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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Key Strategies in Industry for Circular Economy: Analysis of Remanufacturing and Beneficial Reuse

Manufacturing, in the effort to be more sustainable, is increasingly focusing on energy efficiency and waste reduction. DOE's Better Buildings Better Plants Program has established Waste Reduction Network that works with 32 industrial partners to achieve higher material efficiency and reduce waste. United States generates 7.6 Billion Tons of industrial solid waste as estimated by EPA. In a linear economy as the economy grows, so does the waste – increasing strain on resources and the environment. The Circular Economy (CE) model keeps the available resources in circulation for longer period of time easing the burden on the environment.Remanufacturing and (Beneficial) reuse are widely accepted channels in 9R methodology and established pillars of CE. This chapter reviews the two key strategies and their adoption in different industrial sectors. It reviews the key barriers faced by manufacturers in implementing these methodologies and discusses the possible solutions to those barriers. The chapter also reviews impact of these CE strategies on sustainability, material efficiency and the economic and social benefits. Finally, this chapter presents two case studies from DOE's Better Plants partners – one on remanufacturing of components in heavy vehicles industry and one on beneficial reuse of spent foundry sand, a non-hazardous solid waste, and discusses the project impacts.

Chaudhari, Subodh↗
DOE OSTI2015

Elliptical quantum dots as on-demand single photons sources with deterministic polarization states

In quantum information, control of the single photon's polarization is essential. Here, we demonstrate single photon generation in a pre-programmed and deterministic polarization state, on a chip-scale platform, utilizing site-controlled elliptical quantum dots (QDs) synthesized by a top-down approach. The polarization from the QD emission is found to be linear with a high degree of linear polarization and parallel to the long axis of the ellipse. Single photon emission with orthogonal polarizations is achieved, and the dependence of the degree of linear polarization on the QD geometry is analyzed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

SU-E-T-89: Comprehensive Quality Assurance Phantom for the Small Animal Radiation Research Platform

Purpose: Use of Small Animal Radiation Research Platform (SARRP) systems for conducting state-of-the-art image guided radiotherapy (IGRT) research on small animals has become more common over the past years. The purpose of this work is to develop and test the suitability and performance of a comprehensive quality assurance (QA) phantom for the SARRP. Methods: A QA phantom was developed for carrying out daily, monthly and annual QA tasks including imaging, dosimetry and treatment planning system (TPS) performance evaluation of the SARRP. The QA phantom consists of nine (60×60×5 mm3) KV-energy tissue equivalent solid water slabs that can be employed for annual dosimetry QA with film. Three of the top slabs are replaceable with ones incorporating Mosfets or OSLDs arranged in a quincunx pattern, or a slab drilled to accommodate an ion chamber insert. These top slabs are designed to facilitate routine daily and monthly QA tasks such as output constancy, isocenter congruency test, treatment planning system (TPS) QA, etc. One slab is designed with inserts for image QA. A prototype of the phantom was applied to test the performance of the imaging, planning and treatment delivery systems. Results: Output constancy test results showed daily variations within 3%. For isocenter congruency test, the phantom could be used to detect 0.3 mm deviations of the CBCT isocenter from the radiation isocenter. Using the Mosfet in phantom as target, the difference between TPS calculations and measurements was within 5%. Image-quality parameters could also be assessed in terms of geometric accuracy, CT number accuracy, linearity, noise and image uniformity, etc. Conclusion: The developed phantom can be employed as a simple tool for comprehensive performance evaluation of the SARRP. The study provides a reference for development of a comprehensive quality assurance program for the SARRP, with proposed tolerances and frequency of required tests.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Nuclear data covariances are critical input to determine upper sub-critical limits and to design experiments to increase it [Slides]

This presentation discusses how Upper Subcritical Limits (USL) are key parameters to determine operational limits in nuclear criticality safety evaluations. It also discusses an example of plutonium casting operation using tantalum at LANL PF-4. The Whisper tool at Los Alamos relies on many inputs, including covariance data, leading the presentation to ask if an existing benchmark data be used in Whisper to adjust nuclear data and covariances to justify a higher USL. If not, Whisper can be used to help design an optimal new benchmark experiment. The presentation also seeks to determine what the possible impacts are on USL and operational limits for plutonium casting. In conclusion, nuclear data covariances are used for by Whisper for: GSSL adjustment of nuclear data and covariances, identification of most similar existing benchmark experiments to application, simulation of Upper Subcritical Limit of application, and input to optimization techniques for designing most appropriate new benchmark experiment(s) to meet requirements. This requires a complete set of nuclear data covariances, benchmarks and k-effective sensitivity profiles (for both benchmarks and applications). The presentation concludes by asking if end users should trust results that depend on current covariance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Key Strategies in Industry for Circular Economy : Analysis of Remanufacturing and Beneficial Reuse

Manufacturing, in the effort to be more sustainable, is increasingly focusing on energy efficiency and waste reduction. DOE’s Better Buildings Better Plants Program has a Waste Reduction Network that works with 32 industrial partners to achieve higher material efficiency and reduce waste. United States generates 7.6 Billion Tons of industrial solid waste as estimated by EPA. In a linear economy as the economy grows, so does the waste – increasing strain on resources and the environment. The Circular Economy (CE) model keeps the available resources in circulation for longer period of time easing the burden on the environment. Remanufacturing and (Beneficial) re-use are widely accepted channels in 9R methodology and established pillars of CE. This paper reviews the two key strategies and their adoption in different industrial sectors. It reviews the key barriers faced by manufacturers in implementing these methodologies and discusses the possible solutions to those barriers. The paper also reviews impact of these CE strategies on sustainability, material efficiency and the economic and social benefits. Finally, this paper presents two case studies from DOE’s Better Plants partners – one on remanufacturing of components in heavy vehicles industry and one on beneficial reuse of spent foundry sand, a non-hazardous solid waste, and discusses the project impacts.

Chaudhari, Subodh↗
DOE OSTI2023

Reinforcement Learning of Structured Stabilizing Control for Linear Systems With Unknown State Matrix

This paper delves into designing feedback control gains for a continuous-time linear quadratic regulator (LQR) problem that is constrained to certain predefined structure with unknown state matrix. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. Here we first formulate a model-based framework using dynamic programming (DP) to embed the structural constraint to the LQR gain computation in the continuous-time setting, and then subsequently, formulate a policy iteration RL algorithm that can alleviate the requirement of known state matrix in conjunction with maintaining the feedback gain structure. The design enables a distributed learning control design which is necessary for many large-scale cyber-physical systems. Theoretical guarantees are provided for stability and convergence of the structured reinforcement learning (SRL) algorithm. We validate our theoretical results with numerical simulations on a multi-agent networked linear time-invariant (LTI) dynamic system.

42 ENGINEERING↗
DOE OSTI2015

SU-E-T-255: Development of a Michigan Quality Assurance (MQA) Database for Clinical Machine Operations

Purpose: A unified database system was developed to allow accumulation, review and analysis of quality assurance (QA) data for measurement, treatment, imaging and simulation equipment in our department. Recording these data in a database allows a unified and structured approach to review and analysis of data gathered using commercial database tools. Methods: A clinical database was developed to track records of quality assurance operations on linear accelerators, a computed tomography (CT) scanner, high dose rate (HDR) afterloader and imaging systems such as on-board imaging (OBI) and Calypso in our department. The database was developed using Microsoft Access database and visual basic for applications (VBA) programming interface. Separate modules were written for accumulation, review and analysis of daily, monthly and annual QA data. All modules were designed to use structured query language (SQL) as the basis of data accumulation and review. The SQL strings are dynamically re-written at run time. The database also features embedded documentation, storage of documents produced during QA activities and the ability to annotate all data within the database. Tests are defined in a set of tables that define test type, specific value, and schedule. Results: Daily, Monthly and Annual QA data has been taken in parallel with established procedures to test MQA. The database has been used to aggregate data across machines to examine the consistency of machine parameters and operations within the clinic for several months. Conclusion: The MQA application has been developed as an interface to a commercially available SQL engine (JET 5.0) and a standard database back-end. The MQA system has been used for several months for routine data collection.. The system is robust, relatively simple to extend and can be migrated to a commercial SQL server.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Determination of Collision Cross Sections Using a Fourier Transform Electrostatic Linear Ion Trap Mass Spectrometer

Collision cross sections (CCSs) were determined from the frequency-domain linewidths in a Fourier transform electrostatic linear ion trap. With use of an ultrahigh-vacuum precision leak valve and nitrogen gas, transients were recorded as the background pressure in the mass analyzer chamber was varied between 4× 10{sup -8} and 7 × 10{sup -7} Torr. The energetic hard-sphere ion–neutral collision model, described by Xu and coworkers, was used to relate the recorded image charge to the CCS of the molecule. In lieu of our monoisotopically isolating the mass of interest, the known relative isotopic abundances were programmed into the Lorentzian fitting algorithm such that the linewidth was extracted from a sum of Lorentzians. Although this works only if the isotopic distribution is known a priori, it prevents ion loss, preserves the high signal-to-noise ratio, and minimizes the experimental error on our homebuilt instrument. Six tetraalkylammonium cations were used to correlate the CCS measured in the electrostatic linear ion trap with that measured by drift-tube ion mobility spectrometry, for which there was an excellent correlation (R{sup 2} ≈ 0.9999). Although the absolute CCSs derived with our method differ from those reported, the extracted linear correlation can be used to correct the raw CCS. With use of [angiotensin II]{sup 2+} and reserpine, the corrected CCSs (334.9 ± 2.1 and 250.1 ± 0.5, respectively) were in good agreement with the reported ion mobility spectrometry CCSs (335 and 254.3, respectively). With sufficient signal-to-noise ratio, the CCSs determined are reproducible to within a fraction of a percent, comparable to the uncertainties reported on dedicated ion mobility instruments. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

SU-G-JeP2-04: Comparison Between Fricke-Type 3D Radiochromic Dosimeters for Real-Time Dose Distribution Measurements in MR-Guided Radiation Therapy

Purpose: To assess MR signal contrast for different ferrous ion compounds used in Fricke-type gel dosimeters for real-time dose measurements for MR-guided radiation therapy applications. Methods: Fricke-type gel dosimeters were prepared in 4% w/w gelatin prior to irradiation in an integrated 1.5 T MRI and 7 MV linear accelerator system (MR-Linac). 4 different ferrous ion (Fe2?) compounds (referred to as A, B, C, and D) were investigated for this study. Dosimeter D consisted of ferrous ammonium sulfate (FAS), which is conventionally used for Fricke dosimeters. Approximately half of each cylindrical dosimeter (45 mm diameter, 80 mm length) was irradiated to ∼17 Gy. MR imaging during irradiation was performed with the MR-Linac using a balanced-FFE sequence of TR/TE = 5/2.4 ms. An approximate uncertainty of 5% in our dose delivery was anticipated since the MR-Linac had not yet been fully commissioned. Results: The signal intensities (SI) increased between the un-irradiated and irradiated regions by approximately 8.6%, 4.4%, 3.2%, and 4.3% after delivery of ∼2.8 Gy for dosimeters A, B, C, and D, respectively. After delivery of ∼17 Gy, the SI had increased by 24.4%, 21.0%, 3.1%, and 22.2% compared to the un-irradiated regions. The increase in SI with respect to dose was linear for dosimeters A, B, and D with slopes of 0.0164, 0.0251, and 0.0236 Gy{sup −1} (R{sup 2} = 0.92, 0.97, and 0.96), respectively. Visually, dosimeter A had the greatest optical contrast from yellow to purple in the irradiated region. Conclusion: This study demonstrated the feasibility of using Fricke-type dosimeters for real-time dose measurements with the greatest optical and MR contrast for dosimeter A. We also demonstrated the need to investigate Fe{sup 2+} compounds beyond the conventionally utilized FAS compound in order to improve the MR signal contrast in 3D dosimeters used for MR-guided radiation therapy. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. LH- 102SPS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Fermilab proton accelerator complex status and improvement plans

Fermilab carries out an extensive program of accelerator-based high energy particle physics research at the Intensity Frontier that relies on the operation of 8 GeV and 120 GeV proton beamlines for a n umber of fixed target experiments. Routine operation with a world-record 700kW of average 120 GeV beam power on the neutrino target was achieved in 2017 as the result of the Proton Improvement Plan (PIP) upgrade. There are plans to further increase the power to 900 – 1000 kW. The next major upgrade of the FNAL accelerator complex, called PIP-II, is under development. It aims at 1.2MW beam power on target at the start of the LBNF/DUNE experiment in the middle of the next decade and assumes replacement of the existing 40-years old 400 MeV normal-conducting Linac with a modern 800 MeV superconducting RF linear accelerator. There are several concepts to further double the beam power to >2.4MW after replacement of the existing 8 GeV Booster synchrotron. In this article we discuss current performance of the Fermilab proton accelerator complex, the upgrade plans for the next two decades and the accelerator R&D program to address cost and performance risks for these upgrades.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Data-driven gradient optimization for field emission management in a superconducting radio-frequency linac

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission-induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings. Published by the American Physical Society 2025

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Optimal Sizing and Operation of a Hybrid Clean Energy Center

In this paper, we consider the deployment of battery energy storage systems (BESSs) paired with photovoltaics (PV) to form a hybrid clean energy center (HCEC), where the BESS is equipped for two main purposes: i) reducing the volatility of PV output and ii) shifting energy to meet an export limit. We develop a linear dynamical model to characterize the output of an HCEC and express the volatility and export power as functions of the battery's dispatch operations. We also propose an optimal sizing method via second-order cone programming (SOCP) to maximize the net benefits of the HCEC. Numerical studies are performed to illustrate and validate the proposed method.

Battery energy storage systems, photovoltaics, sec↗
DOE OSTI2018

Modelling feedbacks between human and natural processes in the land system

Abstract. The unprecedented use of Earth's resources by humans, in combination with increasing natural variability in natural processes over the past century, is affecting the evolution of the Earth system. To better understand natural processes and their potential future trajectories requires improved integration with and quantification of human processes. Similarly, to mitigate risk and facilitate socio-economic development requires a better understanding of how the natural system (e.g. climate variability and change, extreme weather events, and processes affecting soil fertility) affects human processes. Our understanding of these interactions and feedback between human and natural systems has been formalized through a variety of modelling approaches. However, a common conceptual framework or set of guidelines to model human–natural-system feedbacks is lacking. The presented research lays out a conceptual framework that includes representing model coupling configuration in combination with the frequency of interaction and coordination of communication between coupled models. Four different approaches used to couple representations of the human and natural system are presented in relation to this framework, which vary in the processes represented and in the scale of their application. From the development and experience associated with the four models of coupled human–natural systems, the following eight lessons were identified that if taken into account by future coupled human–natural-systems model developments may increase their success: (1) leverage the power of sensitivity analysis with models, (2) remember modelling is an iterative process, (3) create a common language, (4) make code open-access, (5) ensure consistency, (6) reconcile spatio-temporal mismatch, (7) construct homogeneous units, and (8) incorporating feedback increases non-linearity and variability. Following a discussion of feedbacks, a way forward to expedite model coupling and increase the longevity and interoperability of models is given, which suggests the use of a wrapper container software, a standardized applications programming interface (API), the incorporation of standard names, the mitigation of sunk costs by creating interfaces to multiple coupling frameworks, and the adoption of reproducible workflow environments to wire the pieces together.

58 GEOSCIENCES↗
DOE OSTI2022

Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program

Quantum-empowered electromagnetic transients program (QEMTP) is a promising paradigm for tackling EMTP's computational burdens. Nevertheless, no existing studies truly achieve a practical and scalable QEMTP operable on today's noisy-intermediate-scale quantum (NISQ) computers. The strong reliance on noise-free and fault-tolerant quantum devices--which appears to be decades away--hinder practical applications of current QEMTP methods. Here, we devise a NISQ-QEMTP methodology which for the first time transitions the QEMTP operations from ideal, noise-free quantum simulators to real, noisy quantum computers. The main contributions lie in: (1) a shallow-depth QEMTP quantum circuit for mitigating noises on NISQ quantum devices; (2) practical QEMTP linear solvers incorporating executable quantum state preparation and measurements for nodal voltage computations; (3) a noise-resilient QEMTP algorithm leveraging quantum resources logarithmically scaled with power system dimension; (4) a quantum shifted frequency analysis (QSFA) for accelerating QEMTP by exploiting dynamic phasor simulations with larger time steps; (5) a systematical analysis on QEMTPs performance under various noisy quantum environments. Extensive experiments systematically verify the accuracy, efficacy, universality and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

INL Senior Project

What did my team set out to accomplish:? Can I put a custom Machine Learning Model on FPGA?? Can I analyze network traffic in real time?? Can a QSFP port be used with an FPGA?? Does a visual representation of the latent space enhance our understanding of network traffic?? What is QSFP QSFP (Quad Small Form-Factor Pluggable)? QSFP supports transfer speeds generally up to 100Gb/s? Runs 4 parallel lines running up to 28 Gb/s? Why the latent space is important to our project ?Latent space is the compressed mapping of data points in a non-linear fashion? Create an understanding of the relationship of data collected? Can represent that relationship of a single network packet in 3 points (X, Y, Z) Project Outline FPGA? Custom Xilinx Petalinux Image for the operating system? Python program to collect packets and run them through the DPU (Data Processing Unit)? The program then sends the information over a socket to a computer? Display Program? Python Program that collects the information sent from the FPGA and display it in a graph

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme

Lagrangian methods to solve the inviscid Euler equations produce numerical oscillations near shock waves. A common approach to reducing these oscillations is to add artificial viscosity (AV) to the discrete equations. The AV term acts as a dissipative mechanism that attenuates oscillations by smearing the shock across a finite number of computational cells. However, AV introduces several control parameters that are not determined by the underlying physical model, and hence, in practice are tuned to the characteristics of a given problem. We seek to improve the standard quadratic-linear AV form by replacing it with a learned neural function that reduces oscillations relative to exact solutions of the Euler equations, resulting in a hybrid numerical-neural hydrodynamic solver. Because AV is an artificial construct that exists solely to improve the numerical properties of a hydrodynamic code, there is no offline ‘viscosity data’ against which a neural network can be trained before inserting into a numerical simulation, thus requiring online training. We achieve this via differentiable programming, i.e. end-to-end backpropagation or adjoint solution through both the neural and differential equation code, using automatic differentiation of the hybrid code in the Julia programming language to calculate the necessary loss function gradients. A novel offline pre-training step accelerates training by initializing the neural network to the default numerical AV scheme, which can be learned rapidly by space-filling sampling over the AV input space. We find that online training over early time steps of simulation is sufficient to learn a neural AV function that reduces numerical oscillations in long-term hydrodynamic shock simulations. These results offer an early proof-of-principle that online differentiable training of hybrid numerical schemes with novel neural network components can improve certain performance aspects existing in purely numerical schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multilane Automated Driving With Optimal Control and Mixed-Integer Programming

Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks’ energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin’s minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user’s expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Here, comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%–13.7% reduction in energy consumption and a 5.2%–10.3% reduction in the travel time, along with navigational improvements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms

Computational singular perturbation (CSP) is a method to analyze dynamical systems. It targets the decoupling of fast and slow dynamics using an alternate linear expansion of the right-hand side of the governing equations based on eigenanalysis of the associated Jacobian matrix. This representation facilitates diagnostic analysis, detection and control of stiffness, and the development of simplified models. For this work, we have implemented CSP in a C++ open-source library CSPlib using the Kokkos parallel programming model to address portability across diverse heterogeneous computing platforms, i.e., multi/many-core CPUs and GPUs. We describe the CSPlib implementation and present its computational performance across different computing platforms using several test problems. Specifically, we test the CSPlib performance for a constant pressure ignition reactor model on different architectures, including IBM Power 9, Intel Xeon Skylake, and NVIDIA V100 GPU. The size of the chemical kinetic mechanism is varied in these tests. As expected, the Jacobian matrix evaluation, the eigensolution of the Jacobian matrix, and matrix inversion are the most expensive computational tasks. When considering the higher throughput characteristic of GPUs, GPUs performs better for small matrices with higher occupancy rate. CPUs gain more advantages from the higher performance of well-tuned and optimized linear algebra libraries such as OpenBLAS.

97 MATHEMATICS AND COMPUTING↗