FAST-DERMS: An Architecture to Control the Grid of the Future
FAST-DERMS is a controls architecture for secure and transactive management of distributed energy solutions.
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An Offload NIC for NASA, NLR, and Grid Computing
This work addresses distributed data management and access dynamically configurable high-speed access to data distributed and shared over wide-area high-speed network environments. An offload engine NIC (network interface card) is proposed that scales at nX10-Gbps increments through 100-Gbps full duplex. The Globus de facto standard was used in projects requiring secure, robust, high-speed bulk data transport. Novel extension mechanisms were derived that will combine these technologies for use by GridFTP, bandwidth management resources, and host CPU (central processing unit) acceleration. The result will be wire-rate encrypted Globus grid data transactions through offload for splintering, encryption, and compression. As the need for greater network bandwidth increases, there is an inherent need for faster CPUs. The best way to accelerate CPUs is through a network acceleration engine. Grid computing data transfers for the Globus tool set did not have wire-rate encryption or compression. Existing technology cannot keep pace with the greater bandwidths of backplane and network connections. Present offload engines with ports to Ethernet are 32 to 40 Gbps f-d at best. The best of ultra-high-speed offload engines use expensive ASICs (application specific integrated circuits) or NPUs (network processing units). The present state of the art also includes bonding and the use of multiple NICs that are also in the planning stages for future portability to ASICs and software to accommodate data rates at 100 Gbps. The remaining industry solutions are for carrier-grade equipment manufacturers, with costly line cards having multiples of 10-Gbps ports, or 100-Gbps ports such as CFP modules that interface to costly ASICs and related circuitry. All of the existing solutions vary in configuration based on requirements of the host, motherboard, or carriergrade equipment. The purpose of the innovation is to eliminate data bottlenecks within cluster, grid, and cloud computing systems, and to add several more capabilities while reducing space consumption and cost. Provisions were designed for interoperability with systems used in the NASA HEC (High-End Computing) program. The new acceleration engine consists of state-ofthe- art FPGA (field-programmable gate array) core IP, C, and Verilog code; novel communication protocol; and extensions to the Globus structure. The engine provides the functions of network acceleration, encryption, compression, packet-ordering, and security added to Globus grid or for cloud data transfer. This system is scalable in nX10-Gbps increments through 100-Gbps f-d. It can be interfaced to industry-standard system-side or network-side devices or core IP in increments of 10 GigE, scaling to provide IEEE 40/100 GigE compliance.
Awrach, James↗
Supporting ARPA-E Power Grid Optimization (Final Report)
Pacific Northwest National Laboratory (PNNL), Arizona State University (ASU), Georgia Institute of Technology (Georgia Tech), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), The University of Texas at Austin (UT), and the University of Wisconsin-Madison (UW-M) supported the ARPA-E Grid Optimization (GO) Competition by providing a common problem formulation, data format, datasets, evaluation mechanism, scoring, rules, and results that resulted in the awarding of $\$9.24$ million dollars to teams from academia, industry, and national labs for solving three sets of increasingly difficult non-linear, security- constrained AC Optimal Powerflow (AC-OPF) optimization problems in order to increase the efficiency of the US Electric Grid. It is estimated that a 1% increase in efficiency can save $\$1$ billion. Current industry practices typically use a linear DC model (DC-OPF) in order solve the OPF problem within the time constraints of the operation schedule. The GO Competition challenges the best power engineers, mathematicians, and computer scientists to make possible operational decisions based on accurate physical models. To accomplish this, the GO Competition created a series of Challenges and funded teams to produce the best solver. Challenge 1 was to solve the security constrained Alternating Current Optimal Power Flow (ACOPF) problem. Challenge 2 extended that to by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment (UC). Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. While Challenge 3 was being developed, the entrants were invited to find better solutions to the Challenge 2 synthetic datasets with no restrictions on time, hardware, or algorithms. The Challenge 2 solutions turned out to be very good. Challenge 3 expanded the Challenge 2 problem further by using multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. These problems included active bid-in demand and topology optimization. Together the Challenges used nearly 30 million CPU hours. Since each team was working on the same problem, using the same data, and running on the same hardware, fair comparisons could be drawn as to the best solver. The datasets were varied enough, however, that the best solver for one dataset was not necessarily the best at another, so cumulative scores were used. The process was managed by the PNNL maintained website https://GOCompetition.energy.gov, where Entrants could find information about the problem, the data, the rules, submit their solver for evaluation, and see the scores of all the competing teams on a Leaderboard. Interest was world-wide but only American teams were eligible for prizes. The Competition has produced 34 journal articles 115 papers and been cited over 500 times in the literature, including 12 dissertations (4 from foreign countries; Columbia (2), Germany, and Italy) and 3 from the DOE ExaScale project. Software developed by Pearl Street Technologies for Challenges 1 and 2 is now deployed by Southwest Power Pool (SPP) and Midcontinent Independent Service Operator (MISO). Other teams have received inquiries from venture capitalists. Google DeepMind has thanked the Competition for making the datasets developed for the Competition public. They are using it to train machine learning models. The larger datasets have billions of unknowns to be solved for, but only a small percent matter in the final solution. Knowing what unknowns are important can dramatically speedup the solution.
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Direct Nonlinear Approximation for Security Region Boundary of Integrated Energy Systems: A Polynomial Chaos Expansion Solution
The strong interdependence of electricity, gas, and heating systems can facilitate fault propagation within integrated energy systems (IESs), posing significant challenges to secure operation. This paper proposes a polynomial chaos expansion (PCE)-based approximation method to accurately characterize the IES security region boundary (IES–SRB). By integrating the Karush-Kuhn-Tucker conditions with PCE theory, the IES-SRB approximation problem is reformulated as a set of nonlinear equations concerning the approximation coefficients. Using the Galerkin projection method, these equations are further transformed into a system of projection equations that govern the polynomial approximation coefficients in the IES-SRB approximation. To reduce computational complexity while maintaining high approximation accuracy, a piecewise polynomial approximation method is proposed. Numerical studies on the E39-G20-H6 and E118-G96-H52 IES test systems demonstrate that the proposed method can accurately and effectively construct IES security regions.
Wu, Chenghao [Northeast Electric Power University]↗
Efficiently Verifiable Quantum Advantage on Near-Term Analog Quantum Simulators
Existing schemes for demonstrating quantum computational advantage are subject to various practical restrictions, including the hardness of verification and challenges in experimental implementation. Meanwhile, analog quantum simulators have been realized in many experiments to study novel physics. In this work, we propose a quantum advantage protocol based on verification of an analog quantum simulation, in which the verifier need only run an O ( λ 2 ) -time classical computation, and the prover need only prepare O ( 1 ) samples of a history state and perform O ( λ 2 ) single-qubit measurements, for a security parameter λ . We also propose a near-term feasible strategy for honest provers and discuss potential experimental realizations. Published by the American Physical Society 2025
Liu, Zhenning (ORCID:000000020794419X)↗
D3: A Collaborative Infrastructure for Aerospace Design
DARWIN is a NASA developed, Internet-based system for enabling aerospace researchers to securely and remotely access and collaborate on the analysis of aerospace vehicle design data, primarily the results of wind-tunnel testing and numeric (e.g., computational fluid dynamics) model executions. DARWIN captures, stores and indexes data, manages derived knowledge (such as visualizations across multiple data sets) and provides an environment for designers to collaborate in the analysis of the results of testing. DARWIN is an interesting application because it supports high volumes of data, integrates multiple modalities of data display (e.g. images and data visualizations), and provides non-trivial access control mechanisms. DARWIN enables collaboration by allowing not only sharing visualizations of data, but also commentary about and view of data.
Walton, Joan↗
Making Wireless Networks Secure for NASA Mission Critical Applications using Virtual Private Network (VPN) Technology
With so many security issues involved with wireless networks, the technology has not been fully utilized in the area of mission critical applications. These applications would include the areas of telemetry, commanding, voice and video. Wireless networking would allow payload operators the mobility to take computers outside of the control room to their offices and anywhere else in the facility that the wireless network was extended. But the risk is too great of having someone sit just inside of your wireless network coverage and intercept enough of your network traffic to steal proprietary data from a payload experiment or worse yet hack back into your system and do even greater harm by issuing harmful commands. Wired Equivalent Privacy (WEP) is improving but has a ways to go before it can be trusted to protect mission critical data. Today s hackers are becoming more aggressive and innovative, and in order to take advantage of the benefits that wireless networking offer, appropriate security measures need to be in place that will thwart hackers. The Virtual Private Network (VPN) offers a solution to the security problems that have kept wireless networks from being used for mission critical applications. VPN provides a level of encryption that will ensure that data is protected while it is being transmitted over a wireless local area network (IAN). The VPN allows a user to authenticate to the site that the user needs to access. Once this authentication has taken place the network traffic between that site and the user is encapsulated in VPN packets with the Triple Data Encryption Standard (3DES). 3DES is an encryption standard that uses a single secret key to encrypt and decrypt data. The length of the encryption key is 168 bits as opposed to its predecessor DES that has a 56-bit encryption key. Even though 3DES is the common encryption standard for today, the Advance Encryption Standard (AES), which provides even better encryption at a lower cycle cost is growing acceptance. The user computer running the VPN client and the. target site that is running the . VPN firewall exchange this encryption key and therefore are the only ones that are able to decipher the data. The level of encryption offered by the VPN is making it possible for wireless networks to pass the strict security policies that have kept them from being used in the past. Now people will be able to benefit from the many advantages that wireless networking has to offer in the area of mission critical applications.
Nichols, Kelvin F.↗
Making Wireless Networks Secure for NASA Mission Critical Applications Using Virtual Private Network (VPN) Technology
With so many security issues involved with wireless networks, the technology has not been fully utilized in the area of mission critical applications. These applications would include the areas of telemetry, commanding, voice and video. Wireless networking would allow payload operators the mobility to take computers outside of the control room to their off ices and anywhere else in the facility that the wireless network was extended. But the risk is too great of having someone sit just inside of your wireless network coverage and intercept enough of your network traffic to steal proprietary data from a payload experiment or worse yet hack back into your system and do even greater harm by issuing harmful commands. Wired Equivalent Privacy (WEP) is improving but has a ways to go before it can be trusted to protect mission critical data. Today s hackers are becoming more aggressive and innovative, and in order to take advantage of the benefits that wireless networking offer, appropriate security measures need to be in place that will thwart hackers. The Virtual Private Network (VPN) offers a solution to the security problems that have kept wireless networks from being used for mission critical applications. VPN provides a level of encryption that will ensure that data is protected while it is being transmitted over a wireless local area network (LAN). The VPN allows a user to authenticate to the site that the user needs to access. Once this authentication has taken place the network traffic between that site and the user is encapsulated in VPN packets with the Triple Data Encryption Standard (3DES). 3DES is an encryption standard that uses a single secret key to encrypt and decrypt data. The length of the encryption key is 168 bits as opposed to its predecessor DES that has a 56-bit encryption key. Even though 3DES is the common encryption standard for today, the Advance Encryption Standard (AES), which provides even better encryption at a lower cycle cost is growing acceptance. The user computer running the VPN client and the target site that is running the VPN firewall exchange this encryption key and therefore are the only ones that are able to decipher the data. The level of encryption offered by the VPN is making it possible for wireless networks to pass the strict security policies that have kept them from being used in the past. Now people will be able to benefit from the many advantages that wireless networking has to offer in the area of mission critical applications.
Nichols, Kelvin F.↗
FAIR Ecosystems for Science at Scale
High Performance Computing (HPC) centers provide resources to users who require greater scale to “get science done”. They deploy infrastructure with singular hardware architectures, cutting-edge software environments, and stricter security measures as compared with users’ own resources. As a result, users often create and configure digital artifacts in ways that are specialized for the unique infrastructure at a given HPC center. Each user of that center will face similar challenges as they develop specialized solutions to take full advantages of the center’s resources, potentially resulting in significant duplication of effort. Much duplicated effort could be avoided, however, if users of these centers found it easier to discover others’ solutions and artifacts as well as share their own. The FAIR principles address this problem by presenting guidelines focused around metadata practices to be implemented by vaguely defined “communities”; in practice, these tend to gather by domain (e.g. bioinformatics, geosciences, agriculture). Domain-based communities can unfortunately end up functioning as silos that tend both to inhibit sharing of solutions and best practices as well as to encourage fragile and unsustainable improvised solutions in the absence of best-practice guidance. We propose that these communities pursuing “science at scale” be nurtured both individually and collectively by HPC centers so that users can take advantage of shared challenges across disciplines and potentially across HPC centers. We describe an architecture based on the EOSC-Life FAIR Workflows Collaboratory, specialized for use with and inside HPC centers such as the Oak Ridge Leadership Computing Facility (OLCF), and we speculate on user incentives to encourage adoption. We note that a focus on FAIR workflow components rather than FAIR workflows is more likely to benefit the users of HPC centers.
Wilkinson, Sean [ORNL] (ORCID:0000000214437479)↗
Regularizing the linearly extrapolated BDF2 scheme for incompressible flows with time relaxation
This paper presents a highly-efficient finite element scheme for the time relaxation model (TRM). The efficiency is achieved through the second-order BDF2 time-stepping scheme with linear extrapolation (BDF2LE). The accuracy of the scheme is also greatly enhanced through the use of the divergence-free Scott-Vogeulis finite elements, and van Cittert approximate deconvolution. A complete finite element analysis is provided, which includes rigorous proofs for the stability, well-possessedness, and convergence of both velocity and pressure solutions. Furthermore, we also demonstrate that the inclusion of the linear time relaxation term preserves the long-time stability of the unregularized BDF2LE scheme. Finally, numerical experiments are presented that demonstrate the added stability and accuracy that time relaxation can provide.
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Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method
Not Available
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Reconfigurable Hardware Adapts to Changing Mission Demands
A new class of computing architectures and processing systems, which use reconfigurable hardware, is creating a revolutionary approach to implementing future spacecraft systems. With the increasing complexity of electronic components, engineers must design next-generation spacecraft systems with new technologies in both hardware and software. Derivation Systems, Inc., of Carlsbad, California, has been working through NASA s Small Business Innovation Research (SBIR) program to develop key technologies in reconfigurable computing and Intellectual Property (IP) soft cores. Founded in 1993, Derivation Systems has received several SBIR contracts from NASA s Langley Research Center and the U.S. Department of Defense Air Force Research Laboratories in support of its mission to develop hardware and software for high-assurance systems. Through these contracts, Derivation Systems began developing leading-edge technology in formal verification, embedded Java, and reconfigurable computing for its PF3100, Derivational Reasoning System (DRS ), FormalCORE IP, FormalCORE PCI/32, FormalCORE DES, and LavaCORE Configurable Java Processor, which are designed for greater flexibility and security on all space missions.
Source record↗
Secure THz Communication in 6G: A Two-Stage DRL Approach for IRS-Assisted NOMA
Not Available
Shahwar, Muhammad [College of Computer Science and↗
Certified randomness using a trapped-ion quantum processor
Although quantum computers can perform a wide range of practically important tasks beyond the abilities of classical computers, realizing this potential remains a challenge. An example is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy. Certified randomness has many applications but is impossible to achieve solely by classical computation. Here we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the Internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations: a client generates quantum ‘challenge’ circuits using a small randomness seed, sends them to an untrusted quantum server to execute and verifies the results of the server. We analyse the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of 1.1 × 10 18 floating-point operations per second across multiple supercomputers, we certify 71,313 bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of present-day quantum computers.
computer science↗
ARPA-E Grid Optimization (GO) Competition Challenge 1
The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public. Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition. Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found. For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.
ACOPF↗
Analysis of soot formation from aviation fuels in laminar counterflow flames
Combustion emissions from aviation contribute to the formation of condensation trail (contrail) that can lead to the formation of anthropogenic cirrus clouds. Ice particles that form contrails are observed to have a linear correlation with soot particle number density. Synthetic aviation fuels (SAFs) offer a promising route to mitigate the production of soot particles while also increasing energy security. Although studies have focused on combustion and spray behavior, the detailed investigation of soot formation processes for different jet fuels and their impact on models for computational fluid dynamics (CFD) applications is not well understood. Moreover, experimental measurements of soot for canonical flames using Synthetic aviation fuels (SAF) for model validation remain scarce. To address this, we use employed the Lawrence Livermore National Laboratory (LLNL) detailed soot model based on the discrete sectional method. Additionally, we develop two reduced chemical mechanisms for Jet-A and Alcohol-to-Jet (C1) that are suitable for turbulent flame simulations and couple them with the Hybrid Method of Moments (HMOM). The detailed and reduced model frameworks are validated against experimental measurements of soot volume fraction (ƒ ν ) from a counterflow burner experiment previously reported in the literature. Given the good agreement between modeling results and experimental measurements for the (1) spatial distribution of ƒ ν and (2) the non-linear variation of peak ƒ ν with strain rate, we further investigate the modeled sub-processes (nucleation, condensation, surface growth, and oxidation) using the LLNL model to analyze the assumptions in the reduced model framework. Furthermore, the results indicate a significant contribution from resonant radicals to the surface growth of soot particles, which are not accounted for in the current implementation of HMOM and could help reconcile soot predictions by the reduced model with observations.
Counterflow↗
Cyber-Physical Challenges and Opportunities for Securing Inverter-Based Resources
This presentation is a deep dive into cyber-physical challenges and opportunities. How NREL's cybersecurity tools enable better quantification, management and governance for securing inverter-based resources.
97 MATHEMATICS AND COMPUTING↗
NeuroSymbolic Approaches as a Vector for Assured Artificial Intelligence
The deployment of artificial intelligence systems in critical applications requires higher levels of assurance for safety, security, and interpretability. While neurosymbolic (NESY) approaches combining neural networks with symbolic reasoning offer potential advantages for assured AI, existing differentiable neurosymbolic frameworks face significant limitations including computational overhead and performance constraints. This report investigates the ISED (InferSampleEstimateDescend) framework as an alternative approach that enables neurosymbolic learning without requiring endtoend differentiability. We evaluate ISED’s utility for geointelligence applications by comparing neurosymbolic models against standard neural networks on aircraft classification tasks using the RarePlanes and MTARSI imagery datasets. Our results demonstrate that while ISEDbased models achieve slightly lower accuracy (89.7% vs 92.1% on RarePlanes; 91.1% vs 92.5% on MTARSI), they provide critical explainability capabilities that enable tracing incorrect predictions back to specific attribute misclassifications. We also present an automated pipeline that generates both attributeclass mappings and neurosymbolic model architectures from natural language descriptions, significantly reducing the manual effort required for NESY model deployment. These findings suggest that ISED offers a promising direction for developing assured AI systems where interpretability and reasoning transparency are prioritized alongside performance.
97 MATHEMATICS AND COMPUTING↗