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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bootstrap and amplitudes: a hike in the landscape of quantum field theory

This article is an introduction to two currently very active research programs, the Conformal Bootstrap and Scattering Amplitudes. Rather than attempting full surveys, the emphasis is on common ideas and methods shared by these two seemingly very different programs. In both fields, mathematical and physical constraints are placed directly on the physical observables in order to explore the landscape of possible consistent quantum field theories. We give explicit examples from both programs: the reader can expect to encounter boiling water, ferromagnets, pion scattering, and emergent symmetries on this journey into the landscape of local relativistic quantum field theories. Here, the first part is written for a general physics audience. The second part includes further details, including a new on-shell bottom-up reconstruction of the CP 1 model with the Fubini-Study metric arising from re-summation of the n-point interaction terms derived from amplitudes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Candidate Core Designs for the Transformational Challenge Reactor

Early cycle activities under the Transformational Challenge Reactor (TCR) program focused on analyzing and maturing four reactor core design concepts: two fast-spectrum systems and two thermal-spectrum systems. A rapid, iterative approach has been implemented through which designs can be modified and analyzed and subcomponents can be manufactured in parallel over time frames of weeks rather than months or years. To meet key program initiatives (e.g., timeline, material use), several constraints—including fissile material availability (less than 250 kg of HALEU), component availabilities, materials compatibility, and additive manufacturing capabilities—were factored into the design effort, yielding small (less than one cubic meter in volume) cores with near-term viability. The fast-spectrum designs did not meet the fissile material constraint, so the thermal-spectrum systems became the primary design focus. Since significant progress has been made on advanced moderator materials (YH x ) under the TCR program, gas-cooled thermal-spectrum systems using less than 250 kg of HALEU that occupy less than 1 m 3 are now feasible. The designs for two of these systems have been evolved and matured. In both thermal-spectrum design concepts, bidirectional coolant flow is used. Coolant flows down through YH x moderator elements and is reversed in a bottom manifold and core support structure, and then flows up though or around the fuel elements. The main difference between the two thermal-spectrum design concepts is the fuel elements—one uses traditional UO 2 ceramic fuel, and the other uses UN-bearing TRISO fuel particles embedded inside a SiC matrix. Finally, core neutronics and thermal performance for these systems are assessed and summarized herein.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Formulations and Valid Inequalities for Optimal Black Start Allocation in Power Systems

The restoration of a power system after a blackout starts around units with enhanced technical capabilities, referred to as black start units (BSUs). We examine the planning problem of optimally allocating these units on the grid subject to a budget constraint. We present a mixed integer programming model based on current literature in power systems. Binary variables are associated with the allocation of BSUs and with the energization state of buses, branches, and generators of the power system over a time horizon. We extract a substructure of the feasible region which imposes the requirement that each island that appears during the restoration process must have at least one operational generator. We discuss three equivalent reformulations for this requirement. We introduce a family of exponentially many, polynomially separable, valid inequalities to strengthen the formulation. Under simplifying assumptions, we show that the convex hull of the feasible region is a full-dimensional polyhedron, and prove that some of the constraints we introduced are facet-defining. We perform experiments to examine the difference in strength between the formulations as well as the computational times to solve the problem to near optimality for synthetic instances of the IEEE-39, IEEE-118, Illinois-200, WECC-225, IEEE-300, South Carolina-500, and Texas-2000 power systems. We illustrate a use case of the model. We conclude by suggesting extensions of the current work for future research.

Black Start Allocation↗
DOE OSTI2024

Shortest path network interdiction with asymmetric uncertainty

Abstract This paper considers an extension of the shortest path network interdiction problem that incorporates robustness to account for parameter uncertainty. The shortest path interdiction problem is a game of two players with conflicting agendas and capabilities: an evader, who traverses the arcs of a network from a source node to a sink node using a path of shortest length, and an interdictor, who maximizes the length of the evader's shortest path by interdicting arcs on the network. It is usually assumed that the parameters defining the network are known exactly by both players. We consider the situation where the evader assumes the nominal parameter values while the interdictor uses robust optimization techniques to account for parameter uncertainty or sensor degradation. We formulate this problem as a nonlinear mixed‐integer semi‐infinite bilevel program and show that it can be converted into a mixed‐integer linear program with a second order cone constraint. We use random geometric networks and transportation networks to perform computational studies and demonstrate the unique decision strategies that our variant produces. Solving the shortest path interdiction problem with asymmetric uncertainty protects the interdictor from investing in a strategy that hinges on key interdictions performing as promised. It also provides an alternate strategy that mitigates the risk of these worst‐case possibilities.

Punla‐Green, She'ifa Z.↗
DOE OSTI2024

The geometry of the modular bootstrap

Abstract We explore the geometry behind the modular bootstrap and its image in the space of Taylor coefficients of the torus partition function. In the first part, we identify the geometry as an intersection of planes with the convex hull of moment curves onR + ⊗ℤ, with boundaries characterized by the total positivity of generalized Hankel matrices. We phrase the Hankel constraints as a semi-definite program, which has several advantages, such as the validity of bounds irrespective of spin truncation. We derive bounds on the gap, twist-gap, and the space of Taylor coefficients themselves. We find that if the gap is above$$ {\Delta }_{\textrm{gap}}^{\ast } $$ ∆ gap ∗ , where$$ \frac{c-1}{12}<{\Delta}_{\textrm{gap}}^{\ast }<\frac{c}{12} $$ c − 1 12 < Δ gap ∗ < c 12 , all coefficients become bounded on both sides and kinks develop in the space. In the second part, we propose an analytic method of imposing the integrality condition for the degeneracy number in the spinless bootstrap, which leads to a non-convex geometry. We find that even at very low derivative order this condition rules out regions otherwise allowed by bootstraps at high derivative order.

Physics↗
DOE OSTI2021

Delivery drone route planning over a battery swapping network

Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.

drone battery swapping↗
DOE OSTI2020

Resilient co-expansion planning between gas and electric distribution networks against natural disasters

Resiliently designed and constructed integrated gas-electric distribution networks (GEDNs) against natural disasters are crucial to social welfare. In this study, a two-stage robust optimisation-based co-expansion planning model is proposed to attain an integrated GEDN with a given resilience level, by optimising the investment strategies of hardening and selective expansion of power distribution feeders and natural gas pipelines, as well as the location and capacity of natural-gas-fired distributed generation. In the first stage, the overall annual investment and operation cost is minimised under normal operation conditions while in the second stage, the feasibility of the investment decisions under the identified worst-case natural disaster scenario is checked with an adjustable load shedding cost criterion. The proposed model is formulated as a mixed integer second-order cone programming problem with the column and constraint generation algorithm employed to seek the optimal solution. Case studies on two integrated GEDNs demonstrate the performance of the proposed methodology.

Zou, Bo↗
DOE OSTI2021

A two-stage service restoration method for electric power distribution systems

Improving the reliability of power distribution systems is critically important for both utilities and customers. This calls for an efficient service restoration module within a distribution management system to support the implementation of self-healing smart grid networks. Although the emerging smart grid technologies, including distributed generators (DGs) and remote-controlled switches, enhance the self-healing capability and allow faster recovery, they still pose additional complexity to the service restoration problem, especially under cold load pickup (CLPU) conditions. Herein, a novel two-stage restoration framework is proposed to generate a restoration solutions with a sequence of control actions. The first stage generates a restoration plan that supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islanding methods. The second stage generates an optimal sequence of switching operations to bring the outaged system quickly to the final restored configuration. The problem is formulated as a mixed-integer linear program that incorporates system connectivity, operating constraints, and the CLPU models. It is demonstrated that on using a multi-feeder test case, the proposed framework is effective in utilizing all available resources to quickly restore the service and generate an optimal sequence of switching actions to be used by the operator to reach the desired optimal configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Convex Q-Learning in Continuous Time with Application to Dispatch of Distributed Energy Resources

Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a priori knowledge regarding policy or value function structure. This paper explores algorithm design in the continuous time domain, with a finite-horizon optimal control objective. The main contributions are (i) The new Q-ODE: a model-free characterization of the Hamilton-Jacobi-Bellman equation. (ii) A formulation of Convex Q-learning that avoids approximations appearing in prior work. The Bellman error used in the algorithm is defined by filtered measurements, which is necessary in the presence of measurement noise. (iii) Convex Q-learning with linear function approximation is a convex program. It is shown that the constraint region is bounded, subject to an exploration condition on the training input. (iv) The theory is illustrated in application to resource allocation for distributed energy resources, for which the theory is ideally suited.

Lu, Fan↗
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 OSTI2016

SU-C-202-01: Incorporating Time-Dependent Hypoxia in IMRT Planning

Purpose: To incorporate uncertainties in oxygenation of tumor cells into radiation therapy planning via robust optimization. The model is demonstrated using a clinical prostate cancer case. Methods: The tumor oxygenation levels are determined based on pre- and mid-treatment PET scans. To account for oxygenation changes, we use a radio-sensitivity factor for the effective dose necessary to treat hypoxic cells. Due to the unpredictable nature of re-oxygenation of hypoxic cells, the radio-sensitivity factor and its change is modeled to reside in a time-dependent uncertainty set. This uncertainty can adapt to both pre- and mid-treatment scans, resulting in a two-stage robust treatment planning model. We develop a robust counterpart reformulation that transforms the original NP-hard problem into a linear program consisting of finitely many linear constraints, which can be solved efficiently. Results: The robust plan is compared to both the corresponding fractionated plan which ignores hypoxia, and to the commonly practiced dose-escalated plan that uniformly increases dose based on initial oxygen concentrations. For PTV, the robust plan improves D{sub 50}, D{sub 95}, and EUD by 4% on average compared to fractionated, and by 11% compared to the optimal escalated plan. Moreover, the robust plan spared organs at risk similar to the fractionated plan, while the escalated plan led to over 170% increase of Bladder D{sub 30}. Conclusion: The proposed robust adaptive model can improve tumor coverage by increasing dose to hypoxic tumor voxels that is overlooked by common fractionated treatments. In addition, the robust model anticipates the re-oxygenation process, thus reducing the excess dose at later fractions in comparison to uniform dose-escalation methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A Surety Engineering Framework and Process to Address Ethical Legal and Social Issues for Artificial Intelligence

The ethical, legal, and social issues (ELSI) surrounding Artificial Intelligence (AI) can have as great of an impact on the technologies’ success as technical issues such as safety, reliability, and security. Addressing these risks can counter potential program failures, legal and ethical battles, constraints to scientific research, and product vulnerabilities. This paper presents a surety engineering framework and process that can be applied to AI to identify and address technical, ethical, legal and societal risks. Extending sound engineering practices to incorporate a method to “engineer” ELSI can offer the scientific rigor required to significantly reduce the risk of AI vulnerabilities. Modeling the specification, design, evaluation and quality/risk indicators for AI provides a foundation for a risk-informed decision process that can benefit researchers and stakeholders alike as they use it to critically examine both substantial and intangible risks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Development of a Planar Focusing Collector for CSP

In the field of the concentrating solar power (CSP) technology, the parabolic trough is a common choice for a solar collector. Parabolic troughs are highly reflective curved mirrors that take incident sunlight and focus it onto a line-shaped receiver that runs down the center of troughs. The receivers are generally heat-absorbing pipes containing a heat transfer fluid that delivers the collected thermal energy to steam turbines or engines for generating electricity. However, parabolic troughs are constrained by wind loads and cost of materials and support structure. The goal of this project was to develop a planar focusing collector (PFC) that would serve as an alternative to the parabolic trough. An ideal PFC is ultrathin (less than a few microns) and lightweight, and could be manufactured in a manner that is scalable and low cost. Furthermore, these features would be attractive to CSP technology because of the potential to reduce cost and complexity of installation and maintenance. The work pursued in this project attempted to actualize the PFC concept by utilizing advances in the emerging area of metasurfaces, which rely on the nanopatterning of surfaces to control the amplitude and phase of light at length scales smaller than an optical wavelength. Academic pursuits of metasurface technology predominately employ electron-beam lithography (EBL) for fabrication because the technique offers highly reliable nanopatterning of surfaces with a spatial resolution of ~10 nm. However, due to the techno-economic constraints of the DOE’s SunShot program, particularly cost-effective scalability, the use of EBL was avoided in this project. Rather, fabrication approaches that lend themselves to scalable manufacturing by way of stamping or imprinting were favored. Ultimately, the tradeoffs with using such methods was the larger feature size—on the order of ~150 um critical dimension—resulting in poorer control of light compared to structures fabricated by EBL. A summary of our 2-year investigation on this topic is presented herein. We begin by presenting highlights of our 1 st -year efforts using plasmonics, i.e., metallic nanostructures, as the building block for our PFC. The limitations in this approach are subsequently summarized. We then review the strategy we employed during the final year of our project, in particular utilizing insulating or dielectric materials as the light controlling medium. We round out the report discussing the results of our findings from using this approach. The report is relatively self-contained in that and no prior knowledge of optics or nanotechnology is assumed and technical jargon is kept at a minimum.

14 SOLAR ENERGY↗
DOE OSTI2019

Mesoscale Design of Magnetoelectric Heterostructures and Nanocomposites

Multiferroic materials have a coexistence of at least two ferroic orders. The coupling interaction between the different order parameters can produce new phenomena, such as the magnetoelectric (ME) effect. The ME response is the appearance of an electric polarization P upon applying a magnetic field H, and/or vice versa. Magnetoelectricity has been observed as an intrinsic effect in some natural materials at low temperature; however, such single phase materials suffer from extremely weak ME exchange. Better alternatives are two-phase composites consisting of magnetostrictive and piezoelectric phases, which have been shown to have large ME coefficients. Said composites exploit a unique product ME tensor property that depends upon the individual strictions of the piezoelectric and magnetostrictive phases, the elastic stiffness and integrity of the interphase interfaces, the phase distribution and the dimensionality of its connectivity, and any elastic constraint; amongst other things. This research program between D. Viehland PI and S.Priya at Virginia Tech investigated the mesoscale physics and chemistry of such two-phase ME heterostructures. The results of the proposed research are relevant to new generations of multifunctional materials involving heterogeneous components. How the product ME property be optimized? The answers lies with the interphase-interfaces, where the atomic layers coordinate stress transfer from one phase to another. It was the primary goal of this research program to discover such fundamental relationship for two phase ME heterostructures and nanocomposites. This research program integrated all the components required to fully investigate the ME phenomenon: starting from synthesis of nanomaterials with diversified interfacial characteristics; to full electrical, magnetic, and structural characterizations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Testing the Nature of Dark Matter with Extremely Large Telescopes

For nearly 40 years, dark matter has been widely assumed to be cold and collisionless. Cold dark matter models make fundamental predictions for the behavior of dark matter on small (<10 kpc) scales. These predictions include cuspy density profiles at the centers of dark matter halos and a halo mass function that increases as dN/dM ~ M^-1.9 down to very small masses. We suggest two observational programs relying on extremely large telescopes to critically test these predictions, and thus shed new light on the nature of dark matter. (1) Combining adaptive optics-enabled imaging with deep spectroscopy to measure the three-dimensional motions of stars within a sample of Local Group dwarf galaxies that are the cleanest dark matter laboratories known in the nearby universe. From these observations the inner slope of the dark matter density profile can be determined with an accuracy of 0.20 dex, enabling a central cusp to be distinguished from a core at 5 sigma significance. (2) Diffraction-limited AO imaging and integral field spectroscopy of gravitationally lensed galaxies and quasars to quantify the abundance of dark substructures in the halos of the lens galaxies and along the line of sight. Observations of 50 lensed arcs and 50 multiply-imaged quasars will be sufficient to measure the halo mass function over the range 10^7 < M < 10^10 Msun at cosmological scales, independent of the baryonic and stellar composition of those structures. These two observational probes provide complementary information about the small scale structure, with a joint self-consistent analysis mitigating limitations of either probe. This program will produce the strongest existing constraints on the properties of dark matter on small scales, allowing conclusive tests of alternative warm, fuzzy, and self-interacting dark matter models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Snowmass 2021 Cosmic Frontier White Paper: Cosmology with Millimeter-Wave Line Intensity Mapping

Next-generation tests of fundamental physics and cosmology using large scale structure require measurements over large volumes of the Universe, including high redshifts inaccessible to present-day surveys. Line intensity mapping, an emerging technique that detects the integrated emission of atomic and molecular lines without resolving sources, can efficiently map cosmic structure over a wide range of redshifts. Observations at millimeter wavelengths detect far-IR emission lines such as CO/[CII], and take advantage of observational and analysis techniques developed by CMB experiments. These measurements can provide constraints with unprecedented precision on the physics of inflation, neutrino masses, light relativistic species, dark energy and modified gravity, and dark matter, among many other science goals. In this white paper we forecast the sensitivity requirements for future ground-based mm-wave intensity mapping experiments to enable transformational cosmological constraints. We outline a staged experimental program to steadily improve sensitivity, and describe the necessary investments in developing detector technology and analysis techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Energy Efficiency and Infrastructure Upgrades on Nuclear Clean-up Sites: The Hanford Experience - 18641

The Hanford site, located in southeastern Washington, produced much of our nation's weapons-grade plutonium for the Defense nuclear weapons program. This production lasted the better part of four decades with production ending in 1987. Since 1987, the focus of the Hanford site has turned to clean-up activities. Mission Support Alliance (MSA) is the integrator of the multi-contractor effort to clean up the Hanford site. MSA collaborates with the U.S. Department of Energy (DOE) and all Hanford site contractors in finding and implementing new, cost-saving, safe and practical solutions to cleanup challenges with a continual focus on innovative processes and technologies that reduce costs, energy use and bring safety improvements to the site. Support Services include environmental integration and land management; fleet and road maintenance; water/electric and utilities; cyber security and information management as well as the preservation of the site's cultural artifacts. This paper describes the process, challenges, and focus on infrastructure upgrade and energy efficiency projects in the context of a multi-contractor nuclear clean-up site. Project development and new technology deployment at Hanford has long been plagued with economic challenges. For example, despite extensive auditing, analysis, and project development, most of the energy-related projects on the Hanford site have gone unfunded due to the low cost of electricity, the relatively high cost of labor, and costs associated with detailed safety and cultural reviews. - The Hanford site is a direct-service customer of the Bonneville Power Administration (BPA) and has a marginal electricity rate of $0.033/kWh. - Labor costs on the Hanford site track high because of the requirements associated with the safety and security necessary to operate a legacy nuclear weapons complex. - In partnership with three federally recognized and other affected Native American tribes (Yakama Nation, Confederated Tribes of the Umatilla Indian Reservation, the Nez Perce Tribe, and Wanapum People), most activities on the Hanford site are afforded in-depth cultural reviews. To work within these constraints, the MSA Energy Management team has embarked on a three-pronged approach to energy project development: Program Visibility: The MSA Energy Management team has promoted both project opportunities and requirements across the Hanford site. This information comes in the form of site-wide newsletters, group meetings, e-mails, success stories, and recognition of staff. Single-point Analysis: To by-pass the inevitable duplications of effort, learning curve issues, and to improve project consistency/delivery, the MSA Energy Management team serves as the single resource to the site for energy project analysis. This resource includes initial assessment, engineering analysis, efficiency calculation and project recommendations. Contractor/utility Interface: In addition to the infrastructure under its own purview, MSA provides all support services for the contractor/utility interface. These activities include formulation and submission of incentive-based efficiency projects, project tracking, and incentive awards. All incentive awards are forwarded to the relevant contractor and then highlighted in the visibility campaign. Successful energy efficiency projects at Hanford have included high efficiency heating, ventilation, air conditioning (HVAC) systems, interior and exterior lighting/lighting control systems, pump and motor systems, and the right-sizing of transformers and area-wide electrical distribution systems. Prior to the initiation of this program and structure (roughly 3 years ago) the average annual energy efficiency incentive applied for and received was under $10,000, with an annual energy savings of approximately 36,000 kilowatt hours (kWh). In fiscal year 2017, the annual incentive received was over $419,000, with identified energy savings of over 1.3 million kWh. Details and case studies of the major project accomplishments are included. The success of this program can be traced to an understanding of the constraints, the development of a program to address these constraints, and good communication/coordination across DOE and the multiple contractors serving the Hanford site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

solveME: fast and reliable solution of nonlinear ME models

Background Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints. Results Here, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60× speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints. Conclusions Just as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields.

Biochemistry & Molecular Biology↗