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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

High photon-phonon pair generation rate in a two-dimensional optomechanical crystal

Integrated optomechanical systems are a leading platform for manipulating, sensing, and distributing quantum information, but are limited by residual optical heating. Here, we demonstrate a two-dimensional optomechanical crystal (OMC) geometry with increased thermal anchoring and a mechanical mode at 7.4 GHz, well aligned with the operation range of cryogenic microwave hardware and piezoelectric transducers. The eight times better thermalization than current one-dimensional OMCs, large optomechanical coupling rates, g 0 /2π ≈ 880 kHz, and high optical quality factors, Q opt = 2.4 × 10 5 , allow ground-state cooling (n m = 0.32) of the acoustic mode from 3 K and entering the optomechanical strong-coupling regime. In pulsed sideband asymmetry measurements, we show ground-state operation (n m < 0.45) at temperatures below 10 mK, with repetition rates up to 3 MHz, generating photon-phonon pairs at ≈ 147 kHz. Our results extend optomechanical system capabilities and establish a robust foundation for future microwave-to-optical transducers with entanglement rates exceeding state-of-the-art superconducting qubit decoherence rates.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Feasibility of source-free DAS logging for next-generation borehole imaging

Characterizing and monitoring geologic formations around a borehole are crucial for energy and environmental applications. However, conventional wireline sonic logging usually cannot be used in high-temperature environments nor is the tool feasible for long-term monitoring. We introduce and evaluate the feasibility of a source-free distributed-acoustic-sensing (DAS) logging method based on borehole DAS ambient noise. Our new logging method provides a next-generation borehole imaging tool. The tool is source free because it uses ever-present ambient noises as sources and does not need a borehole sonic source that cannot be easily re-inserted into a borehole after well completion for time-lapse monitoring. The receivers of our source-free DAS logging tool are fiber optic cables cemented behind casing, enabling logging in harsh, high-temperature environments, and eliminating the receiver repeatability issue of conventional wireline sonic logging for time-lapse monitoring. We analyze a borehole DAS ambient noise dataset to obtain root-mean-squares (RMS) amplitudes and use these amplitudes to infer subsurface elastic properties. We find that the ambient noise RMS amplitudes correlate well with anomalies in conventional logging data. The source-free DAS logging tool can advance our ability to characterize and monitor subsurface geologic formations in an efficient and cost-effective manner, particularly in high-temperature environments such as geothermal reservoirs. Further validation of the source-free DAS logging method using other borehole DAS ambient noise data would enable the new logging tool for wider applications.

58 GEOSCIENCES↗
DOE OSTI2022

Deep compressed seismic learning for fast location and moment tensor inferences with natural and induced seismicity

Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗
DOE OSTI2023

In-field strain and temperature measurements in a (RE)Ba 2 Cu 3 O 7–x coil via Rayleigh-backscattering interrogated optical fibers

(RE)Ba 2 Cu 3 O 7-x (REBCO) conductors have overcome technical challenges related to manufacturing quality, length homogeneity, scale-up of piece-length, and joints. There is one remaining technical challenge, however, which is common to all high temperature superconductors and lies in effective detection of failure to prevent material degradation. An innovative technique based on optical fibers interrogated by Rayleigh backscattering has been shown to have advantages over voltage taps at detecting incipient faults. Prior work has experimentally demonstrated the technique in several implementation scenarios, including direct integration of optical fibers into superconducting conductors and cables to create a class of ‘SMART’ conductors and cables that are able to monitor their own health. In this paper, the magnet monitoring technique based on Rayleigh backscattering interrogated optical fibers has been experimentally studied in a model coil subject to external magnetic field, where different fiber integration methods are used to increase selectivity of the fiber sensor to temperature. Results show that the spectral shift displays different features during strain and thermal transients. The implications of the results in terms of potential and limitations of each sensor as well as strain-temperature decoupling are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-resolution Bayesian spatial autocorrelation (SPAC) quasi-3-D Vs model of Utah FORGE site with a dense geophone array

SUMMARY We expand the application of spatial autocorrelation (SPAC) from typical 1-D Vs profiles to quasi-3-D imaging via Bayesian Monte Carlo inversion (BMCI) using a dense nodal array (49 nodes) located at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site. Combinations of 4 and 9 geophones in subarrays provide for 36 and 25 1-D Vs profiles, respectively. Profiles with error bars are determined by calculating coherency functions that fit observations in a frequency range of 0.2–5 Hz. Thus, a high-resolution quasi-3-D Vs model from the surface to 2.0 km depth is derived and shows that surface-parallel sedimentary strata deepen to the west, consistent with a 3-D seismic reflection survey. Moreover, the resulting Vs profile is consistent with a Vs profile derived from distributed acoustic sensing (DAS) data located in a borehole at the FORGE site. The quasi-3-D velocity model shows that the base of the basin dips ∼22° to the west and topography on the basement interface coincident with the Mag Lee Wash suggests that the bedrock interface is an unconformity.

Geochemistry & Geophysics↗
DOE OSTI2021

Network analysis of earthquake ground motion spatial correlation: a case study with the San Jacinto seismic nodal array

SUMMARY The spatial correlation of earthquake ground motion intensity can be measured from strong motion data; however, the data used in past studies is sparsely sampled in space, and only the interstation distance was considered as a correlation variable. These limitations mean that we have only weak constraints on the true correlation structure of ground motion and that potentially important aspects of spatial correlation are unconstrained. In this study, we combine a large-N seismic array and graph analytics to explore this issue at a local scale using small local and regional earthquakes. Our result suggests site conditions, and how they interact with the incident seismic wavefield, strongly condition the spatial correlation of ground motion. Future progress in characterizing ground motion spatial variability will require dense wavefield measurements, either through nodal deployments, or perhaps distributed acoustic sensing measurements, of seismic wavefields. Aftershock sequences of major earthquakes would provide particularly data-rich targets of opportunity.

58 GEOSCIENCES↗
DOE OSTI2021

Phase-weighted slant stacking for surface wave dispersion measurement

SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.

58 GEOSCIENCES↗
DOE OSTI2022

Image-domain DAS 3D VSP elastic transmission tomography

SUMMARY Full-wavefield elastic imaging of active-source seismic data acquired by downhole receivers commonly offers higher-resolution subsurface images in the vicinity of a borehole compared to conventional surface seismic data sets, which can lack higher-frequency wavefield components due to longer travel paths and increased attenuation. An increasingly used approach for downhole acquisition is vertical seismic profiling (VSP), which has become more attractive when coupled with distributed acoustic sensing (DAS) using optical fibres installed in wells. The main difficulty for generating high-quality images with full-wavefield imaging tools for DAS VSP data, though, is the need for an accurate velocity model. To build plausible velocity models using active-source DAS VSP data, we adopt a 3-D image-domain elastic transmission tomography technique, originally developed for surface-recorded passive (microseismic) data, by exchanging the source and receiver positions (i.e. reciprocity) to mimic a passive-seismic surface monitoring scenario. The inversion approach exploits various images for each source constructed through time-reverse imaging (TRI) of downgoing P- and S-wave first-arrival waveforms. The TRI process uses the kinetic term of the (extended) PS energy imaging condition that exhibits sufficient sensitivity to velocity model errors. The method automatically updates the P- and S-wave velocity models to optimize image focusing via adjoint-state inversion. We illustrate the efficacy of the adopted elastic inversion technique using an active-source DAS 3-D VSP field data set acquired in the North Slope of Alaska. The numerical experiments demonstrate that the inverted elastic velocity models can be further used in full-wavefield acoustic/elastic imaging algorithms to obtain accurate subsurface images.

Oren, Can (ORCID:000000019534265X)↗
DOE OSTI2026

Real-Time GPU-Accelerated OFDR With an Integrated Auxiliary Interferometer

A GPU-accelerated optical frequency domain reflectometry (OFDR) system with an improved integrated auxiliary interferometer is proposed. Unlike conventional approaches that require separate auxiliary interferometers and multiple detection channels, the proposed OFDR system embeds this functionality directly into the signal via an intentional beat component. This enables self-calibration of laser nonlinearity while maintaining a cost-effective hardware configuration. Building on this simplified configuration, the system leverages GPU acceleration with an NVIDIA RTX 4070 Ti to achieve real-time performance, delivering high-throughput signal processing for continuous OFDR interrogation. The signal processing pipeline comprises signal capture, resampling for nonlinearity compensation, and frequency shift computation, all optimized for parallel execution. Hardware benchmarking demonstrates substantial acceleration over CPU implementations, achieving up to a 45× speedup for resampling and frequency shift computations and enabling processing latencies below 30 ms. Thermal response validation is conducted under two complementary scenarios: localized heating using a water bath and cryogenic-temperature conditions using liquid nitrogen. Under localized heating, the system achieves an accuracy of 0.249 °C with a thermal sensitivity of 5.971 GHz/°C, while cryogenic-temperature validation demonstrates a frequency shift response with a sensitivity of 2.383 GHz/°C and an accuracy of 2.04 °C. The high acceleration of the proposed GPU-accelerated OFDR system and its accuracy are achieved by exploiting CUDA-based stride indexing, enabling efficient parallel segmentation and processing of large datasets without additional memory copies. The benchmarking results confirm the robustness, accuracy, and deployability of the proposed OFDR system across a wide temperature range, establishing it as a practical platform for real-time distributed fiber sensing in structurally dynamic environments.

Harb, Salah [Lawrence Berkeley National Laboratory↗
DOE OSTI2020

A Digital Twin Approach to Study Additive Manufacturing Processing Using Embedded Optical Fiber Sensors and Numerical Modeling

One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This article reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the laser engineered net shaping (LENS) process. Here by using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes, and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this article demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Minimization of Measurement Uncertainty in Optical Frequency Domain Reflectometry

Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.

42 ENGINEERING↗
DOE OSTI2021

DASSA v0.0.1

DASSA (Parallel DAS Data Storage and Analysis) provides a data storage engine and analysis engine for data from distributed acoustic sensing and other methods. It supports various data analysis operations, from FFT , signal filter, cross-correlation, compression, stacking etc.

Dong, Bin↗
DOE OSTI2022

Seismic monitoring of well integrity

Seismic tube waves, produced by flexure of the well boundary, pressure changes in the fluid in the well, and deformation of the material immediately surrounding the well, are particularly sensitive to variations in the state of the well. In this work, we evaluate a direct approach for generating and observing tube waves as a means of detecting well damage. While we find that it can be difficult to reliably excite observable tube waves without a very strong surface source, time-frequency techniques can be employed to increase the detectability of tube wave reflections. New technologies, particularly distributed acoustic sensing, hold great promise for evaluating well integrity by monitoring tube waves, temperature changes, and seismic noise due to well deformation and fluid leakage.

58 GEOSCIENCES↗
DOE OSTI2022

Distributed Brillouin fiber laser sensor

Brillouin fiber sensors can provide distributed strain and temperature measurements over long distances in standard off-the-shelf fiber by measuring the Brillouin frequency shift as a function of position along a fiber. The primary drawback of these systems is their limited sensitivity, which results from the challenge in identifying the Brillouin frequency shift to within a small fraction of the Brillouin linewidth. In this work, we introduce a technique that overcomes this fundamental limitation by establishing a series of lasing modes that experience Brillouin amplification at discrete spatial locations in the test fiber. The linewidth narrowing and high intensity associated with the lasing transition enable precise measurements of this lasing frequency. As an initial demonstration, we present a sensor that simultaneously excites 40 lasing modes in a 400 m fiber, providing a measurement of the strain at 40 discrete locations with a spatial resolution of 4 m. Each sensor exhibits a minimum detectable strain as low as 4 nε/Hz 1/2 with a dynamic range of >5 mε and a bandwidth of ~10 kHz. As the first demonstration that Brillouin lasing can be used for distributed fiber sensing, this work establishes an approach that could enable ultra-high strain sensitivity using off-the-shelf fiber.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Overcoming the fundamental limit of quantum transduction via intraband entanglement

A quantum transducer converts an input signal to an output probe at a distant frequency band while maintaining the quantum information with high fidelity, which is crucial for quantum networking and distributed quantum sensing and computing. In terms of microwave–optical quantum transduction, the state-of-the-art quantum transducers suffer low transduction efficiency from weak nonlinear coupling, wherein increasing pump power to enhance efficiency inevitably leads to thermal noise from heating. Moreover, we reveal that the efficiency-bandwidth product of a cavity electro-optical or electro-optomechanical transducer is fundamentally limited by pump power and nonlinear coupling coefficient, irrespective of cavity engineering efforts. To overcome this fundamental limit, we propose to noiselessly boost the transduction efficiency by consuming intraband entanglement (e.g., microwave–microwave or optical–optical entanglement in the case of microwave–optical transduction). Via a squeezer–coupler–antisqueezer sandwich structure, the protocol enhances the transduction efficiency to unity in the ideal lossless case, given an arbitrarily weak pump and nonlinear coupling. In practical cavity systems, our entanglement-assisted protocol surpasses the non-assisted fundamental limit of the efficiency-bandwidth product and reduces the threshold cooperativity for positive quantum capacity by a factor proportional to two-mode squeezing gain. Given a fixed cooperativity, our approach increases the broadband quantum capacity by orders of magnitude. The entanglement-assisted advantage is robust to ancilla loss and cavity detuning.

Shi, Haowei [Southern California U.] (ORCID:000000↗
DOE OSTI2025

Utah FORGE: 16B(78)-32 RFS DSS Strain Change Rate vs. Depth During 16A(78)-32 Stimulation

This dataset contains strain change rate versus depth data acquired using a Rayleigh frequency shift (RFS) distributed strain sensing (DSS) system during hydraulic stimulation of well 16A(78)-32 at the Utah FORGE site in April 2024. The data were collected from an optical fiber installed in the annulus of production well 16B(78)-32, approximately 300 feet from the injection well. The dataset includes tabulated strain data and an explanation of the methodology used to generate the frac log, which integrates strain change rate signals over selected time windows to identify fracture events.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Observations and Modeling of Fiber-Optics Strain on Hydraulic Fracture Height Growth in HFTS-2

Understanding fracture height growth can be of great significance to optimizing field development and improving recovery. The Hydraulic Fracturing Test Site 2 (HFTS-2) has provided a unique opportunity and an advanced dataset to allow us to observe and understand fracture geometries rigorously. Low frequency distributed acoustic sensing (LF-DAS) data from a vertical well in HFTS-2 showed three key observations: (i) excessive upward height growth (>1000 ft) and limited downward growth of the hydraulic fractures during pumping, (ii) considerable additional upward fracture height growth (~300 ft) after well shut in, and (iii) very complex LF-DAS strain rate patterns for a small fiber-to-stage offset. Advanced geomechanical modeling was performed to simulate the hydraulic fracture propagation and the resulting strain responses in the vertical direction. The modeling results demonstrated asymmetric upward and downward fracture height growths as observed in HFTS-2 with a similar upward height growth rate. Simulated waterfall plots of vertical strain rate showed distinct patterns for different fiber-to-fracture distances. The upward-growing fracture tip can be clearly identified by the interfaces between compressing and extending zones. It was also found that the complex strain rate patterns observed in HFTS-2 for small fiber-stage offsets were not caused by the mechanical layering but possibly result from the simultaneous propagation of multiple hydraulic fractures at different rates. Furthermore, the simulation results improved the understating of the HFTS-2 LF-DAS data and the simulated strain rate patterns could also serve as templates for fracture height interpretation from LF-DAS data in future.

58 GEOSCIENCES↗