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Tuning the magnetocaloric response in half-Heusler/Heusler MnNi 1 + x Sb solid solutions
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Ionic Tuning of Cobaltites at the Nanoscale
Control of materials through custom design of ionic distributions is one of the most promising approaches for developing future technologies ranging from spintronic logic and memory devices to energy storage. Perovskites have shown particular promise for ionic-devices due to their high ion mobility and sensitivity to chemical stoichiometry. In this work, we demonstrate control of ion distributions in (La,Sr)CoO3. Commensurate with control of the oxygen stoichiometry, the Co valence and saturation magnetization show a smooth continuous variation. In contrast, magnetoresistance measurements show a sharp, step-wise transition in the magnetic anisotropy and resistivity over the same range of oxygen stoichiometry. These results suggest significant phase separation, with conductive magnetic regions and oxygen-deficient, insulating, non-magnetic regions, forming percolated networks. Indeed, X-ray diffraction identifies oxygen vacancy ordering, including the emergence of Brownmillerite ordering. The unusual appearance of the Brownmillerite phase under ambient temperature is further confirmed by high resolution scanning transmission electron microscopy. This work demonstrates room-temperature ionic control of magnetism, electrical conductivity and crystalline structure, in a 40 nm film, presenting new opportunities for ionic devices that leverage multiple material properties.
Dakota and Pyomo for Closed and Open Box Controller Gain Tuning.
Abstract not provided.
Detection of regional infrasound signals using array data: Testing, tuning, and physical interpretation
This work quantifies the physical characteristics of infrasound signal and noise, assesses their temporal variations, and determines the degree to which these effects can be predicted by time-varying atmospheric models to estimate array and network performance. An automated detector that accounts for both correlated and uncorrelated noise is applied to infrasound data from three seismo-acoustic arrays in South Korea (BRDAR, CHNAR, and KSGAR), cooperatively operated by Korea Institute of Geoscience and Mineral Resources (KIGAM) and Southern Methodist University (SMU). Arrays located on an island and near the coast have higher noise power, consistent with both higher wind speeds and seasonably variable ocean wave contributions. On the basis of the adaptive F-detector quantification of time variable environmental effects, the time-dependent scaling variable is shown to be dependent on both weather conditions and local site effects. Significant seasonal variations in infrasound detections including daily time of occurrence, detection numbers, and phase velocity/azimuth estimates are documented. These time-dependent effects are strongly correlated with atmospheric winds and temperatures and are predicted by available atmospheric specifications. As a result, this suggests that commonly available atmospheric specifications can be used to predict both station and network detection performance, and an appropriate forward model improves location capabilities as a function of time.
Notch-Jagged complex structure implicates a catch bond in tuning ligand sensitivity
Notch receptor activation initiates cell fate decisions and is distinctive in its reliance on mechanical force and protein glycosylation. The 2.5-angstrom-resolution crystal structure of the extracellular interacting region of Notch1 complexed with an engineered, high-affinity variant of Jagged1 (Jag1) reveals a binding interface that extends ~120 angstroms along five consecutive domains of each protein. O-Linked fucose modifications on Notch1 epidermal growth factor–like (EGF) domains 8 and 12 engage the EGF3 and C2 domains of Jag1, respectively, and different Notch1 domains are favored in binding to Jag1 than those that bind to the Delta-like 4 ligand. Jag1 undergoes conformational changes upon Notch binding, exhibiting catch bond behavior that prolongs interactions in the range of forces required for Notch activation. In conclusion, this mechanism enables cellular forces to regulate binding, discriminate among Notch ligands, and potentiate Notch signaling.
Spectral aspects of cavity tuned absorption in organic photovoltaic films
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Tuning the transmission lineshape of a photonic-crystal slab guided resonance mode by polarization control
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Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers
In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.
Tuning the DARHT Axis-II linear induction accelerator focusing
Flash radiography of large hydrodynamic experiments driven by high explosives is a well-known diagnostic technique in use at many laboratories, and the Dual-Axis Radiography for Hydrodynamic Testing (DARHT) facility at Los Alamos produces flash radiographs of large hydrodynamic experiments. Two linear induction accelerators (LIAs) make the bremsstrahlung radiographic source spots for orthogonal views of each test. The 2-kA, 20-MeV Axis-I LIA creates a single 60-ns radiography pulse. The 1.7-kA, 16.5-MeV Axis-II LIA creates up to four radiography pulses by kicking them out of a longer pulse that has a 1.6-μs flattop. The Axis-II injector, LIA, kicker, and downstream transport (DST) to the bremsstrahlung converter are described. Adjusting the magnetic focusing and steering elements to optimize the electron-beam transport through an LIA is often called 'tuning.' As in all high-current LIAs, the focusing field is designed to be as close to that of the ideal continuous solenoid as physically possible. In ideal continuous solenoidal transport a smoothly varying beam size can easily be found for which radial forces balance, and the beam is said to be 'matched' to the focusing field. A 'mismatched' beam exhibits unwanted oscillations in size, which are a source of free energy that contributes to emittance growth. This is undesirable, because in the absence of beam-target effects, the radiographic spot size is proportional to the emittance. Tuning the Axis-II LIA is done in two steps. First, the solenoidal focusing elements are set to values designed to provide a matched beam with little or no envelope oscillations, and little or no beam-breakup (BBU) instability growth. Then, steering elements are adjusted to minimize the motion of the centroid of a well-centered beam at the LIA exit. This article only describes the design of the tune for the focusing solenoids. The DARHT Axis-II LIA was required to be re-tuned after installing an accelerator cell to replace a failed solenoid in March of 2012. We took advantage of this opportunity to improve the design of the focusing tune with better models of the remaining partially failed solenoids, better estimates of beam initial conditions, and better values for pulsed-power voltages. As with all previous tunes for Axis-II, this one incorporates measures to mitigate beam-breakup (BBU) instability, image displacement instability (IDI), corkscrew (sweep), and emittance growth. Section II covers the general approach to of design of focusing solenoid tunes for the DARHT Axis-2 LIA. Section III explains the specific requirements and simulations needed to design the tune for the injector, which includes the thermionic electron source, diode, and six induction cells. Section IV explains the requirements and simulations for tuning the main accelerator, which consists of 68 induction cells. Finally, Section V explores sensitivity of the tune to deviations of parameters from nominal, random variations, and uncertainties in values. Four appendices list solenoid settings for this new tune, discuss comparisons of different simulation codes, show halo formation in mismatched beams, and present a brief discussion of the beam envelope equation, which is the heart of the method used to design LIA solenoid tunes.
Tuning and Robustness Analysis for the Orion Absolute Navigation System
The Orion Multi-Purpose Crew Vehicle (MPCV) is currently under development as NASA's next-generation spacecraft for exploration missions beyond Low Earth Orbit. The MPCV is set to perform an orbital test flight, termed Exploration Flight Test 1 (EFT-1), some time in late 2014. The navigation system for the Orion spacecraft is being designed in a Multi-Organizational Design Environment (MODE) team including contractor and NASA personnel. The system uses an Extended Kalman Filter to process measurements and determine the state. The design of the navigation system has undergone several iterations and modifications since its inception, and continues as a work-in-progress. This paper seeks to show the efforts made to-date in tuning the filter for the EFT-1 mission and instilling appropriate robustness into the system to meet the requirements of manned space ight. Filter performance is affected by many factors: data rates, sensor measurement errors, tuning, and others. This paper focuses mainly on the error characterization and tuning portion. Traditional efforts at tuning a navigation filter have centered around the observation/measurement noise and Gaussian process noise of the Extended Kalman Filter. While the Orion MODE team must certainly address those factors, the team is also looking at residual edit thresholds and measurement underweighting as tuning tools. Tuning analysis is presented with open loop Monte-Carlo simulation results showing statistical errors bounded by the 3-sigma filter uncertainty covariance. The Orion filter design uses 24 Exponentially Correlated Random Variable (ECRV) parameters to estimate the accel/gyro misalignment and nonorthogonality. By design, the time constant and noise terms of these ECRV parameters were set to manufacturer specifications and not used as tuning parameters. They are included in the filter as a more analytically correct method of modeling uncertainties than ad-hoc tuning of the process noise. Tuning is explored for the powered-flight ascent phase, where measurements are scarce and unmodelled vehicle accelerations dominate. On orbit, there are important trade-off cases between process and measurement noise. On entry, there are considerations about trading performance accuracy for robustness. Process Noise is divided into powered flight and coasting ight and can be adjusted for each phase and mode of the Orion EFT-1 mission. Measurement noise is used for the integrated velocity measurements during pad alignment. It is also used for Global Positioning System (GPS) pseudorange and delta- range measurements during the rest of the flight. The robustness effort has been focused on maintaining filter convergence and performance in the presence of unmodeled error sources. These include unmodeled forces on the vehicle and uncorrected errors on the sensor measurements. Orion uses a single-frequency, non-keyed GPS receiver, so the effects due to signal distortion in Earth's ionosphere and troposphere are present in the raw measurements. Results are presented showing the efforts to compensate for these errors as well as characterize the residual effect for measurement noise tuning. Another robustness tool in use is tuning the residual edit thresholds. The trade-off between noise tuning and edit thresholds is explored in the context of robustness to errors in dynamics models and sensor measurements. Measurement underweighting is also presented as a method of additional robustness when processing highly accurate measurements in the presence of large filter uncertainties.
Application of step-response lambda tuning to proportional-integral controllers in water resource recovery facilities
Proportional-integral-derivative (PID) controllers in water resource recovery facilities (WRRFs) feedback control loops are commonplace. While simple to implement, such control loops are rarely tuned optimally or systematically. Heuristic tuning approaches are commonly applied with varying degrees of success using trial-and-error, ad hoc tuning rules, or duplication of tuning values from a similar system. However, there are effective methods, such as lambda tuning, produce acceptable tuning with limited effort. These are based on the step-response method, where a manual process perturbation is used to define the relationship between the manipulated and controlled variables. Based on such an experiment, a simple process model is constructed and used to determine the controller tuning values. In this work, we used the step-response method and lambda tuning for two control systems in full-scale WRRFs. This led to responsive and stable behavior of the controlled system as defined by the absolute average error of the controlled variable to setpoint and standard deviation of the manipulated variable. Tuning of feedback control loops can be completed successfully through a systematic approach, and this work suggests that tuning tools, like lambda, should be part of all wastewater treatment control engineers' toolbox.
Algorithms to Automate LCLS Undulator Tuning
Automation of the LCLS undulator tuning offers many advantages to the project. Automation can make a substantial reduction in the amount of time the tuning takes. Undulator tuning is fairly complex and automation can make the final tuning less dependent on the skill of the operator. Also, algorithms are fixed and can be scrutinized and reviewed, as opposed to an individual doing the tuning by hand. This note presents algorithms implemented in a computer program written for LCLS undulator tuning. The LCLS undulators must meet the following specifications. The maximum trajectory walkoff must be less than 5 {micro}m over 10 m. The first field integral must be below 40 x 10{sup -6} Tm. The second field integral must be below 50 x 10{sup -6} Tm{sup 2}. The phase error between the electron motion and the radiation field must be less than 10 degrees in an undulator. The K parameter must have the value of 3.5000 {+-} 0.0005. The phase matching from the break regions into the undulator must be accurate to better than 10 degrees. A phase change of 113 x 2{pi} must take place over a distance of 3.656 m centered on the undulator. Achieving these requirements is the goal of the tuning process. Most of the tuning is done with Hall probe measurements. The field integrals are checked using long coil measurements. An analysis program written in Matlab takes the Hall probe measurements and computes the trajectories, phase errors, K value, etc. The analysis program and its calculation techniques were described in a previous note. In this note, a second Matlab program containing tuning algorithms is described. The algorithms to determine the required number and placement of the shims are discussed in detail. This note describes the operation of a computer program which was written to automate LCLS undulator tuning. The algorithms used to compute the shim sizes and locations are discussed.
Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution-Phase UV/Vis Absorption Energy Prediction
Time-dependent density functional theory (TDDFT) combined with range-separated hybrid (RSH) functionals and a tuned range-separation parameter γ offers a computationally economical approach for high-throughput excited- state property predictions. The γ-tuning procedure in the gas phase is well established. However, no agreement on the best γ- tuning procedure has been made when considering the solvent effect with implicit solvent models like the polarizable continuum model (PCM). To answer that question, this study created a diverse dataset with 937 molecules with experimental solutionphase UV/vis absorption spectra. Three γ-tuning methods, the gasphase γ-tuning (GPγT), the partial vertical γ-tuning (PVγT), and the strict vertical γ-tuning (SVγT), were evaluated for the ωPBEh functional over the entire dataset. Additional benchmarks are done for the optimally tuned screened range-separated hybrid combined with the PCM approach (SRSH-PCM) and the solvation-mediated tuning procedure (sol-med-OT). Our findings revealed that the optimal γ-values obtained by the PVγT and the SVγT are significantly smaller than the GPγT. This trend holds consistently across all molecules in our dataset, and we explained the origin of this phenomenon. TDDFT calculations with PVγTand SVγT-tuned γ-values and default global Fock exchange fraction achieve superior performance compared to those using GPγTtuned or default γ and slightly outperform SRSH-PCM and sol-med-OT with similar or lesser computational cost. Furthermore, we found that the smaller γ-values from SVγT captured the expected 1/(εR) asymptotic behavior in the solution phase, resulting in accurate prediction of solution-phase CT excitations, consistent with the screened asymptote behavior encoded in SRSH-PCM. These results show that SVγT is the best scheme for high-throughput UV/vis absorption spectrum calculations using the ωPBEh functional from a data-driven perspective.
Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications
Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.
A Study on Effect of Tuning Capacitor in Dynamic Radioisotope Power Systems
This study presents the effect of tuning capacitors on the power factor and power output of a gas spring-based free-piston Stirling convertor—Advanced Stirling convertor—when the convertor runs on a forced-oscillation type controller. More specifically, this paper studies how off-nominal tuning capacitance values affects the Stirling convertor in terms of the piston stability and electrical power output, when the convertor is operated by forced-oscillation type controller, such as an alternating current bus controller, and operating conditions of the convertor are unchanged. It has been widely known that tuning capacitors are important circuit components to stabilize the piston amplitude of the free-piston Stirling convertors and correct the power factor for high electrical power output. Recent modeling analysis conducted by NASA Glenn Research Center, however, suggests that while the stability of the convertor is affected by the tuning capacitor value, the power factor and the electrical power output are not affected by the tuning capacitor value, as long as the convertor is controlled by a forced-oscillation controller and the convertor operating conditions, such as the hot-end/cold-end temperatures, piston amplitude, and operating frequency, are unchanged. To verify the modeling analysis results, a test setup is devised where the convertor is controlled by a forced-oscillation controller and the piston amplitude, hot-end temperature, and charge pressure are controlled to 4mm, 760˚C, and 485psig, respectively, while the tuning capacitor value is changed from its nominal value, 840μF to 1040μF and 1240μF. Furthermore, the vibration test results of the same convertor with the three tuning capacitor values are also presented in this paper to show the effect of the tuning capacitor value on the convertor stability. The test results suggest that when the convertor is controlled by a forced-oscillation, the tuning capacitor value determines the convertor stability but not the power factor nor electrical power output. Therefore, the tuning capacitance value in forced-oscillation controllers should be determined based on the convertor stability, system mass, and volume.
Energy cost savings and expected payback for Re-tuning the controls of US Army buildings
The Headquarters Department of the Army (HQDA) sponsored a study to determine the national impact of deploying the Re-tuning™ methodology in 5 buildings types that account for over 40% of the Army’s conditioned building stock. Re-tuning is a systematic process that improves operational efficiency and reduces energy consumption at no- or low-cost through the building automation system (BAS) by correcting operational problems that plague buildings. The study relied on successful demonstration of the re-tuning methodology at 4 pilot US Army installations that informed a holistic effort that included simulating 12 individual re-tuning energy efficiency measures (EEMs) and 6 packages of EEMs in 5 selected Army building prototype models that represent 311 million ft2 (28.9 million m2) of the Army’s conditioned floor space. Both the baseline buildings and the packages were customized to capture the expected outcomes of re-tuning a diverse set of buildings. The study highlighted the benefit of individual re-tuning EEMs and economics of implementing packages of EEMs in applicable Army buildings across 16 climates and 2 building vintages. The average whole-building energy savings ranged from 10.8% to 40.5% by building type, with the company operations facility (COF) building having the highest value proposition from re-tuning. In addition, the study reveals that all large office (LO) buildings in the Army are economical to re-tune. The total modeled cost savings potential for re-tuning the five building types across the US Army is $204/1,000 sf ($220/100 m2), or $64M annually. This cost savings represents 5.6% of all the Army’s energy expenditures.