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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hypersonic Fluid–Structure Interaction on a Cone–Slice–Ramp Geometry

In this study, fluid–structure interactions were measured between a representative control surface and the hypersonic flow deflected by it. The control surface is simplified as a spanwise finite ramp placed on a longitudinal slice of a cone. The front surface of the ramp contains a thin panel designed to respond to the unsteady fluid loading arising from the shock-wave/boundary-layer interactions. Experiments were conducted at Mach 5 and Mach 8 with ramps of different angles. High-speed schlieren captured the unsteady flow dynamics and accelerometers behind the thin panel measured its structural response. Panel vibrations were dominated by natural modes that were excited by the broadband aerodynamic fluctuations arising in the flowfield. However, increased structural response was observed in two distinct flow regimes: 1) attached or small separation interactions, where the transitional regime induced the strongest panel fluctuations. This was in agreement with the observation of increased convective undulations or bulges in the separation shock generated by the passage of turbulent spots, and 2) large separated interactions, where shear layer flapping in the laminar regime produced strong panel response at the flapping frequency. In addition, panel heating during the experiment caused a downward shift in its natural mode frequencies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A Data-Driven Algorithm for Enabling Delay Tolerance in Resilient Microgrid Controls Using Dynamic Mode Decomposition

The increased implementation of smart grid technologies in the power distribution grid presents unique opportunities that enable resiliency, but also brings challenges motivating needs for novel solutions and mitigation techniques. The bi-directional power and data flow allow for the grid to operate with increased resiliency, which is the ability to avoid discontinuity of service to end-use loads during extreme events. However, in applications where control of the distribution grid or microgrid relies on communication networks, the degradation of communication systems in the form of loss or high latency can cause maloperation and result in loss of end-use loads. Here this paper presents a novel framework to enable delay tolerance of centralized microgrid control schemes to mitigate communication system latency impacts and guarantee successful control action. We demonstrate the delay tolerance on a control scheme that operates a battery energy storage system (BESS) to offset the sudden loss of generation and maintain system frequency. During periods of severely degraded communication system performance, the proposed delay-tolerant algorithm compensates for the latency by utilizing a data-driven model generated at the device level using dynamic mode decomposition (DMD) to determine the performance of the communications. The DMD technique predicts the system’s frequency using device-level terminal measurements and provides updated control signals. The HELICS cosimulation platform evaluates the cyber-physical interaction of the power system model in GridLAB-D, the centralized control agent in Python, and the discrete network model in NS-3. The framework is tested and validated on the IEEE-123 node system modified to represent a networked remote microgrid model, and the results show an improvement in the dynamic performance

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Passive cooling designs to improve heat resilience of homes in underserved and vulnerable communities

Disadvantaged communities face a growing threat to staying safe during heat waves, especially during coincident power outages. This study develops a methodology to evaluate the effectiveness of passive cooling measures (those that operate without power) to improve residential building heat resilience. Building performance is simulated for representative homes and on district scales in two disadvantaged communities in Fresno, California. Eleven passive measures are evaluated using four heat resilience metrics with and without grid power. Results show performance of the mitigation measures varies by building characteristics, surrounding environment, and power scenario. The two most effective measures were installing solar-control window films and adding roof insulation. For pre-1978 single-family homes, these two measures can reduce unmet degree-hours (UDH) indoors by 12% and 11% respectively without grid power, or 28% and 37% with grid power. Their respective UDH reductions at district scale typically range 8% — 20% and 4% — 12% without grid power, or 14% — 44% and 8% — 51% with grid power. Top floors have higher overheating risk than lower floors during extreme heat events with coincident power outages. Natural ventilation can help, reducing UDH by 21% — 26%. The methodology and findings from this study can help cities, communities, and utilities develop effective and targeted strategies to promote greater residential heat resilience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Analysis of Long-Term Quality Control Data for a 137 Cs Dosimetry Calibration Source

Strict quality assurance programs are required for many radiological applications, but these seldom exist for verifying dosimetry calibration sources. After initial characterization of a dosimetry calibration facility, quality control procedures are recommended to ensure the early detection of any changes or malfunctions. These also result in refined knowledge about average dose rate and experimental variations in dose delivery. This paper describes the implementation of a phase I quality control protocol for a 137 Cs dosimetry calibration source and includes an analysis of the resulting data collected over a 24-mo period. During this time, substantial data was collected to establish trial control limits. Air kerma rate measurements were obtained using an ion chamber and were adjusted for decay, corrected for ambient temperature, pressure and humidity, and then analyzed using quality control charts. Three variations of rational subgrouping methods were used in order to find assignable causes of error, and Nelson's Rules were followed to detect any non-random statistical variations. Measurements were subgrouped according to same-day measurements in order to detect positional errors as well as atmospheric correction errors. Additionally, measurements were subgrouped according to analogous experimental setups in order to detect failure in equipment or incorrect settings. Both were analyzed using the X-bar and R chart method. Similarly, individuals and moving ranges charts were used to carefully examine each position in order to observe any situational errors that may occur which include timing, positional, or interference errors. Each method was successful in identifying unique out-of-control data points that occurred during the phase I application of forming control limits. Furthermore, over the 24-mo period, enough data points were deemed in-control to establish reliable trial limits. Future experiments will include the phase II application of gaining more reliable measurements in order to fine-tune the limits, as well as performing a designed experiment, where variables are purposefully changed in order to test the variation of the data.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Competition within low-density bacterial populations as an unexpected factor regulating carbon decomposition in bulk soil

Bacterial decomposition of organic matter in soils is generally believed to be mainly controlled by the access bacteria have to organic substrate. The influence of bacterial traits on this control has, however, received little attention. Using the substrate-dependent Monod growth model, we develop a bioreactive transport model to screen the interactive impacts of spatial dispersion and bacterial traits on mineralization. Bacterial traits primarily involved in the bacterial response to the substrate concentration, such as the maximum specific uptake rate and efficiency, the adaptation time of the uptake rate and the initial population density, are considered. We compare the model results with two sets of previously performed cm-scale soil-core experiments in which the mineralization of the pesticide 2,4-D was measured under well-controlled initial distributions and transport conditions. Bacterial dispersion away from the initial substrate location induced a significant increase in 2,4-D mineralization. Additionally, it reveals an increase of specific uptake rates at lower bacterial densities, more than compensating the decrease of specific uptake rates caused by substrate dilution. This regulation of bacterial activities by density, caused by the local depletion of substrate by competing bacteria, becomes dominant for bacteria with an efficient uptake of substrate at low substrate concentrations (a common feature of oligotrophs). Such oligotrophs, commonly found in soils, compete with each other for substrate even at remarkably low population densities. The ratio-dependent Contois growth model, which includes a density regulation in the expression of the uptake efficiency, is more accurate and convenient to calibrate than the substrate-dependent Monod model, at least under these conditions. In view of their strong interactions, bioreactive and transport processes cannot be handled independently but should be integrated, in particular when reactive processes of interest are carried out by oligotrophs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Prototype for Unclassified Algorithm

Continuing previous efforts to investigate and develop the Unclassified Radioisotope Algorithm, the goal of the FY19-FY20 effort was to develop a prototype detector system which uses the algorithm to confirm warhead attributes related to the presence of either weapons grade plutonium (WGPu) or highly enriched uranium (HEU). The final deliverable is a prototype attribute measurement system built with common, commercially available gamma radiation detector components, capable of confirming the presence of specific, complex radioactive sources of interest, without the collection and storage of gamma energy spectra. This is accomplished by processing each gamma pulse as it is received, applying weight values based on the energy and incrementing or decrementing scalar counters which can be compared with expected values to determine if the measured source is consistent with WGPu or HEU. This report documents the design of the prototype system as well as the development of the algorithm and performance testing results. While the previously conceptualized, simple algorithm resulted in a prohibitive amount of false positives, the goal for a simple attribute measurement system capable of verifying Ba-133 and Ra-226 (weapons grade plutonium and highly enriched uranium surrogate testing sources) at over 95% accuracy with sub 5% false positive rate was demonstrated.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Optimal Zeno Dragging for Quantum Control: A Shortcut to Zeno with Action-Based Scheduling Optimization

The quantum Zeno effect asserts that quantum measurements inhibit simultaneous unitary dynamics when the “collapse” events are sufficiently strong and frequent. This applies in the limit of strong continuous measurement or dissipation. It is possible to implement a dissipative control that is known as “Zeno dragging” by dynamically varying the monitored observable, and hence also the eigenstates, which are attractors under Zeno dynamics. This is similar to adiabatic processes, in that the Zeno-dragging fidelity is highest when the rate of eigenstate change is slow compared to the measurement rate. We demonstrate here two theoretical methods for using such dynamics to achieve control of quantum systems. The first, which we shall refer to as “shortcut to Zeno,” is analogous to the shortcuts to adiabaticity (counterdiabatic driving) that are frequently used to accelerate unitary adiabatic evolution. In the second approach, we apply the Chantasri-Dressel-Jordan stochastic action [PRA 88, 042110 (2013)], and demonstrate that the extremal-probability readout paths derived from this are well suited to setting up a Pontryagin-style optimization of the Zeno-dragging schedule. A fundamental contribution of the latter approach is to show that an action suitable for measurement-driven control optimization can be derived quite generally from statistical arguments. Implementing these methods on the Zeno dragging of a qubit, we find that both approaches yield the same solution, namely, that the optimal control is a unitary that matches the motion of the Zeno-monitored eigenstate. We then show that such a solution can be more robust than a unitary-only operation and we comment on solvable generalizations of our qubit example embedded in larger systems. These methods open up new pathways toward systematically developing dynamic control of Zeno subspaces to realize dissipatively stabilized quantum operations. Published by the American Physical Society 2024

Physics↗
DOE OSTI2021

Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning

We report building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While simulation codes are used to model ICF implosions, incomplete physics and the need for approximations deteriorate their predictive capability. Identification of relationships between controllable design inputs and measurable outcomes can help guide the future design of experiments and development of simulation codes, which can potentially improve the accuracy of the computational models used to simulate ICF implosions. In this article, we leverage developments in machine learning (ML) and methods for ML feature importance/sensitivity analysis to identify complex relationships in ways that are difficult to process using expert judgment alone. We present work using random forest (RF) regression for prediction of yield, velocity, and other experimental outcomes given a suite of design parameters, along with an assessment of important relationships and uncertainties in the prediction model. We show that RF models are capable of learning and predicting on ICF experimental data with high accuracy, and we extract feature importance metrics that provide insight into the physical significance of different controllable design inputs for various ICF design configurations. These results can be used to augment expert intuition and simulation results for optimal design of future ICF experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Automatic Waveform Quality Control for Surface Waves Using Machine Learning

Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise. This process has usually been completed by human experts labeling each waveform visually, which is time consuming and tedious for large data sets. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, K-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. To speed up signal quality assessment, we trained these five machine learning (ML) methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show that the two trained models agree with labels from human analysts but required only 0.4% of the time. Although the original (human) quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our investigations demonstrate the capability of the automated processing using these two ML models to reduce outliers in surface-wave-related measurements without human quality control screening.

58 GEOSCIENCES↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2023

The ARENA Test Bed – A Versatile Resource for I&C Development and Validation

The Accelerated and Real-time Experimental Nodal Assessment (ARENA) Test Bed at the Pacific Northwest National Laboratory (PNNL) is a versatile resource for development and validation of instrumentation and control (I&C) technologies. This capability was created to facilitate in-situ testing of nuclear electrical cables in various simulated operational environments. Using cable trays, a control box, and selected test components, low voltage cables can be staged to experience local adverse environments such as elevated temperature and water immersion. Cable condition can be continuously monitored over time to track the effect of local stresses using nondestructive assessment tools. A heads-up display (ARENA TV) plots key data in real-time for users. The ARENA Test Bed has recently been used to evaluate the potential for spread spectrum time domain reflectometry (SSTDR) to monitor thermal aging of a portion of live cable powering a three-phase motor. The arrangement provided the opportunity to directly compare the performance of the novel online SSTDR method with offline results from the more standard frequency domain reflectometry (FDR) method. The ability of SSTDR and FDR to identify the presence of water in immersed shielded and unshielded cables and to detect ground faults was also assessed. A digital twin is being developed to track and predict FDR signals from a thermally aging conceptual cable region to compare with measured signals from the ARENA physical counterpart. The test bed concept addresses an important need in nuclear I&C monitoring tool development. New tools and techniques can be developed in the test bed and validated versus known methods and physical measurements. Digital twins and machine learning engines can be populated with measured data in a controlled environment that would not be readily available in the actual nuclear power plant. Proposed monitoring strategies can be confirmed for effectiveness through objective evaluation. It is anticipated that the PNNL ARENA Test Bed will be a valuable resource in advancing nuclear plant instrumentation.

nuclear electrical cables, ARENA Test Bed, conditi↗
DOE OSTI2026

Enhancing Building Resilience: Maintaining Energy Efficiency and Thermal Comfort During Power Outages in Cold Climates

The increasing frequency and intensity of extreme weather events, such as heatwaves and cold snaps, present significant challenges to building energy performance and occupant comfort. Highly correlated with climate events are widespread long duration power interruptions that may affect thousands of buildings and millions of customers. This study evaluates the impact of building energy performance and occupant thermal comfort in medium-sized office buildings in a cold climate region. Using energy models representing pre-1980 and 2019 vintages, simulations were conducted to assess energy performance under typical weather conditions and occupant thermal comfort during power interrupted extreme cold snap and heatwave climate events under both current 2020s and future 2050s weather conditions. The results show a projected 33% increase in cooling energy demand and a 19% reduction in heating energy by 2050. Findings reveal that older buildings are more susceptible to cold discomfort during cold snaps, while modern airtight buildings are more vulnerable to overheating during heatwaves. Various passive energy efficiency measures, such as improved infiltration control, thermal windows, solar-controlled windows, and cool envelopes, were evaluated for their ability to mitigate thermal discomforts. Solar controlled windows and weatherstripping contribute to reducing cold thermal discomfort by 21% during a power-interrupted cold snap. Solar-controlled windows were found to reduce hot thermal discomfort by 34% during a future power-interrupted heatwave. The study highlights the importance of targeted retrofitting strategies to enhance thermal resilience, especially during power outages, to ensure occupant safety and comfort during extreme climate events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Computational Fluid Dynamics-Based Modeling of Methane Flows Around Oil and Gas Equipment

Recent studies estimate that emissions from oil and gas production facilities contribute between 20 and 50% of the total methane ( CH 4 ) emitted in the US; therefore, quantifying and reducing these emissions are crucial for achieving climate goals. Methane quantification depends on both measuring methane concentrations and converting them to emissions through a modeling framework. Currently, simple atmospheric dispersion models are primarily used to quantify emissions and concentrations, but these estimates are highly uncertain when quantifying emissions from complex aerodynamic sources, such as oil and gas facilities. This investigation used a CFD modeling approach, which can account for aerodynamic complexity but has hitherto not been used to model methane concentrations downwind of a methane release of a known rate, and compared it against in situ measurements. High-time-resolution (1 Hz) methane concentration and meteorological data were measured during experiments conducted at the METEC on 21 March and 11 July 2024. The METEC site configuration, measured wind data, and controlled emission rates were used as input for the CONVERGE CFD model to model downwind CH 4 concentration. The modeling was carried out between 20 and 70 m, from two different points of release in two separate controlled-release experiments, one from a separator and another from a wellhead. In these experiments, we found that the CFD model could predict the CH 4 concentrations downwind of the release to a good degree. The model was evaluated on multiple metrics to assess its performance in estimating methane concentrations at typical fence line distances (∼30 m). These results help us to understand external flows and the ability of CFD models to predict downwind concentrations in aerodynamically complex environments.

03 NATURAL GAS↗
DOE OSTI2025

Laser aberration signatures in expelled electrons from a tenuous gas

We describe a mechanism by which the aberration content of a focused, multiterawatt laser is imprinted upon the forward angular distribution of electrons ionized within and ponderomotively expelled from the focal volume. In our experiments, the laser aberration type and magnitude are controllably varied, and the measured electron distributions are correspondingly modified in a way consistent with predictions from numerical simulations. This imprint mechanism shows potential for enabling the development of accurate focal-spot characterization of intense lasers when fired at full power and is being developed for extension to petawatt laser systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A secure measurement unit for an inspection system used in nuclear arms-control verification

Verification of arms control treaties may require information barriers to protect sensitive data acquired during the verification process. These information barriers are commonly implemented in software, but must store and operate on data that are vulnerable to attack and tampering. Here, we present a new Secure Measurement Unit based on a modified pipeline SAR ADC architecture. The idea is to move the information barrier back into the digitization process so that prohibited data is never created. A prototype 65nm CMOS Secure Measurement Unit (SMU) was tested using U-235, U-238, Co-60, Cs-137 and Am-241 sources. Here, the prototype accurately digitized allowable signals while being immune to side-channel attacks on signals within preset forbidden regions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Hardware-in-the-Loop Testing of Wide-Area Damping Controller for Field Implementation in Large-scale Power Grid

In our previous work, an adaptive measurement-driven wide-area damping controller (WADC) for suppressing inter-area oscillations has been proposed and a hardware prototype was developed and validated through hardware-in-the-loop tests. As a continuation of the work, this paper introduces a WADC software prototype to handle the realistic challenges for field implementation in the control room of the power grid. The WADC software is developed and operated as an openPDC adapter with a graphical user interface (GUI) to monitor the WADC inputs and output, the communication delays and other variables. The software prototype has been fully tested through an enhanced hardware-in-the-loop (HIL) test setup. Its performance is verified under various realistic communication uncertainties, such as random time delays and data losses, with different communication protocols. The experiment results have proven the WADC software can deliver sufficient damping to suppress the targeted oscillation mode in handling various communication uncertainties for future field deployment.

Jia, Xinlan↗
DOE OSTI2021

Long‐term continuous cropping affects ecoenzymatic stoichiometry of microbial nutrient acquisition: a case study from a Chinese Mollisol

Abstract BACKGROUND Soil‐ and plant‐produced extracellular enzymes drive nutrient cycling in soils and are assumed to regulate supply and demand for carbon (C) and nutrients within the soil. Thus, agriculture management decisions that alter the balance of plant and supplemental nutrients should directly alter extracellular enzyme activities (EEAs), and EEA stoichiometry in predictable ways. We used a 12‐year experiment that varyied three major continuous grain crops (wheat, soybean, and maize), each crossed with mineral fertilizer (WCF, SCF and MCF, respectively) or not fertilized (WC, SC and MC, respectively, as controls). In response, we measured the phospholipid fatty acids (PLFAs), EEAs and their stoichiometry to examine the changes to soil microbial nutrient demand under the continuous cropping of crops, which differed with respect to the input of plant litter and fertilizer. RESULTS Fertilizer generally decreased soil microbial biomass and enzyme activity compared to non‐fertilized soil. According to enzyme stoichiometry, microbial nutrient demand was generally C‐ and phosphorus (P)‐limited, but not nitrogen (N)‐limited. However, the degree of microbial resource limitation differed among the three crops. The enzymatic C:N ratio was significantly lower by 13.3% and 26.8%, whereas the enzymatic N:P ratio was significantly higher by 9.9% and 42.4%, in MCF than in WCF and SCF, respectively. The abundances of arbuscular mycorrhizal fungi and aerobic PLFAs were significantly higher in MCF than in WCF and SCF. CONCLUSION These findings are crucial for characterizing enzymatic activities and their stoichiometries that drive microbial metabolism with respect to understanding soil nutrient cycles and environmental conditions and optimizing practices of agricultural management. © 2021 Society of Chemical Industry.

Chen, Xu↗