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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Simulations of SITI Cookoff Experiments Carried Out with Different Lots of PBX 9502

A pressure dependent cookoff model for PBX 9502 was developed by Hobbs’ et. al. PBX 9502 is composed of 95% by mass triaminotrinitrobenzene (TATB) and a 5% by mass chlorotrifluoroethylene/vinylidine fluoride binder. The objective in this study is to implement this cookoff model in Aria to simulate Sandia Instrumented Thermal Ignition (SITI) experiments that were carried out with different manufacturing lots of PBX 9502. The SITI design consists of solid cylinders (1" diameter × 1" height) of insensitive high explosive (IHE) confined by a cylindrical aluminum case. An electric heater is wrapped around the lateral surface of the case. This heater produces a temperature heating ramp on the outer surface of the case. Internal thermocouples measure the IHE temperature rise from the center to locations close to the IHE-aluminum interface. The energetic material is heated until thermal ignition occurs. Pressure is measured with a static pressure transducer installed on top of the confinement case. Two–dimensional axisymmetric heat conduction finite element models were implemented to simulate these experiments using four options of the PBX 9502 cookoff model. In addition, the predictive ability of this thermal decomposition model is evaluated using Latin Hypercube Sampling (LHS) techniques.

42 ENGINEERING↗
DOE OSTI2017

LHC searches for dark sector showers

This paper proposes a new search program for dark sector parton showers at the Large Hadron Collider (LHC). These signatures arise in theories characterized by strong dynamics in a hidden sector, such as Hidden Valley models. A dark parton shower can be composed of both invisible dark matter particles as well as dark sector states that decay to Standard Model particles via a portal. The focus here is on the specific case of ‘semi-visible jets,’ jet-like collider objects where the visible states in the shower are Standard Model hadrons. We present a Simplified Model-like parametrization for the LHC observables and propose targeted search strategies for regions of parameter space that are not covered by existing analyses. Following the ‘mono-X’ literature, the portal is modeled using either an effective field theoretic contact operator approach or with one of two ultraviolet completions; sensitivity projections are provided for all three cases. We additionally highlight that the LHC has a unique advantage over direct detection experiments in the search for this class of dark matter theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Laying a Foundation for Performance Assessment (PA) Model Development - 19267

Performance Assessment (PA) models are abstractions of real-world conditions. As abstractions, modelers must make a number of decisions regarding model complexity and which features, events, and processes (FEPs) will be represented. To ensure that such decisions are transparent to stakeholders, modelers should document their approach and how this approach influences conceptual model development. This paper describes one such approach. Examples are used to describe an analysis of the potential FEPs. Through the FEPs analysis, all potentially relevant FEPs can be identified. These potentially relevant FEPs are then used to develop a detailed conceptual model known as the Central Scenario, which includes a description of the assessment context, the key features of the site and the closure system, and the expected processes of the system. From this Central Scenario conceptual model, mathematical models can be developed and implemented within modeling software and used to demonstrate compliance to PA performance objectives. FEPs which are beyond the scope of the Central Scenario are identified and used as the basis for developing alternative conceptual models (ACMs) or sensitivity analyses. Once these models and modeling cases are defined, the necessary inputs can be identified and defined. (author)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Developing Model Constraints on Northern Extra-Tropical Carbon Cycling Based on measurements of the Abundance and Isotopic Composition of Atmospheric CO 2

The objective of this project was to perform CO 2 data syntheses and modeling activities to address two central questions: 1) how much has the seasonal cycle in atmospheric CO 2 at northern high latitudes changed since the 1960s, and 2) how well do prognostic biospheric models represent these changes. This project also supported the continuation of the Scripps time series of CO 2 isotopes and concentration at ten baseline stations distributed globally.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Multi-objective Microgrid Assessment and Sizing Framework for Economic and Resilience Benefits

This paper presents an innovative multi-objective assessment and sizing framework for battery energy storage system paired with photovoltaics for a cost-effective and resilient microgrid. In the proposed framework, we simultaneously consider i) economic benefits from bundling grid services in grid-connected mode and ii) enhanced resilience by operating the microgrid in island mode upon outages of the main grid. Instead of simply treating resilience as another monetary value stream, this paper models resilience and economic benefits as two separate objectives. Multi-objective optimization is developed to explore different sizing and dispatch options and generate the Pareto front to assist decision-making in system design. Case studies on a real-world microgrid project in Puget Sound Energy system are presented to illustrate the proposed modeling and sizing methods.

Battery energy storage, Microgrid, Optimal Sizingp↗
DOE OSTI2015

Near-equipartition jets with log-parabola electron energy distribution and the blazar spectral-index diagrams

Fermi-LAT analyses show that the γ-ray photon spectral indices Γ{sub γ} of a large sample of blazars correlate with the νF{sub ν} peak synchrotron frequency ν{sub s} according to the relation Γ{sub γ}=d−klog ν{sub s}. The same function, with different constants d and k, also describes the relationship between Γ{sub γ} and peak Compton frequency ν{sub C}. This behavior is derived analytically using an equipartition blazar model with a log-parabola description of the electron energy distribution (EED). In the Thomson regime, k=k{sub EC}=3b/4 for external Compton (EC) processes and k=k{sub SSC}=9b/16 for synchrotron self-Compton (SSC) processes, where b is the log-parabola width parameter of the EED. The BL Lac object Mrk 501 is fit with a synchrotron/SSC model given by the log-parabola EED, and is best fit away from equipartition. Corrections are made to the spectral-index diagrams for a low-energy power-law EED and departures from equipartition, as constrained by absolute jet power. Analytic expressions are compared with numerical values derived from self-Compton and EC scattered γ-ray spectra from Lyα broad-line region and IR target photons. The Γ{sub γ} versus ν{sub s} behavior in the model depends strongly on b, with progressively and predictably weaker dependences on γ-ray detection range, variability time, and isotropic γ-ray luminosity. Implications for blazar unification and blazars as ultra-high energy cosmic-ray sources are discussed. Arguments by Ghisellini et al. that the jet power exceeds the accretion luminosity depend on the doubtful assumption that we are viewing at the Doppler angle.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Skewering the silos: using Brick to enable portable analytics, modeling and controls in buildings

Nearly all large commercial buildings have heating, ventilation and air conditioning (HVAC) systems, lighting systems, safety and other systems controlled by a computer—a dedicated server with a building energy management system (BMS). However, these BMSs are proprietary with each building’s assets (that is, fans, valves, pumps, and their setpoints) named and coded uniquely by the BMS vendor or engineer; building analytics and control algorithms are written specific to the assets and the building. Thus, any control updates or analytics to improve building performance—especially critical to reduce greenhouse emissions or improve load flexibility—are labor intensive and costly. The Brick schema was developed so the same analysis or control algorithms can work on a variety of buildings if each is digitally represented in a Brick data model. The goal of this project was to further the development of Brick to extend it beyond an academic project with demonstrated success in a small field study, to a practical choice for industrial and commercial stakeholders seeking to realize value from building data. To do this, we executed four objectives: (1) expand the Brick schema including its modeling capabilities and vocabulary, (2) develop tools for integrating Brick with existing digital technologies and representations in buildings, (3) develop an open-source analytics platform to facilitate use of Brick in delivering data value, and (4) demonstrate Brick-driven analytics and controls in real settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

An analysis of the hybrid internal mass modeling approach in EnergyPlus

Accurate simulation of building system dynamics is particularly important for understanding building energy flexibility. Among all dynamics in a building, a zone temperature’s variation is especially important, as it significantly affects a building’s electricity load profile when its heating, ventilating, and air conditioning (HVAC) system is controlled with on/off cycles or setpoint reset strategies. To accurately simulate a zone temperature’s dynamics, internal mass needs to be modeled carefully. In this paper, we compare the two internal mass modeling approaches provided by EnergyPlus, i.e., internal mass object and zone air capacitance multiplier, to better understand their impacts on zone temperature simulation. Real building zone temperature dynamic data from small- and medium-sized office buildings are analyzed and compared with simulated data. In particular, we illustrate the effectiveness of a hybrid method of the two EnergyPlus modeling approaches, which yields more realistic zone temperature dynamics, especially when the zone is conditioned with heat pump systems with on/off cycling.

Chen, Zhelun↗
DOE OSTI2021

DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ~20%. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

galaxies: interactions↗
DOE OSTI2017

A Gibbs Energy Minimization Approach for Modeling of Chemical Reactions in a Basic Oxygen Furnace

In modern steelmaking, the decarburization of hot metal is converted into steel primarily in converter processes, such as the basic oxygen furnace. The objective of this work was to develop a new mathematical model for top blown steel converter, which accounts for the complex reaction equilibria in the impact zone, also known as the hot spot, as well as the associated mass and heat transport. An in-house computer code of the model has been developed in Matlab. The main assumption of the model is that all reactions take place in a specified reaction zone. The mass transfer between the reaction volume, bulk slag, and metal determine the reaction rates for the species. The thermodynamic equilibrium is calculated using the partitioning of Gibbs energy (PGE) method. The activity model for the liquid metal is the unified interaction parameter model and for the liquid slag the modified quasichemical model (MQM). The MQM was validated by calculating iso-activity lines for the liquid slag components. The PGE method together with the MQM was validated by calculating liquidus lines for solid components. The results were compared with measurements from literature. The full chemical reaction model was validated by comparing the metal and slag compositions to measurements from industrial scale converter. The predictions were found to be in good agreement with the measured values. Furthermore, the accuracy of the model was found to compare favorably with the models proposed in the literature. The real-time capability of the proposed model was confirmed in test calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multiple Degradation Mechanisms in Reinforced Concrete Structures, Modeling and Risk Analysis

The overarching goal of this project is to complement ongoing Department of Energy (DOE) Light Water Reactor Sustainability (LWRS)-funded Grizzly concrete modeling development efforts by providing improved multi-physics models for incorporation into the Grizzly and BlackBear codes. Objectives of this work identified at the outset of this project include: 1. Coupling between mechanical damage and transport processes. The mechanical damage will be characterized by nonlinear mechanical constitutive models appropriate for concrete used in nuclear power plants (NPPs). 2. Improved representation of the coupling among the transport process, building on the current moisture and thermal transport models in Grizzly. The parameters for coupling among heat and mass transport in the multi-physics model will be experimentally determined. 3. Coupling among different scale levels of concrete constituents, fine and coarse aggregates, cement paste, hydration products, and pore structure. 4. Probabilistic analysis of the random nature of the heterogeneous concrete structure and the environmental factors (ambient temperature and humidity) and their multiple effects on concrete deterioration. 5. Benchmark verification, validation and uncertainty quantification of the thermo-hygro- chemo-mechanical (THCM) concrete formulation. The end goal of this work was to provide a comprehensive and robust simulation capability that can be applied at the engineering scale for analysis of realistic deterioration scenarios in NPP concrete structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Application of Ocean Thermal Energy Conversion (OTEC) Systems for Powering Safety Monitoring Systems of Offshore Oil and Gas Operations, OESI 2.0 M-1 T-1-P1.1 – Objective 3 Report: Collocation of OTEC with Offshore Oil and Gas Activities

This report is a component of a comprehensive research initiative aimed at evaluating the technoeconomic feasibility of deploying Ocean Thermal Energy Conversion (OTEC) systems to power safety monitoring systems for offshore oil and gas operations in the Gulf of America (GOA). The overarching study has three primary objectives: summarizing existing oil and gas equipment monitoring systems, modeling OTEC as a renewable marine energy source, and exploring the collocation of OTEC power sources with offshore oil and gas activities to offset power demand. This report specifically addresses Objective 3, focusing on the integration of OTEC systems with floating oil and gas facilities.

02 PETROLEUM↗
DOE OSTI2015

Laser scanner data processing and 3D modeling using a free and open source software

The laser scanning is a technology that allows in a short time to run the relief geometric objects with a high level of detail and completeness, based on the signal emitted by the laser and the corresponding return signal. When the incident laser radiation hits the object to detect, then the radiation is reflected. The purpose is to build a three-dimensional digital model that allows to reconstruct the reality of the object and to conduct studies regarding the design, restoration and/or conservation. When the laser scanner is equipped with a digital camera, the result of the measurement process is a set of points in XYZ coordinates showing a high density and accuracy with radiometric and RGB tones. In this case, the set of measured points is called “point cloud” and allows the reconstruction of the Digital Surface Model. Even the post-processing is usually performed by closed source software, which is characterized by Copyright restricting the free use, free and open source software can increase the performance by far. Indeed, this latter can be freely used providing the possibility to display and even custom the source code. The experience started at the Faculty of Engineering in Catania is aimed at finding a valuable free and open source tool, MeshLab (Italian Software for data processing), to be compared with a reference closed source software for data processing, i.e. RapidForm. In this work, we compare the results obtained with MeshLab and Rapidform through the planning of the survey and the acquisition of the point cloud of a morphologically complex statue.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

An optimization model for collection, haul, transfer, treatment and disposal of infectious medical waste: Application to a Greek region

Highlights: • We developed an optimization model to minimize cost of infectious waste management. •Two different software were used for model solution. • Model optimizes locations and capacities of treatment plants and transfer stations. • The higher percentage of total cost was due to construction of the treatment plant. • Sensitivity analysis with respect to fuel cost and waste production was conducted. - Abstract: The objective of this work was to develop an optimization model to minimize the cost of a collection, haul, transfer, treatment and disposal system for infectious medical waste (IMW). The model calculates the optimum locations of the treatment facilities and transfer stations, their design capacities (t/d), the number and capacities of all waste collection, transport and transfer vehicles and their optimum transport path and the minimum IMW management system cost. Waste production nodes (hospitals, healthcare centers, peripheral health offices, private clinics and physicians in private practice) and their IMW production rates were specified and used as model inputs. The candidate locations of the treatment facilities, transfer stations and sanitary landfills were designated, using a GIS-based methodology. Specifically, Mapinfo software with exclusion criteria for non-appropriate areas was used for siting candidate locations for the construction of the treatment plant and calculating the distance and travel time of all possible vehicle routes. The objective function was a non-linear equation, which minimized the total collection, transport, treatment and disposal cost. Total cost comprised capital and operation costs for: (1) treatment plant, (2) waste transfer stations, (3) waste transport and transfer vehicles and (4) waste collection bins and hospital boxes. Binary variables were used to decide whether a treatment plant and/or a transfer station should be constructed and whether a collection route between two or more nodes should be followed. Microsoft excel software was used as installation platform of the optimization model. For the execution of the optimization routine, two completely different software were used and the results were compared, thus, resulting in higher reliability and validity of the results. The first software was Evolver, which is based on the use of genetic algorithms. The second one was Crystal Ball, which is based on Monte Carlo simulation. The model was applied to the Region of East Macedonia – Thrace in Greece. The optimum solution resulted in one treatment plant located in the sanitary landfill area of Chrysoupolis, required no transfer stations and had a total management cost of 38,800 €/month or 809 €/t. If a treatment plant is sited in the most eastern part of the Region, i.e., the industrial area of Alexandroupolis, the optimum solution would result in a transfer station of 23 m{sup 3}, located near Kavala General Hospital, and a total cost of 39,800 €/month or 831 €/t. A sensitivity analysis was conducted and two alternative scenarios were optimized. In the first scenario, a 15% rise in fuel cost and in the second scenario a 25% rise in IMW production were considered. At the end, a cost calculation in €/t/km for every type of vehicle used for haul and transfer was conducted. Also, the cost of the whole system was itemized and calculated in €/t/km and €/t. The results showed that the higher percentage of the total cost was due to the construction of the treatment plant.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Final Technical Report for DE-SC0005467

The objective of this project is to gain a comprehensive understanding of the key atmospheric mechanisms and physical processes associated with temperature extremes in order to better interpret and constrain uncertainty in climate model simulations of future extreme temperatures. To achieve this objective, we first used climate observations and a reanalysis product to identify the key atmospheric circulation patterns associated with extreme temperature days over North America during the late twentieth century. We found that temperature extremes were associated with distinctive signatures in near-surface and mid-tropospheric circulation. The orientations and spatial scales of these circulation anomalies vary with latitude, season, and proximity to important geographic features such as mountains and coastlines. We next examined the associations between daily and monthly temperature extremes and large-scale, recurrent modes of climate variability, including the Pacific-North American (PNA) pattern, the northern annular mode (NAM), and the El Niño-Southern Oscillation (ENSO). The strength of the associations are strongest with the PNA and NAM and weaker for ENSO, and also depend upon season, time scale, and location. The associations are stronger in winter than summer, stronger for monthly than daily extremes, and stronger in the vicinity of the centers of action of the PNA and NAM patterns. In the final stage of this project, we compared climate model simulations of the circulation patterns associated with extreme temperature days over North America with those obtained from observations. Using a variety of metrics and self-organizing maps, we found the multi-model ensemble and the majority of individual models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) generally capture the observed patterns well, including their strength and as well as variations with latitude and season. The results from this project indicate that current models are capable of simulating the large-scale meteorological patterns associated with daily temperature extremes and they suggest that such models can be used to evaluate the extent to which changes in atmospheric circulation will influence future changes in temperature extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Structure formation and exotic compact objects in a dissipative dark sector

Here, we present the complete history of structure formation in a simple dissipative dark-sector model. The model has only two particles: a dark electron, which is a subdominant component of dark matter, and a dark photon. Dark-electron perturbations grow from primordial overdensities, become non-linear, and form dense dark galaxies. Bremsstrahlung cooling leads to fragmentation of the dark-electron halos into clumps that vary in size from a few to millions of solar masses, depending on the particle model parameters. In particular, we show that asymmetric dark stars and black holes form within the Milky Way from the collapse of dark electrons. These exotic compact objects may be detected and their properties measured at new high-precision astronomical observatories, giving insight into the particle nature of the dark sector without the requirement of non-gravitational interactions with the visible sector. Our analysis demonstrates that a full study of the structure-formation history is needed to assess correctly the viability of exotic compact objects in dissipative dark-sector models.

79 ASTRONOMY AND ASTROPHYSICS↗