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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.
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Computer program predicts performance of fluorine-hydrogen main tank injection pressurization system for full range of liquid-hydrogen-fueled space vehicles. Analytical model includes provisions for heat transfer, injectant jet penetration, and ullage gas mixing. Analysis predicts GF2 usage, ullage gas and tank wall temperatures, and LH2 evaporation.
Optimal digital control of nonlinear multivariable constrained systems was studied. The optimal controller in the form of an algorithm was improved and refined by reducing running time and storage requirements. A particularly difficult system of nine nonlinear state variable equations was chosen as a test problem for analyzing and improving the controller. Lengthy analysis, modeling, computing and optimization were accomplished. A remote interactive teletype terminal was installed. Analysis requiring computer usage of short duration was accomplished using Tuskegee's VAX 11/750 system.
The development of an interactive/batch gridded information system (array of cells georeferenced to USGS quad sheets) and interfacing application programs (e.g., hydrologic models) is discussed. This system allows non-programer users to request any data set(s) stored in the data base by inputing any random polygon's (watershed, political zone) boundary points. The data base information contained within this polygon can be used to produce maps, statistics, and define model parameters for the area. Present/proposed conditions for the area may be compared by inputing future usage (land cover, soils, slope, etc.). This system, known as the Hydrologic Analysis Program (HAP), is especially effective in the real time analysis of proposed land cover changes on runoff hydrographs and graphics/statistics resource inventories of random study area/watersheds.
A method for analysis of progressive failure in the Computational Structural Mechanics Testbed is presented in this report. The relationship employed in this analysis describes the matrix crack damage and fiber fracture via kinematics-based volume-averaged variables. Damage accumulation during monotonic and cyclic loads is predicted by damage evolution laws for tensile load conditions. The implementation of this damage model required the development of two testbed processors. While this report concentrates on the theory and usage of these processors, a complete list of all testbed processors and inputs that are required for this analysis are included. Sample calculations for laminates subjected to monotonic and cyclic loads were performed to illustrate the damage accumulation, stress redistribution, and changes to the global response that occur during the load history. Residual strength predictions made with this information compared favorably with experimental measurements.
A method for analysis of progressive failure in the Computational Structural Mechanics Testbed is presented in this report. The relationship employed in this analysis describes the matrix crack damage and fiber fracture via kinematics-based volume-averaged damage variables. Damage accumulation during monotonic and cyclic loads is predicted by damage evolution laws for tensile load conditions. The implementation of this damage model required the development of two testbed processors. While this report concentrates on the theory and usage of these processors, a complete listing of all testbed processors and inputs that are required for this analysis are included. Sample calculations for laminates subjected to monotonic and cyclic loads were performed to illustrate the damage accumulation, stress redistribution, and changes to the global response that occurs during the loading history. Residual strength predictions made with this information compared favorably with experimental measurements.
Over the past decade, due in large part to heavy investment in the field of Advanced Air Mobility (AAM), significant progress in rotorcraft-focused modeling tools has been made. Such progress has notably increased AAM rotorcraft modeling capabilities in the topics of conceptual design, preliminary design, and more recently flight dynamics. Yet, due to recent and persistent increases in extreme weather events, an emerging interest has been raised in utilizing such modeling capabilities for aiding in emergency relief efforts and other public good missions. This paper uses wildfire fighting as a representative public good mission and demonstrates the relevance of the NASA Revolutionary Vertical Lift Technology (RVLT) rotorcraft toolchain to such missions. An emphasis is placed on flight dynamics modeling and control because of the hazards and challenges associated with the atmospheric environment of wildfires. In this work, the NASA FlightCODE tool was used to analyze both a UH-60 and the NASA six-passenger quadcopter reference model hovering in an experimentally informed wildfire turbulent environment. Preliminary results of this study estimate actuator usage exceedances and disturbance rejection capabilities of the vehicles’ translational rate command systems. Leveraging the RVLT toolchain, refinement and expansion of this work could lead to handling qualities envelope estimation and design optimization for wildfire turbulent environments. This would provide pilots with additional information to make real-time decisions in high-risk scenarios and begins preparations for simulating these dangerous environments for pilot training and experimentation.
Over the past decade, due in large part to heavy investment in the field of Advanced Air Mobility (AAM), significant progress in rotorcraft-focused modeling tools has been made. Such progress has notably increased AAM rotorcraft modeling capabilities in the topics of conceptual design, preliminary design, and more recently flight dynamics. Yet, due to recent and persistent increases in extreme weather events, an emerging interest has been raised in utilizing such modeling capabilities for aiding in emergency relief efforts and other public good missions. This paper uses wildfire fighting as a representative public good mission and demonstrates the relevance of the NASA Revolutionary Vertical Lift Technology (RVLT) rotorcraft toolchain to such missions. An emphasis is placed on flight dynamics modeling and control because of the hazards and challenges associated with the atmospheric environment of wildfires. In this work, the NASA FlightCODE tool was used to analyze both a UH-60 and the NASA six-passenger quadcopter reference model hovering in an experimentally informed wildfire turbulent environment. Preliminary results of this study estimate actuator usage exceedances and disturbance rejection capabilities of the vehicles’ translational rate command systems. Leveraging the RVLT toolchain, refinement and expansion of this work could lead to handling qualities envelope estimation and design optimization for wildfire turbulent environments. This would provide pilots with additional information to make real-time decisions in high-risk scenarios and begins preparations for simulating these dangerous environments for pilot training and experimentation.
Over the past decade, electronic, electrical, and electromechanical (EEE) parts for space applications have undergone significant changes, largely driven by CubeSat and commercial space developers pushing the boundaries on the utilization of commercial parts in space. Global product shortages and shipping delays are still impacting space flight project deadlines. Many projects have turned to automotive grade resistors as an alternate to their MIL-SPEC counterparts to fulfill requirements. In addition, automotive grade resistors may offer designers a wider range of parts to consider. A recent NASA study recommended the use of high-volume manufactured commercial components for space applications provided these components show evidence of stringent fabrication controls and thorough reliability monitoring practices [1]. Automotive grade components have stringent qualification requirements per the Automotive Electronic Council (AEC). However, the end user usually does not have insight into the practices the manufacturer may use to reduce/eliminate infant mortality nor for compliance to all datasheet specifications. Screening, Life and Accelerated Life testing on a set of standard automotive-grade chip resistors is proposed to evaluate the reliability of these components. Requirements from both the AEC-Q (Automotive Electronic Council Qualification) and EEE-INST-002 (Instructions for EEE Parts Selection, Screening, Qualification, and Derating) for resistors is compared and discussed. The resistors have been tested by using a modified methodology from EEE-INST-002 to evaluate their reliability for space flight projects. The findings of this study indicate that the underlying degradation mechanisms at rated temperature and power are best represented by power law models with a fitted exponent between 0 and 1. A linear model is more conservative which compensates for potential model uncertainty given the wide range of design and materials used in automotive resistors, while still providing useful long-term resistance drift estimates. No electrical anomalies or failures were observed throughout the 1,000-hour Life Tests other than small in tolerance resistance drift aging. Degradation models were utilized to quantify and extrapolate the long-term resistance drift under operating conditions for the components. The models demonstrated that some automotive-grade resistors are likely to operate 10 years at nominal usage conditions while others might fail earlier.
Over the past decade, electronic, electrical, and electromechanical (EEE) parts for space applications have undergone significant changes, largely driven by CubeSat and commercial space developers pushing the boundaries on the utilization of commercial parts in space. Global product shortages and shipping delays are still impacting space flight project deadlines. Many projects have turned to automotive grade resistors as an alternate to their MILSPEC counterparts to fulfill requirements. In addition, automotive grade resistors may offer designers a wider range of parts to consider. A recent NASA study recommended the use of high-volume manufactured commercial components for space applications provided these components show evidence of stringent fabrication controls and thorough reliability monitoring practices. Automotive grade components have stringent qualification requirements per the Automotive Electronic Council (AEC). However, the end user usually does not have insight into the practices the manufacturer may use to reduce/eliminate infant mortality nor for compliance to all datasheet specifications. Screening, Life and Accelerated Life testing on a set of standard automotive-grade chip resistors is proposed to evaluate the reliability of these components. Requirements from both the AEC-Q (Automotive Electronic Council Qualification) and EEE-INST-002 (Instructions for EEE Parts Selection, Screening, Qualification, and Derating) for resistors is compared and discussed. The resistors have been tested by using a modified methodology from EEEINST-002 to evaluate their reliability for space flight projects. The findings of this study indicate that the underlying degradation mechanisms at rated temperature and power are best represented by power law models with a fitted exponent between 0 and 1. A linear model is more conservative which compensates for potential model uncertainty given the wide range of design and materials used in automotive resistors, while still providing useful long-term resistance drift estimates. No electrical anomalies or failures were observed throughout the 1000-hour Life Tests other than small in tolerance resistance drift aging. Degradation models were utilized to quantify and extrapolate the long-term resistance drift under operating conditions for the components. The models demonstrated that some automotive-grade resistors are likely to operate 10 years at nominal usage conditions while others might fail earlier.
Studies have shown that irrigated agriculture has substantial effects on local to regional hydrologic and atmospheric processes. These effects include increased evapotranspiration and near surface humidity, decreased air and ground temperature, augmented downwind precipitation, enhanced recharge, depletion of source water stocks, and overall changes to the regional water budget. However, developers of global offline and coupled modeling systems have been slow to incorporate irrigation. This presentation will describe progress towards simulating irrigation worldwide in the Land Information System (LIS) and Global Land Data Assimilation System (GLDAS). We have developed and refined the ability to simulate sprinkler irrigation, furrow irrigation, paddy irrigation, and drip irrigation. We have reconciled multiple global and national irrigation, land cover, and crop type databases in an effort to (1) distinguish irrigated and rainfed agriculture, (2) simulate the proper distribution of crops (as sub-grid tiles) in each model grid pixel, and (3) apply the most likely irrigation method in each tile based on the crop type and country or state level information on the relative usage of sprinkler, flood, and drip irrigation. Using agricultural water withdrawal data, as available, for calibration and evaluation, we show that the implemented approach is promising but that much fine tuning remains to be done. In particular, irrigation model parameters that produce good results in one location often produce less satisfying results in others.
The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of chemical mechanisms for implementation in computational reacting flow solvers. However, for many applications, the resulting model still requires implicit methods for efficient time integration. Here, in this paper, we outline an approach to formulating the QSSA reduction that is coupled with a strategy to generate C++ source code to evaluate the net species production rates, and the chemical Jacobian. The code-generation component employs a symbolic approach enabling a simple and effective strategy to analytically compute the chemical Jacobian. For computational tractability, the symbolic approach needs to be paired with common subexpression elimination which can negatively affect memory usage. Several solutions are outlined and successfully tested on a 3D multipulse ignition problem, thus allowing portable application across chemical model sizes and GPU capabilities. The implementation of the proposed method is available at https://github.com/AMReX-Combustion/PelePhysics under an open-source license.
Two multi-scale-type turbulence models are implemented in the PAB3D solver. The models are based on modifying the Reynolds Averaged Navier-Stokes (RANS) equations. The first scheme is a hybrid RANS/LES model utilizing the two-equation (k(epsilon)) model with a RANS/LES transition function dependent on grid spacing and the computed turbulence length scale. The second scheme is a modified version of the Partially Averaged Navier-Stokes (PANS) model, where the unresolved kinetic energy parameter (f(sub k)) is allowed to vary as a function of grid spacing and the turbulence length scale. This parameter is estimated based on a novel two-stage procedure to efficiently estimate the level of scale resolution possible for a given flow on a given grid for Partial Averaged Navier-Stokes (PANS). It has been found that the prescribed scale resolution can play a major role in obtaining accurate flow solutions. The parameter f(sub k) varies between zero and one and equal to one in the viscous sub layer, and when the RANS turbulent viscosity becomes smaller than the LES viscosity. The formulation, usage methodology, and validation examples are presented to demonstrate the enhancement of PAB3D's time-accurate and turbulence modeling capabilities. The accurate simulations of flow and turbulent quantities will provide valuable tool for accurate jet noise predictions. Solutions from these models are compared to RANS results and experimental data for high-temperature jet flows. The current results show promise for the capability of hybrid RANS/LES and PANS in simulating such flow phenomena.
Bottom-up urban energy models are crucial for understanding current energy use patterns and informing design strategies. However, accurately characterizing these models to represent different communities remains a challenge due to the extensive data needed for simulating existing energy use behavior. This data includes information related to human activities and building characteristics, all of which correlate with socioeconomic factors. To overcome this challenge, we developed an automated framework that utilizes both top-down and bottom-up data, to predict unknown building and occupant characteristics that are needed for more accurate and equitable modeling and analytics. Our framework, integrated into the URBANopt district energy modeling platform, uses statistical data models from ResStock. URBANopt models co-located buildings and neighborhoods. At this scale there are data gaps in building characteristic data, such as materials, insulation, occupancy, income, and energy usage of the buildings. To address this data gap, we use ResStock data, representative at the census tract scale, and develop machine-learning and deeplearning techniques to disaggregate it to individual buildings. By mapping unique occupant, building and economic properties to URBANopt energy models, we gain detailed insights into the variability of building energy use across different neighborhoods. This insight helps deploy technologies for co-located buildings and supports targeted upgrades for communities with unique economic and demographic characteristics, ensuring energy equity. Accurate characterization of energy models allows us to develop equitable strategies tailored to diverse neighborhoods, whether underserved or affluent. Our automated framework streamlines energy modeling and provides a reliable tool for building energy characterization.
With buildings contributing significantly to electricity usage, enabling demand flexibility becomes a challenge, especially when accounting for occupant comfort. This study introduces an innovative co-simulation framework integrating multiple models: heating, ventilation, and air conditioning (HVAC) system, building zone load, indoor airflow, supervisory control, and occupant comfort and behavior. Uniquely, this framework allows for a comprehensive and dynamic analysis of building systems and occupant interactions in demand response events. Using this framework, we conducted a case study using a typical small office building model. Specifically, we focused on three areas: (1) the impact of indoor airflow modeling on energy use, occupant comfort, and behaviors forecasting, (2) the impact of occupant behaviors on demand flexibility, and (3) occupant comfort and behaviors under demand response events. Key performance indicators such as energy use, flexibility factor, durations of occupant discomfort and occupant behaviors were analyzed. Our findings indicated variations in energy usage and occupant comfort within demand flexibility events, marked by uncertainty boundaries, with variability in demand shedding up to 57.9%. Here, we concluded that this framework is suitable for analyzing typical commercial buildings and their HVAC systems in terms of demand flexibility potential under the impact of occupant behaviors.
A low-cost and reliable system assesses the integrity of composite fan-containment structures. The system utilizes a network of miniature sensors integrated with the structure to scan the entire structural area for any impact events and resulting structural damage, and to monitor degradation due to usage. This system can be used to monitor all types of composite structures on aircraft and spacecraft, as well as automatically monitor in real time the location and extent of damage in the containment structures. This diagnostic information is passed to prognostic modeling that is being developed to utilize the information and provide input on the residual strength of the structure, and maintain a history of structural degradation during usage. The structural health-monitoring system would consist of three major components: (1) sensors and a sensor network, which is permanently bonded onto the structure being monitored; (2) integrated hardware; and (3) software to monitor in-situ the health condition of in-service structures.
Hydrodynamic testing at Los Alamos National Laboratory would benefit from a process improvement for the requirements process. Cameo was used as a digital solution for requirements management to allow Lead Engineers to track requirements more effectively. This was identified as a process improvement throughout this Capstone project. This report includes a project proposal, business case, literature review, methodology, project plan, data analysis, decision-making report, financial analysis report, discussion, and conclusion. Initially, this project focused on figuring out a solution for the hydrotest requirement process improvements. The scope narrowed to focus on the use of Cameo for requirement capture and management. During this Capstone, four tests had digital models produced for requirements management in Cameo. The initial model was the baseline, with core requirements used across the tests. Commonalities in tests were used and the core requirements allowed for process efficiencies. In the data analysis, it was seen that overall, the implementation of using Cameo for requirements resulted in a decreasing trend for both schedule and normalized cost. Tests have different complexity levels which is also a factor in how long the requirements process will take. Additional data is needed to continue analyzing process improvements. Through decisionmaking and financial analysis, the recommendation was to use Cameo for requirements process improvement. Multiple experts provided feedback for requirements that were then captured within models. Numerous tangible and intangible benefits were identified with this process improvement. For return on investment, the metric of success was schedule reduction, which was overall seen. Four tests were analyzed, so future analysis will be needed. There is also not a great financial risk because the main cost would be purchasing more licenses. Individuals must generate requirements whether using this software or not. Overall, training is needed to help improve the skillsets of Lead Engineers but is already being supported on a regular frequency. A desktop guide was started associated with explaining the process, however, it is a work in progress. The team plans on adding additional information in the digital models to help status when requirements are met using verification methods and artifacts. From working on this project an improved understanding of Cameo and requirements was the result. There are future opportunities to extend the usage for requirements management and progress will continue after this project.
The durability of platinum group metal-free (PGM-free) electrocatalysts is a major barrier to their usage in polymer electrolyte fuel cell cathodes. C and N removal from active sites may play an important role in the catalyst’s ability to maintain high activity. While C degradation mechanisms are kinetically controlled, previous studies have focused on thermodynamic descriptors. In this work, we develop a temperature-dependent kinetic descriptor of C and N stability using an electron beam-damage model. Our approach considers the electron beam energy threshold (EBET) describing the knock-on displacement of C and N atoms as a stability descriptor for atomic structures. The stability of different sites is calculated to be different showing this approach can discriminate between similar sites with varied configurations. Additionally, we provide important insight regarding TEM beam damage of proposed active sites. We calculate 60 keV electrons can damage some proposed active site structures even at room temperature.
Transitioning from fossil-fuel power generation to renewable energy generation and energy storage in remote locations has the potential to reduce both carbon emissions and cost. Here, this study presents a techno-economic analysis for implementation of a hybrid renewable energy system at the South Pole in Antarctica, which currently hosts several high-energy physics experiments with nontrivial power needs. A tailored model of resource availability and economics for solar photovoltaics, wind turbine generators, lithium-ion energy storage, and long-duration energy storage at this site is explored in different combinations with and without existing diesel energy generation. The Renewable Energy Integration and Optimization (REopt) platform is used to determine the optimal system component sizing and the associated system economics and environmental benefit. We find that the least-cost system includes all three energy generation sources and lithium-ion energy storage. For an example steady-state load of 170 kW, this hybrid system includes 180 kW-DC of photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery energy storage system. This system reduces diesel consumption by 95% compared to an all -diesel configuration, resulting in approximately 1200 metric tons of carbon footprint avoided annually. Over the course of a 15-year analysis period the reduced diesel usage leads to a net savings of 57 million United States dollars, with a time to payback of approximately two years. All the scenarios modeled show that the transition to renewables is highly cost effective under the unique economics and constraints of this extremely remote site.