes as alternatives to the traditional flammable carbonate-based ones. However, integrating phosphorus-based electrolyte poses challenges, including capacity fading and compatibility issues with graphite material. Here, a high-throughput (HTP) electrochemical characterization method, similar to PH test paper, is introduced to fast screen compatible phosphorus-based electrolyte for use with graphite. Among 1,740 combinations of phosphorus-based electrolytes and graphite materials, 101 promising combinations are identified for further evaluation. These identified phosphorus-based electrolytes and graphite materials are evaluated and optimized in Li/Graphite half-cells and Graphite/LiFePO 4 (LFP) full-cells using commercial-level electrodes. The desolvation energy of a complex with one Li+ and four solvent molecules of phosphate or carbonate is calculated by density functional theory (DFT). Key parameters such as viscosity, ionic conductivity, and flammability of the electrolytes are thoroughly tested and optimized. The modified phosphate-dominant electrolyte (2 M LiFSI TEP/DME/EC (6/2/2, volume ratio) + 5 wt.% VEC + 5 wt.% FEC) demonstrates excellent thermal stability with lithiated graphite and showcases superior cycling performance in the Graphite/LFP full cell, surpassing prior research findings in phosphate-dominant electrolytes. By the rapid HTP screening and identification of compatible electrolyte-graphite combinations, this approach contributes to expediting the development of safer LIBs for EVs and ESS.

25 ENERGY STORAGE↗
DOE OSTI2025

Electronic and Geometric Contributors to Hydrogen Binding in Uranium Oxide Grain Boundaries

Hydrogen induced corrosion of uranium, which leads to the formation of toxic and pyrophoric UH 3 , raises significant safety concerns for long-term storage of nuclear materials. Previous work suggests hydrogen diffuses through the grain boundaries (GBs) of the passivating oxide layer to initiate hydriding reactions. However, the atomistic mechanisms underlying this phenomenon and the structural factors that control its initiation are not well understood. To address this knowledge gap, here we use a high-throughput density functional theory (DFT) workflow to investigate the adsorption of H and H 2 in the defective bulk UO 2 . Specifically, we have exhaustively investigated the adsorption of H (107 sites) and H 2 (26 sites) in three different coincident site lattice (CSL) GBs: Σ3, Σ5, and Σ9. Compared to the binding energies in pristine UO 2 , we observe significantly stronger hydrogen adsorption at these GB sites. Interestingly, we find that the trends in H and H 2 adsorption vary considerably across the three GB models. In particular, while a small number of sites in Σ5 and Σ9 show exothermic adsorption of H and H 2 , respectively, no such sites are found in Σ3. These results provide fundamental atomistic insights that could guide the development of future corrosion mitigation strategies for the storage of nuclear materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗