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Analysis on the effectiveness of instruction tuned embedding models for the reduction of Artificial Intelligence hallucinations compared to the standard embedding models.
A finite element NASTRAN model of the complete forward rotor transmission housing for the Boeing Vertol CH-47 helicopter was developed and applied to reduce transmission vibration/noise at its source. In addition to a description of the model, a technique for vibration/noise prediction and reduction is outlined. Also included are the dynamic response as predicted by NASTRAN, test data, the use of strain energy methods to optimize the housing for minimum vibration/noise, and determination of design modifications which will be manufactured and tested. The techniques presented are not restricted to helicopters but are applicable to any power transmission system. The transmission housing model developed can be used further to evaluate static and dynamic stresses, thermal distortions, deflections and load paths, fail-safety/vulnerability, and composite materials.
Electrification and use of renewable hydrogen is currently a necessity for decarbonizing the iron-and-steel industry. In this regard, hydrogen plasma smelting reduction (HPSR) is a novel pathway that is being explored for reduction of iron ore. HPSR provides several decarbonization merits compared to conventional blast furnaces. Firstly, the use of renewable hydrogen drastically reduces the CO2 emissions compared to the use of coke. Secondly, renewable electricity in the form of a thermal plasma for making reactive hydrogen species (radicals, ions) are more efficient at reducing iron ore compared to neutral H2. Thirdly, a molten product compatible with downstream processes is obtained from the intense heat transfer from the plasma. However, the scale-up of this technology requires fundamental exploration of hydrogen plasma dynamics and its interaction with complex solid material that include phase changing iron-ore and slag. In this work, we present a first principles continuum scale model for thermal plasmas in Ar/H2 gas mixtures typically used for HPSR. The thermal plasma governing equations for mass, momentum and energy with Lorentz force and Joule heating source terms are solved along with electromagnetic equations for electrostatic and magnetic vector potential. Our solver will be based on Pele, a suite of reacting flow solvers designed for advanced scientific computing architectures (Henry De Frahan et al., Proceedings of SIAM Parallel Processing, 13-25, 2024), and will utilize adaptive mesh generation for enhanced resolutions at locations of intense physicochemical interactions. This study will present the impact of Ar to H2 ratios on excited/dissociated hydrogen species concentrations, plasma temperature and conductivity along with the impact of outgassed species (water, metal vapor, O, OH radicals) from ore surface on gas phase chemistry. Furthermore, the heat and species flux to the surface will be quantified as a function of applied voltages in a transferred arc configuration.
Abstract. This study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. The goal of this study was to determine whether MFSE can use multiple models of varying cost and accuracy to reduce the computational cost of estimating the mean and variance of the projected mass change of a glacier. The problem size and complexity were chosen to reflect the challenges posed by future continental-scale studies while still facilitating a computationally feasible investigation of MFSE methods. When quantifying uncertainty introduced by a high-dimensional parameterization of the basal friction field, MFSE was able to reduce the mean-squared error in the estimates of the statistics by well over an order of magnitude when compared to a single-fidelity approach that only used the highest-fidelity model. This significant reduction in computational cost was achieved despite the low-fidelity models used being incapable of capturing the local features of the ice-flow fields predicted by the high-fidelity model. The MFSE algorithms were able to effectively leverage the high correlation between each model's predictions of mass change, which all responded similarly to perturbations in the model inputs. Consequently, our results suggest that MFSE could be highly useful for reducing the cost of computing continental-scale probabilistic projections of sea-level rise due to ice-sheet mass change.
In the domain of deep learning (DL), dimension reduction is crucial for enhancing training efficiency and mitigating overfitting, particularly when managing complex data such as three-dimensional (3D) saturation data. The 3D saturation data in the context of geological carbon storage (GCS) presents unique challenges due to its inherent sparsity and the abrupt transitions at plume boundaries, known as shock fronts. To address the challenges, we proposed a novel DL framework that integrates dimension reduction with advanced 3D reconstruction techniques. Our model leveraged latent variables derived from 2D average saturation data, offering a robust and efficient solution tailored to the intricate dynamics of 3D saturation fields. The proposed framework can extract the critical features of the high-dimensional data while reducing the variable numbers, which is more tractable for DL models and enhances the model robustness and accuracy. Therefore, it provides a novel approach for modeling and analyses in complex geological scenarios, which finds great potential applications in environmental monitoring and energy storage.
The importance of global geomagnetic field models for the reduction of magnetic surveys is discussed. It is demonstrated that a numerical model with adequate secular variation correction, provides a suitable representation of the regional field. The limitations of the presently available models are reported, with emphasis on the International Geomagnetic Reference Field.
Quantitative criteria are presented for model simplification, or order reduction, such that the reduced order model may be used to synthesize and evaluate a control law, and the stability and stability robustness obtained using the reduced-order model will be preserved when controlling the full-order system. The error introduced due to model simplification is treated as modeling uncertainty, and some of the results from multivariable robustness theory are brought to bear on the model simplification problem. A numerical procedure developed previously is shown to lead to results that meet the necessary criteria. The procedure is applied to reduce the model of a flexible aircraft. Also, the importance of the control law itself, in meeting the modeling criteria, is underscored. An example is included that demonstrates that an apparently robust control law actually amplifies modest modeling errors in the critical frequency region, and leads to undesirable results. The cause of this problem is identified to be associated with the canceling of lightly-damped transmission zeroes in the plant.
The marine data set archived at the National Geophysical Data Center (NGDC) consists of shipborne surveys conducted by various institutes worldwide. This data set spans four decades (1953, 1958, 1960-1987), and contains almost 13 million total intensity observations. These are often less than 1 km apart. These typically measure seafloor spreading anomalies with amplitudes of several hundred nanotesla (nT) which, since they originate in the crust, interfere with main field modeling. The source for these short wavelength features are confined within the magnetic crust (i.e., sources above the Curie isotherm). The main field, on the other hand, is of much longer wavelengths and originates within the earth's core. It is desirable to extract the long wavelength information from the marine data set for use in modeling the main field. This can be accomplished by averaging the data along the track. In addition, those data which are measured during periods of magnetic disturbance can be identified and eliminated. Thus, it should be possible to create a data set which has worldwide data distribution, spans several decades, is not contaminated with short wavelengths of the crustal field or with magnetic storm noise, and which is limited enough in size to be manageable for the main field modeling. The along track filtering described above has proved to be an effective means of condensing large numbers of shipborne magnetic data into a manageable and meaningful data set for main field modeling. Its simplicity and ability to adequately handle varying spatial and sampling constraints has outweighed consideration of more sophisticated approaches. This filtering technique also provides the benefits of smoothing out short wavelength crustal anomalies, discarding data recorded during magnetically noisy periods, and assigning reasonable error estimates to be used in the least square modeling. A useful data set now exists which spans 1953-1987.
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Airframe noise is a significant part of the overall noise of transport aircraft during the approach and landing phases of flight. Airframe noise reduction is currently emphasized under the Environmentally Responsible Aviation (ERA) and Fixed Wing (FW) Project goals of NASA. A promising concept for trailing-edge-flap noise reduction is a flexible structural element or link that connects the side edges of the deployable flap to the adjacent main-wing structure. The proposed solution is distinguished by minimization of the span-wise extent of the structural link, thereby minimizing the aerodynamic load on the link structure at the expense of increased deformation requirement. Development of such a flexible structural link necessitated application of hyperelastic materials, atypical structural configurations and novel interface hardware. The resulting highly-deformable structural concept was termed the FLEXible Side Edge Link (FLEXSEL) concept. Prediction of atypical elastomeric deformation responses from detailed structural analysis was essential for evaluating feasible concepts that met the design constraints. The focus of this paper is to describe the many challenges encountered with hyperelastic finite element modeling and the nonlinear structural analysis of evolving FLEXSEL concepts. Detailed herein is the nonlinear analysis of FLEXSEL concepts that emerged during the project which include solid-section, foamcore, hollow, extended-span and pre-stressed concepts. Coupon-level analysis performed on elastomeric interface joints, which form a part of the FLEXSEL topology development, are also presented.
Achieving full decarbonization of all economic sectors remains a challenge, especially in niche markets. For example, remote communities and industrial or mining activities detached from the main electric grid heavily rely on fossil fuels, similar to urban and industrial microgrids with combined heat and power needs. A combination of renewables and energy storage is often not suitable due to cost, reliability, intermittency, and large storage requirements. Small nuclear reactors with a flexible purpose could serve these applications. Microreactors (MR) are a class of reactors that are compact, factory manufactured, transportable, and self-regulating. Typically, they generate much less power than their large reactor counterparts. The main advantages of microreactors include the versatile nature of the energy produced, the reliability of supply, and freedom from having to transport and store large quantities of fuels on-site, coupled with the absence of dependence on an electrical grid. A strong business case is needed to move from the microreactor prototype to the commercialization phase. In fact, fossil fuels are still relatively inexpensive, and in the near term, carbon credits will be available to virtually compensate for emissions. For microreactors, one of the main costs in operation and maintenance (O&M) is their staffing levels. In this study, we investigate how to optimize the number (and thus the cost) of workers, moving from a traditional, fully manned, on-site personnel approach to an unmanned, remote personnel approach. We examine four different staffing models that can be implemented as the technology matures and evolves. We estimate the staffing needs of each model and build a business case to justify the substitution of on-site personnel with adequate technologies. To do so, we propose a cost model to quantify potential cost reductions from automating O&M activities. The model accounts for both the reduction in cost derived from the reduced number of full-time-equivalent (FTE) employees and the increase in cost derived from the need to buy new control hardware as needed. Applying the cost model that we created to different scenarios, an on-site O&M cost reduction exceeding 80% can be expected. Additionally, we found that it is more impactful to focus on automating routine O&M tasks rather than attempting to automate transient management (shutdowns, restarts, monitoring condition deviations). In fact, transients typically account for less than 1% of the total FTE time spent on the reactors.
...This paper presents results from a demonstration of a new sequential filter model and data reduction technique, dubbed the enhanced filter, performed with radio tracking data obtained from the Galileo spacecraft.
Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results.
Water heating accounts for about 18% of a typical US home’s energy use. Modern water heaters have enabled control options through APIs, offering customers the opportunity to reduce their energy cost and peak demand by dynamically adjusting settings. A water heater’s capacity to store energy using its storage tank makes it an asset for peak demand reduction and energy cost savings. For this reason, a mixed-integer linear programming model is proposed to minimize the energy cost of a high-performance water heater while also reducing the peak demand of the residential household under a time-of-use utility rate by dynamically changing the water heater’s running mode. Specifically, a multi-objective optimization model is formulated to determine the mode settings of the water heater considering hot water use, time-of-use rate, and peak demand limit of the residential household. The mode settings are associated with different dead bands of water temperature for triggering on/off action of the heat pump and heating element. A 66-gal hybrid electric high performance water heater was used for numerical simulation and practical experiments. The simulation results were well aligned with measurements of practical experiments, validating the soundness of the thermodynamic model. In addition, reductions of energy cost, enabling affordability, and reducing peak demand are demonstrated. The research team also developed a software framework with dashboards to automatically and continuously monitor and manage devices.
A wind tunnel experiment was conducted in the NASA Glenn Research Center anechoic 9- by 15-Foot Low-Speed Wind Tunnel to investigate two new advanced noise reduction technologies in support of the NASA Fundamental Aeronautics Program Subsonic Fixed Wing Project. The goal of the experiment was to demonstrate the noise reduction potential and effect on fan model performance of the two noise reduction technologies in a scale model Ultra-High Bypass turbofan at simulated takeoff and approach aircraft flight speeds. The two novel noise reduction technologies are called Over-the-Rotor acoustic treatment and Soft Vanes. Both technologies were aimed at modifying the local noise source mechanisms of the fan tip vortex/fan case interaction and the rotor wake-stator interaction. For the Over-the-Rotor acoustic treatment, two noise reduction configurations were investigated. The results showed that the two noise reduction technologies, Over-the-Rotor and Soft Vanes, were able to reduce the noise level of the fan model, but the Over-the-Rotor configurations had a significant negative impact on the fan aerodynamic performance; the loss in fan aerodynamic efficiency was between 2.75 to 8.75 percent, depending on configuration, compared to the conventional solid baseline fan case rubstrip also tested. Performance results with the Soft Vanes showed that there was no measurable change in the corrected fan thrust and a 1.8 percent loss in corrected stator vane thrust, which resulted in a total net thrust loss of approximately 0.5 percent compared with the baseline reference stator vane set.
In the Hemispheric Transport of Air Pollution Phase 2 (HTAP2) exercise, a range of global atmospheric general circulation and chemical transport models performed coordinated perturbation experiments with 20% reductions in emissions of anthropogenic aerosols, or aerosol precursors, in a number of source regions. Here, we compare the resulting changes in the atmospheric load and vertically resolved profiles of black carbon (BC), organic aerosols (OA) and sulfate (SO4/ from 10 models that include treatment of aerosols. We use a set of temporally, horizontally and vertically resolved profiles of aerosol forcing efficiency (AFE) to estimate the impact of emission changes in six major source regions on global radiative forcing (RF) pertaining to the direct aerosol effect, finding values between. 51.9 and 210.8mW/sq m/Tg for BC, between -2.4 and -17.9mW/sq m/Tg for OA and between -3.6 and -10.3W/sq m/Tg for SO4. In most cases, the local influence dominates, but results show that mitigations in south and east Asia have substantial impacts on the radiative budget in all investigated receptor regions, especially for BC. In Russia and the Middle East, more than 80 % of the forcing for BC and OA is due to extra-regional emission reductions. Similarly, for North America, BC emissions control in east Asia is found to be more important than domestic mitigations, which is consistent with previous findings. Comparing fully resolved RF calculations to RF estimates based on vertically averaged AFE profiles allows us to quantify the importance of vertical resolution to RF estimates. We find that locally in the source regions, a 20% emission reduction strengthens the radiative forcing associated with SO4 by 25% when including the vertical dimension, as the AFE for SO4 is strongest near the surface. Conversely, the local RF from BC weakens by 37% since BC AFE is low close to the ground. The fraction of BC direct effect forcing attributable to intercontinental transport, on the other hand, is enhanced by one-third when accounting for the vertical aspect, because long-range transport primarily leads to aerosol changes at high altitudes, where the BC AFE is strong. While the surface temperature response may vary with the altitude of aerosol change, the analysis in the present study is not extended to estimates of temperature or precipitation changes.
In this CRADA project, Lawrence Livermore National Laboratory, Stanford University, SLAC National Laboratory, and TotalEnergies collaboratively executed a multidisciplinary investigation of electrochemical reduction of CO2 to produce sustainable fuels and chemicals. Overall, the project led to an increased understanding of the fundamental processes involved in CO2 electrolysis, from the atomistic scale to the full electrolyzer device scale, ultimately leading to design guidelines for CO2 electrolyzers that will help in their future commercialization. As the model systems, Ag- and Cu-based catalysts were investigated in various forms depending on the electrochemical platform that was utilized to study the activity, selectivity, and durability towards electrochemical CO2 reduction. By employing experimental, theoretical, and computational techniques, the project team experimentally validated multi-physics models, evaluated the experimental levers that lead to increased electrolyzer reaction selectivity and energy efficiency, and used computational optimization to design higher performance electrodes. The learnings of this project were extensively documented in publicly available peer-reviewed journal publications and conference presentations, which serve as a foundation for further work to build from.