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Results for “conditional shift”

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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Biostimulation of Salicornia europaea L. crops with plant growth-promoting bacteria in laboratory and field conditions: effects on growth and metabolite profile

Abstract Aim The objective of the work was to assess the effect of biostimulation with selected plant growth-promoting bacteria on growth and metabolite profile of Salicornia europaea. Methods and results Salicornia europaea seeds were inoculated with different combinations of plant growth-promoting bacteria Brevibacterium casei EB3, Pseudomonas oryzihabitans RL18, and Bacillus aryabhattai SP20. Plants germinated from inoculated seeds were grown either in laboratory conditions or in a saline crop field. Fresh and dry weight were determined at the end of the experiment, for biomass quantification. The microbiological quality of fresh shoots for human consumption as salad greens was assessed, and the persistence of the inoculated strains in the plant rhizosphere was confirmed by next-generation sequencing (Illumina) of the 16S rDNA gene. The primary metabolite profile of biostimulated plants was characterized by GC–TOF-MS. In laboratory conditions, inoculation with the two strains Br. casei EB3 and Ps. oryzihabitans RL18 caused the most significant increase in biomass production (fresh and dry weight), and caused a shift in the central metabolic pathways of inoculated plants toward amino acid biosynthesis. In the field experiment, no significant biostimulation effect was detected with any of the tested inoculants. Seed inoculation had no significant effect on the microbiological quality of the edible parts. The persistence of inoculants was confirmed in both experiments. Conclusions Manipulation of the plant microbiome can trigger primary metabolic reconfiguration and modulate the plant metabolism while promoting plant growth.

Ferreira, Maria J.↗
DOE OSTI2024

Response Of Salt Intrusion In A Tidal Estuary To Regional Climatic Forcing (FVCOM Output for the baseline scenario)

Salinity distribution in a large tidal estuary is subject to estuarine adjustment under the influences of multiple physical drivers such as freshwater pulses and sea level rise, and is crucial to upstream water quality, aquaculture, and ecosystem functions of the estuary. To better understand the estuarine salinity response to climate change, the unstructured-grid Finite Volume Community Ocean Model was implemented to simulate the salt intrusion in the Delaware Bay Estuary. The model was first validated by multiple observational data sets and subsequently applied in an idealized setting to examine the response of salt front to freshwater pulses in high flow conditions, followed by a long-term drought condition supported by a multi-decadal streamflow drought analysis in the estuary. The model results showed that after the freshwater pulses the salt front location moved further upstream with sea level rise. Under the simulated long-term drought condition, the adjustment timescale of salt intrusion varies nonlinearly with sea level rise. With a significant increase in sea level rise, the adjustment timescale starts to decrease. This shift suggests a transition into a different regime where the estuary becomes more stratified, as indicated by an increasing bulk Simpson number with rising sea levels. These findings have been summarized into a journal manuscript. The data included in this information release request are FVCOM model output associated with the numerical simulations described in the journal manuscript.

Liu, Jinliang↗
DOE OSTI2021

Application of Eh-pH Diagrams on Acid Leaching Systems for the Recovery of REEs from Bastnaesite, Monazite and Xenotime

Bastnaesite, monazite and xenotime are rare earth minerals (REMs) that are typical sources for rare earth elements (REEs). To advance the understanding of their leaching and precipitation behavior in different hydrometallurgical processes, Eh-pH diagrams were constructed and modified using the HSC 9.9 software. The aqueous stability of rare earth elements in H 2 O and acid leaching systems, i.e., the REE-Ligands-H 2 O systems, were depicted and studied based on the Eh-pH diagrams. This study considers the most relevant lixiviants, their resulting equilibrium states and the importance in the hydrometallurgical recovery of rare earth elements (REMs). A literature review was performed summarizing relevant Eh-pH diagrams and associated thermodynamic data. Shifting stability regions for REEs were discovered with additions of acid ligands and a narrow stability region for soluble REE-(SO 4 /Cl/NO 3 ) complexes under highly acidic conditions. As such, the recovery of REEs can be enhanced by adjusting pH and Eh values. In addition, the Eh-pH diagrams of the major contaminants (i.e., Fe, Ca and Al) in leaching systems were studied. The resulting Eh-pH diagrams provide possible insights into potential passivation on the particle surfaces due to the formation of an insoluble product layer.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Future North Atlantic tropical cyclone intensities in thermodynamically modified historical environments

Tropical cyclones (TCs) have ranked as the deadliest and most financially crippling natural disasters in the United States. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study we apply a fixed-constraint storyline approach that holds storm tracks and initial conditions constant to probe future TC intensity in the North Atlantic Basin. First, we simulate 618 historical TC events using the Risk Analysis Framework for Tropical Cyclones (RAFT)'s deep-learning intensity model. Next, we apply warming signals derived from eight CMIP6 climate scenarios and rerun each event to explore how intensities respond across scenarios. Finally, we develop an interactive dashboard that allows users to explore individual storm simulations and the scenario-modified environmental drivers. Together, this dataset and tool provide a clear, illustrative way to investigate how TC intensity responds to changes in air-sea state.

Climate Change↗
DOE OSTI2022

316L Stainless Steel Wire Arc Additive Manufacturing and Characterization for Potential SNF Canister Production

In this paper, 316L stainless steel (316L SS) straight walls, curved walls, and vessels were printed by wire arc additive manufacturing (WAAM) using the 316L SS welding wire, to demonstrate the feasibility of spent nuclear fuel (SNF) canisters using this advanced manufacturing technique. Helium leak test with leak tight criteria of 1 × 10-7 ref-cc/s were performed on printed vessels with various wall thickness, and testing pressures were from 0.345 MPa (50 psi) to 1.724 MPa (250 psi). Printed wall microstructures were characterized using optical microscopy (OM) and scanning electron microscopy (SEM). Tensile specimens were machined out from printed walls along the length, height, and thickness directions, respectively, and tested at different temperature conditions, room temperature, 80 °C, 150 °C and 250 °C, respectively. Results showed t hat, all five WAAM-printed canisters with different thickness passed the helium leaktight criteria of radioactive materials containers by the ANSI N14.5; Dispersed micrometer-level size inclusions/voids were observed at printed wall cross sections, dendritic microstructures were found with grain size variation through the welding torch shifting direction, and very small amount of δ-ferrite and σ-phase were also observed in those cross sections; The printed walls were ductile, all tensile specimens broke with large plastic deformation/elongation and without brittle failure, and printed specimens yield strengths and tensile strengths under room temperature and 80 °C conditions are higher than minimum values of 316L SS for pressure vessels and for general applications at room temperature required by the ASTM standard, respectively.

Tang, Wei↗
DOE OSTI2022

Use of Mini-CT Specimens for Fracture Toughness Characterization of Irradiated Highly Embrittled Weld

In the 1990’s, the Heavy Section Steel Irradiation Program at the ORNL performed investigation of the shape of the fracture toughness master curve for reactor pressure vessel steel highly embrittled because of irradiation exposure. A radiation-sensitive reactor pressure vessel (RPV) weld with intentionally enhanced copper content, designated KS-01, has been characterized in terms of static initiation (KJc) and Charpy impact toughness in the unirradiated and irradiated conditions. The objective of this original project was to investigate the ability of highly embrittled material to maintain the shape of the unirradiated transition fracture toughness curve, as well as to examine the ability of the Charpy 41-J shift to predict the fracture toughness shift at such high level of embrittlement. Irradiation of this weld was performed at the University of Michigan Ford Reactor in the new HSSI irradiation-anneal-reirradiation (IAR) facility. Broken specimens from that project have been saved in ORNL storage. To verify applicability of Mini-CT specimens for fracture toughness characterization of RPV materials as part of the DOE Light Water Reactor Sustainability Program, Mini-CT specimens were machined from broken Charpy specimens and tested according to ASTM E1921 standard. As result of this study, the fracture toughness of this weld derived by testing Mini-CT specimens in the unirradiated and irradiated conditions is compared to previously reported fracture toughness derived by large number of conventional specimens.

Sokolov, Mikhail A.↗
DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Short-term Electricity Price Forecasting with Constrained Regressors

The volatility of electricity price presents a challenge to market participants as their decision-making process are highly depend on the accuracy of price forecasts. However, there is growing empirical evidence of increasing price volatility and price spikes in electricity markets as a result of variable renewable energy generation, extreme weather events, and other factors. The distribution shift caused by spikes in electricity price data differentiates the forecasting tasks from other renewable energy sources. Moreover, the observations may be compromised by cyberattacks and thus not available in the testing phase. To this end, we propose a Similarity-Enhanced Electricity Decomposition Forecasting model (SEED-Forecaster) to address the missing response problem and spikes capturing in short-term electricity price forecasting. The effectiveness of the proposed framework is tested on real-world electricity price data from California Independent System Operator (CAISO). Numerical results of case studies show that the proposed SEED-Forecsater can enhance forecasting performance, particularly in capturing electricity spikes, even under conditions without regressors during testing stage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Influence of Molybdenum on Rapid Solidification Microstructure and Microsegregation in Primary Ferrite Solidified Stainless Steel

Due to the widespread use and familiarity of austenitic stainless steels in many industries these alloys are often the first materials used when adapting new processing techniques. The rapid cooling and solidification rates that occur during fusion based additive manufacturing processes like powder bed fusion cause a fundamental shift in the solidification behavior of the material. While the effects that Cr and Ni have on the microstructure have been fairly well documented across a range of solidification conditions, the impact of alloying elements like Mo are not as well understood at rapid solidification rates. For this study, four custom stainless steel alloy feedstocks were made with targeted modification of the Cr and Mo concentrations to maintain a constant Cr/Ni eq ratio of ~1.7. Two-piston splat quenching was used to produce rapidly solidified samples and the solidification mode, phase, and cell size were investigated. Solidification rates were estimated to be between 0.4 and 1.5m/s. A comparison of the microsegregation and partitioning behavior of Cr, Ni and Mo in ferrite solidified material was also performed for the different alloy compositions. Variations of the Mo concentration between each feedstock showed no measurable influence on the solidification/cooling rates, solidification morphology, or the partitioning of Mo. Furthermore, higher concentrations of Mo were found to significantly increase the amount of ferrite phase in the microstructure by suppressing the ferrite to austenite massive transformation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Flow-Assisted Selective Mineral Extraction from Seawater

The sustainable production of critical materials from natural sources requires a paradigm shift away from currently used resource-intensive processes. In this paper, we report a single-step, laminar co-flow method (LCM) that leverages non-equilibrium conditions to selectively extract pure Mg(OH) 2 from natural seawater. Conventional seawater-based Mg extraction involves adding individual or a combination of precipitants to obtain Mg(OH) 2 , but the co-existence of Ca 2+ unavoidably results in CaCO 3 impurities requiring additional purification steps. Here we show that the non-equilibrium conditions in LCM achieved using a microfluidics device and by simply co-injecting a NaOH solution with seawater can result in improved selectivity for Mg(OH) 2 unlike in conventional bulk mixing method. The resulting precipitates are characterized for composition and the process yield and purity are optimized through systematic variations of the reaction time and the concentration of NaOH. This is the first demonstration of LCM for selective separation, and as a one-step process that does not rely on novel sorbents, membranes, or external stimuli, it is easy to scale-up. LCM has the potential to be broadly relevant to selective separations from complex feed streams and diverse chemistries—enabling more sustainable materials extraction and processing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Diurnal Dynamics of Gross Primary Productivity Using Observations From the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R Series at an Oak Savanna Ecosystem

Gross primary productivity (GPP) is the largest flux in the global carbon cycle and satellite-based GPP estimates have long been used to study the trends and interannual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar-orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite-R series (GOES-R) to test the ability of subdaily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We compared three methods to estimate GPP: (a) a light-use efficiency model, (b) a linear relationship between the product of near-infrared reflectance of vegetation and photosynthetically active radiation (LIN-NIR v P) and EC tower GPP, and (c) a light response curve (LRC-NIR v P) between NIR v P and EC GPP. The LRC-NIR v P achieved the lowest mean absolute error for winter (2 µmol CO 2 m –2 s –1 ), spring (2.51 µmol CO 2 m –2 s –1 ), summer (1.43 µmol CO 2 m –2 s –1 ), and fall (1.35 µmol CO 2 m –2 s –1 ). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation toward the morning hours during the dry summers. The LRC-NIRvP and the light-use efficiency model were in agreement with these patterns of a shift in peak daily GPP toward the morning hours during summer. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Reactive CO 2 capture via controlled amine speciation in non-aqueous electrolytes

Current efforts to integrate CO 2 capture and electrochemical conversion (reactive capture) are often performed under aqueous conditions, resulting in undesired hydrogen evolution and reliance on precious metal catalysts and pure CO 2 streams. Here, in this study, we explore reactive capture in aprotic media. By shifting the amine–CO 2 adduct speciation to carbamic acid (instead of carbamate) in dimethyl sulfoxide, we increased CO 2 uptake threefold compared with an aqueous medium, suppressing hydrogen evolution and supporting a 78% Faradaic efficiency towards CO over an earth-abundant zinc catalyst. Under simulated high-oxygen-content flue gas (17% CO 2 , 17% O 2 , 66% N 2 ), we also obtained up to 43% CO Faradaic efficiency over multiple capture–conversion cycles. Our findings showcase the confluence of reactant speciation, electrolyte composition and electrocatalyst design in enabling selective and active electrochemical transformations.

Gomes, Reginaldo J. [Univ. of Chicago, IL (United ↗
DOE OSTI2022

Demonstration of Intelligent HVAC Load Management With Deep Reinforcement Learning: Real-World Experience of Machine Learning in Demand Control

We report that buildings account for 40% of total primary energy consumption and 30% of all CO 2 emissions worldwide. A large portion of building energy consumption is due to heating, ventilation, and air-conditioning (HVAC) systems. In the summer, for example, more than 50% of a building’s electricity consumption is used for cooling. With proper energy management, buildings can provide load shifting, peak shaving, frequency regulation, and many other demand response services.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Techno-Economic Assessment of Residential Heat Pump Integrated with Thermal Energy Storage

Phase change material (PCM)-based thermal energy storage (TES) can provide energy and cost savings and peak demand reduction benefits for grid-interactive residential buildings. Researchers established that these benefits vary greatly depending on the PCM phase change temperature (PCT), total TES storage capacity, system configuration and location and climate of the building. In this study, preliminary techno-economic performance is reported for a novel heat pump (HP)-integrated TES system using an idealized approach. A simplified HP-TES was modeled for 1 year of space heating and cooling loads for a residential building in three different climates in the United States. The vapor compression system of the HP was modified to integrate with TES, and all heat transfer to and from the TES was mediated by the HP. A single PCM was used for heating and cooling, and the PCT and TES capacity were varied to observe their effects on the building’s energy consumption, peak load shifting and cost savings. The maximum reduction in electric consumption, utility cost and peak electric demand were achieved at a PCT of 30 °C for New York City and 20 °C for Houston and Birmingham. Peak energy consumption in Houston, New York City, and Birmingham was reduced by 47%, 53%, and 70%, respectively, by shifting peak load using a time-of-use utility schedule. TES with 170 MJ storage capacity allowed for maximum demand shift from on-peak to off-peak hours, with diminishing returns once the TES capacity equaled the daily building thermal loads experienced during the most extreme ambient conditions.

25 ENERGY STORAGE↗
DOE OSTI2022

RADiCAL—Precision Timing, Ultracompact, Radiation-Hard Electromagnetic Calorimetry

To address the challenges of providing high-performance calorimetry in future hadron collider experiments under conditions of high luminosity and high radiation (FCC-hh environments), we conducted R&D on advanced calorimetry techniques suitable for such operation, based on scintillation and wavelength-shifting technologies and photosensor (SiPM and SiPM-like) technology. In particular, we focused our attention on ultra-compact radiation-hard EM calorimeters based on modular structures (RADiCAL modules) consisting of alternating layers of the very dense absorber and scintillating plates, read out via radiation hard wavelength shifting (WLS) solid fiber or capillary elements to photosensors positioned either proximately or remotely, depending upon their radiation tolerance. RADiCAL modules provide the capability to measure simultaneously and with high precision the position, energy and timing of EM showers. This paper provides an overview of the instrumentation and photosensor R&D associated with the RADiCAL program.

Anderson, Thomas↗
DOE OSTI2023

Machine Learning-Based Model Predictive Control of Two-Time-Scale Systems

In this study, we present a general form of nonlinear two-time-scale systems, where singular perturbation analysis is used to separate the dynamics of the slow and fast subsystems. Machine learning techniques are utilized to approximate the dynamics of both subsystems. Specifically, a recurrent neural network (RNN) and a feedforward neural network (FNN) are used to predict the slow and fast state vectors, respectively. Moreover, we investigate the generalization error bounds for these machine learning models approximating the dynamics of two-time-scale systems. Next, under the assumption that the fast states are asymptotically stable, our focus shifts toward designing a Lyapunov-based model predictive control (LMPC) scheme that exclusively employs the RNN to predict the dynamics of the slow states. Additionally, we derive sufficient conditions to guarantee the closed-loop stability of the system under the sample-and-hold implementation of the controller. A nonlinear chemical process example is used to demonstrate the theory. In particular, two RNN models are constructed: one to model the full two-time-scale system and the other to predict solely the slow state vector. Both models are integrated within the LMPC scheme, and we compare their closed-loop performance while assessing the computational time required to execute the LMPC optimization problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Barriers and Opportunities for Energy Technology Adoption in Juneau, Alaska

This report presents findings from a qualitative study examining barriers and opportunities for air source heat pump (ASHP) and electric vehicle (EV) adoption in Juneau, Alaska, with a particular focus on manufactured and multifamily housing. The analysis draws on community insights from end users and middle actors to better understand how technology adoption unfolds in contexts with distinct logistical, infrastructural, and housing constraints. The report is organized according to key barriers and opportunities identified through stakeholder input, providing a structured understanding of adoption dynamics across technologies and housing types. These insights are intended to inform program design and support more effective electrification strategies tailored to local conditions. The study team employed qualitative methods to capture both in-depth and high-level perspectives on technology adoption. Data collection included: 1) two 2-hour focus groups with a total of five end users and seven middle actors, enabling detailed and structured discussion and 2) ten semistructured interviews with manufactured home owners, multifamily landlords, and one tenant, providing complementary insights across housing contexts. Focus groups captured accounts of shared challenges and opportunities while interviews offered more concise reflections on individual experiences. Together, these methods enabled a more comprehensive understanding of both systemic barriers and lived experiences with ASHPs and EVs. The findings reveal that adoption of electrification technologies is shaped by a combination of economic, logistical, and informational factors that vary across housing types, technology characteristics, user groups, and other demographic factors. Addressing these factors requires tailored strategies that reflect local conditions and user experiences. The insights in this report can provide a foundation for organizations such as AEL&P to refine program design, support more effective outreach, and anticipate shifts in energy demand associated with increased electrification. More broadly, the study highlights the importance of incorporating community perspectives when developing electrification initiatives to ensure they are both practical and responsive to real-world constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗