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DOE OSTI2023

Mechanisms of metabolic adaptation in the duckweed Lemna gibba: an integrated metabolic, transcriptomic and flux analysis

Abstract Background Duckweeds are small, rapidly growing aquatic flowering plants. Due to their ability for biomass production at high rates they represent promising candidates for biofuel feedstocks. Duckweeds are also excellent model organisms because they can be maintained in well-defined liquid media, usually reproduce asexually, and because genomic resources are becoming increasingly available. To demonstrate the utility of duckweed for integrated metabolic studies, we examined the metabolic adaptation of growing Lemna gibba cultures to different nutritional conditions. Results To establish a framework for quantitative metabolic research in duckweeds we derived a central carbon metabolism network model of Lemna gibba based on its draft genome. Lemna gibba fronds were grown with nitrate or glutamine as nitrogen source. The two conditions were compared by quantification of growth kinetics, metabolite levels, transcript abundance, as well as by 13 C-metabolic flux analysis. While growing with glutamine, the fronds grew 1.4 times faster and accumulated more protein and less cell wall components compared to plants grown on nitrate. Characterization of photomixotrophic growth by 13 C-metabolic flux analysis showed that, under both metabolic growth conditions, the Calvin-Benson-Bassham cycle and the oxidative pentose-phosphate pathway are highly active, creating a futile cycle with net ATP consumption. Depending on the nitrogen source, substantial reorganization of fluxes around the tricarboxylic acid cycle took place, leading to differential formation of the biosynthetic precursors of the Asp and Gln families of proteinogenic amino acids. Despite the substantial reorganization of fluxes around the tricarboxylic acid cycle, flux changes could largely not be associated with changes in transcripts. Conclusions Through integrated analysis of growth rate, biomass composition, metabolite levels, and metabolic flux, we show that Lemna gibba is an excellent system for quantitative metabolic studies in plants. Our study showed that Lemna gibba adjusts to different nitrogen sources by reorganizing central metabolism. The observed disconnect between gene expression regulation and metabolism underscores the importance of metabolic flux analysis as a tool in such studies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Data and code for Daily and Multi-Day Extreme Rainfall Analysis Under Future Climates Using Stochastic Storm Transposition and NEX-GDDP-CMIP6 Over CONUS

This data package provides inputs, codes, and outputs for a comprehensive analysis of projected changes in extreme precipitation across 10 regions of the continental United States, using 34 downscaled Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections, Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) dataset. These models are part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a coordinated climate modeling framework widely used to assess climate change impacts. The analysis applies a stochastic storm transposition method to quantify changes in extreme rainfall under two Shared Socioeconomic Pathway (SSP) climate scenarios—SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions)—compared to historical conditions (1995–2014 vs. 2081–2100). The dataset includes rainfall depth estimates for extreme events with return periods from 2 to 500 years across multiple storm durations (1, 3, and 5 days) for each of the 10 U.S. regions. Weighted ensemble statistics are derived from individual ESM performance against historical precipitation patterns, enabling robust uncertainty quantification through both sign-based and permutation-test-based model agreement assessments. Key analyses address: (1) relative changes in extreme precipitation for each climate scenario, (2) differences between SSP scenarios (SSP5-8.5 vs. SSP2-4.5), (3) contrasts between rare and frequent events, and (4) variations between multi-day and daily storm durations. The workflow produces ensemble statistics—median, 5th, 25th, 75th, and 95th percentiles—along with model agreement metrics that identify regions and event types with robust climate change signals. The dataset includes: processed rainfall depth outputs (netCDF format) from the RainyDay Python package, ESM weights from historical performance evaluation using DayMet observations, ensemble statistics across all storm dimensions, and figures summarizing key findings.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Determining the Appropriate Emergency Planning Attributes for Microreactors

Micro-reactor designs incorporate design features that provide very low reactor decay heat power at 24 hours after shutdown that is manageable in comparison with larger reactors. This attribute translates into a low probability of core damage negligible offsite dose. This paper provides the conceptual framework for appropriately structuring emergency planning requirements for micro-reactors while ensuring the U.S. Nuclear Regulatory Commission’s (NRC) commitment to safety is met. Due to the diversity in reactor designs comprising the micro-reactor community, this paper identifies the necessary concepts that should be considered to ensure emergency plans for micro-reactors are appropriate for the risk. This paper does not mandate specific design features for micro-reactors but provides reactor designers a conceptual framework for a scalable, graded approach to Emergency Planning Standards that should be considered in their individual designs in order to support simplified emergency plans. Given that accident source terms associated with micro-reactors are essentially negligible when compared with those for large light water reactors, revisions to emergency planning standards are justified. A graded approach to implementing emergency planning guidance can be used to appropriately structure micro-reactor emergency plans and reduce the size of the emergency planning zone and plume exposure pathway. Appropriately structuring emergency planning requirements will better optimize licensee and offsite agencies emergency planning resources and reduce the resources associated with emergency planning.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Signature Analysis Utilizing a Dynamic Molten Salt Reactor Model for MC&A

Moving from traditional fixed fuel nuclear reactor systems to a mobile, dynamic fuel system that has dissolved special nuclear material in a molten salt is a paradigm shift in several respects. One major consideration is how to develop effective nuclear material controls and accounting for these novel reactor systems. The Molten Salt Reactor Experiment is one example of a critical molten salt system and provided a significant reference library based on the documented effort. But it was low thermal power that was not intended to reflect a commercial scale electricity production design. Therefore, to facilitate and assist vendors with domestic licensing considerations, research is underway to identify methods that could be used for domestic safeguards approaches for these novel reactor systems. This research presents the results of a signature analysis of data generated from three simulated scenarios using the molten salt demonstration reactor model defined in the Transient Simulation Framework of Reconfigurable Modules. Each scenario provides 1 hour isotope inventories over a 180 day period. The scenarios investigated provide test cases to examine if direct gamma-ray spectroscopy of the fuel can be used to identify changes comparing a base case (no reactivity control and fixed fission contribution) to an insertion of reactivity (10 pcm no change in fission composition) and a change in fission composition. The analysis demonstrates that monitoring the total count rate in a highly collimated high-resolution photon energy spectrum is sensitive to perturbations imposed into the reactor model. The total photon count rate changes ≈2% for the fission composition change and ≈4.5% for the reactivity insertion compared to the base case. However, both scenarios show an increase in the total photon count rate. The total photon count rate can be used to identify changes due to power (number of fissions) but not due to a change in the material undergoing fission. To distinguish between the two cases of increased power, the photon spectrum would require an intensive analysis technique. A photon peak count rate ratio analysis could be used to identify changes in the fissile material fission generation in the core through identification of a static peak that shows little variation to the source of fission and a highly varying peak. The photon peak strength will ultimately be determined by the isotope’s fission yield. A preliminary analysis investigating the coupling of the isotope’s fission yield and its concentration in the fuel salt derived from the modeling has been performed. A ratio analysis of the photon peak count rates of 140 La to 99 Mo demonstrated that the reactivity insertion creates a distinct difference in the ratio compared to the fission composition change scenario.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗

DOE OSTI2024

Reconstruction and Selection of Neutrino Interactions in MicroBooNE using Deep Convolutional Neural Networks

In this document, we describe a new reconstruction workflow developed for the MicroBooNE experiment. It features the use of Deep Convolutional Neural Networks trained to recognize key structures within the data sufficient for the 3D reconstruction of neutrino interactions within the detector. As a test of the reconstruction utility, the products of the reconstruction workflow are used to select inclusive charged-current (CC) $\nu_e$ and $\nu_\mu$ interactions in both simulated and real MicroBooNE data. In simulation, our $\nu_e$ and $\nu_\mu$ selections achieve an efficiency of 57% and 68\%, respectively, with a purity of 91% and 96%, respectively. We find that these selections are competitive with the inclusive selections used for the most recent MicroBooNE LEE searches. In particular, the CC-$\nu_e$ inclusive selection efficiency improves by over 20% while also improving sample purity. As a first step in quantifying potential bias, the data and Monte Carlo expectati ons are compared for both selections using the MicroBooNE open data. Within statistical and systematic uncertainties, both the electron and muon CC-inclusive event samples agree. A comparison of the real data events chosen by our work and another reconstruction framework shows that the two analyses each identify a sizeable fraction of events the other does not. This suggests that future analyses integrating the strengths of each could lead to combined gains. This work demonstrates, for the first time on real LArTPC data, state-of-the-art neutrino interaction reconstruction centered around deep learning algorithms.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

An FSI Reweighter for 𝑣 Interactions on Heavy Nuclei

Final State Interaction (FSI) processes in $\nu$--Pb differ significantly from those in neutrino--carbon ($\nu$--C), and MINER$\nu$A is particularly sensitive to these differences, especially in pion absorption. This work develops an FSI reweighting method for $\nu$--Pb (and C) interactions in MINER$\nu$A. We compare GENIE~2, the currently used generator version, with GENIE~3 predictions of the initial hadron energy as a function of FSI fate, and apply these weights to simulated observables. We identify changes in the FSI composition, providing a framework for estimating FSI-related systematic uncertainties and interpreting their effects in the data.

Moreno-Palacios, Oscar [Coll. William and Mary]↗

DOE OSTI2025

Galactic Chemical Evolution of Short-lived Radioactive Isotopes Produced by Explosive Nucleosynthesis: 60 Fe and 53 Mn

Several short-lived radionuclides (SLRs) are know to have existed in the early solar system (ESS). These species, which typically decay with half-lives of the order of a few million years, can be used to probe the timescales of events preceding the birth of the Sun. We investigate the ESS origin of 53 Mn, produced by core-collapse supernovae (CCSNe) and Type Ia supernovae (SNe Ia), and 60 Fe, produced exclusively by CCSNe. We model the evolution of the radioactive-to-stable abundance ratios of these SLRs with a Galactic chemical evolution (GCE) framework accounting for different SN yields, SN Ia delay times, and other Galactic features (K). A further set of models is calculated assuming that SNe Ia did not contribute any 53 Mn to the ESS. The predicted ratios are compared to meteoritic ratios to derive a distribution of solar isolation times that includes uncertainties due to stochastic chemical enrichment and measurements of the ESS values. The isolation times are then compared to those of 107 Pd and 182 Hf calculated in previous work. A self-consistent solution can be found within the current uncertainties, especially when using the GCE setups with K = 1.6 and 2.3, although the maximum likelihood for the 60 Fe distribution is typically ∼4–5 Myr shorter than for 53 Mn. The predicted 60 Fe/ 53 Mn ratio, instead, is completely inconsistent with the ESS value; this could be resolved using a larger fraction of faint CCSNe than usually considered in GCE models.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1981

Survey and documentation of emerging technologies for the Satellite Power System (SPS)

The genesis of the solar power satellite (SPS) concept is reviewed historically and the original assumptions and guidelines which led to development of the SPS reference system design concept are discussed. Some guidelines are applicable to almost any SPS design, but others can be changed, leading to new and perhaps preferable systems. In order to stimulate new SPS concepts and to facilitate comparative assessment of emerging SPS technologies, one useful approach is to break the overall system into functional parts. The system functions which must be performed by any SPS concept and the interrelations between them are discussed and a systematic framework is presented for assessing the wide variety of system concepts and subsystem technologies which have been proposed. About 80 alternative SPS technologies are reviewed.

Glaser, P.↗

NASA NTRS1984

The chemistry of rare earth elements in the solar nebula

The high concentration of rare earth elements (REE) in primitive CaS suggests that the REE along with the other normally lithophile elements form stable sulfides under the unusual conditions which existed during the formation of enstatite chrondites. In order to acquire a more quantitative framework in which to interpret these data, the behavior of the REE in systems with solar, or slightly fractionated solar, composition is being studied. These new data introduce modest changes in the behavior of some of the REE when compared to previous studies. For example, the largest differences are in the stabilities of the gaseous monoxides of Ce, Eu, Tb, Ho, and Tm, all of which now appear to be less stable than previously thought, and YbO(g) which is somewhat more stable. Much more significant are the changes in REE distribution in the gas phase in fractionated systems, especially those made more reducing by changing the C/O ratio from the solar value of 0.6 to about 1.0. In almost all cases, the exceptions being Eu, Tm and Yb whose elemental gaseous species dominate, the monosulfides become more abundant. Moreover, the solid oxides of Eu, Tm and Yb become less stable under more reducing conditions which, in effect, should reduce the condensation temperature of all REE in more reduced systems.

Larimer, J. W.↗

NASA NTRS1986

Sound propagation over curved barriers

Wide barriers with curved tops are studied with emphasis placed on circumstances whereby the local radius of curvature R of the barrier is continuous along the surface and is large compared to a wavelength. Results analogous to those given by Hayek et al. (1978) are reviewed and extended to cases where the radius of curvature and the surface impedance may vary with position. Circumstances not easily interpreted within the framework of the model proposed by Keller (1956) and Hayek et al. are also considered.

Pierce, Allan D.↗

NASA NTRS1997

Fossilization of Coniform (Phormidium) Stromatolites In Siliceous Thermal Springs, Yellowstone National Park

We have studied fossilization processes in well-developed cyanobacterial mats present in mid-terrace ponds of silica-precipitating thermal springs of Yellowstone National Park. These mats occur over temperatures of approx.35-60 C and are dominated by species of Phormidium. Within Bonded systems two end member environments have been distinguished based temperature, depth, and the stability of spring inflows, each differing substantially in the style of stromatolite morphogenesis and fossilization. Type 1 systems include shallow, ephemeral ponds where water flow and temperature fluctuates widely on a seasonal basis; mats typically secrete rapidly up to the air-water interface, forming exposed islands. Silicification occurs primarily by the wicking of water onto the surface of exposed mats, leading to the evaporative concentration of silica near the surface. pH microelectronic measurements in partially-exposed columns show strong gradients in pH with values exceeding 10.5 in the upper am of silicifying mats, decreasing to <9.0 a few mm below the surface. High oxygen concentrations lead to the rapid oxidation of most organic materials. In Type I systems, the tops of coniform and columnar stromatolites become silicified first, followed by the bases. This typically leads to extensive fragmentation during the initial stages of burial. Case 2 systems include deeper ponds where the water flow, temperature and depth are seasonally more stable, and where mats develop larger-scale tufts and columns. Alkalinity in permanently submerged mats increases into tuft interiors from approx.9.0 near the surface to >10.0 at depth. Moderate silicification is apparent throughout mat frameworks, although there is frequently a densely silicified core near the base. In Type 2 systems, preservation of the coniform and columnar architecture of stromatoilites is much more robust. Sub-fossil examples suggest minimal fragmentation prior to burial. Comparative rapid analysis of the phone zones of submerged and exposed (silicifying) mats demonstrated the recovery of comparable levels of esterified fatty acids in both types of mats. Membrane lipids apparently remain intact during the early stages of silicification, although their fate during early diagenesis is still being evaluated.

Farmer, Jack↗

NASA NTRS2000

Learning to tell Neoproterozoic time

In 1989, the International Commission on Stratigraphy established a Working Group on the Terminal Proterozoic Period. Nine years of intensive, multidisciplinary research by scientists from some two dozen countries have markedly improved the framework for the correlation and calibration of latest Proterozoic events. Three principal phenomena--the Marinoan ice age, Ediacaran animal diversification, and the beginning of the Cambrian Period--specify the limits and character of this interval, but chemostratigraphy and biostratigraphy based on single-celled microfossils (acritarchs), integrated with high-resolution radiometric dates, provide the temporal framework necessary to order and evaluate terminal Proterozoic tectonic, biogeochemical, climatic, and biological events. These data also provide a rational basis for choosing the Global Stratotype Section and Point (GSSP) that will define the beginning of this period. A comparable level of stratigraphic resolution may be achievable for the preceding Cryogenian Period, providing an opportunity to define this interval, as well, in chronostratigraphic terms--perhaps bounded at beginning and end by the onset of Sturtian glaciation and the decay of Marinoan ice sheets, respectively. Limited paleontological, isotopic, and radiometric data additionally suggest a real but more distant prospect of lower Neoproterozoic correlation and stratigraphic subdivision.

NASA Discipline Exobiology↗

NASA NTRS2018

Can Semi-Volatile Organic Aerosols Lead to Fewer Cloud Particles?

The impact of condensing organic aerosols on activated cloud number concentration is examined in a new aerosol microphysics box model, MATRIX-VBS. The model includes the volatility-basis set (VBS) framework coupled with the aerosol microphysical scheme MATRIX (Multiconfiguration Aerosol TRacker of mIXing state) that resolves aerosol mass and number concentrations and aerosol mixing state. By including the condensation of organic aerosols, the new model produces less activated particles compared to the original model, which treats organic aerosols as non-volatile. Parameters such as aerosol chemical composition, mass and number concentrations, and particle sizes which affect activated cloud number concentration are thoroughly tested via a suite of Monte-Carlo simulations. Results show that by considering semi-volatile organics in MATRIX-VBS, there is lower activated particle number concentration, except in cases with low cloud updrafts, in clean environment at above freezing temperatures, and in polluted environments at high temperature (310K) and extremely low humidity conditions.

Gao, Chloe Y.↗

NASA NTRSDate not supplied

A Comparison of Model Predictive Control Architectures for Application to Electrified Aircraft Propulsion Systems

As electrified aircraft propulsion (EAP) systems continue to mature, more sophisticated hardware and software are being developed to balance operations among electric machines and gas-turbine engines. In hybrid-electric propulsion systems, the increased complexity resulting from integrating turbine-engine shafts with electric machines necessitates control methodologies to account for various physical domains. Ideal controllers for hybrid-electric engines manage systems, subsystems, and their interactions in a coordinated fashion, able to account for safety and performance goals while being computationally efficient. In a previous work, linear model predictive control (MPC) schemes were implemented in centralized and distributed frameworks on a nonlinear turbofan engine model as a proof of concept. However, these schemes were not evaluated for computational complexity, prompting further study. The research presented here develops hierarchical MPC schemes to reduce the computational burden of the previous MPC schemes. A two-tier framework is implemented, where a slower sampling MPC controls electric machines and determines fan-speed tracking goals for a faster sampling controller, which is either a MPC or a proportional-integral (PI) controller. The proposed designs are compared to the centralized MPC investigated previously, and performance is measured via fan speed tracking error, energy storage state-of-charge, and computation time. Results reveal that the hierarchical MPC scheme employing a lower-level PI controller improves computation time while maintaining comparable tracking and state-of-charge regulation to the centralized scheme.

model predictive control↗

NASA NTRSDate not supplied

A Comparison of Model Predictive Control Architectures for Application to Electrified Aircraft Propulsion Systems

model predictive control↗

DOE OSTI2021

Geothermal Operational Optimization with Machine Learning

The Geothermal Operational Optimization with Machine Learning (GOOML) project has developed a generic and extensible component-based system modeling framework to study complex geothermal fields using a data-driven approach. Through building a digital twin of a geothermal steam field with the GOOML modeling framework, operators can analyze historical and forecasted power production, explore possible steam field configurations, and optimize real world operations, all in a cost-effective digital environment. The GOOML modeling software is based on a historical data-assimilation framework that uses first-principal thermodynamics to model steam field components using historical data, and a forecast framework that uses machine-learning-driven models of steam field components to predict future operations. This modeling framework creates countless new opportunities for digital exploration of steam field design and operations. To date, digital twins have been developed for several steam fields in New Zealand and the United States. These digital twins have been validated by comparing hindcast predictions against historical production data. Field design and operations have been explored using genetic optimization and reinforcement learning. Initial results show compelling and often surprising opportunities for improved design and operation of fields with 2 to 5 percent improvements in annual energy production. GOOML is driving a step-change in geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind intelligent geothermal systems model.

40 EE - Geothermal Technologies Office (EE-4G)↗

DOE OSTI2022

Automated quantum error mitigation based on probabilistic error reduction

Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.

McDonough, Benjamin↗