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DOE OSTI2011

Impact of instrumental systematics on the CMB bispectrum

We study the effects of instrumental systematics on the estimation of primordial non-Gaussianity using the cosmic microwave background (CMB) bispectrum from both the temperature and the polarization anisotropies. For temperature systematics we consider gain fluctuation and beam distortions. For polarization we consider effects related to known instrumental systematics: calibration, pixel rotation, differential gain, pointing, and ellipticity of the instrument beam. We consider these effects at the next to leading order, which we refer to as nonlinear systematic effects. We find that if the instrumental response is linearly proportional to the received CMB intensity, then only the shape of the primordial CMB bispectrum, if there is any, will be distorted. We show that the nonlinear response of the instrument can in general result in spurious non-Gaussian features on both the CMB temperature and polarization anisotropies, even if the primordial CMB is completely Gaussian. We determine the level for both the linear and nonlinear systematics parameters for which they would cause no significant degradation of our ability to constrain the primordial non-Gaussianity amplitude f{sub NL}. We find that the nonlinear systematics are potentially a bigger worry for extracting the primordial non-Gaussianity than the linear systematics, especially because the current and near future CMB probes are optimized for CMB power-spectrum measurements which are not particularly sensitive to the nonlinear instrument response. We find that if instrumental nonlinearities are not controlled by dedicated calibration, the effective local non-Gaussianity can be as large as f{sub NL}{approx}O(10) before the corresponding nonlinearities show up in the CMB dipole measurements. The higher-order multipoles are even less sensitive to instrumental nonlinearities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2011

Data compression of measurements of peculiar velocities of supernovae type Ia

We study the compression of information present in the correlated perturbations to the luminosity distance in the low-redshift (z<0.1) supernovae Ia due to peculiar velocities of these supernovae. We demonstrate that the naieve compression into angular velocity power spectrum does not work efficiently, due to thickness of the spherical shell over which the supernovae are measured. Instead, we show that measurements can be compressed into measurements of f{sup 2}P(k), where f is the logarithmic rate of growth of linear perturbations and P(k) is their power spectrum. We develop an optimal quadratic estimator and show that it recovers all information for {Lambda}CDM models for surveys of N{approx}10,000 or more supernovae. We explicitly demonstrate robustness with respect to the assumed fiducial model and the number of power spectrum bins. Using mock catalogues of supernovae Ia we estimate that future low-redshift surveys will be able to probe {sigma}{sub 8} to 6% accuracy with 10 000 supernovae Ia.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators

Tensor algebra operations represent an important class of algorithms used across many applications, including machine learning, scientific computing, and data analytics. As a result, the efficient generation of custom accelerators for tensor operations has received increased attention. Previous efforts have produced automated tools enabling users to prototype and explore optimized accelerators. However, little effort has been focused on the host-accelerator interaction in these tools. Efficient use of hardware accelerators requires knowledge about the accelerator's capabilities (operations, data formats, and opcode support), the host CPU microarchitecture (e.g., memory hierarchy), the host-accelerator interface, and the application's features (which code regions should be mapped onto an accelerator). Manually rewriting the original applications to facilitate improved custom accelerator mapping is an error-prone and time-consuming endeavor. To cope with this, we propose AXI4MLIR, a new framework to automatically generate and optimize the communication between the host CPU and arbitrary accelerators that implement linear algebra algorithms. AXI4MLIR extends the MLIR compiler framework to automatically generate efficient host-accelerator driver code for accelerators with AXI-based interfaces. Our compiler extensions enable automatic driver code generation while carefully considering the host's memory hierarchy and target accelerator features. To demonstrate the flexibility and utility of AXI4MLIR, we test it with diverse use cases that include different types of accelerators, tiling scenarios, and dataflow schemes. We compare our experimental results to manual implementations of host-accelerator driver code and find that our approach can reduce CPU cache references by 56% and deliver up to a 1.65x speedup.

Bohm Agostini, Nicolas↗

DOE OSTI2018

On the Information in Spike Timing: Neural Codes Derived from Polychronous Groups

There is growing evidence regarding the importance of spike timing in neural information processing, with even a small number of spikes carrying information, but computational models lag significantly behind those for rate coding. Experimental evidence on neuronal behavior is consistent with the dynamical and state dependent behavior provided by recurrent connections. This motivates the minimalistic abstraction investigated in this paper, aimed at providing insight into information encoding in spike timing via recurrent connections. We employ information-theoretic techniques for a simple reservoir model which encodes input spatiotemporal patterns into a sparse neural code, translating the polychronous groups introduced by Izhike-vich into codewords on which we can perform standard vector operations. Finally, we show that the distance properties of the code are similar to those for (optimal) random codes. In particular, the code meets benchmarks associated with both linear classification and capacity, with the latter scaling exponentially with reservoir size.

computational modeling↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

Distributed energy resources (DERs) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Generic Multi-Frequency Modelling of Converter-Connected Renewable Energy Generators Considering Frequency and Sequence Couplings

Frequency and sequence couplings can compromise the trustworthiness of multi-frequency models for converter-based systems. There have been effective attempts to address the couplings mainly by linearized averaged models. Only a few studies have been conducted on practical optimization of such models with enormous matrices and experimental results. This paper provides a generic theory for coupling patterns and proposes a multi-frequency modelling method to detect and address only the main couplings in the sequence domain for converter-connected renewable energy generators. The proposed generic model is based on empirical tests using small-signal perturbations and adopting Fourier transform on the switching converter response. The proposed theory and modelling methodology are verified using a 7MVA grid emulator for voltage perturbation tests on a 2MVA photo-voltaic converter. Accordingly, the couplings can exist in more generic forms, including multiples of perturbation and fundamental frequencies. To the best of our knowledge, the patterns with the multiples of the perturbation frequency have been overlooked in the literature. Furthermore, the mirror frequency concept is valid for all coupling patterns and is included in the proposed model. Besides, the proposed patterns and the environment noise levels have been practical criteria for selecting the main couplings.

frequency and sequence couplings↗

DOE OSTI2015

MO-FG-BRA-06: Material Optimisation for Nanoparticle-Sensitized Radiotherapy

Purpose: To evaluate the potential applications of nanoparticles of different elemental compositions as X-ray radiosensitizers. Methods: Geant4 Monte Carlo models were used to model the interactions of 10 nm radius nanoparticles of elements ranging from silicon to mercury with either monoenergetic keV X-rays or a 6 MV Linac spectrum. Secondary electron spectra emitted from nanoparticles and resulting radial doses were calculated. Potential biological impacts were evaluated through the use of the Local Effect Model. Results: For keV radiation, fluoresence photons and energetic photoelectrons deposit the majority of energy emitted by nanoparticles, over long ranges. However, short-range energy deposition is dominated by Auger electrons. While total energy deposition increases with atomic number, short-range energy deposition has a more complex behaviour, with local maxima around germanium (Z=32) and gadolinium (Z=64). For megavoltage radiation, Compton electrons deposit the majority of energy, but short- range depositions are dominated by interactions with secondary electrons. As a Result, energy deposition following ionising events following Linac irradiation is largely independent of atomic number.When analysed through the Local Effect Model, these effects translate into significant variations in predicted cell killing with atomic number for keV exposures, but comparatively limited effects for megavoltage exposures. Conclusions: Although heavier elements typically lead to greater increases in dose within target volumes, on the nanometre scale effects are typically dominated by low-energy secondary electrons, whose distribution has a complex relationship with both nanoparticle composition and beam energy.These results suggest that maximum radiosensitization may not necessarily be achieved through maximizing atomic number, but rather a balance between overall interaction cross-section and local dose distribution. The different behaviours seen when comparing keV and MV behaviours may also provide future mechanisms for separating physical and biological mechanisms of nanoparticle sensitisation, a key step in their development as a potential therapeutic agent.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Modelling and Analysis of the Corrosion Characteristics of Ferritic-Martensitic Steels in Supercritical Water

The dependencies of weight gain of 9-12 Cr ferritic-martensitic steels in supercritical water on each of seven principal independent variables (temperature, oxygen concentration, flow rate, exposure time, and key chemical composition and surface condition of steels) have been predicted using a supervised artificial neural network (ANN). The relative significance of each independent variable was uncovered by fuzzy curve analysis, which ranks temperature and exposure time as the most important. The optimized ANN, not only satisfactorily represents the experimentally-known non-linear relationships between the corrosion characteristics of F-M steels and the key independent variables (demonstrating the effectiveness of this technique), but also predicts and reveals that the effects of oxygen concentration on the weight gains, to a certain degree, is influenced by the flow rate and temperature. Finally, according to the ANN predicted-results, departure of oxidation kinetics from the parabolic law, and basic cause of chromium content in steel substrate influencing the corrosion rate, and the synergetic effects of dissolved oxygen concentration, flow rate, and temperature, are discussed and analyzed.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes

Lagrangian particle tracking schemes allow a wide range of flow and transport processes to be simulated accurately, but a major challenge is numerically implementing the inter-particle interactions in an efficient manner. This article develops a multi-dimensional, parallelized domain decomposition (DDC) strategy for mass-transfer particle tracking (MTPT) methods in which particles exchange mass dynamically. We show that this can be efficiently parallelized by employing large numbers of CPU cores to accelerate run times. In order to validate the approach and our theoretical predictions we focus our efforts on a well-known benchmark problem with pure diffusion, where analytical solutions in any number of dimensions are well established. In this work, we investigate different procedures for “tiling” the domain in two and three dimensions (2-D and 3-D), as this type of formal DDC construction is currently limited to 1-D. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-D for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work. For an optimally subdivided diffusion problem, the 2-D parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to around 2700 cores, reducing a 5 h simulation to 8 s, while the 3-D algorithm maintains appreciable speedup up to 1700 cores.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2011

Computation of Large Covariance Matrices by SAMMY on Graphical Processing Units and Multicore CPUs

Computational power of Graphical Processing Units and multicore CPUs was harnessed by the nuclear data evaluation code SAMMY to speed up computations of large Resonance Parameter Covariance Matrices (RPCMs). This was accomplished by linking SAMMY to vendor-optimized implementations of the matrix-matrix multiplication subroutine of the Basic Linear Algebra Library to compute the most time-consuming step. The U-235 RPCM computed previously using a triple-nested loop was re-computed using the NVIDIA implementation of the subroutine on a single Tesla Fermi Graphical Processing Unit, and also using the Intel's Math Kernel Library implementation on two different multicore CPU systems. A multiplication of two matrices of dimensions 16,000 x 20,000 that had previously taken days, took approximately one minute on the GPU. Similar performance was achieved on a dual six-core CPU system. The magnitude of the speed-up suggests that these, or similar, combinations of hardware and libraries may be useful for large matrix operations in SAMMY. Uniform interfaces of standard linear algebra libraries make them a promising candidate for a programming framework of a new generation of SAMMY for the emerging heterogeneous computing platforms.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2019

Linear Approximation Line Pack Model for Integrated Electricity and Natural Gas Systems OPF

This poster provides an overview on a linear approximation line pack model for integrated electricity and natural gas systems OPF.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning: Preprint

Distributed energy resources (DER) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline MPC controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2018

Using Contact to Increase Robot Performance for Glovebox D and D Tasks - 18227

Glovebox decommissioning tasks usually require manipulating relatively heavy objects in a highly constrained environment. Thus, contact with the surroundings becomes inevitable. In order to allow the robot to interact with the environment in a natural way, we present a contact-implicit motion planning framework. This framework enables the system, without the specification in advance of a contact plan, to make and break contacts to maintain stability while performing a manipulation task. In this method, we use linear complementarity constraints to model rigid body contacts and find a locally optimal solution for joint displacements and magnitudes of support forces. Then, joint torques are calculated such that the support forces have the highest priority. We evaluate our framework in a 2.5D, quasi-static simulation in which a humanoid robot with planar arms manipulates a heavy object. Our results suggest that the proposed method provides the robot with the ability to balance itself by generating support forces on the environment while simultaneously performing the manipulation task. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Quandary

Quandary numerically simulates and optimizes the time-evolution of open quantum systems. The underlying dynamics are modelled by Lindblad's master equation, a linear ordinary differential equation (ODE) describing quantum systems interacting with the environment. Quandary solves this ODE numerically by applying a time-stepping integration scheme, and utilizes a gradient-based optimization approach to determine optimal control pulses that drive the quantum system to a desired target state. Two optimization objectives are considered: (a) Unitary gate optimization that finds controls to realize a unitary gate transformation, and (b) optimal reset that aims to drive the quantum system to the ground states. Gradient-based optimization schemes utilizing Petsc's Tao optimization package are applied to generate control pulses that minimize the respective measure. To evaluate the gradient of the objective function, the discrete adjoint method is used while leveraging techniques from Algorithmic Differentiation to produce exact and consistent gradients. To mitigate excessive execution run times, the software can be build together with the XBraid software library which provides a parallelization strategy to distribute the time-evolution of the underlying dynamics onto multiple processor.

Petersson, NilsA.↗

DOE OSTI2021

Juqbox.jl

The Juqbox.jl package implements functionality for solving the quantum optimal control problem for realizing logical gates in closed quantum systems. The dynamics of the quantum system is modeled by Schroedinger's equation, which takes to form of a linear system of ordinary differential equations (ODE). Juqbox.jl solves this ODE by numerical time stepping and applies a gradient-based optimization technique to determine control pulses for driving an initial state to a final state, according to the desired logical gate transformation. To evaluate the gradient, Juqbox.jl applies the ``first discretize, then optimize'' approach based on a discrete adjoint time stepping technique. The actual optimization is performed by the open source Ipopt libaray. Juqbox.jl is written in the Julia programming language which, among many other features, provides a convenient interface to the Ipopt library.

PETERSSON, NILSA.↗

DOE OSTI2011

Travel Demand Modeling

This chapter describes the principal types of both passenger and freight demand models in use today, providing a brief history of model development supported by references to a number of popular texts on the subject, and directing the reader to papers covering some of the more recent technical developments in the area. Over the past half century a variety of methods have been used to estimate and forecast travel demands, drawing concepts from economic/utility maximization theory, transportation system optimization and spatial interaction theory, using and often combining solution techniques as varied as Box-Jenkins methods, non-linear multivariate regression, non-linear mathematical programming, and agent-based microsimulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

OptiBench: An Optimization Benchmark Tool for Renewable Energy Problems

We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Renewable Energy Laboratory's (NREL) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗