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Results for “advanced multivariable control methods”

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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31 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Biomimetic model-based advanced control strategy integrated with multi-agent optimization for nonlinear chemical processes

Here, a novel framework is proposed for integrating biomimetic-based advanced control and multi-agent optimization approaches for nonlinear chemical process applications. In particular, a Biologically-Inspired Optimal Control Strategy, denoted as BIO-CS, is combined with multi-agent optimization (MAO) techniques to provide optimal solutions for dynamic systems. In this combined framework, the BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents’ local interactions. Also, the MAO uses the capabilities of heuristic-based optimization techniques by sharing process information to obtain optimal operating setpoints for the controller considering an overall process objective. The applicability of the proposed method is demonstrated using a nonlinear, multivariable, process model of a fermentation system. Specifically, the optimal operating points are computed by the MAO implementation for setpoint tracking, trajectory tracking and plant-model mismatch scenarios for BIO-CS application. Results of the developed framework are compared to a gradient-based Sequential Quadratic Programming (SQP) technique and a classical proportional-integral (PI) controller in terms of optimization and control studies, respectively. As an additional contribution, BIO-CS is also cast as a model predictive controller (MPC) for the first time and compared to the agent-based BIO-CS approach in terms of computational time and tracking error. Closed-loop control results show up to 46% improvement in tracking performance during transient for the multi-agent BIO-CS when compared to BIO-CS as MPC for additional computational expense. The obtained results illustrate the capabilities of this novel integrated framework including BIO-CS as MPC to achieve desired nonlinear system performance for various scenarios.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1983

A project management system for the X-29A flight test program

The project-management system developed for NASA's participation in the X-29A aircraft development program is characterized from a theoretical perspective, as an example of a system appropriate to advanced, highly integrated technology projects. System-control theory is applied to the analysis of classical project-management techniques and structures, which are found to be of closed-loop multivariable type; and the effects of increasing project complexity and integration are evaluated. The importance of information flow, sampling frequency, information holding, and delays is stressed. The X-29A system is developed in four stages: establishment of overall objectives and requirements, determination of information processes (block diagrams) definition of personnel functional roles and relationships, and development of a detailed work-breakdown structure. The resulting system is shown to require a greater information flow to management than conventional methods. Sample block diagrams are provided.

Stewart, J. F.↗
NASA NTRS1984

Periodic control of the individual-blade-control helicopter rotor

Results of an investigation into methods of controller design for an individual helicopter rotor blade in the high forward-flight speed regime are described. This operating condition poses a unique control problem in that the perturbation equations of motion are linear with coefficients that vary periodically with time. The design of a control law was based on extensions to modern multivariate synthesis techniques and incorporated a novel approach to the reconstruction of the missing system state variables. The controller was tested on both an electronic analog computer simulation of the out-of-plane flapping dynamics, and on a four foot diameter single-bladed model helicopter rotor in the M.I.T. 5x7 subsonic wind tunnel at high levels of advance ratio. It is shown that modal control using the IBC concept is possible over a large range of advance ratios with only a modest amount of computational power required.

Mckillip, R. M., Jr.↗
DOE OSTI2026

Anomaly Detection for Online Monitoring of Thermocouple Sensors in the Advanced Test Reactor

This study explores data-driven anomaly detection methods to analyze sensor fail- ures in the Advanced Gas Reactor (AGR) nuclear fuel irradiation experiments. Specifically, we examine failures of thermocouples (TCs), which are critical for mon- itoring and controlling in-reactor temperatures during operation. Failures were pri- marily observed during abrupt power transitions and manifested as sensor drop-outs, drifts, or unexplained behavior. We applied three time-series analysis techniques— rolling mean smoothing, matrix profile, and vector auto-regression (VAR)—to de- tect anomalies in TC data prior to failure events. The rolling mean method effec- tively highlighted deviations aligned with reported failures, while the matrix profile provided partial early warning but sometimes flagged normal fluctuations during power-down periods. VAR shows potential in capturing multivariate dependencies but requires further calibration. A rare case of TC drift was also documented, which did not result in failure, underscoring the challenge of building predictive models with sparse positive examples. Our findings demonstrate that traditional statistical tools can aid anomaly detection but have limited predictive power without richer training data. We propose future directions including synthetic data generation, real- time surrogate modeling, and multi-modal feature integration. This work provides a foundation for applying robust anomaly detection frameworks to mission-critical sensor systems in experimental settings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Water structure and electric fields at the interface of oil droplets

Interfacial water exhibits rich and complex behaviour, playing an important part in chemistry, biology, geology and engineering. However, there is still much debate on the fundamental properties of water at hydrophobic interfaces, such as orientational ordering, the concentration of hydronium and hydroxide, improper hydrogen bonds and the presence of large electric fields. This controversy arises from the challenges in measuring interfacial systems, even with the most advanced experimental techniques and theoretical approaches available. Here we report on an in-solution, interface-selective Raman spectroscopy method using multivariate curve resolution to probe hexadecane-in-water emulsions, aided by a monomer-field theoretical model for Raman spectroscopy. Our results indicate that oil-water emulsion interfaces can exhibit reduced tetrahedral order and weaker hydrogen bonding, along with a substantial population of free hydroxyl groups that experience about 95 cm -1 redshift in their stretching mode compared with planar oil-water interfaces. Given the known electrostatic zeta potential characteristic of oil droplets, we propose the existence of a strong electric field (about 50-90 MV cm -1 ) emanating from the oil phase. This field is inferred indirectly but supported by control experiments and theoretical estimates. These observations are either absent or opposite in the molecular hydrophobic interface formed by small solutes or at planar oil-water interfaces. Instead, water structural disorder and enhanced electric fields emerge as unique features of the mesoscale interface in oil-water emulsions, potentially contributing to the accelerated chemical reactivity observed at hydrophobic-water interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Altered fibrin clot properties in advanced lung cancer: strong impact of cigarette smoking

Background: Dense fibrin networks resistant to lysis have been reported in patients at high risk of thromboembolism. Little is known about fibrin clot properties in cancer. We investigated fibrin clot properties and their determinants in patients with inoperable lung cancer. Methods: We enrolled 150 patients with advanced lung cancer prior to therapy and 90 control subjects matched by age, sex, cardiovascular disease, and diabetes. Plasma clot permeability (K{sub s}), turbidimetric analysis of clot formation, clot lysis time (CLT), microparticle-associated tissue factor (MP-TF) activity, thrombin generation, and serum cotinine levels were determined. Results: Lung cancer patients, compared with controls, formed at a faster rate (− 8.1% lag phase) denser plasma fibrin networks (− 27.2% K{sub s}) that displayed impaired lysis (+ 26.5% CLT), along with 19.5% higher MP-TF activity and 100% higher peak thrombin generated, also after adjustment for potential confounders. Cotinine levels were associated with fibrin maximum absorbance (r = 0.20, p = 0.016) and K{sub s} (r = − 0.50, p < 0.0001) in cancer patients. On multivariate regression analysis, an increase in cotinine levels was a predictor of low K{sub s} (the lower quartile, < 5.8 × 10{sup −9} cm{sup 2}; odds ratio = 1.21 per 10 ng/ml, 95% confidence interval 1.02–1.46), but not CLT. Conclusion: Advanced lung cancer is associated with the prothrombotic plasma clot phenotype largely driven by smoking.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Breast Reconstruction in an Underserved Population: A Retrospective Study

Background: Breast reconstruction can help restore the shape and appearance of breasts after surgery. Studies have shown that minority and uninsured patients are less likely to receive breast reconstruction after mastectomy. Objective: We sought to determine if post-mastectomy reconstruction varied by patient ethnicity and insurance status in a medically underserved population. Methods: This was a retrospective study of mastectomy patients seen at Bellevue Hospital Center, a safety-net hospital in New York City, between January 2010 and December 2015. The Chi square test was used to compare patient characteristics versus type of reconstruction chosen and likelihood of reconstruction. Logistic regression was used to examine likelihood of reconstruction, controlling for patient insurance status, race, age, stage at presentation, and contralateral prophylactic mastectomy. Results: Of the 750 patients included in the database, 220 underwent mastectomy. Overall, 73.6% of our patient population received breast reconstruction. Patients with Medicare insurance were less likely to get reconstruction compared with patients with other types of insurance (37.5%, p = 0.04). Hispanic patients were most likely to receive reconstruction (89.1%), followed by Black patients (80%) and Asian patients (66.7%) [p = 0.03]. There were no significant associations between patient race or stage at presentation and type of reconstruction. In a multivariate logistic regression, advancing age was associated with a decreased likelihood of reconstruction (adjusted odds ratio 0.91, p < 0.001). Conclusions: In our underserved patient population, patients received breast reconstruction at rates higher than the national average. Institutional availability of patient navigators and preoperative counseling may contribute to more equal access to breast reconstruction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRSDate not supplied

Towards Formalization of Advanced Linear Algebra with Applications to Dynamical Systems using PVS

Linear Algebra is essential for numerous aerospace problems of interest. Formal reasoning about hybrid systems that contain variables modeled by differential equations rely on concepts from Linear Algebra such as eigenvalues, matrix decompositions, and matrix valued functions. For example, the long-term dynamics of a system of differential equations depend on the stability/instability of its equilibrium points, which often reduces to an eigenvalue problem. This talk will embark on a quest to formalize theorems and results about eigenvalues and eigenvectors using PVS. We shall start our journey with 2 x 2 complex matrices, where we will apply our PVS code to a simple example of a dynamical system. Since it can be difficult or impossible to give simple expressions of eigenvalues for larger matrices (i.e. 5 x 5 or higher), we then move towards specifying the power method for verified computation of eigenvalue approximations in PVS. This effort requires development of multivariate complex arithmetic. At the end of the day, having such additions to the PVS NASA libraries will help move towards the use of formal methods to verify concepts of control theory and system level verification.

Linear Algebra↗
DOE OSTI2018

Highly Selective and Stable Multivariable Gas Sensors for Enhanced Robustness and Reliability of SOFC Operation

GE Global Research, in partnership with SUNY Polytechnic Institute (SUNY Poly) and GE–Fuel Cells LLC, designed, built, and tested multi-gas sensor prototypes for in situ monitoring of gases produced during steam reforming in solid oxide fuel cell (SOFC) systems. To be widely adopted and to deliver a lower operating cost for customers, SOFC systems should enhance their operation reliability and improve stack robustness. These needs can be achieved by early diagnostics of potential upsets by using in-situ gas sensors for the real-time monitoring of gas mixture compositions. Thus, this project objective was to achieve selectivity and stability of sensing of gases for SOFC applications by implementing a new generation of gas sensors, known as multivariable sensors. Such sensors are intended to complement the mature traditional analytical instruments based on sensor performance and design characteristics. The key sensor-performance characteristics include accurate detection and quantitation of several gases in their mixtures and stability of operation in unattended applications. The key design characteristics of sensors include their unobtrusive form factor and cost-effectiveness. The duration of the project was 18 months with the project structure that included three technical tasks such as (1) design and laboratory validation of multivariable sensors for selective detection of H 2 and CO gases, (2) laboratory validation of sensor stability over a two-week testing, and (3) field-validation of the developed multivariable sensor system prototype at GE–Fuel Cells factory. Multi-gas detection was achieved with multivariable photonic sensors designed to operate at elevated temperature. The sensor design was based on three-dimensional (3D) bio-inspired photonic nanostructure that was fabricated using materials responsive to gases of interest such as H 2 and CO. The gas discrimination and the rejection of interferences such as CH 4 , H 2 O, and others was performed by optimizing the design parameters of the 3D photonic nanostructures such as their composition and morphology. Planar sol-gel and sputtered sensing films were fabricated as controls. As a result of the fabrication, functionalization, excitation, and performance testing of 3D photonic sensors, we developed initial design rules for high temperature multivariable sensors. These design rules pave the way for the new generation of cost-effective industrial sensors. Stability of sensor operation was tested in the laboratory conditions under variations of the ambient environments. Stability tests were designed in the order of enhanced complexity and realism of sensor operation and included quantitation of a single gas, quantitation of a single gas in the presence of interferences, and quantitation of two and three gases. We have found that conventional machine learning data analysis stools were unable to correct for the sensor drift. Thus, we have developed new methods of machine learning data analysis that provided us with needed performance stability. Such performance improvement in gas sensing was a significant milestone in bringing our multivariable sensors to practical applications. The project culminated with initial field validation of developed multivariable sensor prototypes at GE–Fuel Cells LLC. The in situ data generated by the sensor system in the lab and field conditions provided inputs that advanced our fundamental understanding of gas sensing at high temperatures to enable sensors for SOFC applications and allowed development of the recommendations for further deliverables.

47 OTHER INSTRUMENTATION↗
NASA NTRS1986

Robust detection, isolation and accommodation for sensor failures

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous techniques

Emami-Naeini, A.↗
DOE OSTI2020

Multi-Gas Sensors for Enhanced Reliability of SOFC Operation

GE Research, in partnership with SUNY Polytechnic Institute (SUNY Poly), designed, built, and tested gas sensors for in situ monitoring of H 2 and CO anode tail gases produced with on-site steam reforming in solid oxide fuel cell (SOFC) systems. The knowledge of the H 2 /CO ratio of these anode tail gases should allow accurate determination and control of the efficiency of the reforming process in the SOFC system and should deliver a lower operating cost for SOFC customers. The project objectives were to achieve multi-gas monitoring capability with a single multivariable sensor, and to sustain this performance in the presence of gaseous interferences and a potential poison for the sensor. The duration of the project was 24 months with the project structure that included three technical tasks such as (1) development of design rules of photonic nanostructures for H 2 and CO gas detection, (2) laboratory validation of photonic nanostructures for selective H 2 and CO gas detection, and (3) validation of photonic nanostructures for initial stability and poison-resistance against H 2 S. To build multivariable sensors for selective H 2 and CO gas detection in the presence of interferences, we expanded our earlier knowledge of multi-gas sensors into new fabrication and functionalization methodologies as well as into new methodologies for the spectral data analysis of multi-gas responses. We have advanced our design rules of the three-dimensional (3D) photonic nanostructures that allowed detection of H 2 and CO at high temperatures as individual gases and as their mixtures and rejection of interferences such as CO 2 , H 2 O, CH 4 , and other hydrocarbons for SOFC applications. Our advanced design rules should be attractive for building the new generation of cost-effective industrial sensors. Stability and poison resistance of our 3D photonic nanostructures was tested in the laboratory conditions. While initially we utilized conventional machine learning data analysis tools, we have found that they were unable to correct for the sensor drift. Thus, we have implemented new methods of machine learning for the analysis of our spectral data. These learnings pave the way to move the future studies into advanced testing of effects of interferences, aging and field tests. In future, our work will continue to advance our sensing designs to operate in conditions with known and unknown interferences by implementing nanostructures with enhanced spectral diversity of responses to gaseous species of interest and interferences. Our systematic reduction of technical risks in this completed project and in future studies will ensure transition of this sensing technology to commercialization.

03 NATURAL GAS↗
DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗