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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fuel Property Effects on Stochastic Preignition Events During Engine Load Transitions

Stochastic preignition (SPI) is an abnormal combustion phenomenon that can cause catastrophic engine damage. There have been several proposed mechanisms of SPI, where a uniform source is still not certain, however, SPI tendencies have been shown to be influenced by engine operating conditions, oil composition, engine age, and fuel chemical and physical properties. Laboratory research and testing for SPI propensity is challenging given the stochastic nature of events, as well as the potential for significant degradation of the engine platform and measuring equipment over time. Thus, SPI specific experiments are generally conducted under either sustained or cyclic patterning of steady-state operating conditions to avoid the influence of transient engine boundary conditions on test parameters of interest (e.g. oil additive package, fuel properties, engine speed/load, etc.). In this work a cyclically varying SPI test sequence involves a 5 min engine warmup period at a low engine load of around 4 bar gross indicated mean effective pressure (IMEPg), followed by a transition to high load (~20 bar IMEPg) at a constant 2000 rev/min engine speed for a total of 25 min. This individual test sequence load schedule is then sequentially repeated 10 times to generate significant statistical data for analysis. This work examines the influence of fuel chemical and physical properties on SPI tendency during the unsteady portion of the 10-cycle sequence (the first 5 min of the high load operation in each sequence of the loading cycle) which has been discarded from previous analyses due to the uncertainty in engine operating and thermal boundary conditions. Results from this analysis suggest an increasing trend in the ratio of SPI events during the unsteady test period relative to the steady test period with increasing fuel Reid Vapor Pressure (RVP), implying differences in uncontrolled ignition source terms, possibly from, fuel wall interactions and retention during the load transition phase of the test.

Splitter, Derek [ORNL] (ORCID:0000000174044047)↗
NASA NTRS2016

The NANOGrav Nine-Year Data Set: Limits on the Isotropic Stochastic Gravitational Wave Background

We compute upper limits on the nanohertz-frequency isotropic stochastic gravitational wave background (GWB) using the 9 year data set from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration. Well-tested Bayesian techniques are used to set upper limits on the dimensionless strain amplitude (at a frequency of 1 yr(exp -1) for a GWB from supermassive black hole binaries of A(sub gw) less than 1.5 x 10(exp -15). We also parameterize the GWB spectrum with a broken power-law model by placing priors on the strain amplitude derived from simulations of Sesana and McWilliams et al. Using Bayesian model selection we find that the data favor a broken power law to a pure power law with odds ratios of 2.2 and 22 to one for the Sesana and McWilliams prior models, respectively. Using the broken power-law analysis we construct posterior distributions on environmental factors that drive the binary to the GW-driven regime including the stellar mass density for stellar-scattering, mass accretion rate for circumbinary disk interaction, and orbital eccentricity for eccentric binaries, marking the first time that the shape of the GWB spectrum has been used to make astrophysical inferences. Returning to a power-law model, we place stringent limits on the energy density of relic GWs, OMEGA(sub gw) (f) h squared less than 4.2 x 10(exp -10). Our limit on the cosmic string GWB, OMEGA(sub gw) (f) h squared less than 2.2 x 10(exp -10), translates to a conservative limit on the cosmic string tension with G mu less than 3.3 x 10(exp -8), a factor of four better than the joint Planck and high-l‚ cosmic microwave background data from other experiments.

Arzoumanian, Z.↗
DOE OSTI2022

Deep learning categorization of infrasound array data

Here we develop a deep learning-based infrasonic detection and categorization methodology that uses convolutional neural networks with self-attention layers to identify stationary and non-stationary signals in infrasound array processing results. Using features extracted from the coherence and direction-of-arrival information from beamforming at different infrasound arrays, our model more reliably detects signals compared with raw waveform data. Using three infrasound stations maintained as part of the International Monitoring System, we construct an analyst-reviewed data set for model training and evaluation. We construct models using a 4-category framework, a generalized noise vs non-noise detection scheme, and a signal-of-interest (SOI) categorization framework that merges short duration stationary and non-stationary categories into a single SOI category. We evaluate these models using a combination of k-fold cross-validation, comparison with an existing “state-of-the-art” detector, and a transportability analysis. Although results are mixed in distinguishing stationary and non-stationary short duration signals, f-scores for the noise vs non-noise and SOI analyses are consistently above 0.96, implying that deep learning-based infrasonic categorization is a highly accurate means of identifying signals-of-interest in infrasonic data records.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

High-fidelity multiphysics load following and accidental transient modeling of microreactors using NEAMS tools: Application of NEAMS codes to perform multiphysics modeling analyses of micro-reactor concepts

The feasibility of modeling microreactors using high-fidelity models with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) tools is investigated in this report. Three overarching questions guided this research: can NEAMS tools readily be applied for high-fidelity multiphysics modeling of different types of transients in microreactor designs; how accurate are the results obtained; and are improvements needed in accuracy or user experience of NEAMS tools, especially considering newly developed capabilities? This work builds upon FY-2022 work, and two microreactor concepts considering heat pipe (HP-MR) and gas-cooled (GC-MR) technologies were further analyzed using high-fidelity multiphysics simulations. The NEAMS tools considered and coupled within the MultiApp environment are Griffin for neutronics, BISON for thermo-mechanics, Sockeye for heat pipe modeling (in HP-MR), SAM for 1D Fluid – 3D solid modeling of coolant channels and system modeling of balance of plant components (in GC-MR), and the SWIFT code for hydrogen redistribution in hydride moderator. The Heat Pipe MicroReactor (HP-MR) concept was further analyzed in FY-2023 to demonstrate the stochastic TRISO failure modeling capability in BISON to check operational limits of the TRISO fuel. A new full-core Gas-Cooled MicroReactor (GC-MR) model was developed based on the initial assembly-model used in Y-2022 and used for steady-state and accidental depressurization transient simulations. Accuracy of the simulations performed was assessed through 1) verification analyses completed on the different physics with code-to-code comparison, and 2) validation of the multiphysics simulations based on modeling of the Kilopower Reactor Using Stirling Technology (KRUSTY) experiment. In FY-2023, the mesh and model of KRUSTY was updated to closely match publicly available data, and the neutronic model was verified and validated against experimental control rod worth measurements. The multiphysics model of KRUSTY was developed and used for steady-state analysis and for modeling reactivity insertion transient. The calculated power increase and stabilization agrees well with experimental data following adjustment in fuel thermal expansion coefficient. As an important component of this project, the ANL team gathered experience with a wide range of NEAMS tools: the MOOSE Mesh System, Griffin, BISON, SWIFT, Sockeye, SAM, Workbench, and the MOOSE MultiApp System, and provided assessment of new capabilities. Noteworthy are the user assessment of the “vapor-only” flow model in Sockeye and development of a multiphysics startup transient in HP-MR unit cell for use as tutorial in Sockeye. The full-core GC-MR model was used for assessment of SAM for balance of plant modeling and for demonstrating the SWIFT code capability for hydrogen redistribution modeling in multiphysics transient analyses. In this process, several bugs/issues were identified and reported to developers. Finally, the assembly GC-MR model developed in FY-2022 coupling Griffin, BISON and SAM through flow blockage and rod ejection transients was published to the National Reactor Innovation Center (NRIC) Virtual Test Bed (VTB). The Heat Pipe MicroReactor (HP-MR) concept high-fidelity multiphysics coupling of Griffin/BISON/Sockeye in load-following and heat pipe failure transients was also published on the VTB. Those submissions are enabling thorough review of these models as well as wide distribution to industry, regulator, and university users. In this analysis, several new research questions were uncovered, and follow-up analyses are recommended to further improve some models, consider additional transients, and continue development of VTB models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Adaptive Recovery Model: Designing Systems for Testing Tracing and Vaccination to Support COVID-19 Recovery Planning.

This report documents a new approach to designing disease control policies that allocate scarce testing, contact tracing, and vaccination resources to better control community transmission of COVID19 or similar diseases. The Adaptive Recovery Model (ARM) combines a deterministic compartmental disease model with a stochastic network disease propagation model to enable us to simulate COVID-19 community spread through the lens of two complementary modeling motifs. ARM contact networks are derived from cell-phone location data that have been anonymized and interpreted as individual arrivals to specic public locations. Modeling disease spread over these networks allows us to identify locations within communities conducive to rapid disease spread. ARM applies this model- and data-derived abstractions of community transmission to evaluate the effectiveness of disease control measures including targeted social distancing, contact tracing, testing and vaccination. The architecture of ARM provides a unique capacity to help decision makers understand how best to deploy scarce testing, tracing and vaccination resources to minimize disease-spread potential in a community. This document details the novel mathematical formulations underlying ARM, presents a dynamical stability analysis of the deterministic model components, a sensitivity analysis of control parameters and network structure, and summarizes a process for deriving contact networks from cell-phone location data. An example use case steps through applying ARM to evaluate three targeted social distancing policies using Bernalillo County, New Mexico as an exemplar test locale. This step-by-step analysis demonstrates how ARM can be used to measure the relative performance of competing public health policies. Initial scenario tests of ARM shows that ARMs design focus on resource utilization rather than simple incidence prediction can provide decision makers with additional quantitative guidance for managing ongoing public health emergencies and planning future responses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A nonsmooth nonconvex optimization algorithm for two-stage optimization problems

An optimization algorithm for a group of nonsmooth nonconvex problems inspired by two-stage stochastic programming problems is proposed. The main challenges for these problems include (1) the problems lack the popular lower-type properties such as prox-regularity assumed in many nonsmooth nonconvex optimization algorithms, (2) the objective can not be analytically expressed and (3) the evaluation of function values and subgradients are computationally expensive. To address these challenges, this report first examines the properties that exist in many two-stage problems, specifically upper-C 2 objectives. Then, we show that quadratic penalty method for securityconstrained alternating current optimal power flow (SCACOPF) contingency problems can make the contingency solution functions upper-C 2 . Based on these observations, a simplified bundle algorithm that bears similarity to sequential quadratic programming (SQP) method is proposed. It is more efficient in implementation and computation compared to conventional bundle methods. Global convergence analysis of the algorithm is presented under novel and reasonable assumptions. The proposed algorithm therefore fills the gap of theoretical convergence for smoothed SCACOPF problems. The inconsistency that might arise in our treatment of the constraints are addressed through a penalty algorithm whose convergence analysis is also provided. Finally, theoretical capabilities and numerical performance of the algorithm are demonstrated through numerical examples.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

Secondary Fragmentation of Comet Shoemaker-Levy 9 and the Ramifications for the Progenitor's Breakup in July 1992

Comprehensive analysis of discrete events of secondary fragmentation leads to a conceptually new understanding of the process of disintegration of comet Shoemaker-Levy 9. We submit that the jovian tidal forces inflicted extensive cracks throughout the interior of the original nucleus but did not split it apart. The initial disruption was apparently accomplished by stresses exerted on the cracked object by its fast rotation during the early post-perijove period of time. We argue that this disruption was in fact a rapid sequence of episodes during July 1992 that gave birth to the 12 on-train, or primary, fragments: A, C, D, E, G, H, K, L, Q (later Q(sub 1)), R, S, and W. The discrete events of secondary fragmentation, which gave birth to the off-train fragments, are understood in this scenario as stochastic manifestations of the continuing process of progressive disintegration. Of the 13 off-train fragments considered, nine were secondary--B, F, G(sub 2), M, N, P (later P(sub 2) or P(sub 2a)), Q(sub 2), U, and V--and four tertiary (J, P(sub 1), P(sub 2b), and T). The separation parameters of 11 off-train fragments were determined. The vectorial distribution of separation velocities of these fragments shows a strong concentration toward a great circle, unquestionably an effect of the approximately conserved angular momentum of the progenitor comet since the time of its initial disruption. Also apparent is their clumping (except for P(sub 1)) to a segment along the great circle, implying that the fragments were consistently released from one side of their parents, thus explaining for the first time why the off-train fragments preferentially appeared on one side of the nuclear train. In order to obtain a consistent solution, our model requires that the points of separation be on the antisolar side of the parent fragments, where thermal stresses are likely to enhance the effect of rotation. The episodes of secondary fragmentation are found to have nine months after the close encounter with Jupiter in early July 1992, and the separation velocities ranged between 0.36 and 1.7 m/s. The spin-axis position is determined to have been nearly in the jovicentric orbit plane, which rules out the Asphaug-Benz-Solem strengthless aggregate model as a plausible breakup hypothesis. Since the separation velocities are rotational in nature, they cannot substantially exceed the critical limit for centrifugal breakup and offer an estimate for the original nuclear dimensions. The comet's nucleus is found to have been approximately 10 km in diameter and spinning rapidly. With the exception of P(sub 1) and apparently also P(sub 2) and F, no nongravitational deceleration was detected in the motions of the off-train fragments. Serious doubts are cast on continuing appreciable activity of any of these fragments. Indeed, when it was necessary to introduce a deceleration into the equations of motion, the effect appears to have been due to the action of solar radiation pressure on the centroid of centimeter-sized particulates in the disintegrating condensations.

Sekanina, Z.↗
DOE OSTI2024

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗
DOE OSTI2021

EGR Dilution and Fuel Property Effects on High-Efficiency Spark-Ignition Flames

Modern spark ignition internal combustion engines rely on fast combustion rates and high dilution to achieve high brake thermal efficiencies. To accomplish this, new engine designs have moved towards increased tumble ratios and stroke-to-bore ratios. Increased tumble ratios correlate positively with increases in turbulent kinetic energy and improved fuel and residual gas mixing, all of which favor faster and more efficient combustion. Longer stroke-to-bore ratios allow higher geometric compression ratios and use of late intake valve closing to control peak compression pressures and temperatures. The addition of dilution to improve efficiency is limited by the resulting increase in combustion instabilities manifested by cycle-to-cycle variability. A number of effects - preferential diffusion, turbulence-combustion interactions, stochastic flow patterns, laminar-turbulent flame kernel transitions, and relative length and velocity scales between flame and turbulence - are believed to be responsible for the increase in cycle-to-cycle variations, where their contributions are likely interlinked. Several studies have shown the influence of stochastic flow characteristics on the nature of combustion instabilities, such as velocity patterns on flame kernel formation and cycle-to-cycle variations in residual gas. However, few have focused on the specific effects of fuel properties. The objective of this work is to contrast the effects of dilution on propane stoichiometric combustion against gasoline. Dilution tolerance experiments were conducted in a purpose-built high stroke-to-bore ratio single cylinder engine with both gasoline and LPG. Three-dimensional full cycle computational fluid dynamics (CFD) simulations employing a level-set combustion approach and Reynolds averaged Navier-Stokes (RANS) turbulence modeling was used to qualitatively assess the changes in length and velocity scales for turbulence and the flame. The experimental results showed that LPG can tolerate higher exhaust gas recirculation (EGR) dilution under a variety of conditions. Analysis of CFD simulations showed that propane flames are likely less sensitive to influences from the flow field due to less thickening of the flame and higher effective flame speeds.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1988

Dynamical interpretation of conditional patterns

While great progress is being made in characterizing the 3-D structure of organized turbulent motions using conditional averaging analysis, there is a lack of theoretical guidance regarding the interpretation and utilization of such information. Questions concerning the significance of the structures, their contributions to various transport properties, and their dynamics cannot be answered without recourse to appropriate dynamical governing equations. One approach which addresses some of these questions uses the conditional fields as initial conditions and calculates their evolution from the Navier-Stokes equations, yielding valuable information about stability, growth, and longevity of the mean structure. To interpret statistical aspects of the structures, a different type of theory which deals with the structures in the context of their contributions to the statistics of the flow is needed. As a first step toward this end, an effort was made to integrate the structural information from the study of organized structures with a suitable statistical theory. This is done by stochastically estimating the two-point conditional averages that appear in the equation for the one-point probability density function, and relating the structures to the conditional stresses. Salient features of the estimates are identified, and the structure of the one-point estimates in channel flow is defined.

Adrian, R. J.↗
NASA NTRS1995

Interdecadal Variations in the Alaska Gyre

Climatic dynamic topography variations in the Alaska gyre during the period 1968-1990 are described with an objective analysis of more than 12000 STD and XBT stations, and COADS wind stress data Interannual the dynamic height and SST variations were correlated and were consistent with recently described large-scale climatic shifts in the North Pacific. The gyre was centered more to the east, circulation appeared stronger, and SST was lower during the early to mid-1970s than during the 1980s. The Aleutian low (NP and PNA indices) intensified during the interim, but the response did not appear as a gyre spinup. Instead, the associated wind stress anomalies forced a slowly varying dynamic height anomaly across the eastern and northern part of the gyre through Ekman convergence, which had the effect of displacing the gyre's low somewhat to the WSW in the 1980s. The wind curl spectrum was white, and the slow oceanic response was modeled as stochastic-forced climate variability with a simple first-order Markov autoregression process. Forcing was assumed to be Ekman pumping of the pycnocline, and the damping coefficient was estimated from the data to be approx. 1 yr. A hindcast with observed winds gave estimated dynamic height patterns similar to those observed, with a canonical correlation of 0.79 at 99% confidence. This response was weak in the western half of the gyre, where slow baroclinic variability may have been influenced by long Rossby wave propagation. A simple autoregression simulation using artificial white noise forcing shows the evolution of decadal variations similar in nature to those observed. This result, along with the low frequency correlation between dynamic height and SST, suggests that the upper-ocean climatic variability in this region is primarily wind forced.

Lagerloef, Gary S.E.↗
DOE OSTI2025

Developing a Nuclear Quality Assurance Compliant Design Methodology for Neutronic Analysis of Xe-100 Design

The primary objective of this work is to develop a design methodology compliant with nuclear quality assurance standards for the Xe-100 neutronic design verification studies. To achieve this, a Monte Carlo model of the Xe-100 reactor was constructed using the exclusion principle, transformation technique, and universe-based level specification following Idaho National Laboratory (INL) NQA level-1 compliant standards and an NQA-1 compliant version of MCNP6. The model encompasses the entire reactor core structures, including the upper plenum, core region, and lower plenum sections, along with all sub-components. The active core section was represented using the spectral regions, each comprising a particular fuel composition and temperature averaged over the considered zone, calculated by X-energy using Very Superior Old Programs (VSOP). Additionally, a component-wise temperature map was implemented into the model, not only for the core region but also for the structural components. Temperature-dependent cross-section libraries, along with thermal scattering law libraries, generated using INL NQA-1 compliant version of NJOY21, were utilized for each isotope in the burnt fuel and the structural materials. Furthermore, the volume of each modeled component was estimated using a stochastic approach with the ray tracing method in MCNP and criticality calculations were performed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Formation and coarsening of epitaxially-supported metal nanoclusters

This mini-review describes developments over the last ~30 years in characterizing the nucleation & growth of epitaxially-supported metal nanoclusters (NCs) or islands during vapor deposition, as well as their post-deposition coarsening. A beyond-mean-field treatment for homogeneous nucleation & growth corrects the deficiencies of traditional treatments in describing, e.g., the island size distribution, but also necessitates consideration of the spatial distribution of islands and their capture zones. We discuss advances in modeling capabilities, including those based upon on an ab-initio level treatment of periphery diffusion kinetics, for description of the non-equilibrium growth shapes of these NCs, focusing on 2D NCs. For post-deposition coarsening of arrays of NCs, there is generally a competition between Ostwald Ripening (OR) and Smoluchowski Ripening (SR). SR is also known as Particle Migration & Coalescence. For 2D NCs in homoepitaxial systems, conventional OR is observed on pristine fcc(111) surfaces, dramatically enhanced OR in the presence of even trace amounts of chalcogens for Cu(111) and Ag(111), and anomalous OR on anisotropic fcc(110) surfaces. The unexpected discovery of SR for fcc(100) homoepitaxial systems prompted extensive analysis of the underlying diffusivities of 2D NCs as a function of size, as well as of NC coalescence dynamics. A comprehensive understanding of these processes is now available. Self-assembly of 3D NCs during deposition, issues related to heterogeneous nucleation, directed assembly, NC growth structure selection, and coarsening are addressed. Finally, for SR of 3D epitaxial NCs, recent insights into the size-dependence of diffusivity are described.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Coarse-grained methods for heterogeneous vesicles with phase-separated domains: Elastic mechanics of shape fluctuations, plate compression, and channel insertion

We develop coarse-grained particle approaches for studying the elastic mechanics of vesicles with heterogeneous membranes having phase-separated domains. We perform simulations both of passive shape fluctuations and of active systems where vesicles are subjected to compression between two plates or subjected to insertion into narrow channels. Analysis methods are developed for mapping particle configurations to continuum fields with spherical harmonics representations. Heterogeneous vesicles are found to exhibit rich behaviors where the heterogeneity can amplify surface two-point correlations, reduce resistance during compression, and augment vesicle transport times in channels. The developed methods provide general approaches for characterizing the mechanics of coarse-grained heterogeneous systems taking into account the roles of thermal fluctuations, geometry, and phase separation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Dual adaptive control: Design principles and applications

The design of an actively adaptive dual controller based on an approximation of the stochastic dynamic programming equation for a multi-step horizon is presented. A dual controller that can enhance identification of the system while controlling it at the same time is derived for multi-dimensional problems. This dual controller uses sensitivity functions of the expected future cost with respect to the parameter uncertainties. A passively adaptive cautious controller and the actively adaptive dual controller are examined. In many instances, the cautious controller is seen to turn off while the latter avoids the turn-off of the control and the slow convergence of the parameter estimates, characteristic of the cautious controller. The algorithms have been applied to a multi-variable static model which represents a simplified linear version of the relationship between the vibration output and the higher harmonic control input for a helicopter. Monte Carlo comparisons based on parametric and nonparametric statistical analysis indicate the superiority of the dual controller over the baseline controller.

Mookerjee, Purusottam↗
DOE OSTI2024

Differentiable stochastic halo occupation distribution

ABSTRACT In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep reinforcement learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models. As a particular example, we focus on the problem of estimating parameters of halo occupation distribution (HOD) models that are used to connect galaxies with their dark matter haloes. Using a combination of continuous relaxation and gradient re-parametrization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogue realizations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalogue generated from the Bolshoi simulation using a standard HOD model and find near-identical posteriors as standard Markov chain Monte Carlo techniques with an increase of ∼8× in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Acquire and Test IDAES Framework Components

The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.

24 POWER TRANSMISSION AND DISTRIBUTION↗