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At least 505 records · Page 28

Rare Events via Cross-Entropy Population Monte Carlo

Rare events are events that happen with very low frequency. Estimating rare event probabilities using Monte Carlo techniques is computationally expensive, often to the point of intractability, and special methods are required. Importance sampling (IS) is a well known technique that uses a proposal distribution in place of a target distribution to lower the variance of the estimator. Key to the success of IS methods is the choice of a proposal distribution, or the parameters governing the distribution. Adaptive importance sampling improves the parameters of a family or population of proposal distributions iteratively through trials. We present a novel cross-entropy population Monte Carlo algorithm, which adapts the parameters of proposals through the cross-entropy method. The proposed method stands apart from previous work in that we are not optimizing a mixture distribution. Instead, we leverage deterministic mixture weights and optimize the distributions individually through a reinterpretation of the typical derivation of the cross-entropy method. Demonstrations on rare event examples show that the algorithm can outperform existing resampling based population Monte Carlo methods, especially for higher-dimensional problems. Finally, we also demonstrate efficacy on a conjunction analysis problem.

97 MATHEMATICS AND COMPUTING↗

NREL Hydrogen Sensor Testing Laboratory

Relevance: Detection is recognized as a critical element for hydrogen facility safety design and supports risk mitigation. Detection methodologies will support validation of H2 behavior research. Hydrogen point sensors play a critical role for safety and process monitoring, but other methodologies can be developed. Approach: NREL Sensor Laboratory tests and verifies sensor performance for manufacturers, developers, end-users, regulatory agencies and SDOs/CDOs NREL deployment activity supports regulatory requirement verification, hydrogen behavior models, and method development for use by stakeholders. Accomplishments and Progress: NREL's R&D accomplishments have supported developers, industry, and SDOs by providing sensor performance and deployment expertise not otherwise available. Development of alternative detection strategies for hydrogen applications have been initiated. HyWAM and advanced detection methodologies deployments at H2@Scale Facilities are being implemented. Collaborations: Collaboration with government laboratories, universities, private organizations and regulatory agencies has leveraged the NREL Sensor Laboratory's success in advancing hydrogen safety sensors and process control. Proposed Future Work: NREL will support hydrogen deployment by the proper implementation of hydrogen sensors and advanced detection strategies. NREL will continue to support science-based codes and standards. This effort will be guided by the needs of the hydrogen community.

codes and standards↗

Between a Map and a Data Rod

A Digital Divide has long stood between how NASA and other satellite-derived data are typically archived (time-step arrays or maps) and how hydrology and other point-time series oriented communities prefer to access those data. In essence, the desired method of data access is orthogonal to the way the data are archived. Our approach to bridging the Divide is part of a larger NASA-supported data rods project to enhance access to and use of NASA and other data by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the larger hydrology community. Our main objective was to determine a way to reorganize data that is optimal for these communities. Two related objectives were to optimally reorganize data in a way that (1) is operational and fits in and leverages the existing Goddard Earth Sciences Data and Information Services Center (GES DISC) operational environment and (2) addresses the scaling up of data sets available as time series from those archived at the GES DISC to potentially include those from other Earth Observing System Data and Information System (EOSDIS) data archives. Through several prototype efforts and lessons learned, we arrived at a non-database solution that satisfied our objectivesconstraints. We describe, in this presentation, how we implemented the operational production of pre-generated data rods and, considering the tradeoffs between length of time series (or number of time steps), resources needed, and performance, how we implemented the operational production of on-the-fly (virtual) data rods. For the virtual data rods, we leveraged a number of existing resources, including the NASA Giovanni Cache and NetCDF Operators (NCO) and used data cubes processed in parallel. Our current benchmark performance for virtual generation of data rods is about a years worth of time series for hourly data (9,000 time steps) in 90 seconds. Our approach is a specific implementation of the general optimal strategy of reorganizing data to match the desired means of access. Results from our project have already significantly extended NASA data to the large and important hydrology user community that has been, heretofore, mostly unable to easily access and use NASA data.

machine learning↗

Evaluation of Response Surface Experiment Designs for Distributed Propulsion Aircraft Aero-Propulsive Modeling

Modern distributed hybrid and electric propulsion aircraft, including vertical, short, and conventional takeoff and landing configurations, exhibit significant aero-propulsive complexity and a large number of interacting test factors. This paper presents the development and evaluation of experiment designs for aero-propulsive characterization of distributed propulsion aircraft. Five different foundational response surface designs are evaluated to inform the development of two sequential design approaches tailored to complex aircraft aerodynamic characterization experiments. The first approach, which builds on sequential face-centered central composite designs, has been used previously to develop aero-propulsive models for complex aircraft using wind tunnel testing. The second approach is a new design strategy leveraging a regular I-optimal and nested I-optimal design that was developed for this study. The two sequential design strategies are compared for experiments with a large number of test factors using pre-experiment design evaluation metrics, as well as modeling results obtained from simulated wind tunnel data for the NASA LA-8 aircraft. The design evaluation metrics show that the sequential I-optimal base design has higher statistical power, lower correlation among candidate regressors, lower prediction variance, and more precise parameter estimates. The simulated wind tunnel experiments conducted using each design reveal that the sequential I-optimal base design has better predictive capability with fewer test points. The experiment design and evaluation procedures are described in detail to inform future aerodynamic characterization experiments for complex aircraft.

design of experiments↗

Impact of undoped substrates on high performance silicon solar cells

Today's highest-efficiency silicon solar cells typically operate near the threshold between low-level and high-level injection. It is not well understood if pushing further into a regime in which the cell operating point is solidly in high-level injection at all times of the day has further benefits for the solar cell performance. From a reliability perspective, cells fabricated on lower doped silicon have a larger breakdown voltage. This advantage can affect the design of modules allowing higher voltages and a relaxation of the number of bypass diodes needed. In this project, we present a comprehensive assessment, both experimental and using simulation, of how bulk resistivity, light intensity, and operation temperature impact the performance of the silicon solar cell. This work incorporates a comprehensive device physics analysis assisted by numerical simulation. The source code is now available under General Public License (GPL3), and to further leverage the findings of this project and outreach people outside of the scientific community, we are working with www.pveducation.org (which receives >1 million visitors a year) to create interactive content using our simulation results. The simulation results indicate that high bulk resistivity wafers (>>10 Ωcm) require bulk Shockley-Read-Hall (SRH) lifetimes in the millisecond range to outperform wafers with standard bulk resistivities (<10 Ωcm). Additionally, above bulk resistivities of 10 Ωcm (the exact value depends on the bulk characteristics of the wafer), the cell efficiency is weakly dependent on the bulk resistivity. As a result, ingot manufactures may have an opportunity to further reduce wafer cost by growing higher resistivity ingots that are more tolerant to resistivity variations. This project is particularly relevant today, as solar cell architectures with improved surface passivation and milliseconds lifetimes wafers are commercially available, leveraging potential benefits of using higher bulk resistivities. Outside of interdigitated back contact (IBC) cell, reported studies on high resistivity silicon (>100 Ωcm) are very limited. To the best of our knowledge, this project provides for the first-time experimental insight on solar cells fabricated on wafers with bulk resistivities up to several thousand Ωcm, delivering a comprehensive vision of their performance under real-world temperature and light intensity operation conditions. We manufactured and characterized solar cells with bulk resistivities in the range of 1 Ωcm to >15k Ωcm. Under standard testing conditions (STC), we measured solar cells efficiencies over 20% over the entire range of bulk resistivities, using our baseline cell processing. To evaluate the cell performance in real-world operation conditions, the solar cells were measured at different temperatures (25-80°C) and at different light intensities (0.1-1 suns). The measurements show that the bulk resistivity does not impact the solar cell response to temperature and light intensity. Similar thermal coefficients (TC) were measured for standard and high bulk resistivities, and they are comparable with the TC values reported in the literature for standard bulk resistivities <10 Ωcm. After light soaking, the solar cell didn’t show signs of light-induced degradation (LID). This result was expected since n-type float zone (FZ) wafers were used in this work, i.e. low traces of boron and low concentration of oxygen (oxygen is typically found in the seed end of Czochralski (CZ) ingots). We measured for high bulk resistivities (>10 Ωcm) extremely high breakdown voltages (>1000V). In conclusion, the insight provided by this project can positively impact the levelized cost of energy (LCOE) of the photovoltaic systems through its effect on cell and ingot manufacturing yield, silicon cell power output, and module reliability.

14 SOLAR ENERGY↗

Conceptual Aerodynamic Design of a Tail-Cone Thruster System Under Axi-Symmetric Inlet Distortion

This paper presents a conceptual design of a tail-cone thruster system which is operating under an axisymmetric inlet distortion. An effort to realize the targeted fuel burn saving that was proposed in NASA's STARC_ABL aircraft design is made through a CFD (Computational Fluid Dynamics)-based design approach. This method employs three iterative steps to exploit the CFD tools until the design requirements are met: a quasi-2D through-flow model to design the fan/EGV (Exit Guide Vane), a 3-D RANS (Reynolds Averaging Navier-Stokes) simulation of the single blade row to account for the inlet/fan and the EGV/nozzle interaction, and a 3-D RANS simulation of the airframe with a propulsor installed - propulsion airframe integration (PAI). The design requirements which include the thrust, and shaft power of the propulsor are matched throughout the evaluations coming from two CFD domains, i.e., the turbo-machinery and the PAI. During the switch between these different computational domains, the inlet and exit profiles are matched via the correction factors of the body-force model. The present tail-cone thruster (TCT) aerodynamic design leverages a low-pressure ratio fan (FPR=1.2 to approximately 1.25) of which the camber-line angles are predicted by a quasi-2D through-flow model. The quasi-2D model is derived to analyze the radially distorted flow resulting from the ingested boundary layer at the inlet. It also estimates the appropriate velocity vectors of the metal angles of the fan and EGV which is subjected to different types of vortex at the fan exit. The baseline geometry is revisited and its internal flow-path and exhaust cone are redesigned to illustrate the strong correlation among the components of the propulsor in the PAI domain. The peak efficiency point of the fan/EGV with respect to the blade counts, also known as solidity, and rotational speed is chosen for the cruise condition via parametric studies. The corresponding performance maps are presented. The resulting performance metrics of the new conceptual design of the BLI (Boundary Layer Ingestion) propulsor are analyzed and compared with these of the baseline in the PAI aspect. Finally, ideas of the CFD based design of a BLI propulsor are discussed based on the observations drawn from the numerical results.

Inlet↗

A structured framework for predicting sustainable aviation fuel properties using liquid-phase FTIR and machine learning

Sustainable aviation fuels have the potential to improve efficiency, reduce emissions, and enhance energy security. To help identify viable sustainable aviation fuels and accelerate research, machine learning models have been developed to predict relevant physicochemical properties. However, many models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that offer limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid FTIR spectra can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 ml of sample. The method first decomposes FTIR spectra into fundamental building blocks using non-negative matrix factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15°C, and kinematic viscosity at -20°C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict key properties across a broad range of neat molecules and representative fuels and blends, while enabling interpretation of relationships between compositional elements, such as functional groups or chemical classes, and their resulting properties. This demonstrates strong potential to support sustainable aviation fuel research and development. The models and data are available on an interactive web tool.

Fourier transform infrared spectroscopy↗

Manipulating Pore Topology and Functionality to Promote Fluorocarbon-Based Adsorption Cooling

With the worldwide demand for refrigeration and cooling expected to triple, it is increasingly important to search for alternative energy resources to drive the refrigeration cycles with reduced electricity consumption. Recently, adsorption cooling has gained increased attention since energy reallocation in such systems is based on gas adsorption/desorption, which can be driven by waste/natural heat sources. Eco-friendly sorption-based cooling relies on the cyclic transfer of refrigerant gas from a high to low energy state by the pseudo-compression effect resulting from adsorption and desorption. The driving force for energy transfer relies on heat rather than electricity. The performance of a sorption chiller is primarily influenced by this cyclic sorption behavior, which is characterized as the working capacity of the porous sorbent. Thus increases in this working capacity directly translate to a more compact and efficient cooling system. How-ever, a lack of highly effective sorbent/refrigerant pairs lowers cooling performance, and therefore has limited applicability. To this end, synthetic metal-organic frameworks (MOFs) and covalent organic polymers (COPs) possess higher porosity and greater tunability leading to more substantial potential benefits for adsorption, compared to traditional sorbent materials. Similarly, hydrofluorocarbon refrigerants have more favorable applicability given ease of operation above atmospheric pressures due to suitable saturated vapor pressures and boiling points. For these reasons, our work focuses on an ongoing strategy to promote sorption cooling via improvements in the sorbent/refrigerant pair. Specifically, we target the interaction of hydrofluorocarbon refrigerants with MOF/COP materials at a molecular level by interpreting the host-guest chemistry and the role of framework pore topology. These molecular level differences translate to cooling performance, which is described herein. These strategies include engineering framework porosity (i.e., pore size, pore volume) by using elongated organic linkers and stereochemistry control during synthesis; manipulating the sorb-ate/sorbent interaction by introducing functional moieties or unsaturated metal centers to enhance working capacities in narrow pressure ranges; varying pore topology/morphology to impact adsorption isotherm behavior; and leveraging defective sites within the frameworks to further enhance adsorption capability. Here this atomic level understanding of sorb-ate-sorbent interactions is conducted using various in situ experimental techniques such as synchrotron-based X-ray diffraction, X-ray absorption spectroscopy, in situ Fourier transform infrared spectroscopy, and direct sorption energies determinization with calorimetry. Moreover, the experimentally studied interactions and the corresponding adsorption mechanism are corroborated by computational studies using density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations. Using this approach, we envision the capability to engineer designed frameworks with precise molecular control to target refrigerant molecules and thereby enhance the performance of desired working pairs for sorption-based cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Update on the Ares V to Support Heavy Lift for U.S. Space Exploration Policy

The focus of design efforts in the near future will be on the primary Lunar mission. We are currently just beginning to integrate the design functions from the various centers for this mission. We appreciate all thoughts and ideas for different ways to use the Ares V platform. The Ares V can support an order of magnitude more science instrumentation for outer planetary exploration missions. It can also reach outer planets such as Neptune and other high energy insertion destinations. Current Ares V Point-of-Departure (51.00.48) exceeds Saturn mass capability by approx.40%. Ares V Lunar Capabilities Concept Review analysis focused on meeting lunar requirements and developing margin. Ares V is sensitive to Loiter, Attitude, Power, and Altitude requirements in addition to payload performance. LCCR-approved 51.00.48 POD 5.5-segment steel case booster/6 engine core) Ares V can meet current Human Lunar Return requirements with approx.6 mT of Margin. LCCR-approved 51.00.47 option maintained (5 segment HTPB composite case booster/6 engine core) can meet HLR requirement with more than 9 mT Margin. Ares V team is actively reaching out to external organizations during this early concept phase to ensure that the Ares V vehicle can be leveraged for national security, scientific and commercial development needs.

Creech, Steve↗

NASA’s Secured Airspace for Urban Air Mobility (UAM)

The Urban Air Mobility (UAM) architecture is leveraged from the Unmanned Traffic Management (UTM) concept of operations. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within that environment. As a recognized need, various views of UAM flight information are provided to the public and public safety entities. To accomplish this, among other goals, the Federal Aviation Administration (FAA) can coordinate flight information between the FAA controlled National Airspace System (NAS) and the UAM environments through the FAA-Industry Data Exchange Protocol (FIDXP). This concept of UAM proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical take-off and landing (VTOL) or short take-off and landing (STOL) aircraft to overcome increasing surface congestion. To garner the support of UAM and to realize its potential, an assurance of cybersecurity is critical for public acceptance. Understanding the various components communicating with one-another cybersecurity, like in other industries, has come to the forefront highlighting the need to protect these networks and systems from cyberattacks. With the planned growth and reach of UAM systems, it’s clear that the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. Consequently, as these threats evolve, the UAM cybersecurity capabilities must adapt to these changes as well. While learning is always the goal, the overall intent of this workshop is to make recommendations on the following: (1) how future UAM environments can be protected against cyber-attacks, and (2) what mechanisms should be put in place to detect attacks against UAM environments.

UAM↗

Machine learning framework for predicting uranium enrichments from M400 CZT gamma spectra

A machine learning framework was developed for predicting uranium enrichments from M400 CZT gamma spectra. This framework leverages the availability of a large amount of measured M400 gamma spectra and uses a recently updated version of Gamma Detector Response and Analysis Software (GADRAS) for gamma spectrum analysis and generation. It also leverages the existing machine learning modules in Python for gamma spectrum data processing, curation, model training, benchmarking, and optimization of the deep machine learning models. The framework is used to develop a deep learning model to analyze gamma spectra from a set of U 3 O 8 samples with enrichments ranging from 0.31 to 93.17% and UF 6 cylinders with enrichments ranging from 0.2 to 4.95%, and the model performance is tested using a set of measured spectra and the respective declared enrichment values. Results show that the model can correctly classify 99.35% of the U 3 O 8 sample enrichments, and can predict the samples’ enrichments within an average absolute error of 0.099% (in percentage points of enrichment). For the UF 6 cylinders, the average absolute error was approximately 0.03%, with an accuracy of 98% in classifying discrete enrichment values of UF 6 samples. Finally, the results also show that the model has performed significantly better in terms of predicting enrichments in UF 6 cylinders based on measured gamma spectra than the GEM code, with a standard deviation (of the relative errors) of 2.23% (compared with the 11.51% value for the GEM code) based on results from a set of test data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A Linear-Complexity Tensor Butterfly Algorithm for Compressing High-Dimensional Oscillatory Integral Operators

This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral operators, including Green's functions for wave equations and integral transforms such as Radon transforms and Fourier transforms. The proposed algorithm leverages a tensor extension of the so-called complementary low-rank property of existing matrix butterfly algorithms. The algorithm partitions the discretized integral operator tensor into subtensors of multiple levels and factorizes each subtensor at the middle level as a Tucker-type interpolative decomposition, whose factor matrices are formed in a multilevel fashion. For a d-dimensional (d > 1) integral operator discretized into a 2d-mode tensor with n2d entries, the overall CPU time and memory requirement scale as O(nd), in stark contrast to the O(nd log n) complexity of existing matrix algorithms such as matrix butterfly algorithms and fast Fourier transforms (FFTs), where n is the number of points per direction. When comparing with other tensor algorithms such as quantized tensor train (QTT), the proposed algorithm also shows superior CPU and memory performance for tensor contraction. Remarkably, the tensor butterfly algorithm can efficiently model high-frequency Green's function interactions between two unit cubes, each spanning 512 wavelengths per direction, which represents problems of scale over 512× larger than that existing butterfly algorithms can handle, with the same amount of computation resources. On the other hand, for a problem representing 64 wavelengths per direction, which is the largest size existing algebraic matrix algorithms can handle, our tensor butterfly algorithm exhibits 200x speedups and 30× memory reduction compared with existing ones. Moreover, the tensor butterfly algorithm also permits O(nd)-complexity FFTs and Radon transforms up to d = 6 dimensions.

Kielstra, P Michael↗

A novel implicit hybrid machine learning model and its application for reinforcement learning

A novel methodology to develop implicit hybrid models is presented. PyTorch is used to integrate physics-based equations with machine learning models. Automatic differentiation of the hybrid model is leveraged to solve the implicit equations. Iterative solving enables gradient based updates to the machine learning model. The novel methodology is compared to an explicit hybrid approach on a continuously stirred tank reactor (CSTR). The novel method results in a lower modelling error. Both hybrid models effectively train with noisy data. To test the implicit hybrid model, it is employed as a reinforcement learning (RL) training model. The RL algorithm trained on the hybrid model outperforms real time optimization of the CSTR and performs nearly as well as RL trained directly on the CSTR and a traditional gradient based approach. Training RL directly on the CSTR requires over 60,000 system interactions compared to 6000 historical data points for hybrid model development.

42 ENGINEERING↗

Leveraging FPGA Advantages for Quicker Data Processing for LBNF

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, Jacob↗

A Compact Electron-Proton Spectrometer for Moon / Mars Surface Space Weather Monitoring and Crew Protection (CEPS)

Small mass/low-power sensors are needed for autonomous Moon/Mars surface ops that provide alerts to impending adverse space weather conditions. In order to use current operational forecasting tools, a sensor capable of measuring both high-energy electrons and protons is needed. By combining high-TRL Timepix technology with a CdTe layer, this project leverages known technology to fill a critical gap for surface operations. Typical space weather sensors are at least an order of magnitude higher in mass than is expected from Timepix-based devices and are deployed on robotic assets away from human crews. High fidelity measurements at the crew location and subsequent ability are needed to provide warnings to crews to shelter with a device that can easily be carried on the Lunar and Martian surfaces.​ The aim of this project is the development of compact, low power space weather sensors for crew protection based on the combination of AES flight heritage Timepix hardware with novel CdTe (Cadmium Telluride) sensors. CdTe technology has only recently matured to a point where it can be reliably used with Timepix detectors. These detectors will be able to measure electron spectra in an energy range relevant to the RELEASE model, which is used operationally by NASA to provide crew early warning of Solar Energetic Particle Events as well as protons in the energy ranges relevant to human health allowing for event ‘nowcasting’ and total event crew dose prediction. Compared to existing solutions these detectors will be compact enough to travel with crew, providing a significant advantage to space environment situation awareness. Project results indicate that CdTe-based Timepix sensors respond well to energetic electrons, and particle tracks are sufficiently distinct to be able to measure in a mixed radiation field. Thermal testing results point to a needed design modification to mitigate high temperatures the detector will see in space. Radiation susceptibility testing was conducted to assess the radiation hardness of the CdTe-Timepix assembly. Overall, the assembly continued to operate up to 100 kRad of gamma-ray exposure. The system calibration did show drift over time but recovered once exposure was complete.

Dan James Fry↗

CEPS – A Compact Electron-Proton Spectrometer

Small mass/low-power sensors are needed for autonomous Moon/Mars surface ops that provide alerts to impending adverse space weather conditions. In order to use current operational forecasting tools, a sensor capable of measuring both high-energy electrons and protons is needed. By combining high-TRL Timepix technology with a CdTe layer, this project leverages known technology to fill a critical gap for surface operations. Typical space weather sensors are at least an order of magnitude higher in mass than is expected from Timepix-based devices and are deployed on robotic assets away from human crews. High fidelity measurements at the crew location and subsequent ability are needed to provide warnings to crews to shelter with a device that can easily be carried on the Lunar and Martian surfaces.​ The aim of this project is the development of compact, low power space weather sensors for crew protection based on the combination of AES flight heritage Timepix hardware with novel CdTe (Cadmium Telluride) sensors. CdTe technology has only recently matured to a point where it can be reliably used with Timepix detectors. These detectors will be able to measure electron spectra in an energy range relevant to the RELEASE model, which is used operationally by NASA to provide crew early warning of Solar Energetic Particle Events as well as protons in the energy ranges relevant to human health allowing for event ‘nowcasting’ and total event crew dose prediction. Compared to existing solutions these detectors will be compact enough to travel with crew, providing a significant advantage to space environment situation awareness. Project results indicate that CdTe-based Timepix sensors respond well to energetic electrons, and particle tracks are sufficiently distinct to be able to measure in a mixed radiation field. Detector response functions were developed for both electrons and protons. Measurement results showed response to both electrons and protons in the energy range of interest for human space flight.

Radiation↗

Developing Methods for Exercise System Kinematics Tracking

BACKGROUND How to quantify the load and forces produced by exercise equipment and their Vibration Isolation and Stabilization (VIS) platforms in-flight is an active area of investigation. Kinematic tracking paired with system modeling can provide insights as well as verification and validation of simulations used for system design and development. Traditional motion capture methods can require significant cost in equipment procurement and crew-time, but newer lessons learned can be leveraged [1]. The VIS systems of current and future exercise hardware on the International Space Station (ISS) such as the Cycle Ergometer with Vibration Isolation System (CEVIS) and the European Enhanced Exploration Exercise Device (E4D) are not currently outfitted with IMUs or similar measurement devices. Video-based methods would enable use of multi-purpose, crew-familiar flight equipment. An initial exploration of video-based solutions was performed utilizing 2-camera video from crew cycling on Teal-CEVIS on the ISS. METHODS AND RESULTS Our group has scoped a variety of video-based object tracking methods. To date, we have primarily investigated computer vision toolkits such as openCV. Techniques explored include key-point detection, background subtraction, region-of interest tracking, color-based tracking, tag masking and tracking, and corner detection. Although object-tracking and 6D pose estimation is a rich field, space applications are a unique problem that are challenging for existing software and toolkits. The majority of the existing object-tracking applications involve vehicles/pedestrians and household objects with simple backgrounds. We have identified the following features which pose particular challenges for on-station exercise equipment tracking: Busy and visually cluttered background Low-textured tracking object with relatively small motions Occlusions and motion by human subject and loose, floating objects Limited number of video cameras with no fixed global references Limited ability to add tags, markers, or visual references to the tracking object Lack of training data for Machine Learning (ML) algorithms CONCLUSION We will summarize the efficacy of techniques tested for a ground mock-trial and the on-station exercise trial. It is likely that human-in-loop feedback or a conglomerate of methods is required. ML-based methods, like those implemented for human body tracking [2], may still be a viable option, but more training data and validation is needed.

L B Nilsson↗

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗