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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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DOE OSTI2019

Computationally Driven Discovery of a Family of Layered LiNiB Polymorphs

Abstract Two novel lithium nickel boride polymorphs, RT ‐LiNiB and HT ‐LiNiB, with layered crystal structures are reported. This family of compounds was theoretically predicted by using the adaptive genetic algorithm (AGA) and subsequently synthesized by a hydride route with LiH as the lithium source. Unique among the known ternary transition‐metal borides, the LiNiB structures feature Li layers alternating with nearly planar [NiB] layers composed of Ni hexagonal rings with a B–B pair at the center. A comprehensive study using a combination of single crystal/synchrotron powder X‐ray diffraction, solid‐state 7 Li and 11 B NMR spectroscopy, scanning transmission electron microscopy, quantum‐chemical calculations, and magnetism has shed light on the intrinsic features of these polymorphic compounds. The unique layered structures of LiNiB compounds make them ultimate precursors for exfoliation studies, thus paving a way toward two‐dimensional transition‐metal borides, MBenes.

Gvozdetskyi, Volodymyr↗
DOE OSTI2019

Computationally Driven Discovery of a Family of Layered LiNiB Polymorphs

Abstract Two novel lithium nickel boride polymorphs, RT ‐LiNiB and HT ‐LiNiB, with layered crystal structures are reported. This family of compounds was theoretically predicted by using the adaptive genetic algorithm (AGA) and subsequently synthesized by a hydride route with LiH as the lithium source. Unique among the known ternary transition‐metal borides, the LiNiB structures feature Li layers alternating with nearly planar [NiB] layers composed of Ni hexagonal rings with a B–B pair at the center. A comprehensive study using a combination of single crystal/synchrotron powder X‐ray diffraction, solid‐state 7 Li and 11 B NMR spectroscopy, scanning transmission electron microscopy, quantum‐chemical calculations, and magnetism has shed light on the intrinsic features of these polymorphic compounds. The unique layered structures of LiNiB compounds make them ultimate precursors for exfoliation studies, thus paving a way toward two‐dimensional transition‐metal borides, MBenes.

Gvozdetskyi, Volodymyr↗
DOE OSTI2019

Pareto Optimality Explanation of the Glycolytic Alternatives in Nature

The Entner-Doudoroff (ED) and Embden-Meyerhof-Parnas (EMP) glycolytic pathways are largely conserved across glycolytic species in nature. Is this a coincidence, convergent evolution or there exists a driving force towards either of the two pathway designs? We addressed this question by first employing a variant of the optStoic algorithm to exhaustively identify over 11,916 possible routes between glucose and pyruvate at different pre-determined stoichiometric yields of ATP. Subsequently, we analyzed the thermodynamic feasibility of all the pathways at physiological metabolite concentrations and quantified the protein cost of the feasible solutions. Pareto optimality analysis between energy efficiency and protein cost reveals that the naturally evolved ED and EMP pathways are indeed among the most protein cost-efficient pathways in their respective ATP yield categories and remain thermodynamically feasible across a wide range of ATP/ADP ratios and pathway intermediate metabolite concentration ranges. In contrast, pathways with higher ATP yield (>2) while feasible, are bound within stringent and often extreme operability ranges of cofactor and intermediate metabolite concentrations. The preponderance of EMP and ED is thus consistent with not only optimally balancing energy yield vs. enzyme cost but also with ensuring operability for wide metabolite concentration ranges and ATP/ADP ratios.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Robust vehicle routing under uncertainty via branch-price-and-cut

Here, this paper contemplates how branch-price-and-cut solvers can be employed along with the robust optimization paradigm to address parametric uncertainty in the context of vehicle routing problems. In this setting, given postulated uncertainty sets for customer demands and vehicle travel times, one aims to identify a set of cost-effective routes for vehicles to traverse, such that the vehicle capacities and customer time window constraints are respected under any anticipated demand and travel time realization, respectively. To tackle such problems, we propose a novel approach that combines cutting-plane techniques with an advanced branch-price-and-cut algorithm. Specifically, we use deterministic pricing procedures to generate "partially robust" vehicle routes and then utilize robust versions of rounded capacity inequalities and infeasible path elimination constraints to guarantee complete robust feasibility of routing designs against demand and travel time uncertainty. In contrast to recent approaches that modify the pricing algorithm, our approach is both modular and versatile. It permits the use of advanced branch-price-and-cut technologies without significant modification, while it can admit a variety of uncertainty sets that are commonly used in robust optimization but could not be previously employed in a branch-price-and-cut setting.

42 ENGINEERING↗
DOE OSTI2022

Online eco-routing for electric vehicles using combinatorial multi-armed bandit with estimated covariance

Identifying energy-efficient routes in real-time has significant implications for the energy-optimal operations of electric vehicles (EVs). Here, this study proposes a novel model for EV online eco-routing problem, which obtains the minimal expected energy consumption paths (MECPs) for multiple origin-destination (OD) pairs simultaneously. Specifically, we formulate the routing problem as a bandit problem and solve it with online algorithms. We extend the algorithms by implementing a path elimination mechanism to reduce the candidate path set and introducing the variance and covariance of the energy consumption to reduce the uncertainties. The numerical results show that the proposed algorithms can efficiently obtain near-optimal MECPs, and the solution is significantly better than the widely used shortest trip time path algorithm (STTP) and shortest trip distance path algorithm (SDP). The variation considering link energy covariance and path elimination generates paths that save 4.1% of energy compared to the SDP and 5.4% to the STTP.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Topology Inference of Unknown Networks Based on Robust Virtual Coordinate Systems

Learning and exploring the connectivity of unknown networks represent an important problem in practical applications of communication networks and social-media networks. Modeling large-scale networks as connected graphs is highly desirable to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This paper investigates a simple measurement model in which a small subset of source nodes collect hop distance information from networked nodes in order to generate a virtual coordinate system (VCS) for networks of unknown topology. We establish the VCS to define logical distance among nodes based on principal component analysis and to determine connectivity relationship and effective routing methods. More importantly, we present a robust analytical algorithm to derive the VCS against practical issues of missing and corrupted measurements. We also develop a connectivity inference method which classifies nodes into layers based on the hop distances and derives partial information on network connectivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Phase estimation algorithm for the multibeam optical metrology

Unitary Fourier transform lies at the core of the multitudinous computational and metrological algorithms. Here we show experimentally how the unitary Fourier transform-based phase estimation protocol, used namely in quantum metrology, can be translated into the classical linear optical framework. The developed setup made of beam splitters, mirrors and phase shifters demonstrates how the classical coherence, similarly to the quantum coherence, poses a resource for obtaining information about the measurable physical quantities. Our study opens route to the reliable implementation of the small-scale unitary algorithms on path-encoded qudits, thus establishing an easily accessible platform for unitary computation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Dynamic traffic routing in a network with adaptive signal control

In real traffic networks, travellers’ route choice is affected by traffic control strategies. In this work, we capture the interaction between travellers’ route choice and traffic signal control in a coherent framework. For travellers’ route choice, a VANET (Vehicular Ad hoc NETwork) is considered, where travellers have access to the real-time traffic information through V2V/V2I (Vehicle to Vehicle/Vehicle to Infrastructure) infrastructures and make route choice decisions at each intersection using hyper-path trees. We test our algorithm and control strategy by simulation in OmNet++ (A network communication simulator) and SUMO (Simulation of Urban MObility) under several scenarios. The simulation results show that with the proposed dynamic routing, the overall travel cost significantly decreases. Lastly, it is also shown that the proposed adaptive signal control reduces the average delay effectively, as well as reduces the fluctuation of the average speed within the whole network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SCORE (Synthesis of COnsists as Rolling Energy microgrids) (Final Technical Report)

The SCORE toolset is an open source, web-based application to assess the impact of new powering technologies on railroad performance, specifically technologies capable of both putting power into the rail (motive force) and taking power from the rail (regeneration). SCORE’s primary goal is generate trade studies to analyze different powering and train make-up options to minimize energy usage and greenhouse gas generation. At the core of generating these trade spaces is calculating the optimal powering policy for the train given the makeup of the train, the route, and time constraints. This paper presents details on the algorithms used in SCORE to compute this powering policies that is fast and accurate, discusses its implementation in an Energy-Longitudinal Train Dynamics (E-LTD) model, compares it to naïve approaches, and demonstrates its use across a variety of train/route pairs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Discovery and implementation of a novel pathway for n-butanol production via 2-oxoglutarate

Background: One of the European Union directives indicates that 10% of all fuels must be bio-synthesized by 2020. In this regard, biobutanol—natively produced by clostridial strains—poses as a promising alternative biofuel. One possible approach to overcome the difculties of the industrial exploration of the native producers is the expression of more suitable pathways in robust microorganisms such as Escherichia coli. The enumeration of novel pathways is a powerful tool, allowing to identify non-obvious combinations of enzymes to produce a target compound. Results: This work describes the in silico driven design of E. coli strains able to produce butanol via 2-oxoglutarate by a novel pathway. This butanol pathway was generated by a hypergraph algorithm and selected from an initial set of 105,954 diferent routes by successively applying diferent flters, such as stoichiometric feasibility, size and novelty. The implementation of this pathway involved seven catalytic steps and required the insertion of nine heterologous genes from various sources in E. coli distributed in three plasmids. Expressing butanol genes in E. coli K12 and cultivation in High-Density Medium formulation seem to favor butanol accumulation via the 2-oxoglutarate pathway. The maximum butanol titer obtained was 85±1 mg L-1 by cultivating the cells in bioreactors. Conclusions: In this work, we were able to successfully translate the computational analysis into in vivo applications, designing novel strains of E. coli able to produce n-butanol via an innovative pathway. Our results demonstrate that enumeration algorithms can broad the spectrum of butanol producing pathways. This validation encourages further research to other target compounds.

09 BIOMASS FUELS↗
DOE OSTI2022

Data-centric framework for crystal structure identification in atomistic simulations using machine learning

Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have historically been challenging to construct, requiring significant intuition and effort. Here we propose an alternative framework for a fundamental structural characterization task: classifying atoms according to the crystal structure to which they belong. Our approach is data-centric and favors the employment of Machine Learning over heuristic rules of classification. A group of data-science tools and simple local descriptors of atomic structure are employed together with an efficient synthetic training set. We also introduce the first standard and publicly available benchmark data set for evaluation of algorithms for crystal-structure classification. Further, it is demonstrated that our data-centric framework outperforms all of the most popular heuristic methods—especially at high temperatures when lattices are the most distorted—while introducing a systematic route for generalization to new crystal structures. Moreover, through the use of outlier detection algorithms our approach is capable of discerning between amorphous atomic motifs (i.e., noncrystalline phases) and unknown crystal structures, making it uniquely suited for exploratory materials synthesis simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Accelerating a fluvial incision and landscape evolution model with parallelism

Solving inverse problems, performing sensitivity analyses, and achieving statistical rigour in landscape evolution models require running many model realizations. Parallel computation is necessary to achieve this in a reasonable time. However, no previous landscape evolution algorithm is able to leverage modern parallelism. In this report, I describe an algorithm that can utilize the parallel potential of GPUs and many-core processors, in addition to working well in serial. The new algorithm runs 43× faster (70 s vs. 3000 s on a 10,000×10,000 input) than the previous state-of-the-art and exhibits sublinear scaling with input size. I also identify key techniques for multiple flow direction routing and quickly eliminating landscape depressions and local minima. Complete, well-commented, easily adaptable source code for all versions of the algorithm is available on Github and Zenodo.

58 GEOSCIENCES↗
DOE OSTI2021

Tusas: A fully implicit parallel approach for coupled phase-field equations

In this study, we develop a fully-coupled, fully-implicit approach for phase-field modeling of solidification in metals and alloys. Predictive simulation of solidification in pure metals and metal alloys remains a significant challenge in the field of materials science, as microstructure formation during the solidification process plays a critical role in the properties and performance of the solid material. Our simulation approach consists of a finite element spatial discretization of the fully-coupled nonlinear system of partial differential equations at the microscale, which is treated implicitly in time with a preconditioned Jacobian-free Newton-Krylov method. The approach is algorithmically scalable as well as efficient due to an effective preconditioning strategy based on algebraic multigrid and block factorization. We implement this approach in the open-source Tusas framework, which is a general, flexible tool developed in C++ for solving coupled systems of nonlinear partial differential equations. The performance of our approach is analyzed in terms of algorithmic scalability and efficiency, while the computational performance of Tusas is presented in terms of parallel scalability and efficiency on emerging heterogeneous architectures. We demonstrate that modern algorithms, discretizations, and computational science, and heterogeneous hardware provide a robust route for predictive phase-field simulation of microstructure evolution during additive manufacturing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Joint optimization of logistics infrastructure investments and subsidies in a regional logistics network with CO 2 emission reduction targets

This study proposes an optimization model that simultaneously incorporates the selection of logistics infrastructure investments and subsidies for green transport modes to achieve specific CO 2 emission targets in a regional logistics network. The proposed model is formulated as a bi-level formulation, in which the upper level determines the optimal selection of logistics infrastructure investments and subsidies for green transport modes such that the benefit-cost ratio of the entire logistics system is maximized. The lower level describes the selected service routes of logistics users. A genetic and Frank-Wolfe hybrid algorithm is introduced to solve the proposed model. The proposed model is applied to the regional logistics network of Changsha City, China. Findings show that using the joint scheme of the selection of logistics infrastructure investments and green subsidies is more effective than using them solely. In conclusion, carbon emission reduction targets can significantly affect logistics infrastructure investments and subsidy levels.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Formal Approach For Resilient Reachability based on End-System Route Agility

The deterministic nature of existing routing protocols has resulted into an ossified Internet with static and predictable network routes. This gives persistent attackers (e.g. eavesdroppers and DDoS attackers) plenty of time to study the network and identify the vulnerable links (critical) to plan a devastating and stealthy attack. Recently, route mutation approaches have been proposed to address such issues. However, these approaches incur significantly high overhead and depend upon the availability of disjoint routes in the network, which inherently limit their use for mission critical services. To cope with these issues, we extend the current routing architecture to consider end-hosts as routing elements, and present a formal method based agile defense mechanism to increase resiliency of the existing cyber infrastructure. The major contributions of this paper include: (1) formalization of efficient and resilient End to End (E2E) reachability problem as a constraint satisfaction problem, which identifies the potential end-hosts to reach a destination while satisfying resilience and QoS constraints, (2) design and implementation of a novel decentralized End Point Route Mutation (EPRM) protocol, and (3) design and implementation of planning algorithm to minimize the overlap between multiple flows, for the sake of maximizing the agility in the system. Our implementation and evaluation validates the correctness, effectiveness and scalability of the proposed approach.

Rauf, Usman↗
DOE OSTI2022

Performance Enhancement of APW+lo Calculations by Simplest Separation of Concerns

Full-potential linearized augmented plane wave (LAPW) and APW plus local orbital (APW+lo) codes differ widely in both their user interfaces and in capabilities for calculations and analysis beyond their common central task of all-electron solution of the Kohn–Sham equations. However, that common central task opens a possible route to performance enhancement, namely to offload the basic LAPW/APW+lo algorithms to a library optimized purely for that purpose. To explore that opportunity, we have interfaced the Exciting-Plus (“EP”) LAPW/APW+lo DFT code with the highly optimized SIRIUS multi-functional DFT package. This simplest realization of the separation of concerns approach yields substantial performance over the base EP code via additional task parallelism without significant change in the EP source code or user interface. We provide benchmarks of the interfaced code against the original EP using small bulk systems, and demonstrate performance on a spin-crossover molecule and magnetic molecule that are of size and complexity at the margins of the capability of the EP code itself.

Zhang, Long↗
DOE OSTI2025

Cybersecurity Enhancement in Digital Substations: Hidden Markov Model-Based Smart Cyber Switching and Threat Response

The rising incidence of cyber-attacks on critical infrastructure and power grids poses significant threats to the stability and reliability of electrical substations, with potentially devastating consequences such as extended blackouts. This paper introduces an advanced cybersecurity framework aimed at safeguarding IEC 61850-based substations through the integration of software-defined networking (SDN) and digital twin (DT) technologies. The proposed DT-based framework employs smart cyber switching (SCS) for proactive threat mitigation and concurrent intelligent electronic device (CIED) for swift system restoration, thereby maintaining continuous operational integrity and robust cybersecurity defenses. Central to this framework is the adaptive port controller (APC), which enables dynamic port management to adapt to evolving threats, and an intrusion detection system (IDS) designed to detect and neutralize malicious attacks on IEC 61850-based sampled value (SV) and generic object-oriented substation event (GOOSE) messages within the substation’s communication network. Further, novel predictive intrusion detection and response (PIDR) algorithm is implemented on a digital substation (DS) to predict the best route to be taken by the attacker. The efficacy of these comprehensive cybersecurity frameworks is validated through rigorous simulations and a hardware-in-the-loop (HIL) testbed, showcasing the system’s ability to sustain substation operations amidst cyber-attacks.

Digital substation↗