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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Surface-Enhanced Raman Detection of the CO 2 Moisture Swing

The development of scalable, energy-efficient carbon dioxide (CO 2 ) capture technologies is critical for achieving net-zero emissions. Moisture swing (MS) sorbents offer a promising alternative to traditional thermal regeneration methods by enabling reversible CO 2 binding through humidity-driven ion hydrolysis. In this study, we investigate the anion speciation dynamics in two classes of MS materials─an anion-exchange resin with a bicarbonate anion and activated carbon impregnated with potassium bicarbonate salt─using both sorption measurements and in situ surface-enhanced Raman spectroscopy (SERS). Ni-coated Ag nanowires were employed as SERS substrates to enhance signal intensity and enable the real-time detection of carbonate (CO 3 2– ), bicarbonate (HCO 3 – ), and hydroxide (OH – ) species under controlled humidity conditions in both air and nitrogen atmospheres. The results reveal humidity-dependent interconversion between anionic species with significant spectral shifts confirming the reversible hydrolysis reactions that drive the MS mechanism. Under humid conditions, we observed the depletion of bicarbonate signals and a concurrent increase in carbonate species, consistent with moisture-induced desorption of CO 2 . With the activated carbon samples, we further observed the formation of hydroxide. These findings not only validate the mechanistic models of humidity-driven anion exchange in MS sorbents but also demonstrate the practical potential of SERS as an operando diagnostic tool for monitoring CO 2 capture media. The ability to resolve and semiquantitatively evaluate the reversible transformation of carbonate, bicarbonate, and hydroxide ions under realistic environmental conditions provides valuable insight for the rational design, performance optimization, and quality control of next-generation sorbent materials for direct air capture applications.

CO2 capture↗
DOE OSTI2018

Advanced Analytics Studies Applied to US Department of Veterans Affairs' Corporate Data Warehouse (Initial Draft)

In this report, we describe our experience of applying several advanced analytics algorithms to the US Department of Veterans Affairs’ (VA’s) Corporate Data Warehouse (CDW) electronic health records datasets during FY2017-18. While various algorithms were applied to the CDW data, the main goal of this effort was to provide useful insights into the operational aspects (as opposed to the purely clinical aspect) of this specific implementation. Since there are not many reports in the public literature on advanced analytics applied to this data, this report provides unique insight in this regard. We focused on two machine learning (ML) applications: (1) clinical pathway inference (patterns dominating clinical pathways applied to a male dataset of Stable Ischemic Heart Disease patients as use case) and (2) medical-concept representation learning’s capability to inform and refine cohort membership based on probable patient outcomes. During our study on clinical pathway inference, we used two main modeling techniques: (a) topic probabilistic modeling (latent Dirichlet allocation [LDA]) and (b) feature imbedding. We observed that the applicability of LDA to the pathway inference remains in question. LDA provides results that are intuitive and easy for humans to comprehend; however, when applied to the pathway inference, it also generates pathway patterns that consist of a few dominant pathway components. In addition, LDA provides no information regarding the relationships among components in the same pathway. The outputs of LDA are also found to categorize patients based on their trace data of clinical procedures. However, the results suggest that LDA is biased toward statistically dominant components. This makes it particularly hard to discriminate pathway subbranches. To address the issue, we designed a new pathway inference methodology that integrates temporal ordering of pathway components into LDA results, as existing feature embedding tools such as word2vec are not readily applicable to our task. In addition, we developed our own feature embedding tool customized for patient traces. We conclude this study by suggesting two approaches to use embedding representation of a component into LDA and briefly list our lessons learned and recommendations for future work. For our study on medical-concept representation learning ability to inform and refine cohort membership based on probable patient outcomes, during our empirical evaluation we compared methods from medical-concept learning to standard one of a kind (one-of-K) encoding to evaluate the change in effectiveness as done in Choi, Schuetz, Stewart, and Sun (2016). We performed two primary empirical evaluations. The first evaluation was on a curated collection of 60,000 patients with no more than 1 year of medical history included. The second was a collection of patients with no restriction to the amount of medical history included. The hypothesis across both evaluations is that representation learning is broadly useful independent of downstream processing models; thus, for each experiment, we trained three models—a logistic regression model, a two-layer neural network model, and a nearest-neighbor model—and then averaged the results. Each model was trained using fivefold cross validation. We provided evidence that medical-representation learning improves predictions of the primary diagnosis category of a short patient history through 12 experiments. In addition, we provided evidence that medical-representation learning fails to improve prediction of the primary diagnosis category of an arbitrarily long patient history through 12 experiments. We conclude this study with a brief list of our lessons learned and recommendations for future work. We conclude the report with a discussion of recent technology trends as they relate to our artificial intelligence and, more specifically, to our ML research and approaches as described previously.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Countermeasures for Mitigation of Sensorimotor Decrements Following Head-Down Bed Rest

BACKGROUND Astronauts experience postflight disturbances in postural and locomotor control due to sensorimotor adaptations during spaceflight. These alterations may have adverse consequences if a rapid egress is required after landing. Although exercise is partially effective for mitigating cardiovascular and muscular deconditioning, additional countermeasures are needed to further preserve sensorimotor function for exploration missions. We have identified proprioceptive training and electrical muscle stimulation (EMS) as promising in-flight countermeasures. Since prolonged head down bed rest (HDBR) is a spaceflight analog for body unloading and causes postural and locomotor control decrements that parallel those observed after spaceflight, it can be used to facilitate the development of these countermeasures. METHODS This study will determine the effects of proprioceptive training and EMS on functional task performance and sensorimotor function following 60 days of 6° HDBR. Subjects will be randomly assigned to one of four groups: 1)an EMS arm, 2) a proprioceptive training arm, 3) an exercise plus proprioceptive training arm, and 4) a control arm. The EMS countermeasure will include daily bilateral stimulation of selected bilateral lower extremity muscles (30 minutes per session). Proprioceptive training will be performed three days per week (20 minutes per session) consisting of body-loaded postural tasks in the horizontal position on an air bearing sled. Exercise training will mimic current protocols used on the International Space Station, but treadmill aerobic exercise will be replaced with additional cycling aerobic exercise. Primary outcome measures will include pre and post HDBR functional tests that are representative of high priority exploration mission tasks and require high demand for dynamic control of postural stability. Additional measures will be used to identify the key physiological factors contributing to countermeasure benefits. Given the constrained samples size, a Bayesian modelling approach will be used to quantify the probability that there is an effect of a given magnitude. HARDWARE AND PROTOCOL DEVELOPMENT Proprioceptive countermeasure design enhancements continued as part of the Mars Campaign Office (MCO) Crew Health and Performance (CHP) Crew Health Countermeasures (CHC). The primary goals of this work were to assemble a portable version of the countermeasure system that can be used at the :envihab facility, expand the software feedback system’s capabilities, and develop an actuator loading system that mimics what could be used on the International Space Station. In addition, one major piece of exercise hardware used in previous HDBR studies, the Horizontal Squat Device, was reassembled and restored to full functionality. Human-in-the-loop pilot testing is ongoing, prior to shipping both devices to the :envihab HDBR facility. Evaluations are also underway for the best EMS device technologies and specific methods. Finally, assessment techniques are being translated for administration in the horizontal position (e.g., leg dexterity and foot sole skin sensitivity). Our current goals are to refine the countermeasure training and assessment techniques and integrate protocols across multiple modalities. RELEVANCE The deliverable from this project will be proof-of-concept sensorimotor countermeasure designs for functional task performance with full assessment of efficacy in a spaceflight analog. If one or more countermeasures are effective, they will be translated for validation with the suite of operationally implemented in-flight countermeasures. ACKNOWLEDGEMENT This work is supported by NASA’s Human Research Program Human Health Countermeasures Element and by the Canadian Space Agency (L. Bent).

T R Macaulay↗
NASA NTRSDate not supplied

Artificial Intelligence (AI) Methods for Augmenting the IMPACT Tool Evidence Library

Development of the Evidence Library for use with the IMPACT probability risk assessment tool took several years and involved a staggering amount of effort from a multi-disciplinary team. A very significant amount of the labor effort to collect, assess and finalize the Clinical Finding Form (CliFF) for each of the 119 medical conditions was provided by physician subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team. Many AI tools such as ChatGPT are excellent at summarizing large amounts of information and the current project was initiated to determine how such tools might streamline laborious processes, e.g., review and summarization of many scientific research publications, to execute key steps more efficiently in the process of developing CliFFs. The process for collecting the evidence which is found in the CliFFs is well documented in the Evidence Library Methods document (ELM; HRP-48036*). Using ELM and the CliFF development instructions as a guideline, a team of developers is leveraging Microsoft Azure AI tools and services along with open-source frameworks, to construct an AI-assisted automated pipeline. This pipeline is designed to search, retrieve, and process the necessary data sources, and ultimately help generate the final version of a CliFF. Currently, the large language model evaluates the relevance of each source material to spaceflights, either as direct evidence or as an analog. Additionally, the model assists in extracting keywords and generating brief summaries to enhance augmented retrieval and search processes in later stages of CliFF development. Once the data is ready, the model can perform semantic search and retrieval, generating and extracting valuable information for the CliFF. For instance, it can handle epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. The steps that required reading and summarizing articles were viewed as providing the greatest return on investment since large language models are very efficient and accurate in summarizing large amounts of text. Since labor effort to complete the original CliFF was not recorded with sufficient granularity, comparisons with an AI tool-generated CliFF will provide merely an approximation of time saved. Upon completion of the process, the CliFF for the medical condition “appendicitis” generated with the support of AI-based methods will serve as a proof-of-concept and will be compared to the original appendicitis CliFF to determine if use of the tools resulted in content and conclusory similarity. Based upon the results from face validation of the two CliFFs, modifications to the process will be made if necessary and additional condition CliFFs will be evaluated. Ultimately, CliFFs for the entire set of medical conditions will be created with the assistance of AI tools. Depending on the cost savings realized, CliFFs for additional medical conditions can be created to expand the Evidence Library. Future direction includes specifying the characteristics of the reviewer (prompting the AI tools to generate output assuming the reviewer is a sub-specialist physician, or nurse or EMT/medic) to determine if the effects on AI-generated output are different based on knowledge, skills and abilities. *Exploration Medical Capability Evidence Library Methods, HRP-48036 Rev A, July 2022.

Ali Al↗
DOE OSTI2021

Optimized Carbon Fiber Intermediate Development to Enable High-Volume Manufacturing of Lightweight Automotive Composites

The ongoing pursuit of improved fuel economy and reduced greenhouse gas emissions has resulted in sustained interest for lightweight materials technologies. In this context, carbon fiber composites have captured the imagination of automotive engineers due to the potential to achieve substantial mass reduction when compared to traditional steel construction. That stated, the use of carbon fiber composites in automotive has been limited for the most part to premium supercars and other derivative platforms. In these cases, manufacturing costs are less of an obstacle to implementation, and the performance benefits of carbon fiber have enabled production of structures offering more than 50% weight savings. In practice, translating these low-volume demonstrations onto high-volume vehicle platforms has remained challenging. This can be attributed to several factors, with the absence of suitable high throughput production methodologies being a key impediment. To date, structural, crash critical components have relied upon manufacturing techniques born out of the aerospace industry. This has created a disconnect between automotive production systems that are accustomed to manufacturing multiple parts per minute and the aerospace technologies that have cycle times in the order of hours. Consequently, the focus of this project is the development of manufacturing process technology for carbon fiber composites that can support a mainstream vehicle program at an assumed throughput of 100,000 vehicles per year. In practice, this translates to a part-to-part cycle time of less than 3 minutes. Project participation included contributions from a broad range of academic, industrial and national lab partners. The primary scope of work, being the development of new carbon fiber epoxy compounds that are stable at room temperature and suited to high throughput automated processing. For project management, the work streams were divided into six key areas, with the lead organization in parentheses. • Carbon fiber/epoxy materials formulation development and scale up (Dow) • Simulation of discontinuous near isotropic meso-structure intermediates (Purdue) • Simulation of mechanical performance of compression molded components (Purdue) • Meso-Scale morphological analysis and correlation with structural performance (UTK) • Paint and adhesion durability analysis (MSU) • Demonstrator part design, prototype production, and validation testing (Ford). The primary goal at the commencement of the project was development of a chopped carbon fiber sheet molding compound (SMC) that offered a three times improvement in tensile modulus over a comparable glass-based SMC. In addition to meeting mechanical performance targets, the resin kinetics were modified to achieve a processing cycle time of less than 3 minutes. Other critical-to-quality (CTQ) specifications were also stipulated to account for a broad range of materials and processing characteristics. To achieve the above, staff scientists at Dow Chemical created an extensive series of new epoxy blends for testing and validation. Throughout this development, a key challenge was attaining material performance goals without impacting processing behavior and paintability of finished components. The latter required a new internal mold release system being developed by Dow that was designed to complement the kinetics of the rapid cure epoxy. As a complement to work studies at the industrial partners, the teams from academia executed a series of analytical and experimental studies to investigate potential factors influencing CF-SMC performance. Unit cell models were developed to capture the meso-scale representations of the fiber matrix architecture. Results of this analysis and subsequent morphological investigations led to the design of a novel composite derivative comprising carbon fiber platelets embedded in an epoxy matrix; the platelet size and aspect ratio playing significant role in final composite properties. This approach was a departure from previous research in CF-SMC development whereby bulk filamentization or disassembly of the carbon fiber rovings had been considered the most effective means of achieving both fiber wet through and wet-out. As the course of the academia studies progressed, the aspect ratio of the fiber constituents was further optimized before finalizing material attributes and processing conditions. For the purposes of technology validation, the Ford team led a work stream devoted to the design, fabrication and testing of demonstration components. The carbon fiber SMC material has the potential to displace numerous stampings and castings on an automotive structure but ultimately vehicle closure applications were selected to showcase the abilities of the CF-SMC to achieve both mass reduction and business case for large complex structures. Using target properties established by the Dow staff scientists, the complete closure system for a full-size sedan decklid and a mid-size wagon liftgate were engineered. Prototypes for both applications were fabricated using production representative processing methods to allow for physical testing and performance validation of the CF-SMC structures. Following completion of a testing program that concluded with a FMVSS301 55 mph offset rear crash, the CF-SMC formulation was declared by the Ford team to have met all engineering requirements. To summarize, the joint development activities during this project led to significant technical breakthroughs and achievement of all milestones. The result was the development of a novel, tack-free carbon fiber molding compound that is suited to automated processing. This combined room temperature stability, fast cure kinetics, and internal mold release system facilitates cycle times that are conducive to high-volume production. The VORAFUSE M6400 successfully passed technology validation at Ford and is now eligible for consideration on future production commercial vehicle programs.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Associative Flow Rule Used to Include Hydrostatic Stress Effects in Analysis of Strain-Rate-Dependent Deformation of Polymer Matrix Composites

designing reliable composite engine cases that are lighter than the metal cases in current use. The types of polymer matrix composites that are likely to be used in such an application have a deformation response that is nonlinear and that varies with strain rate. The nonlinearity and the strain-rate dependence of the composite response are due primarily to the matrix constituent. Therefore, in developing material models to be used in the design of impact-resistant composite engine cases, the deformation of the polymer matrix must be correctly analyzed. However, unlike in metals, the nonlinear response of polymers depends on the hydrostatic stresses, which must be accounted for within an analytical model. By applying micromechanics techniques along with given fiber properties, one can also determine the effects of the hydrostatic stresses in the polymer on the overall composite deformation response. First efforts to account for the hydrostatic stress effects in the composite deformation applied purely empirical methods that relied on composite-level data. In later efforts, to allow polymer properties to be characterized solely on the basis of polymer data, researchers at the NASA Glenn Research Center developed equations to model the polymers that were based on a non-associative flow rule, and efforts to use these equations to simulate the deformation of representative polymer materials were reasonably successful. However, these equations were found to have difficulty in correctly analyzing the multiaxial stress states found in the polymer matrix constituent of a composite material. To correct these difficulties, and to allow for the accurate simulation of the nonlinear strain-rate-dependent deformation analysis of polymer matrix composites, in the efforts reported here Glenn researchers reformulated the polymer constitutive equations from basic principles using the concept of an associative flow rule. These revised equations were characterized and validated in an experimental program carried out through a university grant with the Ohio State University, wherein tensile and shear deformation data were obtained for a representative polymer for strain rates ranging from quasi-static to high rates of several hundred per second. Tensile deformation data also were obtained over a variety of strain rates and fiber orientation angles for a representative polymer matrix composite composed using the polymer.

Goldberg, Robert K.↗
NASA NTRS2015

Development of an Infrared Lamp Array for the Smap Spacecraft Thermal Balance Test

NASA launched the SMAP observatory in January 2015 aboard a Delta II into a sun-synchronous orbit around Earth. The science payload of a radar and a radiometer utilizes a shared rotating six-meter antenna to provide a global map of the Earth's soil moisture content and its freeze/thaw state on a global, high-resolution scale in this three-year mission. An observatory-level thermal balance test conducted in May/June 2014 validated the thermal design and demonstrated launch readiness as part of the planned environmental test campaign. An infrared lamp array was designed and used in the thermal balance test to replicate solar heating on the solar array and sunlit side of the spacecraft that would normally be seen in orbit. The design, implementation, and operation of an infrared lamp array used for this nineteen-day system thermal test are described in this paper. Instrumental to the smooth operation of this lamp array was a characterization test performed in the same chamber two months prior to the observatory test to provide insight into its array operation and flux uniformity. This knowledge was used to identify the lamp array power settings that would provide the worst case predicted on-orbit fluxes during eclipse, cold, and hot cases. It also showed the lamp array variation when adjustments in flux were needed. Calorimeters calibrated prior to testing determined a relationship between calorimeter temperature and lamp array flux. This allowed the team to adjust the lamp output for the desired absorbed flux on the solar array. Flux levels were within 10% of the desired value at the center of the solar array with an ability to maintain these levels within 5% during steady state cases. All tests demonstrated the infrared lamp array functionality and furthered lamp array understanding for modeling purposes. This method contributed to a high-fidelity environmental simulation, which was required to replicate the extreme on-orbit thermal environments.

Miller, Jennifer R.↗
NASA NTRS1999

System Identification for Integrated Aircraft Development and Flight Testing [l'Identification Des Systemes Pour le Developpement Integre des Aeronefs et les Essais en Vol]

Over the last decades flight vehicles such as aircraft and helicopters entering service and requiring increased operational effectiveness have with few exceptions experienced prolonged flight test development to achieve full certification. In many cases the original requirements had later to be reduced to enable release to service. The impact on the customer, and manufacturer has been considerable leading to increased costs and or reduced operational capabilities. These costly experiences are largely a result of the flight vehicle not behaving as modelled and designed. The evaluation of flight test data can be used as a tool for validating windtunnel results and mathematical models describing the flight dynamical behaviour. In this sense the uncertainty of important aerodynamic stability and control parameters can be reduced and the confidence of aircraft mathematical models improved. An additional important factor comes from the implementation of active control systems offering the promise of significantly increased flight vehicle performance and operational capability. This approach extends the traditional trade-offs between aerodynamics, structures and propulsion systems to include full- time, full-authority fly-by-wire/light systems. It is imperative that the aerodynamic stability and control parameters of such integrated flight and propulsion control systems have to turn out inflight as predicted, since inherent stability margins will be lower and the flight control system must correct these deficiencies to provide flight critical redundancy and safety. With the methodology of system identification from flight tests it is possible to sense the control inputs and the flight vehicle reactions Such as accelerations, rates and attitudes. The mathematical model, e.g. the model structure and parameters, has to be determined from the relationship of the measured control inputs and the system's responses. The aim of this symposium was to review the present state of the art of flight vehicle system and parameter identification techniques, and to provide a critical appraisal of current methods developed and applied to flight test data in a number of NATO nations. Particular emphasis was placed on practical aspects and lessons learned in order to generate information useful to the flight test community in industry and government agencies. The technical papers share invaluable experience and emphasize the advances of flight vehicle system identification over the last years to the point where confidence and robustness level is now reasonably high. The symposium covered overviews of identification methodologies, flight test techniques, recent aircraft and helicopter application programs, and a session of short papers covering up-to-the-minute flight test results. A final discussion included prepared comments from experts and concluded with key issues learned in the application of system identification and future research needs. The essential benefits to NATO nations can be condensed as follows: More accurate mathematical models for high bandwidth flight control systems, Improved assessment and evaluation of flying qualities, High fidelity mathematical models for flight vehicle development and mission training simulators, and generally, Reduced flight test time and costs.

Advisory Group for Aerospace Research and Developm↗
NASA NTRS1999

Fundamental Study of Material Flow in Friction Stir Welds

The presented research project consists of two major parts. First, the material flow in solid-state, friction stir, butt-welds as been investigated using a marker insert technique. Changes in material flow due to welding parameter as well as tool geometry variations have been examined for different materials. The method provides a semi-quantitative, three-dimensional view of the material transport in the welded zone. Second, a FSW process model has been developed. The fully coupled model is based on fluid mechanics; the solid-state material transport during welding is treated as a laminar, viscous flow of a non-Newtonian fluid past a rotating circular cylinder. The heat necessary for the material softening is generated by deformation of the material. As a first step, a two-dimensional model, which contains only the pin of the FSW tool, has been created to test the suitability of the modeling approach and to perform parametric studies of the boundary conditions. The material flow visualization experiments agree very well with the predicted flow field. Accordingly, material within the pin diameter is transported only in the rotation direction around the pin. Due to the simplifying assumptions inherent in the 2-D model, other experimental data such as forces on the pin, torque, and weld energy cannot be directly used for validation. However, the 2-D model predicts the same trends as shown in the experiments. The model also predicts a deviation from the "normal" material flow at certain combinations of welding parameters, suggesting a possible mechanism for the occurrence of some typical FSW defects. The next step has been the development of a three-dimensional process model. The simplified FSW tool has been designed as a flat shoulder rotating on the top of the workpiece and a rotating, cylindrical pin, which extends throughout the total height of the flow domain. The thermal boundary conditions at the tool and at the contact area to the backing plate have been varied to fit experimental data such as temperature profiles, torque and tool forces. General aspects of the experimentally visualized material flow pattern are confirmed by the 3-D model.

Reynolds, Anthony P.↗
DOE OSTI2019

National Wind Technology Center-Characterization of Atmospheric Conditions

Data provided by the instrumentation located at the National Wind Technology Center (NWTC) is compiled below for the purposes of characterizing the atmospheric operating conditions at the National Renewable Energy Laboratories (NREL) wind site. Conditions are presented conditioned on month of the year to show seasonal variation, and based on characteristic quantities seen in the atmospheric flow including, bulk wind speed, direction, and atmospheric stability. All data reported and analyzed in the following document were collected from instrumentation mounted on the 135 m-tall masts erected at the NWTC near Boulder, Colorado. Each of the two met-masts (named M4 and M5) were sited specifically to measure the inflow wind to several of the utility-scale turbines at the NWTC. The construction, operation, and maintenance of the masts has been supported since their commissioning in 2011 by the Department of Energy, Office of Energy Efficiency and Renewable Energy, Wind and Water Power Program. Data from the masts are publicly available for download or basic visualization, hosted on NREL's website (https://nwtc.nrel.gov/135mData). Data from the masts have been used in the past for a wide range of projects including wind turbine power performance measurements, high fidelity model development and validation, and atmospheric science research. An interactive visualization platform has been developed on NREL's Open Energy Information platform and can be used to reproduce many of the graphics included in the current work. This report describes in detail the atmospheric conditions recorded by M5, the inflow mast for the DOE-owned GE 1.5 MW wind turbine. The report focuses on data recorded from August 2012 to May 2018 and includes: an introduction to the wind climate at the NWTC; a physical description of the met masts; a review of installed instrumentation; and data collection, processing, and quality control methods. In addition to a concise description of the instrumentation from the met masts and the data channels they provide, this report characterizes the behavior of the atmospheric resource first in a cumulative sense, discussing mainly the distributions wind speeds, flow directions and thermodynamic properties at several heights along the towers. Periodicity in atmospheric conditions is then detailed through analysis of diurnal and annual cycles of the data streams. Periods of both low turbulence and extreme atmospheric events that are of interest in terms of wind turbine operation and wind energy research are detailed in the final chapter of this report. Low turbulence events are somewhat rare at the NWTC and are of interest from the perspective of validating the operation of new wind turbine and blade designs. Similarly, the NTWC is known for a relatively higher rate of occurrence of extreme weather events.

17 WIND ENERGY↗
NASA NTRS2007

NASA Langley's Approach to the Sandia's Structural Dynamics Challenge Problem

The objective of this challenge is to develop a data-based probabilistic model of uncertainty to predict the behavior of subsystems (payloads) by themselves and while coupled to a primary (target) system. Although this type of analysis is routinely performed and representative of issues faced in real-world system design and integration, there are still several key technical challenges that must be addressed when analyzing uncertain interconnected systems. For example, one key technical challenge is related to the fact that there is limited data on target configurations. Moreover, it is typical to have multiple data sets from experiments conducted at the subsystem level, but often samples sizes are not sufficient to compute high confidence statistics. In this challenge problem additional constraints are placed as ground rules for the participants. One such rule is that mathematical models of the subsystem are limited to linear approximations of the nonlinear physics of the problem at hand. Also, participants are constrained to use these models and the multiple data sets to make predictions about the target system response under completely different input conditions. Our approach involved initially the screening of several different methods. Three of the ones considered are presented herein. The first one is based on the transformation of the modal data to an orthogonal space where the mean and covariance of the data are matched by the model. The other two approaches worked solutions in physical space where the uncertain parameter set is made of masses, stiffnesses and damping coefficients; one matches confidence intervals of low order moments of the statistics via optimization while the second one uses a Kernel density estimation approach. The paper will touch on all the approaches, lessons learned, validation 1 metrics and their comparison, data quantity restriction, and assumptions/limitations of each approach. Keywords: Probabilistic modeling, model validation, uncertainty quantification, kernel density

Horta, Lucas G.↗
DOE OSTI2021

Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor

Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Nutrient Cycle Impacts on Forest Ecosystem Carbon Cycling: Improved Prediction of Climate Feedbacks from Coupled C–Nutrient Dynamics from Ecosystem to Regional Scales

One of the largest sources of uncertainty in the projected global mean temperature response to anthropogenic carbon (C) emissions is the role of the land surface in contributing to or mediating the rise in atmospheric CO2 [IPCC, 2007]. Development of ecosystem processes in global land surface models (LSMs) is far from complete, with major components such as the nitrogen (N) cycle only recently being implemented in some LSMs [Thornton et al., 2007; Sokolov et al., 2008; Xu-Ri and Prentice, 2008; Fisher et al., 2010]. Inclusion of the N cycle can reduce terrestrial C uptake by up to ~50 Pg C·y-1 globally [Figure 1: Fisher et al., 2010]. However, even these early representations of the N cycle in LSMs are missing critical plant–soil interactions that have been shown empirically to exert a major influence over ecosystem C fluxes. Here we propose to use a newly developed plant N model by PI Fisher as a theoretical and analytical framework for integrating novel field-based methods and long-term flux observations to quantify the impacts of belowground processes on fluxes of C, water and energy, from ecosystem to regional scales. Although the model (Fixation & Uptake of Nitrogen: FUN) is one of the more advanced global plant N models available, the model is dependent on theoretical C flux curves optimizing plant C allocation to root exudates, symbiotic bacteria and fungi, and retranslocation enzymes that have not been confirmed or validated with measurements—primarily because the measurement techniques had not been available, until recently. Parallel to the model developments by Fisher et al. were developments in in situ direct measurement techniques by Phillips et al. [2008; 2011], who were able to measure C fluxes from roots to soil. Not only do these techniques have the capacity to confirm and/or modify the modeling components in the Fisher et al. plant N model, but they also provide insight into how belowground C supply from roots couples the C–N cycles at the micro-site and ecosystem scales. Collectively, the integration of measurements and modeling provides empirical grounding for theoretical process behavior. Three critical components will enable us to successfully carry out this research: I) new methodological approaches developed by co-I Phillips for in situ measurement of root exudates, and belowground C fluxes that allow for testing and refinement of FUN [Phillips et al., 2008; Phillips et al., 2011]; II) an innovative research design that involves data integration across an ecological gradient, including a DOE-supported AmeriFlux tower operated by co-I Dragoni, which is essential to determining the ecosystem flux impacts [Dragoni et al., 2011]; and, III) new results from co-I Evans showing unique spectral signatures of trees in association with ectomycorrhizae (ECM) and arbuscular mycorrhizae (AM) fungi from Landsat data using spectral mixture analysis (SMA), which is key to scaling up belowground processes to regional scales [Andersson et al., 2009]. Each component provides feedback to and is intimately linked with all other components; no part operates in isolation. Our team and approach combines expertise in modeling, remote sensing, ecosystem ecology, plant physiology, soil biogeochemistry, and micrometeorology to address these inherently interdisciplinary problems and questions. This proposal directly responds to the focused Science Area of the FOA: “The role of belowground processes and mechanisms across scales (e.g., soil carbon transformation, root dynamics, mycorrhizal interactions, and plant mediated (e.g., root exudates) biogeochemical transformations) associated with a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A new scintillation hodoscope for CLAS12 and partial wave analysis of the channel yp → pK + K -

This thesis consists of two parts that have contributed to a new meson spectroscopy program, MesonEx, which is currently taking place in Hall B of the upgraded Thomas Jefferson National Accelerator Facility (JLab) in the USA. The first, hardware part, presents design, testing, construction and calibration of a fast-timing scintillation Hodoscope for a new Forward Tagger detector, that has been installed inside the upgraded CEBAF Large Acceptance Spectrometer (CLAS12) which is situated in the Hall B of JLab. The Forward Tagger is a key apparatus for measurement of quasi-real photoproduction of mesons in the MesonEx, and a necessary device for other new hadron spectroscopy programs. The second, software and analysis part, presents contributions to the general HAdron SPEctroscopy CenTre (HASPECT) analysis framework, that has been developed in preparation for the MesonEx data analysis. The software contribution consists of finalizing HASPECT simulation and analysis chains in a model-independent way, and developing a mass-independent partial wave analysis procedure. This procedure has been tested via analysis of the γ p → pK + K - channel from the g 11 a CLAS data, in the photon energy range E γ = 3.0-3.8 GeV and momentum transfer squared range -t = 0.6 - 0.7 (GeV/c 2 ) 2 . The first result of this analysis is the differential cross section for the ϕ(1020) resonance photoproduction. This result has been compared with a previous analysis result, and a good agreement has validated the developed analysis procedure. Furthermore, S , P and D partial waves have been extracted from the data set, using the same procedure, and ambiguous solutions for these partial waves have been calculated, using the method of Barrelet zeros which is for the first time applied to photoproduction of the K + and K - mesons on the proton. Distributions of the calculated solutions have been compared with the fit results. It is found that the physical solution contains contributions of the ϕ(1020) in the P -wave and the a 2(1320) in the D -wave.

Stankovic, Ivana↗
DOE OSTI2017

Examples of Use of SINBAD Database for Nuclear Data and Code Validation

Benchmark experiments can serve several purposes and the availability of suitable experimental data is fundamental to ensure that modelling tools meet the requirements of nuclear industry and licensing needs. Essentially, they are used for: - validation of nuclear cross-section data, - validation of nuclear codes and models used, - qualification and training of users through benchmarking and code comparison exercises. The Shielding Integral Benchmark Archive and Database (SINBAD) project was started in the early 1990's at the OECD Nuclear Energy Agency Data Bank (NEA DB) and the Radiation Safety Information Computational Center (RSICC) as a collection of quality experiments for validating and benchmarking computer codes and data for radiation transport and shielding applications. Currently, the SINBAD database comprises over 100 benchmark compilations and evaluations of relevance to: - reactor shielding, pressure vessel dosimetry (48); - fusion blanket neutronics (31); - accelerator shielding (23). In order to facilitate their use, the data are organized in the standardized format structure. The index and the master document (called abstract) use the Hyper Text Markup Language (HTML) format with links to text files, figures, Portable Document Format (pdf) documents and computer code inputs. The format allows easy access and forward/backward navigation among the data and has proved to be suitable, general enough and above all easy (and low-cost) to maintain. In addition to the characterization of the radiation source, description of the shielding set-up, the instrumentation and the relevant detector measurements, most sets in SINBAD contain also the deterministic or Monte Carlo radiation transport computer model used for the interpretation of the experiment and, where available, results from uncertainty analysis. The set of primary documents used for the benchmark compilation and evaluation are provided in computer readable form. The database is intended for different types of users, including nuclear data evaluators, computer code developers, experiment designers and university students. SINBAD is available from the NEA Data Bank and RSICC (see https://www.oecdnea.org/science/wprs/shielding/sinbad/). Up to now several hundreds of SINBAD packages were distributed from the NEA DB and RSICC. Since 2011 the maintenance and further development of the database is coordinated in the scope of the OECD NEA Nuclear Science Committee (NSC) Working Party on Scientific Issues of Reactor Systems (WPRS) Expert Group on Radiation Transport and Shielding (EGRTS). A key objective of the group is to identify, evaluate and preserve experimental data on shielding benchmarks. The Expert Group maintains close links with the International Reactor Physics Experiment Evaluation (IRPhE) and International Criticality Safety Benchmark Evaluation (ICSBEP) projects, as well as in co-ordination with the JEFF and EFF NEA Data Bank projects and with the SATIF community of accelerator shielding specialists. In the last years, much effort was devoted to the quality review of the available benchmark information.

42 ENGINEERING↗
DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1996

Observations and Analysis of Atmospheric Hydroxyl

Ground-based measurements of sunlight absorption at the OH P(sub 1)(l) resonance line at 308 nm have been made on a continuous basis at Fritz Peak, Colorado. The derived OH vertical column abundances show the persistence of a new seasonal regime which began in 1991. The fall minimum has been consistently depressed about 10-15% below the 1980-1990 average fall values. While the initial onset of depressed fall abundances occurred a few months after the Pinatubo eruption, there has been no fall OH recovery correlating with decreased amounts of volcanic aerosol found since spring 1993. The Colorado data also continues to exhibit an AM-PM asymmetry which varies seasonally, approximately in phase with local total ozone. These observations were presented at the Front Range AGU meeting in February 1996 and were published in Geophysical Research Letters in July 1996 (preprint enclosed). An update through the fall of 1996, when morning abundances were found to be extremely low, was presented at the Fall 1996 AGU meeting (abstract attached). A PEPSIOS instrument of identical design is currently operational and has been used since April 1996 for OH column measurements at New Mexico Tech, Socorro, NM. Title for both instruments was transferred from Florida Atlantic University to New Mexico Tech in February of 1996. Comparative measurements from the two instruments for April-July 1996 indicate small differences in OH column abundances, with New Mexico (34 deg N) abundances about 10% above Colorado (40 deg N) values for comparable solar zenith angles. A more detailed comparison will require at least one full year of data from both locations. New Mexico measurements were obtained on June 10, 1996, concurrently with a balloon launch of the NASA STRAT mission from Fort Sumner, New Mexico. We hope to make use of STRAT measurements H2O, CH4, and O3 which are particularly relevant to OH photochemistry. Additional work at New Mexico Tech involves a comparison of P(sub 1)(1) and Q(sub 1)(3) absorption by the method of Doppler shift of solar limb spectra. These are being used to infer path weighted temperatures and for validations studies on the standard method of analysis using the single P(sub 1)(1) line. Results were presented at the Fall 1996 AGU meeting (abstract attached). A graduate student in the Physics Department at New Mexico Tech has been supported since August 1996. The student is investigating column OH behavior using the NCAR 2-D model of the middle atmosphere. Graduate student support was not available until the start of the second year (Nov. 15, 1996), therefore funds have been transferred from the allocation for the research associate, who resigned from the project July 1, 1996.

Minschwaner, Ken R.↗
DOE OSTI2023

IDAES-PSE 2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings

IDAES↗