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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Degradation mechanisms of 1.3 μm C-doped quantum dot lasers grown on native substrate

Here, the aim of this paper is to analyse the degradation modes of 1.3 μm InAs quantum dot laser diodes (QD LDs) grown by using carbon as a p-type dopant, as an alternative to beryllium. The devices were stressed at high current densities, to favor the onset of excited-state (ES) emission, and to study the related degradation phenomena. The study of QD LDs is of fundamental importance for the photonic integrated circuits (PICs). The investigation is based on two types of stress tests: 1) a current step stress and 2) a constant current stress. With these experiments we demonstrate that a) the current is a determining factor for the onset of the ES during the operation of such devices. b) the onset of ES is responsible for a rapid quenching of the ground state (GS) emission. c) stress induces a significant increase in the threshold current of the devices, that is ascribed to the lowering of the injection efficiency, which in turn can be explained by the easier escape of carriers from quantum dots (QDs) from ES energy levels. d) the contribution of defects on the optical and electrical degradation is also discussed.

42 ENGINEERING↗
DOE OSTI2024

SANSMIC v.1.0

SAND2024-01036O The updated Sandia Solution Mining Code (SANSMIC) modeling software models the dissolution of underground salt cavern walls due to raw-water injection (leaching), assuming an oil blanket. A previous version of this software modeled the impacts of leaching at the Strategic Petroleum Reserve and has been used in research for several decades, but the source code was not available. The new version will enhance current research as additional subsurface storage of energy in solution-mined caverns increases. It will also allow research reproducibility by making the software available to the wider research community. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hart, David↗
DOE OSTI2023

Designing an Intrusion Detection for an Adjustable Speed Drive System Controlling a Critical Process

In this article, we address the cyber-security problem of industrial control systems (ICSs) when their sensor measurements may be compromised due to an attacker who has intercepted those measurements via a network. We introduce a general-purpose method “Dynamic Watermarking (DW)” to detect potential cyber-intrusions on speed sensor measurements within industrial control systems, which deploy an adjustable speed drive (ASD) to control a critical process. The DW method is injecting a random private low-amplitude signal with a zero mean Gaussian distribution, “watermark”, into one of the input phase voltages powering the ASD system. The watermark signal propagates through the system including pulse width modulation (PWM) power conversion stage and motor, then ultimately appears in the speed sensor measurements. By deploying two statistical DW tests with two proper thresholds, the system can detect potential cyber-intrusions or unobservable cyber-attacks such as replay attacks and false data injection attacks (FDIA). The DW method tested on a laboratory-scale ASD system experimentally to protect the system against cyber-intrusions. This system, powered by a commercial PWM drive operating at 208 V, 3-phase, and 3.7 kW, served as our experimental platform.

42 ENGINEERING↗
DOE OSTI2025

Optimization and Performance Characterization of the Second Generation Fermilab Constant Fraction Discriminator Readout ASIC

We present the optimization and performance characterization of the second-generation Fermilab Constant Fraction Discriminator ASIC (FCFD), designed for the readout of AC-coupled low-gain avalanche detector (LGAD) strip-sensors. The FCFD is explicitly engineered to be insensitive to signal-amplitude variations, thereby removing the need for time-walk correction that is required in other LGAD time-stamping readout ASICs. This updated version, referred to as FCFD1.1, incorporates several enhancements over the first iteration to address key challenges in AC-coupled LGAD front-end design. We outline the primary readout-ASIC design considerations for these applications, describe the methodology used to evaluate critical sensor and system parameters, and summarize the additionally implemented features. Performance measurements using injected charge signals and minimum-ionizing particles in test-beams demonstrate a time resolution of approximately 40 ps and a position resolution of roughly \SI{15}{\micro\meter} when tested with beam particles uniformly over the active area of the sensor.

Apresyan, Artur [Fermilab] (ORCID:0000000261860130↗
DOE OSTI2023

Nanoparticle-Surfactant Stabilized Strong Foam for Enhanced Oil Recovery in High-Salinity Fractured Carbonate Reservoirs

Summary Foam flooding can minimize bypassing in gasfloods in fractured reservoirs. Finding a foam formulation effective in high-salinity brine is challenging, especially with divalent cations, e.g., American Petroleum Institute (API) brine (8% NaCl with 2% CaCl2). When formulating with nanoparticles, the colloidal dispersion stability is difficult due to the dramatic reduction in zeta potential and the Debye length at high salinity. The aim of this work was to develop a strong foam in API brine at the ambient temperature, using a nonionic surfactant and ethyl cellulose nanoparticles (ECNP), for gasflooding in fractured carbonate reservoirs. ECNPs was synthesized and dispersed in API brine using a nonionic surfactant (also denoted as SF). SF and SF/ECNP foams were generated, and their stability was studied at atmospheric pressure and 950 psi. Foam mobility was measured in a sandpack at high pressure. Foam flood experiments were conducted in oil-saturated fractured carbonate cores. The nonionic surfactant proved to be a good dispersion agent for ECNP in API brine. The SF/ECNP mixture stabilized foam in API brine, even in the presence of oil. Injecting a partially miscible gas (below its minimum miscibility pressure) as an SF foam into a fractured core more than doubles the oil recovery over injection of the gas alone. The injection of the strong foam (SF/ECNP) further improves the oil recovery over that of the SF foam, indicating the synergy between ECNP and surfactant. ECNP accumulates in the foam lamella and induces larger pressure gradients in the fracture to divert more gas into the matrix for oil displacement.

Engineering↗
DOE OSTI2020

Binder Jet Tooling for Automotive Lighting Industry. CRADA Final Report

The goal of this collaborative project between Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) and Grote Industries, LLC was to develop additively manufactured tooling to produce lighting products in a production environment. The project explored the use of metal powder bed systems to rapidly manufacture moderately sized injection molding tools (less than 8” x 8” x 2”) for automotive lamps. The team designed the mold (male and female), manufactured the mold using the binder jet process, experimented with finishing processes, manufactured parts using the molds, and monitored the wear on the mold to predict life.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Development and Field Testing Novel Natural Gas Surface Process Equipment for Replacement of Water as Primary Hydraulic Fracturing Fluid (Final Report)

Southwest Research Institute® (SwRI ® ), Schlumberger Technology Corporation (SLB), and Chevron Corporation ® (Chevron) developed a novel, optimized, and lightweight process for natural gas (NG) to replace water as a low-cost fracturing medium with a low environmental impact. Hydraulic fracturing is used to increase oil and NG production by injecting high-pressure fluid, primarily water, into a rock formation, which fractures the rock and releases trapped oil and NG. This method was developed to increase yield and make feasible production areas that would not otherwise be viable for large-scale oil and NG extraction using traditional drilling technologies. Since the fracturing fluid is composed of approximately 90% water, one of the principal drawbacks to hydraulic fracturing is its excessive water use and associated large environmental footprint. According to recent data collected at fracture sites within the United States, fracturing applications in North America can consume as much as 9 million gallons of water per well. During the fracturing process, some of the fracturing fluid is permanently lost and the portion that is recovered is contaminated by both fracturing chemicals and dissolved solids from the formation. The recovered water or flow-back represents a significant environmental challenge, as it must be treated before it can be reintroduced into the natural water system. Although there is some recycling for future fracturing, the majority of the flow-back water is hauled from the well site to a treatment facility or to an injection well for permanent underground disposal. To mitigate these issues, an optimized, lightweight and modular surface process using NG to replace a majority of the water was developed as a cost-effective and environmentally clean fracturing fluid. Using NG will result in significantly less consumption since the gas that is injected as a fracturing fluid will be mixed with the formation gas and extracted as if it were from the formation itself. This process will minimize the collection, waste, and treatment of large amounts of water and will reduce the environmental impact of transporting and storing the fracturing fluid. There are two major steps involved in utilizing NG as the primary fracturing medium: (1) increasing the supply pressure of NG to wellhead pressures suitable for fracturing and (2) mixing the required chemicals and proppant needed for the fracturing process at these elevated pressures. The second step (NG-proppant mixing at elevated pressures) still requires demonstration; but very similar processes have been demonstrated in the field with other gases such as nitrogen (N 2 ) and carbon dioxide (CO 2 ). However, the first step (a compact, on-site unit for generating high-pressure NG at costs feasible for fracturing) has not been developed and is currently not commercially available. Due to the inherent compressibility of NG, more energy is required to compress the gas than what is required to pump water (or other incompressible liquids) to the very high pressure required for downhole injection. This project aimed to develop a novel, hybrid method to overcome this challenge. The project accomplishments and findings are discussed in this report. Ultimately, the research and development efforts described herein demonstrate that fracturing with NG foam is a feasible alternative to using water.

02 PETROLEUM↗
DOE OSTI2025

The potential for New Mexico basalts to sequester CO2 with a focus on critical element mobility

This study investigates the potential for New Mexico basalts to serve as geologic CO₂ sequestration media through mineral carbonation. Batch-type experiments were conducted using crushed basalts from the Carrizozo and Taos regions at low PCO₂ (0.7–2 bar) and temperatures up to 40°C over 180 days. The experiments evaluated the kinetics of CO₂-water-rock interaction, cation release, and critical element mobility (Cu, Ni, Co, Zn). Solid and fluid samples were analyzed using SEM, EMPA, ICP-OES, and ICP-MS, and geochemical modeling was performed with GEM-Selektor and PHREEQC. Results indicated that up to 80% of the injected CO₂ was mineralized within 100 days, forming secondary carbonates such as siderite and ankerite. Additionally, significant mobilization of critical metals was observed, which has implications for resource recovery and mine-waste valorization in future CO₂ sequestration projects.

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

Evaluation of two inoculation routes of an adenovirus-mediated viral protein inhibitor in a Crimean-Congo hemorrhagic fever mouse model

Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne nairovirus with a wide geographic spread that can cause severe and lethal disease. No specific medical countermeasures are approved to combat this illness. The CCHFV L protein contains an ovarian tumor (OTU) domain with a cysteine protease thought to modulate cellular immune responses by removing ubiquitin and ISG15 post-translational modifications from host and viral proteins. Viral deubiquitinases like CCHFV OTU are attractive drug targets, as blocking their activity may enhance cellular immune responses to infection, and potentially inhibit viral replication itself. We previously demonstrated that the engineered ubiquitin variant CC4 is a potent inhibitor of CCHFV replication in vitro. A major challenge of the therapeutic use of small protein inhibitors such as CC4 is their requirement for intracellular delivery, e.g., by viral vectors. In this study, we examined the feasibility of in vivo CC4 delivery by a replication-deficient recombinant adenovirus (Ad-CC4) in a lethal CCHFV mouse model. Since the liver is a primary target of CCHFV infection, we aimed to optimize delivery to this organ by comparing intravenous (tail vein) and intraperitoneal injection of Ad-CC4. While tail vein injection is a traditional route for adenovirus delivery, in our hands intraperitoneal injection resulted in higher and more widespread levels of adenovirus genome in tissues, including, as intended, the liver. However, despite promising in vitro results, neither route of in vivo CC4 treatment resulted in protection from a lethal CCHFV infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Correlated interlayer exciton insulator in heterostructures of monolayer WSe 2 and moiré WS 2 /WSe 2

Moiré superlattices in van der Waals heterostructures have emerged as a powerful tool for engineering quantum phenomena. Here, in this work, we report the observation of a correlated interlayer exciton insulator in a double-layer heterostructure composed of a WSe 2 monolayer and a WS 2 /WSe 2 moiré bilayer that are separated by ultrathin hexagonal boron nitride. The moiré WS 2 /WSe 2 bilayer features a Mott insulator state when the density of holes is one per moiré lattice site. When electrons are added to the Mott insulator in the WS 2 /WSe 2 moiré bilayer and an equal number of holes are injected into the WSe 2 monolayer, a new interlayer exciton insulator emerges with the holes in the WSe 2 monolayer and the electrons in the doped Mott insulator bound together through interlayer Coulomb interactions. The interlayer exciton insulator is stable up to a critical hole density in the WSe 2 monolayer, beyond which the interlayer exciton dissociates. Our study highlights the opportunities for realizing quantum phases in double-layer moiré systems due to the interplay between the moiré flat band and strong interlayer electron interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Bipolar planetary nebulae from outflow collimation by common envelope evolution

ABSTRACT The morphology of bipolar planetary nebulae (PNe) can be attributed to interactions between a fast wind from the central engine and the dense toroidal-shaped ejecta left over from common envelope (CE) evolution. Here we use the 3D hydrodynamic adaptive mesh refinement (AMR) code AstroBEAR to study the possibility that bipolar PN outflows can emerge collimated even from an uncollimated spherical wind in the aftermath of a CE event. The output of a single CE simulation via the smoothed particle hydrodynamics (SPH) code phantom serves as the initial conditions. Four cases of winds, all with high enough momenta to account for observed high momenta pre-PN outflows, are injected spherically from the region of the CE binary remnant into the ejecta. We compare cases with two different momenta and cases with no radiative cooling versus application of optically thin emission via a cooling curve to the outflow. Our simulations show that in all cases highly collimated bipolar outflows result from deflection of the spherical wind via the interaction with the CE ejecta. Significant asymmetries between the top and bottom lobes are seen in all cases. The asymmetry is strongest for the lower momentum case with radiative cooling. While real post-CE winds may be aspherical, our models show that collimation via ‘inertial confinement’ will be strong enough to create jet-like outflows even beginning with maximally uncollimated drivers. Our simulations reveal detailed shock structures in the shock-focused inertial confinement (SFIC) model and develop a lens-shaped inner shock that is a new feature of SFIC-driven bipolar lobes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The hidden structure of hydrodynamic transport in random fracture networks

We study the large-scale dynamics and prediction of hydrodynamic transport in random fracture networks. The flow and transport behaviour is characterized by first passage times and displacement statistics, which show heavy tails and anomalous dispersion with a strong dependence on the injection condition. The origin of these behaviours is investigated in terms of Lagrangian velocities sampled equidistantly along particle trajectories, unlike classical sampling strategies at a constant rate. The velocity series are analysed by their copula density, the joint distribution of the velocity unit scores, which reveals a simple, albeit hidden, correlation structure that can be described by a Gaussian copula. Based on this insight, we derive a Langevin equation for the evolution of equidistant particle speeds. In this framework, particle motion is quantified by a stochastic time-domain random walk, the joint density of particle position, and speed satisfies a Klein–Kramers equation. The upscaled theory quantifies particle motion in terms of the characteristic fracture length scale and the distribution of Eulerian flow velocities. That is, it is predictive in the sense that it does not require the a priori knowledge of transport attributes. The upscaled model captures non-Fickian transport features, and their dependence on the injection conditions in terms of the velocity point statistics and average fracture length. It shows that the first passage times and displacement moments are dominated by extremes occurring at the first step. The presented approach integrates the interaction of flow and structure into a predictive model for large-scale transport in random fracture networks.

42 ENGINEERING↗
DOE OSTI2021

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

We report the extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.

58 GEOSCIENCES↗
DOE OSTI2023

Carrier dynamics in blue, cyan, and green InGaN/GaN LEDs measured by small-signal electroluminescence

We study the carrier dynamics for c-plane InGaN/GaN light-emitting diodes (LEDs) with various emission wavelengths near the green gap using a small-signal electroluminescence method. The LEDs were grown by Lumileds using state-of-the-art growth conditions. Radiative and non-radiative recombination rates are numerically separated, and the carrier recombination lifetime and carrier density are obtained. Experiment shows that the causes of efficiency reduction at longer wavelength in the present structures are injection efficiency decrease, radiative recombination rate decrease, and imbalance of the increase in Auger–Meitner and radiative terms due to the interplay between the carrier–current density relationship and the quantum-confined Stark effect (QCSE). Here, the effects of QCSE, phase-space filling, and the carrier–current density relationship on efficiency reduction at longer wavelengths are examined separately with experimental data and Schrödinger–Poisson calculations. In addition, we confirm the scaling law between C(n) and B(n) under electrical injection and find that the increase in carrier density at a given current density is the primary cause for lower radiative efficiency at high current density in longer wavelength LEDs. Conversely, we do not observe a significant efficiency reduction at longer wavelengths from extrinsic material degradation.

42 ENGINEERING↗
DOE OSTI2021

Parametric Study of SANSMIC Input and Resulting Impact on Predicted Cavern Geometry and Leaching Efficiency

The Sandia Solution Mining Code (SANSMIC) has been used for many years to examine the development of salt cavern geometry, both in a confirmatory manner with comparisons made to real-world sonar data and in a predictive manner when updated sonar data are not available. SANSMIC models require some modeling choices in order to incorporate real-world data. Key modeling choices include the vertical resolution of cavern geometry to implement, as well as how to incorporate daily raw water injection data into the SANSMIC model. This report documents five studies that address the impact of the modeling choices on the predicted cavern geometries and calculated leaching efficiencies. In most cases, hypothetical cylindrical initial cavern geometries are used to provide a common baseline against which to test the systematic variation of input variables including cavern radius, oil-brine-interface (OBI) depth, vertical cell size, raw water injection rate, raw water injection duration, workover time, and number of leaching stages. The use of smaller cell sizes is recommended moving forward to provide a better one-to-one relationship between sonar data and the modeled cavern. A new methodology for incorporating raw water injection data is also recommended, in order to more closely model real-world injection and workover times. Overall, the systematic studies performed here have increased our confidence in previous SANSMIC model results, as well future use of the code for predicting leaching effects on cavern geometries. Some minor changes to modeling choices are recommended, which can easily be applied with the version of SANSMIC currently under development.

42 ENGINEERING↗
DOE OSTI2025

Edge ML for CAN bus intrusion detection in AVs

Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

CONTROL AND DATA ACQUISITION IN A CYBER-PHYSICAL MIDSTREAM TESTBED

This thesis presents the development of a laboratory-scale cyber–physical midstream pipeline testbed designed to address this gap and support research in industrial control systems security. The platform integrates pumps, valves, sensors, programmable logic controllers (PLCs), and a human–machine interface (HMI) to emulate the monitoring and control architecture of real pipeline operations. The physical process is implemented as a closed-loop liquid circulation system designed to replicate flow behavior characteristic of midstream pipeline infrastructure. The testbed enables real-time data acquisition of key process variables, including flow rate and pressure facilitating the generation of datasets representative of normal pipeline operation. A threat model encompassing common ICS attack vectors was developed, including sensor spoofing, command injection, false data injection, denial-of-service attacks, and relay manipulation. Multiple attack scenarios were implemented and evaluated to demonstrate how cyber intrusions targeting sensors, actuators, networks, and software propagate into measurable physical consequences in pipeline flow and pressure. The developed platform serves as a practical, cost-effective environment for experimentation, education, and future cybersecurity research in midstream pipeline systems.

42 ENGINEERING↗
DOE OSTI2023

Linewidth narrowing in self-injection-locked on-chip lasers

Abstract Stable laser emission with narrow linewidth is of critical importance in many applications, including coherent communications, LIDAR, and remote sensing. In this work, the physics underlying spectral narrowing of self-injection-locked on-chip lasers to Hz-level lasing linewidth is investigated using a composite-cavity structure. Heterogeneously integrated III–V/SiN lasers operating with quantum-dot and quantum-well active regions are analyzed with a focus on the effects of carrier quantum confinement. The intrinsic differences are associated with gain saturation and carrier-induced refractive index, which are directly connected with 0- and 2-dimensional carrier densities of states. Results from parametric studies are presented for tradeoffs involved with tailoring the linewidth, output power, and injection current for different device configurations. Though both quantum-well and quantum-dot devices show similar linewidth-narrowing capabilities, the former emits at a higher optical power in the self-injection-locked state, while the latter is more energy-efficient. Lastly, a multi-objective optimization analysis is provided to optimize the operation and design parameters. For the quantum-well laser, minimizing the number of quantum-well layers is found to decrease the threshold current without significantly reducing the output power. For the quantum-dot laser, increasing the quantum-dot layers or density in each layer increases the output power without significantly increasing the threshold current. These findings serve to guide more detailed parametric studies to produce timely results for engineering design.

47 OTHER INSTRUMENTATION↗