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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 OSTI2022

Optimizing Sensor Count and Placement to Detect Bond Wire Lift-Offs and Surface Defects in High-Power IGBT Modules Using Low-Cost Piezo-Electric Resonators

This manuscript presents the most recent results and findings to identify bond wire lift-offs and surface defects in high-power isolated gate bipolar junction transistor (IGBT) modules. The authors of this manuscript formerly proposed a low-cost, piezoelectric resonator-based measurement unit to detect bond wire related degradation in larger IGBT modules. Since high-power IGBT modules are expensive, it was apparent that evaluating our proposed method by inducing controlled damage to fresh (new) IGBTs may not be cost-effective especially when multiple sets of data need to be captured by inducing damage to different levels. In order to overcome this limitation, IGBT bond wires have been mimicked using a 3D printed enclosure, a PCB, and copper wires with dimensions very closely resembling a real IGBT. Using this method, multiple test devices can be built at the cost of a real IGBT, and the proposed technique could be fine-tuned without damaging expensive, real IGBTs. Our recent findings can be used to determine real IGBT degradation and bond wire lift-offs using only two sensors, as opposed to six transducers used in the first iteration of the setup. In addition to optimizing the sensor count, we have also identified the best possible locations of these sensors by attempting multiple placements inside the IGBT casing.

condition monitoring↗
DOE OSTI2020

Nationwide HVAC Energy-Saving Potential Quantification for Office Buildings with Occupant-Centric Controls in Various Climates

The occupant-centric control (OCC) is receiving an increasing attention due to its ability to reduce building heating ventilation and air-conditioning (HVAC) system energy consumptions while not affecting the occupant thermal comfort. This paper aims to investigate and quantify the nationwide energy-saving potential of implementing the occupant-centric HVAC controls in typical office buildings using a whole building simulation software EnergyPlus. First, the medium office and large office from the Department of Energy (DOE) Commercial Prototype Building Models (CBPM) were enhanced to have detailed layouts and dynamic occupancy schedules. Then, a comprehensive simulation plan was created by incorporating the multiple zone-level and system-level occupant-centric building HVAC controls recommended by the updated ASHRAE Standard 90.1 – 2019 and ASHRAE Guideline 36 – 2018. Three control scenarios with different occupancy sensing methods were identified in this simulation plan. A nation-wide parametric analysis which includes two building types, three occupancy sensing scenarios, two building code versions, and 16 U.S. climate zones was carried out. The simulation results of the key control variables and HVAC energy consumption suggest that generally, both the occupancy presence sensor and occupant counting sensor could achieve energy savings for the office buildings in majority of the scenarios. However, compared with the occupancy presence sensor, which could support both the temperature setpoint reset and operational breathing zone airflow rate reset for the unoccupied zones, the occupant counting sensor only brings a marginal benefit. Besides, a higher HVAC energy-saving ratio could be achieved in the heating-dominated zone, since the energy reduction brought with the minimum outdoor airflow rate reset is stronger in the heating mode.

Pang, Zhihong↗
DOE OSTI2023

Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

The energy performance of commercial buildings is greatly influenced by occupants which are highly variable and among the most unpredictable components of a building's operation. While most building control systems use fixed, predetermined occupancy schedules, these fixed occupancy levels can be quite different from actual occupancy. This can cause unnecessary energy consumption, particularly from heating, ventilation, and air conditioning (HVAC) and lighting systems which are responsible for approximately 60% of commercial buildings' energy use. The use of occupancy counting sensor systems integrated with building management system controls is one method that can be used to improve the energy-consuming performance of buildings. However, there is no standardized universal methodology and metrics to evaluate their reliability. The aim of this research is to develop a uniform evaluation methodology to assess the reliability of occupancy counting sensor systems in a controlled laboratory environment. The developed testing methodology includes both “typical” scenarios representing the occupancy scenarios of a typical commercial building, and “failure” testing scenarios which represent a range of potential scenarios that may impact a sensor system's reliability. These methods were then implemented in a case study to evaluate the performance of two novel occupancy counting sensor systems (i.e., door-centric, and camera-based). Results suggest that typical testing results can be used to compare the overall performance of the occupancy counting sensor systems; however, failure testing is also important to understand the weaknesses of the sensor system in order to select the suitable one for the intended use of the commercial building. In addition, the proposed methodology includes a modified confusion matrix which enables the ability to identify if failures are caused by over or under counting occupants and to what extent this occurs over the testing period.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Massively distributed fiber strain sensing using Brillouin lasing

Brillouin based distributed fiber sensors present a unique set of characteristics amongst fiber sensing architectures. They are able to measure absolute strain and temperature over long distances, with high spatial resolution, and very large dynamic range in off-the-shelf fiber. However, Brillouin sensors traditionally provide only modest sensitivity due to the weak dependence of the Brillouin frequency on strain and the high signal to noise ratio required to identify the resonance’s peak frequency to within a small fraction of its linewidth. Recently, we introduced a technique which substantially improves the precision of Brillouin fiber sensors by exciting a series of lasing modes in a fiber loop cavity that experience Brillouin amplification at discrete locations in the fiber. The narrow-linewidth and high intensity of the lasing modes enabled ultra-low noise Brillouin sensors with large dynamic range. However, our initial demonstration was only modestly distributed: measuring strain at 40, non-contiguous positions along a 400 m fiber. In this work, we greatly extend this methodology to enable fully distributed sensing at 1000 contiguous locations along 3.5 km of fiber—an order of magnitude increase in sensor count and range. This highly-multiplexed Brillouin fiber laser sensor provides a strain noise as low as 34 nɛ/√Hz and we analyze the limiting factors in this approach.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation

This study addresses the problem of calibrating utility-maximizing nested logit activity-based travel demand model-systems. After estimation, it is common practice to use aggregate measurements to calibrate the estimated model-system’s parameters prior to their application in transportation planning, policy making, and operations. However, calibration of activity-based model-systems has received much less attention. Existing calibration approaches are myopic heuristics in the sense that they do not consider the fundamental inter-dependencies among choice-models and do not have a systematic way to adjust model parameters. Also, other purely simulation-based approaches do not perform well in large-scale applications. In this study, we focus on utility-maximizing nested logit activity-based model-systems and calibrating aggregate statistics such as activity shares, mode shares, time-dependent & mode-specific OD flows, and time-dependent & mode-specific sensor counts. We formulate the calibration problem as a simulation-based optimization problem and propose a stochastic gradient-based solution procedure to solve it. The solution procedure relies on microsimulation to calculate expectations of the aggregate statistics of interest to the calibration problem. Additionally, we derive approximate analytical expressions for the gradient of the objective function —that are evaluated through microsimulation on mini-batches of the population. The proposed solution procedure is sensitive to the fundamental structure of the activity-based model-system and is non-myopic in considering the dependencies across its model components. The formulated optimization problem is non-convex, highly nonlinear, and potentially has multiple-minima. Lastly, we show —through a real-world application— that the proposed solution procedure outperforms other state-of-the-art purely simulation-based optimization approaches in terms of computational efficiency, stability, and convergence. We also compare various gradient-based solution algorithms to determine the best algorithm to update the parameters. This work has the potential to facilitate wider and easier application of activity-based model-systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose

Abstract Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e−/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Materials Science↗
DOE OSTI2023

Simulation, Challenge Testing & Validation of Occupancy Recognition & CO 2 Technologies (Final Project Report)

This final report covers the results of the development of testing methods of occupancy sensor systems connected to HVAC controls. This project focused on the development of test methods to evaluate the performance of HVAC-connected occupancy sensor systems, including occupancy presence, occupancy counting, and CO 2 sensor systems in commercial and residential buildings. This included evaluation of the reliability, ease of commissioning, and energy savings potential of these sensor systems. The results of this work help to standardize the methods used to evaluate performance, to enable the ability to compare sensor system performance following the same methods, to understand which sensor systems perform better or worse compared to others. The U.S. building stock’s energy usage can benefit substantially from having building system operations informed by reliable occupancy recognition and CO 2 measurements, however to date, standard methods have not been in place to ensure that sensor systems’ reported performance is uniformly evaluated. The results of this work support the development of a standard or guideline that outlines the developed methods of testing. This project also included the testing of both off-the-shelf and novel SENSOR team-developed low-cost occupancy sensor systems, using the developed methods of testing. The results of this testing helped to inform further development and improvement of new low-cost sensor systems that will benefit from being used in residential and commercial buildings throughout the country.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Fusion-based occupancy sensing for building systems

Sensing and control apparatus for a building HVAC system includes interior and boundary sensors, such as cameras and thermal sensors, generating sensor signals conveying occupancy-related features for an area. A controller uses the sensor signals to produce an occupancy estimate and to generate equipment-control signals to cause the HVAC system to supply conditioned air to the area based on the occupancy estimate. The controller includes fusion systems collectively generating the occupancy estimate by corresponding fusion calculations, the fusion systems producing a boundary occupancy-count change based on sensor signals from the boundary sensors, an interior occupancy count based on sensor signals from the interior sensors, and the overall occupancy estimate. Fusion may be of one or multiple types including cross-modality fusion across different sensor types, within-modality fusion across different instances of same-type sensors, and cross-algorithm fusion using different algorithms for the same sensor(s).

Konrad, Janusz L.↗
DOE OSTI2024

Dark counts in optical superconducting transition-edge sensors for rare-event searches

Superconducting transition-edge sensors (TESs) are a type of quantum sensor known for their high single-photon detection efficiency and low background. This makes TESs ideal for particle-physics experiments searching for rare events. In this work, we present a comprehensive characterization of the background in optical TESs, distinguishing three types of events: electrical-noise, high-energy, and photonlike events. We introduce computational methods to automate the classification of events. We experimentally verify and simulate the source of the high-energy events. We also isolate the photonlike events, the expected signal in dielectric haloscopes searching for dark-matter dark photons, and achieve a photonlike dark-count rate of 3.6× 10 − 4 in the 0.8–3.2 eV energy range.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Quantifying Operational Drivers of Multimodal Biometric Verification in Aerial Surveillance

Multimodal biometric verification is increasingly applied across operational contexts ranging from close-range security cameras and building-mounted surveillance to long-range ground sensors and unmanned aerial system (UAS) imagery. Variations in acquisition conditions—such as image resolution, viewing geometry, and motion artifacts—pose significant challenges for cross-domain algorithmic generalization. This study evaluates two independent multimodal biometric verification systems developed under the Intelligence Advanced Research Projects Activity (IARPA) Biometric Recognition and Identification at Altitude and Range (BRIAR) program, comparing performance on close-range and aerial datasets. Close-range video served as a baseline to quantify the decline in verification performance on aerial footage. The dataset included six UAS platforms, spanning small quadcopters at 10m altitude to medium-sized fixed-wing aircraft at 360m. Mixed-effects logistic regression identified image resolution (head and body pixel counts), head height, sensor characteristics, and algorithm selection as primary determinants of verification success, whereas demographic attributes and mission gait were not significant predictors. Activity type and collection site influenced performance in close-range data but had negligible impact on UAS imagery. These results clarify modality-specific strengths and limitations and highlight opportunities to enhance cross-domain biometric verification.

Peluso, Alina [ORNL] (ORCID:0000000328950406)↗
DOE OSTI2022

Skipper-CCDs: Current applications and future

This work briefly discusses the potential applications of the Skipper-CCD technology in astronomy and reviews its current use in dark matter and neutrino experiments. An overview of the ongoing efforts to build multi-kilogram experiments with these sensors is given, in the context of the Oscura experiment. First results from the characterization of Oscura sensors from the first 200 mm wafer-fabrication run with a new vendor are presented. The overall yield of the electron counting capability of these sensors is 71%. In conclusion, a noise of 0.087 e - RMS, with 1225 samples/pix, and a dark current of (0.031±0.013) e - /pix/day at 140 K were measured.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Skipper-CCDs: current applications and future

This work briefly discusses the potential applications of the Skipper-CCD technology in astronomy and reviews its current use in dark matter and neutrino experiments. An overview of the ongoing efforts to build multi-kilogram experiments with these sensors is given, in the context of the Oscura experiment. First results from the characterization of Oscura sensors from the first 200 mm wafer-fabrication run with a new vendor are presented. The overall yield of the electron counting capability of these sensors is 71%. A noise of 0.087 e$^-$ RMS, with 1225 samples/pix, and a dark current of (0.031$\pm$0.013) e$^-$/pix/day at 140 K were measured.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A zone-level occupancy counting system for commercial office spaces using low-resolution time-of-flight sensors

Understanding the locations of occupants in a commercial built environment is critical for realizing energy savings by delivering lighting, heating, and cooling only where it is needed. In this paper, we present an indoor occupancy counting system using a sparse array of inexpensive, low-resolution, and privacy-preserving time-of-flight sensors. We develop and validate an algorithm for zonal occupancy counting that can deal with multiple people walking underneath the sensors in arbitrary directions, and evaluate the system both in realistic simulations of office spaces and in a real-world installation. Finally, we found that our system has an error rate of around 0.4%, resulting in highly accurate person localization and zone counting using only a few sensors per space.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control

As occupancy sensing technologies become mature, various occupancy sensors are increasingly deployed in commercial buildings for pervasive occupancy monitoring. These sensors provide occupant-count data, which contains rich spatiotemporal information about occupancy patterns. With long-term occupant-count data collected from a commercial building, we design three different predictive models that capture the occupancy dynamics and examine how a model predictive control of the HVAC system benefits from actual occupancy count prediction. Our analysis reveals that mispredictions of occupancy states, especially false positives and false negatives, may introduce inefficient control that leads to energy waste or user discomfort. To address this issue, we take a step further to design an adaptive model predictive controller that minimizes inefficient control actions according to misprediction types and distributions. A comprehensive evaluation is performed in OpenBuild and EnergyPlus simulators to study the effectiveness of the proposed end-to-end control strategy. The evaluation shows that the proposed solution reduces energy consumption by 29.5% while improving the average weighted occupants comfort by 86.7% in Predicted Mean Vote (PMV) over the fixed schedule strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Indoor Occupant Counting by RF Backscattering

Building HVAC (heating, ventilation and air conditioning) consumes approximately 13% of all energy consumption in USA. Motion detectors, cameras and user programmable thermostats have been shown to be ineffective for HVAC controls to save energy, mostly due to the user concerns of comfort, reliability and privacy. A new HVAC control system based on real-time occupant counting that is fully automated, highly accurate, economically sensible and preserving privacy and aesthetics can thus bring forth a disruptive impact to this large energy sector. Our indoor occupant monitoring technology is based on the radio-frequency identification system (RFID), deployed in the room, not on the occupants. One reader with four antennas can be deployed on the ceiling or behind the ceiling panels for every thousand square feet in home, office and assisted living, with or without room partitions. The sticker-like passive tag, 10 cents each and maintenance-free, are profusely hidden on the wall or inside the furniture at arbitrary position, preserving privacy and aesthetics. The large number of tags can realize diverse observation points to accommodate arbitrary room layouts, which is impractical by other active units of camera, infrared, radar or lidar. With 20 tags, the system can reliably detect the number of occupants. For 100 tags, occupant posture and location can be known. The technology has been verified in the research labs and test buildings with very high accuracy. When the real-time occupant number can be accurately known without assuming devices on occupants or occupant motion, the building HVAC system can be automated to achieve building energy saving without sacrificing occupant comfort. According to our limited testing in a few types of building models and the simplified cost calculation, the RFID system has low overall cost in production, deployment, operation and maintenance. The signal processing algorithm based on machine learning requires very small number of training cases as most learning is transferrable for various layouts, and very low computational needs during operation, according to our testing in four different room sizes and layouts. In our preliminary estimate from HVAC saving alone, the RFID system can potentially pay for itself within 1.5 years, in addition to the other enhancement in building automation systems (BAS). Our commercialization strategy and business pitch deck focus on venturing this Cornell occupant monitoring technology into BAS and energy management markets. We have identified three broad BAS market segments of senior living, residential buildings, and office buildings. We have put together the minimum viable product characteristics for these identified segments, including analyses on total cost and competing technologies, as well as the fit and technical gaps for these market segments. We intend to bring the technology to market by licensing or partnering with existing BAS vendors. A list of potential collaborators and licensing partner candidates was assembled for different aspects of integrating our technology into a potential product that can be used with BAS.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Progress Toward Fast Decay Energy Spectroscopy for Actinide Analysis

Decay energy spectroscopy (DES) is an increasingly popular technique for measuring isotopic composition of actinide samples for nuclear safeguards applications. Current approaches for actinide DES utilize milligram-scale external gold absorbers (> 0.1 nJ/K) that are integrated with actinide samples through mechanical kneading and are thermally connected to microcalorimeters using indium or gold wire bonds. This leads to relatively slow sensor rise time and, consequently, limits counting speed to a few counts per second. We are developing faster metallic magnetic calorimeter-based DES by integrating actinide samples with magnetic sensor materials. This reduces signal rise time and enables high counting speed while maintaining the ability to knead the radioactive source with the absorber. We have measured signal rise time of 0.7 μs with a 1.5 mg external gold absorber using this approach. Here, we also demonstrated online DES operation using an Ortec DSPEC 50, a commercially available data acquisition system developed for semiconductor detectors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗