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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Utilizing Environmental Tracers to Reduce Groundwater Flow and Transport Model Parameter Uncertainties

Abstract Non‐uniqueness in groundwater model calibration is a primary source of uncertainty in groundwater flow and transport predictions. In this study, we investigate the ability of environmental tracer information to constrain groundwater model parameters. We utilize a pilot point calibration procedure conditioned to subsets of observed data including: liquid pressures, tritium ( 3 H), chlorofluorocarbon‐12 (CFC‐12), and sulfur hexafluoride (SF 6 ) concentrations; and groundwater apparent ages inferred from these environmental tracers, to quantify uncertainties in the heterogeneous permeability fields and infiltration rates of a steady‐state 2‐D synthetic aquifer and a transient 3‐D model of a field site located near Riverton, Wyoming (USA). To identify the relative data worth of each observation data type, the post‐calibration uncertainties of the optimal parameters for a given observation subset are compared to that from the full observation data set. Our results suggest that the calibration‐constrained permeability field uncertainties are largest when liquid pressures are used as the sole calibration data set. We find significant reduction in permeability uncertainty and increased predictive accuracy when the environmental tracer concentrations, rather than apparent groundwater ages, are used as calibration targets in the synthetic model. Calibration of the Riverton field site model using environmental tracer concentrations directly produces infiltration rate estimates with the lowest uncertainties, however; permeability field uncertainties remain similar between the environmental tracer concentration and apparent groundwater age calibration scenarios. This work provides insight on the data worth of environmental tracer information to calibrate groundwater models and highlights potential benefits of directly assimilating environmental tracer concentrations into model parameter estimation procedures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Performance of Fire Danger Indices and Their Utility in Predicting Future Wildfire Danger Over the Conterminous United States

Abstract Predicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control

Abstract Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris . This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Local and utility-wide cost allocations for a more equitable wildfire-resilient distribution grid

Climate-induced extreme weather conditions make electricity infrastructure more vulnerable. They increase the risk of power-line-ignited wildfires which can, in turn, jeopardize electric power delivery. Here, leveraging machine learning, we show that lower-income communities in California not only have lower fractions of power distribution lines undergrounded, but overhead lines and poles in their neighbourhoods are also more vulnerable to wildfires. Should they bear the cost of undergrounding fire-prone lines themselves, they would have to pay a disproportionately higher cost per household. We propose a cost allocation scheme with an income threshold below which the cost is borne by utility-wide ratepayers and above which the cost is borne locally. This scheme can not only minimize the average of undergrounding costs per household as a share of income, but also homogenize such cost–income ratios across communities. Furthermore, our research demonstrates the opportunity to appropriately integrate existing policies to make electricity infrastructure affordable, equitable and reliable amidst climate change.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Evidence for the utility of quantum computing before fault tolerance

Quantum computing promises to offer substantial speed-ups over its classical counterpart for certain problems. However, the greatest impediment to realizing its full potential is noise that is inherent to these systems. The widely accepted solution to this challenge is the implementation of fault-tolerant quantum circuits, which is out of reach for current processors. Here we report experiments on a noisy 127-qubit processor and demonstrate the measurement of accurate expectation values for circuit volumes at a scale beyond brute-force classical computation. We argue that this represents evidence for the utility of quantum computing in a pre-fault-tolerant era. These experimental results are enabled by advances in the coherence and calibration of a superconducting processor at this scale and the ability to characterize and controllably manipulate noise across such a large device. We establish the accuracy of the measured expectation values by comparing them with the output of exactly verifiable circuits. In the regime of strong entanglement, the quantum computer provides correct results for which leading classical approximations such as pure-state-based 1D (matrix product states, MPS) and 2D (isometric tensor network states, isoTNS) tensor network methods break down. These experiments demonstrate a foundational tool for the realization of near-term quantum applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Traceable and Scalable Food Balance Sheets from Agricultural Commodity Supply and Utilization Accounts (2010–2022)

Abstract The Food Balance Sheets (FBS), compiled by the Food and Agriculture Organization (FAO), serve as a cornerstone dataset for studies on agricultural development, food security, and dietary health, providing a broad overview of global and regional food systems. However, its limited transparency and scalability hinder its application in empirical analysis and multisector dynamic modeling. Here, we present a traceable Food Balance Sheets (T-FBS) dataset, developed from detailed Supply Utilization Accounts (SUA) using a novel Primary Commodity equivalent (PCe) aggregation approach. This framework enables the aggregation of commodity flows along supply chains while ensuring consistency and balance across multiple dimensions. The T-FBS dataset includes 57 PCe commodities across 195 regions for the period 2010–2022, consolidated from over 500 SUA products. While T-FBS closely aligns with FAO-FBS at aggregate levels for dietary energy and macronutrients, it identifies key uncertainties in other elements (e.g., feed, trade, stocks). By enhancing methodological transparency, traceability, and scalability, T-FBS strengthens the robustness of food system studies and fosters future research and collaboration within the open-source community.

agriculture↗
DOE OSTI2023

Split selectable marker systems utilizing inteins facilitate gene stacking in plants

The ability to stack multiple genes in plants is of great importance in the development of crops with desirable traits but can be challenging due to limited selectable marker options. Here we establish split selectable marker systems using protein splicing elements called “inteins” for Agrobacterium-mediated co-transformation in plants. First, we show that such a split selectable marker system can be used effectively in plants to reconstitute a visible marker, RUBY, from two non-functional fragments through tobacco leaf infiltration. Next, to determine the general applicability of our split selectable marker systems, we demonstrate the utility of these systems in the model plants Arabidopsis and poplar by successfully stacking two reporters eYGFPuv and RUBY, using split Kanamycin or Hygromycin resistance markers. In conclusion, this method enables robust plant co-transformation, providing a valuable tool for the simultaneous insertion of multiple genes into both herbaceous and woody plants efficiently.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

An orthogonal metabolic framework for one-carbon utilization

Metabolic engineering often entails concurrent engineering of substrate utilization, central metabolism, and product synthesis pathways to maximize the conversion of a carbon substrate into desired product(s). Here, we report an alternative approach using synthetic pathways for C1 bioconversion that are orthogonal to the host metabolic network, thus minimizing interdependency on native metabolism and enabling more efficient biocatalysts. Here, the engineered pathways are based on formyl-CoA elongation (FORCE) reactions catalyzed by the enzyme 2-hydroxyacyl-CoA lyase (HACL) and generate multi-carbon products directly from C1 elongation units in the form of formyl-CoA. Herein, we use thermodynamic and stoichiometric analyses to evaluate different FORCE pathway variants, including aldose elongation, α-reduction, and aldehyde elongation. Promising variants were further prototyped using cell-free systems (purified enzymes and cell extracts) as well as resting and growing cultures of non-methylotrophic bacterium Escherichia coli. We demonstrate that C1 substrates formate, formaldehyde, and methanol can be used as inputs for FORCE pathways and that FORCE reactions can serve as a platform for varied product synthesis including glycolate, ethylene glycol, ethanol, and glycerate. Furthermore, the orthogonal FORCE pathways have the potential to be integrated with host metabolism for synthetic methylotrophy by the production of native growth substrates as demonstrated in a two-strain culture system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Gel composite electrolyte – an effective way to utilize ceramic fillers in lithium batteries

Achieving synergy between ion-conducting polymers and ceramics in a composite electrolyte has been proven to be difficult as the complicated ceramic/polymer interface presents challenges to understand and control. In this work, we report a strategy to utilize discrete ceramic fillers to form a gel composite electrolyte with enhanced transport properties for lithium metal batteries. The matrix of the composite membrane is crosslinked poly(ethylene oxide) with bis(trifluoromethane)sulfonimide lithium salt (LiTFSI). The membrane is plasticized with tetraethylene glycol dimethyl ether (TEGDME). The incorporation of doped-lithium aluminum titanium phosphate particles (LICGC™) into the membrane greatly improves the membrane's cycling characteristics against the lithium electrode, exhibiting lower interfacial impedance, lower overpotential and higher rate capability. The underpinnings of the superior performance of the gel composite electrolyte are discussed in depth. LICGC™ can immobilize the TFSI - anions in the polymer matrix and simultaneously promote Li+ transport by increasing the plasticizer to Li + ratio. Further, the transport enhancement is achieved without sacrificing mechanical properties. The composite membrane shows significantly improved handleability and processability. This work sheds light on the design strategy for a safe electrolyte towards stable Li metal batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

N-dopants optimize the utilization of spontaneously formed photocharges in organic solar cells

Non-fullerene acceptors (NFAs) not only bring rapid efficiency progress to organic solar cells (OSCs) but have also generated scientific interest in re-evaluating the photocharge generation route via spontaneous or heterojunction-assisted charge separation. In this work, we propose the addition of n-dopants as a strategy to optimize the utilization of spontaneously formed photocharges in NFAs and construct ultralow donor-content dilute bulk heterojunction (BHJ) devices to validate the dopant's roles. N-dopants enhance the transport length of spontaneously formed photocharges over twofold and alleviate the bimolecular recombination with balanced carrier mobility during charge extraction. This improves the photocurrent of ultralow donor-content dilute BHJ devices by over 2.4 times. Moreover, these benefits are successfully conducted to the BHJ devices, and enhancement of conversion efficiencies is observed in both the 100 nm and 400 nm thick OSCs. Our results provide a feasible way to optimize the intrinsic photocharges in NFAs and renew the possibility of efficient single-component OSCs as inorganic photovoltaic techniques.

14 SOLAR ENERGY↗
DOE OSTI2026

Towards utility-scale electronic structure with sample-based quantum bootstrap embedding

One of the main applications for which quantum computers are hoped to find utility is in simulating ground state energies and other observables of molecular chemical systems. The recently proposed sample-based diagonalization method is a readily implementable method for this task on current-day hardware using short circuit depths and has been demonstrated on as many as 85 qubits in recent studies. In this work, we combine the recently proposed quantum bootstrap embedding (QBE) method with sampled-based diagonalization (QBE-SQD) and present the first benchmarking study of the QBE method on real quantum hardware, ibm_pittsburgh, a Heron r3 processor with 156 qubits. Our test system is a hydrogen ring with 8 hydrogen atoms in the cc-pVDZ basis. We show that for this system, QBE-SQD using an active space of (8e, 19o) per fragment with a 43 qubit footprint produces a ground state energy accuracy which exceeds that of an SQD calculation with an (8e, 30o) active space with a 67 qubit footprint when using a comparable number of Slater determinants. This demonstrates that the use of quantum bootstrap embedding techniques is a promising path towards extending the capabilities of state-of-the-art quantum eigensolvers on near-term devices.

Bierman, Joel [North Carolina State University, Ra↗
DOE OSTI2026

Energy utilization strategies in lignocellulosic biorefineries

Lignocellulosic biorefineries can generate various energy-rich side streams, including biogas from anaerobic digestion, a lignin stream from biomass pretreatment, and conversion residues. This study evaluates different chemical energy utilization strategies in lignocellulosic biorefineries based on energy and carbon efficiencies, cost, and greenhouse gas mitigation potential. Specifically, using modeling and optimization, we examine strategies that include electricity generation, biogas upgrade to biomethane, lignin valorization, and carbon capture and storage. We find that upgrading biogas to biomethane demonstrates the highest energy efficiency and leads to the lowest minimum fuel selling price for the main product, ethanol. The biorefinery with carbon capture and storage achieves the lowest net carbon footprint. Lignin valorization has the highest carbon footprint due to the additional materials required for lignin depolymerization, despite its potential to produce high value bioproducts.

Aboagye, Emmanuel A. [Princeton Univ., NJ (United ↗
DOE OSTI2023

Utilizing grid–supportive load response to shape resilient frequency control of the power grid

The increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid-supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent-based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single-area and multi-area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation.

24 POWER TRANSMISSION AND DISTRIBUTION↗