SEARCH · Search NASA

Results for “simulation output analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,639 records · Page 91

All sources NASA NTRS DOE OSTI

NASA NTRS2006

System Would Detect Foreign-Object Damage in Turbofan Engine

A proposed data-fusion system, to be implemented mostly in software, would further process the digitized and preprocessed outputs of sensors in a turbofan engine to detect foreign-object damage (FOD) [more precisely, damage caused by impingement of such foreign objects as birds, pieces of ice, and runway debris]. The proposed system could help a flight crew to decide what, if any, response is necessary to complete a flight safely, and could aid mechanics in deciding what post-flight maintenance action might be needed. The sensory information to be utilized by the proposed system would consist of (1) the output of an accelerometer in an engine-vibration-monitoring subsystem and (2) features extracted from a gas path analysis. ["Gas path analysis" (GPA) is a term of art that denotes comprehensive analysis of engine performance derived from readings of fuel-flow meters, shaft-speed sensors, temperature sensors, and the like.] The acceleration signal would first be processed by a wavelet-transform-based algorithm, using a wavelet created for the specific purpose of finding abrupt FOD-induced changes in noisy accelerometer signals. Two additional features extracted would be the amplitude of vibration (determined via a single- frequency Fourier transform calculated at the rotational speed of the engine), and the rate of change in amplitude due to an FOD-induced rotor imbalance. This system would utilize two GPA features: the fan efficiency and the rate of change of fan efficiency with time. The selected GPA and vibrational features would be assessed by two fuzzy-logic inference engines, denoted the "Gas Path Expert" and the "Vibration Expert," respectively (see Figure 1). Each of these inference engines would generate a "possibility" distribution for occurrence of an FOD event: Each inference engine would assign, to its input information, degrees of membership, which would subsequently be transformed into basic probability assignments for the gas path and vibration components. The outputs of the inference engines would be fused by use of Dempster s combination algorithm (more precisely, an algorithm, based on the Dempster-Shafer-Yager theory of evidence, for fusing uncertain or imprecise information) to provide a reduced body of information to a human or computer decision maker. Figure 2 depicts some outputs generated in response to simulated accelerometer and GPA signals

Torso, James A.↗

DOE OSTI2021

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

36 MATERIALS SCIENCE↗

NASA NTRS2013

Sample Analysis at Mars Instrument Simulator

The Sample Analysis at Mars Instrument Simulator (SAMSIM) is a numerical model dedicated to plan and validate operations of the Sample Analysis at Mars (SAM) instrument on the surface of Mars. The SAM instrument suite, currently operating on the Mars Science Laboratory (MSL), is an analytical laboratory designed to investigate the chemical and isotopic composition of the atmosphere and volatiles extracted from solid samples. SAMSIM was developed using Matlab and Simulink libraries of MathWorks Inc. to provide MSL mission planners with accurate predictions of the instrument electrical, thermal, mechanical, and fluid responses to scripted commands. This tool is a first example of a multi-purpose, full-scale numerical modeling of a flight instrument with the purpose of supplementing or even eliminating entirely the need for a hardware engineer model during instrument development and operation. SAMSIM simulates the complex interactions that occur between the instrument Command and Data Handling unit (C&DH) and all subsystems during the execution of experiment sequences. A typical SAM experiment takes many hours to complete and involves hundreds of components. During the simulation, the electrical, mechanical, thermal, and gas dynamics states of each hardware component are accurately modeled and propagated within the simulation environment at faster than real time. This allows the simulation, in just a few minutes, of experiment sequences that takes many hours to execute on the real instrument. The SAMSIM model is divided into five distinct but interacting modules: software, mechanical, thermal, gas flow, and electrical modules. The software module simulates the instrument C&DH by executing a customized version of the instrument flight software in a Matlab environment. The inputs and outputs to this synthetic C&DH are mapped to virtual sensors and command lines that mimic in their structure and connectivity the layout of the instrument harnesses. This module executes, and thus validates, complex command scripts prior to their up-linking to the SAM instrument. As an output, this module generates synthetic data and message logs at a rate that is similar to the actual instrument.

Benna, Mehdi↗

DOE OSTI2021

Efficient Contingency Analysis in Power Systems via Network Trigger Nodes

Modeling failure dynamics within a power system is a complex and challenging process due to multiple inter-dependencies and convoluted inter-domain relationships. Subject matter experts (SMEs) are interested in understanding these failure dynamics for reducing the impact from future disasters (i.e., losses or failures of power system components, such as transmission lines). Contingency analysis (CA) tools enable such ’what-if’ scenario analyses to evaluate the impacts on the power system. Analyzing all possible contingencies among N system components can be computationally expensive. An important step for performing CA is identifying a set of k ‘trigger’ components, which when failed initially can significantly impact the overall system by causing multiple failures. Currently SMEs focus on identifying these trigger components by running expensive simulations on all possible subsets, which quickly becomes infeasible. Hence finding a relevant set of trigger components (contingencies) rapidly to enable efficient and useful CA is crucial.In a collaboration between computer scientists and power system experts, we propose an efficient method for performing CA by exploiting network inter-dependencies in power system components. First, we construct a network with multiple electric grid infrastructure components and dependencies as connections among them. We reformulate the problem of finding a set of trigger components as a problem of identifying critical nodes in the network, which can cascade power failures through connected nodes and cause significant damage to the network. To guide the practical CA tools, we develop a network-based model with a probabilistic edge-weights setup using intricate domain rules. Then we conduct an empirical study on real power system data in the US for both regional and national levels. Firstly, we use power system datasets for the US to create a national-scale domain-driven model. Secondly, we demonstrate that network-based model outperforms the outputs from a real CA tool and show on average 25 × improved selection of contingencies, thereby showcasing practical benefits to the power experts.

Tabassum, Anika↗

DOE OSTI2019

3D Gamma Source Mapping and Intervention Analysis - 19243

3D gamma source mapping and intervention analysis, through Transco Products, Inc.'s RadVision3D{sup R}, uses the N-Visage Scanner{sup TM} by Createc combining laser scanning, optical imaging, and CZT (Cadmium Zinc Telluride) technologies to capture plant geometry, images, and gamma radiation data. By capturing multiple scans in an area of interest a full 3D analysis can be performed using N-Visage Fusion{sup TM} software by Createc which develops a 3D model showing radioactive sources in the environment and allowing various interventions, such as shielding or source removal, to be modeled virtually. The output of a RadVision3D{sup R} project is a scaled virtual environment that the user can 'walk through' on a PC for training and work planning activities. Included in the viewer are dose planes for the as-scanned conditions as well as for each intervention, showing the dose rate at any point in the room. A virtual dosimeter is built into the viewer to allow users to be fully immersed in the environment in which they will be working, without being exposed to actual radiation or other hazardous conditions. This technology can help save dose at operating nuclear power plants when used for planning large work scopes in high radiation areas and is valuable as a characterization tool for decommissioning facilities. Adopters of 3D gamma source mapping and intervention analysis save time, money, and dose by evaluating options well in advance of the performance of any work, and optimizing interventions using accurate modelling techniques to plan work evolutions. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Effects of Inflow Spatiotemporal Discretization on Wake Meandering and Turbine Structural Response using FAST.Farm

FAST.Farm is a newly developed multiphysics, midfidelity engineering tool that can be used to predict turbine power and structural loads of wind turbines in a wind farm with minimal computational expense. Previous studies have shown similarities and differences in wind turbine performance between FAST.Farm and high-fidelity large-eddy simulations (LES) using both LES precursor-generated inflow and TurbSim-generated synthetic inflow. While conservative resolutions have been used to date, no formal spatial or temporal discretization study has been performed for the FAST.Farm model wind domains. This work aims to study the effects of varying the spatial and temporal discretization of the wind domains on wake meandering (low-resolution domain) and turbine structural response (high-resolution domains) and resulting wake and load calculations. The purpose of this study is to establish convergence criteria and recommendations for discretization values in terms of rotor diameter (D, expressed in meters) that will maximize computational efficiency. To ensure a percent error of ≤ 1% in standard deviation of wake center position relative to the finest included resolution, a low-resolution time step of 0.024D s, 0.016D s, and 0.0079D s and a low-resolution spatial discretization of 0.079D m, 0.16D m, and 0.24D m is recommended for 8 m/s, 12 m/s, and 18 m/s mean ambient wind speed at hub height, respectively. These guidelines are likely applicable to other implementations of the dynamic wake meandering model. To ensure a percent error of ≤ 1% in standard deviation of all considered structural response outputs, a high-resolution time step that captures the highest influential excitation and natural frequencies in the system and a high-resolution spatial discretization comparable to the maximum airfoil cord length is recommended. Lastly, these guidelines are likely applicable to any aeroelastic analysis.

17 WIND ENERGY↗

DOE OSTI2018

A stochastic techno-economic analysis of shrub willow production using EcoWillow 3.0S: A Stochastic Techno-Economic Analysis of Shrub Willow Production Using EcoWillow 3.0S

EcoWillow 3.0S is a techno-economic analysis (TEA) tool that calculates realistic willow biomass production costs using data from field trials of willow production in New York State. The model represents the input parameters for diesel fuel price, wood price, and willow yield as data-derived probability distributions rather than as single values. This study uses EcoWillow 3.0S with Oracle Crystal Ball to perform a stochastic TEA of shrub willow production in the Northeastern United States. Two outputs obtained from this model are a 24-year net present value (NPV) and minimum selling price (MSP) of shrub willow production under price and yield uncertainty. The MSP for dry willow ranges within a 95% confidence interval from $\$ 59$. -$\$ 102$/Mg. The NPV, also within a 95% confidence interval, ranges from -$\$ 153$ 819 to $\$ 207$ 129 for 40 ha of willow harvested seven times over 24 years. The relevance of this study is to utilize the TEA methodology to assess the financial feasibility of shrub willow production as a bioenergy feedstock. The results of this analysis provide the bioenergy field with NPV and MSP probability distributions on shrub willow production that have previously been absent from the refereed literature. Using data collected from field operations over a number of years, EcoWillow 3.0S captures some of the key inputs that influence the variation in the delivered cost of willow biomass crops.

09 BIOMASS FUELS↗

DOE OSTI2014

Representation of Solar Capacity Value in the ReEDS Capacity Expansion Model

An important issue for electricity system operators is the estimation of renewables' capacity contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to the resource variability. Reliability-based methods, particularly assessment of the Effective Load-Carrying Capacity, are considered to be the most robust and widely-accepted techniques for addressing this resource variability. This report compares estimates of solar PV capacity value by the Regional Energy Deployment System (ReEDS) capacity expansion model against two sources. The first comparison is against values published by utilities or other entities for known electrical systems at existing solar penetration levels. The second comparison is against a time-series ELCC simulation tool for high renewable penetration scenarios in the Western Interconnection. Results from the ReEDS model are found to compare well with both comparisons, despite being resolved at a super-hourly temporal resolution. Two results are relevant for other capacity-based models that use a super-hourly resolution to model solar capacity value. First, solar capacity value should not be parameterized as a static value, but must decay with increasing penetration. This is because -- for an afternoon-peaking system -- as solar penetration increases, the system's peak net load shifts to later in the day -- when solar output is lower. Second, long-term planning models should determine system adequacy requirements in each time period in order to approximate LOLP calculations. Within the ReEDS model we resolve these issues by using a capacity value estimate that varies by time-slice. Within each time period the net load and shadow price on ReEDS's planning reserve constraint signals the relative importance of additional firm capacity.

14 SOLAR ENERGY↗

DOE OSTI2015

Development of an accurate EPID-based output measurement and dosimetric verification tool for electron beam therapy

Purpose: To develop an efficient and robust tool for output measurement and absolute dose verification of electron beam therapy by using a high spatial-resolution and high frame-rate amorphous silicon flat panel electronic portal imaging device (EPID). Methods: The dosimetric characteristics of the EPID, including saturation, linearity, and ghosting effect, were first investigated on a Varian Clinac 21EX accelerator. The response kernels of the individual pixels of the EPID to all available electron energies (6, 9, 12, 16, and 20 MeV) were calculated by using Monte Carlo (MC) simulations, which formed the basis to deconvolve an EPID raw images to the incident electron fluence map. The two-dimensional (2D) dose distribution at reference depths in water was obtained by using the constructed fluence map with a MC simulated pencil beam kernel with consideration of the geometric and structural information of the EPID. Output factor measurements were carried out with the EPID at a nominal source–surface distance of 100 cm for 2 × 2, 3 × 3, 6 × 6, 10 × 10, and 15 × 15 cm{sup 2} fields for all available electron energies, and the results were compared with that measured in a solid water phantom using film and a Farmer-type ion chamber. The dose distributions at a reference depth specific to each energy and the flatness and symmetry of the 10 × 10 cm{sup 2} electron beam were also measured using EPID, and the results were compared with ion chamber array and water scan measurements. Finally, three patient cases with various field sizes and irregular cutout shapes were also investigated. Results: EPID-measured dose changed linearly with the monitor units and showed little ghosting effect for dose rate up to 600 MU/min. The flatness and symmetry measured with the EPID were found to be consistent with ion chamber array and water scan measurements. The EPID-measured output factors for standard square fields of 2 × 2, 3 × 3, 6 × 6, 10 × 10, 15 × 15 cm{sup 2} agreed with film and ion chamber measurements. The average discrepancy between EPID and ion chamber/film measurements was 0.81% ± 0.60% (SD) and 1.34% ± 0.75%, respectively. For the three clinical cases, the difference in output between the EPID- and ion chamber array measured values was found to be 1.13% ± 0.11%, 0.54% ± 0.10%, and 0.74% ± 0.11%, respectively. Furthermore, the γ-index analysis showed an excellent agreement between the EPID- and ion chamber array measured dose distributions: 100% of the pixels passed the criteria of 3%/3 mm. When the γ-index was set to be 2%/2 mm, the pass rate was found to be 99.0% ± 0.07%, 98.2% ± 0.14%, and 100% for the three cases. Conclusions: The EPID dosimetry system developed in this work provides an accurate and reliable tool for routine output measurement and dosimetric verification of electron beam therapy. Coupled with its portability and ease of use, the proposed system promises to replace the current film-based approach for fast and reliable assessment of small and irregular electron field dosimetry.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Transportation and System Analysis Collaborations in Support of a Federal Consolidated Interim Storage Facility

The U.S. Department of Energy’s Integrated Waste Management (IWM) program under the Office of Nuclear Energy is planning for the future transportation, storage, and eventual disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant and waste custodian sites across the United States. To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the IWM program has been sponsoring the development and application of system analysis tools capable of analyzing various options for managing SNF and HLW. With these tools, integrated waste management system (IWMS) architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analyses and assessments typically use these modeling and simulation tools to investigate implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, start and stop dates of facilities, etc. The Next Generation System Analysis Model (NGSAM) is an agent-based simulation toolkit that is used for a system-level simulation and analysis focused on SNF management in the United States. NGSAM’s primary purpose is to provide a system analyst with capabilities to model the IWMS and gain insights into SNF and HLW management alternatives including the impact of system choices, associated cost estimates, and development of integrated yet flexible approaches. An analyst using NGSAM can define several factors like the number of storage facilities, capacity at each facility, transportation schedules, shipment rates, and other conditions. IWM is also developing the Stakeholder Tool for Assessing Radioactive Transportation (START). START is a web-based geospatial tool developed to provide the visualization and initial evaluation of transportation options associated with future SNF and HLW shipment planning and operations. This includes characterizing safety, economic, and environmental conditions at and in proximity to shipment origins as well as along prospective transportation routes. Information from the START tool can be presented/shared as maps, graphics, geospatial files, and tabular form to enable easy data representation and export functionality. The system analysis, NGSAM, and START teams have been working closely for several years before formalizing this collaboration. START provides the SNF transportation routing information for use in NGSAM. System analysts use the NGSAM tool to generate results (schedule, costs, infrastructure acquisitions, shipment rates, etc.). Subject-matter experts and system analysts work together to inform how NGSAM should model the waste management system. Specifically, this paper discusses the collaborations between the system analysis, NGSAM, and START teams and their accomplishments over the past year. Some examples of these efforts include calibrating and adding data to the START output files to meet NGSAM needs, gaining a better understanding of START data used in NGSAM as well as how updates in START data could result in an improved NGSAM analysis. Detailed examples of various tasks that have been performed by the team will be discussed in the full paper. Continued efforts in this direction are expected in the coming years.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Modeling and Analysis of Hybrid Pixel Detector Deficiencies for Scientific Applications

Semiconductor hybrid pixel detectors often consist of a pixellated sensor layer bump bonded to a matching pixelated readout integrated circuit (ROIC). The sensor can range from high resistivity Si to III-V materials, whereas a Si CMOS process is typically used to manufacture the ROIC. Independent, device physics and electronic design automation (EDA) tools are used to determine sensor characteristics and verify functional performance of ROICs respectively with significantly different solvers. Some physics solvers provide the capability of transferring data to the EDA tool. However, single pixel transient simulations are either not feasible due to convergence difficulties or are prohibitively long. A simplified sensor model, which includes a current pulse in parallel with detector equivalent capacitor, is often used; even then, spice type top-level (entire array) simulations range from days to weeks. In order to analyze detector deficiencies for a particular scientific application, accurately defined transient behavioral models of all the functional blocks are required. Furthermore, various simulations, such as transient, noise, Monte Carlo, inter-pixel effects, etc. of the entire array need to be performed within a reasonable time frame without trading off accuracy. The sensor and the analog front-end can be modeling using a real number modeling language, as complex mathematical functions or detailed data can be saved to text files, for further top-level digital simulations. Parasitically aware digital timing is extracted in a standard delay format (sdf) from the pixel digital back-end layout as well as the periphery of the ROIC. For any given input, detector level worst-case and best-case simulations are performed using a Verilog simulation environment to determine the output. Each top-level transient simulation takes no more than 10-15 minutes. The impact of changing key parameters such as sensor Poissonian shot noise, analog front-end bandwidth, jitter due to clock distribution etc. can be accurately analyzed to determine ROIC architectural viability and bottlenecks. Hence the impact of the detector parameters on the scientific application can be studied.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2005

Development of the Architectural Simulation Model for Future Launch Systems and its Application to an Existing Launch Fleet

A significant portion of lifecycle costs for launch vehicles are generated during the operations phase. Research indicates that operations costs can account for a large percentage of the total life-cycle costs of reusable space transportation systems. These costs are largely determined by decisions made early during conceptual design. Therefore, operational considerations are an important part of vehicle design and concept analysis process that needs to be modeled and studied early in the design phase. However, this is a difficult and challenging task due to uncertainties of operations definitions, the dynamic and combinatorial nature of the processes, and lack of analytical models and the scarcity of historical data during the conceptual design phase. Ultimately, NASA would like to know the best mix of launch vehicle concepts that would meet the missions launch dates at the minimum cost. To answer this question, we first need to develop a model to estimate the total cost, including the operational cost, to accomplish this set of missions. In this project, we have developed and implemented a discrete-event simulation model using ARENA (a simulation modeling environment) to determine this cost assessment. Discrete-event simulation is widely used in modeling complex systems, including transportation systems, due to its flexibility, and ability to capture the dynamics of the system. The simulation model accepts manifest inputs including the set of missions that need to be accomplished over a period of time, the clients (e.g., NASA or DoD) who wish to transport the payload to space, the payload weights, and their destinations (e.g., International Space Station, LEO, or GEO). A user of the simulation model can define an architecture of reusable or expendable launch vehicles to achieve these missions. Launch vehicles may belong to different families where each family may have it own set of resources, processing times, and cost factors. The goal is to capture the required resource levels of the major launch elements and their required facilities. The model s output can show whether or not a certain architecture of vehicles can meet the launch dates, and if not, how much the delay cost would be. It will also produce aggregate figures of missions cost based on element procurement cost, processing cost, cargo integration cost, delay cost, and mission support cost. One of the most useful features of this model is that it is stochastic where it accepts statistical distributions to represent the processing times mimicking the stochastic nature of real systems.

Rabadi, Ghaith↗

DOE OSTI2024

Transportation and Systems Analysis Collaborations in Support of a Federal Consolidated Interim Storage Facility

The U.S. Department of Energy’s Integrated Waste Management (IWM) program under the Office of Nuclear Energy is planning for the future transportation, storage, and eventual disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant sites across the United States. To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the IWM program has been sponsoring the development and application of system analysis tools capable of analyzing various options for managing SNF and HLW. With these tools, integrated waste management system (IWMS) architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analyses and assessments typically use these modeling and simulation tools to investigate implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, start and stop dates of facilities, etc. The Next Generation System Analysis Model (NGSAM) is an agent-based simulation toolkit that is used for a system-level simulation and analysis focused on SNF management in the United States. An analyst using NGSAM has the ability to define several factors like the number of storage facilities, capacity at each facility, transportation schedules, shipment rates, and other conditions. NGSAM’s primary purpose is to provide a system analyst with capabilities to model the IWMS and gain insights into SNF and HLW management alternatives including the impact of system choices, associated cost estimates, and development of integrated yet flexible approaches. IWM is also developing the Stakeholder Tool for Assessing Radioactive Transportation (START). START is a web-based geospatial tool developed to provide visualization and initial evaluation of transportation options associated with future SNF and HLW shipment planning and operations. This includes characterizing safety, economic, and environmental conditions on and in proximity to shipment origins as well as along prospective transportation routes. Information from the START tool can be presented/shared as maps, graphics, geospatial files, and tabular form to enable easy data representation and export functionality. The system analysis, NGSAM, and START teams had been working closely for several years before formalizing this collaboration. START provides the SNF routing information for use in NGSAM. System analysts use the NGSAM tool to generate results (schedule, costs, infrastructure acquisitions, shipment rates, etc.). Subject-matter experts and system analysts work together to inform how NGSAM should model the waste management system. Specifically, this paper discusses the collaborations between the system analysis, NGSAM, and START teams and their accomplishments over the past year. Some examples of these efforts include calibrating and adding data to the START output files to meet NGSAM needs, gaining a better understanding of START data used in NGSAM, as well as how updates in START data could result in an improved NGSAM analysis. Detailed examples of various tasks that have been performed by the team will be discussed in the full paper. Continued efforts in this direction are expected in the coming years.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2006

Remote Sensing Time Series Product Tool

The TSPT (Time Series Product Tool) software was custom-designed for NASA to rapidly create and display single-band and band-combination time series, such as NDVI (Normalized Difference Vegetation Index) images, for wide-area crop surveillance and for other time-critical applications. The TSPT, developed in MATLAB, allows users to create and display various MODIS (Moderate Resolution Imaging Spectroradiometer) or simulated VIIRS (Visible/Infrared Imager Radiometer Suite) products as single images, as time series plots at a selected location, or as temporally processed image videos. Manually creating these types of products is extremely labor intensive; however, the TSPT development tool makes the process simplified and efficient. MODIS is ideal for monitoring large crop areas because of its wide swath (2330 km), its relatively small ground sample distance (250 m), and its high temporal revisit time (twice daily). Furthermore, because MODIS imagery is acquired daily, rapid changes in vegetative health can potentially be detected. The new TSPT technology provides users with the ability to temporally process high-revisit-rate satellite imagery, such as that acquired from MODIS and from its successor, the VIIRS. The TSPT features the important capability of fusing data from both MODIS instruments onboard the Terra and Aqua satellites, which drastically improves cloud statistics. With the TSPT, MODIS metadata is used to find and optionally remove bad and suspect data. Noise removal and temporal processing techniques allow users to create low-noise time series plots and image videos and to select settings and thresholds that tailor particular output products. The TSPT GUI (graphical user interface) provides an interactive environment for crafting what-if scenarios by enabling a user to repeat product generation using different settings and thresholds. The TSPT Application Programming Interface provides more fine-tuned control of product generation, allowing experienced programmers to bypass the GUI and to create more user-specific output products, such as comparison time plots or images. This type of time series analysis tool for remotely sensed imagery could be the basis of a large-area vegetation surveillance system. The TSPT has been used to generate NDVI time series over growing seasons in California and Argentina and for hurricane events, such as Hurricane Katrina.

Predos, Don↗

NASA NTRS2015

GeneLab: A Systems Biology Platform for Spaceflight Omics Data

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. Resources to support large numbers of spaceflight investigations are limited. NASA's GeneLab project is maximizing the science output from these experiments by: (1) developing a unique public bioinformatics database that includes space bioscience relevant "omics" data (genomics, transcriptomics, proteomics, and metabolomics) and experimental metadata; (2) partnering with NASA-funded flight experiments through bio-sample sharing or sample augmentation to expedite omics data input to the GeneLab database; and (3) developing community-driven reference flight experiments. The first database, GeneLab Data System Version 1.0, went online in April 2015. V1.0 contains numerous flight datasets and has search and download capabilities. Version 2.0 will be released in 2016 and will link to analytic tools. In 2015 Genelab partnered with two Biological Research in Canisters experiments (BBRIC-19 and BRIC-20) which examine responses of Arabidopsis thaliana to spaceflight. GeneLab also partnered with Rodent Research-1 (RR1), the maiden flight to test the newly developed rodent habitat. GeneLab developed protocols for maxiumum yield of RNA, DNA and protein from precious RR-1 tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected. GeneLab is establishing partnerships with at least three planned flights for 2016. Organism-specific nationwide Science Definition Teams (SDTs) will define future GeneLab dedicated missions and ensure the broader scientific impact of the GeneLab missions. GeneLab ensures prompt release and open access to all high-throughput omics data from spaceflight and ground-based simulations of microgravity and radiation. Overall, GeneLab will facilitate the generation and query of parallel multi-omics data, and deep curation of metadata for integrative analysis, allowing researchers to uncover cellular networks as observed in systems biology platforms. Consequently, the scientific community will have access to a more complete picture of functional and regulatory networks responsive to the spaceflight environment.. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and enable emerging terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space. As a result, open access to the data will foster new hypothesis-driven research for future spaceflight studies spanning basic science to translational science.

proteomics↗

DOE OSTI2025

A computational investigation of high-flux, plate-and-frame membrane modules for industrial carbon capture

In this work, we study the application of membrane-based separation systems for carbon capture, considering plate-and-frame membrane modules. The successful deployment of membrane CO 2 capture system relies on high-performing membranes as well as effective membrane modules that can fully exploit the developed membranes. A plate-and-frame membrane module is especially attractive for CO 2 capture from industrial flue gas due to its lower pressure drop compared to its counterparts such as spiral wound modules and hollow fiber modules. To design better plate-and-frame modules, we investigate their basic unit - a single membrane stack through a combination of computational modeling and experimental investigations. The modeling approach is based on Computational Fluid Dynamics (CFD) to represent a multiphysics problem, including the fluid flow and diffusion processes within a membrane module. We use experimental data collected under different operating conditions to validate the CFD model. Numerical results suggest a good agreement between experiments and model outputs for the CO 2 recovery, CO 2 mole fraction in the retentate and permeate, and stage-cut. The CFD model is able to predict accurately the flow behavior, providing valuable insights on the effects of fluid dynamics on mass transfer of CO 2 . We also carry out a sensitivity analysis to identify the effect of key parameters on the CO 2 recovery and the CO 2 purity of the outlet streams.

CFD simulation↗

DOE OSTI2016

Analyses of Model-Based Predictive Control for a S-CO 2 Brayton Cycle Power Converter

The supercritical carbon dioxide (S-CO 2 ) Brayton cycle is a promising alternative to steam cycles as an energy converter for advanced nuclear reactors as well as fossil-fired and concentrated solar power plants. For advanced reactors, such as Sodium-cooled Fast Reactors (SFRs), the S-CO 2 cycle offers the benefits of high cycle efficiency, very compact turbomachinery, and elimination of sodium-water reactions, all of which may enable improved economics. The efficiency gains calculated for the S-CO 2 cycle are particularly significant if advantage can be taken of the sharp changes in the carbon dioxide thermo-physical properties near the critical point. On the other hand, the same property variations give rise to challenges in the cycle design, analysis, and control. The onset of two-phase flow (where the minimum cycle temperature and pressure drop drop below the critical value) can degrade compressor performance and potentially cause damage to the compressor blades. In previous work, the feasibility of the Model-based Predictive Control (MPC) approach was assessed for improving regulation of the precooler outlet temperature. A linear reduced-order model for the precooler was generated and for this simplified standalone system operating with an MPC controller the response of outlet temperature was evaluated through simulations. These evaluations included the presence of temperature measurement time delay representing a degraded sensor. It was demonstrated that the MPC controller maintained stable precooler temperature for those delay cases where a proportional-integral controller became unstable. In the present work the simulations have been extended to evaluate the MPC controller operating under more realistic conditions, namely in the split-flow recompression cycle non-linear plant. This compares with the previous standalone reduced-order linear system described above. In the present work the MPC-based control scheme was implemented in a GPASS 1-D systems code representation of the split-flow recompression cycle. The performance of the controller was again investigated for degraded sensor conditions and compared to that of a proportional-integral controller. The results were qualitatively similar to the previous results with the MPC controller and again providing superior stability characteristics. This work provides a sound starting point for the development of model-based control systems. In particular, the MPC control approach applied to Multiple Input Multiple Output systems can be used to synchronize the operation of the different system components to improve control system dynamic performance.

20 FOSSIL-FUELED POWER PLANTS↗