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NASA NTRSDate not supplied

Understanding the Impact of Long Duration Space Radiation on Biology and Nutrients in Food

No food or bioregenerative life support system (BLSS) is likely to be deployed on Mars prior to being fully vetted on lunar long duration missions. BLSS systems developed for lunar missions will trade well in terms of up mass and volume with prepackaged food for Mars missions, but only if they are reliable. There are significant differences in the gravitational and radiation environments between the moon and Mars, and one key challenging aspect for a BLSS on Mars missions is the extremely long durations. This results in the exposure of seeds, spores, or other dormant stages of organisms to both natural and radiation induced degradation over time frames of two or more years before they might be needed for growth. This can result in poor or no germination and sickly crops. A poorly functioning BLSS represents a threat to crew health. To ensure that a BLSS food production system will function well, we need to understand, at an early stage, the impact of these long missions on the biology. This is important to meet the Mars Exploration Program Analysis Group (MEPAG) goals.

C. D. Quincy↗
NASA NTRS2021

Low and High Temperature Non-Thermonuclear Fusion Approaches to Energy Production

We are responding to the RFI points A1, A2 and A3. We appreciate the need for a high density, high power, environmentally-benign and carbon-neutral power system. Although fusion has been considered for over 70 years, the nearly intractable means of maintaining a hydrogen plasma at temperatures exceeding the center of the Sun increasingly delays not only commercialization but even the scientific breakeven point. As a recent JASON report notes, the world-wide energy grid is moving towards decentralization which is counter to the large tokamak fusion de-vices necessary given their underdense plasmas. NASA has spent several years understanding and developing Lattice Confinement Fusion as an alternative to thermonuclear fusion by exploring many means of loading and triggering fusion reactions in deuterated metal lattices. Triggering has run the gamut from bremsstrahlung-initiated photo-neutrons to the role of electron screening in assisting these reactions. Electron screening may be responsible for both nuclear effects in deuterium pumped metals as well as fast neutrons observed from electrolytically driven cathodes. Electron screened fusion rate enhancement has been observed by others, including the Lawrence Berkeley National Laboratory. NASA’s need for deep space power drove this research with a goal of a ten-year lifetime for a multi-kWe power system. Previous NASA studies have explored the use of fusion systems for nuclear thermal propulsion for manned spaceflight to the outer planets. Despite these designated uses, small-scale terrestrial power applications as well as medical isotope production are possible once scaling is achieved. ARPA-E can contribute to the Nation’s Green Energy mandates by funding research in this field that builds on condensed matter nuclear science findings by the US Navy and NASA.

L. P. Forsley↗
NASA NTRS1987

Radio OH observations of P/Halley with the NRAO 43-m telescope

Results of the radio OH emission from Comet Halley obtained with the 43-m NRAO telescope over the period from July 1985 to May 1986 show the global properties to be close to those predicted by Schloerb and Gerard (1985). The OH production rates are found to be well correlated with heliocentric magnitude estimates and to roughly follow the production rates determined from UV OH observations. After correction for the Greenstein effect, the mean velocities suggest that there is anisotropic outgassing of the parent molecule. An increase in the linewidths of the OH emission near and after perihelion is also noted.

Claussen, M. J.↗
NASA NTRS2011

Enhancements in Deriving Smoke Emission Coefficients from Fire Radiative Power Measurements

Smoke emissions have long been quantified after-the-fact by simple multiplication of burned area, biomass density, fraction of above-ground biomass, and burn efficiency. A new algorithm has been suggested, as described in Ichoku & Kaufman (2005), for use in calculating smoke emissions directly from fire radiative power (FRP) measurements such that the latency and uncertainty associated with the previously listed variables are avoided. Application of this new, simpler and more direct algorithm is automatic, based only on a fire's FRP measurement and a predetermined coefficient of smoke emission for a given location. Attaining accurate coefficients of smoke emission is therefore critical to the success of this algorithm. In the aforementioned paper, an initial effort was made to derive coefficients of smoke emission for different large regions of interest using calculations of smoke emission rates from MODIS FRP and aerosol optical depth (AOD) measurements. Further work had resulted in a first draft of a 1 1 resolution map of these coefficients. This poster will present the work done to refine this algorithm toward the first production of global smoke emission coefficients. Main updates in the algorithm include: 1) inclusion of wind vectors to help refine several parameters, 2) defining new methods for calculating the fire-emitted AOD fractions, and 3) calculating smoke emission rates on a per-pixel basis and aggregating to grid cells instead of doing so later on in the process. In addition to a presentation of the methodology used to derive this product, maps displaying preliminary results as well as an outline of the future application of such a product into specific research opportunities will be shown.

Ellison, Luke↗
NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
DOE OSTI2025

Patulin Inhibition of Specific Apple Microbiome Members Uncovers Hanseniaspora uvarum as a Potential Biocontrol Agent

Penicillium expansum is a major postharvest pathogen of apples, causing loss in fruits through tissue damage, as well as in apple products due to contamination with the mycotoxin patulin. During infections, patulin is a cultivar-dependent virulence factor that facilitates apple lesion development. Patulin also has characterized antimicrobial activity and is important for inhibiting other competitive phytopathogens, but the role of this inhibitory activity has not been investigated in the context of the apple microbiome. Here, in our current study, we isolated 68 apple microbiota and characterized their susceptibility to P. expansum extracts. We found gram-negative bacteria and basidiomycete yeast to demonstrate largely patulin-specific growth inhibition compared with gram-positive and ascomycete isolates. From co-cultures, we identified a Hanseniaspora and Gluconobacter pairing that reduced P. expansum biomass and found that H. uvarum alone is sufficient to reduce apple disease progression in vivo. We investigated possible mechanisms of H. uvarum biocontrol activity and found modest inhibition on apple puree plates, as well as a trend toward lower patulin levels at the wound site. Active biocontrol activity required live yeast, which was also effective in controlling Botrytis cinerea apple infections. Lastly, we explored the breadth of H. uvarum biocontrol activity with over 30 H. uvarum isolates and found consistent inhibition of P. expansum apple disease.

Hanseniaspora uvarum↗
NASA NTRS2004

Acquisition of a Gas Chromatograph/Mass Spectrometer System for Laboratory Study of Prebiotic Organic Geochemical Processes on the Early Earth, Mars, and Meteorites

This was a major equipment grant that provided funds ($72K) for purchase of a benchtop gas chromatograph-mass spectrometer (GC-MS) for use in experimental studies of prebiotic organic compounds. An Agilent model 689015973 GC-MS was purchased and installed in the PI's lab in August of 2003. The instrument is now being used for a variety of research projects. The primary use of the instrument is to analyze and quantify organic products of laboratory experiments conducted by the PI. One example is shown, which shows organic products (predominantly n-alkanes) formed during Fischer-Tropsch-type abiotic synthesis under hydrothermal conditions. The analytical capabilities of the GC- MS allowed identification of the numerous organic products of this as well as other laboratory experiments. A key use of the instrument in this research is that the mass spectrometer capabilities allow use of isotopically labeled reactants to trace the progress of reactions and evaluate background contaminants. collaborative projects with other scientists involved in exobiology & astrobiology research (e.g., Mitch Schulte, NASA Ames; Katrina Edwards, Woods Hole Oceanographic Institution). For instance, an analysis of membrane lipids of an lithoautotrophic iron-oxidizing bacteria being grown on basalt as a source of metabolic energy, a project where the instrument is being used to evaluate possible biomarker compounds from these organisms is shown. These iron oxidizers are thought to be similar to those living within the ocean crust, and are being investigated as possible analog organisms to those on the early Earth or crust of Mars. The instrument has also been used by an outside investigator (graduate student Brandon Canfeld, Arizona State University) for identification and isotopic characterization of experimental products of abiotic organic synthesis experiments he is conducting with Dr. John Holloway. analysis of quality control samples for other NASA-funded projects. For instance, an analysis of residual hydrocarbon contaminants on the internal surface of the shell of an atmospheric sounding rocket is shown. This analysis was used to help determine the source of the contaminating compounds. In the future, the instrument will continue to be used for quality control analysis in clean rooms and instrument construction facilities within the Laboratory for Atmospheric and Space Physics, where the GC-MS is housed.

McCollom, Thomas↗
NASA NTRS2005

You Can't Get There From Here! Problems and Potential Solutions in Developing New Classes of Complex Systems

The explosion of capabilities and new products within the sphere of Information Technology (IT) has fostered widespread, overly optimistic opinions regarding the industry, based on common but unjustified assumptions of quality and correctness of software. These assumptions are encouraged by software producers and vendors, who at this late date have not succeeded in finding a way to overcome the lack of an automated, mathematically sound way to develop correct systems from requirements. NASA faces this dilemma as it envisages advanced mission concepts that involve large swarms of small spacecraft that will engage cooperatively to acheve science goals. Such missions entail levels of complexity that beg for new methods for system development far beyond today's methods, which are inadequate for ensuring correct behavior of large numbers of interacting intelligent mission elements. New system development techniques recently devised through NASA-led research will offer some innovative approaches to achieving correctness in complex system development, including autonomous swarm missions that exhibit emergent behavior, as well as general software products created by the computing industry.

Hinchey, Michael G.↗
NASA NTRS2024

On the Use of SMAP Soil Moisture for Forecasting NDVI Over CONUS Cropland Regions

Vegetation health forecasting (NDVI as a proxy) informs decision-makers about the end of season crop yield productivity but is not well-documented. This study tests improvements in vegetation health forecasting by developing a data-driven Dynamic Agricultural Productivity Indicator ( DAPI ), which simultaneously incorporates satellite-based root zone soil moisture (RZSM) and satellite-based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology-based NDVI forecasts, with the largest improvements in water-limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food-insecure areas, predicting crop price changes, and projecting expected commodities market trends.

Manh Le↗
DOE OSTI2022

Photopolymerizable Sol-gel Synthesis for Faster Manufacturing of Surrogate Nuclear Debris

Simulated debris is used to train first responders for potential nuclear detonations. Sol gel chemistry has been used to produce surrogate material for these training exercises, but the current average production time is 4 hours. Optimization using UV-polymerizable sol gel precursors can potentially decrease time as well as cost of the production process. FT-IR spectroscopy was used to measure and compare gelation kinetics of the standard and photopolymerizable sol gel synthetic procedures. Addition of the UV-polymerizable precursors and controlling the aqueous phase volume resulted in a 120-fold decrease in vitrification time.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗
NASA NTRSDate not supplied

Implementation of A New Microwave Scattering Database and A Forward Model for Active Microwave Sensors in CRTM

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. CRTM is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk scattering lookup tables in order to perform all-sky RT calculations. However, the current CRTM lookup tables for microwave frequencies were generated based on the Mie theory by assuming spherical frozen particles. The scattering lookup tables generated using the DDA technique has shown to largely improve the RT scattering calculations in the MW region. This presentation targets (i) the implementation and validation of a DDA database that was originally developed for the ARTS RT model into CRTM, and (ii) developing the CRTM active sensor module that takes advantage of the backscattering coefficients computed using the DDA method. The DDA database only provides single scattering properties of different habits, while CRTM requires bulk scattering properties. The CRTM cloud coefficients were previously generated based on the effective radius for representing the size of the particles. However, effective radius is neither measurable nor provided by the NWP models, thus need to be estimated from other geophysical variables such as water content. Therefore, in addition to calculating the CRTM bulk scattering properties from the DDA single scattering database, the CRTM was also largely modified to use cloud water content (kg.m-3), instead of effective radius, for performing the interpolation over size/mass of the particles. CRTM already requires water content as input, thus no extra variables are required for performing scattering calculations using the new ARTS DDA database. The CRTM scattering modules search for effective radius in cloud coefficient files and will use the cloud water content if the effective radius dimension is not found in the cloud coefficient files. Figure 1 shows the CRTM simulated brightness temperatures computed using different cloud coefficients versus ATMS observed values over Hurricane Irma on September 7, 2017 at 18:00 UTC. We used all the cloud water content values included in ERA5 with default CRTM/DDA habits for water, rain, snow, ice, hail, and graupel. ERA5 does not provide separate water content values for ice, hail, and graupel, thus the ice water content values were divided between ice, hail, and graupel clouds similar to what was explained in the previous section. In channels with a frequency lower than 90 GHz, emission from water and rain clouds can compensate for cloud scattering so that cloud contaminated Tbs are larger than corresponding clear sky Tbs. The DDA simulations for channels 1-7 largely perform better than the Mie simulations. The DDA simulations show a mix of small negative and positive simulated minus observed values, while the Mie results show large negative biases. The weighting functions for some of the ATMS temperature sounding channels (channels 9-15) peak mostly above the clouds, therefore the measured Tbs become less sensitive to clouds so that the results of both Mie and DDA become very similar. The Mie lookup tables generate excessive scattering for channel 16, but not enough scattering for the water vapor channels. In the specific case of Hurricane Maria, the DDA lookup tables do not generate enough scattering for channel 16, but the DDA results are much more consistent with observations for water vapor channels than for channel 16. It should be noted that the results may vary if we use other habits to represent snow, hail, and graupel in the DDA simulations. Although these results clearly show the advantage of the DDA database over the Mie dataset, different error sources such as error in the observations, displacement of clouds in the ERA5 reanalysis, and also lack of convective clouds or in general errors in the input atmospheric and cloud profiles contribute to the differences between the simulated and observed values. Aside from the improvements in the simulations, a major advantage of the new dataset is a large number of habits that can be used to tune the data assimilation systems to perform well in different weather conditions.

Isaac Moradi↗
DOE OSTI2022

Supplementary Data for "Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-fidelity Model for Unconventional Oil and Gas Wells" by Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O'Malley, D., Marquis, M., Vactor, R.T., and Cunha, L.

The Bello et al. study evaluates the impact of contrasting pressure drawdown on gas productivity and the resulting economics of a well in the Marcellus Shale of the Appalachian Basin. This research applies a techno-economic analysis approach to help identify potential ways pressure management strategies can be used to improve cumulative recovery of hydraulically fractured horizontal wells while maintaining project profitability. Gas production forecast outlook scenarios of the Marcellus Shale Energy and Environment Laboratory Laboratory's MIP-3H well were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning (PIML) workflow and 2) via traditional reservoir simulation in Computer Modeling Group’s (CMG) GEM Compositional & Unconventional Simulator. Cash flow and other economic metrics of interest were compiled on the production outlook using the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Unconventional Shale Well Economic Model (UShWEM).The sheets within this Microsoft ExcelTM workbook provide the economic metric outputs for the baseline condition and the one-at-a-time (OAT) sensitivity analysis of UShWEM's input parameters for each of the production scenarios evaluated.

Fracture Network Model↗
DOE OSTI2022

Particulate and dissolved metabolite distributions along a latitudinal transect of the western Atlantic Ocean

Abstract Metabolites, or the small organic molecules that are synthesized by cells during metabolism, comprise a complex and dynamic pool of carbon in the ocean. They are an essential currency in interactions at the population and community levels of biological organization. Characterizing metabolite distributions inside microbial cells and dissolved in seawater is essential to understanding the controls on their production and fate, as well as their roles in shaping marine microbial food webs. Here, we apply a targeted metabolomics method to quantify particulate and dissolved distributions of a suite of biologically relevant metabolites including vitamins, amino acids, nucleic acids, osmolytes, and intermediates in biosynthetic pathways along a latitudinal transect in the western Atlantic Ocean. We find that, in the upper 200 m of the water column, most particulate or intracellular metabolites positively covary with the most abundant microbial taxa. In contrast, dissolved metabolites exhibited greater variability with differences in distribution between ocean regions. Although fewer particulate metabolites were detected below 200 m, the particulate metabolites identified in the deep ocean may be linked to adaptive physiological strategies of deep‐sea microbes. Based on the identified metabolite distributions, we propose relationships between certain metabolites and microbial populations, and find that dissolved metabolite distributions are not directly related to their particulate abundances.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Dark freeze-out cogenesis

We propose a new mechanism where a multi-component dark sector generates the observed dark matter abundance and baryon asymmetry and thus addresses the coincidence between the two. The thermal freeze-out of dark matter annihilating into meta-stable dark partners sets the dark matter relic abundance while providing the out-of-equilibrium condition for baryogenesis. The meta-stable state triggers baryon asymmetry production by its decay well after the freeze-out and potentially induces a period of early matter domination before its decay. The dark matter and baryon abundances are related through number conservation within the dark sector (cogenesis). The “coincidence” is a natural outcome with GeV- to TeV-scale symmetric dark matter and the dark sector’s interactions with the Standard Model quarks. We present a UV-complete model and explore its phenomenological predictions, including dark matter direct detection signals, LHC signatures of new massive particles with color charges and long-lived particles with displaced vertices, dark matter-induced nucleon conversions, (exotic) dark matter indirect detection signals, and effects on the cosmological matter power spectrum. As a side result, we provide a novel analytical treatment for dark sector freeze-out, which may prove useful in the study of related scenarios.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A reacting multiphase computational flow model for 2,3-butanediol synthesis in industrial-scale bioreactors

The microbe, Zymomonas mobilis, can efficiently convert sugars to 2,3 butanediol (BDO), an important intermediate for downstream chemical products, only in a well-controlled microaerated environment. However, controlling oxygen distribution in industrial-scale bioreactors is challenging, and at-scale BDO production is hard to optimize using traditional engineering methods. This study takes a step towards addressing this problem through a computational model for reacting multiphase flows in large-scale bioreactors. A phenomenological metabolic model was first developed and validated against experiments, and then it was coupled to a multiphase computational fluid dynamics (CFD) solver using a subcycling algorithm for simulating long conversion times (~30 hours). Large-scale (~500 m 3 ) bubble column simulations using this coupled approach demonstrated a 25% improvement in BDO yield with low sparging rates (0.002 m/s) and low height-to-diameter ratio (0.875) compared to a baseline bubble column (35 m height, 5 m diameter) with a superficial gas velocity of 0.06 m/s.

09 BIOMASS FUELS↗