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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Full-Scale Soft-Sensor Implementations Enable WRRF Hybrid Digital-Twins

Digital twins (DTs) are on the rise in the wastewater treatment industry and, with ever-increasinguse of online data in more complex applications, it is bringing us towards a digital revolution withinwater resource recovery facilities (WRRFs). An integral data requirement is dynamic real-timeinfluent concentrations. However, few WRRFs have the ability to deploy sensors to generate therequired dynamic influent concentrations. This paper presents an innovative approach to generatedynamic influent profiles with a soft sensor primarily using bioreactor airflow rates and DT models.It has been implemented on three different full-scale facilities and demonstrate it is feasible toretrieve 15-minute interval diurnal influent concentrations. This study overcomes a key bottleneckin DT applications - the lack of realistic high-resolution influent profiles - fostering the developmentof DTs. The fact that the data used in this study are commonly available ensures wide applicabilityfor most WRRFs and prepares them for the advanced controls that digital twins can unlock.

Johnson, Bruce↗
NASA NTRS2003

Temperature, stress, and corrosive sensing apparatus utilizing harmonic response of magnetically soft sensor element (s)

A temperature sensing apparatus including a sensor element made of a magnetically soft material operatively arranged within a first and second time-varying interrogation magnetic field, the first time-varying magnetic field being generated at a frequency higher than that for the second magnetic field. A receiver, remote from the sensor element, is engaged to measure intensity of electromagnetic emissions from the sensor element to identify a relative maximum amplitude value for each of a plurality of higher-order harmonic frequency amplitudes so measured. A unit then determines a value for temperature (or other parameter of interst) using the relative maximum harmonic amplitude values identified. In other aspects of the invention, the focus is on an apparatus and technique for determining a value for of stress condition of a solid analyte and for determining a value for corrosion, using the relative maximum harmonic amplitude values identified. A magnetically hard element supporting a biasing field adjacent the magnetically soft sensor element can be included.

Grimes, Craig A.↗
NASA NTRS2002

Tracking the harmonic response of magnetically-soft sensors for wireless temperature, stress, and corrosive monitoring

This paper describes the application of magnetically-soft ribbon-like sensors for measurement of temperature and stress, as well as corrosive monitoring, based upon changes in the amplitudes of the higher-order harmonics generated by the sensors in response to a magnetic interrogation signal. The sensors operate independently of mass loading, and so can be placed or rigidly embedded inside nonmetallic, opaque structures such as concrete or plastic. The passive harmonic-based sensor is remotely monitored through a single coplanar interrogation and detection coil. Effects due to the relative location of the sensor are eliminated by tracking harmonic amplitude ratios, thereby, enabling wide area monitoring. The wireless, passive, mass loading independent nature of the described sensor platform makes it ideally suited for long-term structural monitoring applications, such as measurement of temperature and stress inside concrete structures. A theoretical model is presented to explain the origin and behavior of the higher-order harmonics in response to temperature and stress. c2002 Elsevier Science B.V. All rights reserved.

Non-NASA Center↗
NASA NTRS2017

Elastomeric Sensing of Pressure with Liquid Metal and Wireless Inductive Coupling

This project describes resistance-based soft sensors filled with liquid metal, which permit measurements of large strains (0 percent to 110 percent), associated with small forces of less than 30 Newtons. This work also demonstrates a methodology for wireless transfer of these strain measurements without connected electrodes. These sensors allow intermittent detection of pressure on soft membranes with low force. Adapting these sensors for passive wireless pressure sensing will eliminate the need for embedded batteries, and will allow the sensors to transmit pressure data through non-conductive materials including glass and acrylic. The absence of batteries allows us to embed these sensors into materials for long-term use because the sensors only use passive analog circuit elements. We found the oxidation of the liquid metal (eutectic gallium indium) plays a role in the repeatability of the soft sensors. We investigated how the oxidation layer affected the behavior of the sensor by encapsulating materials (silicone, fluorosilicone, and PVC) with varied permeabilities to oxygen. We measured the effects of mechanical loading on the oxidation layer and the effects of wireless inductive coupling on the oxidation layer. We concluded our research by investigating the effects of embedding self-resonant circuits into polydimethylsiloxane (PDMS). Efforts to design engineered systems with soft materials are a growing field with progress in soft robotics, epidermal electronics, and wearable electronics. In the field of soft robotics, PDMS-based grippers are capable of picking up delicate objects because their form-fitting properties allow them to conform to the shape of objects more easily than conventional robotic grippers. Epidermal devices also use PDMS as a substrate to hold electronic components such as radios, sensors, and power supply circuits. Additionally, PDMS-based soft sensors can monitor human motion with liquid metal embedded within micro-channels. Passive wireless sensors have applications in structural health monitoring and medical health monitoring. Doctors can take wireless blood pressure measurements inside arteries to monitor the progression of heart disease. Glaucoma patients can use this technology to monitor the pressure in their eyes to track the progression of the disease.

Dick, Jacob↗
NASA NTRS1993

More About Detecting Sensor Failures In A Turbofan Engine

Advanced Detection, Isolation, and Accommodation (ADIA) algorithm helps digital electronic multivariable-control system of advanced turbofan engine cope with failures of sensors in real time. Algorithm includes four major elements: hard-sensor-failure detection-and-isolation logic, soft-sensor-failure detection-and-isolation logic, accommodation filter, and interface switch matrix.

Delaat, John C.↗
NASA NTRS1981

Model degradation effects on sensor failure detection

This paper discusses the effects of imperfect modeling on the detection and isolation of sensor failures. For systems with non-zero set points, deterministic inputs or non-zero noise biases, the model mismatch appears as a bias on the stochastic innovation process. This bias, if left unaccounted for, would be sufficient to declare a false alarm failure in one or more sensors. A practical design procedure based upon the Generalized Likelihood Ratio (GLR) form uses a finite data window sequential t-test to detect and isolate model mismatch effects and soft sensor failures. Application to an eighth order model of the QCSEE turbofan engine is discussed.

Leininger, G. G.↗
NASA NTRS1998

Real-Time Sensor Validation, Signal Reconstruction, and Feature Detection for an RLV Propulsion Testbed

A real-time system for validating sensor health has been developed in support of the reusable launch vehicle program. This system was designed for use in a propulsion testbed as part of an overall effort to improve the safety, diagnostic capability, and cost of operation of the testbed. The sensor validation system was designed and developed at the NASA Lewis Research Center and integrated into a propulsion checkout and control system as part of an industry-NASA partnership, led by Rockwell International for the Marshall Space Flight Center. The system includes modules for sensor validation, signal reconstruction, and feature detection and was designed to maximize portability to other applications. Review of test data from initial integration testing verified real-time operation and showed the system to perform correctly on both hard and soft sensor failure test cases. This paper discusses the design of the sensor validation and supporting modules developed at LeRC and reviews results obtained from initial test cases.

Jankovsky, Amy L.↗
NASA NTRS1989

Signal and data processing of small targets 1989; Proceedings of the Meeting, Orlando, FL, Mar. 27-29, 1989

The present conference on digital signal processing, association and filtering techniques, and multiple-sensor/multiple-tracking techniques, discusses single-frame velocity estimation, efficient target extraction for laser radar imagery, precision target tracking for small extended objects, IR clutter partitioning for matched filter design, the maximum-likelihood approach to gamma circumvention, position estimation for optical point targets using staring detector arrays, and a multiple-scan signal processing technique for area-moving target indication. Also discussed are a proportional integral estimator, the prediction of track purity in tracking performance evaluations, synchronization and fault-tolerance in a distributed tracker, the benefits of soft sensors and probabilistic fusion, and testing track initiation algorithms fusing two-dimensional tracks.

Drummond, Oliver E.↗
DOE OSTI2025

Operational resilience of additively manufactured parts to stealthy cyberphysical attacks using geometric and process digital twins

Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.

Additive Manufacturing↗
NASA NTRS1997

Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

Jorgensen, Charles C.↗
NASA NTRS1998

Neural Network-Based Sensor Validation for Turboshaft Engines

Sensor failure detection, isolation, and accommodation using a neural network approach is described. An auto-associative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures.

Moller, James C.↗
NASA NTRS1998

Feedback Linearized Aircraft Control Using Dynamic Cell Structure

A Dynamic Cell Structure (DCS ) Neural Network was developed which learns a topology representing network (TRN) of F-15 aircraft aerodynamic stability and control derivatives. The network is combined with a feedback linearized tracking controller to produce a robust control architecture capable of handling multiple accident and off-nominal flight scenarios. This paper describes network and its performance for accident scenarios including differential stabilator lock, soft sensor failure, control, stability derivative variation, and turbulence.

Jorgensen, C. C.↗
DOE OSTI2025

A High-Speed, High-Resolution Transition Edge Sensor Spectrometer for Soft X-Rays at the Advanced Photon Source

This project explores the design and development of a transition edge sensor (TES) spectrometer for resonant soft X-ray scattering (RSXS) measurements developed in collaboration between Argonne National Laboratory (ANL) and the National Institute of Standards and Technology (NIST). Soft X-ray scattering is a powerful technique for studying the electronic and magnetic properties of materials on a microscopic level. However, the lack of high-performance soft X-ray spectrometers has limited the potential of this technique. TES spectrometers have the potential to overcome these limitations due to their high energy resolution, high efficiency, and broad energy range. This project aims to optimize the design of a TES spectrometer for RSXS measurements and more generally soft X-ray spectroscopy at the Advanced Photon Source (APS) 29-ID, leading to improved understanding of advanced materials. We will present a detailed description of the instrument design and implementation. The spectrometer consists of a large array of approximately 250 high-speed and high-resolution pixels. The pixels have saturation energies of approximately 1 keV, sub-ms pulse duration and energy resolution of approximately 1 eV. The array is read out using microwave multiplexing chips with MHz bandwidth per channel, enabling efficient data throughput. To facilitate measurement of samples in situ under ultra-high vacuum conditions at the beamline, the spectrometer is integrated with an approximately 1 m long snout.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Multiphysics Modeling Framework for Soft PVC Gel Sensors with Experimental Comparisons

Polyvinyl chloride (PVC) gels have recently been found to exhibit mechanoelectrical transduction or sensing capabilities under compressive loading applications. This phenomenon is not wholly understood but has been characterized as an adsorption-like phenomena under varying amounts and types of plasticizers. A different polymer lattice structure has also been tested, thermoplastic polyurethane, which showed similar sensing characteristics. This study examines mechanical and electrical properties of these gel sensors and proposes a mathematical framework of the underlying mechanisms of mechanoelectrical transduction. COMSOL Multiphysics is used to show solid mechanics characteristics, electrostatic properties, and transport of interstitial plasticizer under compressive loading applications. The solid mechanics takes a continuum mechanics approach and includes a highly compressive Storakers material model for compressive loading applications. The electrostatics and transport properties include charge conservation and a Langmuir adsorption migration model with variable diffusion properties based on plasticizer properties. Results show both plasticizer concentration gradient as well as expected voltage response under varying amounts and types of plasticizers. Experimental work is also completed to show agreeance with the modeling results.

42 ENGINEERING↗
DOE OSTI2024

Flexible Soft X-Ray Image Sensors based on Metal Halide Perovskites With High Quantum Efficiency

Soft X-ray imaging is a powerful tool to explore the structure of cells, probe material with nanometer resolution, and investigate the energetic phenomena in the universe. Conventional soft X-ray image sensors are by and large Si-based charge coupled devices that suffer from low frame rates, complex fabrication processes, mechanical inflexibility, and required cooling below -60 °C. Here, a soft X-ray photodiode is reported based on low-cost metal halide perovskite with comparable performance to commercial Si-based device. Nanothrough network electrode minimized the optical loss due to the shadowing of insensitive layers, while a multidimensional perovskite heterojunction is generated to reduce the photo-generated carrier loss. Further, this strategy promoted a record quantum efficiency of 8 × 10 3 % without cooling, several orders of magnitude greater than the previously achieved. Flexible and curved soft X-ray imaging arrays are fabricated based on this high-performance device structure, demonstrating stable soft X-ray response and sharp imaging capabilities. This work highlights the low-cost and efficient perovskite photodiode as a strong candidate for the next-generation soft X-ray image sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Surface Energy Modulated Self‐Assembly of Percolative Gallium Nanodroplet Films for Stretchable Electronics

Advancements in soft robotics and wearable technologies have led to an increased need for flexible and stretchable electronic systems. Liquid metals like Gallium (Ga) and its alloys offer high potential in the field of stretchable electronics and photonics due to their inherent high conductivity and malleability. However, their high surface tension and native oxide formation hinder uniform wetting and continuous film formation to get conducting films. Here, we report a fabrication strategy to realize electrically conductive Ga thin films with effective thicknesses around 100 nm on polymeric and elastomeric substrates. By enhancing the surface energy of the elastomer, we create uniform Ga nanodroplets capable of sustaining stretchability up to 200% strain. Our devices exhibit electronic conduction primarily through tunneling and display strain tunable piezoresistive characteristics, which are explained using Kirchhoff's resistive network and Simmon's analytical tunneling current model. To showcase the device's capability in fields of stretchable electronics, we demonstrate several functional prototypes, including flexible devices integrated with Light Emitting Diodes (LEDs), curvature and pressure sensors, and soft robotic grippers capable of sensing object curvature. The demonstrated devices highlight the potential of this approach for scalable integration of stretchable electronics in soft robotic and wearable platforms.

42 ENGINEERING↗
DOE OSTI2026

Development of Data-Driven Models for Performance Prediction and Chemical Dosing of a Full-Scale Controlled Phosphorus Precipitation Reactor

This study evaluated the use of data-driven models to improve control of a struvite precipitation reactor that removes phosphorus from wastewater while producing a fertilizer product. The researchers developed predictive models for influent orthophosphate concentration, effluent orthophosphate concentration, and phosphorus removal using operational data from a full-scale MagPrex™ reactor at a water resource recovery facility in Denver, Colorado. Model predictions were used to recommend magnesium chloride dosing adjustments needed to achieve a target effluent phosphorus concentration. Several machine learning approaches were tested, with ridge regression providing the best predictions for influent orthophosphate concentration and phosphorus removal, and XGBoost providing the best predictions for effluent orthophosphate concentration. Simulation results indicated that the decision-support approach could correctly identify dosing adjustments in most cases and reduce chemical use. Full-scale implementation achieved lower accuracy due to changing operating conditions and limited historical data in some operating ranges. Here, the results demonstrate the potential of data-driven tools to support phosphorus recovery process control while also identifying practical limitations that affect deployment in full-scale systems.

42 ENGINEERING↗
DOE OSTI2025

Design considerations for a Digital Twin built to improve nitrification performance at a water resource recovery facility

A Digital Twin built around Activated Sludge Model No. 1 was deployed at a full-scale water resource recovery facility. Its design included a waste rate recommender system based on automatic scenario analyses, where influent loads and waste rates are varied to determine their impact on nitrification. At the same frequency as these scenario analyses, scheduled auto-calibrations allow for nitrifier maximum specific growth rate (μmax-NITO) soft sensing, the only kinetic parameter shown to require adjustment if the objective is aeration tank effluent ammonia forecasting accuracy. By integrating temperature forecasting over the next three sludge ages, this Digital Twin approach creates opportunities for advancing waste rate decisions in anticipation of seasonal temperature changes, optimizing ammonia control authority under varying influent loads, and furnishing valuable insights for future capital projects requiring nitrifier kinetic understanding and modelling.

54 ENVIRONMENTAL SCIENCES↗