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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Comparison of Multisine Peak Factor Minimization Algorithms for Aircraft System Identification(Presentation)

Two phase-optimized multisine peak factor minimization algorithms are presented and evaluated. The first algorithm minimizes peak factor by iteratively clipping the peaks of generated multisine signals. The second algorithm optimizes peak factor indirectly through minimization of an approximation of the infinity norm of the multisine. Algorithm performance was evaluated as a function of different signal properties, including the number of harmonics, harmonic spacing, and number of snow harmonics (extra harmonics included for further reduction of the peak factor). The two algorithms are compared against results obtained by minimizing peak factor directly using a simplex algorithm, which has been a common approach when designing phase-optimized multisines for system identification flight tests. Sample results show that the clipping and infinity norm algorithms produced multisine signals with comparable peak factors that were lower than that of the simplex algorithm. However, the clipping algorithm runs an order of magnitude faster than the other two algorithms, which also makes it practical to repeat the algorithm multiple times to achieve even lower peak factors.

flight test↗
DOE OSTI2020

Deep Learning for Automated Identification of Eels in Sonar Data

Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.

13 HYDRO ENERGY↗
DOE OSTI2022

QONNX: Representing Arbitrary-Precision Quantized Neural Networks

We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce support for low precision quantization in existing ONNX-based quantization formats by leveraging integer clipping, resulting in two new backward-compatible variants: the quantized operator format with clipping and quantize-clip-dequantize (QCDQ) format. We then introduce a novel higher-level ONNX format called quantized ONNX (QONNX) that introduces three new operators -- Quant, BipolarQuant, and Trunc -- in order to represent uniform quantization. By keeping the QONNX IR high-level and flexible, we enable targeting a wider variety of platforms. We also present utilities for working with QONNX, as well as examples of its usage in the FINN and hls4ml toolchains. Finally, we introduce the QONNX model zoo to share low-precision quantized neural networks.

Pappalardo, Alessandro↗
DOE OSTI2022

The Effect of Inverter Loading Ratio on Energy Estimate Bias: Preprint

Subhourly effects, particularly variability in solar irradiance, can lead to underestimation of inverter clipping losses and overestimation of energy in hourly photovoltaic system performance models, particularly for systems with high inverter loading ratios. Direct simulation of this error can be complicated by factors such as the representation of spatial and temporal variability in hourly weather data and transient system conditions. In this work we take an alternative approach using real system power measurements to show that energy predictions from typical industry models suffer from a bias that increases with inverter loading ratio. We also show that this loading ratio-dependent bias is strongly correlated with an empirical subhourly inverter clipping bias derived from real power plant data. Finally, we show that this bias is not necessarily specific to any one model or weather dataset by recreating similar biases with alternatives of each.

clipping↗
DOE OSTI2024

Artificial ground reflector size and position effects on energy yield and economics of single‐axis‐tracked bifacial photovoltaics

Abstract Artificial ground reflectors improve bifacial energy yield by increasing both front and rear‐incident irradiance. Studies have demonstrated an increase in energy yield due to the addition of artificial reflectors; however, they have not addressed the effect of varying reflector dimensions and placement on system performance and the impact of these parameters on the reflectors' financial viability. We studied the effect of high albedo (70% reflective) artificial reflectors on single‐axis‐tracked bifacial photovoltaic systems through ray‐trace modeling and field measurements. In the field, we tested a range of reflector configurations by varying reflector size and placement and demonstrated that reflectors increased daily energy yield up to 6.2% relative to natural albedo for PERC modules. To confirm the accuracy of our model, we compared modeled and measured power and found a root mean square error (RMSE) of 5.4% on an hourly basis. We modeled a typical meteorological year in Golden, Colorado, to demonstrate the effects of artificial reflectors under a wide range of operating conditions. Seventy percent reflective material can increase total incident irradiance by 1.9%–8.6% and total energy yield by 0.9%–4.5% annually after clipping is considered with a DC–AC ratio of 1.2. Clipping has a significant effect on reflector impact and must be included when assessing reflector viability because it reduces reflector energy gain. We calculated a maximum viable cost for these improvements of up to $2.50–4.60/m 2 , including both material and installation, in Golden. We expanded our analysis to cover a latitude range of 32–48°N and demonstrated that higher‐latitude installations with lower energy yield and higher diffuse irradiance content can support higher reflector costs. In both modeling and field tests, and for all locations, the ideal placement of the reflectors was found to be directly underneath the module due to the optimized rear irradiance increase.

14 SOLAR ENERGY↗
DOE OSTI2021

A universal and facile approach for building multifunctional conjugated polymers for human-integrated electronics

Polymer semiconductors have shown distinct promise for the development of human- integrated electronics, owing to their solution processability and mechanical softness. However, numerous functional properties required for this application domain face synthetic challenges to be imparted onto conjugated polymers and thus combined with efficient charge- transport property. Here, we develop a “click-to-polymer” (CLIP) synthesis strategy for conjugated polymers, which uses a click reaction for the facile and versatile attachment of diverse types of functional units to a pre-synthesized conjugated-polymer precursor. With four types of functional groups, we show that functionalized polymers from this CLIP method can still retain good charge-carrier mobility. Here, we take two realized polymers to showcase the photo-patternable property and biochemical sensing function, both of which advance the state of the art of realizing these two types of functions on conjugated polymers. We expect the expanded use of this synthesis approach can largely enrich the functional properties from conjugated polymers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A unified large language model–based framework for heterogeneous PV image diagnosis

With advances in imaging technologies, modern photovoltaic (PV) systems generate large volumes of heterogeneous image data, including visible, electroluminescence (EL), and infrared (IR) images. Existing PV image analysis models, particularly deep learning approaches, are typically task-specific and lack cross-modality generalization. To address this limitation, this paper proposes an open-source large language model (LLM)–based unified framework for heterogeneous PV image diagnostics. Through task-aware diagnostic prompting, the framework enables analysis of visible, EL, and IR images within a single pipeline, supporting both zero-shot and few-shot inference and binary and multiclass classification. It is compatible with state-of-the-art multimodal LLMs, including ChatGPT, Gemini, Claude, Qwen, and CLIP. The framework is evaluated on PV module condition classification (clean, soiling, snow, hail, and bird droppings) using visible images, cell crack detection using EL images, and hotspot detection using IR images. GPT-5.1 in few-shot mode achieves the best performance, with classification accuracy exceeding 97.3%. Open-source models such as Qwen and CLIP also deliver competitive results on visible images (around 90% accuracy), though their performance is more limited on EL and IR modalities. On the full ELPV dataset, the framework achieves 83.5% zero-shot accuracy, within 2.8% of the supervised CNN baseline, confirming scalability to larger benchmarks. Practical aspects such as reproducibility, response latency, and confidence estimation are systematically analyzed. The framework operates across PV image modalities without modality- or task-specific training, making it well suited as a rapid pre-screening tool to support downstream detailed diagnostics. A benchmark dataset of diverse labeled PV images is also released.

Li, Baojie↗
DOE OSTI2023

MHC-II dynamics are maintained in HLA-DR allotypes to ensure catalyzed peptide exchange

Presentation of antigenic peptides by major histocompatibility complex class II (MHC-II) proteins determines T helper cell reactivity. The MHC-II genetic locus displays a large degree of allelic polymorphism influencing the peptide repertoire presented by the resulting MHC-II protein allotypes. During antigen processing, the human leukocyte antigen (HLA) molecule HLA-DM (DM) encounters these distinct allotypes and catalyzes exchange of the placeholder peptide CLIP by exploiting dynamic features of MHC-II. Here, we investigate 12 highly abundant CLIP-bound HLA-DRB1 allotypes and correlate dynamics to catalysis by DM. Despite large differences in thermodynamic stability, peptide exchange rates fall into a target range that maintains DM responsiveness. A DM-susceptible conformation is conserved in MHC-II molecules, and allosteric coupling between polymorphic sites affects dynamic states that influence DM catalysis. As exemplified for rheumatoid arthritis, we postulate that intrinsic dynamic features of peptide–MHC-II complexes contribute to the association of individual MHC-II allotypes with autoimmune disease.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values

Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two algorithms, Shift-NMF and Nearly-NMF, that can handle both the noisiness of the input data and also any introduced negativity. Both of these algorithms use the negative data space without clipping or masking and recover non-negative signals without any introduced positive offset that occurs when clipping or masking negative data. We demonstrate this numerically on both simple and more realistic examples, and prove that both algorithms have monotonically decreasing update rules.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Leaf demography spectra, February 2017, PA-SLZ: Panama

This dataset contains leaf reflectance spectra of sunlit canopy leaves from trees at the San Lorenzo Protected Area (PA-SLZ), Panama. Spectra were measured with a full-spectrum (350 -2500 nm) spectroradiometer with a leaf clip attachment. Leaves previously documented in a demography survey were targeted, and each leaf spectrum is paired with species identification, relative leaf position on each branch and estimated leaf age in days. Leaves were measured from the following species: Apeiba membranacea, Carapa guianensis, Guatteria dumetorum, Miconia borealis, Tachigali versicolor, Terminalia amazonia, Tocoyena pittieri and Vochysia ferruginea Unprocessed spectral data are included as SVC *.sig files, and metadata, including sample details, are presented in *.xlsx files. Leaf reflectance spectra of sunlit canopy leaves from trees at the San Lorenzo Protected Area (PA-SLZ), Panama. Spectra were measured with a full-spectrum (350 -2500 nm) spectroradiometer with a leaf clip attachment. Leaves previously documented in a demography survey were targeted, and each leaf spectrum is paired with species identification, relative leaf position on each branch and estimated leaf age in days. Leaves were measured from the following species: Apeiba membranacea, Carapa guianensis, Guatteria dumetorum, Miconia borealis, Tachigali versicolor, Terminalia amazonia, Tocoyena pittieri and Vochysia ferruginea Unprocessed spectral data are included as SVC *.sig files, and metadata, including sample details, are presented in *.xlsx files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep ML for inference from indirect measurements

The magic of deep machine learning (DML): Neural networks that can beat Go grandmasters. Networks that have better than human accuracy at classifying images in over 100 categories. Making still pictures move realistically. Super-resolution versions of very old film clips. Realistic, high-resolution, images of people that have never existed. Realistic insertion of people into video clips. Network generated music and writing that passes for human created. How can this technology be leveraged for physics and engineering?

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

EFT validity issues in Vector Boson Scattering processes

Vector Boson Scattering (VBS) processes are regarded as the best lab to study the $VVVV$ quartic couplings, where $V = W, Z$. Such studies are carried in the framework of Effective Field Theories (EFT), but the EFT formalism is often not used in a fully consistent way. We discuss the limitations of the EFT approach to describe New Physics effects in VBS data. We argue that the "clipping" technique is the most theory-motivated way to do data analysis in the EFT language and discuss first results from an analysis of CMS Run 2 data on the $WZ$ and same-sign $WW$ process, with and without "clipping" implemented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1975

Thermostructural testing of instrumented ATJ-S graphite nosetip models in the AFFDL 50 MW rent facility

Instrumented ATJ-S graphite shell nosetip models were tested in the AFFDL 50 MW RENT Facility. The inside surface of the models were instrumented with seven thermocouples and three clip-on extensometers. One thermocouple was located at the inside stagnation point and three thermocouples were located at 120 degree intervals on the aft portion of the skirt of the model. In addition, three thermocouples at 120 degree intervals plus three clip-on extensometers at 120 degree intervals offset 60 degrees from the thermocouples were located in the critical region. The outside surface temperature of the models was monitored in two locations by optical pyrometers. Finally, the tests were recorded by three high speed motion picture cameras. Pressure models and calorimeters were also tested in order to help define the flow field condition and heating distribution around the models.

Budde, C. L.↗
NASA NTRS1977

NTS-2 retroreflector array

The NTS-II Laser Retroreflector consists of a honeycomb tray onto which are mounted 44 fused silica cube corners with a 50-millimeter hexagonal entrance pupil and single infrared cube. A thermal shield with a highly reflective outer surface and a black inner surface was incorporated to shroud the assembly protecting it from extreme high temperature exposure thus reducing cube corner thermal distortions to a minimum. A dummy retroreflector, utilizing simulated aluminum cube corners was fabricated and vibration tested to qualification levels to substantiate the integrity of the tray and cube mounting clips. This dummy was further used in the spacecraft spin balance test. The flight Laser Retroreflector was tested to the flight level vibration spectrum to verify that the unit will survive its expected vibration environment. During this test several GFE cube corners exhibited slight crazing at the clip support mounting holes. These cubes were reworked and retested and proved to be satisfactory.

Source record↗
NASA NTRS1982

Reflectance of litter accumulation levels at five wavelengths within the 0.5- to 2.5 micron waveband

Reflectance was measured for 1-m2 range grass plots with two canopy treatments (standing and clipped) and four levels of litter accumulation and for grain sorghum with two canopy treatments. Reflectance was significantly higher at the 0.65- to 1.65-, and 2.20-micrometer. Wavelengths for both grass and grain sorghum canopies when the canopies were clipped and the resulting litter was removed. The natural accumulation of litter under the grass canopy did not significantly affect reflectance. The 1.65- and 2.20-micrometer wavelength reflectances of the live grass and the intact litter were 21.8% and 16.2%, respectively, and those of grain sorghum were 21.8% and 16.5%, respectively.

Gerbermann, A. H.↗
NASA NTRS1980

Miniature spectrally selective dosimeter

A miniature spectrally selective dosimeter capable of measuring selected bandwidths of radiation exposure on small mobile areas is described. This is achieved by the combination of photovoltaic detectors, electrochemical integrators (E-cells) and filters in a small compact case which can be easily attached in close proximity to and substantially parallel to the surface being measured. In one embodiment two photovoltaic detectors, two E-cells, and three filters are packaged in a small case with attaching means consisting of a safety pin. In another embodiment, two detectors, one E-cell, three filters are packaged in a small case with attaching means consisting of a clip to clip over a side piece of an eye glass frame.

Adams, R. R.↗
NASA NTRS1987

Program for Development of Artificial Intelligence

C Language Integrated Production System (CLIPS) computer program is shell for developing expert systems. Designed to enable research, development, and delivery of artificial intelligence on conventional computers. Primary design goals for CLIPS are portability, efficiency, and functionality. Meets or out-performs most microcomputer- and minicomputer-based artificial-intelligence tools. Written in C.

Riley, Gary↗
NASA NTRS1987

Investigation of leading-edge flap performance on delta and double-delta wings at supersonic speeds

An investigation of the aerodynamic performance of leading-edge flaps on three clipped delta and three clipped double-delta wing planforms with aspect ratios of 1.75, 2.11, and 2.50 was conducted in the Langley Unitary Plan Wind Tunnel at Mach numbers of 1.60, 1.90, and 2.16. A primary set of fullspan leading-edge flaps with similar root and tip chords were investigated on each wing, and several alternate flap planforms were investigated on the aspect-ratio-1.75 wings. All leading-edge flap geometries were effective in reducing the drag at lifting conditions over the range of wing aspect ratios and Mach numbers tested. Application of a primary flap resulted in better flap performance with the double-delta planform than with the delta planform. The primary flap geometry generally yielded better performance than the alternate flap geometries tested. Trim drag due to flap-induced pitching moments was found to reduce the leading-edge flap performance more for the delta planform than for the double-delta planform. Flow-visualization techniques showed that leading-edge flap deflection reduces crossflow shock-induced separation effects. Finally, it was found that modified linear theory consistently predicts only the effects of leading-edge flap deflection as related to pitching moment and lift trends.

Covell, Peter F.↗