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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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At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Characterization of Internal Insulation Thermal Performance

A test campaign was performed to build an SRM-insulation-material thermal-performance database using MSFC’s Solid Fuel Torch that could be leveraged to enhance understanding of the relationship between ablation rates and internal-environment parameters and improve insulation performance predictions. Seven hot-fire tests were performed along with data analysis that revealed the thermal performance differences among the tested materials. Functional Data Analysis was employed to create a model of material ablation rate as a function of six covariates. The relative importance of several environmental parameters was indicated, and the gaps in the operational space covered by the database were identified.

Ablation↗
NASA NTRS2024

Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVA on the Moon and Mars are expected to be higher compared to microgravity EVA currently conducted from the International Space Station (ISS). Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We used the applied cognitive task analysis method as outlined by Militello and Hutton (1998) and conducted interviews with subject matter experts (SMEs) in EVA at NASA Johnson Space Center in summer 2024. Interviews focused on exploration EVA and elicited feedback from SMEs on the cognitive skills required for specific EVA tasks including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis will inform ongoing work to identify the key cognitive domains for safe decision-making during exploration mission class EVA on the Moon and Mars. This work will importantly provide a link between cognitive domains identified in the cognitive task analysis and operationally-relevant performance metrics collected during spaceflight and simulation EVA that can be tracked and assessed by NASA mission operations. Ultimately, this work will aid in the development and application of exploration class cognitive performance monitoring capabilities to improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗
NASA NTRSDate not supplied

Overview of Rotor Hover Performance Capabilities at Low Reynolds Number for Mars Exploration

The Evolutionary aLgorithm for Iterative Studies of Aeromechanics (ELISA) software was developed in support of the Rotorcraft Optimization for the Advancement of Mars eXploration (ROAMX) project. ELISA was developed to enable aerodynamic rotor hover optimization for low Reynolds number flows in the Mars atmosphere. ELISA comprises two modules. The first module is dedicated to airfoil optimization and allows for the creation of multi-objective Pareto optimal (PO) airfoil sets with the airfoil performance evaluation performed using OVERFLOW. The second module is dedicated to rotor hover performance optimization and generates multi-objective PO rotor sets with the rotor performance evaluation performed using the comprehensive analysis code CAMRAD II. This paper presents recent updates to the ELISA optimization toolset. The airfoil module now includes variation in section Reynolds number, alongside simultaneous maximization of section lift and minimization of section drag. Consequently, the rotor optimization module can query PO airfoil sets (as a function of section lift, drag, and Reynolds number) and generate PO C81 decks tailored to specific Reynolds numbers, eliminating the need for adequate initial chord guesses and allowing for arbitrary rotor solidities to be studied. Furthermore, the rotor optimization has been extended to incorporate a third dimension, alongside maximization of blade loading and minimization of rotor power. This enables optimization across a relevant density range on Mars, presenting the lowest power rotor hover geometry, for each attainable blade loading, for each density. The goal of this work is to present the relevance of recent updates to the ELISA optimization toolset, by showing full rotor hover optimization using unconventional airfoils across a practical Mars density range, and by presenting various optimizations for changing blade numbers with unconstrained solidity in the Mars atmosphere.

Rotor↗
NASA NTRSDate not supplied

Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVAs on the Moon and Mars are expected to be higher compared to microgravity EVAs currently conducted from the International Space Station. Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We conducted a cognitive task analysis with 15 astronauts and subject matter experts in EVA operations and research. Interviews focused on surface exploration EVA and elicited feedback from experts on the cognitive skills required for specific EVA tasks, including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis informs ongoing exploration EVA for Moon to Mars. This work identifies the specific cognitive challenges that astronauts are likely to encounter during surface exploration EVA, and provides the foundation for: (1) prioritized and targeted cognitive performance measurement and functional performance tests, (2) EVA simulation design at varying levels of cognitive workload, and (3) the development of training and other technologies that can improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗
DOE OSTI2020

Geomancy: Automated Performance Enhancement through Data Layout Optimization

Large distributed storage systems such as high- performance computing (HPC) systems used by national or international laboratories require sufficient performance and scale for demanding scientific workloads and must handle shifting workloads with ease. Ideally, data is placed in locations to optimize performance, but the size and complexity of large storage systems inhibit rapid effective restructuring of data layouts to maintain performance as workloads shift. To address these issues, we have developed Geomancy, a tool that models the placement of data within a distributed storage system and reacts to drops in performance. Using a combination of machine learning techniques suitable for temporal modeling, Geomancy determines when and where a bottleneck may happen due to changing workloads and suggests changes in the layout that mitigate or prevent them. Our approach to optimizing throughput offers benefits for storage systems such as avoiding potential bottlenecks and increasing overall I/O throughput from 11% to 30%.

Bel, Oceane M.↗
DOE OSTI2020

A Visual Analytics Framework for Reviewing Streaming Performance Data

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.

Human-centered computing↗
DOE OSTI2022

A Knowledge-based Framework for Building Energy Model Performance Verification

Building energy modeling (BEM) has been widely used by researchers, regulators, and engineers to quantify building energy performance. Quality assurance (QA) and quality control (QC) of the model's performance are essential parts of such analysis. Currently, QA/QC is done in a manual and ad-hoc manner, which is tedious, error-prone, and time-consuming when QA/QC a large number of models. To solve these challenges, we propose a a dAta-driveN buIlding perforMance verificATion framEwork (ANIMATE), which conducts automated output-based verification of building operations requirements (especially for time-series output-based verification of control requirements). While this framework was developed for verifying energy model performance, it can be extended for other applications such as BEM software testing and performance verification of real buildings in the field.

Chen, Yan↗
DOE OSTI2023

Optimization of a Residential Air Source Heat Pump using Refrigerants with GWP <150 for Improved Performance and Reduced Emission

Using low-GWP refrigerants can reduce the Green House Gas (GHG) emission of heat pump systems. Heat exchangers and compressors are the key components and have a prominent impact on system performance, significant research is devoted to reducing the cost of the heat exchangers while achieving the same or better system performance with refrigerant charge reduction.To better understand the environmental impacts of optimized systems with low-GWP refrigerants, Life Cycle Climate Performance (LCCP) evaluation method was used to evaluate the direct and indirect emissions of the system over the course of its lifetime from manufacturing to disposal. The DOE/ORNL Heat Pump Design Model (HPDM) is used to evaluate the performance of heat pumps. Multi-objective optimizations using Particle Swarm Optimization (PSO) algorithm are performed on a 3-ton R410A residential air source heat pump on market. Seven R410A alternatives, i.e., R32, R454B, R454C, R455A, R457A, R1234yf and R1234ze(E) are investigated. The last five fluids have GWP lower than 150.As a result, 5.5%-12.8% seasonal energy efficiency ratio 2 (SEER2) improvement is achieved, and the optimized systems reduce life cycle CO2 emission by 8.5%-28.6% with GWP lower than 150 refrigerants. The optimal heat exchangers can fit into the original R410A fan-coil units; therefore, the proposed design method establishes a production and installation path to produce cost-effective low-GWP heat pumps easily accepted by end users.

Li, Zhenning↗
DOE OSTI2020

Modeling Interfaces to Support Low-Level Waste Disposal System Performance Assessments - 20366

In low-level waste (LLW) disposal sites, interfaces between cementitious materials, used as waste forms and/or engineered barriers, and the surrounding soil or backfill material are often encountered. Reactions across these interfaces may lead to chemical and structural alteration of the cementitious and backfill materials that may ultimately affect long-term performance. For example, the ingress of carbon dioxide from soil gas into a waste tank concrete shell at the Hanford Site or Savannah River Site (SRS) or a low-level waste disposal vault at SRS leads to carbonation of cement hydration products (e.g. portlandite, calcium-silicate hydrate or C-S-H, and ettringite). The result of carbonation is a decrease in pH in the cement paste portion of the tank wall or vault concrete structure that could ultimately lead to de-passivation of the embedded structural steel and, eventually, to cracking. Cracking can lead to increased ingress of water into the structure, corresponding increased release of constituents of concern, and to subsequent increased transport of these constituents to the surrounding environment. The mobility of trace constituents (e.g., radionuclides of concern) in waste forms may increase in response to changes in pH, pore structure, and mineralogical gradients. Depletion of portlandite results in subsequent decalcification of C-S-H that leads to changes in the cement strength and may lead to structural failure. At the boundary between waste forms and concrete vaults, the migration of sulfate ions from the salt waste form (e.g., the saltstone waste form used for LLW disposal at the Savannah River Site in Saltstone Disposal Units, or SDUs) into the barrier or vault concrete has been predicted to result in expansive mineral phase formation (or 'sulfate attack'). Expansive mineral formation could result in cracking and potential loss of structural integrity in the vault concrete. In the performance assessment (PA) for the SRS SDUs, prediction of both the carbonation and sulfate attack fronts are critical to assessing the long-term performance of these LLW disposal vaults. A general purpose geochemical reactive transport model has been developed in LeachXS/ORCHESTRA to evaluate typical interfaces for LLW disposal environments, including SDUs at SRS. The model may be used to predict the long-term performance of interfaces between cementitious materials and saltstone or backfill materials with respect to primary phases by considering mass transport, cement chemistry, geochemical speciation, and multi-ionic diffusion over interfaces between different materials. The present study compares the performance and interactions between vault concrete and saltstone waste form for a representative SDU scenario. The materials were characterized using pH-dependence leaching test (EPA 1313) and semi-dynamic transport tests (EPA 1315) with test results used to parameterize the interface model. The development of a credible inter model provides an important basis for supporting future PAs where carbonation and sulfate attack are important aging and degradation mechanisms. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Incorporating the Impacts of Climate Change on Hydrology in a Performance Assessment Model - 20403

The evidence of climate change is increasingly well-documented and impacts should be incorporated in performance assessment studies. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. Probabilistic modeling is a core requirement for quantifying uncertainty and evaluating its impacts. Not evaluating future climate states in a performance assessment because of the existence of uncertainty is contradictory to good modeling practices - the most uncertain issues and parameters require the most attention in effective probabilistic modeling. Excluding climate change limits development of modeling information that could aid in effective decision making. In this work we develop methods to use the output from hydrologic models and analysis of historical aerial imagery to quantify and implement the impacts of climate change on hydrologic processes at a nuclear waste site in West Valley, New York. Specifically, we used the HELP (Hydraulic Performance of Landfill Performance) model to characterize key hydrologic processes under both current and future climate conditions to assess the impacts of changing climate on hydrology. A suite of previous climatic models were reviewed and synthesized to produce a cohesive representation of the current state of knowledge of the impact of climate change on important model inputs such as precipitation. Output from the HELP simulations was coupled to the GoldSim model that was used to develop the Probabilistic Performance Assessment (PPA) approach through the application of a novel 'nearest neighbor' technique. First, several thousand realizations were generated from the HELP model using a Latin Hypercube experimental design to ensure adequate coverage of the parameter space of explanatory variables used to drive HELP. For example, porosity is a physical parameter that is used as an input to both HELP and the GoldSim PA model. We then ran sensitivity analysis (SA) algorithms on the output of HELP for each of the responses of interest. For each predictor, each time we build an SA model we get a different value for the sensitivity index (SI). From the collection of all the SA models, the average was computed among all of the SIs to represent the predictor within the context of the nearest neighbor approach. That is, we conduct SA on each HELP outcome for each scenario. This gives us parameter sensitivity indices for the outcomes. We average the parameter sensitivity indices across the outcomes to get the average SI for a scenario. For each realization that is generated from the Goldsim PA model, Goldsim generates random values for physical/empirical parameters that HELP uses as well. For each vector of physical/empirical parameters that Goldsim generates, the vector from the 5,000 HELP runs that is most 'similar' to the Goldsim vector is computed using the nearest neighbor approach. In this context 'similar' means minimization of the SA-weighted sum of the absolute differences among the 5,000 values computed for this statistic, where each value corresponds to a different HELP realization. In order to account for the impacts of climate change, this process was repeated using the spatially downscaled future climate projections. For each of the key parameters of interest, it was assumed that a linear change depicted the relationship between the values for the present day and those for 2100. In this way, the climatically-driven changes in key parameters used to inform the GoldSim model are quantified and incorporated into the PA model output for the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Development of a Conditioned System-Level Groundwater Model to Evaluate Long-Term Groundwater Impacts within a Performance Assessment - 20115

A preliminary performance assessment (PA) of single-shell tank Waste Management Area (WMA) A-AX located at the U.S. Department of Energy (DOE)'s Hanford Site in southeastern Washington is being conducted to satisfy the requirements of DOE Order 435.1 [1] as it relates to closure of the single-shell radioactive waste tanks in the WMA A-AX tank farms. A PA assesses the fate, transport, and impacts of radionuclides within a low-level radioactive waste disposal facility in its assumed closure configuration and the subsequent potential doses to humans over a 1,000-year compliance period and a 10,000-year performance evaluation period. The WMA A-AX preliminary PA evaluation is structured around the complementary use of process-level and system-level models to calculate the facility performance against established DOE Order 435.1 [1] performance objectives. Process-level models are those that represent a detailed phenomenological representation of processes of concern in the PA. Process models typically only represent one or a few of the components of the PA, such as groundwater flow and transport, and must be integrated with other modeling elements to perform PA calculations. System-level models are those that are abstracted from the process models, retaining the essential features of the process model, while allowing integration of all aspects of the PA in a single modeling framework. System-level models are often characterized by coarser numerical discretization, lower dimensionality, or other similar simplifications compared to the process-level model. Traditionally, a three-dimensional (3-D) process-level model is utilized primarily to evaluate the long-term impact on groundwater and the potential doses to individuals who consume contaminated groundwater. System-level models are also utilized to evaluate the groundwater pathway in PAs. These models typically have reduced dimensionality (1-D) and are conditioned utilizing flow fields (Darcy fluxes) and moisture content distributions that are abstracted from the process level models. The abstraction approach assures that the flow field in both models is consistent for a specific set of input parameters for flow, differing only in the discretization and dimensionality of the two models. The preliminary WMA A-AX PA incorporates a detailed representation of the geological system and hydraulic properties within the 3-D model STOMP{sup C} numerical code so that the effects of relevant features and processes on water flow and radionuclide transport in the subsurface can be evaluated. The complementary system-level model is developed utilizing the GoldSim{sup C} code to implement a simplified, 1-D equivalent model to represent the groundwater pathway. Contaminant transport through the vadose zone and unconfined aquifer for Tc-99 and I-129 were evaluated in each of the models. Adjustments to the saturated portion of the GoldSim{sup C} 1-D model were required to mimic the dispersion effect captured with the 3-D STOMP{sup C} model. Once this conditioning was conducted, highly similar results for the transport of Tc-99 and I-129 were achieved at the point of calculation, located 100 m downgradient from the WMA A-AX fenceline. These radionuclides represent elements that are regarded as primary dose drivers in the PA groundwater pathway analysis. The high degree of conformance between the two models suggests that the use of the equivalent 1-D system model is suitable for evaluating the full suite of radionuclides that will be released from the tank sources within WMA A-AX over the 1,000-year compliance period and over the 10,000-year evaluation period. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Current TRISO Fuel Performance Capabilities and Considerations for Expanded Operational Envelopes

Current TRISO Fuel Performance Capabilities and Considerations for Expanded Operational Envelopes. US-DOE TRISO Fuel Development, AGR Fuel Design and Performance Requirements, AGR Program Fuel Irradiations, UCO Fuel Performance Evaluation Results, Performance Limiting Phenomena, Coated-Particle-Fueled Reactor Concepts and Fuel Designs, Expanded Fuel Performance Envelope, and Accelerated Fuel Qualification.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Fuel Performance Evaluation of THOR-C Experiments

The Temperature Heatsink Overpower Response Commissioning (THOR-C) and THOR-Metal (THOR-M) experiments will be performed as part of an ongoing project for testing sodium fast reactor fuels with the Japan Atomic Energy Agency (JAEA). The THOR-C experiments consist of fresh metallic fuel pins and have been analyzed using the ABAQUS, Ansys codes and the BISON fuel performance code. THOR-M-Loss of Flow-1 (THOR-M-LOF-1) is designed to test an EBR-II irradiated fuel pin under LOF conditions. Simulation of the THOR-MLOF-1 experiment required first simulating the base irradiation of the fuel pin in EBR-II. MFUEL module of SAS4A/SASSYS-1 [1] is a physics-based metallic fuel performance model applicable to the normal operation, transient scenarios and fuel failure modeling including scenarios with bulk fuel melting. The model has been validated using EBR-II normal operation, separate effect transient tests as well as TREAT M-Series transient tests [2]. In this study, MFUEL models has been utilized together with a new capsule heat transfer model developed in this project. The new heat transfer model was necessary due to (1) significant amount of heat losses that required 2D heat transfer, (2) the presence of a titanium heat sink, rejecting a significant amount of heat, and (3) stagnant coolant conditions, which are inconsistent with SAS4A/SASSYS-1 (SAS) heat transfer model. Updates to SAS4A/SASSYS-1 and MFUEL has been described below, followed by a preliminary validation effort using the results from THOR-C-2 fresh fuel capsule experiment. A previous study for THOR-C-2 analysis using BISON code is also utilized in this study to model this test [3]. [1] D. O’Grady, A. J. Brunett, L. Ibarra, A. Karahan, T. Kim, T. S. Sumner, R. Thomas, T. H. Fanning, “The SAS4A/SASSYS-2 Version 5.7 Safety Analysis Code System,” Argonne National Laboratory,ANL/NSE-SAS/5.7, (2023). [2] A. Karahan, T. Kim, T. Fanning, D. O’Grady, “Validation of MFUEL Metal Fuel Performance Models of SAS4A/SASSYS-1,” Argonne National Laboratory, ANL/NSE-23/11, (2023). [3] M. Mihelish, A. Zabriskie, K. Paaren, P. Medvedev, C. Jensen, “Fuel Performance Predictions for the TREAT THOR-C Experiments,” Idaho National Laboratory, INL/RPT-23-73397, Revision 0, (2023)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Rancor-HUNTER: A Virtual Plant and Operator Environment for Predicting Human Performance

Advances in simulation capabilities to model physical systems have outpaced the development of simulations for humans using those physical systems. There is an argument that the infinite span of potential human behaviors inherently render human modeling more challenging than physical systems. Despite this challenge, the need for modeling humans interacting with these complex systems is paramount. As technologies have improved, many of the failure modes originating from the physical systems have been solved. This means the overall proportion of human errors has increased, such that it is not uncommon to be the primary driver of system failure in modern complex systems. Moreover, technologies such as automated systems may introduce emerging contexts that can cause new, unanticipated modes of human error. Therefore, it is now more important than ever to develop models of human behavior to realize overall system error reductions and achieve established safety margins. To support new and novel concepts of operations for the anticipated wave of advanced nuclear reactor deployments, human factors and human reliability analysis researchers need to develop advanced simulation-based approaches. This talk presents a simulation environment suitable to both collect data and then perform Monte Carlo simulations to evaluate human performance and develop better models of human behavior. Specifically, the Rancor Microworld Simulator models a complex energy production system in a simplified manner. Rancor includes computer-based procedures, which serve as a framework to automatically classify human behaviors without manual, subjective experimenter coding during scenarios. This method supports a detailed level of analysis at the task level. It is feasible for collecting large sample sizes required to develop quantitative modelling elements that have historically challenged traditional full-scope simulator study approaches. Additionally, the other portion of this experimental platform, the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), is presented to show how the collected data can be used to evaluate novel scenarios based on the contextual factors, or performance shaping factors, derived from Rancor simulations. Rancor-HUNTER is being used to predict operator performance with new procedures, such as results from control room modernization or new-build situations. Rancor-HUNTER is also proving a useful surrogate platform to model human performance for other complex systems.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1951

Effects of Altitude on Turbojet Engine Performance

Component and over-all performance characteristics of several turbojet engines investigated in the altitude facilities of the NACA Lewis Laboratory during the last several years are summarized to indicate the effects of altitude on turbojet engine performance. Data presented show that failure of turbojet engine performance to generalize for all altitudes can be traced to reductions in compressor efficiency, corrected air flow, and combustion efficiency at altitude. In addition, it is shown that although engines of different design may have equal thrusts at sea level, the thrusts at altitude may vary widely because of differences in compressor performance characteristics from one engine to another.

COMPRESSORS - EFFICIENCY↗
NASA NTRS2015

Survey and Assessment of Crew Performance Evaluation Methods Applicable to Human Spacecraft Design

Space is an unforgiving environment where the actions of the crew play a critical role in their health and safety. Given the limited number of crewmembers typically onboard a spacecraft and the multitude of complex systems they must operate, the performance of each individual is of paramount importance. Spacecraft habitat layout and operations are two main drivers affecting crew performance efficiency. Having the capability to analyze and compare crew performance across various spacecraft configurations can help identify improvements early in the conceptual design process where changes are less costly to implement, ultimately reducing overall project costs and improving long-term operations of the system. Currently, there are few comprehensive methods readily available for evaluating crew integration within a spacecraft in the conceptual design phase. In order to address this shortcoming, the goal of this work was to analyze various specialized evaluation methods found in analogous industries that have potential application to human spacecraft design. A survey of more than 400 human performance evaluation methods was completed. Over twenty different attributes were identified for each method and a variety of analyses were conducted to characterize and evaluate their potential use for assessing human spacecraft design options. The analysis revealed a particular deficiency of quantitative evaluation methods that are applicable early in the systems engineering design phase. It also identified five existing methods that could be supplemented to achieve the needs of an early design evaluation method. Additional discussion describes potential issues that must be overcome when developing a method specific for use in human spacecraft evaluations.

crew performance↗
DOE OSTI2020

Temperature-dependent Battery Performance of a Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT Cathode and In-situ Heat Generation on Cycling

Excellent structural stability, high operating voltage, and high capacity have made Na 3 V 2 (PO 4 ) 2 F 3 a promising cathode material for sodium-ion batteries. However, high-temperature battery performances and heat generation measurements have not been systematically reported yet. Carbon-coated Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT (multi-walled carbon nanotube) samples are fabricated by a hydrothermal-assisted sol-gel method and the electrochemical performances are evaluated at three different temperatures (25, 45, and 55 °C). Furthermore, the well-crystallized Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT samples exhibit good cycling stability at both low and high temperatures; they deliver an initial discharge capacity of 120–125 mAhg -1 at a 1 C rate with a retention of 53 % capacity after 1,400 cycles with 99 % columbic efficiency. The half-cell delivers a capacity of 100 mAhg -1 even at a high rate of 10 C at room temperature. Furthermore, the Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT samples show good long-term durability; the capacity loss is an average of 0.05 % per cycle at a 1 C rate at 55 °C. Additionally, ionic diffusivity and charge transfer resistance are evaluated as functions of state of charge, and they explain the high electrochemical performance of the Na 3 V 2 (PO 4 ) 2 F 3 @MWCNT samples. In-situ heat generation measurements reveal reversible results upon cycling owing to the high structural stability of the material. Excellent electrochemical performances are also demonstrated in the full-cell configuration with hard carbon as well as antimony Sb/C anodes.

25 ENERGY STORAGE↗
DOE OSTI2022

How Should Researchers Measure Perovskite-Based Monolithic Multijunction Solar Cells’ Performance? A Calibration Lab's Perspective

Perovskite multijunctions (PVSK MJs) have made remarkable progress with monolithic PVSK/PVSK tandems surpassing the efficiency of single-junction (1 J) PVSK cells and PVSK/Si cells reported to exceed the 30% efficiency mark. These efficiencies are reported at standard test conditions (STC), established by the photovoltaic (PV) community to facilitate comparison between devices and technologies. Herein, it is discussed why an accurate STC performance measurement for a MJ is more complicated than for a 1 J cell and the special aspects to be considered when measuring the current-voltage characteristics and the performance of PVSK-containing MJ cells are emphasized. It is discussed why a spectrally adjustable solar simulator is needed and the sequence of accurate performance measurement, namely measurement of the spectral response of each junction, adjustment of the spectrum, and appropriate protocols for measuring the power output of the device at STC, is presented. For all these, common errors and pitfalls that sometime lead to misleading interpretation of the results are presented, and the methods to evaluate the accuracy of the data when a spectrally adjustable solar simulator is not available are recommended. Finally, first step is taken toward recommending performance measurement approaches when high throughput is required as will eventually be in a production line.

14 SOLAR ENERGY↗