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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Simple Non-Planckian Radiation Source for ICF and HED Simulations (Rev.1)

The purpose of this paper is to present a simple way to build frequency dependent spectral (FDS) sources for use in inertial confinement fusion and high energy density physics simulations. This process takes an arbitrary temperature history and converts it into a radiation source with both Planckian and non-Planckian components, the latter of which is to describe high energy photon energies emitted by gold hohlraums. This method is then used to generate spectral energy sources for use in inertial confinement fusion (ICF) and high energy density (HED) simulations which are compared to integrated laser simulations along with experimental measurements.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

DEEP: A Database of Energy Efficiency Performance to Accelerate Energy Retrofitting of Commercial Buildings

The paper presents a method and process to establish a database of energy efficiency performance (DEEP) to enable quick and accurate assessment of energy retrofit of commercial buildings. DEEP was compiled from results of about 35 million EnergyPlus simulations. DEEP provides energy savings for screening and evaluation of retrofit measures targeting the small and medium-sized office and retail buildings in California. The prototype building models are developed for a comprehensive assessment of building energy performance based on DOE commercial reference buildings and the California DEER prototype buildings. The prototype buildings represent seven building types across six vintages of constructions and 16 California climate zones. DEEP uses these prototypes to evaluate energy performance of about 100 energy conservation measures covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and domestic hot water. DEEP consists the energy simulation results for individual retrofit measures as well as packages of measures to consider interactive effects between multiple measures. The large scale EnergyPlus simulations are being conducted on the super computers at the National Energy Research Scientific Computing Center of Lawrence Berkeley National Laboratory. The pre-simulation database is a part of an on-going project to develop a web-based retrofit toolkit for small and medium-sized commercial buildings in California, which provides real-time energy retrofit feedback by querying DEEP with recommended measures, estimated energy savings and financial payback period based on users’ decision criteria of maximizing energy savings, energy cost savings, carbon reduction, or payback of investment. The pre-simulated database and associated comprehensive measure analysis enhances the ability to performance assessments of retrofits to reduce energy use for small and medium buildings and business owners who typically do not have resources to conduct costly building energy audit. DEEP will be migrated into the DEnCity - DOE’s Energy City, which integrates large-scale energy data for multi-purpose, open, and dynamic database leveraging diverse source of existing simulation data.

Hoon Lee, Sang↗
DOE OSTI2025

Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials

There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Transfer Learning for HVAC System Fault Detection

Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity. Yet the lack of labeled data, such as normal versus faulty operational status, has slowed the application of machine learning to HVAC systems. In addition, for any particular building, there may be an insufficient number of observed faults over a reasonable amount of time for training. To overcome these challenges, we present a transfer methodology for a novel Bayesian classifier designed to distinguish between normal operations and faulty operations. The key is to train this classifier on a building with a large amount of sensor and fault data (for example, via simulation or standard test data) then transfer the classifier to a new building using a small amount of normal operations data from the new building. We demonstrate a proof-of-concept for transferring a classifier between architecturally similar buildings in different climates and show few samples are required to maintain classification precision and recall.

transfer learning, Building HVAC, Bayesian framewo↗
DOE OSTI2026

High-Voltage DC MULTI-terminal SIMulation (HVDC MULTISIM): Technical Program Summary

Expansion of the power grid in the USA is essential to fulfilling the rising energy demand of the country. In particular, the transmission grid, a network of electrical energy corridors that enable the flow of large amounts of energy from the point of generation to the point of consumption, needs significant expansion to cater to this growth of electrification. The current infrastructure is also dated, and any new transmission corridor should be based on a vision of building a modern infrastructure that is futureproof. High voltage DC (HVDC) transmission technology falls in this category: it is the most economical way to build large transmission lines and relies on sophisticated electronics and flexible controls, rather than just passive components like switchgear and transformers. This project is aimed at establishing the technical and economic feasibility of a network of HVDC lines that are interconnected to form a multi-terminal DC network. HVDC transmission is not a new technology and United States has a number of such lines; however, these are point to point transmissions and do not form a DC grid. Modeling and analyzing DC grids is challenging since there are no established modeling tools and the traditional ways of modeling AC grids fall short of providing the fidelity required the fast dynamics of the DC grid. This project aims to build a software-in-the-loop simulator for a multi-terminal HVDC grid, called MULTISIM and, establish new control and protection algorithms to operate such a connected DC system. NLR, a key partner, established the core framework of the MULTISIM simulator and validated its functionality. The GE Vernova team, created a full-scale model of a four-terminal HVDC network using PSCAD (Power Systems Computer Aided Design) software and established the baseline performance of the system during normal and fault operation. The project was started on 10/12024 with a kick-off meeting held on 12/11/2024 and ran through two quarters till termination. All the tasks, milestones and deliverables during this period were met. Several interim reports detailing the various tasks were submitted. The following sections provide details on the completed tasks till the project pause and termination after Q2.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Development of a coupled thermo-hydro-mechanical model in discontinuous media for carbon sequestration

Geomechanical alteration of porous media is generally ignored for most shallow subsurface applications, whereas CO 2 injection, migration, and trapping in deep saline aquifers will be controlled by coupled multifluid flow, energy transfer, and geomechanical processes. The accurate assessment of the risks associated with potential leakage of injected CO 2 and the design of effective injection systems requires that we represent these coupled processes within numerical simulators. The objectives of this study were to develop a coupled thermal-hydro-mechanical model into a single software, and to examine the coupling of thermal, hydrological, and geomechanical processes for simulation of CO 2 injection into the subsurface for carbon sequestration. A numerical model is developed to couple nonisothermal multiphase hydrological and geomechanical processes for prediction of multiple interconnected processes for carbon sequestration in deep saline aquifers. The geomechanics model was based on Rigid Body-Spring Model (RBSM), one of the discrete methods to model discontinuous rock system. Poisson’s effect that was often ignored by RBSM was considered in the model. The simulation of large-scale and long-term coupled processes in carbon capture and storage projects requires large memory and computational performance. Global Array Toolkit was used to build the model to permit the high performance simulations of the coupled processes. The model was used to simulate a case study with several scenarios to demonstrate the impacts of considering coupled processes and Poisson’s effect for the prediction of CO 2 sequestration.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Atomistic Cohesive Zone Models for Interface Decohesion in Metals

Using a statistical mechanics approach, a cohesive-zone law in the form of a traction-displacement constitutive relationship characterizing the load transfer across the plane of a growing edge crack is extracted from atomistic simulations for use within a continuum finite element model. The methodology for the atomistic derivation of a cohesive-zone law is presented. This procedure can be implemented to build cohesive-zone finite element models for simulating fracture in nanocrystalline or ultrafine grained materials.

Yamakov, Vesselin I.↗
DOE OSTI2015

DEEP: Database of Energy Efficiency Performance

A database of energy efficiency performance (DEEP) is a presimulated database to enable quick and accurate assessment of energy retrofit of commercial buildings. DEEP was compiled from results of about 10 million EnergyPlus simulations. DEEP provides energy savings for screening and evaluation of retrofit measures targeting the small and medium-sized office and retail buildings in California. The prototype building models are developed for a comprehensive assessment of building energy performance based on DOE commercial reference buildings and the California DEER [sic] prototype buildings. The prototype buildings represent seven building types across six vintages of constructions and 16 California climate zones. DEEP uses these prototypes to evaluate energy performance of about 100 energy conservation measures covering envelope, lighting, heating, ventilation, air conditioning, plug loads, and domestic hot war. DEEP consists the energy simulation results for individual retrofit measures as well as packages of measures to consider interactive effects between multiple measures. The large scale EnergyPlus simulations are being conducted on the super computers at the National Energy Research Scientific Computing Center (NERSC) of Lawrence Berkeley National Laboratory. The pre-simulation database is a part of the CEC PIER project to develop a web-based retrofit toolkit for small and medium-sized commercial buildings in California, which provides real-time energy retrofit feedback by querying DEEP with recommended measures, estimated energy savings and financial payback period based on users' decision criteria of maximizing energy savings, energy cost savings, carbon reduction, or payback of investment. The pre-simulated database and associated comprehensive measure analysis enhances the ability to performance assessments of retrofits to reduce energy use for small and medium buildings and business owners who typically do not have resources to conduct costly building energy audit.

Hong, Tianzhen↗
DOE OSTI2023

Metal–Carbodithioate-Based 3D Semiconducting Metal–Organic Framework: Porous Optoelectronic Material for Energy Conversion

Solar energy conversion requires the working compositions to generate photoinduced charges with high potential and the ability to deliver charges to the catalytic sites and/or external electrode. These two properties are typically at odds with each other and call for new molecular materials with sufficient conjugation to improve charge conductivity but not as much conjugation as to overly compromise the optical band gap. In this work, we developed a semiconducting metal-organic framework (MOF) prepared explicitly through metal-carbodithioate"(-CS 2 ) n M" linkage chemistry, entailing augmented metal-linker electronic communication. The stronger ligand field and higher covalent character of metal-carbodithioate linkages-when combined with spirofluorene-derived organic struts and nickel-(II) ion-based nodes-provided a stable, semiconducting 3D-porous MOF, Spiro-CS 2 Ni. This MOF lacks long-range ordering and is defined by a flexible structure with non-aggregated building units, as suggested by reverse Monte Carlo simulations of the pair distribution function obtained from total scattering experiments. The solvent-removed "closed pore"material recorded a Brunauer-Emmett-Teller area of similar to 400m 2 /g, where the "open pore" form possesses90 wt % solvent-accessible porosity. Electrochemical measurements suggest that Spiro-CS 2 Ni possesses a band gap of 1.57 eV(sigma = 10 -7 S/cm at -1.3 V bias potential),which can be further improved by manipulating the d-electron configuration through an axial coordination (ligand/substrate), the latter of which indicates usefulness as an electrocatalyst and/or a photoelectrocatalyst(upon substrate binding). Transient-absorption spectroscopy reveals a long-lived photo-generated charge-transfer state (τ CR = 6.5 mu s) capable of chemical transformation under a biased voltage. Spiro-CS 2 Ni can endure a compelling range of pH(1-12 for weeks) and hours of electrochemical and photoelectrochemical conditions in the presence of water and organic acids. We believe this work provides crucial design principles for low-density, porous,light-energy-conversion materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-driven prediction of scaling and ignition of inertial confinement fusion experiments

Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

DRDMannTurb: A Python package for scalable, data-driven synthetic turbulence

Synthetic turbulence models (STMs) are used in wind engineering to generate realistic flow fields and are employed as inputs to industrial wind simulations. Examples include prescribing inlet conditions in large eddy simulations that model loads on wind turbines and tall buildings. We are interested in STMs capable of generating fluctuations based on prescribed second-moment statistics since such models can simulate environmental conditions that closely resemble on-site observations. To this end, the widely used Mann model (see Mann, 1994, 1998) is the inspiration for DRDMannTurb. The Mann model is described by three physical parameters: a magnitude parameter influencing the global variance of the wind field and corresponding to the Kolmogorov constant multiplied by the rate of viscous dissipation of the turbulent kinetic energy to the two-thirds, αϵ 2/3 , a turbulence length scale parameter L, and a nondimensional parameter Γ related to the lifetime of the eddies. A number of studies, as well as international standards (e.g., those by the International Electrotechnical Commission (IEC)), include recommended values for these three parameters with the goal of standardizing wind simulations according to observed energy spectra. Yet, having only three parameters, the Mann model faces limitations in accurately representing the diversity of observable spectra. This Python package enables users to extend the Mann model and more accurately fit field measurements through flexible neural network models of the eddy lifetime function. Following Keith et al. (2021), we refer to this class of models as Deep Rapid Distortion (DRD) models. DRDMannTurb also includes a general module implementing an efficient method for synthetic turbulence generation based on a domain decomposition technique. This technique is also described in Keith et al. (2021).

17 WIND ENERGY↗
DOE OSTI2014

Dose rates near the Service Buildings for Al, Cu, Fe and Pb targets

Fluka simulations were performed to model fault conditions that result in the electron beam hitting different materials found in the storage ring (SR), namely aluminum in beam pipes, copper in magnet coils, iron in magnet yokes and lead in local shields. The primary goal of the simulations was to determine dose distributions outside the bulk shielding due to beam losses for different fault conditions. These predicted doses can be used to provide guidance regarding the need for additional shielding or administrative safeguards. A secondary goal was to compare the simulated dose at three different locations with the dose obtained from analytical calculations using the thick target approximation. The analytical calculations are quick and easy and do not require detailed modeling of the geometry. If these calculations can indeed provide reasonably accurate estimates of the dose then they can be used instead of the detailed FLUKA simulations which require more time, effort and computational resources. However the assumptions embedded in the analytical methods result in the maximum possible yield of secondary particles and will therefore overestimate the radiation dose and the radiation shielding required.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

model predictive control↗
NASA NTRSDate not supplied

Fluid-Structure Interaction Simulations of the ASPIRE SR01 Supersonic Parachute

High-fidelity computational fluid dynamics (CFD) simulations have so far only played exploratory and supporting roles in the study and qualification of parachutes for planetary entry. The last few years have seen the maturation of coupled computational methods that are capable of modeling the complex fluid-structure interactions between a parachute canopy and the supersonic flow in the wake of an entry vehicle in flight conditions. One of the primary goals of these methods is to predict the peak opening load experienced by the parachute during inflation. The Launch, Ascent, and Vehicle Aerodynamics (LAVA)team is developing efficient, high-fidelity numerical methods to perform such challenging fluid-structure interaction simulations. A loose coupling approach is used to advance the solutions of a Cartesian ghost cell immersed boundary method CFD solver and a finite element computational structural dynamics (CSD) solver in space and time. The coupled solver is employed to simulate the ASPIRE SR01 flight test, where a build-to-print version of the Mars Science Laboratory parachute was inflated in supersonic conditions in the upper terrestrial atmosphere. The simulations conducted in the current paper predict a peak opening load that is within 10% of that from the flight test. Grid convergence with respect to the volume and structural domains is demonstrated, and less than 1% variation in the peak opening load is predicted between all grid resolutions.

ARMD↗
NASA NTRS2013

Human and Robotic Mission to Small Bodies: Mapping, Planning and Exploration

This study investigates the requirements, performs a gap analysis and makes a set of recommendations for mapping products and exploration tools required to support operations and scientific discovery for near- term and future NASA missions to small bodies. The mapping products and their requirements are based on the analysis of current mission scenarios (rendezvous, docking, and sample return) and recommendations made by the NEA Users Team (NUT) in the framework of human exploration. The mapping products that sat- isfy operational, scienti c, and public outreach goals include topography, images, albedo, gravity, mass, density, subsurface radar, mineralogical and thermal maps. The gap analysis points to a need for incremental generation of mapping products from low (flyby) to high-resolution data needed for anchoring and docking, real-time spatial data processing for hazard avoidance and astronaut or robot localization in low gravity, high dynamic environments, and motivates a standard for coordinate reference systems capable of describing irregular body shapes. Another aspect investigated in this study is the set of requirements and the gap analysis for exploration tools that support visualization and simulation of operational conditions including soil interactions, environment dynamics, and communications coverage. Building robust, usable data sets and visualisation/simulation tools is the best way for mission designers and simulators to make correct decisions for future missions. In the near term, it is the most useful way to begin building capabilities for small body exploration without needing to commit to specific mission architectures.

Neffian, Ara V.↗
NASA NTRS2005

Analysis of Wake VAS Benefits Using ACES Build 3.2.1: VAMS Type 1 Assessment

The FAA and NASA are currently engaged in a Wake Turbulence Research Program to revise wake turbulence separation standards, procedures, and criteria to increase airport capacity while maintaining or increasing safety. The research program is divided into three phases: Phase I near term procedural enhancements; Phase II wind dependent Wake Vortex Advisory System (WakeVAS) Concepts of Operations (ConOps); and Phase III farther term ConOps based on wake prediction and sensing. The Phase III Wake VAS ConOps is one element of the Virtual Airspace Modelling and Simulation (VAMS) program blended concepts for enhancing the total system wide capacity of the National Airspace System (NAS). This report contains a VAMS Program Type 1 (stand-alone) assessment of the expected capacity benefits of Wake VAS at the 35 FAA Benchmark Airports and determines the consequent reduction in delay using the Airspace Concepts Evaluation System (ACES) Build 3.2.1 simulator.

Smith, Jeremy C.↗
NASA NTRS2002

Software for Processing Flight and Simulated Data of the ATIC Experiment

ATIC (Advanced Thin Ionization Calorimeter) is a balloon borne experiment designed to measure the cosmic ray composition for elements from hydrogen to iron and their energy spectra from approx.50 GeV to near 100 TeV. It consists of a Si-matrix detector to determine the charge of a CR particle, a scintillator hodoscope for tracking, carbon interaction targets and a fully active BGO calorimeter. ATIC had its first flight from McMurdo, Antarctica from 28/12/2000 to 13/01/2001. The ATIC flight collected approximately 25 million events. A C++-class library for building different programs for processing flight and simulated data of the ATIC balloon experiment is described. This library is compatible with the ROOT-system and includes classes and methods for solving a number of problems as the following: Reading data files in different formats (raw-data format, ROOT-format, ASCII-format, different formats for simulated data); Transferring all these formats to the only inner format of the library; Reconstruction of trajectories of primary particles with BGO calorimeter only. The Monte-Carlo simulations with GEANT code were used to obtain the basic tables for computing error corridors and chi(sup 2)-values for the trajectories. Obtaining error corridors for searching for signal of primary particle in the Si-matrix; Searching for hit of primary particle in the Si-matrix with using of error corridor and other criteria (chi(sup 2)-values, agreement between signals in Si-matrix and in the upper layer of scintillator and others); Determination of charge of primary particle; Determination of energy deposit in BGO calorimeter.

Panov, A. D.↗
DOE OSTI2016

Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.

54 ENVIRONMENTAL SCIENCES↗