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Integrated In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) connects individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. MATLAB was favorable to use as the main software integration tool due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of technologies, including but not limited to, the water processing Lunar Auger Dryer for ISRU (LADI) system and an oxygen extraction carbothermal reduction process. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing heater energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Efforts to update the MAIT tool are ongoing to accommodate and scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

DOE OSTI2020

Continuous and discrete modeling of HIV-1 decline on therapy

Mathematical models have shed light on the dynamics of HIV- 1 infection in vivo. In this paper, we generalize continuous mathematical models of drug therapy for HIV-1 by Perelson et al. (Science 271:1582–1586, 1996) and Perelson and Nelson (SIAM Rev 41:3–44, 1999) on time scales, i.e., a nonempty closed subset of real numbers in order to derive new discrete models that predict the total concentration of plasma virus as a function of time. Here, one of our main goals is to compare discrete mathematical models with the continuous model in Perelson et al. (1996) where HIV infected patients were given protease inhibitors and sampled frequently thereafter. For the comparison, we use experimental data collected in Perelson et al. (1996) and estimate the parameters such as the virion clearance rate and the rate of loss of infected cells by fitting the total concentration of plasma virus to this data set. Our results show that discrete systems describe the best fit. In the previous models of this study, the efficacy of protease inhibitor is assumed to be perfect. Motivated by Perelson and Nelson (1999), we end the paper with a mathematical model of imperfect protease inhibitor and reverse transcriptase (RT) inhibitor combination therapy of HIV-1 infection on time scales with its stability analysis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Dynamic Material Flow Analysis of Silicon Photovoltaic Modules to Support a Circular Economy Transition

Solar photovoltaics (PV) are the fastest growing renewable energy technologies for clean, cheap, and sustainable electricity generation. To prepare for rapid scale-up, the PV industry needs to project material requirements to build out all aspects of the supply chain appropriately and plan to handle large volumes of module waste. Impacts of deploying different material circularity strategies to reduce waste and conserve primary resources need to be quantified to inform sustainable material management. Here, we introduce the photovoltaic dynamic material flow analysis (PV DMFA) model based on PV electricity generation. The model quantifies material flows and stocks in the cradle-to-cradle life cycles of utility-scale c-Si PV systems in the United States through 2100. We present case studies for solar flat glass and aluminum frame materials under various scenarios to project the impacts of PV performance, reliability, and processing parameters, material circularity strategies, and module design shifts. In the absence of circularity measures, ~100 million MT of flat glass and ~12 million MT of aluminum would be needed for PV installations by 2100 to meet projected growth in domestic utility PV demand to nearly 1000 TWh in 2100. With optimistic but feasible improvements in efficiency, reliability, and circularity, material intensity and waste could be reduced by nearly 50%. Efficient module collection, minimally intrusive recycling, and careful scrap handling and cleaning could improve material circularity in the PV value chain. This model serves as a sustainability data support tool that may aid in the circular economy transition for PV systems.

circular economy↗

DOE OSTI2023

Lifetime and Degradation Study of Poly(Methyl Methacrylate) via a Data-Driven Study Protocol Approach

We report to optimize and extend the service life of polymeric materials in outdoor environments, a domain knowledge-based and data-driven approach was utilized to quantitatively investigate the temporal evolution of degradation modes, mechanisms, and rates under various stepwise accelerated exposure conditions. Six formulations of poly(methyl methacrylate) (PMMA) with different combinations of stabilizing additives, including one unstabilized formulation, were exposed in three accelerated weathering conditions. Degradation was dependent on wavelength as samples in UV light at 340 nm (UVA) exposure showed the most yellowing. The unstabilized PMMA formulation showed much higher yellowness index values (59.5) than stabilized PMMA formulations (2–12). Urbach edge analysis shows a shift toward longer wavelength from 285 to 500 nm with increasing exposure time and an increased absorbance around 400 nm of visible region as the unstabilized samples increase in yellowing. The degradation mechanisms of PMMA were tracked using induced absorbance to dose at specific wavelengths that correspond to known degradation mechanisms. The degradation pathway of PMMA was modeled in a framework using network structural equation modeling (netSEM). netSEM showed changes in degradation pathway as PMMA transition stages of degradation.

36 MATERIALS SCIENCE↗

DOE OSTI2023

SPERLU Selective Process for Efficient Removal of Lignin and Upgrading (Final Technical Report)

To meet the growing demand for bio-based chemicals and improve the profitability and efficiency of the emerging biorefinery industry, lignin is an abundant and attractive feedstock. Spero Renewables has developed a proprietary and patent-pending technology, the Selective Process for Efficient Removal of Lignin and Upgrading (SPERLU). The technology is a catalytic deconstruction and upgrading of lignin in lignocellulosic biomass or commercially available technical lignin to produce multi-functional phenol (MFP) products. The SPERLU process has been demonstrated and vetted on a lab scale to convert commercially available technical lignin to MFPs in yields of 60-90% (based on lignin). The MFP products can be separated and further upgraded through catalytic or biological means. Spero performed a detailed kinetic study of the SPERLU process and used the resulting kinetic data to design and construct a mini-pilot-scale reactor for routine production of hundreds of grams of MFP products. Spero’s lignin-based MFPs have been demonstrated for use as a drop-in replacement for bisphenol A (BPA) in the synthesis of thermoset polymers. Spero’s lignin-based thermoset polymers exhibit excellent mechanical properties and can be used in many applications. Additionally, a collaboration with the National Renewable Energy Lab (NREL) was used to evaluate the feasibility of upgrading SPERLU products through biological means into valuable chemicals. A comprehensive technoeconomic analysis (TEA) and Life-cycle analysis (LCA) have been completed and support the case for commercialization of the SPERLU technology. Through this project Spero has significantly de-risked the SPERLU technology and is actively planning larger scale pilot projects.

09 BIOMASS FUELS↗

NASA NTRS1980

Overview of escalator applications in rail transit

The difference in operating environment and in construction between escalators in transit and nontransit use, the impact of recent escalator innovations, and areas which could benefit from urban mass transportation administration sponsored research and development are determined. Several factors causing a more severe transit escalator operating environment are identified. There are no significant design differences between transit and nontransit escalators. Recent innovations that have affected performance and cost include outdoor escalators, extra flat steps at both landings, and modular escalators. Data were collected by interviews at transit agencies. Long term, unscheduled, escalator maintenance records were available for analysis from one property. A description of escalator operating principles is provided. Transit represents less than 5% of the U.S. escalator market. Transit agencies have limited leverage on escalator industry practices. A substantial impact on transit escalator cost and performance can be achieved by research identifying when and how to apply and specify several of the more recent innovations. Purchase of escalators under long term (25 year) maintenance contracts is one method that has been used to promote escalators manufactured for minimum life cycle cost.

Deshpande, G.↗

NASA NTRS1984

Flat-plate module efficiency versus cost tradeoffs

The Five Year Research Plan energy cost methodology and in depth analyses based on the extensive data that are relevant to PV systems are used to facilitate the accomplishment of the $0.15/kWh energy cost goal. An equation is given for the five year energy cost methodology. The allocation guidelines are designed to be consistent with flat solar array (FSA) milestones for module cost, module efficiency, and the programmatic goal for energy cost. They are research targets that appear to be achievable, given prior accomplishments and planned activities in the areas of low cost silicon purification, low cost sheet material, high efficiency cell processing, low cost, long life encapsulants, and automated fabrication method. Extensive sensitivity analysis work has been performed that shows that these guidelines represent an efficient way to meet the intent of the DOE program.

Aster, R. W.↗

NASA NTRS2002

Space Flight Qualification Program for the AMS-2 Commercial Cryocoolers

The Alpha Magnetic Spectrometer-02 (AMS-02) experiment is a state-of-the-art particle physics detector containing a large superfluid helium-cooled superconducting magnet. Highly sensitive detector plates inside the magnet measure a particle's speed, momentum, charge, and path. The AMS-02 experiment will study the properties and origin of cosmic particles and nuclei including antimatter and dark matter. AMS-02 will be installed on the International Space Station on Utilization Flight-4. The experiment will be run for at least three years. To extend the life of the stored cryogen and minimize temperature gradients around the magnet, four Stirling-cycle Sunpower M87N cryocoolers will be integrated with AMS-02. The cryocooler cold tip will be connected via a flexible strap to the outer vapor cooled shield of the dewar. Initial thermal analysis shows the lifetime of the experiment is increased by a factor of 2.8 with the use of the cryocooler. The AMS-02 project selected the Sunpower M87 cryocoolers and has asked NASA Goddard to qualify the cryocoolers for space flight use. This paper describes the interfaces with the cryocoolers and presents data collected during testing of the two engineering model cryocoolers. Tests include thermal performance characterization and launch vibration testing. Magnetic field compatibility testing will be presented in a separate paper at the conference.

Shirey, K. A.↗

NASA NTRS2008

Signal Detection Theory Applied to Helicopter Transmission Diagnostic Thresholds

Helicopter Health Usage Monitoring Systems (HUMS) have potential for providing data to support increasing the service life of a dynamic mechanical component in the transmission of a helicopter. Data collected can demonstrate the HUMS condition indicator responds to a specific component fault with appropriate alert limits and minimal false alarms. Defining thresholds for specific faults requires a tradeoff between the sensitivity of the condition indicator (CI) limit to indicate damage and the number of false alarms. A method using Receiver Operating Characteristic (ROC) curves to assess CI performance was demonstrated using CI data collected from accelerometers installed on several UH60 Black Hawk and AH64 Apache helicopters and an AH64 helicopter component test stand. Results of the analysis indicate ROC curves can be used to reliably assess the performance of commercial HUMS condition indicators to detect damaged gears and bearings in a helicopter transmission.

Dempsey, Paula J.↗

NASA NTRS2009

Signal Detection Theory Applied to Helicopter Transmission Diagnostic Thresholds

Dempsey, Paula J.↗

NASA NTRS2012

Rapid Analysis, Self-Calibrating Array for Air Monitoring

Human space missions have critical needs for monitoring and control for life support systems. These systems have monitoring needs that include feedback for closed loop processes and quality control for environmental factors. Sensors and monitoring technologies assure that the air environment and water supply for the astronaut crew habitat fall within acceptable limits, and that the life support system is functioning properly and efficiently. The longer the flight duration and the more distant the destination, the more critical it becomes to have carefully monitored and automated control systems for life support. Past experiments with the JPL ENose have demonstrated a lifetime of the sensor array, with the software, of around 18 months. The lifetime of the calibration, for some analytes, was as long as 24 months. We are working on a sensor array and new algorithms that will include sensor response time in the analysis. The preliminary array analysis for two analytes shows that the analysis time, of an event, can be dropped from 45 minutes to less than10 minutes and array training time can be cut substantially. We will describe the lifetime testing of an array and show lifetime data on individual sensors. This progress will lead to more rapid identification of analytes, and faster training time of the array.

Self Calibrating↗

DOE OSTI2021

Economic and Environmental Assessment of Biological Conversions of Agile BioFoundry (ABF) Bio-Derived Chemicals

Bio-derived chemicals are an essential part of the growing bioeconomy. They possess the potential to boost a new domestic bioproduct industry, improve the sustainability of integrated biomanufacturing, and reduce U.S. dependence on fossil energy. The Agile BioFoundry (ABF) consortium, a Department of Energy (DOE)-sponsored collaboration, is investigating biobased pathways to produce advantaged products including advanced biofuels, fuel intermediates, and bioproducts. ABF is integrating advanced computational tools for biological engineering, process design and data analysis, and economic/sustainability modeling tools into a comprehensive and dynamic platform for biomanufacturing of microbes and using them to produce key metabolic intermediates or beachhead molecules which may be derivatized into several distinct bioproducts of industrial interest. In this presentation, we first discuss a methodology to select a single exemplar product molecule to represent each beachhead pathway based on similarities with other end-molecule options (yields/titers/rates, fermentation operation mode, oxygen requirements, general separations challenges, etc.), in order to maintain a reasonable number of cases for rigorous process simulation. We then use techno-economic analysis (TEA) and life-cycle analysis (LCA) to highlight sensitivities and trends around economic performance and environmental footprint, reflecting examples for two selected ABF technology pathways to bio-derived chemicals: 1) adipic acid production via muconic acid fermentation from mixed sugars with Pseudomonas putida and 2) cineole via geranyl diphosphate with Rhodosporidium toruloides. We present multi-variable scan plots highlighting key drivers on costs and greenhouse gas emissions in order to identify the parameter space in which each pathway could ultimately achieve economic and environmental sustainability meeting or exceeding commodity product benchmarks, thus prioritizing future R&D focus areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Pixelated CZT Technology for the Detection, Identification, and Localization of Gamma-Ray Sources - 18311

Radioactive waste-management applications require good measurements to qualitatively and quantitatively characterize waste materials. Often these measurements must be done in situ, and the user wishes to learn something of the isotopic and spatial distribution of the source. Therefore, there is a need for a portable instrument that can map radioactive contamination and quantify sources. This presentation introduces several instruments that have been applied to this task, along with example measurements in relevant applications. All instruments are based on 3D-position-sensitive CdZnTe (CZT) detectors. This technology routinely achieves better than 1.1% FWHM energy resolution at 662 keV and operates at room temperature with a startup time under 2 minutes. All are packaged in portable enclosures with WiFi and Bluetooth capabilities for wireless communication. These capabilities allow the technology to be used in more advanced measurements in the field. A unique capability of these detectors is the ability to image the spatial distribution of gamma rays from a single stationary measurement. The first product, Polaris-H, has been in use at commercial nuclear power plants since 2013. The system, currently used at over 50% of the US nuclear-power fleet, integrates a 3D-position-sensitive CZT detector, associated readout electronics, an embedded computer, a battery with over 7-hour life, an optical camera, and a laser range finder in a portable water-proof enclosure. The system connects wirelessly to a tablet which displays a gamma-ray spectrum and isotope-specific images of the gamma-ray distribution in all directions in real time. The CZT detectors have a dynamic range from 50 keV up to 3 MeV. Coded aperture or pinhole imaging is used for energies below 250 keV, and Compton imaging is used for energies above 250 keV. List-mode data is saved to an external USB memory stick to enable more advanced analysis, including isotopic activity analysis, via H3D's post-processing software. Additional systems based on the same architecture have been developed to optimize the technology for other applications. These include Polaris-H Quad, which provides over 3 times the sensitivity for applications involving weaker sources, and Polaris-H Shield, which uses a collimator to provide images and isotope information over a narrower field of view in order to measure weaker sources in the presence of stronger sources of the same isotope. H3D has also developed custom systems that have been mounted on robots and drones that enable remote operation of the technology. Results from the measurements collected at commercial nuclear power plants and other applicable sites will be presented to show how these instruments can be used to detect, identify, and localize gamma-ray sources. Advanced analysis techniques to show the accurate quantification of the gamma-ray source will also be presented. Applications range from characterizing simple objects, to locating hottest points, estimating contamination distributions on an isotope-by-isotope basis, and differentiating scatter and direct emissions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Imperfect World of beta beta-decay Nuclear Data Sets

The precision of double-beta ββ-decay experimental half lives and their uncertainties is reanalyzed. The method of Benford's distributions has been applied to nuclear reaction, structure and decay data sets. First-digit distribution trend for ββ-decay T 2v 1/2 is consistent with large nuclear reaction and structure data sets and provides validation of experimental half-lives. A complementary analysis of the decay uncertainties indicates deficiencies due to small size of statistical samples, and incomplete collection of experimental information. Further experimental and theoretical efforts would lead toward more precise values of-decay half-lives and nuclear matrix elements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

The tomato DWD motif-containing protein DDI1 interacts with the CUL4–DDB1-based ubiquitin ligase and plays a pivotal role in abiotic stress responses

Highlights: • We identify DDI1 as a DAMAGED DNA BINDING PROTEIN1 (DDB1)-interacting protein. • DDI1 interacts with the CUL4–DDB1-based ubiquitin ligase in the nucleus. • DDI1 plays a positive role in regulating abiotic stress response in tomato. - Abstract: CULLIN4(CUL4)–DAMAGED DNA BINDING PROTEIN1 (DDB1)-based ubiquitin ligase plays significant roles in multiple physiological processes via ubiquitination-mediated degradation of relevant target proteins. The DDB1–CUL4-associated factor (DCAF) acts as substrate receptor in the CUL4–DDB1 ubiquitin ligase complex and determines substrate specificity. In this study, we identified a tomato (Solanum lycopersicum) DDB1-interacting (DDI1) protein as a DCAF protein involved in response to abiotic stresses, including UV radiation, high salinity and osmotic stress. Co-immunoprecipitation and bimolecular fluorescence complementation assay indicated that DDI1 associates with CUL4–DDB1 in the nucleus. Quantitative RT-PCR analysis indicated the DDI1 gene is induced by salt, mannitol and UV-C treatment. Moreover, transgenic tomato plants with overexpression or knockdown of the DDI1 gene exhibited enhanced or attenuated tolerance to salt/mannitol/UV-C, respectively. Thus, our data suggest that DDI1 functions as a substrate receptor of the CUL4–DDB1 ubiquitin ligase, positively regulating abiotic stress response in tomato.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

LncRNA SNHG20 knockdown suppresses the osteosarcoma tumorigenesis through the mitochondrial apoptosis pathway by miR-139/RUNX2 axis

Highlights: • SNHG20 was up-regulated in osteosarcoma samples and its high-expression indicated the poor prognosis. • SNHG20 knockdown inhibited the proliferation, invasion and promoted the apoptosis of osteosarcoma cells in vitro. • SNHG20 knockdown up-regulated the expression levels of caspase-9, caspase-3 and Bax. • miR-139 both targeted with the 3′-UTR of RUNX2 and SNHG20. • SNHG20/miR-139/RUNX2 axis modulates the osteosarcoma tumorigenesis and apoptosis via mitochondrial apoptosis. Increasing evidence has indicated the important roles of long noncoding RNAs (lncRNAs) in human osteosarcoma tumorigenesis. In present study, we aim to investigate the roles of lncRNA SNHG20 (small nucleolar RNA host gene 20) in osteosarcoma tumorigenesis and explore the in-depth molecular mechanism. Results showed that lncRNA SNHG20 expression was up-regulated in osteosarcoma samples and its high-expression indicated the poor prognosis. Loss-of-functional experiments indicated that SNHG20 knockdown inhibited the proliferation, invasion and induced the apoptosis of osteosarcoma cells in vitro. Specifically, SNHG20 knockdown up-regulated the expression levels of caspase-9, caspase-3 and Bax, indicating that SNHG20 knockdown accelerated the apoptosis of osteosarcoma cells via mitochondrial apoptosis pathway. Bioinformatics analysis revealed that miR-139 both targeted with the 3′-UTR of runt-related transcription factor 2 (RUNX2) and SNHG20, which was verified by luciferase reporter assay and RNA immunoprecipitation (RIP). In conclusion, our data reveals that lncRNA SNHG20/miR-139/RUNX2 axis modulates the osteosarcoma tumorigenesis and apoptosis via mitochondrial apoptosis pathway, providing a novel insight for the pathophysiological process.

60 APPLIED LIFE SCIENCES↗