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DOE OSTI2021

Probabilistic Voltage Sensitivity based Preemptive Voltage Monitoring in Unbalanced Distribution Networks

With increasing penetration of renewable energy and active consumers, control and management of power distribution networks has become challenging. Renewable energy sources can cause random voltage fluctuations as their output power depends on weather conditions. Conventional voltage control schemes such as tap changers and capacitor banks lack the foresight required to quickly alleviate voltage violations. Thus, there is an urgent need for effective approaches for predicting and mitigating voltage violations as a result of random fluctuations in power injections. This work proposes a novel voltage monitoring approach based on low-complexity, data-driven probabilistic voltage sensitivity analysis. The usefulness of this work is not only in predicting voltage violations in unbalanced distribution grids, but also in opening up the door for optimal voltage control. Using system data and forecasts, the proposed approach predicts the distribution of system node voltages which is then used to to identify nodes that may violate the nominal operational limits with high probability. The method is tested on the IEEE 37 node distribution system considering integrated distributed solar energy sources. The method is validated against the classic load flow based method and offers over 95% accuracy in predicting voltage violations.

Abujubbeh, Mohammad↗

DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗

DOE OSTI2023

Protecting and Defending against Autonomous Control Systems and Digital Twin Cyber Attacks: Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Models in Nuclear Power Plants (Final)

Navigating through the complex tapestry of technological advancements, "Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Model in Nuclear Power Plants" stands at the intersection of cybersecurity and nuclear power plant operations, embarking on a journey through the intricacies of securing digital twins against malicious cyber activities. As nuclear power plants progressively integrate digital twin technology and machine learning models to optimize operations and ensure system reliability, they inadvertently expose themselves to a new spectrum of vulnerabilities, notably in the realm of hyperparameter attacks. Hyperparameters, integral in machine learning model tuning and optimal performance of digital twins, have emerged as a target for adversaries aiming to destabilize the predictive capabilities and therefore, the operational accuracy of these digital entities within critical infrastructures like nuclear plants. This paper, therefore, meticulously threads the needle through the development of a robust response strategy, poised to shield these digital reflections against calculated hyperparameter manipulations, ensuring that the digital twin can effectively and securely function as a reliable proxy for its physical counterpart. The ensuing sections delve into the orchestrated maelstrom of multi-rate time-changing intelligent coordinated hyperparameter attacks and the implementation of event-triggered predictive control, laying down a structured, predictive, and responsive framework that safeguards the nexus where the digital and physical realms of nuclear power plants coalesce. The operational integrity of digital twins in nuclear power plants depends critically on the security of machine learning hyperparameters. This study makes two different contributions. First, a decision-based idea known as a multi-rate time changing intelligent coordinated hyperparameter attack is put forth. In this attack, many hyperparameters are repeatedly changed using both random and intelligent optimal techniques by the attacker. These assaults introduce varied rates at different attack steps, compromise various amounts of hyperparameters, and improve stealth and flexibility. Second, a technique is developed for event triggered predictive control to rapidly respond to potential hyperparameter attacks. This control integrates a sliding window framework, retaining a history of previous data points and employing linear regression to predict the next data point from the current dataset. The control gain K is determined using the Lyapunov-Krasovskii method, and subsequently, an action is developed. Finally, the outcome of the simulation demonstrates the viability of the proposed method for defending nuclear power plant digital twins from hyperparameter attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1974

In flight ground control of high drag satellites utilizing on-board accelerometer data and rapid orbit prediction techniques

High drag satellites frequently require precise verification of orbital maneuvers and the accurate prediction of perigee height. An in-flight ground support system designed to monitor and compute orbital state and maneuvers is described. The use of on-board three-axis accelerometer data in a flight support software system to perform on-line maneuver analysis and atmospheric model updating is discussed. In addition, automated analytic techniques to rapidly and accurately predict perigee height following a maneuver are described, as well as semianalytic averaging techniques designed to predict a decaying orbital state for mission control.

Fuchs, A. J.↗

NASA NTRS1977

Computer assisted thermal-vacuum testing

In testing complex systems and components under dynamic thermal-vacuum environments, it is desirable to optimize the environment control sequence in order to reduce test duration and cost. This paper describes an approach where a computer is utilized as part of the test control operation. Real time test data is made available to the computer through time-sharing terminals at appropriate time intervals. A mathematical model of the test article and environmental control equipment is then operated on using the real time data to yield current thermal status, temperature analysis, trend prediction and recommended thermal control setting changes to arrive at the required thermal condition. The data acquisition interface and the time-sharing hook-up to an IBM-370 computer is described along with a typical control program and data demonstrating its use.

Petrie, W.↗

DOE OSTI2026

EASY-SHIFT v Alpha

The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.

Grant, Peter [Lawrence Berkeley National Laborator↗

NASA NTRS1994

Software reliability studies

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Our research has shown that by improving the quality of the data one can greatly improve the predictions. We are working on methodologies which control some of the randomness inherent in the standard data generation processes in order to improve the accuracy of predictions. Our contribution is twofold in that we describe an experimental methodology using a data structure called the debugging graph and apply this methodology to assess the robustness of existing models. The debugging graph is used to analyze the effects of various fault recovery orders on the predictive accuracy of several well-known software reliability algorithms. We found that, along a particular debugging path in the graph, the predictive performance of different models can vary greatly. Similarly, just because a model 'fits' a given path's data well does not guarantee that the model would perform well on a different path. Further we observed bug interactions and noted their potential effects on the predictive process. We saw that not only do different faults fail at different rates, but that those rates can be affected by the particular debugging stage at which the rates are evaluated. Based on our experiment, we conjecture that the accuracy of a reliability prediction is affected by the fault recovery order as well as by fault interaction.

Hoppa, Mary Ann↗

NASA NTRS1947

Comparison of Wind-Tunnel Predictions with Flight Measurements of the Longitudinal-Stability and -Control Characteristics of a Douglas BTD-1 Airplane

Low Mach number longitudinal-stability and control characteristics as predicted by use of wind tunnel data from a powered 3/16-scale model are compared with flight test measurements of a Navy BTD-1 airplane. The accuracy of the wind tunnel data and the discrepancies involved in attempting to correlate with flight data are discussed and analyzed. The comparison showed that wind tunnel predictions were, in general, in good agreement with flight test data. The predicted values were for the most part sufficiently accurate to show the satisfactory and unsatisfactory characteristics in the preliminary design stage and to indicate possible methods of improvement. The discrepancies which did occur were attributed principally to physical dissimilarities between model and airplane and the instability to determine accurately the flight power conditions. The effect of Mach number was considered negligible since the maximum flight test value was about 0.5. In order to simulate more closely the flight conditions and hence obtain more accurate data for predictions, it appears desirable to perform large-scale tests of unorthodox control surfaces such as the sealed vaned elevators with which the airplane was equipped.

Bunnell, Mort V.↗

NASA NTRS1975

Fracture mechanics correlation of boron/aluminum coupons containing stress risers

The mechanical behavior of boron/aluminum near stress risers has been studied and reported. This effort was directed toward defining the tensile behavior of both unidirectional and (0/ plus or minus 45) boron/aluminum using linear elastic fracture mechanics (LEFM). The material used was 5.6-mil boron in 6061 aluminum, consolidated using conventional diffusion bonding techniques. Mechanical properties are reported for both unidirectional and (0/ plus or minus 45) boron/aluminum, which serve as control data for the fracture mechanics predictions. Three different flawed specimen types were studied. In each case the series of specimens remained geometrically similar to eliminate variations in finite size correction factors. The fracture data from these tests were reduced using two techniques. They both used conventional LEFM methods, but the existence of a characteristic flaw was assumed in one case and not the other. Both the data and the physical behavior of the specimens support the characteristic flaw hypothesis. Cracks were observed growing slowly in the (0/ plus or minus 45) laminates, until a critical crack length was reached at which time catastrophic failure occurred.

Adsit, N. R.↗

NASA NTRS1981

AD-1 oblique wing aircraft program

A NASA program for evaluation of the handling and flying characteristics of the AD-1 oblique wing aircraft is discussed. The vehicle was flown to compare wind tunnel predictions with aerodynamic data, explore the control system requirements, and obtain a preliminary assessment of the aeroelastic effects. The fiberglass sandwich skin aircraft is designed for 8 g positive and 4 g negative loading at 175 knots, while the wing pivot can withstand 25 g loading. Flight monitoring was accomplished with a 41 channel pulse code modulation system for telemetry and by averaging of pilot ratings. Maneuvering tests are outlined, noting that pilot ratings indicated acceptable handling at up to 50 deg sweep. It is concluded that acceptable flying qualities can be achieved with a 60 deg sweep, and that aeroelastic tailoring can be used to satisfy cruise design technology.

Mcmurtry, T. C.↗

NASA NTRS1991

Role of computer graphics in space telerobotics - Preview and predictive displays

The application of computer graphics in space telerobotics research and development work is briefly reviewed and illustrated by specific examples implemented in real time operation. The applications are discussed under the following four major categories: preview displays, predictive displays, sensor data displays, and control system status displays.

Bejczy, Antal K.↗

DOE OSTI2023

A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management. Without accessibility to other data sources, the onsite observed temperatures or the airport temperatures are used in forecast models. In this paper, we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations (PWS) to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework. The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs. Our approach is distinguished from existing literature in various aspects. First, we leverage the crowdsourcing weather data from PWS in addition to public data sources. In this way, the data is at much finer time resolution (e.g., at 5-minute frequency) and spatial resolution (e.g., arbitrary location vs grid). Second, our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance. Here, we demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio, Texas, USA. In general, the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50% compares with persistent model and has 90% chance to outperform airport forecast in short-term forecasting. In a real-time setting, the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon. Moreover, we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance. Finally, we implement our model in other cities such as Syracuse and Chicago to test the model's performance in different landforms and climate types.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1997

Deadbeat Predictive Controllers

Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.

Juang, Jer-Nan↗

NASA NTRS1983

Minimum testing of the space shuttle orbiter for stability and control derivatives

A unique approach has been developed for stability and control derivative testing of the Space Shuttle orbiter during entry. Shuttle Program requirements have necessitated a minimum of testing. Therefore, flight tests concentrate on potential control problem areas predicted from wind tunnel data as well as anomalies discovered during flight testing. The object is to use the test data to remove center of gravity (cg), angle of attack, and elevon placards which are a function of these potential control problems. To ensure successful aerodynamic extraction from a minimum of testing, the following special measures have been taken. Exact maneuvers are designed preflight on Shuttle simulators. These maneuvers are duplicated and implemented during the flight by onboard software. An onboard instrumentation system was designed especially for aerodynamic parameter identification. State-of-the-art techniques are used in extracting aerodynamics.

Cooke, D. R.↗

NASA NTRS2010

Predicting Ares I Reaction Control System Performance by Utilizing Analysis Anchored with Development Test Data

The Ares I launch vehicle is an integral part of NASA s Constellation Program, providing a foundation for a new era of space access. The Ares I is designed to lift the Orion Crew Module and will enable humans to return to the Moon as well as explore Mars.1 The Ares I is comprised of two inline stages: a Space Shuttle-derived five-segment Solid Rocket Booster (SRB) First Stage (FS) and an Upper Stage (US) powered by a Saturn V-derived J-2X engine. A dedicated Roll Control System (RoCS) located on the connecting interstage provides roll control prior to FS separation. Induced yaw and pitch moments are handled by the SRB nozzle vectoring. The FS SRB operates for approximately two minutes after which the US separates from the vehicle and the US Reaction Control System (ReCS) continues to provide reaction control for the remainder of the mission. A representation of the Ares I launch vehicle in the stacked configuration and including the Orion Crew Exploration Vehicle (CEV) is shown in Figure 1. Each Reaction Control System (RCS) design incorporates a Gaseous Helium (GHe) pressurization system combined with a monopropellant Hydrazine (N2H4) propulsion system. Both systems have two diametrically opposed thruster modules. This architecture provides one failure tolerance for function and prevention of catastrophic hazards such as inadvertent thruster firing, bulk propellant leakage, and over-pressurization. The pressurization system on the RoCS includes two ambient pressure-referenced regulators on parallel strings in order to attain the required system level single Fault Tolerant (FT) design for function while the ReCS utilizes a blow-down approach. A single burst disk and relief valve assembly is also included on the RoCS to ensure single failure tolerance for must-not-occur catastrophic hazards. The Reaction Control Systems are designed to support simultaneously firing multiple thrusters as required

Stein, William B.↗

NASA NTRS2015

Inflight Characterization of the Cassini Spacecraft Propellant Slosh and Structural Frequencies

While there has been extensive theoretical and analytical research regarding the characterization of spacecraft propellant slosh and structural frequencies, there have been limited studies to compare the analytical predictions with measured flight data. This paper uses flight telemetry from the Cassini spacecraft to get estimates of high-g propellant slosh frequencies and the magnetometer boom frequency characteristics, and compares these values with those predicted by theoretical works. Most Cassini attitude control data are available at a telemetry frequency of 0.5 Hz. Moreover, liquid sloshing is attenuated by propellant management device and attitude controllers. Identification of slosh and structural frequency are made on a best-effort basis. This paper reviews the analytical approaches that were used to predict the Cassini propellant slosh frequencies. The predicted frequencies are then compared with those estimated using telemetry from selected Cassini burns where propellant sloshing was observed (such as the Saturn Orbit Insertion burn). Determination of the magnetometer boom structural frequency is also discussed.

Lee, Allan Y.↗

NASA NTRS2015

Inflight Characterization of the Cassini Spacecraft Propellant Slosh and Structural Frequencies

Lee, Allan Y.↗

NASA NTRS2015

Numerical Investigation of Microgravity Tank Pressure Rise Due to Boiling

The ability to control self-pressurization in cryogenic storage tanks is essential for NASAs long-term space exploration missions. Predictions of the tank pressure rise in Space are needed in order to inform the microgravity design and optimization process. Due to the fact that natural convection is very weak in microgravity, heat leaks into the tank can create superheated regions in the liquid. The superheated regions can instigate microgravity boiling, giving rise to pressure spikes during self-pressurization. In this work, a CFD model is developed to predict the magnitude and duration of the microgravity pressure spikes. The model uses the Schrage equation to calculate the mass transfer, with a different accommodation coefficient for evaporation at the interface, condensation at the interface, and boiling in the bulk liquid. The implicit VOF model was used to account for the moving interface, with bounded second order time discretization. Validation of the models predictions was carried out using microgravity data from the Tank Pressure Control Experiment, which flew aboard the Space Shuttle Mission STS-52. Although this experiment was meant to study pressurization and pressure control, it underwent boiling during several tests. The pressure rise predicted by the CFD model compared well with the experimental data. The ZBOT microgravity experiment is scheduled to fly on February 2016 aboard the ISS. The CFD model was also used to perform simulations for setting parametric limits for the Zero-Boil-Off Tank (ZBOT) Experiments Test Matrix in an attempt to avoid boiling in the majority of the test runs that are aimed to study pressure increase rates during self-pressurization. *Supported in part by NASA ISS Physical Sciences Research Program, NASA HQ, USA

pressure rise↗