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At least 1,459 records · Page 81

Selecting Tasks for Evaluating Human Performance as a Function of Gravity

A challenge in understanding human performance as a function of gravity is determining which tasks to research. Initial studies began with treadmill walking, which was easy to quantify and control. However, with the development of pressurized rovers, it is less important to optimize human performance for ambulation as rovers will likely perform gross translation for them. Future crews are likely to spend much of their extravehicular activity (EVA) performing geology, construction and maintenance type tasks, for which it is difficult to measure steady-state-workloads. To evaluate human performance in reduced gravity, we have collected metabolic, biomechanical and subjective data for different tasks at varied gravity levels. Methods: Ten subjects completed 5 different tasks including weight transfer, shoveling, treadmill walking, treadmill running and treadmill incline walking. All tasks were performed shirt-sleeved at 1-g, 3/8-g and 1/6-g. Off-loaded conditions were achieved via the Active Response Gravity Offload System. Treadmill tasks were performed for 3 minutes with reported oxygen consumption (VO2) averaged over the last 2 minutes. Shoveling was performed for 3 minutes with metabolic cost reported as ml O2 consumed per kg material shoveled. Weight transfer reports metabolic cost as liters O2 consumed to complete the task. Statistical analysis was performed via repeated measures ANOVA. Results: Statistically significant metabolic differences were noted between all 3 gravity levels for treadmill running and incline walking. For the other 3 tasks, there were significant differences between 1-g and each reduced gravity, but not between 1/6-g and 3/8-g. For weight transfer, significant differences were seen between gravities in both trial-average VO2 and time-to-completion with noted differences in strategy for task completion. Conclusion: To determine if gravity has a metabolic effect on human performance, this research may indicate that tasks should be selected that require the subject to work vertically against the force of gravity.

Norcross, J. R.↗

Selecting Tasks for Evaluating Human Performance as a Function of Gravity

A challenge in understanding human performance as a function of gravity is determining which tasks to research. Initial studies began with treadmill walking, which was easy to quantify and control. However, with the development of pressurized rovers, it is less important to optimize human performance for ambulation as pressurized rovers will likely perform gross translation for them. Future crews are likely to spend much of their extravehicular activity (EVA) performing geology, construction,a nd maintenance type tasks. With these types of tasks, people have different performance strategies, and it is often difficult to quantify the task and measure steady-state metabolic rates or perform biomechanical analysis. For many of these types of tasks, subjective feedback may be the only data that can be collected. However, subjective data may not fully support a rigorous scientific comparison of human performance across different gravity levels and suit factors. NASA would benefit from having a wide variety of quantifiable tasks that allow human performance comparison across different conditions. In order to determine which tasks will effectively support scientific studies, many different tasks and data analysis techniques will need to be employed. Many of these tasks and techniques will not be effective, but some will produce quantifiable results that are sensitive enough to show performance differences. One of the primary concerns related to EVA performance is metabolic rate. The higher the metabolic rate, the faster the astronaut will exhaust consumables. The focus of this poster will be on how different tasks affect metabolic rate across different gravity levels.

Norcross, Jason R.↗

Design Optimization of Energized Composite Using Simulation and Experimental Methods

As electric vehicles (EVs) are evolving, innovative technologies like “energized composite” that can store energy in the car's body helps extend its range per charge. The composite's unique ability to function as both structural body panel and charge storage medium stems from its unique pattern design between “electrochemical areas (EcA)” and “epoxy area (EpA)”. Herein, a design optimization study is presented to obtain a balanced ratio between EcA versus EpA to maximize the charge storage ability of the composite while maintaining a decent tensile and bending strength. Simulations using ANSYS software and experimental confirmation using universal testing machines and electrochemical analyzers are used to derive optimum ratios between EcA and EpA. Uniaxial tension test and 3-point bend test have been performed to optimize the tensile and bend strengths, whereas cyclic voltammetry, galvanic charge–discharge, and electrochemical impedance spectroscopy are used to determine the electrochemical performance of various design configurations by modulating the ratios of EcA versus EpA. Overall, the highest achieved energy storage per lamina is 2531 mWh m−2 for a maximum of 81.6% EcA with a tensile strength of 417.73 MPa and bending strength of 263.13 MPa. This study is highly beneficial for EVs and aerospace applications.

energy storage↗

Testing and optimizing MST coaxial collinear arrays, part 6.4A

Many clear-air VHF wind profiles use coaxial collinear (COCO) arrays for their antenna. A COCO array is composed of long lines of half-wave dipoles spaced one-half wavelength apart. An inexpensive method of checking a COCO array is described and its performance is optimized by measuring and then correcting the relative rf phase among its lines at their feed point. This method also gives an estimate of the rf current amplitude among the lines. The strength and location of the sidelobes in the H-plane of the array can be estimated.

Warnock, J. M.↗

Optimization of Triply Periodic Minimal Surface Heat Exchanger to Achieve Compactness, High Efficiency, and Low-Pressure Drop

With advancements in additive manufacturing (AM) techniques, high-quality triply periodic minimal surface (TPMS) structures can now be produced. TPMS walled heat exchangers (HX) hold significant potential for industrial applications and are receiving increasing attention. This paper explores the impact of various TPMS design variables on flow and thermal performance to optimize TPMS heat exchangers for compactness, high efficiency, and low pressure drop. The design variables examined include the type of TPMS lattice, unit cell size, wall thickness, aspect ratio, TPMS orientation, and equivalent thickness. The study reveals that the flow and heat transfer performance of TPMS structures are significantly affected by these design variables. For the Gyroid, Diamond, and SplitP lattices, performance is nearly identical when the surface-to-volume ratio is kept constant. The average velocity of the fluid in the TPMS HX should be 0.3 m/s. The corresponding Re is between 300~800. Thin wall thickness, small equivalent thickness, and flat lattice configurations can significantly reduce pressure drop while maintaining the overall heat transfer coefficient. Additionally, the angle between the flow direction and TPMS orientation can increase pressure drop. Three aluminum heat exchangers were successfully printed using an AM machine, and testing results are comparable with theoretical prediction.

42 ENGINEERING↗

Atikokan Digital Twin: Machine learning in a biomass energy system

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

09 BIOMASS FUELS↗

Freedom of the mixer rotation axis improves performance in the quantum approximate optimization algorithm

Variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA) are particularly attractive candidates for implementation on near-term quantum processors. As hardware realities such as error and qubit connectivity will constrain achievable circuit depth in the near future, new ways to achieve high performance at low depth are of great interest. In this work, we present a modification to QAOA that adds additional variational parameters in the form of freedom of the rotation axis in the XY plane of the mixer Hamiltonian. Here, via numerical simulation, we show that this leads to a drastic performance improvement over standard QAOA at finding solutions to the MaxCut problem on graphs of up to seven qubits. Furthermore, we explore the Z-phase error mitigation properties of our modified Ansatz, its performance under a realistic error model for a neutral atom quantum processor, and the class of problems it can solve in a single round.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Parameter Optimization Toolbox for NS-3 network optimization, NS-3 Parameter Optimization Framework [SWR-18-60]

This simulation-based parameter optimization framework is proposed to tune parameters of different types of communication networks using ns-3 to achieve the optimal network performance. It consists of three main components: an ns-3 packet reporting module; a sampler running simulations with all possible parameter sets for the input parameter variables by using a parallel executor at each generation; and a hybrid optimization algorithm for tuning configurable parameters of hybrid designs and application parameter variables. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient descent function to quickly achieve an approximate globally optimum solution. This software is designed to be used in a multi-core processing Linux environment and run over a long duration of time. The execution time varies depending mainly upon the nature of the ns-3 configuration being simulated. This software includes a custom ns-3 QoS measurement application which must be included with the ns-3 source code during installation of the software.

Hasandka, Adarsh↗

Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms (Final Report)

Online tuning of particle accelerators is necessary in order to achieve optimal machine performance. However, it is also a major challenge due to the large parameter space which must be searched and the fact that many of the desir- able objectives compete with one another, and so will not reach their optimal values simultaneously. In order to mitigate these issues, we have explored using dimension-reduction techniques to reduce the size of the parameter space which must be searched and multi-objective genetic algorithms to obtain the sets of tuning parameters which provide pareto-optimal values for the objectives. These methods have enabled us to obtain improved values of the vertical emittance at the Cornell Electron Storage Ring (CESR), and to do so with greater control of orbit errors. Algorithmic tuning is a multidisciplinary endeavour, requiring expertise in beam dynamics, diagnostics, control systems and computer science, and thus a key practical problem is to formulate a common language in which experts with different specialties can communicate. We developed a solution to this problem in the form of a generic accelerator software interface that allows for rapid prototyping of optimization and control algorithms. Our interface is built on the Experimental Physics and Industrial Control Systems (EPICS) and makes possible testing control code in simulation before deployment on real accelerators, as well as deployment of third-party optimization code.

43 PARTICLE ACCELERATORS↗

Range Process Simulation Tool

Range Process Simulation Tool (RPST) is a computer program that assists managers in rapidly predicting and quantitatively assessing the operational effects of proposed technological additions to, and/or upgrades of, complex facilities and engineering systems such as the Eastern Test Range. Originally designed for application to space transportation systems, RPST is also suitable for assessing effects of proposed changes in industrial facilities and large organizations. RPST follows a model-based approach that includes finite-capacity schedule analysis and discrete-event process simulation. A component-based, scalable, open architecture makes RPST easily and rapidly tailorable for diverse applications. Specific RPST functions include: (1) definition of analysis objectives and performance metrics; (2) selection of process templates from a processtemplate library; (3) configuration of process models for detailed simulation and schedule analysis; (4) design of operations- analysis experiments; (5) schedule and simulation-based process analysis; and (6) optimization of performance by use of genetic algorithms and simulated annealing. The main benefits afforded by RPST are provision of information that can be used to reduce costs of operation and maintenance, and the capability for affordable, accurate, and reliable prediction and exploration of the consequences of many alternative proposed decisions.

Phillips, Dave↗

Experimental study of a tri-functional propane hydronic heat pump

This study investigates the performance of a propane hydronic heat pump system for space cooling, heating, and water heating applications, focusing on various operational conditions, with a total refrigerant charge below 1200 g. Cooling performance was evaluated at different outdoor temperatures, compressor stages (capacity levels), and water flow rates. The results showed that the coefficient of performance (COP) for cooling was higher at lower outdoor temperatures, with peak values exceeding 5.0 under low-stage operation and lower water flow rate. The cooling capacity increased with higher flow rates and compressor stages, reaching 12 kW. Space heating performance, evaluated at ambient temperatures ranging from −8.3 °C to 16.7 °C, revealed a decrease in COP with lower temperatures. However, high-stage operation maintained higher heating capacities even at low temperatures. The system demonstrated high Heating Seasonal Performance Factors (HSPF), exceeding 10.0. For water heating, the COP decreased with lower ambient temperatures and higher supply water temperatures. The system efficiently heated an 189-L water tank, outperforming conventional water heaters, with process COPs exceeding 4.0 under cooling-season conditions. Despite some efficiency loss due to defrosting at low temperatures, the propane heat pump consistently delivered strong performance. Seasonal energy efficiency ratios (SEER) and seasonal heating COP values indicated significant overall efficiency. The study concludes that propane is a promising refrigerant for heat pump systems, with future research needed to future reduce total system charge and optimize system performance through advanced controls and heat exchanger designs.

Hu, Yifeng [ORNL] (ORCID:0000000242875185)↗

Improving Efficacy and Safety of Pharmacological Treatment Through Precision Health and Pharmacogenomics

INTRODUCTION: Future spaceflight will require increased crew medical autonomy as exploration class missions expand in duration and distance from Earth, especially for Mars missions. As mission duration increases, it will be essential to have appropriate amounts of effective medication to ensure the maintenance of crew health and performance. Conversely, mass and volume constraints will become more severe as future spaceflight expands beyond low Earth orbit, where resupply is difficult or becomes impossible. These constraints thus convey an urgency to tailor medications for individual crewmembers and further examine appropriate dosing regimens. BACKGROUND: Precision Health is an exciting area of medicine focused on maintaining an individual’s health and performance through in-depth understanding of an individual’s unique clinical and environmental history, genetic makeup, and molecular profiles. This approach can be adapted to better predict, monitor, and address physiological responses to the spaceflight environment. A subset of this field is pharmacogenomics (PGX), the study of how the expressed genome impacts drug responses with the goal of prescribing the right dose of the right drug at the right time. Specifically, PGX testing provides valuable information on an individual’s precise allelic variations to guide physicians in making informed decisions on drug choice and dosing to avoid adverse events and maximize efficacy. The study goal was to identify which current space pharmacy drugs could be evaluated using PGX testing and to understand the potential impact on the health and wellness of the astronaut population. Additionally, we sought to evaluate clinically available FDA-approved PGX testing solutions to better understand its applicability. METHODS: A complete list of drugs on the ISS was analyzed for risk and likelihood of drug failure and PGX actionability. This analysis encompassed both astronauts’ personal medications, including supplements and over the counter drugs (n=151) contained in the ISS medical accessory kit (IMAK), and ISS MedKit formulary medications (n=95). Duplicate medications and different formulations were removed, which resulted in a total of 157 drugs used in the subsequent analysis. A 5x5 risk assessment table was produced by examining the likelihood of drug failure compared to the consequence of drug failure (LxC). Likelihood of individual drug failure was defined by whether existing processes are sufficient to prevent ineffective treatment or impactful side effect events, as ranked from 1 (very low, can easily be prevented) to 5 (very high, cannot be prevented) during a Mars mission. In contrast, the consequence of drug failure was defined by impact to safety, schedule, cost, or technical criteria and ranked from 1 (very low) to 5 (very high). An assessment of PGX reference laboratories is currently underway to evaluate sample requirements, benefit analysis (cost vs. utility of allele variant analysis), relevance to inflight medication usage, quality of reporting in enabling clinical application, and ease of integration into electronic medical records. RESULTS: Risk assessments (LxC 5x5 table) indicated 128 medications were in the green zone where risk is acceptable, with the remaining 29 of the medications in the yellow or red zone driven predominantly due to drug failure or safety concerns. We found that current PGX testing results could impact 21% of the total medications in the ISS MedKit and IMAK; of these, 9 medications currently have direct clinically actionable guidance available. Results of the clinical PGX solution evaluations as related to these medications will be presented. CONCLUSION: PGX testing has demonstrated clear benefits in terrestrial medicine and clinical environments for the selection of proper medications, avoiding adverse drug reactions, and maximizing drug efficacy. We propose that similar benefits would be bestowed on the astronaut and commercial spaceflight passenger population by performing preemptive preflight PGX testing to reduce risk of mission failure due to ineffective or toxic medications, improve drug efficacy, and further open the door to countermeasure research. For example, PGX results could allow tailoring of specific medications at optimal doses more precisely to each individual astronaut, particularly in areas of space motion sickness, sleep aids, and analgesics. An additional benefit is that PGX results could provide information for better planning of the components of a space pharmacy for deep space missions to be more effective and efficient in the utilization of limited pharmaceutical resources. Finally, while PGX testing of the astronaut corps is not currently conducted, this approach could provide immediate impact in support of mission success by reducing risks, optimizing astronaut performance, and providing valuable insights into long-term astronaut health. Such advancements in clinical decision making are important next steps in building dynamic individual risk profiles for astronauts, increasing selection of the best treatment choice, and providing tailored countermeasures for individual crewmembers.

Pharmacogenomics↗

Ducted Fuel Injection Provides Consistently Lower Soot Emissions in Sweep to Full-Load Conditions

Earlier studies have proven how ducted fuel injection (DFI) substantially reduces soot for low- and mid-load conditions in heavy-duty engines, without significant adverse effects on other emissions. Nevertheless, no comprehensive DFI study exists showing soot reductions at high- and full-load conditions. This study investigated DFI in a single-cylinder, 1.7-L, optical engine from low- to full-load conditions with a low-net-carbon fuel consisting of 80% renewable diesel and 20% biodiesel. Over the tested load range, DFI reduced engine-out soot by 38.1–63.1% compared to conventional diesel combustion (CDC). This soot reduction occurred without significant detrimental effects on other emission types. Thus, DFI reduced the severity of the soot–NO x tradeoff at all tested conditions. While DFI delivered considerable soot reductions in thepresent study, previous DFI studies at low- and mid-load conditions delivered larger soot reductions (>90%) compared to CDC operation at the same conditions. Therefore, the DFI configuration used here has been deemed nonoptimal (in terms of parameters such as the injector-spray and piston geometries), and several improvements are recommended for future studies with high-load DFI. Further, these improvements include employing better spray-duct alignment, a deeper piston bowl with a smaller injector umbrella angle, and a fuel injector that opens and closes faster. The study also suggests future research to make DFI ready for commercialization, such as metal-engine tests to ensure desirable DFI performance over an engine’s complete speed/load map. Overall, this study supports the continued development and commercialization of DFI to meet upcoming emissions regulations for heavy-duty vehicles. Specifically, multicylinder engine experiments and CFD simulations should be utilized to optimize the performance and clarify the full potential of DFI.

33 ADVANCED PROPULSION SYSTEMS↗

Simultaneous Aerodynamic Analysis and Design Optimization (SAADO) for a 3-D Flexible Wing

The formulation and implementation of an optimization method called Simultaneous Aerodynamic Analysis and Design Optimization (SAADO) are extended from single discipline analysis (aerodynamics only) to multidisciplinary analysis - in this case, static aero-structural analysis - and applied to a simple 3-D wing problem. The method aims to reduce the computational expense incurred in performing shape optimization using state-of-the-art Computational Fluid Dynamics (CFD) flow analysis, Finite Element Method (FEM) structural analysis and sensitivity analysis tools. Results for this small problem show that the method reaches the same local optimum as conventional optimization. However, unlike its application to the win,, (single discipline analysis), the method. as I implemented here, may not show significant reduction in the computational cost. Similar reductions were seen in the two-design-variable (DV) problem results but not in the 8-DV results given here.

Gumbert, Clyde R.↗

Simultaneous Aerodynamic Analysis and Design Optimization (SAADO) for a 3-D Flexible Wing

The formulation and implementation of an optimization method called Simultaneous Aerodynamic Analysis and Design Optimization (SAADO) are extended from single discipline analysis (aerodynamics only) to multidisciplinary analysis - in this case, static aero-structural analysis and applied to a simple 3-D wing problem. The method aims to reduce the computational expense incurred in performing shape optimization using state-of-the-art Computational Fluid Dynamics (CFD) flow analysis, Finite Element Method (FEM) structural analysis and sensitivity analysis tools. Results for this small problem show that the method reaches the same local optimum as conventional optimization. However, unlike its application to the rigid wing (single discipline analysis), the method, as implemented here, may not show significant reduction in the computational cost. Similar reductions were seen in the two-design-variable (DV) problem results but not in the 8-DV results given here.

Gumbert, Clyde R.↗

Aerostructural Level Set Topology Optimization for a Common Research Model Wing

The purpose of this work is to use level set topology optimization to improve the design of a representative wing box structure for the NASA common research model. The objective is to minimize the total compliance of the structure under aerodynamic and body force loading, where the aerodynamic loading is coupled to the structural deformation. A taxi bump case was also considered, where only body force loads were applied. The trim condition that aerodynamic lift must balance the total weight of the aircraft is enforced by allowing the root angle of attack to change. The level set optimization method is implemented on an unstructured three-dimensional grid, so that the method can optimize a wing box with arbitrary geometry. Fast matching and upwind schemes are developed for an unstructured grid, which make the level set method robust and efficient. The adjoint method is used to obtain the coupled shape sensitivities required to perform aerostructural optimization of the wing box structure.

Dunning, Peter D.↗

Demonstration of MgCr 2– x Mn x O 4 Spinel Oxide Cathodes in High-Voltage Mg Batteries

Solid-solution oxide spinels with high redox voltages and facile Mg 2+ mobility have been identified as promising candidates for practical, high-voltage cathodes in Mg batteries. In this work, we discuss the development of MgCr 2-x Mn x O 4 [x = 0.5, 1, 1.2] solid-solution spinel oxides as a cathode material and their electrochemical performance paired with an Mg anode in a full cell. This work presents the first demonstration of full cells with these materials. Mg-Cr-Mn spinel oxides with varying Cr and Mn contents were synthesized using alternative synthetic routes for optimal electrochemical performance. High-resolution synchrotron powder X-ray diffraction (PXRD), solid-state nuclear magnetic resonance (NMR) spectroscopy, and electron microscopy showed that these different synthetic routes resulted in changes in structures and particle morphologies, which in turn affect the electrochemical performance. Particularly, the urea coprecipitation synthetic route resulted in high-surface-area particles that enabled lower overpotentials and increased discharge capacity. The high surface area also resulted in expedited structural degradation caused by the irreversible migration of Mg 2+ into normally vacant 16c sites in the spinel lattice. This structural degradation was lessened by using a hydrosauna-urea synthesis method, which decreased the Mg/Mn inversion ratio while retaining high-surface-area particles with good cycling performance. Furthermore, our findings highlight the necessity for high surface area or nanostructured spinel oxide cathodes with minimized Mg-Mn inversion to enable spinel oxide cathodes in Mg full cells.

Mg anode↗