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DOE OSTI2024

Dynamics of hydrogen–ammonia–natural gas lean-premixed high-pressure flames

The influence of fuel composition on self-excited combustion instabilities in a high-pressure combustor operated with ammonia, hydrogen, and natural gas fuels is characterized with high-frequency pressure measurements and imaging of the flame structure. A micromix multi-stage injector with 19 elements is used to introduce the fuel blend premixed with heated air into an optically accessible combustor operated at ~1.1 MPa. As hydrogen is substituted for natural gas, longitudinal thermoacoustic instabilities are observed in the combustor with pressure fluctuation amplitudes as large as 8% of the mean chamber value. In the absence of natural gas, limit-cycle instability magnitude is largely insensitive to ammonia addition. However, fuel compositions with > 20% natural gas result in positive correlation between instability amplitude and ammonia concentration. Spatial distribution of heat release in the combustor evaluated from OH* chemiluminescence imaging reveals axial growth in the regions of heat release fluctuation as hydrogen decreases, thus correlating flame length to instability amplitude. Distinct transitions from the first harmonic of the fundamental longitudinal acoustic mode (~1100 Hz) to the fundamental mode (~550 Hz) are observed with root mean square pressure fluctuations exceeding 7% of the mean chamber value. Here, these cases correspond to hydrogen mole fractions ≤ 50%. Analysis of the phase relationship between pressure fluctuations upstream and downstream of the flame zone indicates acoustic coupling of the injector as a key contributor to instability growth.

03 NATURAL GAS↗

DOE OSTI2022

Predicting spring green-up across diverse North American grasslands

Vegetation phenology influences many ecosystem and climate processes, such as carbon uptake and energy and water cycles. Thus, understanding drivers of vegetation phenology is crucial for predicting current and future impacts of climate change on ecological systems. Existing models can accurately predict the date of spring green-up in temperate forests but tend to perform poorly in grassland systems. We hypothesize this is because most do not incorporate water availability, a primary limiting factor for grassland plants. In this study, we used long-term datasets of digital imagery from the PhenoCam Network of 43 diverse North American grassland sites (195 site-years) to test existing spring phenology models, as well as develop several new models that incorporate precipitation or soil moisture (53 models). As a result, most of the new models performed substantially better, with the best model requiring sufficient accumulated precipitation followed by warm temperatures to trigger spring onset (root mean square error, RMSE, between predicted and observed dates = 16.0 days). Importantly, the best model performed well across all grassland types using a single set of parameters, from temperate to arid grasslands. Since plants are adapted to their local climates, model performance was further improved when parameters were independently optimized for four separate climate regions (RMSE = 10.4 days). Therefore, both sufficient precipitation and temperature are required for grassland green-up, but optimal thresholds vary by region. Running the top model with projected climate data (representative concentration pathway 8.5) suggests that, depending on the climate region, spring onset will occur up to 12 days earlier within 100 years in temperature-limited sites, but the trend is unclear for precipitation-limited sites (3.5 ± 8.0 days later). This new phenology model improves our ability to understand and predict grassland dynamics, with implications for both current and future ecosystem processes related to carbon and water cycling.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2006

The Space Technology-7 Disturbance Reduction System Precision Control Flight Validation Experiment Control System Design

As originally proposed, the Space Technology-7 Disturbance Reduction System (DRS) project, managed out of the Jet Propulsion Laboratory, was designed to validate technologies required for future missions such as the Laser Interferometer Space Antenna (LISA). The two technologies to be demonstrated by DRS were Gravitational Reference Sensors (GRSs) and Colloidal MicroNewton Thrusters (CMNTs). Control algorithms being designed by the Dynamic Control System (DCS) team at the Goddard Space Flight Center would control the spacecraft so that it flew about a freely-floating GRS test mass, keeping it centered within its housing. For programmatic reasons, the GRSs were descoped from DRS. The primary goals of the new mission are to validate the performance of the CMNTs and to demonstrate precise spacecraft position control. DRS will fly as a part of the European Space Agency (ESA) LISA Pathfinder (LPF) spacecraft along with a similar ESA experiment, the LISA Technology Package (LTP). With no GRS, the DCS attitude and drag-free control systems make use of the sensor being developed by ESA as a part of the LTP. The control system is designed to maintain the spacecraft s position with respect to the test mass, to within 10 nm/the square root of Hz over the DRS science frequency band of 1 to 30 mHz.

O'Donnell, James R.↗

NASA NTRS1997

Verifying Stability of Dynamic Soft-Computing Systems

Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent systems that are flexible and robust. Although recent research have shown that certain class of neuro-fuzzy controllers can be proven bounded and stable, they are implementation dependent and difficult to apply to the design and validation process. Many practitioners adopt the trial and error approach for system validation or resort to exhaustive testing using prototypes. In this paper, we describe our on-going research towards establishing necessary theoretic foundation as well as building practical tools for the verification and validation of soft-computing systems. A unified model for general neuro-fuzzy system is adopted. Classic non-linear system control theory and recent results of its applications to neuro-fuzzy systems are incorporated and applied to the unified model. It is hoped that general tools can be developed to help the designer to visualize and manipulate the regions of stability and boundedness, much the same way Bode plots and Root locus plots have helped conventional control design and validation.

Wen, Wu↗

NASA NTRS1997

Comparative Model Evaluation Studies of Biogenic Trace Gas Fluxes in Tropical Forests

Simulation modeling can play a number of important roles in large-scale ecosystem studies, including synthesis of patterns and changes in carbon and nutrient cycling dynamics, scaling up to regional estimates, and formulation of testable hypotheses for process studies. Recent comparative studies have shown that ecosystem models of soil trace gas exchange with the atmosphere are evolving into several distinct simulation approaches. Different levels of detail exist among process models in the treatment of physical controls on ecosystem nutrient fluxes and organic substrate transformations leading to gas emissions. These differences are is in part from distinct objectives of scaling and extrapolation. Parameter requirements for initialization scalings, boundary conditions, and time-series driven therefore vary among ecosystem simulation models, such that the design of field experiments for integration with modeling should consider a consolidated series of measurements that will satisfy most of the various model requirements. For example, variables that provide information on soil moisture holding capacity, moisture retention characteristics, potential evapotranspiration and drainage rates, and rooting depth appear to be of the first order in model evaluation trials for tropical moist forest ecosystems. The amount and nutrient content of labile organic matter in the soil, based on accurate plant production estimates, are also key parameters that determine emission model response. Based on comparative model results, it is possible to construct a preliminary evaluation matrix along categories of key diagnostic parameters and temporal domains. Nevertheless, as large-scale studied are planned, it is notable that few existing models age designed to simulate transient states of ecosystem change, a feature which will be essential for assessment of anthropogenic disturbance on regional gas budgets, and effects of long-term climate variability on biosphere-atmosphere exchange.

Potter, C. S.↗

DOE OSTI2022

Computational Methods to Characterize Panel Loading Conditions for Accelerated Testing

Panel cracking and degradation due to wind loading are known to have a detrimental effect on power output. Recreating these damaging conditions in a controlled experimental setting requires an understanding of the loads being generated at the panel surface as a function of both wind speed, wind direction, and panel orientation. To better understand these relationships, a computational fluid dynamics (CFD) simulation package was constructed using an open-source Python library for solving partial differential equations with the finite element method. This CFD package allows the simulated wind speed and panel orientation of a multi-panel array to be easily changed and provides the capability to measure the traction forces at discrete points along the sun-facing and ground-facing surface of an interior panel residing in the wake of one or more upstream panels. A parameter exploration was performed in which the panel angle was varied in even increments from -70 deg to 70 deg and the wind speed was varied from 2 to 30 m/s. During post processing, the measured traction along the panel surface was averaged spatially and interpreted as a time-varying signal, where further processing of this signal yielded the root mean square amplitude and a characteristic frequency associated with the loading. This study found that higher wind speeds are generally associated with increased amplitude of loading and that the panel orientation angle can significantly exacerbate or mitigate this loading. These outcomes are presented along with current work on higher-fidelity verification simulations and recommendations for performing accelerated experimental testing.

loading↗

DOE OSTI2018

Pore-size resolved water vapor adsorption kinetics of white cement mortars as viewed from proton NMR relaxation

The dynamic pore structure of partially saturated cement-based material is rather essential to quantify its mass transport and many other properties. To clarify the influence of water removal and re-entry, the low-field nuclear magnetic resonance technique is employed to characterize the pore structure of two white cement mortars during isopropanol exchange and water vapor adsorption kinetics. It is found that, after isopropanol replacement the pore structures of mortars become remarkably coarsen due to the significant collapse of C-S-H interlayer pores, which are only partly reversible at water re-saturation. Consistently, water vapor adsorption is not a simple progressively filling process from finer to coarser pores, but accompanied by continuous expansion of C-S-H gel adsorbing water in priority. Furthermore, the irrecoverable contraction of C-S-H gel is the physical root of irreversible shrinkage. The smaller BET surface area using nitrogen than water is also attributed to the collapse of interlayer pores after drying.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Directional Laplacian Centrality for Cyber Situational Awareness

Cyber operations is drowning in diverse, high-volume, multi-source data. To get a full picture of current operations and identify malicious events and actors, analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, they typically use signature-based detection methods. We introduce a general method rooted in spectral graph theory to discover patterns and anomalies without a priori knowledge of signatures. We derive and propose a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix in the direction of a vertex. To build intuition about our measure, we show how it identifies the most central vertices in standard network datasets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important IP addresses in network flow data. Using both real and synthetic network flow data, we conduct several experiments to test our measure’s sensitivity to two types of injected attack profiles and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

Predicting Dynamic-to-Static Correction Factor from Petrophysical Data and Chemostratigraphy using Unsupervised Machine Learning

Estimating static mechanical properties of stratigraphic layers is critical for optimizing subsurface engineering applications. To estimate dynamic-to-static correction factor F ds (static-to-dynamic Young’s modulus ratio) across the Caney shale interval in Oklahoma, USA, we integrated triaxial test measurements and petrophysical data, including well logs and X-ray fluorescence (XRF) using unsupervised machine learning (ML). We used a novel workflow that includes principal component analysis (PCA) to reduce data set dimensionality of well logs and XRF data sets—both separately and combined—creating three scenarios, and later applied inverse distance weighting (IDW) to derive F ds profiles for these scenarios. Furthermore, we applied K-means clustering on each scenario to predict depositional facies, and built a stiffness zonation profile through chemostratigraphic analysis of the terrigenous elements to validate the predicted F ds . The predicted F ds profile from each scenario using the PCA-IDW method was compared with the constant F ds approach from our previous study by calculating the root mean square error (RMSE). The combined data sets scenario yielded the lowest RMSE value of 0.113, while the RMSE values for the well logs and XRF scenarios were 0.131 and 0.129, respectively. In addition, the predicted F ds from the XRF scenario well-matched the stiffness zonation from the chemostratigraphic analysis that was built using the optimized K-means clustering of nine clusters for that scenario. These methods and findings offer a valuable tool for refining lithological classification and improving the F ds profile, potentially enhancing drilling and stimulation strategies for subsurface energy engineering applications.

clastic rock↗

NASA NTRS2011

Signal Processing Methods for Liquid Rocket Engine Combustion Stability Assessments

The J2X Gas Generator engine design specifications include dynamic, spontaneous, and broadband combustion stability requirements. These requirements are verified empirically based high frequency chamber pressure measurements and analyses. Dynamic stability is determined with the dynamic pressure response due to an artificial perturbation of the combustion chamber pressure (bomb testing), and spontaneous and broadband stability are determined from the dynamic pressure responses during steady operation starting at specified power levels. J2X Workhorse Gas Generator testing included bomb tests with multiple hardware configurations and operating conditions, including a configuration used explicitly for engine verification test series. This work covers signal processing techniques developed at Marshall Space Flight Center (MSFC) to help assess engine design stability requirements. Dynamic stability assessments were performed following both the CPIA 655 guidelines and a MSFC in-house developed statistical-based approach. The statistical approach was developed to better verify when the dynamic pressure amplitudes corresponding to a particular frequency returned back to pre-bomb characteristics. This was accomplished by first determining the statistical characteristics of the pre-bomb dynamic levels. The pre-bomb statistical characterization provided 95% coverage bounds; these bounds were used as a quantitative measure to determine when the post-bomb signal returned to pre-bomb conditions. The time for post-bomb levels to acceptably return to pre-bomb levels was compared to the dominant frequency-dependent time recommended by CPIA 655. Results for multiple test configurations, including stable and unstable configurations, were reviewed. Spontaneous stability was assessed using two processes: 1) characterization of the ratio of the peak response amplitudes to the excited chamber acoustic mode amplitudes and 2) characterization of the variability of the peak response's frequency over the test duration. This characterization process assists in evaluating the discreteness of a signal as well as the stability of the chamber response. Broadband stability was assessed using a running root-mean-square evaluation. These techniques were also employed, in a comparative analysis, on available Fastrac data, and these results are presented here.

Kenny, R. Jeremy↗

DOE OSTI2023

Hierarchical Modeling to Enhance Spectrophotometry Measurements—Overcoming Dynamic Range Limitations for Remote Monitoring of Neptunium

A robust hierarchical model has been demonstrated for monitoring a wide range of neptunium concentrations (0.75–890 mM) and varying temperatures (10–80 °C) using chemometrics and feature selection. The visible–near infrared electronic absorption spectrum (400–1700 nm) of monocharged neptunyl dioxocation (Np(V) = NpO2+) includes many bands, which have molar absorption coefficients that differ by nearly 2 orders of magnitude. The shape, position, and intensity of these bands differ with chemical interactions and changing temperature. These challenges make traditional quantification by univariate methods unfeasible. Measuring Np(V) concentration over several orders of magnitude would typically necessitate cells with varying path length, optical switches, and/or multiple spectrophotometers. Alternatively, the differences in the molar extinction coefficients for multiple absorption bands can be used to quantify Np(V) concentration over 3 orders of magnitude with a single optical path length (1 mm) and a hierarchical multivariate model. In this work, principal component analysis was used to distinguish the concentration regime of the sample, directing it to the relevant partial least squares regression submodels. Each submodel was optimized with unique feature selection filters that were selected by a genetic algorithm to enhance predictions. Through this approach, the percent root mean square error of prediction values were ≤1.05% for Np(V) concentrations and ≤4% for temperatures. This approach may be applied to other nuclear fuel cycle and environmental applications requiring real-time spectroscopic measurements over a wide range of conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1999

CUSP Energetic Particles: Confinement, Acceleration and Implications

The cusp energetic particle (CEP) event is a new magnetospheric phenomenon. The events were detected in the dayside cusp for hours, in which the measured helium ions had energies up to 8 MeV. All of these events were associated with a dramatic decrease and large fluctuations in the local magnetic field strength. During January 1999 - December 1999 covered by this report, I have studied the CEP events by analyzing the POLAR, GEOTAIL, and WIND particle and magnetic field data measured during the geomagnetic quiet periods in 1996 and one geomagnetic storm period in 1998. The simultaneous observations indicated that the ion fluxes in the CEP events were higher than that in both the upstream and the downstream from the bow shock. The pitch angle distribution of the helium ions in the CEP events was found to peak around 90 deg. It was found that the mirror parameter, defined as the ratio of the square root of the integration of the parallel turbulent power spectral component over the ultra-low frequency (ULF) ranges to the mean field in the cusp, is correlated with the intensity of the cusp MeV helium flux, which is a measure of the influence of mirroring interactions and an indication of local effect. It was also found that the turbulent power of the local magnetic field in the ultra-low frequency (ULF) ranges is correlated with the intensity of the cusp energetic helium ions. Such ULF ranges correspond to periods of about 0.33-500 seconds that cover the gyroperiods, the bounce periods, and the drift periods of the tens keV to MeV charged particles when they are temporarily confined in the high-altitude dayside cusp. These observations represent a discovery that the high-altitude dayside cusp is a new acceleration and dynamic trapping region of the magnetosphere. The cusp geometry is connected via gradient and curvature drift of these energized ions to the equatorial plasma sheet as close as the geostationary orbit at local midnight. It implies that the dayside cusp is potentially an important source of magnetospheric particles. The discovery of the CEP events has been recognized as one of the most significant results from POLAR. I was invited to give a talk at 1999 IUGG meeting to interpret the CEP events. This discovery has also been written into the web-based Space Physics Text Book (http://www.oulu.fi/- spaceweb/textbook/cusp.html).

Chen, Jiasheng↗

NASA NTRS2019

Uncertainty in Soil Moisture Retrievals: an Ensemble Approach Using SMOS L-Band Microwave Data

The uncertainty of soil moisture (SM) retrievals from satellite brightness temperature (TB) observations depends primarily on the choice of radiative transfer model (RTM) parameters, prior SM information and TB inputs. This paper studies the sensitivity of several (quasi-)operational and experimental SM retrieval products from the Soil Moisture Ocean Salinity (SMOS) mission to these choices at 11 reference sites, located in 7 watersheds across the United States (US). Different literature-based RTM parameter sets cause large biases between retrievals. Whereas typical RTM parameter sets are calibrated for SM retrievals, it is shown that a parameter set carefully optimized for TB forward modeling can also be used for retrieving SM. It is also shown that the inclusion of dynamic prior SM estimates in a Bayesian retrieval scheme can strongly improve SM retrievals, regardless of the choice of RTM parameters, and that the use of multi-angular and multi-polarization TB does not necessarily lead to superior retrievals compared to retrievals based on TB data at a single incidence angle and polarization. The second part of this paper evaluates ensemble uncertainty metrics for SM retrievals obtained by propagating a wide range of RTM parameters through the RTM. As expected for bounded variables, the spread in the ensemble SM retrievals is smallest for wet and dry SM values and highest for intermediate SM values. After removal of the strong long-term mean bias associated with the RTM parameter values for individual ensemble members, the remaining anomaly ensemble SM spread of 0.037 cu m/cu m approximates the actual time series unbiased root-mean-square-difference of 0.042 cu m/cu m between ensemble mean retrievals and in situ reference data across the reference sites. However, the temporal variability in the anomaly ensemble spread reveals higher-order biases in the retrieval error, which should be accounted for when characterizing retrieval error.

Jan Quets↗

NASA NTRSDate not supplied

Manual Crew Override of Vehicle Landings Following G-Transitions

BACKGROUND Manual control during exploration spaceflight consists of both planned automated supervisory control and unplanned crew override. This crew override capability is critical to enable overall mission success during landing contingencies. However, the introduction of manual override capabilities must be implemented to enable crews to mitigate risks introduced by human error. Adaptive changes in the sensorimotor system can manifest during g-transitions as spatial disorientation. While training and landing aids enable successful landing through disorientation, these adaptive changes may increase cognitive demand that needs to be accounted for in the manual control strategy. It is important to characterize these effects as soon as possible following the G-transition to develop appropriate countermeasures. METHODS In this study, we will examine operational piloting tasks following International Space Station (ISS) missions in a simulated lunar landing using a six-degree-of-freedom (6DOF) motion base. The motion-based simulation will be implemented in our laboratory at the Johnson Space Center (JSC) and will be available within hours following the return from commercial crew landings. The primary goals of this study are (1) to understand the impact of spaceflight on crew ability to perform manual crew override tasks, (2) to examine how adaptive changes in vestibular and cognitive function relate to changes in manual crew override proficiency, and (3) compare performance during late “just-in-time” on-board training with early post-flight crew performance. The impact of spaceflight on piloting capability will be assessed from pre- versus post-flight changes in crewmembers assigned to either short duration (< 30 day) or long duration (~6- month) missions to the International Space Station (ISS). Individual differences in post-flight vestibular and cognitive changes include motion sickness reports, measures of tilt motion perception accuracy and precision, and dual-task tracking. During the 6DOF lunar simulation, the crew will manually takeover attitude and rate-of-descent to the nominal or re-designated landing aim point during the approach phase. The outcome measures for the lunar crew override tasks will be the percent time maintaining actual vehicle states, e.g., attitude and rate-of-descent, within recommended guidance during the landing approach, number and maximum deviation outside limits, and root mean square error (RMSE). Given that “just-in-time” (JIT) training is an operational expectation for the Human Landing System (HLS) program, all participants will perform late inflight JIT training for each manual crew override task in which they will participate. Crew proficiency will be captured inflight during JIT training that will be implemented on a laptop with hand controllers to allow the crewmember to practice the landing task procedures like the approach implemented for JIT training with Shuttle landing and ISS telerobotic tasks. COUNTERMEASURE UPDATES The lunar lander simulation has been implemented onto a two-laptop display system where subjects select from alternative landing points, based upon avoiding hazards that the on-board system identifies during the landing. The subject makes inputs using a rotational hand controller (i.e., joystick) and a translational hand controller, which are processed by simulated vehicle dynamics to update the vehicle attitude and rate of descent. Subjects will utilize a combination of flight, situation, and status displays to monitor the state of the simulated vehicle. The simulation exports tilt and translation movements to a 6DOF motion base synced to visual movements and provides representative vestibular cues to the subject while performing the task inside an enclosed cabin featuring a simulated lunar out-the-window view and a heads-down lander cockpit view. RELEVANCE This project will deliver an operational demonstration of crew override capability following spaceflight and identify potential deficits that may require remediation. Comparison of individual vestibular and cognitive changes with crew override performance will help better characterize the manual control risks associated with sensorimotor alterations. The inclusion of “just-in-time” on-board training will ensure we are characterizing changes in override proficiency with this expected countermeasure in place. ACKNOWLEDGEMENTS: The authors acknowledge contributions from Draper, HLS Joint Test Panel, Crew HLS Interfaces for Piloting Working Group, and Dynamic Skills Trainer (DST) Lab toward the development of the lunar landing simulation. This project is funded by the Human Health Countermeasures Element.

Austin M Bollinger↗

DOE OSTI2022

Bedrock Vadose Zone Storage Dynamics Under Extreme Drought: Consequences for Plant Water Availability, Recharge, and Runoff

Bedrock vadose zone water storage (i.e., rock moisture) dynamics are rarely observed but potentially key to understanding drought responses. Exploiting a borehole network at a Mediterranean blue oak savanna site—Rancho Venada—we document how water storage capacity in deeply weathered bedrock profiles regulates woody plant water availability and groundwater recharge. The site is in the Northern California Coast Range within steeply dipping turbidites. In a wet year (water year 2019; 647 mm of precipitation), rock moisture was quickly replenished to a characteristic storage capacity, recharging groundwater that emerged at springs to generate streamflow. In the subsequent rainless summer growing season, rock moisture was depleted by about 93 mm. In two drought years that followed (212 and 121 mm of precipitation) the total amount of rock moisture gained each winter was about 54 and 20 mm, respectively, and declines were documented exceeding these amounts, resulting in progressively lower rock moisture content. Oaks, which are rooted into bedrock, demonstrated signs of water stress in drought, including reduced transpiration rates and extremely low water potentials. In the 2020–2021 drought, precipitation did not exceed storage capacity, resulting in variable belowground water storage, increased plant water stress, and no recharge or runoff. Rock moisture deficits (rather than soil moisture deficits) explain these responses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Numerical Investigation of the Spark Discharge Process in a Crossflow

The present study numerically investigates the spark discharge process under crossflow conditions using a thermal equilibrium plasma solver that fully couples the electromagnetic physics and fluid dynamics in a computational framework. Numerical results are validated by the comparison with experimental data. Here, the spark discharge experiment is performed in a constant volume vessel using an inductive coil ignition system for automotive applications, and the evolution of the spark channel is measured using high-speed imaging. The crossflow in the gap between the spark-plug electrodes is generated by a rotating fan with two different fan speeds, and the flow velocity across the gap is characterized by particle image velocimetry (PIV) measurement. A computational fluid dynamics (CFD) solver is employed to simulate the crossflow and provide the flow field variables (velocity, pressure, temperature) to the plasma solver. The crossflow velocity predicted in the flow simulation agrees well with the PIV data in that the non-uniform velocity profiles at monitoring points are reproduced by the CFD code. With the crossflow initialization in the plasma solver, the simulated spark discharge process from the breakdown to spark discharge matches the experimental data, including the voltage and circuit waveforms and the high-speed images of the spark channel evolution. The stretch of spark channel captured by plasma simulations agrees with the measured data. The plasma simulation reveals that the mean temperature of the spark channel is maintained at 5000 K during the discharge phase, and the temperature varies along the spark channel so that the highest value is obtained at the spark root on the center electrode. Overall, the results presented in this paper are meant to provide valuable information about the properties of the plasma generated by the spark discharge.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Estimating and Calibrating DER Model Parameters Using Levenberg–Marquardt Algorithm in Renewable Rich Power Grid

The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57 × 10 –7 .

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2012

A Change Impact Analysis to Characterize Evolving Program Behaviors

Change impact analysis techniques estimate the potential effects of changes made to software. Directed Incremental Symbolic Execution (DiSE) is an intraprocedural technique for characterizing the impact of software changes on program behaviors. DiSE first estimates the impact of the changes on the source code using program slicing techniques, and then uses the impact sets to guide symbolic execution to generate path conditions that characterize impacted program behaviors. DiSE, however, cannot reason about the flow of impact between methods and will fail to generate path conditions for certain impacted program behaviors. In this work, we present iDiSE, an extension to DiSE that performs an interprocedural analysis. iDiSE combines static and dynamic calling context information to efficiently generate impacted program behaviors across calling contexts. Information about impacted program behaviors is useful for testing, verification, and debugging of evolving programs. We present a case-study of our implementation of the iDiSE algorithm to demonstrate its efficiency at computing impacted program behaviors. Traditional notions of coverage are insufficient for characterizing the testing efforts used to validate evolving program behaviors because they do not take into account the impact of changes to the code. In this work we present novel definitions of impacted coverage metrics that are useful for evaluating the testing effort required to test evolving programs. We then describe how the notions of impacted coverage can be used to configure techniques such as DiSE and iDiSE in order to support regression testing related tasks. We also discuss how DiSE and iDiSE can be configured for debugging finding the root cause of errors introduced by changes made to the code. In our empirical evaluation we demonstrate that the configurations of DiSE and iDiSE can be used to support various software maintenance tasks

Rungta, Neha Shyam↗