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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.

Cloud computing↗
DOE OSTI2023

Enabling Hyper-Differential Sensitivity Analysis for Ill-Posed Inverse Problems

Inverse problems constrained by partial differential equations (PDEs) play a critical role in model development and calibration. In many applications, there are multiple uncertain parameters in a model that must be estimated. However, high dimensionality of the parameters and computational complexity of the PDE solves make such problems challenging. A common approach is to reduce the dimension by fixing some parameters (which we will call auxiliary parameters) to a best estimate and use techniques from PDE-constrained optimization to estimate the other parameters. In this article, hyper-differential sensitivity analysis (HDSA) is used to assess the sensitivity of the solution of the PDE-constrained optimization problem to changes in the auxiliary parameters. Foundational assumptions for HDSA require satisfaction of the optimality conditions which are not always practically feasible as a result of ill-posedness in the inverse problem. Here we introduce novel theoretical and computational approaches to justify and enable HDSA for ill-posed inverse problems by projecting the sensitivities on likelihood informed subspaces and defining a posteriori updates. Our proposed framework is demonstrated on a nonlinear multiphysics inverse problem motivated by estimation of spatially heterogeneous material properties in the presence of spatially distributed parametric modeling uncertainties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

GPU-resident sparse direct linear solvers for alternating current optimal power flow analysis

Integrating renewable resources within the transmission grid at a wide scale poses significant challenges for economic dispatch as it requires analysis with more optimization parameters, constraints, and sources of uncertainty. This motivates the investigation of more efficient computational methods, especially those for solving the underlying linear systems, which typically take more than half of the overall computation time. In this paper, we present our work on sparse linear solvers that take advantage of hardware accelerators, such as graphical processing units (GPUs), and improve the overall performance when used within economic dispatch computations. We treat the problems as sparse, which allows for faster execution but also makes the implementation of numerical methods more challenging. We present the first GPU-native sparse direct solver that can execute on both AMD and NVIDIA GPUs. We demonstrate significant performance improvements when using high-performance linear solvers within alternating current optimal power flow (ACOPF) analysis. Furthermore, we demonstrate the feasibility of getting significant performance improvements by executing the entire computation on GPU-based hardware. Finally, we identify outstanding research issues and opportunities for even better utilization of heterogeneous systems, including those equipped with GPUs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

A discrete model for compressible flows in heterogeneous media

This work deals with the building of a discrete model able to describe and to predict the evolution of complex gas flows in heterogeneous media. In many physical applications, large scales numerical simulation is no longer possible because of a lack of computing resources. Indeed the medium topology may be complex due to the presence of many obstacles (walls, pipes, equipments, geometric singularities etc.). Aircraft powerplant compartments are examples where topology is complex due to the presence of pipes, ducts, coolers and other equipment. Other important examples are gas explosions and large scale dispersion of hazardous materials in urban places, cities or underground involving obstacles such as buildings and various infrastructures. In all cases efficient safety responses are required. Then a new discrete model is built and solved in reasonable execution times for large cells volumes including such obstacles. Quantitative comparisons between experimental and numerical results are shown for different significant test cases, showing excellent agreement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Electrocatalytic synthesis of heterocycles from biomass-derived furfuryl alcohols

It is very attractive yet underexplored to synthesize heterocyclic moieties pertaining to biologically active molecules from biomass-based starting compounds. Herein, we report an electrocatalytic Achmatowicz reaction for the synthesis of hydropyranones from furfuryl alcohols, which can be readily produced from biomass-derived and industrially available furfural. Taking advantage of photo-induced polymerization of a bipyridyl ligand, we demonstrate the facile preparation of a heterogenized nickel electrocatalyst, which effectively drives the Achmatowicz reaction electrochemically. A suite of characterization techniques and density functional theory computations were performed to aid the understanding of the reaction mechanism. It is rationalized that the unsaturated coordination sphere of nickel sites in our electrocatalyst plays an important role at low applied potential, not only allowing the intimate interaction between the nickel center and furfuryl alcohol but also enabling the transfer of hydroxide from nickel to the bound furfuryl alcohol.

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

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

42 ENGINEERING↗
DOE OSTI2017

Exploiting the flexibility and the polarization of ferroelectric perovskite surfaces to achieve efficient photochemistry and enantiospecificity

This research project explored the catalytic properties of complex surfaces of functional materials. The PI used first-principles density functional theory (DFT) calculations to explore a tightly integrated set of properties. The physical properties of complex functional materials that influence surface chemistry were explored, including bulk and surface electric dipoles, and surface conductivity. The energetic, compositional, electronic, and chemical properties of the surfaces of these materials were explored in detail, and connections between material properties and chemical reactivity were established. This project led to 28 publications, including Nat. Comm., JACS, 3 PRL, 7 PRB, 2 ACS Nano, 2 Nano Lett., 4 JPCL, 2 JCP, Chem. Mater., ACS Appl. Mater. Interfaces, Phys. Rev. Appl., and a U.S. Patent on surface catalysts. The key accomplishments in this project involved work in six coordinated areas: pioneering ways to control bulk dipoles in order to dynamically affect catalysis, exploring novel ways of bringing charge to the surface for redox catalysis, nonstoichiometric surfaces offering new sites for heterogeneous catalysis, illustrating how surface catalysis responds to applied pressure, catalytic growth of carbon-based materials, and new computational methods allowing more accurate exploration of molecule-surface interactions

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

w18_empower-aids - HIV Envelope at Multiple Scales [Slides]

Being the sole viral protein entity expressed on the surface of the Human Immunodeficiency Virus (HIV) envelope, the Envelope Glycoprotein (Env) remains the only plausible immunological target. Nevertheless, extreme dynamic heterogeneity, structural complexity, and system size make the study of these glycoproteins immensely challenging. We have utilized the Institutional Computing (IC) resources to overcome some of these challenges in computational modeling of the Env glycoproteins. This integrated technique can sufficiently sample a physiologically relevant conformational space accessible to carbohydrates, in a very short time. This method has been validated by quantitatively comparing to experimental cryoEM maps of the HIV Env protein. We have employed graph theory to capture the glycan shield topological network, pinpoint potential interaction pathways, and identify concerted behavior of the glycans. Analyses of various network attributes, such as relative centrality of different glycan positions, identification of communities, and critical subnetwork features, have aided in detailed examination of the glycan shield. Starting from select structures modeled by the pipeline, we are performing large-scale atomistic MD simulations to study the temporal behavior of this system, which will help elucidate biologically relevant antibody behavior. This method can be seamlessly extended to benefit research in Zika, Ebola and other high-density glycosylated systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Interpretation of borehole strain measurements using surrogate modeling-based optimization

Interpreting strain data measured during well testing requires inverting poroelastic forward models set up to represent an aquifer or reservoir. One approach is to use stochastic methods to conduct the inversion. Subsurface parameters such as elastic modulus, permeability, and geometry of heterogeneities are estimated by searching the parameter space. This is feasible, but cumbersome, requiring more than a week of computation using many hundreds of computer nodes in one of our earlier analyses. This motivated us to consider alternative methods, including an artificial neural network (ANN) as a surrogate model.

Roudini, Soheil↗
DOE OSTI2025

Applying Corrective Machine Learning in the E3SM Atmosphere Model in C++ (EAMxx)

The Simplified Cloud-Resolving E3SM Atmosphere Model (SCREAM) is the newest addition to the family of Earth System Models capable of explicitly resolving convective systems. SCREAM is a kilometer-scale configuration of the advanced E3SM Atmosphere Model (EAMxx), designed for heterogeneous systems. While the enhanced accuracy of kilometer-scale modeling offers significant benefits, it comes with a substantial computational cost, limiting feasible simulation durations to only a few years, even on the fastest supercomputers. Machine learning presents an opportunity for scientists to achieve the high accuracy of storm-resolving models at a significantly reduced cost. Building on the previous success of applying corrective machine learning (ML) to the FV3 model, this study explores the effects of implementing corrective ML in EAMxx-SCREAM. We also address the computational challenges of integrating the corrective ML, which is written in Python, with the C++/Kokkos EAMxx driver, as well as the potential pitfalls of generalizing an approach that was effective with one atmosphere model to another.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

A View on Future Building System Modeling and Simulation

This chapter presents what a future environment for building system modeling and simulation may look like. As buildings continue to require increased performance and better comfort, their energy and control systems are becoming more integrated and complex. We therefore focus in this chapter on the modeling, simulation and analysis of building energy and control systems. Such systems can be classified as heterogeneous systems because they involve multiple domains, such as thermodynamics, fluid dynamics, heat and mass transfer, electrical systems, control systems and communication systems. Also, they typically involve multiple temporal and spatial scales, and their evolution can be described by coupled differential equations, discrete equations and events. Modeling and simulating such systems requires a higher level of abstraction and modularisation to manage the increased complexity compared to what is used in today's building simulation programs. Therefore, the trend towards more integrated building systems is likely to be a driving force for changing the status quo of today's building simulation programs. Thischapter discusses evolving modeling requirements and outlines a path toward a future environment for modeling and simulation of heterogeneous building systems.A range of topics that would require many additional pages of discussion has been omitted. Examples include computational fluid dynamics for air and particle flow in and around buildings, people movement, daylight simulation, uncertainty propagation and optimisation methods for building design and controls. For different discussions and perspectives on the future of building modeling and simulation, we refer to Sahlin (2000), Augenbroe (2001) and Malkawi and Augenbroe (2004).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

WE-A-17A-04: Development of An Ultra-Fast Monte Carlo Dose Engine for High Dose Rate Brachytherapy

Purpose: Current clinical brachytherapy dose calculations are based on AAPM TG43 guidelines, which approximate the patient geometry as a large water phantom. This ignores heterogeneities and tends to overestimate skin dose. Although Monte Carlo (MC) dose calculations have been recognized as the most accurate method, its associated long computational time is a major bottleneck for routine clinical applications. This work aims to develop a GPU-based ultra-fast MC dose engine (gBMC) for HDR brachytherapy to provide clinical users with accurate sub-minute dose calculations. Methods: Standard photon transport with discrete events including: Compton scattering, Rayleigh scattering and Photoelectric effect, was implemented. Secondary electrons were transported under the continuous slowing down approximation. To reduce the GPU thread divergence, photons and electrons were separately transported. Transport of photons was grouped according to energy. The source model in gBMC can be either a phase-space file generated using Geant4 or a parameterized source model. This dose engine was validated against TG43 in a water phantom and against Geant4 calculations in heterogeneous patient geometries. Results: A phase space file was generated for the Varian VS2000 Ir-192 source. In a water phantom, the calculated radial dose function was within 0.6% of the TG43 calculations for radial distances from 1 cm to 20 cm. The anisotropy functions were within 1% for radial distances from 1 cm to 20 cm except for polar angles larger than 173°. Local point-dose differences were within 2%. In a Mammosite breast cancer case with 22 dwell locations, gBMC and Geant4 isodose lines compared well. The computation time was about 28 seconds using the phase-space file source and 20 seconds using the parameterized source to simulate 1 billion particles, yielding less than 1% statistical uncertainty. Conclusion: The gBMC dose engine makes it possible to use fast and accurate MC dose calculations for clinical work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-DE-201-12: Thermal and Dosimetric Properties of a Ferrite-Based Thermo-Brachytherapy Seed

Purpose: The novel thermo-brachytherapy (TB) seed provides a simple means of adding hyperthermia to LDR prostate permanent implant brachytherapy. The high blood perfusion rate (BPR) within the prostate motivates the use of the ferrite and conductive outer layer design for the seed cores. We describe the results of computational analyses of the thermal properties of this ferrite-based TB seed in modelled patient-specific anatomy, as well as studies of the interseed and scatter (ISA) effect. Methods: The anatomies (including the thermophysical properties of the main tissue types) and seed distributions of 6 prostate patients who had been treated with LDR brachytherapy seeds were modelled in the finite element analysis software COMSOL, using ferrite-based TB and additional hyperthermia-only (HT-only) seeds. The resulting temperature distributions were compared to those computed for patient-specific seed distributions, but in uniform anatomy with a constant blood perfusion rate. The ISA effect was quantified in the Monte Carlo software package MCNP5. Results: Compared with temperature distributions calculated in modelled uniform tissue, temperature distributions in the patient-specific anatomy were higher and more heterogeneous. Moreover, the maximum temperature to the rectal wall was typically ∼1 °C greater for patient-specific anatomy than for uniform anatomy. The ISA effect of the TB and HT-only seeds caused a reduction in D90 similar to that found for previously-investigated NiCu-based seeds, but of a slightly smaller magnitude. Conclusion: The differences between temperature distributions computed for uniform and patient-specific anatomy for ferrite-based seeds are significant enough that heterogeneous anatomy should be considered. Both types of modelling indicate that ferrite-based seeds provide sufficiently high and uniform hyperthermia to the prostate, without excessively heating surrounding tissues. The ISA effect of these seeds is slightly less than that for the previously-presented NiCu-based seeds.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

We present Π4U,{sup 1} an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

A Beam-Specific Planning Target Volume (PTV) Design for Proton Therapy to Account for Setup and Range Uncertainties

Purpose: To report a method for explicitly designing a planning target volume (PTV) for treatment planning and evaluation in heterogeneous media for passively scattered proton therapy and scanning beam proton therapy using single-field optimization (SFO). Methods and Materials: A beam-specific PTV (bsPTV) for proton beams was derived by ray-tracing and shifting ray lines to account for tissue misalignment in the presence of setup error or organ motion. Range uncertainties resulting from inaccuracies in computed tomography-based range estimation were calculated for proximal and distal surfaces of the target in the beam direction. The bsPTV was then constructed based on local heterogeneity. The bsPTV thus can be used directly as a planning target as if it were in photon therapy. To test the robustness of the bsPTV, we generated a single-field proton plan in a virtual phantom. Intentional setup and range errors were introduced. Dose coverage to the clinical target volume (CTV) under various simulation conditions was compared between plans designed based on the bsPTV and a conventional PTV. Results: The simulated treatment using the bsPTV design performed significantly better than the plan using the conventional PTV in maintaining dose coverage to the CTV. With conventional PTV plans, the minimum coverage to the CTV dropped from 99% to 67% in the presence of setup error, internal motion, and range uncertainty. However, plans using the bsPTV showed minimal drop of target coverage from 99% to 94%. Conclusions: The conventional geometry-based PTV concept used in photon therapy does not work well for proton therapy. We investigated and validated a beam-specific PTV method for designing and evaluating proton plans.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Recent developments in the production of liquid fuels via catalytic conversion of microalgae: experiments and simulations

Due to continuing high demand, depletion of non-renewable resources and increasing concerns about climate change, the use of fossil fuel-derived transportation fuels faces relentless challenges both from a world markets and an environmental perspective. The production of renewable transportation fuel from microalgae continues to attract much attention because of its potential for fast growth rates, high oil content, ability to grow in unconventional scenarios, and inherent carbon neutrality. Moreover, the use of microalgae would minimize ‘‘food versus fuel’’ concerns associated with several biomass strategies, as microalgae do not compete with food crops in the food chain. This paper reviews the progress of recent research on the production of transportation fuels via homogeneous and heterogeneous catalytic conversions of microalgae. This review also describes the development of tools that may allow for a more fundamental understanding of catalyst selection and conversion processes using computational modelling. The catalytic conversion reaction pathways that have been investigated are fully discussed based on both experimental and theoretical approaches. Finally, this work makes several projections for the potential of various thermocatalytic pathways to produce alternative transportation fuels from algae, and identifies key areas where the authors feel that computational modelling should be directed to elucidate key information to optimize the process.

09 BIOMASS FUELS↗